From 073c5e0a5d3b37bffd225e0ee52e5d340fb3599d Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Thu, 25 Jun 2020 17:21:17 +0200
Subject: [PATCH 01/22] Factor out plotting module as optional dependency

---
 filter_functions/__init__.py       | 10 ++------
 filter_functions/numeric.py        | 17 ++++++-------
 filter_functions/plotting.py       |  8 +++----
 filter_functions/pulse_sequence.py |  4 ++--
 filter_functions/types.py          | 38 +++++++++++++++++++-----------
 setup.py                           | 17 ++++++++-----
 tests/test_core.py                 | 27 +++++++++------------
 tests/test_plotting.py             |  7 ++++++
 8 files changed, 68 insertions(+), 60 deletions(-)

diff --git a/filter_functions/__init__.py b/filter_functions/__init__.py
index e535416..1b092b9 100644
--- a/filter_functions/__init__.py
+++ b/filter_functions/__init__.py
@@ -20,23 +20,17 @@
 # =============================================================================
 """Package for efficient calculation of generalized filter functions"""
 
-from . import analytic, basis, numeric, plotting, pulse_sequence, util
+from . import analytic, basis, numeric, pulse_sequence, util
 from .basis import Basis
 from .numeric import (error_transfer_matrix, infidelity,
                       liouville_representation)
-from .plotting import (
-    plot_bloch_vector_evolution, plot_error_transfer_matrix,
-    plot_filter_function, plot_pulse_correlation_filter_function,
-    plot_pulse_train)
 from .pulse_sequence import (PulseSequence, concatenate, concatenate_periodic,
                              extend, remap)
 
 __all__ = ['Basis', 'PulseSequence', 'analytic', 'basis', 'concatenate',
            'concatenate_periodic', 'error_transfer_matrix', 'extend',
            'infidelity', 'liouville_representation', 'numeric',
-           'plot_bloch_vector_evolution', 'plot_error_transfer_matrix',
-           'plot_filter_function', 'plot_pulse_correlation_filter_function',
-           'plot_pulse_train', 'plotting', 'pulse_sequence', 'remap', 'util']
+           'pulse_sequence', 'remap', 'util']
 
 __version__ = '0.2.4'
 __license__ = 'GNU GPLv3+'
diff --git a/filter_functions/numeric.py b/filter_functions/numeric.py
index ec8f967..46e7821 100644
--- a/filter_functions/numeric.py
+++ b/filter_functions/numeric.py
@@ -63,7 +63,6 @@
 
 from . import util
 from .basis import Basis, ggm_expand
-from .plotting import plot_infidelity_convergence
 from .types import Coefficients, Operator
 
 __all__ = ['calculate_control_matrix_from_atomic',
@@ -813,8 +812,8 @@ def infidelity(pulse: 'PulseSequence',
         Return the smallness parameter :math:`\xi` for the given spectrum.
     test_convergence: bool, optional
         Test the convergence of the integral with respect to the number of
-        frequency samples. Plots the infidelities against the number of
-        frequency samples. See *S* and *omega* for more information.
+        frequency samples. Returns the number of frequency samples and the
+        corresponding fidelities. See *S* and *omega* for more information.
 
     Returns
     -------
@@ -831,9 +830,6 @@ def infidelity(pulse: 'PulseSequence',
     convergence_infids: array_like
         Array with infidelities calculated in convergence test.
         Only if *test_convergence* is ``True``.
-    (fig, ax): tuple
-        The matplotlib figure and axis instances used for plotting.
-        Only if *test_convergence* is ``True``.
 
     .. _notes:
 
@@ -898,6 +894,10 @@ def infidelity(pulse: 'PulseSequence',
         Section A: General, Atomic and Solid State Physics, 303(4), 249–252.
         https://doi.org/10.1016/S0375-9601(02)01272-0
 
+    See Also
+    --------
+    error_transfer_matrix: Calculate the full process matrix.
+    plotting.plot_infidelity_convergence: Convenience function to plot results.
     """
     # Noise operator indices
     idx = util.get_indices_from_identifiers(pulse, n_oper_identifiers, 'noise')
@@ -941,10 +941,7 @@ def infidelity(pulse: 'PulseSequence',
                 test_convergence=False
             )
 
-        fig, ax = plot_infidelity_convergence(n_samples,
-                                              convergence_infids.sum(axis=1))
-
-        return n_samples, convergence_infids, (fig, ax)
+        return n_samples, convergence_infids
 
     if which == 'total':
         if not pulse.basis.istraceless:
diff --git a/filter_functions/plotting.py b/filter_functions/plotting.py
index 1ace148..af0c5f1 100644
--- a/filter_functions/plotting.py
+++ b/filter_functions/plotting.py
@@ -571,8 +571,8 @@ def plot_infidelity_convergence(n_samples: Sequence[int],
     ax[1].grid()
 
     ax[0].plot(n_samples, infids, 'o-')
-    ax[1].semilogy(n_samples[1:],
-                   np.abs(np.diff(infids, axis=-1))/infids[1:]*100, 'o-')
+    ax[1].semilogy(n_samples, np.abs(np.gradient(infids, axis=0))/infids*100,
+                   'o-')
 
     return fig, ax
 
@@ -764,8 +764,8 @@ def plot_error_transfer_matrix(
     cbar = fig.colorbar(im, cax=grid.cbar_axes[0])
     cbar.set_label(cbar_label)
     if colorscale == 'log':
-        labels = cbar.ax.get_yticklabels()
+        labels = cbar.get_ticklabels()
         labels[len(labels) // 2] = ''
-        labels = cbar.ax.set_yticklabels(labels)
+        labels = cbar.set_ticklabels(labels)
 
     return fig, grid
diff --git a/filter_functions/pulse_sequence.py b/filter_functions/pulse_sequence.py
index f5864bc..e4f34a8 100644
--- a/filter_functions/pulse_sequence.py
+++ b/filter_functions/pulse_sequence.py
@@ -152,8 +152,8 @@ class PulseSequence:
     >>> omega = np.logspace(-1, 2, 500)
     >>> F = pulse.get_filter_function(omega)    # shape (1, 500)
     >>> # Plot the resulting filter function:
-    >>> from filter_functions import plot_filter_function
-    >>> fig, ax, leg = plot_filter_function(pulse)
+    >>> from filter_functions import plotting
+    >>> fig, ax, leg = plotting.plot_filter_function(pulse)
 
     Attributes
     ----------
diff --git a/filter_functions/types.py b/filter_functions/types.py
index f5a4030..eb37ed7 100644
--- a/filter_functions/types.py
+++ b/filter_functions/types.py
@@ -23,24 +23,34 @@
 """
 from typing import Mapping, Optional, Sequence, Tuple, Union
 
-from matplotlib import axes, colors, figure, legend
-from mpl_toolkits import axes_grid1
 from numpy import ndarray
-from qutip import Qobj
 
-State = Union[ndarray, Qobj]
-Operator = Union[ndarray, Qobj]
+try:
+    from matplotlib import axes, colors, figure, legend
+    from mpl_toolkits import axes_grid1
+
+    Axes = axes.Axes
+    Colormap = Union[colors.Colormap, str]
+    Figure = figure.Figure
+    Grid = axes_grid1.ImageGrid
+    Legend = legend.Legend
+    FigureAxes = Tuple[Figure, Axes]
+    FigureAxesLegend = Tuple[Figure, Axes, Legend]
+    FigureGrid = Tuple[Figure, Grid]
+except ImportError:
+    pass
+
+try:
+    from qutip import Qobj
+
+    State = Union[ndarray, Qobj]
+    Operator = Union[ndarray, Qobj]
+except ImportError:
+    State = ndarray
+    Operator = ndarray
+
 Coefficients = Sequence[float]
 Hamiltonian = Sequence[Sequence[Union[Operator, Coefficients]]]
 PulseMapping = Sequence[Sequence[Union['PulseSequence',
                                        Union[Sequence[int], int],
                                        Optional[Mapping[str, str]]]]]
-
-Axes = axes.Axes
-Colormap = Union[colors.Colormap, str]
-Figure = figure.Figure
-Grid = axes_grid1.ImageGrid
-Legend = legend.Legend
-FigureAxes = Tuple[Figure, Axes]
-FigureAxesLegend = Tuple[Figure, Axes, Legend]
-FigureGrid = Tuple[Figure, Grid]
diff --git a/setup.py b/setup.py
index 3896f6c..264e464 100644
--- a/setup.py
+++ b/setup.py
@@ -25,6 +25,16 @@ def extract_version(version_file):
                      'to install this package.\n')
     exit(1)
 
+extras_require = {'plotting': ['matplotlib'],
+                  'bloch_sphere_visualization': ['qutip', 'matplotlib'],
+                  'fancy_progressbar': ['tqdm', 'requests'],
+                  'doc': ['jupyter', 'nbsphinx', 'numpydoc', 'sphinx',
+                          'pandoc', 'sphinx_rtd_theme'],
+                  'tests': ['pytest', 'coverage', 'coveralls']}
+
+extras_require['all'] = [dep for deps in extras_require.values()
+                         for dep in deps]
+
 setup(name='filter_functions',
       version=extract_version(read('filter_functions', '__init__.py')),
       description='Package for efficient calculation of generalized filter functions',
@@ -37,12 +47,7 @@ def extract_version(version_file):
       package_dir={'filter_functions': 'filter_functions'},
       install_requires=['numpy', 'scipy', 'matplotlib', 'qutip', 'opt_einsum',
                         'sparse'],
-      extras_require={
-          'fancy_progressbar': ['tqdm', 'requests'],
-          'doc': ['ipython', 'ipykernel', 'nbsphinx', 'numpydoc', 'sphinx',
-                  'jupyter_client', 'sphinx_rtd_theme'],
-          'tests': ['pytest', 'coverage', 'coveralls'],
-      },
+      extras_require=extras_require,
       test_suite='tests',
       classifiers=[
           'Programming Language :: Python :: 3',
diff --git a/tests/test_core.py b/tests/test_core.py
index 19b1176..975c342 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -797,9 +797,6 @@ def test_calculate_error_vector_correlation_functions(self):
                 )
 
     def test_infidelity_convergence(self):
-        import matplotlib
-        matplotlib.use('Agg')
-
         omega = {
             'omega_IR': 0,
             'omega_UV': 2,
@@ -816,27 +813,25 @@ def S(omega):
         complicated_pulse = testutil.rand_pulse_sequence(2, 100, 3, 3)
 
         with self.assertRaises(TypeError):
-            n, infids, (fig, ax) = ff.infidelity(simple_pulse, S, [],
-                                                 test_convergence=True)
+            n, infids = ff.infidelity(simple_pulse, S, [],
+                                      test_convergence=True)
 
         with self.assertRaises(TypeError):
-            n, infids, (fig, ax) = ff.infidelity(simple_pulse, [1, 2, 3],
-                                                 dict(spacing='foobar'),
-                                                 test_convergence=True)
+            n, infids = ff.infidelity(simple_pulse, [1, 2, 3],
+                                      dict(spacing='foobar'),
+                                      test_convergence=True)
 
         with self.assertRaises(ValueError):
-            n, infids, (fig, ax) = ff.infidelity(simple_pulse, S,
-                                                 dict(spacing='foobar'),
-                                                 test_convergence=True)
+            n, infids = ff.infidelity(simple_pulse, S, dict(spacing='foobar'),
+                                      test_convergence=True)
 
         # Test with default args
-        n, infids, (fig, ax) = ff.infidelity(simple_pulse, S, {},
-                                             test_convergence=True)
+        n, infids = ff.infidelity(simple_pulse, S, {}, test_convergence=True)
 
         # Test with non-default args
         identifiers = testutil.rng.choice(complicated_pulse.n_oper_identifiers,
                                           testutil.rng.randint(1, 4))
 
-        n, infids, (fig, ax) = ff.infidelity(complicated_pulse,
-                                             S, omega, test_convergence=True,
-                                             n_oper_identifiers=identifiers)
+        n, infids = ff.infidelity(complicated_pulse, S, omega,
+                                  test_convergence=True,
+                                  n_oper_identifiers=identifiers)
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index ec1f382..45b8cff 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -331,3 +331,10 @@ def test_plot_error_transfer_matrix(self):
                                                       **figure_kw)
 
         plt.close('all')
+
+    def test_plot_infidelity_convergence(self):
+        def S(omega):
+            return omega**0
+
+        n, infids = ff.infidelity(simple_pulse, S, {}, test_convergence=True)
+        fig, ax = plotting.plot_infidelity_convergence(n, infids)

From 1dbf79206918a4e32bd0f73e087d25ef37103b9e Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Thu, 25 Jun 2020 17:22:29 +0200
Subject: [PATCH 02/22] Factor out qutip as optional dependency

---
 filter_functions/basis.py          |   9 ++-
 filter_functions/plotting.py       | 104 ++++++++++++++++++++---------
 filter_functions/pulse_sequence.py |  14 ++--
 filter_functions/util.py           |  42 +++++++-----
 setup.py                           |   3 +-
 tests/test_basis.py                |  11 +--
 tests/test_core.py                 |  14 ++--
 tests/test_precision.py            |  18 ++---
 tests/test_sequencing.py           |  28 ++++----
 tests/test_util.py                 |  22 +++---
 tests/testutil.py                  |   2 +-
 11 files changed, 158 insertions(+), 109 deletions(-)

diff --git a/filter_functions/basis.py b/filter_functions/basis.py
index cdcad38..c2b1eba 100644
--- a/filter_functions/basis.py
+++ b/filter_functions/basis.py
@@ -47,7 +47,6 @@
 import opt_einsum as oe
 from numpy import linalg as nla
 from numpy.core import ndarray
-from qutip import Qobj
 from scipy import linalg as sla
 from sparse import COO
 
@@ -172,11 +171,11 @@ def __new__(cls, basis_array: Sequence, traceless: Optional[bool] = None,
                 basis = np.empty((len(basis_array), *basis_array[0].shape),
                                  dtype=complex)
                 for i, elem in enumerate(basis_array):
-                    if isinstance(elem, ndarray):
+                    if isinstance(elem, ndarray):   # numpy array
                         basis[i] = elem
-                    elif isinstance(elem, Qobj):
+                    elif hasattr(elem, 'full'):     # qutip.Qobj
                         basis[i] = elem.full()
-                    elif isinstance(elem, COO):
+                    elif hasattr(elem, 'todense'):  # sparse array
                         basis[i] = elem.todense()
                     else:
                         raise TypeError('At least one element invalid type!')
@@ -424,7 +423,7 @@ def pauli(cls, n: int) -> 'Basis':
         """
         normalization = np.sqrt(2**n)
         combinations = np.indices((4,)*n).reshape(n, 4**n)
-        sigma = util.tensor(*np.array(util.P_np)[combinations], rank=2)
+        sigma = util.tensor(*util.paulis[combinations], rank=2)
         sigma /= normalization
         return cls(sigma, btype='Pauli', skip_check=True)
 
diff --git a/filter_functions/plotting.py b/filter_functions/plotting.py
index af0c5f1..7f0c3ee 100644
--- a/filter_functions/plotting.py
+++ b/filter_functions/plotting.py
@@ -42,52 +42,72 @@
 """
 from itertools import product
 from typing import Optional, Sequence, Union
+from unittest import mock
+from warnings import warn
 
 import matplotlib.pyplot as plt
 import numpy as np
-from matplotlib import colors, lines
-from mpl_toolkits.axes_grid1 import ImageGrid
+from matplotlib import colors, lines  # , collections
+from mpl_toolkits import axes_grid1, mplot3d
 from numpy import ndarray
-from qutip import Bloch, Qobj, basis, expect
 
 from . import numeric, util
 from .types import (Axes, Coefficients, Colormap, Figure, FigureAxes,
                     FigureAxesLegend, FigureGrid, Grid, Operator, State)
 
-__all__ = ['plot_bloch_vector_evolution', 'plot_error_transfer_matrix',
-           'plot_filter_function', 'plot_infidelity_convergence',
+__all__ = ['plot_error_transfer_matrix', 'plot_filter_function',
+           'plot_infidelity_convergence',
            'plot_pulse_correlation_filter_function', 'plot_pulse_train']
 
+try:
+    import qutip as qt
+    __all__.append('plot_bloch_vector_evolution')
+except ImportError:
+    warn('Qutip not installed. plot_bloch_vector_evolution() is not available')
+    qt = mock.Mock()
+
 
 def get_bloch_vector(states: Sequence[State]) -> ndarray:
     r"""
     Get the Bloch vector from quantum states.
     """
-    if isinstance(states[0], Qobj):
+    try:
+        import qutip as qt
+    except ImportError as err:
+        raise RuntimeError('Requirements not fulfilled. ' +
+                           'Please install Qutip') from err
+
+    if isinstance(states[0], qt.Qobj):
         a = np.empty((3, len(states)))
-        X, Y, Z = util.P_qt[1:]
+        X, Y, Z = qt.sigmax(), qt.sigmay(), qt.sigmaz()
         for i, state in enumerate(states):
-            a[:, i] = [expect(X, state),
-                       expect(Y, state),
-                       expect(Z, state)]
+            a[:, i] = [qt.expect(X, state),
+                       qt.expect(Y, state),
+                       qt.expect(Z, state)]
     else:
-        a = np.einsum('...ij,kil,...lm->k...', np.conj(states), util.P_np[1:],
-                      states)
-    return a
+        a = np.einsum('...ij,kil,...lm->k...',
+                      np.conj(states), util.paulis[1:], states)
+    return a.real
 
 
-def init_bloch_sphere(**bloch_kwargs) -> Bloch:
+def init_bloch_sphere(**bloch_kwargs) -> qt.Bloch:
     """A helper function to create a Bloch instance with a default viewing
     angle and axis labels."""
+    try:
+        import qutip as qt
+    except ImportError as err:
+        raise RuntimeError('Requirements not fulfilled. ' +
+                           'Please install Qutip') from err
+
     bloch_kwargs.setdefault('view', [-150, 30])
-    b = Bloch(**bloch_kwargs)
+    b = qt.Bloch(**bloch_kwargs)
     b.xlabel = [r'$|+\rangle$', '']
     b.ylabel = [r'$|+_i\rangle$', '']
     return b
 
 
 def get_states_from_prop(U: Sequence[Operator],
-                         psi0: State = basis(2, 0),
+                         psi0: Optional[State] = None,
                          prop: str = 'total') -> ndarray:
     r"""
     Get the the quantum state at time t from the propagator and the inital
@@ -106,7 +126,10 @@ def get_states_from_prop(U: Sequence[Operator],
     with :math:`t_0\equiv 0` and :math:`t_n\equiv t`.
 
     """
-    psi0 = psi0.full() if isinstance(psi0, Qobj) else psi0
+    if psi0 is None:
+        psi0 = np.c_[1:-1:-1]  # |0>
+
+    psi0 = psi0.full() if hasattr(psi0, 'full') else psi0  # qutip.Qobj
     d = max(psi0.shape)
     states = np.empty((len(U), d, 1), dtype=complex)
     if prop == 'total':
@@ -122,11 +145,12 @@ def get_states_from_prop(U: Sequence[Operator],
 
 def plot_bloch_vector_evolution(pulse: 'PulseSequence',
                                 psi0: Optional[State] = None,
-                                b: Optional[Bloch] = None,
+                                b: Optional[qt.Bloch] = None,
                                 n_samples: Optional[int] = None,
+                                cmap: Optional[Colormap] = None,
                                 show: bool = True,
                                 return_Bloch: bool = False,
-                                **bloch_kwargs) -> Union[None, Bloch]:
+                                **bloch_kwargs) -> Union[None, qt.Bloch]:
     r"""
     Plot the evolution of the Bloch vector under the given pulse sequence.
 
@@ -138,22 +162,24 @@ def plot_bloch_vector_evolution(pulse: 'PulseSequence',
     psi0: Qobj or array_like, optional
         The initial state before the pulse is applied. Defaults to
         :math:`|0\rangle`.
-    b: Bloch, optional
+    b: qutip.Bloch, optional
         If given, the QuTiP Bloch instance on which to plot the time evolution.
     n_samples: int, optional
         The number of time points to be sampled.
+    cmap: matplotlib colormap, optional
+        The colormap for the trajectory.
     show**: bool, optional
         Whether to show the sphere (by calling :code:`b.make_sphere()`).
     return_Bloch: bool, optional
         Whether to return the :class:`qutip.bloch.Bloch` instance
     bloch_kwargs: dict, optional
-        A dictionary with keyword arguments to be fed into the Bloch
+        A dictionary with keyword arguments to be fed into the qutip.Bloch
         constructor (if *b* not given).
 
     Returns
     -------
-    b: Bloch
-        The Bloch instance
+    b: qutip.Bloch
+        The qutip.Bloch instance
 
     Raises
     ------
@@ -164,6 +190,8 @@ def plot_bloch_vector_evolution(pulse: 'PulseSequence',
     --------
     qutip.bloch.Bloch: Qutip's Bloch sphere implementation.
     """
+    from mpl_toolkits.mplot3d import Axes3D
+
     # Raise an exception if not a one-qubit pulse
     if not pulse.d == 2:
         raise ValueError('Plotting Bloch sphere evolution only implemented ' +
@@ -171,7 +199,11 @@ def plot_bloch_vector_evolution(pulse: 'PulseSequence',
 
     # Parse default arguments
     if b is None:
-        b = init_bloch_sphere(**bloch_kwargs)
+        figsize = bloch_kwargs.pop('figsize', [5, 5])
+        view = bloch_kwargs.pop('view', [-60, 30])
+        fig = plt.figure(figsize=figsize)
+        axes = mplot3d.Axes3D(fig, azim=view[0], elev=view[1])
+        b = init_bloch_sphere(fig=fig, axes=axes, **bloch_kwargs)
 
     if n_samples is None:
         # 5 time points during  the smallest time interval in pulse.t. Being
@@ -179,9 +211,6 @@ def plot_bloch_vector_evolution(pulse: 'PulseSequence',
         # max out at 5000.
         n_samples = min([5000, 5*int(pulse.t[-1]/np.diff(pulse.t).min())])
 
-    if psi0 is None:
-        psi0 = basis(2)
-
     times = np.linspace(pulse.t[0], pulse.t[-1], n_samples)
     n_cops = len(pulse.c_opers)
     coeffs = np.zeros((n_cops, len(times)))
@@ -194,14 +223,29 @@ def plot_bloch_vector_evolution(pulse: 'PulseSequence',
     propagators = pulse.propagator_at_arb_t(times)
     points = get_bloch_vector(get_states_from_prop(propagators, psi0))
     b.add_points(points, meth='l')
+
+    # The following enables a color gradient for the trajectory, but only works
+    # by patching matplotlib, see
+    # https://github.com/matplotlib/matplotlib/issues/17755
+    # points = get_bloch_vector(
+    #     get_states_from_prop(propagators, psi0)
+    # ).T.reshape(-1, 1, 3)
+    # points[:, :, 1] *= -1  # qutip convention
+    # segments = np.concatenate([points[:-1], points[1:]], axis=1)
+
+    # if cmap is None:
+    #     cmap = plt.get_cmap('winter')
+
+    # colors = cmap(np.linspace(0, 1, n_samples - 1))
+    # lc = collections.LineCollection(segments[:, :, :2], colors=colors)
+    # b.axes.add_collection3d(lc, zdir='z', zs=segments[:, :, 2])
+
     if show:
         b.make_sphere()
 
     if return_Bloch:
         return b
 
-    return
-
 
 def plot_pulse_train(pulse: 'PulseSequence',
                      c_oper_identifiers: Optional[Sequence[int]] = None,
@@ -715,7 +759,7 @@ def plot_error_transfer_matrix(
             figsize = figure_kw.pop('figsize', (8*n_cols, 6*n_rows))
             fig = plt.figure(figsize=figsize, **figure_kw)
 
-        grid = ImageGrid(fig, **grid_kw)
+        grid = axes_grid1.ImageGrid(fig, **grid_kw)
     else:
         if len(grid) != len(n_oper_inds):
             raise ValueError('Size of supplied ImageGrid instance does not ' +
diff --git a/filter_functions/pulse_sequence.py b/filter_functions/pulse_sequence.py
index e4f34a8..d3bd1cb 100644
--- a/filter_functions/pulse_sequence.py
+++ b/filter_functions/pulse_sequence.py
@@ -49,7 +49,6 @@
 import numpy as np
 from numpy import linalg as nla
 from numpy import ndarray
-from qutip import Qobj
 
 from . import numeric, util
 from .basis import (Basis, equivalent_pauli_basis_elements,
@@ -1011,7 +1010,8 @@ def _parse_Hamiltonian(H: Hamiltonian, n_dt: int,
         coeffs = args[0]
         identifiers = list(args[1])
 
-    if not all(isinstance(oper, (ndarray, Qobj)) for oper in opers):
+    if not all(isinstance(oper, ndarray) or hasattr(oper, 'full')
+               for oper in opers):
         raise TypeError('Expected operators in '.format(H_str) +
                         'to be NumPy arrays or QuTiP Qobjs!')
 
@@ -1019,13 +1019,13 @@ def _parse_Hamiltonian(H: Hamiltonian, n_dt: int,
         raise TypeError('Expected coefficients in '.format(H_str) +
                         'to be a sequence')
 
-    # Convert qt.Qobjs to full arrays
+    # Convert qutip.Qobjs to full arrays
     try:
-        opers = np.array([oper.full() if isinstance(oper, Qobj) else oper
+        opers = np.array([oper.full() if hasattr(oper, 'full') else oper
                           for oper in opers])
     except ValueError:
         raise TypeError("Couldn't parse operators in {}. ".format(H_str) +
-                        "Are you sure they are all 2d arrays or Qobjs?")
+                        "Are you sure they are all 2d arrays or qutip.Qobjs?")
 
     # Check correct dimensions for the operators
     if set(oper.ndim for oper in opers) != {2}:
@@ -1747,7 +1747,7 @@ def remap(pulse: PulseSequence, order: Sequence[int], d_per_qubit: int = 2,
 
     Examples
     --------
-    >>> X, Y = util.P_np[1:3]
+    >>> X, Y = util.paulis[1:3]
     >>> XY, YX = util.tensor(X, Y), util.tensor(Y, X)
     >>> pulse = PulseSequence([[XY, [np.pi/2], 'XY']], [[YX, [1], 'YX']], [1],
     ...                       Basis.pauli(2))
@@ -1912,7 +1912,7 @@ def extend(pulse_to_qubit_mapping: PulseMapping,
     Examples
     --------
     >>> import filter_functions as ff
-    >>> I, X, Y, Z = ff.util.P_np
+    >>> I, X, Y, Z = ff.util.paulis
     >>> X_pulse = ff.PulseSequence([[X, [np.pi/2], 'X']],
     ...                            [[X, [1], 'X'], [Z, [1], 'Z']],
     ...                            [1], basis=ff.Basis.pauli(1))
diff --git a/filter_functions/util.py b/filter_functions/util.py
index ded367f..cab3f1c 100644
--- a/filter_functions/util.py
+++ b/filter_functions/util.py
@@ -83,7 +83,6 @@
                     Tuple, Union)
 
 import numpy as np
-import qutip as qt
 from numpy import linalg as nla
 from numpy import ndarray
 
@@ -171,17 +170,22 @@ def _get_notebook_name() -> str:
 except ImportError:
     tqdm = None
 
-__all__ = ['P_np', 'P_qt', 'abs2', 'all_array_equal', 'dot_HS',
+__all__ = ['paulis', 'abs2', 'all_array_equal', 'dot_HS',
            'get_sample_frequencies', 'hash_array_along_axis', 'mdot',
            'oper_equiv', 'progressbar', 'remove_float_errors', 'tensor',
            'tensor_insert', 'tensor_merge', 'tensor_transpose']
 
 # Pauli matrices
-P_qt = [qt.qeye(2),
-        qt.sigmax(),
-        qt.sigmay(),
-        qt.sigmaz()]
-P_np = [P.full() for P in P_qt]
+paulis = np.array([
+    [[1, 0],
+     [0, 1]],
+    [[0, 1],
+     [1, 0]],
+    [[0, -1j],
+     [1j, 0]],
+    [[1, 0],
+     [0, -1]],
+])
 
 
 def abs2(x: ndarray) -> ndarray:
@@ -461,7 +465,7 @@ def tensor_insert(arr: ndarray, *args, pos: Union[int, Sequence[int]],
 
     Examples
     --------
-    >>> I, X, Y, Z = P_np
+    >>> I, X, Y, Z = paulis
     >>> arr = tensor(X, I)
     >>> r = tensor_insert(arr, Y, Z, arr_dims=[[2, 2], [2, 2]], pos=0)
     >>> np.allclose(r, tensor(Y, Z, X, I))
@@ -634,7 +638,7 @@ def tensor_merge(arr: ndarray, ins: ndarray, pos: Sequence[int],
 
     Examples
     --------
-    >>> I, X, Y, Z = P_np
+    >>> I, X, Y, Z = paulis
     >>> arr = tensor(X, Y, Z)
     >>> ins = tensor(I, I)
     >>> r1 = tensor_merge(arr, ins, pos=[1, 2], arr_dims=[[2]*3, [2]*3],
@@ -758,7 +762,7 @@ def tensor_transpose(arr: ndarray, order: Sequence[int],
 
     Examples
     --------
-    >>> I, X, Y, Z = P_np
+    >>> I, X, Y, Z = paulis
     >>> arr = tensor(X, Y, Z)
     >>> transposed = tensor_transpose(arr, [1, 2, 0], arr_dims=[[2, 2, 2]]*2)
     >>> np.allclose(transposed, tensor(Y, Z, X))
@@ -847,7 +851,7 @@ def oper_equiv(psi: Union[Operator, State],
 
     Parameters
     ----------
-    psi, phi: Qobj or array_like
+    psi, phi: qutip.Qobj or array_like
         Vectors or operators to be compared
     eps: float
         The tolerance below which the two objects are treated as equal, i.e.,
@@ -858,12 +862,13 @@ def oper_equiv(psi: Union[Operator, State],
 
     Examples
     --------
-    >>> psi = qt.sigmax()
-    >>> phi = qt.sigmax()*np.exp(1j*1.2345)
+    >>> psi = paulis[1]
+    >>> phi = paulis[1]*np.exp(1j*1.2345)
     >>> oper_equiv(psi, phi)
     (True, 1.2345)
     """
-    psi, phi = [obj.full() if isinstance(obj, qt.Qobj) else obj
+    # Convert qutip.Qobj's to numpy arrays
+    psi, phi = [obj.full() if hasattr(obj, 'full') else obj
                 for obj in (psi, phi)]
 
     if eps is None:
@@ -895,7 +900,7 @@ def dot_HS(U: Operator, V: Operator, eps: float = None) -> float:
 
     Parameters
     ----------
-    U, V: Qobj or ndarray
+    U, V: qutip.Qobj or ndarray
         Objects to compute the inner product of.
 
     Returns
@@ -905,15 +910,16 @@ def dot_HS(U: Operator, V: Operator, eps: float = None) -> float:
 
     Examples
     --------
-    >>> U, V = qt.sigmax(), qt.sigmay()
+    >>> U, V = paulis[1:3]
     >>> dot_HS(U, V)
     0.0
     >>> dot_HS(U, U)
     2.0
     """
-    if isinstance(U, qt.Qobj):
+    # Convert qutip.Qobj's to numpy arrays
+    if hasattr(U, 'full'):
         U = U.full()
-    if isinstance(V, qt.Qobj):
+    if hasattr(V, 'full'):
         V = V.full()
 
     if eps is None:
diff --git a/setup.py b/setup.py
index 264e464..0796a23 100644
--- a/setup.py
+++ b/setup.py
@@ -45,8 +45,7 @@ def extract_version(version_file):
       author_email='tobias.hangleiter@rwth-aachen.de',
       packages=['filter_functions'],
       package_dir={'filter_functions': 'filter_functions'},
-      install_requires=['numpy', 'scipy', 'matplotlib', 'qutip', 'opt_einsum',
-                        'sparse'],
+      install_requires=['numpy', 'scipy', 'opt_einsum', 'sparse'],
       extras_require=extras_require,
       test_suite='tests',
       classifiers=[
diff --git a/tests/test_basis.py b/tests/test_basis.py
index e8b64c6..204795d 100644
--- a/tests/test_basis.py
+++ b/tests/test_basis.py
@@ -25,6 +25,7 @@
 from itertools import product
 
 import numpy as np
+import qutip as qt
 from sparse import COO
 
 import filter_functions as ff
@@ -40,9 +41,9 @@ def test_basis_constructor(self):
         with self.assertRaises(TypeError):
             _ = ff.Basis(1)
 
-        # All elements should be either COO, Qobj, or ndarray
-        elems = [ff.util.P_np[1], ff.util.P_qt[2],
-                 COO.from_numpy(ff.util.P_np[3]), ff.util.P_qt[0].data]
+        # All elements should be either sparse, Qobj, or ndarray
+        elems = [ff.util.paulis[1], qt.sigmay(), qt.qeye(2).data,
+                 COO.from_numpy(ff.util.paulis[3]), [[0, 1], [1, 0]]]
         with self.assertRaises(TypeError):
             _ = ff.Basis(elems)
 
@@ -54,7 +55,7 @@ def test_basis_constructor(self):
             _ = ff.Basis(testutil.rng.randn(5, 2, 2))
 
         # Properly normalized
-        self.assertEqual(ff.Basis.pauli(1), ff.Basis(ff.util.P_np))
+        self.assertEqual(ff.Basis.pauli(1), ff.Basis(ff.util.paulis))
 
         # Non traceless elems but traceless basis requested
         with self.assertRaises(ValueError):
@@ -123,7 +124,7 @@ def test_basis_properties(self):
 
             base._print_checks()
 
-        basis = ff.util.P_np[1].view(ff.Basis)
+        basis = ff.util.paulis[1].view(ff.Basis)
         self.assertTrue(basis.isorthonorm)
         self.assertArrayEqual(basis.T, basis.view(np.ndarray).T)
 
diff --git a/tests/test_core.py b/tests/test_core.py
index 975c342..68eb14f 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -229,10 +229,10 @@ def test_pulse_sequence_constructor(self):
 
         # Fewer identifiers than opers
         pulse_2 = ff.PulseSequence(
-            [[util.P_np[1], [1], 'X'],
-             [util.P_np[2], [1]]],
-            [[util.P_np[1], [1]],
-             [util.P_np[2], [1], 'Y']],
+            [[util.paulis[1], [1], 'X'],
+             [util.paulis[2], [1]]],
+            [[util.paulis[1], [1]],
+             [util.paulis[2], [1], 'Y']],
             [1]
         )
         self.assertArrayEqual(pulse_2.c_oper_identifiers, ('A_1', 'X'))
@@ -240,7 +240,7 @@ def test_pulse_sequence_constructor(self):
 
     def test_pulse_sequence_attributes(self):
         """Test attributes of single instance"""
-        X, Y, Z = util.P_np[1:]
+        X, Y, Z = util.paulis[1:]
         n_dt = testutil.rng.randint(1, 10)
 
         # trivial case
@@ -445,7 +445,7 @@ def test_pulse_sequence_attributes(self):
 
     def test_pulse_sequence_attributes_concat(self):
         """Test attributes of concatenated sequence."""
-        X, Y, Z = util.P_np[1:]
+        X, Y, Z = util.paulis[1:]
         n_dt_1 = testutil.rng.randint(5, 11)
         x_coeff_1 = testutil.rng.randn(n_dt_1)
         z_coeff_1 = testutil.rng.randn(n_dt_1)
@@ -639,7 +639,7 @@ def test_pulse_correlation_filter_function(self):
         Test calculation of pulse correlation filter function and control
         matrix.
         """
-        X, Y, Z = util.P_np[1:]
+        X, Y, Z = util.paulis[1:]
         T = 1
         omega = np.linspace(-2e1, 2e1, 250)
         H_c, H_n, dt = dict(), dict(), dict()
diff --git a/tests/test_precision.py b/tests/test_precision.py
index 65a1291..be252f3 100644
--- a/tests/test_precision.py
+++ b/tests/test_precision.py
@@ -35,8 +35,8 @@ class PrecisionTest(testutil.TestCase):
     def test_FID(self):
         """FID"""
         tau = abs(testutil.rng.randn())
-        FID_pulse = ff.PulseSequence([[ff.util.P_np[1]/2, [0]]],
-                                     [[ff.util.P_np[3]/2, [1]]],
+        FID_pulse = ff.PulseSequence([[ff.util.paulis[1]/2, [0]]],
+                                     [[ff.util.paulis[3]/2, [1]]],
                                      [tau])
 
         omega = ff.util.get_sample_frequencies(FID_pulse, 50, spacing='linear')
@@ -54,7 +54,7 @@ def test_SE(self):
         H_c, dt = testutil.generate_dd_hamiltonian(n, tau=tau, tau_pi=tau_pi,
                                                    dd_type='cpmg')
 
-        H_n = [[ff.util.P_np[3]/2, np.ones_like(dt)]]
+        H_n = [[ff.util.paulis[3]/2, np.ones_like(dt)]]
 
         SE_pulse = ff.PulseSequence(H_c, H_n, dt)
         omega = ff.util.get_sample_frequencies(SE_pulse, 100, spacing='linear')
@@ -66,7 +66,7 @@ def test_SE(self):
         # Test again with a factor of one between the noise operators and
         # coefficients
         r = testutil.rng.randn()
-        H_n = [[ff.util.P_np[3]/2*r, np.ones_like(dt)/r]]
+        H_n = [[ff.util.paulis[3]/2*r, np.ones_like(dt)/r]]
 
         SE_pulse = ff.PulseSequence(H_c, H_n, dt)
         # Comparison to filter function defined with omega**2
@@ -83,7 +83,7 @@ def test_6_pulse_CPMG(self):
         H_c, dt = testutil.generate_dd_hamiltonian(n, tau=tau, tau_pi=tau_pi,
                                                    dd_type='cpmg')
 
-        H_n = [[ff.util.P_np[3]/2, np.ones_like(dt)]]
+        H_n = [[ff.util.paulis[3]/2, np.ones_like(dt)]]
 
         CPMG_pulse = ff.PulseSequence(H_c, H_n, dt)
         omega = ff.util.get_sample_frequencies(CPMG_pulse, 100, spacing='log')
@@ -103,7 +103,7 @@ def test_6_pulse_UDD(self):
         H_c, dt = testutil.generate_dd_hamiltonian(n, tau=tau, tau_pi=tau_pi,
                                                    dd_type='udd')
 
-        H_n = [[ff.util.P_np[3]/2, np.ones_like(dt)]]
+        H_n = [[ff.util.paulis[3]/2, np.ones_like(dt)]]
 
         UDD_pulse = ff.PulseSequence(H_c, H_n, dt)
         # Comparison to filter function defined with omega**2
@@ -122,7 +122,7 @@ def test_6_pulse_PDD(self):
         H_c, dt = testutil.generate_dd_hamiltonian(n, tau=tau, tau_pi=tau_pi,
                                                    dd_type='pdd')
 
-        H_n = [[ff.util.P_np[3]/2, np.ones_like(dt)]]
+        H_n = [[ff.util.paulis[3]/2, np.ones_like(dt)]]
 
         PDD_pulse = ff.PulseSequence(H_c, H_n, dt)
         # Comparison to filter function defined with omega**2
@@ -141,7 +141,7 @@ def test_5_pulse_CDD(self):
         H_c, dt = testutil.generate_dd_hamiltonian(n, tau=tau, tau_pi=tau_pi,
                                                    dd_type='cdd')
 
-        H_n = [[ff.util.P_np[3]/2, np.ones_like(dt)]]
+        H_n = [[ff.util.paulis[3]/2, np.ones_like(dt)]]
 
         CDD_pulse = ff.PulseSequence(H_c, H_n, dt)
         # Comparison to filter function defined with omega**2
@@ -179,7 +179,7 @@ def test_liouville_representation(self):
 
             if d == 2:
                 U_liouville = numeric.liouville_representation(
-                    ff.util.P_np[1:], basis)
+                    ff.util.paulis[1:], basis)
                 self.assertArrayAlmostEqual(U_liouville[0],
                                             np.diag([1, 1, -1, -1]),
                                             atol=np.finfo(float).eps)
diff --git a/tests/test_sequencing.py b/tests/test_sequencing.py
index a0d7a95..208e376 100644
--- a/tests/test_sequencing.py
+++ b/tests/test_sequencing.py
@@ -71,7 +71,7 @@ def test_concatenate_without_filter_function(self):
                                                          tau_pi=tau_pi,
                                                          dd_type='cpmg')
 
-        n_oper = util.P_np[3]
+        n_oper = util.paulis[3]
         H_n_SE = [[n_oper, np.ones_like(dt_SE)]]
         SE_1 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
         SE_2 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
@@ -135,7 +135,7 @@ def test_concatenate_with_filter_function_SE1(self):
                                                          tau_pi=tau_pi,
                                                          dd_type='cpmg')
 
-        H_n_SE = [[util.P_np[3], np.ones_like(dt_SE)]]
+        H_n_SE = [[util.paulis[3], np.ones_like(dt_SE)]]
         SE_1 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
         SE_2 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
 
@@ -143,7 +143,7 @@ def test_concatenate_with_filter_function_SE1(self):
                                                              tau_pi=tau_pi,
                                                              dd_type='cpmg')
 
-        H_n_CPMG = [[util.P_np[3], np.ones_like(dt_CPMG)]]
+        H_n_CPMG = [[util.paulis[3], np.ones_like(dt_CPMG)]]
         CPMG = ff.PulseSequence(H_c_CPMG, H_n_CPMG, dt_CPMG)
 
         SE_1.cache_filter_function(omega)
@@ -177,7 +177,7 @@ def test_concatenate_with_filter_function_SE2(self):
                                                          tau_pi=tau_pi,
                                                          dd_type='cpmg')
 
-        H_n_SE = [[util.P_np[3], np.ones_like(dt_SE)]]
+        H_n_SE = [[util.paulis[3], np.ones_like(dt_SE)]]
         SE_1 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
         SE_2 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
 
@@ -185,7 +185,7 @@ def test_concatenate_with_filter_function_SE2(self):
                                                              tau_pi=tau_pi,
                                                              dd_type='cpmg')
 
-        H_n_CPMG = [[util.P_np[3], np.ones_like(dt_CPMG)]]
+        H_n_CPMG = [[util.paulis[3], np.ones_like(dt_CPMG)]]
         CPMG = ff.PulseSequence(H_c_CPMG, H_n_CPMG, dt_CPMG)
 
         SE_2.cache_filter_function(omega)
@@ -217,7 +217,7 @@ def test_concatenate_with_filter_function_SE12(self):
                                                          tau_pi=tau_pi,
                                                          dd_type='cpmg')
 
-        H_n_SE = [[util.P_np[3], np.ones_like(dt_SE)]]
+        H_n_SE = [[util.paulis[3], np.ones_like(dt_SE)]]
         SE_1 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
         SE_2 = ff.PulseSequence(H_c_SE, H_n_SE, dt_SE)
 
@@ -225,7 +225,7 @@ def test_concatenate_with_filter_function_SE12(self):
                                                              tau_pi=tau_pi,
                                                              dd_type='cpmg')
 
-        H_n_CPMG = [[util.P_np[3], np.ones_like(dt_CPMG)]]
+        H_n_CPMG = [[util.paulis[3], np.ones_like(dt_CPMG)]]
         CPMG = ff.PulseSequence(H_c_CPMG, H_n_CPMG, dt_CPMG)
 
         SE_1.cache_filter_function(omega)
@@ -263,14 +263,14 @@ def test_concatenate_4_spin_echos(self):
                                                          tau_pi=tau_pi,
                                                          dd_type='cpmg')
 
-        H_n_SE = [[util.P_np[3], np.ones_like(dt_SE)]]
+        H_n_SE = [[util.paulis[3], np.ones_like(dt_SE)]]
         SE = [ff.PulseSequence(H_c_SE, H_n_SE, dt_SE) for _ in range(4)]
 
         H_c_CPMG, dt_CPMG = testutil.generate_dd_hamiltonian(4*n, tau=4*tau,
                                                              tau_pi=tau_pi,
                                                              dd_type='cpmg')
 
-        H_n_CPMG = [[util.P_np[3], np.ones_like(dt_CPMG)]]
+        H_n_CPMG = [[util.paulis[3], np.ones_like(dt_CPMG)]]
         CPMG = ff.PulseSequence(H_c_CPMG, H_n_CPMG, dt_CPMG)
 
         SE[testutil.rng.randint(0, len(SE)-1)].cache_filter_function(omega)
@@ -458,7 +458,7 @@ def test_different_n_opers(self):
 
     def test_concatenation_periodic(self):
         """Test concatenation for periodic Hamiltonians"""
-        X, Y, Z = util.P_np[1:]
+        X, Y, Z = util.paulis[1:]
         A = 0.01
         omega_0 = 1
         omega_d = omega_0
@@ -521,7 +521,7 @@ class ExtensionTest(testutil.TestCase):
 
     def test_extend_with_identity(self):
         """Test extending a pulse to more qubits"""
-        ID, X, Y, Z = util.P_np
+        ID, X, Y, Z = util.paulis
         n_dt = 10
         coeffs = testutil.rng.randn(3, n_dt)
         ids = ['X', 'Y', 'Z']
@@ -718,7 +718,7 @@ def test_caching(self):
         self.assertIsNone(extended_pulse._F)
 
     def test_accuracy(self):
-        ID, X, Y, Z = util.P_np
+        ID, X, Y, Z = util.paulis
         XI = util.tensor(X, ID)
         IX = util.tensor(ID, X)
         XII = util.tensor(X, ID, ID)
@@ -934,7 +934,7 @@ def test_accuracy(self):
                                     atol=1e-8)
 
     def test_exceptions(self):
-        X = util.P_np[1]
+        X = util.paulis[1]
         n_dt = 10
         omega = np.linspace(0, 1, 50)
 
@@ -1052,7 +1052,7 @@ def test_caching(self):
             self.assertFalse(remapped_ggm_pulse.is_cached(attr))
 
     def test_accuracy(self):
-        paulis = np.array(util.P_np)
+        paulis = np.array(util.paulis)
         I, X, Y, Z = paulis
         amps = testutil.rng.randn(testutil.rng.randint(1, 11))
         pulse = ff.PulseSequence(
diff --git a/tests/test_util.py b/tests/test_util.py
index 7396152..6136678 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -47,9 +47,9 @@ def test_cexp(self):
 
     def test_get_indices_from_identifiers(self):
         pulse = PulseSequence(
-            [[util.P_np[3], [2], 'Z'],
-             [util.P_np[1], [1], 'X']],
-            [[util.P_np[2], [2]]],
+            [[util.paulis[3], [2], 'Z'],
+             [util.paulis[1], [1], 'X']],
+            [[util.paulis[2], [2]]],
             [1]
         )
         idx = util.get_indices_from_identifiers(pulse, ['X'], 'control')
@@ -101,7 +101,7 @@ def test_tensor(self):
                 self.assertArrayEqual(util.tensor(A, B, rank=1), np.kron(A, B))
 
         i, j = testutil.rng.randint(0, 4, (2,))
-        A, B = util.P_np[i], util.P_np[j]
+        A, B = util.paulis[i], util.paulis[j]
         self.assertArrayEqual(util.tensor(A, B), np.kron(A, B))
 
         args = [testutil.rng.randn(4, 1, 2), testutil.rng.randn(3, 2),
@@ -119,7 +119,7 @@ def test_tensor(self):
         self.assertEqual(msg, str(err.exception))
 
     def test_tensor_insert(self):
-        I, X, Y, Z = util.P_np
+        I, X, Y, Z = util.paulis
         arr = util.tensor(X, I)
 
         with self.assertRaises(ValueError):
@@ -258,7 +258,7 @@ def test_tensor_insert(self):
 
     def test_tensor_merge(self):
         # Test basic functionality
-        I, X, Y, Z = util.P_np
+        I, X, Y, Z = util.paulis
         arr = util.tensor(X, Y, Z)
         ins = util.tensor(I, I)
         r1 = util.tensor_merge(arr, ins, pos=[1, 2], arr_dims=[[2]*3, [2]*3],
@@ -366,7 +366,7 @@ def test_tensor_merge(self):
 
     def test_tensor_transpose(self):
         # Test basic functionality
-        paulis = np.array(util.P_np)
+        paulis = np.array(util.paulis)
         I, X, Y, Z = paulis
         arr = util.tensor(I, X, Y, Z)
         arr_dims = [[2]*4]*2
@@ -512,8 +512,8 @@ def test_all_array_equal(self):
 
     def test_get_sample_frequencies(self):
         pulse = PulseSequence(
-            [[util.P_np[1], [np.pi/2]]],
-            [[util.P_np[1], [1]]],
+            [[util.paulis[1], [np.pi/2]]],
+            [[util.paulis[1], [1]]],
             [abs(testutil.rng.randn())]
         )
         # Default args
@@ -539,8 +539,8 @@ def test_get_sample_frequencies(self):
 
     def test_symmetrize_spectrum(self):
         pulse = PulseSequence(
-            [[util.P_np[1], [np.pi/2]]],
-            [[util.P_np[1], [1]]],
+            [[util.paulis[1], [np.pi/2]]],
+            [[util.paulis[1], [1]]],
             [abs(testutil.rng.randn())]
         )
 
diff --git a/tests/testutil.py b/tests/testutil.py
index d66a043..f819ed4 100644
--- a/tests/testutil.py
+++ b/tests/testutil.py
@@ -197,7 +197,7 @@ def rand_pulse_sequence(d: int, n_dt: int, n_cops: int = 3, n_nops: int = 3,
 
 d = 16
 H = np.empty((6, d, d), dtype=float)
-Id, Px, Py, Pz = util.P_np
+Id, Px, Py, Pz = util.paulis
 # Exchange Hamiltonians
 H[0] = 1/4*sum(util.tensor(P, P, Id, Id) for P in (Px, Py, Pz)).real
 H[1] = 1/4*sum(util.tensor(Id, P, P, Id) for P in (Px, Py, Pz)).real

From 25d4e7deb89d5f5dda57750f84fc438a14b4c6ad Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Thu, 25 Jun 2020 17:45:28 +0200
Subject: [PATCH 03/22] Update documentation and examples

---
 .../examples/advanced_concatenation.ipynb     |    12 +-
 .../calculating_error_transfer_matrices.ipynb | 13963 +++++++++++++-
 doc/source/examples/extending_pulses.ipynb    |     6 +-
 doc/source/examples/getting_started.ipynb     |    12 +-
 doc/source/examples/periodic_driving.ipynb    |  3657 +++-
 .../examples/quantum_fourier_transform.ipynb  | 15396 +++++++++++++++-
 doc/source/examples/qutip_integration.ipynb   |     7 +-
 examples/qft.py                               |     3 +-
 8 files changed, 32879 insertions(+), 177 deletions(-)

diff --git a/doc/source/examples/advanced_concatenation.ipynb b/doc/source/examples/advanced_concatenation.ipynb
index 0c06ef8..24b368c 100644
--- a/doc/source/examples/advanced_concatenation.ipynb
+++ b/doc/source/examples/advanced_concatenation.ipynb
@@ -88,7 +88,7 @@
     "d = 2\n",
     "H = np.empty((2, d, d), dtype=complex)\n",
     "\n",
-    "Id, Px, Py, Pz = ff.util.P_np\n",
+    "Id, Px, Py, Pz = ff.util.paulis\n",
     "H[0] = 1/2*Px\n",
     "H[1] = 1/2*Pz\n",
     "\n",
@@ -184,8 +184,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from filter_functions import plotting\n",
+    "\n",
     "for gate in gate_types:\n",
-    "    ff.plot_bloch_vector_evolution(H[gate], n_samples=501, figsize=(4, 4))"
+    "    plotting.plot_bloch_vector_evolution(H[gate], n_samples=501,\n",
+    "                                         figsize=(4, 4))"
    ]
   },
   {
@@ -203,9 +206,8 @@
    "source": [
     "pulses = ('Y', 'X_1', 'X_2')\n",
     "for gate_type, hadamard in H.items():\n",
-    "    fig, ax, leg = ff.plot_pulse_correlation_filter_function(hadamard,\n",
-    "                                                             xscale='linear',\n",
-    "                                                             figsize=(9, 6))\n",
+    "    fig, ax, leg = plotting.plot_pulse_correlation_filter_function(\n",
+    "        hadamard, xscale='linear', figsize=(9, 6))\n",
     "\n",
     "    # Adjust the titles to something more meaningful\n",
     "    for i in range(3):\n",
diff --git a/doc/source/examples/calculating_error_transfer_matrices.ipynb b/doc/source/examples/calculating_error_transfer_matrices.ipynb
index fc048cc..4265af8 100644
--- a/doc/source/examples/calculating_error_transfer_matrices.ipynb
+++ b/doc/source/examples/calculating_error_transfer_matrices.ipynb
@@ -75,7 +75,7 @@
     "d = 16\n",
     "H = np.empty((6, d, d), dtype=complex)\n",
     "\n",
-    "Id, Px, Py, Pz = util.P_np\n",
+    "Id, Px, Py, Pz = util.paulis\n",
     "# Exchange Hamiltonians\n",
     "H[0] = 1/4*sum(util.tensor(P, P, Id, Id) for P in (Px, Py, Pz))\n",
     "H[1] = 1/4*sum(util.tensor(Id, P, P, Id) for P in (Px, Py, Pz))\n",
@@ -233,38 +233,4103 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
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\n",
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OPK93Eyj2m9inpWbn2UOATcfKMwFFiu7FeRHncut1Mp5zBjUhiMANsF87TEd2gA2cLEnNgWUbUYDEeTUEZuJxZUwdXbvtgjDHyEBzgcewU3QHrdN3vTNbScBgX8OX29GTjr1n0zuFsEV2QFM8mgsYyV4cVrHujlvcgonMZSIeYaYBEFZcAYLXluWHnCcUMJyhcMOKRyMXcX9V6XZc1O9xthTk8pbjYT4MBzANqEwurXodUC86Ht2GW+FJ4flG9W2zehP7VVyX006e67CFpRUvwbl1g3iVosChGJHvDttyMSimTxoGLHn+XVPlVQwkVh4EPT9w99haqcd0811/IPKlv3pfu7NY6kfeb1ArGSv/vJwIQmVG403KWteVWKO0OmIG3cHTbIEvtvuIvLTYbgvqovMqhaOaVPbL2L9epWSt+FdP3DjeDeL1PQ6f4WwUVjeslLoun83SxyqVDIhs88WeEWZ7m8QolQyjHVApGSTTpw0ruvtVzyELcgZH92m3D32eUgmY28IvvMyETHK+P/LY5bhepphP/eWlP6Au2dHajewDqqmeB2czTRj83Pf3b1Dr9Le4mD+iR73Hy1AKHGAmsmyR90ujG9Y6PQVns3gxah1rPLerdfqhYSG4XxmdznXXdlNtBGoyF8n8Os2HOIHYF3Fti7mIzIXveQs3e5fWs96rNDSx1nLQEdfeMGeLLvpx2jfJdpb7c71nY4w8YOK0Mo9Enkcl++3FTZrrcfciDL90Td3VgjesuJoqideYC0K3HHR2rIeN+a4/lFS8Te9csM3Wt/ENC8j+DBq0JaHAhBq4YwFENZ9idqO8kMdLtAVKplY8GtPtW5Y6vR5nQz/fXnmRMZxRfZ54LM0lezngmPPdPYT2YddFh8EyRxwY7igx9+PEgmhP4dXZvO2sDkOnVWJmUQnOb2s8SLUayh1CjSeHsycHY24yW3WqWdYaK4YL4XWr9dkqU80FwP564a8evd0FLeVlXw+lEdQaqXRa3gnUj/UbRm9HvdmKRit4rdHcDof9Gmw2axyrJOovFi0w7PpiNgdrGqfLg62LV+PHcX37FV+BgSkueKa940K36593b+OanHReyDXsv373Xq0ovCLXluDXd2vxsm7J9yTwNj4GkamEzz/ePb9KTk1uAvbYxvGijWN9ddT9K7ofOaR259W1YUUMF6wm45t5fP5HGb/81/hqe+mUa++7KuvN3H0vMz5henvXJ3qELfaXdxH8fJqXKJynmst93a9eQ4NmObdQcftLO2XfHoGyH779y8/ffPvjp2fz+x9//gSF2oXc+98pE1wPd2q0vvynf/zw/uPPb5/9+/3SwquhjWQbRTsIcRrG8vHCOITvmtoPRALYtQYJmxK9S8jPt7vUpwfv3/Tv2mJp6bHG6H77mjWK29miuIuWQJLRvSWcNfrdhep7vZGVy6m/5eOFHnk41u17THzBQXuZy69i+ivGWwL1bjhb3JrDISuVx8bE57q8eD2ehs1unvHrge9r87zcjpQxYXIa2vti5va+QNrh25/e0RLjV3Tg+Xvl7lPG/wOjfYOR/g8ncjgKCmVuZHN0cmVhbQplbmRvYmoKMTEgMCBvYmoKNDQ3NAplbmRvYmoKMTcgMCBvYmoKPDwgL0JCb3ggWyAtMTAxNiAtMzUxIDE2NjAgMTA2OCBdIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTYzCi9TdWJ0eXBlIC9Gb3JtIC9UeXBlIC9YT2JqZWN0ID4+CnN0cmVhbQp4nDWQMQ5DIQxDd07hCyAlJCFwnkqdfu+/1qF0esZBdqBtRXji06bGUU+LveBx1NhX+TTo0FKxsRM+BUuIiRS8mucALV+GYJBo4dVC5jnrOBgCxtEeWSKM9iK8ULaizyqWja6MY1NXY2yQcXu6/vq7clFfh5zITxlX6AlLbmz0bQ6woEDL0mlr3be8jxsWV6lPSPXHgbrfQf7F/a+nvb9VsTsiCmVuZHN0cmVhbQplbmRvYmoKMTUgMCBvYmoKPDwgL0Jhc2VGb250IC9EZWphVnVTYW5zLU9ibGlxdWUgL0NoYXJQcm9jcyAxNiAwIFIKL0VuY29kaW5nIDw8IC9EaWZmZXJlbmNlcyBbIF0gL1R5cGUgL0VuY29kaW5nID4+IC9GaXJzdENoYXIgMAovRm9udEJCb3ggWyAtMTAxNiAtMzUxIDE2NjAgMTA2OCBdIC9Gb250RGVzY3JpcHRvciAxNCAwIFIKL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0gL0xhc3RDaGFyIDI1NSAvTmFtZSAvRGVqYVZ1U2Fucy1PYmxpcXVlCi9TdWJ0eXBlIC9UeXBlMyAvVHlwZSAvRm9udCAvV2lkdGhzIDEzIDAgUiA+PgplbmRvYmoKMTQgMCBvYmoKPDwgL0FzY2VudCA5MjkgL0NhcEhlaWdodCAwIC9EZXNjZW50IC0yMzYgL0ZsYWdzIDk2Ci9Gb250QkJveCBbIC0xMDE2IC0zNTEgMTY2MCAxMDY4IF0gL0ZvbnROYW1lIC9EZWphVnVTYW5zLU9ibGlxdWUKL0l0YWxpY0FuZ2xlIDAgL01heFdpZHRoIDEzNTAgL1N0ZW1WIDAgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9YSGVpZ2h0IDAgPj4KZW5kb2JqCjEzIDAgb2JqClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAzOTAgNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNgo2MzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYzMiA1NzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1IDYxMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMgNjM1IDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2MzUgNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgzOCA2MDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNTAgNjM1IDQwMCAxMDcwIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAwIDUyMSA0MDAgMTAyOCA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAwIDUwMCAxMDAwIDQ3MSA2MTcgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYzNiA2MzYgMzE4IDUwMAo0MDEgNDcxIDYxNyA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0IDY4NCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDggNzg3IDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA4CjYzMCA2MTMgNjEzIDYxMyA2MTMgNjEzIDYxMyA5OTUgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3OCAyNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0IDU5MiA2MzUgNTkyIF0KZW5kb2JqCjE2IDAgb2JqCjw8ID4+CmVuZG9iagoyMiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMwNCA+PgpzdHJlYW0KeJw9kjuSwzAMQ3udghfIjPiT5PNkJ5X3/u0+MslWgEmJACgvdZmypjwgaSYJ/9Hh4WI75XfYns3MwLVELxPLKc+hK8TcRfmymY26sjrFqsMwnVv0qJyLhk2TmucqSxm3C57DtYnnln3EDzc0qAd1jUvCDd3VaFkKzXB1/zu9R9l3NTwXm1Tq1BePF1EV5vkhT6KH6UrifDwoIVx7MEYWEuRT0UCOs1yt8l5C9g63GrLCQWpJ57MnPNh1ek8ubhfNEA9kuVT4TlHs7dAzvuxKCT0StuFY7n07mrHpGps47H7vRtbKjK5oIX7IVyfrJWDcUyZFEmROtlhui9We7qEopnOGcxkg6tmKhlLmYlerfww7bywv2SzIlMwLMkanTZ44eMh+jZr0eZXneP0BbPNzOwplbmRzdHJlYW0KZW5kb2JqCjIzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzkyID4+CnN0cmVhbQp4nD1SS24FMQjbzym4QKXwTXKeqd7u3X9bm8xUqgovA7YxlJcMqSU/6pKIM0x+9XJd4lHyvWxqZ+Yh7i42pvhYcl+6hthy0ZpisU8cyS/ItFRYoVbdo0PxhSgTDwAt4IEF4b4c//EXqMHXsIVyw3tkAmBK1G5AxkPRGUhZQRFh+5EV6KRQr2zh7yggV9SshaF0YogNlgApvqsNiZio2aCHhJWSqh3S8Yyk8FvBXYlhUFtb2wR4ZtAQ2d6RjREz7dEZcVkRaz896aNRMrVRGQ9NZ3zx3TJS89E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       "text/plain": [
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     "### Calculation using `filter_functions`\n",
     "The calculation is implemented in the `ff.error_transfer_matrix()` function, which takes a `PulseSequence` instance, a noise spectrum and a list of frequencies as arguments. Optionally, we may also pass a list of identifiers corresponding to the noise operators whose contributions we are interested in. In our case we use the exchange terms affected by charge noise. The function will return an array with the separate noise operator contributions on the first axis.\n",
     "\n",
-    "Since the `filter_functions` package assumes two-sided spectra, we need to rescale the above spectrum by a factor of two accordingly. This can be done using `util.symmetrize_spectrum()`. Visualization of the error transfer matrices is implemented by ``ff.plot_error_transfer_matrix()``."
+    "Since the `filter_functions` package assumes two-sided spectra, we need to rescale the above spectrum by a factor of two accordingly. This can be done using `filter_functions.util.symmetrize_spectrum()`. Visualization of the error transfer matrices is implemented by ``filter_functions.plotting.plot_error_transfer_matrix()``."
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+     "output_type": "stream",
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+      "\\\\janeway\\user ag bluhm\\hangleiter\\code\\filter_functions\\filter_functions\\plotting.py:780: MatplotlibDeprecationWarning: default base may change from np.e to 10.  To suppress this warning specify the base keyword argument.\n",
+      "  norm = colors.SymLogNorm(linthresh=linthresh, vmin=Umin, vmax=Umax)\n"
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\n",
-      "text/plain": [
-       "<Figure size 792x1080 with 14 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
     "from itertools import product\n",
     "import matplotlib.pyplot as plt\n",
+    "from filter_functions import plotting\n",
     "fig = plt.figure(figsize=(11, 15))\n",
     "\n",
     "# First basis 16 elements are the Pauli basis on the qubit subspace\n",
@@ -452,7 +4514,7 @@
     "    # We can call plot_transfer matrix with the same arguments as\n",
     "    # error_transfer_matrix in which case the transfer matrix is calculated on\n",
     "    # the fly, or pass a pre-computed transfer matrix to the function\n",
-    "    fig, grid = ff.plot_error_transfer_matrix(\n",
+    "    fig, grid = plotting.plot_error_transfer_matrix(\n",
     "        U=transfer_matrices[key], n_oper_identifiers=identifiers,\n",
     "        basis_labels=basis_labels, grid_kw=dict(rect=10*31+i), figsize=(11, 4),\n",
     "        fig=fig, cbar_label=key, colorscale='log'\n",
@@ -497,6 +4559,8315 @@
       "p(10->10) = 2.3946e-05\n",
       "p(11->11) = 2.3946e-05\n"
      ]
+    },
+    {
+     "data": {
+      "application/pdf": 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+       "     </g>\r\n",
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+       "     </g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_37\">\r\n",
+       "      <!-- II -->\r\n",
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+       "     <g id=\"text_38\">\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_3\">\r\n",
+       "     <g id=\"line2d_39\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"39.091544\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_39\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(47.510809 42.130919)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_4\">\r\n",
+       "     <g id=\"line2d_40\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"45.885662\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_40\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(46.917059 48.925037)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_5\">\r\n",
+       "     <g id=\"line2d_41\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"52.679779\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_41\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(46.917059 55.719154)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_6\">\r\n",
+       "     <g id=\"line2d_42\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"59.473897\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_42\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(43.797059 62.513272)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_7\">\r\n",
+       "     <g id=\"line2d_43\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"66.268015\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_43\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(44.390809 69.30739)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_8\">\r\n",
+       "     <g id=\"line2d_44\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"73.062132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_44\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(43.797059 76.101507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_9\">\r\n",
+       "     <g id=\"line2d_45\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"79.85625\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_45\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(47.510809 82.895625)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_10\">\r\n",
+       "     <g id=\"line2d_46\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"86.650368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_46\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(44.390809 89.689743)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_11\">\r\n",
+       "     <g id=\"line2d_47\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"93.444485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_47\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(44.984559 96.48386)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_12\">\r\n",
+       "     <g id=\"line2d_48\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"100.238603\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_48\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(44.390809 103.277978)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_13\">\r\n",
+       "     <g id=\"line2d_49\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"107.032721\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_49\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(46.917059 110.072096)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_14\">\r\n",
+       "     <g id=\"line2d_50\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"113.826838\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_50\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(43.797059 116.866213)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_15\">\r\n",
+       "     <g id=\"line2d_51\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"120.620956\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_51\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(44.390809 123.660331)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_16\">\r\n",
+       "     <g id=\"line2d_52\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"127.415074\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_52\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(43.797059 130.454449)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_17\">\r\n",
+       "     <g id=\"line2d_53\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"134.209191\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_53\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 137.248566)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_18\">\r\n",
+       "     <g id=\"line2d_54\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"141.003309\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_54\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 144.042684)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_19\">\r\n",
+       "     <g id=\"line2d_55\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"147.797426\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_55\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 150.836801)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_20\">\r\n",
+       "     <g id=\"line2d_56\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"154.591544\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_56\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 157.630919)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_21\">\r\n",
+       "     <g id=\"line2d_57\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"161.385662\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_57\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 164.425037)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_22\">\r\n",
+       "     <g id=\"line2d_58\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"168.179779\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_58\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 171.219154)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_23\">\r\n",
+       "     <g id=\"line2d_59\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"174.973897\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_59\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 178.013272)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_24\">\r\n",
+       "     <g id=\"line2d_60\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"181.768015\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_60\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 184.80739)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_25\">\r\n",
+       "     <g id=\"line2d_61\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"188.562132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_61\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 191.601507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_26\">\r\n",
+       "     <g id=\"line2d_62\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"195.35625\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_62\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 198.395625)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
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+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_63\">\r\n",
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+       "      <g transform=\"translate(41.797059 205.189743)scale(0.08 -0.08)\">\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
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+       "     </g>\r\n",
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+       "      <g transform=\"translate(41.797059 211.98386)scale(0.08 -0.08)\">\r\n",
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+       "    </g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_65\">\r\n",
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+       "      <g transform=\"translate(41.797059 218.777978)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
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+       "      <g transform=\"translate(41.797059 245.954449)scale(0.08 -0.08)\">\r\n",
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+       "     <g id=\"line2d_73\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"331.342353\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_74\">\r\n",
+       "      <!-- II -->\r\n",
+       "      <g transform=\"translate(333.549853 278.414485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_38\">\r\n",
+       "     <g id=\"line2d_74\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"338.136471\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_75\">\r\n",
+       "      <!-- IX -->\r\n",
+       "      <g transform=\"translate(340.343971 281.534485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_39\">\r\n",
+       "     <g id=\"line2d_75\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"344.930588\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_76\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(347.138088 280.940735)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_40\">\r\n",
+       "     <g id=\"line2d_76\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"351.724706\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_77\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(353.932206 281.534485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_41\">\r\n",
+       "     <g id=\"line2d_77\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"358.518824\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_78\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(360.726324 281.534485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_42\">\r\n",
+       "     <g id=\"line2d_78\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"365.312941\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_79\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(367.520441 284.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_43\">\r\n",
+       "     <g id=\"line2d_79\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"372.107059\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_80\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(374.314559 284.060735)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_44\">\r\n",
+       "     <g id=\"line2d_80\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"378.901176\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_81\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(381.108676 284.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_45\">\r\n",
+       "     <g id=\"line2d_81\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"385.695294\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_82\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(387.902794 280.940735)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_46\">\r\n",
+       "     <g id=\"line2d_82\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"392.489412\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_83\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(394.696912 284.060735)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_47\">\r\n",
+       "     <g id=\"line2d_83\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"399.283529\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_84\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(401.491029 283.466985)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_48\">\r\n",
+       "     <g id=\"line2d_84\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"406.077647\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_85\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(408.285147 284.060735)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_49\">\r\n",
+       "     <g id=\"line2d_85\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"412.871765\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_86\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(415.079265 281.534485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_50\">\r\n",
+       "     <g id=\"line2d_86\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"419.665882\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_87\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(421.873382 284.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_51\">\r\n",
+       "     <g id=\"line2d_87\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"426.46\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_88\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(428.6675 284.060735)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_52\">\r\n",
+       "     <g id=\"line2d_88\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"433.254118\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_89\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(435.461618 284.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_53\">\r\n",
+       "     <g id=\"line2d_89\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"440.048235\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_90\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(442.255735 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_54\">\r\n",
+       "     <g id=\"line2d_90\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"446.842353\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_91\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(449.049853 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_55\">\r\n",
+       "     <g id=\"line2d_91\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"453.636471\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_92\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(455.843971 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_56\">\r\n",
+       "     <g id=\"line2d_92\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"460.430588\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_93\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(462.638088 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_57\">\r\n",
+       "     <g id=\"line2d_93\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"467.224706\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_94\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(469.432206 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_58\">\r\n",
+       "     <g id=\"line2d_94\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"474.018824\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_95\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(476.226324 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_59\">\r\n",
+       "     <g id=\"line2d_95\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"480.812941\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_96\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(483.020441 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_60\">\r\n",
+       "     <g id=\"line2d_96\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"487.607059\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_97\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(489.814559 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_61\">\r\n",
+       "     <g id=\"line2d_97\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"494.401176\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_98\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(496.608676 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_62\">\r\n",
+       "     <g id=\"line2d_98\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"501.195294\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_99\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(503.402794 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_63\">\r\n",
+       "     <g id=\"line2d_99\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"507.989412\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_100\">\r\n",
+       "      <!-- $C_{26}$ -->\r\n",
+       "      <g transform=\"translate(510.196912 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_64\">\r\n",
+       "     <g id=\"line2d_100\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"514.783529\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_101\">\r\n",
+       "      <!-- $C_{27}$ -->\r\n",
+       "      <g transform=\"translate(516.991029 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_65\">\r\n",
+       "     <g id=\"line2d_101\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"521.577647\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_102\">\r\n",
+       "      <!-- $C_{28}$ -->\r\n",
+       "      <g transform=\"translate(523.785147 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_66\">\r\n",
+       "     <g id=\"line2d_102\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"528.371765\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_103\">\r\n",
+       "      <!-- $C_{29}$ -->\r\n",
+       "      <g transform=\"translate(530.579265 286.574485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(113.135352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_67\">\r\n",
+       "     <g id=\"line2d_103\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"535.165882\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_104\">\r\n",
+       "      <!-- $C_{30}$ -->\r\n",
+       "      <g transform=\"translate(537.373382 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_68\">\r\n",
+       "     <g id=\"line2d_104\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"541.96\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_105\">\r\n",
+       "      <!-- $C_{31}$ -->\r\n",
+       "      <g transform=\"translate(544.1675 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_69\">\r\n",
+       "     <g id=\"line2d_105\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"548.754118\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_106\">\r\n",
+       "      <!-- $C_{32}$ -->\r\n",
+       "      <g transform=\"translate(550.961618 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_70\">\r\n",
+       "     <g id=\"line2d_106\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"555.548235\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_107\">\r\n",
+       "      <!-- $C_{33}$ -->\r\n",
+       "      <g transform=\"translate(557.755735 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_71\">\r\n",
+       "     <g id=\"line2d_107\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"562.342353\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_108\">\r\n",
+       "      <!-- $C_{34}$ -->\r\n",
+       "      <g transform=\"translate(564.549853 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_72\">\r\n",
+       "     <g id=\"line2d_108\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"569.136471\" xlink:href=\"#m395898b422\" y=\"266.694485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_109\">\r\n",
+       "      <!-- $C_{35}$ -->\r\n",
+       "      <g transform=\"translate(571.343971 286.654485)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_4\">\r\n",
+       "    <g id=\"ytick_37\">\r\n",
+       "     <g id=\"line2d_109\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"25.503309\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_38\">\r\n",
+       "     <g id=\"line2d_110\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"32.297426\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_39\">\r\n",
+       "     <g id=\"line2d_111\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"39.091544\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_40\">\r\n",
+       "     <g id=\"line2d_112\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"45.885662\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_41\">\r\n",
+       "     <g id=\"line2d_113\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"52.679779\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_42\">\r\n",
+       "     <g id=\"line2d_114\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"59.473897\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_43\">\r\n",
+       "     <g id=\"line2d_115\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"66.268015\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_44\">\r\n",
+       "     <g id=\"line2d_116\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"73.062132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_45\">\r\n",
+       "     <g id=\"line2d_117\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"79.85625\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_46\">\r\n",
+       "     <g id=\"line2d_118\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"86.650368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_47\">\r\n",
+       "     <g id=\"line2d_119\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"93.444485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_48\">\r\n",
+       "     <g id=\"line2d_120\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"100.238603\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_49\">\r\n",
+       "     <g id=\"line2d_121\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"107.032721\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_50\">\r\n",
+       "     <g id=\"line2d_122\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"113.826838\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_51\">\r\n",
+       "     <g id=\"line2d_123\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"120.620956\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_52\">\r\n",
+       "     <g id=\"line2d_124\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"127.415074\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_53\">\r\n",
+       "     <g id=\"line2d_125\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"134.209191\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_54\">\r\n",
+       "     <g id=\"line2d_126\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"141.003309\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_55\">\r\n",
+       "     <g id=\"line2d_127\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"147.797426\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_56\">\r\n",
+       "     <g id=\"line2d_128\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"327.945294\" xlink:href=\"#mf0e9fccaf8\" y=\"154.591544\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
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+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_7\">\r\n",
+       "    <g id=\"xtick_73\">\r\n",
+       "     <g id=\"line2d_194\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"65.154118\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_118\">\r\n",
+       "      <!-- II -->\r\n",
+       "      <g transform=\"translate(67.361618 571.920368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_74\">\r\n",
+       "     <g id=\"line2d_195\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"71.948235\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_119\">\r\n",
+       "      <!-- IX -->\r\n",
+       "      <g transform=\"translate(74.155735 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_75\">\r\n",
+       "     <g id=\"line2d_196\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"78.742353\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_120\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(80.949853 574.446618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_76\">\r\n",
+       "     <g id=\"line2d_197\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"85.536471\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_121\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(87.743971 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_77\">\r\n",
+       "     <g id=\"line2d_198\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"92.330588\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_122\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(94.538088 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_78\">\r\n",
+       "     <g id=\"line2d_199\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"99.124706\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_123\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(101.332206 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_79\">\r\n",
+       "     <g id=\"line2d_200\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"105.918824\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_124\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(108.126324 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_80\">\r\n",
+       "     <g id=\"line2d_201\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"112.712941\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_125\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(114.920441 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_81\">\r\n",
+       "     <g id=\"line2d_202\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"119.507059\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_126\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(121.714559 574.446618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_82\">\r\n",
+       "     <g id=\"line2d_203\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"126.301176\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_127\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(128.508676 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_83\">\r\n",
+       "     <g id=\"line2d_204\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"133.095294\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_128\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(135.302794 576.972868)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_84\">\r\n",
+       "     <g id=\"line2d_205\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"139.889412\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_129\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(142.096912 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_85\">\r\n",
+       "     <g id=\"line2d_206\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"146.683529\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_130\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(148.891029 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_86\">\r\n",
+       "     <g id=\"line2d_207\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"153.477647\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_131\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(155.685147 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_87\">\r\n",
+       "     <g id=\"line2d_208\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"160.271765\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_132\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(162.479265 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_88\">\r\n",
+       "     <g id=\"line2d_209\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"167.065882\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_133\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(169.273382 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_89\">\r\n",
+       "     <g id=\"line2d_210\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"173.86\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_134\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(176.0675 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_90\">\r\n",
+       "     <g id=\"line2d_211\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"180.654118\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_135\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(182.861618 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_91\">\r\n",
+       "     <g id=\"line2d_212\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"187.448235\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_136\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(189.655735 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_92\">\r\n",
+       "     <g id=\"line2d_213\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"194.242353\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_137\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(196.449853 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_93\">\r\n",
+       "     <g id=\"line2d_214\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"201.036471\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_138\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(203.243971 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_94\">\r\n",
+       "     <g id=\"line2d_215\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"207.830588\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_139\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(210.038088 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_95\">\r\n",
+       "     <g id=\"line2d_216\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"214.624706\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_140\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(216.832206 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_96\">\r\n",
+       "     <g id=\"line2d_217\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"221.418824\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_141\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(223.626324 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_97\">\r\n",
+       "     <g id=\"line2d_218\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"228.212941\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_142\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(230.420441 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_98\">\r\n",
+       "     <g id=\"line2d_219\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"235.007059\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_143\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(237.214559 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_99\">\r\n",
+       "     <g id=\"line2d_220\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"241.801176\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_144\">\r\n",
+       "      <!-- $C_{26}$ -->\r\n",
+       "      <g transform=\"translate(244.008676 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_100\">\r\n",
+       "     <g id=\"line2d_221\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"248.595294\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_145\">\r\n",
+       "      <!-- $C_{27}$ -->\r\n",
+       "      <g transform=\"translate(250.802794 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_101\">\r\n",
+       "     <g id=\"line2d_222\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"255.389412\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_146\">\r\n",
+       "      <!-- $C_{28}$ -->\r\n",
+       "      <g transform=\"translate(257.596912 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_102\">\r\n",
+       "     <g id=\"line2d_223\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"262.183529\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_147\">\r\n",
+       "      <!-- $C_{29}$ -->\r\n",
+       "      <g transform=\"translate(264.391029 580.080368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(113.135352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_103\">\r\n",
+       "     <g id=\"line2d_224\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"268.977647\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_148\">\r\n",
+       "      <!-- $C_{30}$ -->\r\n",
+       "      <g transform=\"translate(271.185147 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_104\">\r\n",
+       "     <g id=\"line2d_225\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"275.771765\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_149\">\r\n",
+       "      <!-- $C_{31}$ -->\r\n",
+       "      <g transform=\"translate(277.979265 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_105\">\r\n",
+       "     <g id=\"line2d_226\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"282.565882\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_150\">\r\n",
+       "      <!-- $C_{32}$ -->\r\n",
+       "      <g transform=\"translate(284.773382 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_106\">\r\n",
+       "     <g id=\"line2d_227\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"289.36\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_151\">\r\n",
+       "      <!-- $C_{33}$ -->\r\n",
+       "      <g transform=\"translate(291.5675 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_107\">\r\n",
+       "     <g id=\"line2d_228\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"296.154118\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_152\">\r\n",
+       "      <!-- $C_{34}$ -->\r\n",
+       "      <g transform=\"translate(298.361618 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_108\">\r\n",
+       "     <g id=\"line2d_229\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"302.948235\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_153\">\r\n",
+       "      <!-- $C_{35}$ -->\r\n",
+       "      <g transform=\"translate(305.155735 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_8\">\r\n",
+       "    <g id=\"ytick_122\">\r\n",
+       "     <g id=\"line2d_230\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"319.009191\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_154\">\r\n",
+       "      <!-- II -->\r\n",
+       "      <g transform=\"translate(50.037059 322.048566)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_123\">\r\n",
+       "     <g id=\"line2d_231\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"325.803309\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_155\">\r\n",
+       "      <!-- IX -->\r\n",
+       "      <g transform=\"translate(46.917059 328.842684)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_124\">\r\n",
+       "     <g id=\"line2d_232\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"332.597426\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_156\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(47.510809 335.636801)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_125\">\r\n",
+       "     <g id=\"line2d_233\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"339.391544\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_157\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(46.917059 342.430919)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_126\">\r\n",
+       "     <g id=\"line2d_234\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"346.185662\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_158\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(46.917059 349.225037)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_127\">\r\n",
+       "     <g id=\"line2d_235\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"352.979779\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_159\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(43.797059 356.019154)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_128\">\r\n",
+       "     <g id=\"line2d_236\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"359.773897\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_160\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(44.390809 362.813272)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_129\">\r\n",
+       "     <g id=\"line2d_237\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"366.568015\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_161\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(43.797059 369.60739)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_130\">\r\n",
+       "     <g id=\"line2d_238\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"373.362132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_162\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(47.510809 376.401507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_131\">\r\n",
+       "     <g id=\"line2d_239\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"380.15625\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_163\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(44.390809 383.195625)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_132\">\r\n",
+       "     <g id=\"line2d_240\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"386.950368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_164\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(44.984559 389.989743)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_133\">\r\n",
+       "     <g id=\"line2d_241\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"393.744485\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_165\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(44.390809 396.78386)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_134\">\r\n",
+       "     <g id=\"line2d_242\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"400.538603\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_166\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(46.917059 403.577978)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_135\">\r\n",
+       "     <g id=\"line2d_243\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"407.332721\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_167\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(43.797059 410.372096)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_136\">\r\n",
+       "     <g id=\"line2d_244\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"414.126838\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_168\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(44.390809 417.166213)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_137\">\r\n",
+       "     <g id=\"line2d_245\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"420.920956\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_169\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(43.797059 423.960331)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_138\">\r\n",
+       "     <g id=\"line2d_246\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"427.715074\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_170\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 430.754449)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_139\">\r\n",
+       "     <g id=\"line2d_247\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"434.509191\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_171\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 437.548566)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_140\">\r\n",
+       "     <g id=\"line2d_248\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"441.303309\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_172\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 444.342684)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_141\">\r\n",
+       "     <g id=\"line2d_249\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"448.097426\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_173\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 451.136801)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_142\">\r\n",
+       "     <g id=\"line2d_250\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"454.891544\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_174\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 457.930919)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_143\">\r\n",
+       "     <g id=\"line2d_251\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"461.685662\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_175\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 464.725037)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_144\">\r\n",
+       "     <g id=\"line2d_252\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"468.479779\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_176\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 471.519154)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_145\">\r\n",
+       "     <g id=\"line2d_253\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"475.273897\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_177\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 478.313272)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_146\">\r\n",
+       "     <g id=\"line2d_254\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"61.757059\" xlink:href=\"#mf0e9fccaf8\" y=\"482.068015\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_178\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(41.797059 485.10739)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
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+       "     </g>\r\n",
+       "     <g id=\"text_191\">\r\n",
+       "      <!-- II -->\r\n",
+       "      <g transform=\"translate(333.549853 571.920368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_110\">\r\n",
+       "     <g id=\"line2d_267\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"338.136471\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_192\">\r\n",
+       "      <!-- IX -->\r\n",
+       "      <g transform=\"translate(340.343971 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_111\">\r\n",
+       "     <g id=\"line2d_268\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"344.930588\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_193\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(347.138088 574.446618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_112\">\r\n",
+       "     <g id=\"line2d_269\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"351.724706\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_194\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(353.932206 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_113\">\r\n",
+       "     <g id=\"line2d_270\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"358.518824\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_195\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(360.726324 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_114\">\r\n",
+       "     <g id=\"line2d_271\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"365.312941\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_196\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(367.520441 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_115\">\r\n",
+       "     <g id=\"line2d_272\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"372.107059\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_197\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(374.314559 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_116\">\r\n",
+       "     <g id=\"line2d_273\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"378.901176\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_198\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(381.108676 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_117\">\r\n",
+       "     <g id=\"line2d_274\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"385.695294\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_199\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(387.902794 574.446618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_118\">\r\n",
+       "     <g id=\"line2d_275\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"392.489412\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_200\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(394.696912 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_119\">\r\n",
+       "     <g id=\"line2d_276\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"399.283529\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_201\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(401.491029 576.972868)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_120\">\r\n",
+       "     <g id=\"line2d_277\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"406.077647\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_202\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(408.285147 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_121\">\r\n",
+       "     <g id=\"line2d_278\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"412.871765\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_203\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(415.079265 575.040368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_122\">\r\n",
+       "     <g id=\"line2d_279\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"419.665882\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_204\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(421.873382 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_123\">\r\n",
+       "     <g id=\"line2d_280\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"426.46\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_205\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(428.6675 577.566618)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_124\">\r\n",
+       "     <g id=\"line2d_281\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"433.254118\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_206\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(435.461618 578.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_125\">\r\n",
+       "     <g id=\"line2d_282\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"440.048235\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_207\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(442.255735 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_126\">\r\n",
+       "     <g id=\"line2d_283\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"446.842353\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_208\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(449.049853 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_127\">\r\n",
+       "     <g id=\"line2d_284\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"453.636471\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_209\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(455.843971 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_128\">\r\n",
+       "     <g id=\"line2d_285\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"460.430588\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_210\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(462.638088 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_129\">\r\n",
+       "     <g id=\"line2d_286\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"467.224706\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_211\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(469.432206 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_130\">\r\n",
+       "     <g id=\"line2d_287\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"474.018824\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_212\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(476.226324 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_131\">\r\n",
+       "     <g id=\"line2d_288\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"480.812941\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_213\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(483.020441 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_132\">\r\n",
+       "     <g id=\"line2d_289\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"487.607059\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_214\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(489.814559 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_133\">\r\n",
+       "     <g id=\"line2d_290\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"494.401176\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_215\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(496.608676 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_134\">\r\n",
+       "     <g id=\"line2d_291\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"501.195294\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_216\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(503.402794 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_135\">\r\n",
+       "     <g id=\"line2d_292\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_217\">\r\n",
+       "      <!-- $C_{26}$ -->\r\n",
+       "      <g transform=\"translate(510.196912 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_136\">\r\n",
+       "     <g id=\"line2d_293\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_218\">\r\n",
+       "      <!-- $C_{27}$ -->\r\n",
+       "      <g transform=\"translate(516.991029 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_137\">\r\n",
+       "     <g id=\"line2d_294\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"521.577647\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_219\">\r\n",
+       "      <!-- $C_{28}$ -->\r\n",
+       "      <g transform=\"translate(523.785147 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_138\">\r\n",
+       "     <g id=\"line2d_295\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_220\">\r\n",
+       "      <!-- $C_{29}$ -->\r\n",
+       "      <g transform=\"translate(530.579265 580.080368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(113.135352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_139\">\r\n",
+       "     <g id=\"line2d_296\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_221\">\r\n",
+       "      <!-- $C_{30}$ -->\r\n",
+       "      <g transform=\"translate(537.373382 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_140\">\r\n",
+       "     <g id=\"line2d_297\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_222\">\r\n",
+       "      <!-- $C_{31}$ -->\r\n",
+       "      <g transform=\"translate(544.1675 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_141\">\r\n",
+       "     <g id=\"line2d_298\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_223\">\r\n",
+       "      <!-- $C_{32}$ -->\r\n",
+       "      <g transform=\"translate(550.961618 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_142\">\r\n",
+       "     <g id=\"line2d_299\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"555.548235\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_224\">\r\n",
+       "      <!-- $C_{33}$ -->\r\n",
+       "      <g transform=\"translate(557.755735 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_143\">\r\n",
+       "     <g id=\"line2d_300\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_225\">\r\n",
+       "      <!-- $C_{34}$ -->\r\n",
+       "      <g transform=\"translate(564.549853 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_144\">\r\n",
+       "     <g id=\"line2d_301\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"569.136471\" xlink:href=\"#m395898b422\" y=\"560.200368\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_226\">\r\n",
+       "      <!-- $C_{35}$ -->\r\n",
+       "      <g transform=\"translate(571.343971 580.160368)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_10\">\r\n",
+       "    <g id=\"ytick_158\">\r\n",
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+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_159\">\r\n",
+       "     <g id=\"line2d_303\">\r\n",
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+       "    </g>\r\n",
+       "    <g id=\"ytick_160\">\r\n",
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+       "    <g id=\"ytick_161\">\r\n",
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+       "    <g id=\"ytick_163\">\r\n",
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\" y=\"-641.412132\"/>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_13\">\r\n",
+       "    <g id=\"xtick_145\">\r\n",
+       "     <g id=\"line2d_387\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"29.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_235\">\r\n",
+       "      <!-- II -->\r\n",
+       "      <g transform=\"translate(31.8675 833.132132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_146\">\r\n",
+       "     <g id=\"line2d_388\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"34.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_236\">\r\n",
+       "      <!-- IX -->\r\n",
+       "      <g transform=\"translate(36.8675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_147\">\r\n",
+       "     <g id=\"line2d_389\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"39.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_237\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(41.8675 835.658382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_148\">\r\n",
+       "     <g id=\"line2d_390\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"44.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_238\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(46.8675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
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+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_149\">\r\n",
+       "     <g id=\"line2d_391\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_239\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(51.8675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_150\">\r\n",
+       "     <g id=\"line2d_392\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"54.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_240\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(56.8675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_151\">\r\n",
+       "     <g id=\"line2d_393\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"59.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_241\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(61.8675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_152\">\r\n",
+       "     <g id=\"line2d_394\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"64.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_242\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(66.8675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_153\">\r\n",
+       "     <g id=\"line2d_395\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"69.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_243\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(71.8675 835.658382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_154\">\r\n",
+       "     <g id=\"line2d_396\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"74.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_244\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(76.8675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_155\">\r\n",
+       "     <g id=\"line2d_397\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"79.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_245\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(81.8675 838.184632)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_156\">\r\n",
+       "     <g id=\"line2d_398\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"84.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_246\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(86.8675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_157\">\r\n",
+       "     <g id=\"line2d_399\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"89.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_247\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(91.8675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_158\">\r\n",
+       "     <g id=\"line2d_400\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"94.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_248\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(96.8675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_159\">\r\n",
+       "     <g id=\"line2d_401\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"99.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_249\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(101.8675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_160\">\r\n",
+       "     <g id=\"line2d_402\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"104.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_250\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(106.8675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_161\">\r\n",
+       "     <g id=\"line2d_403\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"109.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_251\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(111.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_162\">\r\n",
+       "     <g id=\"line2d_404\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"114.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_252\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(116.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_163\">\r\n",
+       "     <g id=\"line2d_405\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"119.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_253\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(121.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_164\">\r\n",
+       "     <g id=\"line2d_406\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"124.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_254\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(126.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_165\">\r\n",
+       "     <g id=\"line2d_407\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"129.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_255\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(131.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_166\">\r\n",
+       "     <g id=\"line2d_408\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"134.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_256\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(136.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_167\">\r\n",
+       "     <g id=\"line2d_409\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"139.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_257\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(141.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_168\">\r\n",
+       "     <g id=\"line2d_410\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"144.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_258\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(146.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_169\">\r\n",
+       "     <g id=\"line2d_411\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"149.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_259\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(151.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_170\">\r\n",
+       "     <g id=\"line2d_412\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"154.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_260\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(156.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_171\">\r\n",
+       "     <g id=\"line2d_413\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"159.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_261\">\r\n",
+       "      <!-- $C_{26}$ -->\r\n",
+       "      <g transform=\"translate(161.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_172\">\r\n",
+       "     <g id=\"line2d_414\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"164.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_262\">\r\n",
+       "      <!-- $C_{27}$ -->\r\n",
+       "      <g transform=\"translate(166.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_173\">\r\n",
+       "     <g id=\"line2d_415\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"169.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_263\">\r\n",
+       "      <!-- $C_{28}$ -->\r\n",
+       "      <g transform=\"translate(171.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_174\">\r\n",
+       "     <g id=\"line2d_416\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"174.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_264\">\r\n",
+       "      <!-- $C_{29}$ -->\r\n",
+       "      <g transform=\"translate(176.8675 841.292132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(113.135352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_175\">\r\n",
+       "     <g id=\"line2d_417\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"179.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_265\">\r\n",
+       "      <!-- $C_{30}$ -->\r\n",
+       "      <g transform=\"translate(181.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_176\">\r\n",
+       "     <g id=\"line2d_418\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"184.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_266\">\r\n",
+       "      <!-- $C_{31}$ -->\r\n",
+       "      <g transform=\"translate(186.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_177\">\r\n",
+       "     <g id=\"line2d_419\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"189.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_267\">\r\n",
+       "      <!-- $C_{32}$ -->\r\n",
+       "      <g transform=\"translate(191.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_178\">\r\n",
+       "     <g id=\"line2d_420\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"194.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_268\">\r\n",
+       "      <!-- $C_{33}$ -->\r\n",
+       "      <g transform=\"translate(196.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_179\">\r\n",
+       "     <g id=\"line2d_421\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"199.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_269\">\r\n",
+       "      <!-- $C_{34}$ -->\r\n",
+       "      <g transform=\"translate(201.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_180\">\r\n",
+       "     <g id=\"line2d_422\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"204.66\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_270\">\r\n",
+       "      <!-- $C_{35}$ -->\r\n",
+       "      <g transform=\"translate(206.8675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_14\">\r\n",
+       "    <g id=\"ytick_243\">\r\n",
+       "     <g id=\"line2d_423\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"643.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_271\">\r\n",
+       "      <!-- II -->\r\n",
+       "      <g transform=\"translate(15.44 646.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_244\">\r\n",
+       "     <g id=\"line2d_424\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"648.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_272\">\r\n",
+       "      <!-- IX -->\r\n",
+       "      <g transform=\"translate(12.32 651.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_245\">\r\n",
+       "     <g id=\"line2d_425\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"653.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_273\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(12.91375 656.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_246\">\r\n",
+       "     <g id=\"line2d_426\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"658.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_274\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(12.32 661.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_247\">\r\n",
+       "     <g id=\"line2d_427\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"663.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_275\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(12.32 666.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_248\">\r\n",
+       "     <g id=\"line2d_428\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"668.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_276\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(9.2 671.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_249\">\r\n",
+       "     <g id=\"line2d_429\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"673.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_277\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(9.79375 676.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_250\">\r\n",
+       "     <g id=\"line2d_430\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"678.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_278\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(9.2 681.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_251\">\r\n",
+       "     <g id=\"line2d_431\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"683.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_279\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(12.91375 686.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_252\">\r\n",
+       "     <g id=\"line2d_432\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"688.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_280\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(9.79375 691.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_253\">\r\n",
+       "     <g id=\"line2d_433\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"693.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_281\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(10.3875 696.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_254\">\r\n",
+       "     <g id=\"line2d_434\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"698.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_282\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(9.79375 701.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_255\">\r\n",
+       "     <g id=\"line2d_435\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"703.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_283\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(12.32 706.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_256\">\r\n",
+       "     <g id=\"line2d_436\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"708.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_284\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(9.2 711.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_257\">\r\n",
+       "     <g id=\"line2d_437\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"713.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_285\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(9.79375 716.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_258\">\r\n",
+       "     <g id=\"line2d_438\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"718.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_286\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(9.2 721.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_259\">\r\n",
+       "     <g id=\"line2d_439\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"723.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_287\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(7.2 726.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_260\">\r\n",
+       "     <g id=\"line2d_440\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"728.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_288\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(7.2 731.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_261\">\r\n",
+       "     <g id=\"line2d_441\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"733.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_289\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(7.2 736.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_262\">\r\n",
+       "     <g id=\"line2d_442\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"738.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_290\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(7.2 741.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_263\">\r\n",
+       "     <g id=\"line2d_443\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"743.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_291\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(7.2 746.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_264\">\r\n",
+       "     <g id=\"line2d_444\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"748.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_292\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(7.2 751.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_265\">\r\n",
+       "     <g id=\"line2d_445\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"753.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_293\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(7.2 756.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_266\">\r\n",
+       "     <g id=\"line2d_446\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"758.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_294\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(7.2 761.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_267\">\r\n",
+       "     <g id=\"line2d_447\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"763.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_295\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(7.2 766.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_268\">\r\n",
+       "     <g id=\"line2d_448\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"768.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_296\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(7.2 771.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_269\">\r\n",
+       "     <g id=\"line2d_449\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"773.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_297\">\r\n",
+       "      <!-- $C_{26}$ -->\r\n",
+       "      <g transform=\"translate(7.2 776.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_270\">\r\n",
+       "     <g id=\"line2d_450\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"778.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_298\">\r\n",
+       "      <!-- $C_{27}$ -->\r\n",
+       "      <g transform=\"translate(7.2 781.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_271\">\r\n",
+       "     <g id=\"line2d_451\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"783.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_299\">\r\n",
+       "      <!-- $C_{28}$ -->\r\n",
+       "      <g transform=\"translate(7.2 786.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_272\">\r\n",
+       "     <g id=\"line2d_452\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"788.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_300\">\r\n",
+       "      <!-- $C_{29}$ -->\r\n",
+       "      <g transform=\"translate(7.28 791.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(113.135352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_273\">\r\n",
+       "     <g id=\"line2d_453\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"793.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_301\">\r\n",
+       "      <!-- $C_{30}$ -->\r\n",
+       "      <g transform=\"translate(7.2 796.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_274\">\r\n",
+       "     <g id=\"line2d_454\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"798.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_302\">\r\n",
+       "      <!-- $C_{31}$ -->\r\n",
+       "      <g transform=\"translate(7.2 801.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_275\">\r\n",
+       "     <g id=\"line2d_455\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"803.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_303\">\r\n",
+       "      <!-- $C_{32}$ -->\r\n",
+       "      <g transform=\"translate(7.2 806.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_276\">\r\n",
+       "     <g id=\"line2d_456\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"808.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_304\">\r\n",
+       "      <!-- $C_{33}$ -->\r\n",
+       "      <g transform=\"translate(7.2 811.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_277\">\r\n",
+       "     <g id=\"line2d_457\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"813.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_305\">\r\n",
+       "      <!-- $C_{34}$ -->\r\n",
+       "      <g transform=\"translate(7.2 816.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_278\">\r\n",
+       "     <g id=\"line2d_458\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"27.16\" xlink:href=\"#mf0e9fccaf8\" y=\"818.912132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_306\">\r\n",
+       "      <!-- $C_{35}$ -->\r\n",
+       "      <g transform=\"translate(7.2 821.951507)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"text_307\">\r\n",
+       "    <!-- J_12 -->\r\n",
+       "    <g transform=\"translate(104.755 635.412132)scale(0.12 -0.12)\">\r\n",
+       "     <use xlink:href=\"#DejaVuSans-74\"/>\r\n",
+       "     <use x=\"29.492188\" xlink:href=\"#DejaVuSans-95\"/>\r\n",
+       "     <use x=\"79.492188\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "     <use x=\"143.115234\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "  </g>\r\n",
+       "  <g id=\"axes_8\">\r\n",
+       "   <g id=\"patch_11\">\r\n",
+       "    <path d=\"M 228.76 821.412132 \r\n",
+       "L 408.76 821.412132 \r\n",
+       "L 408.76 641.412132 \r\n",
+       "L 228.76 641.412132 \r\n",
+       "z\r\n",
+       "\" style=\"fill:#ffffff;\"/>\r\n",
+       "   </g>\r\n",
+       "   <g clip-path=\"url(#p4707aaa589)\">\r\n",
+       "    <image height=\"180\" id=\"image71929f4ec6\" transform=\"scale(1 -1)translate(0 -180)\" width=\"180\" x=\"228.76\" xlink:href=\"data:image/png;base64,\r\n",
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y=\"-641.412132\"/>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_15\">\r\n",
+       "    <g id=\"xtick_181\">\r\n",
+       "     <g id=\"line2d_459\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"231.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_308\">\r\n",
+       "      <!-- II -->\r\n",
+       "      <g transform=\"translate(233.4675 833.132132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_182\">\r\n",
+       "     <g id=\"line2d_460\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"236.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_309\">\r\n",
+       "      <!-- IX -->\r\n",
+       "      <g transform=\"translate(238.4675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_183\">\r\n",
+       "     <g id=\"line2d_461\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"241.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_310\">\r\n",
+       "      <!-- IY -->\r\n",
+       "      <g transform=\"translate(243.4675 835.658382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_184\">\r\n",
+       "     <g id=\"line2d_462\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"246.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_311\">\r\n",
+       "      <!-- IZ -->\r\n",
+       "      <g transform=\"translate(248.4675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "       <use x=\"29.492188\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_185\">\r\n",
+       "     <g id=\"line2d_463\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_312\">\r\n",
+       "      <!-- XI -->\r\n",
+       "      <g transform=\"translate(253.4675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_186\">\r\n",
+       "     <g id=\"line2d_464\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"256.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_313\">\r\n",
+       "      <!-- XX -->\r\n",
+       "      <g transform=\"translate(258.4675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_187\">\r\n",
+       "     <g id=\"line2d_465\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_314\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(263.4675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_188\">\r\n",
+       "     <g id=\"line2d_466\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"266.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_315\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(268.4675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_189\">\r\n",
+       "     <g id=\"line2d_467\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"271.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_316\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(273.4675 835.658382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_190\">\r\n",
+       "     <g id=\"line2d_468\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"276.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_317\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(278.4675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_191\">\r\n",
+       "     <g id=\"line2d_469\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"281.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_318\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(283.4675 838.184632)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_192\">\r\n",
+       "     <g id=\"line2d_470\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"286.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_319\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(288.4675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_193\">\r\n",
+       "     <g id=\"line2d_471\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"291.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_320\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(293.4675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_194\">\r\n",
+       "     <g id=\"line2d_472\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"296.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_321\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(298.4675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_195\">\r\n",
+       "     <g id=\"line2d_473\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"301.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_322\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(303.4675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_196\">\r\n",
+       "     <g id=\"line2d_474\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"306.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_323\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(308.4675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_197\">\r\n",
+       "     <g id=\"line2d_475\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"311.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_324\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(313.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_198\">\r\n",
+       "     <g id=\"line2d_476\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"316.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_325\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(318.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_199\">\r\n",
+       "     <g id=\"line2d_477\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"321.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_326\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(323.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_200\">\r\n",
+       "     <g id=\"line2d_478\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"326.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_327\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(328.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_201\">\r\n",
+       "     <g id=\"line2d_479\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"331.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_328\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(333.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_202\">\r\n",
+       "     <g id=\"line2d_480\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"336.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_329\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(338.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_203\">\r\n",
+       "     <g id=\"line2d_481\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"341.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_330\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(343.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_204\">\r\n",
+       "     <g id=\"line2d_482\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"346.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_331\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(348.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_205\">\r\n",
+       "     <g id=\"line2d_483\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"351.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_332\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(353.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_206\">\r\n",
+       "     <g id=\"line2d_484\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"356.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_333\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(358.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_207\">\r\n",
+       "     <g id=\"line2d_485\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"361.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_334\">\r\n",
+       "      <!-- $C_{26}$ -->\r\n",
+       "      <g transform=\"translate(363.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_208\">\r\n",
+       "     <g id=\"line2d_486\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"366.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_335\">\r\n",
+       "      <!-- $C_{27}$ -->\r\n",
+       "      <g transform=\"translate(368.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_209\">\r\n",
+       "     <g id=\"line2d_487\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"371.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_336\">\r\n",
+       "      <!-- $C_{28}$ -->\r\n",
+       "      <g transform=\"translate(373.4675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_210\">\r\n",
+       "     <g id=\"line2d_488\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"376.26\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_337\">\r\n",
+       "      <!-- $C_{29}$ -->\r\n",
+       "      <g transform=\"translate(378.4675 841.292132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(113.135352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_211\">\r\n",
+       "     <g id=\"line2d_489\">\r\n",
+       "      <g>\r\n",
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+       "    </g>\r\n",
+       "    <g id=\"xtick_223\">\r\n",
+       "     <g id=\"line2d_537\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"462.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_351\">\r\n",
+       "      <!-- XY -->\r\n",
+       "      <g transform=\"translate(465.0675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_224\">\r\n",
+       "     <g id=\"line2d_538\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"467.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_352\">\r\n",
+       "      <!-- XZ -->\r\n",
+       "      <g transform=\"translate(470.0675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_225\">\r\n",
+       "     <g id=\"line2d_539\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"472.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_353\">\r\n",
+       "      <!-- YI -->\r\n",
+       "      <g transform=\"translate(475.0675 835.658382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_226\">\r\n",
+       "     <g id=\"line2d_540\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"477.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_354\">\r\n",
+       "      <!-- YX -->\r\n",
+       "      <g transform=\"translate(480.0675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_227\">\r\n",
+       "     <g id=\"line2d_541\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"482.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_355\">\r\n",
+       "      <!-- YY -->\r\n",
+       "      <g transform=\"translate(485.0675 838.184632)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_228\">\r\n",
+       "     <g id=\"line2d_542\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"487.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_356\">\r\n",
+       "      <!-- YZ -->\r\n",
+       "      <g transform=\"translate(490.0675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_229\">\r\n",
+       "     <g id=\"line2d_543\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"492.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_357\">\r\n",
+       "      <!-- ZI -->\r\n",
+       "      <g transform=\"translate(495.0675 836.252132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-73\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_230\">\r\n",
+       "     <g id=\"line2d_544\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"497.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_358\">\r\n",
+       "      <!-- ZX -->\r\n",
+       "      <g transform=\"translate(500.0675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-88\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_231\">\r\n",
+       "     <g id=\"line2d_545\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"502.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_359\">\r\n",
+       "      <!-- ZY -->\r\n",
+       "      <g transform=\"translate(505.0675 838.778382)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-89\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_232\">\r\n",
+       "     <g id=\"line2d_546\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"507.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_360\">\r\n",
+       "      <!-- ZZ -->\r\n",
+       "      <g transform=\"translate(510.0675 839.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "       <use x=\"68.505859\" xlink:href=\"#DejaVuSans-90\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_233\">\r\n",
+       "     <g id=\"line2d_547\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"512.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_361\">\r\n",
+       "      <!-- $C_{16}$ -->\r\n",
+       "      <g transform=\"translate(515.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_234\">\r\n",
+       "     <g id=\"line2d_548\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"517.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_362\">\r\n",
+       "      <!-- $C_{17}$ -->\r\n",
+       "      <g transform=\"translate(520.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_235\">\r\n",
+       "     <g id=\"line2d_549\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"522.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_363\">\r\n",
+       "      <!-- $C_{18}$ -->\r\n",
+       "      <g transform=\"translate(525.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_236\">\r\n",
+       "     <g id=\"line2d_550\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"527.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_364\">\r\n",
+       "      <!-- $C_{19}$ -->\r\n",
+       "      <g transform=\"translate(530.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_237\">\r\n",
+       "     <g id=\"line2d_551\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"532.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_365\">\r\n",
+       "      <!-- $C_{20}$ -->\r\n",
+       "      <g transform=\"translate(535.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_238\">\r\n",
+       "     <g id=\"line2d_552\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"537.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_366\">\r\n",
+       "      <!-- $C_{21}$ -->\r\n",
+       "      <g transform=\"translate(540.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_239\">\r\n",
+       "     <g id=\"line2d_553\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"542.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_367\">\r\n",
+       "      <!-- $C_{22}$ -->\r\n",
+       "      <g transform=\"translate(545.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_240\">\r\n",
+       "     <g id=\"line2d_554\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"547.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_368\">\r\n",
+       "      <!-- $C_{23}$ -->\r\n",
+       "      <g transform=\"translate(550.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_241\">\r\n",
+       "     <g id=\"line2d_555\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"552.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_369\">\r\n",
+       "      <!-- $C_{24}$ -->\r\n",
+       "      <g transform=\"translate(555.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_242\">\r\n",
+       "     <g id=\"line2d_556\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"557.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_370\">\r\n",
+       "      <!-- $C_{25}$ -->\r\n",
+       "      <g transform=\"translate(560.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_243\">\r\n",
+       "     <g id=\"line2d_557\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"562.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_371\">\r\n",
+       "      <!-- $C_{26}$ -->\r\n",
+       "      <g transform=\"translate(565.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_244\">\r\n",
+       "     <g id=\"line2d_558\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"567.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_372\">\r\n",
+       "      <!-- $C_{27}$ -->\r\n",
+       "      <g transform=\"translate(570.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_245\">\r\n",
+       "     <g id=\"line2d_559\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"572.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_373\">\r\n",
+       "      <!-- $C_{28}$ -->\r\n",
+       "      <g transform=\"translate(575.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_246\">\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_374\">\r\n",
+       "      <!-- $C_{29}$ -->\r\n",
+       "      <g transform=\"translate(580.0675 841.292132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "       <use transform=\"translate(113.135352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-57\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_247\">\r\n",
+       "     <g id=\"line2d_561\">\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_375\">\r\n",
+       "      <!-- $C_{30}$ -->\r\n",
+       "      <g transform=\"translate(585.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_248\">\r\n",
+       "     <g id=\"line2d_562\">\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_376\">\r\n",
+       "      <!-- $C_{31}$ -->\r\n",
+       "      <g transform=\"translate(590.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_249\">\r\n",
+       "     <g id=\"line2d_563\">\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_377\">\r\n",
+       "      <!-- $C_{32}$ -->\r\n",
+       "      <g transform=\"translate(595.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_250\">\r\n",
+       "     <g id=\"line2d_564\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_378\">\r\n",
+       "      <!-- $C_{33}$ -->\r\n",
+       "      <g transform=\"translate(600.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_251\">\r\n",
+       "     <g id=\"line2d_565\">\r\n",
+       "      <g>\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_379\">\r\n",
+       "      <!-- $C_{34}$ -->\r\n",
+       "      <g transform=\"translate(605.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_252\">\r\n",
+       "     <g id=\"line2d_566\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"607.86\" xlink:href=\"#m395898b422\" y=\"821.412132\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_380\">\r\n",
+       "      <!-- $C_{35}$ -->\r\n",
+       "      <g transform=\"translate(610.0675 841.372132)rotate(-90)scale(0.08 -0.08)\">\r\n",
+       "       <use transform=\"translate(0 0.78125)\" xlink:href=\"#DejaVuSans-Oblique-67\"/>\r\n",
+       "       <use transform=\"translate(69.824219 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
+       "       <use transform=\"translate(114.360352 -15.625)scale(0.7)\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "   <g id=\"matplotlib.axis_18\">\r\n",
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+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"ytick_316\">\r\n",
+       "     <g id=\"line2d_568\">\r\n",
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+       "     <g id=\"line2d_569\">\r\n",
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+       "    </g>\r\n",
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+       "    <g id=\"ytick_324\">\r\n",
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+       "  </clipPath>\r\n",
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+       "</svg>\r\n"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 614.4x460.8 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -626,8 +14301,9 @@
     "    'n_points': 10\n",
     "}\n",
     "\n",
-    "n_omega, infids, (fig, ax) = ff.infidelity(pulses['CNOT'], S, omega,\n",
-    "                                           test_convergence=True)\n",
+    "n_omega, infids = ff.infidelity(pulses['CNOT'], S, omega, test_convergence=True)\n",
+    "print(infids.shape)\n",
+    "fig, ax = plotting.plot_infidelity_convergence(n_omega, infids.sum(axis=1))\n",
     "fig.suptitle('CNOT convergence')"
    ]
   },
@@ -655,7 +14331,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.1"
+   "version": "3.8.3"
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+       "value": " 100/100 [00:01&lt;00:00, 99.80it/s]"
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+       "layout": "IPY_MODEL_5350dca7816d47469901c73d83925038",
+       "style": "IPY_MODEL_e62e9ab7acf8426fa5f34ff285bc51f8",
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+       "style": "IPY_MODEL_dadcefbf44ea489ca1add54a608c8c02",
+       "value": " 100/100 [00:01&lt;00:00, 82.57it/s]"
+      }
+     },
+     "88f59df3764e43ef86d4e504f38ef94d": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
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@@ -983,6 +14753,12 @@
        "layout": "IPY_MODEL_835fc073583440a394db086b060c16ec"
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       "model_module_version": "1.5.0",
@@ -1028,6 +14804,14 @@
       "model_name": "LayoutModel",
       "state": {}
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
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@@ -1036,6 +14820,12 @@
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@@ -1066,12 +14856,42 @@
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+       "style": "IPY_MODEL_adc353eaca4946f3b7157a1b6857d311",
+       "value": " 250/250 [00:02&lt;00:00, 97.10it/s]"
+      }
+     },
      "b0d29cbf316d43628bf732898910984c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
       "state": {}
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+     "b5f452d581f746dab713d94de2f14cd3": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HBoxModel",
+      "state": {
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+        "IPY_MODEL_d07b37848dca4eb5b2e92a9aab87f02c",
+        "IPY_MODEL_86a983452c9e4113bed5b6e1da4a02b6"
+       ],
+       "layout": "IPY_MODEL_caff07cb089a460facd2d7d09bae8e7b"
+      }
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@@ -1104,6 +14924,14 @@
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       }
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@@ -1158,6 +14986,12 @@
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@@ -1170,6 +15004,18 @@
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+      "model_module_version": "1.5.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "bar_style": "success",
+       "description": "Calculating control matrix: 100%",
+       "layout": "IPY_MODEL_a72221ea80d64015a17dac444877ede0",
+       "style": "IPY_MODEL_75642b77c7344521afa46e7b998f261e",
+       "value": 100
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@@ -1196,6 +15042,14 @@
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+       "description": "Calculating control matrix: 100%",
+       "layout": "IPY_MODEL_2a443ad8200d4632a8522b9d26cd4942",
+       "max": 250,
+       "style": "IPY_MODEL_9e1853105e0a4c6098fd0b674f0ef155",
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       "model_module": "@jupyter-widgets/base",
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diff --git a/doc/source/examples/extending_pulses.ipynb b/doc/source/examples/extending_pulses.ipynb
index 6fb29dd..61127b7 100644
--- a/doc/source/examples/extending_pulses.ipynb
+++ b/doc/source/examples/extending_pulses.ipynb
@@ -26,7 +26,7 @@
     "import filter_functions as ff\n",
     "from filter_functions import util\n",
     "\n",
-    "I, X, Y, Z = util.P_np\n",
+    "I, X, Y, Z = util.paulis\n",
     "\n",
     "# In order to be able to remap cached filter functions, we need a separable\n",
     "# basis like the Pauli basis\n",
@@ -209,8 +209,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from filter_functions import plotting\n",
+    "\n",
     "print(swap_14_23.is_cached('F'))\n",
-    "_ = ff.plot_filter_function(swap_14_23)"
+    "_ = plotting.plot_filter_function(swap_14_23)"
    ]
   }
  ],
diff --git a/doc/source/examples/getting_started.ipynb b/doc/source/examples/getting_started.ipynb
index 84fb472..e1f4265 100644
--- a/doc/source/examples/getting_started.ipynb
+++ b/doc/source/examples/getting_started.ipynb
@@ -126,7 +126,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from filter_functions import analytic\n",
+    "from filter_functions import analytic, plotting\n",
     "\n",
     "# Generate logarithmically spaced frequencies over an interval which generally\n",
     "# captures the important regions of the filter function. Since the filter\n",
@@ -135,7 +135,7 @@
     "omega = ff.util.get_sample_frequencies(FID, n_samples=200, spacing='log',\n",
     "                                       symmetric=False)\n",
     "# Plot the filter function.\n",
-    "fig, ax, legend = ff.plot_filter_function(FID, omega)\n",
+    "fig, ax, legend = plotting.plot_filter_function(FID, omega)\n",
     "# Plot the analytic solution into the same plot\n",
     "ax.plot(omega*tau, analytic.FID(omega*tau)/omega**2,\n",
     "        'x-', label='Analytical', zorder=0)\n",
@@ -161,7 +161,8 @@
     "# Initial state the +1 eigenstate of X\n",
     "psi_i = (qt.basis(2, 0) + qt.basis(2, 1))/np.sqrt(2)\n",
     "\n",
-    "ff.plot_bloch_vector_evolution(FID, psi0=psi_i, n_samples=101, figsize=(4, 4))"
+    "plotting.plot_bloch_vector_evolution(FID, psi0=psi_i, n_samples=101,\n",
+    "                                     figsize=(4, 4))"
    ]
   },
   {
@@ -261,14 +262,15 @@
     "omega = ff.util.get_sample_frequencies(SE, n_samples=400, spacing='log',\n",
     "                                       symmetric=False)\n",
     "# Plot the filter function\n",
-    "fig, ax, legend = ff.plot_filter_function(SE, omega, yscale='log')\n",
+    "fig, ax, legend = plotting.plot_filter_function(SE, omega, yscale='log')\n",
     "ax.set_ylim(bottom=1e-13)\n",
     "# Plot the analytical solution\n",
     "ax.plot(omega*tau, analytic.SE(omega*tau)/omega**2, '--',\n",
     "        label='Analytical (Bang-Bang)', zorder=0)\n",
     "legend = ax.legend()\n",
     "\n",
-    "ff.plot_bloch_vector_evolution(SE, psi0=psi_i, n_samples=101, figsize=(4, 4))"
+    "plotting.plot_bloch_vector_evolution(SE, psi0=psi_i, n_samples=101,\n",
+    "                                     figsize=(4, 4))"
    ]
   },
   {
diff --git a/doc/source/examples/periodic_driving.ipynb b/doc/source/examples/periodic_driving.ipynb
index d4bba52..ba67902 100644
--- a/doc/source/examples/periodic_driving.ipynb
+++ b/doc/source/examples/periodic_driving.ipynb
@@ -48,7 +48,7 @@
     "t = np.linspace(0, T, 21)\n",
     "dt = np.diff(t)\n",
     "# Paulis\n",
-    "X, Y, Z = ff.util.P_np[1:]\n",
+    "X, Y, Z = ff.util.paulis[1:]\n",
     "\n",
     "H_c = [[Z, [omega_0/2]*len(dt), 'Z'],\n",
     "       [X, A*np.sin(omega_d*t[1:] + phi), 'X']]\n",
@@ -82,20 +82,951 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 614.4x460.8 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Plot the pulse train for the drive only\n",
-    "_ = ff.plot_pulse_train(X_ATOMIC, c_oper_identifiers=['X'])"
+    "from filter_functions import plotting\n",
+    "_ = plotting.plot_pulse_train(X_ATOMIC, c_oper_identifiers=['X'])"
    ]
   },
   {
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      "name": "stdout",
      "output_type": "stream",
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+      "0.98766778476328\n"
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LEI9P/xq+98pxPPyOJ9BicRX8WLPZjBMnTiAUCuHVV19FS0sLenp6oNFoIAjCNgcTtv5WOVgqkqEoxZTib2xsyEUfqVRKniDt9XrBcRwuXLiA5ubmWvwZisEfkIrcC44DTNY71ZMsPVl1shtAbA2IrQLxwFW8s+dneNOJXyx6PzabDa2trVhZWcHk5CQ6Ojrg8XhACEEul5PX31iDd2VgwsYom0JL8QVBQDgclmeV6XQ6uadsZ4O0tM96hyswYtsJS09WB0qBVBiI3hGzjej23xv1lpL3TQiBx+OBy+XCrVu3MDExgb6+PtjtdgCbzjfS+hsrMFEWCjBhYxRHMaX4qVRKnlWWzWblBunBwcEDe30a5UNebMS2FZaerAxCDoj5N4UstgbkDxg+YjKWnzHQaDTwer3o6OjY1uDd1NS0y8GETRBQBspxrHiEcTA7S/EFQZB/t1eDtDSrLBKJwGQyweFwYGxsDEajsVZ/Qs3gufI9J7elJ6OAoNopUOolHd8UsegqkFhHwS6wxc53Owi9Xo/R0VHE43HMzs7CYDDA6/VCr9ezCQIKw1KRjD0ptOiDUop4PC6vlQmCALvdjvb2doyMjBz5BfJCi0cKQaMFmuwsPVkIorApYLHVTTHLltgraDIqJ2wSTU1NOHXqFILBIK5cuSJX+0oTBGKxGPR6PWvwLgMmbAwAxc8q2zpBuqmpCQ6HAydPnoReX1iD61GBh7LPh5Se1BmADZae3EZu485a2RoQ92+KW7mYDZUpXiKEwOl0wm63Y2lpCRMTE+jq6kJbWxuAzc9YNptlBSYlwoTtCFNMVLZ1VhkhpCoN0oSQXfOy6g2eq0zPHWHpyc3Cj8jdqGxn4YcS6LSVXafhOE4WtIWFBUxMTKC/vx+tra2glLICkxJhxSNHiGJK8aVZZcFgEIlEAlarFU6nEz09PYrNKjsMnufrX9gUTEXuxVFLT8qFH3cis3ymsserlpBotVoMDAwglUptKzAxmUxyg3c2m2UTBAqFRWyNTaGl+DtnlWk0GjgcDvT398NisdTkg8TzPARBqGunBp5UPjXb6OnJdOJuVFZM4Uf5VP89bzKZcPz4cUQiEVy/fh1NTU3o6+uDRqOBKIqIx+OswKQARFYV2VgUWooPAKlUSo7KNjY25FL8/v5+VYiJJGz1jKZCqci92Jqe3IgeXMauZkQRSAbv9pbVSqhrGRW1tLTgzJkzWFtbw6VLl9DW1obOzk65wIRNEDgYtsZW5xRTii8IwrZZZXq9Hg6HA0NDQzCZTKpLb0i2WvUMp3DxSCFIzd2ZVP2kJ3PpO+X4UuFHvtZnBHCktoJBCEFbWxucTidu376Nixcvore3F06nEwArMDkItsZW51BKkclsLjTsVfSxdYJ0NpuFzWaD0+k8tEFaDdRrxCbdQAQCAbweXwRqlBFRc3qSUmAjcjcqS0VqfUa7ITUWNgme59Hb2wuPx4ObN2/KEwSam5t3NXizCQKbsIitztmvQVqaVVbPDdL1JGxbHVYymYw87DTrGsbMUu3OS03pSSEHxAOba2XVKPwoF7Wl+HQ6HYaHh5FIJDA7OwutVov+/n4YDAZ5GoY0wVsNSwk1hQlbfUMIQTwely+qoijCbrejo6MDo6OjqvtwFoOahU0URdn3MhQKQa/Xw+l0YmRkBCaTSd5uNWw6YC/Vo2bpSQIE5jhEV0QkgpuRWr3AEXVmNCwWC06dOoX19XVcvXoVNpsNPT09chWx5GBiMBhUn5WpFKx4pAFYWVmR3QwaqUFabcImTSMIBAJysY3T6cTAwMC+FxCeqOvDVY30JKVAMgT4Z4HoCgDUwSLfHnCcukXBbrfvmiDQ3t4OjuPkCQJ6vR56vb6ub3BLga2xNQDDw8Pbqh8bhVoLmzQjTprcrdVq4XQ6MTQ0BLPZXNA+tJy6hA24m57U32nuLjs9SQAhA0RWgJUbQK5E+yq1wfPqvzxwHIeOjg643W68/vrrmJiYgNfrhd1ul9ffpQKTo9TgzdbYGgBCSMMKW7VH10iN6YFAQJ4R53Q6S26BqEYfW6nwZaYn03EgOA8Eblbm/GoNAVc3BgEajQb9/f3bCkykHtQjOUGACRtDrfA8j1yuspM2KaXy5O5gMAie5+X04s4ZcaWgUWHEtpNC05OisFn8sToNpELVO79awRF+WwRUDxiNRoyNjSEajWJ6ehomkwlerxc6nW5bgYnBYGjoAhMWsTFUS6VSkdlsVo7K4vG4PLlbcnhQEg1XH5Woe6UnCdmM5sK3gbWZozfN26A34vjx45ibm8PS0pJscVUPWK1WnD59GoFAAJcvX4bL5UJXVxc4jsPq6ioymQz6+voatsGbCVsD0KhpBaUatCmliMVicuUoIUQWsqampoo+f1pSH8ImIaUnb/wIWHq11mdTWzT8ZmXh2NgYIpEIpqamYLVaK3IDVAkIIXC5XHA4HFhcXMTExAS6u7shCAIIIQ3d4C0SwqoiGeqknIgtl8vJUVksFkNzc3PVTZyB+khF7sVGrNZnUHs0mrt2aC0tLRgfH99WgejxeOpCDDiOQ3d3tzxBIBgMwu12g+f5xp0gwCK2+qch3oh7UIywSQNPpaiMUgqHw4Genh40NzfX7DnSqKzcn1E4Ws12n09CCDweD1wu164RM/WATqfD4OAgKKUIhUJIpVLo7++HyWRquAITlopkqJbDhC2fz8tFH9FoFBaLBU6nE6dPn4ZOVz3z4YPQcvWxJsPYjVazd0WrRqOBz+dDKpXC7OysbHFVL84+HMehv78fhBA5vdrb2wutVrutwbueJwgwYWsA6vnO6iB2CpvkfRkIBBAIBCAIAhwOBzo6OjA2NqbK56Feha0Bu0eKRrePsEmYTCacOHECoVAI165dkx1A1L7+Jo2Cam5uxvj4OFZXV3Hp0iW0t7ejo6OjISYIMGFjqBZJ2Px+PwKBACKRCMxmMxwOB06ePFkXLitqFFtGYRQ6Pdtms6GlpQXLy8uYnJyUJ2Kr9bXP5/NyJEYIQXt7O1wuF27duoWJiQn09vbC4XAAwLYG73oqMGHFIwzVsTUqi0ajCIfDaG9vx8jISN3dOTLqF22BwgZspvc6OzvhdrsxPz+P5eVl+Hw+WK3WCp5haew1vJfnefT19e2aINDU1FSfEwRYxFb/qP5Ndghbx7yEw2EYjUY4nU6MjY3h8uXLGBoaqvUpMo4ghUZsW9FqtRgcHEQymcTs7Ky8Hqem7EI+n983XarX6zEyMoJ4PI7Z2Vno9Xr09/dDr9fvmiDA87xqrz0sFcmoCdL07kAgsG3My/DwcANGZQSbtqyMekKvLb0YxGw24+TJkwgGg3jllVfgcDjQ3d2timKMQmzCJFP19fV1vPLKK7Db7dsmCMTjcVUXmDBhawDUete0lULHvDQiBASUCVvdUY6wSTgcDthsNrlBWpqAXcvPLKW0oONLRgY2mw3Ly8uYmJhAZ2cn2tvbVV9gwtz9GRUjnU7La2WFjnlhMNSCQafMDdfWBmlp/WpgYABNTU2K7L/SbF0/3DpBwGazgVKqSgcTyuaxNQZqcPjfOuYlFApBo9EUPealMan9B51RPAa9spkEaQK2tH5lMBjQ39+vmp7Lw9BqtXL/3tzcnDxBwGw2ywUmksDVusCEpSIZZZHJZGS3j2QyWfaYl50QQupmdMh+EHCgUM/AVImMwCOaNmFjXUCnJ4U6GD9WVQy6ytyMSetXkkFxW1sbOjs7q/IeV+KzZDKZcPz4cUQiEbz22mtoampCb2+vPEEglUqB53nZwaQWMGFrEKoVse035sXn8yky5mUn0mJ1XQsbIaqoHREpkMzpEc8YkE7zEPJ3XistcGNeD5slgbb2I2bhfwB6hVKReyEZFNvtdty+fRsXL16Ux+NUMtLZ2sNWLi0tLThz5gzW1tZ2CbQgCNscTKr9+WVrbIxD2TrmJZFIwGq1VmzMy06kJm21uzkcBEHtRDkvEsQyRiQzOmQy/L7DRLUWDjHahND1PHq74lCFEtcYo95S8WPwPI/e3l60t7fL6T2fzweLpTLHVvqzRAhBW1sbnE6nLNBSgQywaUb+Z3/2Z/jgBz+I0dFRxY57GCxiaxCUvMur5ZiXnSg1uqa2VFfYNvJaxDIGbKS1yGULf60IIdC2aHFrvRVCcxoUqSO9Omg0VF7YJPR6PUZHRxGLxXDjxg00NTWhr69P8UkUB/WwlYMk0DsneFutVkxNTVV1ogYA1qBd6xNQC1vHvMTjcTQ1NdVkzMtOKjVstJpUOhUpUiCRNSCe0SOd4SHmy5MjXkdgHTJC59QhfDkBIZpX6EzrC6O++gVPzc3Ncnrv0qVL8Hg88Hg8iqXyBEGoaEWyVCCTSCQwOzuLb33rW0gkEmhpaanYMfdCZFWRRxO1jnnZST0Lm2TaDFH55zIncnKKMZvmKmJabLTxMLyjGdGbOcSnEkD+aKUnTYbalONvTe9J5fX9/f2w2Wxl77tSEdtOLBYLTp06hfn5eTzxxBP4i7/4C3zqU59Cc3NzxY8NsDW2hvnLCxGiehjzspN6EzZRFBEKheD3+xEOh2EymUB4ruyIjQLYyGkRyxixkdEgX0SKsRwIIWjp18HS0YrQtRTSt9NHJj1pNlTnIrwfPM/D6/XuWn8rx9Cg0hHbTu677z589rOfRX9/P9761rfi+9//Pjo7Oyt+XLbG1sDU45iXndSDsKXTaTmNm0qlYLPZ4HK5ZHuw569pUUq1v0iBWNaAREaPTFoDsYZPg8ZA4LrHjFSPAZErcQhxdb8mSlCriG0nRqMRx44dQyQSwdTUFFpaWtDb21tS5FWtiE1Ccjn5zd/8TXzgAx+onstQFYWNEPJVAN8G8CcA2gCIAB6llH6xKiewBw0jbJJI5fN52VC4Hse87EQq91cTUnGN3+/f1vIwMDCwZ8tDMVWRWYFDLGNCMqNFNlN+pKc0JicP4zutiMxmkXgtUZJg1wvGGkdsO2lpacH4+DhWVlYwOTkp21sVc4MqCEJVszOiKIIQAkJIVU0aqhyxvRHA/wPg9ymlk4SQJgAThJDnKKVT1TqJrTSMsKVSKVy+fBn5fB42m61hxrxInnS1Jp/PY319Xb5haG5uhtPplCcPHwQh+6d+KIBkTod42oB0RoN8Tv1RNOEIWgf1sHRqEbq6gcxyY6YndVr13QgSQuDxeOByubCwsICJiQn4fL6CizPy+XxVvVljsVjJ62oPPvggvve978HlcuHatWt7bvPss8/iYx/7GARBwEMPPYRPfvKTAJQtHiGE+AC8CCAJIAKgG0AYwGkAHQCmKaVLAJYAgFIaJ4Rcv/M7JmzloNfrcezYsboZT18otYzYUqmUPOQ0n8/DbreXlMbldgibQIF4xojEnSpGWqdRj9bEwf1GM5KrekSuJCCm6vQPqUOkcTipVAqzs7Nyef1hn/9qr7GFw+GSKyI//OEP43d+53fwoQ99aM/fC4KAhx9+GM899xw6Oztx9uxZvOc975H75QSu4L/TQQi5uOX7Rymlj0rfUEpnCSE/AfA5Sum/EkJ+COA/UUpjhJCHADy7dWeEkF5sit7LhZ6A0jSMsEkzkhoNjuOQy1XHDWOr1+X6+ro8gaDcGwYOPDICv1nFmNYil1VfirEczG0aGJ1WRKYzSEwnQdSVOS4RgtXVVbjdblWvRZtMJpw4cQKhUAjXrl3bNl5mL6q9xhaJRNDa2lrSY9/2trdhYWFh399fuHABPp8PXq8XAPDAAw/gySef3BQ2CqDwRE+QUnrPIduMAZDCxmEAN+58fS+Aj0gbEUIsAL4L4HcppbGCz0BhGkbYGpVKF49Irip+vx+JREKeQODz+cq6sxVEAS/5v4OfrP4PzKwnkc9Vt4+n2nA8gW3EAEuXDqErSeT82VqfUlkQQhCNRrG8vIzBwcGKOYEohc1mQ0tLizxepru7e09RrnbEFolEKtbDtrS0hK6uLvn7zs5OvPzynSCpOGE7EEKIEYCBUhomhHQBWKeUZgkhJgAtlNLlO9tpsSlq36KU/qMyRy+NhhE2Nd9VloPSwkYpRSKRkCtFgc2ZWV6vt2xXlUhmDT9Y+hom/M9gNb4AUWiI0KUodBYObW9pQnwph+grCdB0fT4HHOEwNDSEeDyO6elpWCyWir1LVYcAACAASURBVDiBKMnW8TLz8/NYWlrCwMDAtjWuWkRsSvTf7cVe3rjbPr/KLc2PArh+5+uRLV+/A8Dzd45LAHwNwHVK6ecUO3KJNIywAeoYXaM0SgibIAhypWg4HIbZbFasf28m+jL+ZfFruL7+AmKpcFn7aiSaOrQwuVoQfi2N1FwKpM7eluRO0VVTUxNOnz4tO4F0dHTA4/Go+kZSq9VicHAQyWQSMzMz0Ol06O/vh16vV9QEuRDKWWM7jM7OTty+fVv+fnFxER6PZ/MbBSM2bE9DbgA4QwgZBvDvAHznzs/fAuA3AFwlhFy+87P/TCl9WrGzKIKGErZGpFRhk4ac+v1+ZDIZ2Gw2uN1uubesVHJiBj9ZfQwvrn4XC+FryObqO+VWSXgtgeO4EZluPUJXksiv189ztbXgR3ICcTgcciXiwMAArFZrDc/wcMxmM06ePIn19XVcuXIFLpcLgiBUtVI6FovJa2BKc/bsWczMzGB+fh4dHR14/PHH8e1vf3vzlyKAtDLHoZT+3Zav/xWAFwAIIW8G8PE7P/8JVDR4kQmbyilU2CiliEajskWYNOR0eHi47P6Z9fQi/nnxUVwOPAd//DaoWGfhR43RWzm0v60JsVs5xK7GQbPqf/74PS7+UiXiXpGQWpEMzG02GxYXF7GxsYFAIACn01mVqLOcNbZf//Vfxw9/+EMEg0F0dnbiT//0T/HRj34U586dw/nz5+HxePDII4/g3nvvhSAIePDBBzE2Nrb5YGUjtj2hlJ6p7BFKp6GErVFTkfuV+0sWYX6/H7FYDFarFU6ns+xxOpRSvBr5IZ5f+lvcWL+AxEa05H0x7tLcrYW5rRXhqTRS8+qeHMBx+79/pEgoGAziypUrVR0UWiocx6G7uxvLy8sIhUJYWlqCz+dDU1Nl3VXKqYp87LHH9vz500/fze6dO3cO586d271RFYRNzTSUsDUiOyO2rRZh+XweDocD3d3dsFqtZd2BpvNJ/Hj1m3hp9QncjryGXJ4N3KwEvI7AccqIdI8OoctJCBF1Ps/8AcIGbN5EOp1O2Gw23Lp1S1Gj4kpBKQXHcRgeHkY8HsfMzAxMJhO8Xm/F3EjKEbayYcLWGKh5QbtUCCFIp9O4fv06QqEQjEYjnE4njh8/DkOZzgIrqVk8t/goXgk+j/X4ckUc8hl7Y2jl0f72JsTmc4hNJYCcup58ni/s0sDzPPr6+tDW1obZ2Vm5ErHc92YlEEVRLhyRimICgcCu6ddKEo1GayP2LGJjqI1MJiOnGJPJJLLZLBwOBwYHB8uq6KKU4tL6M/jR8jcxE5pAKp1Q8KwZxUIIgdWrg9nTitCrKaRvqceaS8MXV9ZvNBpx/PhxhEIhXL16Vc4kVLMC8TB2Ts8mhMDlcsFut+P27duYmJhAX18f7Ha7YjfJ0Wi0NhGbgsUj9UhDCVu9RmxbZ8MFAgE5zePz+WCxWPDiiy/Ko+bL4YX1x/C3r/3fyCbUFR0cdTQGAtf4nckBlxMQ4rW/1S5W2CSkRunFxUVZKBwOhyo+m/uV+kvTr6XxONL6mxKmxfl8vjYjsVjExqgFgiBgfX0dfr8fkUhEnth95syZin0Q9LwRBisHrZFiIyJCVOfyzpHF5OBhfGczInNZJK7XdnJAqcIG3C3UcLvdmJubw/LyMgYGBqpqPrwXh/Ww6fV6jI6OIhqN4rXXXkNTU1NZTek1L2RjwsaoBlKpsd/vRzabhd1uh8fjwejoaFUqyvT8pi0SryMwOzlkExSZGIve1AThCFoH9LB01HZygEZT/s3VVqEod46aEuxMRe6H1WrFmTNn5KZ0j8cDj8dT9GdUEraaRKssYmsc1JDu2AqlFJFIBH6/H+vr69DpdHA6nRgdHa3J3auRu1vaTAiBvolAY6RIh0UI9dM7fCSQJwes3ZkckKxu+KblletNs1qtGB8fx/LyMiYnJ/f1caw0xbiObG1KL7XqM5FI1M5jkwkbQ0lyuZxc+BGPx9HS0gKn04n+/v6S71QJIRBFseyozsDv7tnhNQQmB4dciiIdpQ3lut8ImN0aGH/Bish0FonpRNUmB2gViNi2QghBR0cHXC4Xbt68KacnK91HtpVCI7ataDQaeL1eef1tcXERPp+voBvTStppHQorHmkcahGxUUq39ZaJogiHw4He3l40Nzcrck4cx1VM2IDN501nJtAYKNIREfkj/IFQI5uTA/SwdGkReiWJ3Frlw2tthYaMarVa2Vx5ZmYGZrMZXq+3KubK5fhEGo1GHDt2DOFwuOC0aiWd/QuCRWyMYhBFEaFQCH6/H+FwGCaTCU6nEydPnqyIvZBGoynpbnMnRu7gSb4cT2Cy88ilRKSjFLQ+jekbFp2FQ9ubm5BYziHyShJ0o3LpSZ2msrMNa2GuLAhC2f11ra2t29KqnZ2daG9v3/O8a9qczVKRjEJIp9MIBoMIBAJIpVKw2WxwuVxlmwoXAsdxioyuMXCFpX20Jg68niITpchtsNyk2rB4tDC5rAi/lkZytjKTA/QViti2Um1zZaVG1khpVbfbLZ+3z+fbFZ1Fo9HaRWxM2BoHJe/2KKWIxWJyipHneTidTgwMDMBsNlc17anUTDa+8FHx4HgCo41Ak95MT9Ialp4zdsNpCOzHjLB06xG6rPzkAJ2uetPoJXPlVCqFmZkZaLXaipgrKz1kdOt5z87OyutvUlTIIrba0VDCBpRnhJzP57G+vo5AIIBIJILm5mY4nU709PTUdMBipadoH4TWQKBxcUjHKHJJFr2pCSFHkYmLEI165JGHBsrljvXa6ltimUwmnDhxomLmypUaMiqd9/r6Oq5evQq73Y6enh5EIhF0dHQofryCYMJ2tEmlUnJvWS6Xg8PhQEdHB8bGxlTTPqCksBEQ0CJLHwlHYGwh0Bo3ozfxCH9gak1ugyKxKiCxIiAVFOV1UKUTh3pdbZqpK2murHTEthO73Y7W1lYsLS3hD//wDxGLxe6Okak2FKwqspE4LGITRRGRSASBQADr6+vQ6/VwOp04duwYjMbqpV+KQVlh40BLtLTQ6AnMLg6ZGGW2XFUkExORWBUQXxGQDu/9vBOF+zRqJWwSkrlye3s7ZmZmZJurcj6jlYrYtsJxHLq6uvCJT3wCv/Ebv4E//uM/RkdHB86ePVvR4+6CRWyNTzablXvLEokEWltbZS9GNZm07sdBM9mKhRAO5SyYEUJgsBJmy1VBKKXYCIlIrIiIrwgFpYAVFzZtbYVNwmAwyObK165dK8tcudIR21bcbjc6Ozvx/ve/H3/0R3+Ej3/843j3u99dlWMDYMJW6xOoBJRSJBIJufCDUgqn0wmv14umpibVpBgLRamqSADgwCliQchsuZRFFChSgU0hS6wINXeCMRrKNwBWEslceWlpqWRzZWkeW7WIRqM4e/YsfvmXf7n6vpFM2BqLpaUlzM3NwWKxwOl04vTp07Vx11YQZVORyt2xMluu8hCyVE4xJv3qqjw16NQlbMDdNJ/b7cbs7CyWl5eLcuGvtrhEIhHYbDYQQqp/M82ErbFwOp1wu92qHlNfLDzPI5PJKLIvjvCK22YxW67CySZFJFbFzeKPdVG1z1UykVbE7aYS6HS6bS78Vqu1pubK+5HNZktaE3z22WfxsY99DIIg4KGHHsInP/nJXdt8/vOfx/nz50EIwfHjx/H1r399e/P5ES8eUd+7tkwMBoMqP4zloGTExpPKfPg3bbk4WNwcNOobnlxT0hERges5zP/vNG4+l4H/ag6poLKipnQ8oOH0mJycRDQaVXjPyiG58JtMJkxOTmJ1dXXfqIxSWtWoqdToUBAEPPzww3jmmWcwNTWFxx57DFNTU9u2WVpawl/+5V/i4sWLuHbtGgRBwOOPP77jBLAZsRXyrwFR1y0OY0+UFbbK9uMxWy6AihSp9c2oLL4iIl9p9xZKFRc2b48PXs8oZmZmoNPp0N/fr8qUPiEEHo8HTqcT8/Pz+5orV7NwBCh9ZM2FCxfg8/ng9XoBAA888ACefPJJjI6Obtsun89jY2MDWq0WqVQKHo9nxwmgYUWrEBpO2OqtMKQQlBA2aYROtXrQjpotl5CjSPo3xSyxJtR9tajJ0CQ3HgcCAVy+fLkqfo6lotVqMTg4iEQigenp6V3mytUWto2NjZLSkEtLS+jq6pK/7+zsxMsvv7xtm46ODvzBH/wBuru7YTQa8a53vQvvete7tu/oiAtbY+XsGpRShS2fz2N1dRWvvPIKfvrTn2JpaUnROVuHsWnLxcFo50DU31VRNPk0RWQhj9svZjD7TBrLP8sitlj/ogYARsNmxEMIgcvlwvj4ODY2NjA5OYlYLFbjs9sfi8WC06dPw2q14tKlS1haWgKltCo9bFsJh8MleV7ulcLceSMRDofx5JNPyhFqMpnEN7/5zd07Y6lIhpopRtg2Njbg9/sRCARkJ5Xu7m5YrVYQQvDUtAGK1PsXQSPZcmXid1OM6XDj5lnNxu2TIHieh8/nQzKZxPT0NIxGI/r7+2tqNbcfW82VX3/9dUxMTMDj8VQ1YotGoyX5RHZ2duL27dvy94uLi7vSjP/yL/+Cvr4+OJ1OAMCv/Mqv4IUXXsAHP/jBuxsd8Yit4YRNjWmScjmoQVsya/b7/QgGg9BoNHC5XBgbG9szFaIhtVknqVdbLrlZ+k4l41FxXDEb9o42zGYzTp06JY+bOWhsS63RaDTo7+9HKpXC1NQU8vk8MplMRUZL7aTUIaNnz57FzMwM5ufn0dHRgccffxzf/va3t23T3d2Nl156CalUCkajET/4wQ9wzz33bN8RGzTKUDs7G7QFQcD6+jr8fr9s1uxyudDX13doukVDqpeK3PP4dWDLta1ZelWAoEynRV1xUHSzNSKan5/HpUuXqj4NuxhMJhO6u7sRCAQqYq68F6U6+2s0GjzyyCO49957IQgCHnzwQdlv8ty5czh//jze+MY34v7778eZM2eg0Whw+vRp/NZv/db2HR3xiI0cUpaqzivPISjV86UWBEHAyy+/jO7ubvj9fqTTaXkeXEtLS1Ef0L+8+T7MJl+s4NkWjpBVjy2XkKVIrAl3ij/U1Sx9KJTCjA1Fd/nMlwpfR5MKNiwWC7xer+r6yQBgZWUF+XweHo8Ht2/fRiAQUMxceS++9a1vIR6P4xOf+ERF9n8YxHEP8IsXC9v478gEpfSewzesH9T3DmQAuGsLJq2XJZNJ5HI5DA0NFey0sBdaop4ms5rbchEglxLhfyWL+CprLJcgRTYPSAUbq6urmJycRHd3N9xut6rSk1JVJM/z6O3tRVtbG2ZnZxUxV96LcDgMl8ul6D6L4ohHbA0pbOXMZKsloigiFArB7/cjHA7DbDbD5XLhzJkzuHjxIvr6+so+ho6rbSpyJ1W35SJAJioiNJdH9PV6CsuqRymCRAhBe3s7HA4Hbt68iZWVFQwODpZ1E6Yk+XweJtNdY2eDwYBjx44pYq68F9FoFIODg4rsqySYsDFqydbJA8lkEq2trXC5XBgeHq7IGoCWU+lonkractHNhungjTxSgcatZFQKQkp/32m1WgwNDSEWi6nK7iqfz+8pWkqYK+9FqVWRisGKRxoPtUdsyWRSTjGKogin04n+/n5YLJaKp290KkpF7mTTlotAY9isnMyX+sEkgJjfNBgOXM8hl1D0NBsejis/amlubsaZM2ewvLyMyclJ9Pb2wul01iw9KQjCvuK61Vx5bm4OS0tLGBgYKCvaLLV4RDFYxMaoNDuHmxoMBjidTpw4cWK7cWkVUGvEtpWSbLkIkM9QxG7nEXwtr4qClHqFKyNi2wohBB0dHXA6nZibm8Py8jIGBwe3pQSrxX4R21Z0Oh1GRkYUMVeWnP1rChO2xkINi9b5fF5OMcZiMbS0tMDlcpU83JQQoojbup5TxwDJQjjUlosA2YSIyHweoTmBFX8ohBIR21a2CsbU1BRaW1vR29tb1YbpgyK2nUjmyisrKyUXw9Rc2FjExlCKvVw/enp60NzcXLbYchynkLCpYzG/UDZtuQg06c30JBWAdETA+kwe8SW2XlYJeK4ylwVJMLauZ0nuGZWmWEutQs2V92NjY6O2hTNM2BilstP1Q6vVwul07uv6UQ6SrVa5i/D1JmwSWgNBPEEx+7+O8Ir4vigbqnIVjKSk9SyXy4XZ2VmsrKxgYGBA8c/LTko1Qd5qrjwzMwOTybTNXHkvKKVVH5OzC1Y80nhU8g1VjutHOSg1uqZehQ2A8kPHGHui4Srv/6jX6zE2NoZwOFyRcvudlCs0FosFp06dgt/vx6VLl+DxeNDR0XHgPmu+JMIiNsZBZDIZOcWYTqdht9vh8XgwOjpataGmigkbb1HgbGpDra8TakXpp4Xnq3dZaG1txfj4OBYXFzExMYH+/n7Y7faqHb8YCCFwu91wOBxYWFjAxMQEfD7fLk/ITCZT+9l1LBXZeJR7p7TT9YPjODidzrJdP8pBKWEzcPUrbCxiqw5avroXZY7j0N3dLacnpfWsalcMFwrP87K58szMjOxeIpkrRyKRkgyQFYUJGwM42PWj5ndfYMIGsIitWmg0tXm/b3UDuXr1KlwuF7q6usrOioiiWJG0oMlkwsmTJxEMBnHlyhW43W50dXXVvocNYMJW6xOoJdV2/SiHg0bXFIOBV6cDe0Go6yVREcoWj2j42s5Yk9xAbt26Jaf7yhGKSk/PdjgcaG1txe3bt/HEE09gaWmJRWw1piGF7aC7s1q6fpTDztE1pWLimw/fSKWo+OVpKLSa2vuJchyH3t5euN1uzMzMYHl5eVu6rxiUqCY+DMlcmVKK8+fPIxgMYn5+XhF/15KgYFWRjYzk+uH3+xEKhWAwGOByuXDy5MmqDBxUCsVSkUzYGIegrVEqci+MRiNOnDghp/va29vR0dFRVEal2B62cujr68OHPvQhXLhwAe973/vw0EMP7Z6VVg0aIGIjhPwTdqcjogAuAvgqpXRf6W5YYVtdXd3l+jEwMFBVtwMlUUrYdJw6F+QLginbniheFVmFcv9ikdJ9r7/+OiYnJ/esRtyPSqcidxKNRnHPPffgK1/5CpaXl6t23G00gLABuAnACeCxO9+/D8AagEEAfwPgN/Z7YEMKGyEE6XRaMdcPNcDzfMMNUC2WBngZ64L0Rhavv/66IoUbSsLzPLxeL9ra2ralJw8r7qpmxAZsCltPTw80Gg26u7urdtxtKCxshJB3A/giAB7AeUrpp5Xb+76cppS+bcv3/0QI+TGl9G2EkFcPeqB63rUK09fXB6vV2hCiBigXsW1Sp89Jw75by0XZ4hGXsw2iKGJiYgKRSETRfSuByWTCiRMn4HA4cPnyZSwuLh44zaPaEZsqqiKBTWEr5N8hEEJ4AH8F4N8BGAXw64SQ0Yqc83achBD5zuDO14473x44uZFdKuoEJYWt2AnJaqFB7lFUj0FnQl9fH8bGxrCwsIDr168jl1PXuARCCFwuF8bHx5FOpzExMYFYLLbnttWO2EoVtmeffRZDQ0Pw+Xz49Kf3DogikQjuv/9+DA8PY2RkBC+++OLeO5MstQr5dzhvADBLKb1JKc0CeBzAe4v400rl9wH8hBDyPCHkhwD+FcAnCCFmAH970AMbMhUJqH8mW7GUK2wbGxtYW1uD3+8HDKQ+g7Z6POcqoPTTotNursNKfVqSjVRXVxfa2tpUlQXheR4+nw/JZBLT09MwGo3o7+/f5uVYjarIrZTi7C8IAh5++GE899xz6OzsxNmzZ/Ge97wHo6PbA6OPfexjePe7343vfOc7yGazSKVS++6ToLD2IAo4CCEXt/zoUUrpo1u+7wBwe8v3iwDeWNDOS4RsTruNABgAMIzNt/lrWwpGvnDQ4xtW2BqNYvvYKKWIx+NYW1uTDZpdLhdOnDiBx2d55KlSac3qoaLraUOj190dbSTZSNlsNty8eROrq6sYHBysrXP9HpjN5m1ejp2dnWhvbwchBPl8vqouJqVEbBcuXIDP54PX6wUAPPDAA3jyySe3CVssFsOPf/xjfOMb3wCwOQ5o3/VFQsFpCltkE7IIUkrvOWCTvT55FY0aKKUiIeSzlNI3AbhS7OMbVtjUdFepBIX0sYmiiHA4jLW1NYTDYVgsFrhcLvT29m67g+VQn5WhDfaSqha9brfTvlarxdDQEGKxGK5fvw6bzYaenh5VVRlLImy32zE/P4/JyUkMDg5WfY0tkUgUPN5GYmlpCV1dXfL3nZ2dePnll7dtc/PmTTidTnzkIx/BlStXMD4+ji9+8Yt73mSQ4oTtMBYBdG35vhNANco9/5kQ8qsA/pEWmX5ja2x1wn6pyHw+j9XVVVy5cgUvvPAC1tbW4HK58KY3vQknT55Ee3v7rhEbHFHPxagomLDtg7I3zwbd/tFYc3Mzzpw5A61Wi4mJCYRCIUWPrQQajQYDAwMYGhrCzMwM1tfXq3ZsaWRNsUK613V75815Pp/H5OQkfvu3fxuXLl2C2Wzedy0O2BS2Qv4VwM8ADBBC+gghOgAPAHiqqD+wNH4PwD8AyBBCYoSQOCFk74XUHTRsxNZobBW2dDqNQCCAtbU1eaBpb29vwa0NHKnPl73RonC1cpCwAXdnqjmdTszMzGBlZaVkV5BKYrFYcPr0aUxMTGB6elp2Mqn0+4gQUvQxOjs7cfv23WWsxcVFeDyeXdt0dnbijW/cXN66//779xe2IiK2w6CU5gkhvwPgf2Gz3P+/UUoPLLdX6LhNhBAbNtfZisol1+cVrgAa6SJIKcXGxgaSySReeuklcBwHl8uF0dFRmEymw3ewA65eX3aWX9gTpd/pRn1h62cGgwHHjx+XXUEKmVFWbQgh0Gq1GBkZweLiojzY1GKpjBl4Pp8vKe159uxZzMzMYH5+Hh0dHXj88cfx7W9/e9s2bW1t6Orqwo0bNzA0NIQf/OAHu4pLJAhHoTEo56lFKX0awNOK7bAACCEPAfgYNlOflwH8HIAXAPzCYY+t0ytc40Mpla3A1tfX5cVvJaYN8HUbsdX6DI4GRn1xF33JFWTrulaxa0yVRBAE6PV6eY3wxo0bsFqt6O3tVbxaMhKJwGq1Fv04jUaDRx55BPfeey8EQcCDDz6IsbExAMC5c+dw/vx5eDwefOlLX8IHPvABZLNZeL1efP3rX99zf8WssamYjwE4C+AlSuk7CCHDAP60kAfW5xWuANR011goO6dzS1ZgPp8PPM/jhRdeUGSEjoaozzKpEOrwJa1LDIbiKx6lsvtEIoHp6Wk0NTVVfKp8oWwtHpHWCJeXlzE5OYne3l44nU7FrhflzGI7d+4czp07t+vnTz99N1A6deoULl68uGub3TSEsKUppek7qV09pfQ1QshQIQ+s/bvuiJPNZhEIBOD3+7GxsQG73Y6Ojg6MjY1VTJw1RD0mt0XBhG1PiMLFIyZ96dGWtK61srKCyclJ9PX1wel0Knh2xUMp3fZZIoSgo6MDTqcTc3NzWF5exuDgYElp/Z2oYsgooOgaWw1ZJIS0APifAJ4jhIRRYDVmwwqbmiO2VCoFv98Pv98PURRlg2az2VyV8+a5+hQ2Fb+kDYXJUF4akRACj8cDh8OB2dlZeV3LaNzdRlAN9vtM6XQ6jIyMIBqNYmpqCq2trejt7S2rNUAtdlqEoO6FjVJ6350v/4QQ8jwAK4BnC3lswwqbmqCUIhaLwe/3IxgMQqfTyc3S1WwclWARW2OhdMRW7Brbfuh0OoyOjiIcDuPatWuKTcQulsNaoKxWK8bHx7G0tISJiYmyokzVCBsnKlo8UmsopT8qZnsmbBVCFEWEQiF5DlxzczNcLlfZ6w470yqloCHqKssuFMIxZasGZqOyM/taW1sxPj4uj5wZGBgoqcCiFERRLEhICSHo7OyEy+XC7OysnJ4sNspUi7A1yBpbyTSssNUiFZnP5+X1skQiAZvNBrfbjeHhYUXuUqVetnIX5LVcnQob07WqYDIoP4yW4zj09fXB7XZjenoaBoNhl6djJSjWdWRnlOlwONDd3V3wPiKRCEZGRko9XeVojDW2kmlYYQOqY4ScTqfl9TKpWbqvrw9NTU2Ki6tiwkbqdNgoE7Y9UToVWclUoWSsvLa2hkuXLqG7u7uiTdOlOvtLUebi4iImJibQ398Pu91+6OPUUjzSCGts5dDQwlYJKKVIJBLw+/0IBALgeR5Op7PkZuliUG6Kdm0W8cuFRWyNASEEbW1tsNvtuHnzJlZWVjA0NFSRz0+pDdPApsBLwisNNh0YGDhwXTwajaojFckitsZFqYiNUopwOCw3S5tMJrjdbkWapYtBKWFjERtjP6qZwpeMlaWqxEoYKyuR4dDr9Th27BhCoRCuXr16YBFMNBotemRNJSCksYpHiqWhha0cBEFAMBiE3+9HNBpFS0sL3G43BgYGauZofuQjNmapVXFIDXzLrFYrzpw5I6f9fD6fYuJQTsS2E5vNhpaWFty6dUs+z53RmWqKR1jExpDIZrPyelk6nYbdbkdXVxeOHTumir64QkbXFIKOq2zKtFKo4CVQJYUOlCxoX1UuxZeQ0n4ul0s2Vh4YGCg7I6L0kFGO42QzZSk9udUAOhaLsTU2FdDQwlaIGCWTSVnMAMDlcmFoaEh1gxSBTT+5YoaN7oe+TiM2lorcGyWfFq7GYbFkrBwIBHD58mV0dHTA4/GUfGOpZMS2FaPRiBMnTsgG0O3t7ejo6Kj6tO59YRHb0YJSimg0KjdL6/V6uN1unDp1SnVjN3aiXMSmPtEuBDVEzY0Ox6ljVp/T6URraysWFhbKMlYWBKGi6+CSAfT09DTe+c531sxdZScNYoJcMg0tbNKFUBRF2Vw4HA6jubkZbrcbXq9XHXdXBaLUGpuhToUNwGZ4UtkOjiMNp6LZQBqNRjZWvnHjBpqbm4s2OMjn81WpVh4ZGcGXvvQlvPe978VHPvIRfOYzn4HL5arocQ+ECVvjEovFMD09/YH5nwAAIABJREFUjWQyCZvNhra2NoyMjFTd0kcplBI2PV+ZWVTVgBCgwq2JRxtC8OqrryqyvqUUFotlmyN/MZZXxTZol0NHRweGh4fx3ve+Fx/96Efx1FNP1SzLwKoiGxitVov+/n5YLJaGSGPxPI9MJlP2fgxc/QobW2fbjZJPiU6rh8vlwuXLl9HZ2Yn29nZVfHa2OvJLxSWDg4OHeq2W2qBdCpFIBDabDffddx/uu+++wx9QSY548Uh9hi4FYjKZKuIAUisUS0XWecTG2AKligobz2lgs9lw5swZJBIJXL58GalUSsEjlIdOp8PY2Bg6Oztx9epV3Lp168CCqmpGbKop9cfdNbZC/jUiDS1sjUa5wpZIJHDz5k1MX5tX8KyqC+tlqywajRaiKIJSCp/PB6/Xi6mpKSwsLChSkasUkvgKgoDJyUlEo9E9t6t2xFZqqf+zzz6LoaEh+Hw+fPrTn953O0EQcPr0afzSL/3SwTtkwta4NEqkJlGssEkVoNPT03jhhRcwPT0NvV6P02NvqOBZVpjGeklVh5bXQafTyRW4ZrMZp06dAgBMTEzsKyC1gOd59PX1YXR0FDdv3sSNGzeQy+W2bVPtiK2UxnJBEPDwww/jmWeewdTUFB577DFMTU3tue0Xv/jFAk2Wj7awNfQaW6PBcdyhd82iKCIcDmNtbQ3hcBgWi2VbBSilFMlcpEpnrDwNdq+iOjSaTVHT6XQQBEEWCql5+saNGzCZTOjv71dNRbHJZMKpU6f2NFYudGyNEkQikZLG8Vy4cEGOjgHggQcewJNPPonR0dFt2y0uLuL73/8+PvWpT+Fzn/vcgfskHGXFI4z6YL+ITRAErK+vY21tTTZh3Touh1IKURSRz+dBKYUO9Vzuz+r9K4lGc7cSkud5EEIgCAIEQYBWq8WJEyfg9/uLrk6sNFuNlefm5rC6uorBwUFF5hcWSiQSkcWpGJaWltDV1SV/39nZiZdffnnXdr/7u7+Lz3zmM4jH44fuk/WxNTCNnIrM5XIIBAJYW1tDKpWC3W5HZ2cnjh07Jm+/VcyAzeeDEFK37Q4Ai9gqjU6z3aSA4zhwHAee55HL5SCKIlwuF+x2O2ZmZmQBUYu5gVarxfDwsGysnM1mqxa1lersv5dR+85r1/e+9z24XC6Mj4/jhz/84eE7ZcLW2FRjJlu1yOfzSCaTuHjxInK5HJxOJ3w+HyyWu1WO0sL/TjFrFJFnxSOVRavZW6A4joNWq5WjN0IIRkZGEA6HceXKFXg8HnR0dKjmfWa1WnH69Gm89NJL8jy1Srvul1oV2dnZidu3b8vfLy4uwuPxbNvmpz/9KZ566ik8/fTTSKfTiMVi+OAHP4hvfvOb++z1aAsbu0yonFQqhfn5ebz88suYmpqCIAgYHR3Fm970JrlHTxRFCIKAfD4vr8FJkdlOUcvlRHzlqxFQUR0XoKKp09OuF2xW976/k8Rta3FJS0sLxsfHkU6ncenSJSQSiSqe7eFI3pOLi4t49dVXkc1mK3asUoXt7NmzmJmZwfz8PLLZLB5//HG85z3v2bbNn//5n2NxcRELCwt4/PHH8c53vvMAUWPl/ixiUxmUUsTjcaytrSEYDEKr1cpeljqdDi+++CKMRiMEQdhWSHJQijEeF/DIX0fw938fxs2bceTzFA/9lIBw9fO8SKgkIGgojEYDjg++Bb/yjv8LZwb/7aHbS8Ul+XxeTnV7vV4kk0ncuHEDra2tis9VKwWpItJgMODEiROKGSvvR6mz2DQaDR555BHce++9EAQBDz74IMbGxgAA586dw/nz53dFcIdyxBu0G17Y6gFKKSKRCNbW1rC+vg6z2Qy32y374kmpRWnNbH19HVar9cAU45o/jy98IYQn/mcES0sJ7CympCIHKDjupGowYVMEo8mIk4Nvwa++4+M4OfD2kvah0WjAcRzy+TwEQYDBYMCpU6dk66u95pVVk509bJKx8vz8PCYnJzE0NLQtjV8u5TRonzt3DufOndv186effnrXz97+9rfj7W9/+4H7Ixyz1Gpo1JLz34lkzLy2tiY3drpcLnmQqSRmOyOz06dPY2pqCi0tLfB6vdv+vpvzWXz2syE882wEfn/qQE/FTWGrP1T6ctYFJpMJJ4fegl9958dxov/fKLLPna0BgiDI1lc3btzA6uoqfD4ftFqtIscrhr162DQaDQYGBhCPx3Hjxg1YrVb09fUpEl3m83nV+Gse9TW2hhc2NZHP5xEMBrG2toZEIgGbzQaPx4PR0dE9y/IltkZmJpMJ4+PjWFhYwMWLFyGKg/jrryTx/PNRhEKF36GJQr0KGyv3LwaT2YRTQ2/Dr/3C72Gs7y0VO87O1gCe53Hs2DEEg8FdvWXV4iDXkaamJtlYeWJiAl6vFw6Ho+RjqW25g5X7MyqKNJV7bW0NmUwGTqcTvb29aG5ulrfZryx/v4vAj360gb/8kgY/fcGIRGIOpeTnqKCOuVtFwyK2QzGbzTg1/Db82jt/D6N9b67acXe2BgiCAIfDAZvNhrm5OaytrWFwcLBqM8sOcx3Zy1h5YGDgUGPlvZD65VSTIWJrbI1NLd5oGxsbWFtbg9/vl/t+hoeHYTabZfEqpiyfUorv/mMCj/5NCJOTMWxsbLUNKu3vE+tU2NRy3VAbZosFZ4b/De7/hd/DSM/P1fRcdrYGAMDg4CCi0SiuXbsGt9uNzs7OiveWFeoTKRkrh0IhvPLKK2hrayv6/BKJhKLrdeXCIjaGIiQSCVnMNBoN3G43Tpw4AYPBIIuXIAgFi5kgiPj612P4xt+F8eqrMWSz5bv6b0XM16ewsQaV7eh0enzp95/FUNc9VTsmpRT/8A9xnP/aGt71riz+4PfHdm2zM3rL5/Nobm6W0+hS8UYpU7ELpVifSJvNhvHxcbz++uvy1O6tmZWDCIfDJRsgVwJWPNLgVCpikwyGpUpGo9EIl8uFe+65B1qtdpuYFVqWn0yK+PJXInjs8TBmZ+PI5ytXtSjm6/OlZxHbdqxme1VEjVKK73w3jq98eR2Xr0SRyWzeaL3wAvCNbzyPJ/5xHAMDu0WA4zjo9Xq5uIRSit7eXrjdbty4cQNNTU3wer0VaQ3I5/NFpxV5nofX64Xb7cb09DRMJhO8Xu+hxS+l+kRWDhaxMQpkq8FwKBRCc3Mz3G43fD7fgZWMB+Xe19cFfOELIXz3iQgWFxMQhOosQou5On3pmbBtg+cq9zpSSvHd78bx5a+s4/Llu2K2k4WFZpx9wxQ+/GEtPv+5M3u+16XiEqk1QKfT4eTJk1hdXZWdQex2u6Lnn8/nSxZMaaqBZKzc09MDl8u17+dYTbPYADBLrVqfgNoRBEGuZIzFYrDZbPKaWaGVjDtZWMjic58P4ftPR+H3J3f1mFUDIVufLz2L2LbDc8pGOpRSPPFEHH/95XVcurS/mO0kn9fi/HngySd/hG9/awRvetNuB5OtrQGSwLW3t8PhcGB6ehqrq6sYGBhQrGReEISyJhDsNFaWpnabTKZd26pN2AgrHmlsSklFbjUY3tjYgN1uR3d3t9wUXYqYvXI1g89/fh3/8oMowv9/e2ceVmW17/HPuzfzKKKQgKLIIKKCc8510lQ0zSEFPM45lKfOzaGO55badPLek3Y7B+malnY7WoHScahUTCGH0sRwQFMxUVERHJBBpr33un8YBIm4Ye8Ne7PX53n28wCuvdZC3v1+399av9933S6utcasIdCWN3xdkTGQwlYdtdrwj7AQgn//u4BVcfcis5KS+u/n5ua6MnTYRYYPP8+/Pu2Nre39wvv70gCVSkVYWBg3b94kLS0NPz8/WrVqZfA2giERW1WqGiunp6fTokUL/P39q20p1NcA2WTIiE0CUFJSUpmWr9FoKoulnZ1/O+KlrgbD+/bd5f1/3uTAgXzy80sb5PfQF22ZZQobKqlsVdFpRb2OZhFCsGVLIavibvLTT3kGidl9c9Kp+eorNW38D7BqlT9jx/jf16am5JLmzZvj4eHBL7/8QlpaGiEhITVGR/piaMT2e9zd3enevTuXL18mNTW1mrNKXl4evr6+RhvLUBRFJo80aWr7wBcVFZGTk0NOTg6KouDl5UWnTp1wdHSsd1r+1m1F/O/qWxz58Q53i8vva2MuaCxU2GTEVh2VoiI1NZXQ0NBqD2E1IYRgy9ZCVq0yvpjVREGBM1On5hIbm8nmTY/i4XH/yQE1lQYEBgZSUFDAqVOnaNGiBW3atKlXaYC+6f51QaVSVe63VdS+BQYGkpeXV+3IqEZHRmxNn4rlw6oGw7m5udjb2+Pt7U3Xrl2xs7Ord1r+p58W8PH625w8qf+eRGOjLTUX6586IoWtGk6OLgQFBXHy5Em8vLzuWyITQrBteyGxsTc5evQOJSUNe7MTQuHwYReCgo+w5LXmvPhi6H1taoreXFxc6NatW2V0FBwcXOesw7qm+9cFR0dHOnfuzI0bN3jppZfIy8ur0euxsZB7bFbA7du3uXbtGrdu3XqgwXBd0vKLi3X874d5bNx4m3NnCyg3YVq+qbBUYZMRW3Vs1Da4u7vTs2dPLly4wJEjRwgNDSU5RSE29gapqQ0vZjVRUuLAX//zLh9/vJfEL3sQ0O7++rWaSgNat25d6Tvp5ORE+/bt9Y7ChBAmLQJXFIWWLVvy9ttvM2bMGF5++WXWrVtnHpGbjNiaPnl5ebRo0YKQkJDKTMa6puXfvq3l/fdvsTkxj4uXGi4t31RoLFTYZMRWHRv1vSVllUrF6Z+9ef99OHo0g9JS83zYyjjvRvfu6cyaZcd/Le+qV2mAra0tXbp0IScnh6NHj9KuXTtatmz50LEayr+xWbNmeHh4sHDhQubOncuaNWsIDb0/Mm1IpPOIFRAQEFApYnXJZKzKmjWFrF5zg/z8YlNOtcHQlNR8UrK5IyO26uTlqRgeeZEjR/IoLraMG1l5uS1xcYLNm5P54vPO9Ohxv/lwTaUB3t7eeHp6kpGRQXZ2NsHBwdjbm8d1fOfOHQYMGMB3331ncqsw/bBuYTOHv4BJuXv3LgsXLuT27dv3nS5dccK0Prz8sjtTpzjeS68WlrGPVhvlxXU3ejUHpLBV51haKfv23bAYUavK9etu/OGJX5g8+XvKy2v+TKnVamxtbbGxsalMLunQoQO+vr4cO3aMrKysGiOz+mSKGkJpaSmOjo5mImqgqAQ2DiV6vZoi5vFXMCFOTk6Eh4cTGRnJsWPHDHLgXv5OW3bt8KeZhwsIy7uRVEVTbB5PunWmyV+xdUPoLHvRRadT8+W/7fBvu5/t2y/X2KYic9LOzq7yYNOK1PuSkhKOHj1KYWFhtfdU1Mc1BIYsee7YsYOQkBACAwNZvnz5ff9++fJlHn/8cUJDQwkLC+P999/Xr+NflyL1eTVFrOI2MWPGDDZu3Mj8+fNZtWpVtX21uvLoo+78khFM//7ugABhnnsZD8NyIzYZslXF0oWtgvx8F6Jjsnnyye8oKCirsU2FwNna2qLT6dDpdAQEBBAcHMyZM2c4f/58ZVRn7Bq22qiaPV0XtFot8+bN45tvvuHUqVN89tlnnDp1qlobGxsbVqxYwenTp/nhhx9YtWrVfW1qQpHCZh2EhoaSkpJCZmYmEydO5MaNG/Xuy95ezc4dIbz791bY2TtaZPRmqcImk0eqI7SWWY9YE0KoOPi9MwHtD7N69dka26hUKmxsbKpFb05OTnTr1g07OztSU1O5ffu2SWrYHkRxcXG9zpg7fPgwgYGBBAQEYGdnR1RUFFu2bKnWplWrVnTr1g24dzhqaGgoV65ceXjnZiRsiqKsVhRlkKIoexVFOa0oSrqiKH825ZhWI2wADg4OxMbGMnPmTEaOHMl3331nUH/PzfUh9UgAvn6uFidu5UUNc9ijsZEBW3WErukIWwXFxY4sWJhP9x57ycoqrLFNRXKJnZ1dZVKYr68vXbp04dKlS2RkZDRYdF/fI2uuXLlC69atK7/38/OrVbQyMzP56aef6N27tx69m4+wAb2BDGCBECIUeBSYpyhKR1MNaFXCBveWC8aOHcv27dt55513eOutt9Bo6v/HDWjnyJnTHZg40RNFUVnM0mT5XcsUNhmxVUdom8ZS5P0onDnjRucuJ3htybEHtlKr1ZXRW0VBdqdOnXB1deXmzZtkZ2ebPO2/vgbINc3rQWJcWFjIuHHj+J//+R+9zohryOQRRVECFUXJVRQlU1GUNEVRbimKcl5RFDdFUUKBs0KIK0KIowBCiALgNGAyDzKrE7YK2rRpw+7du1GpVIwaNYqsrKx696UoCh9/1J7PN/rh7OJkEdGbjNiaBjqthdYj6kl5uR3vvachOGQvx47drLFNRfRma2uLoijodDqcnZ1p1aoVeXl5HD9+nOJi05Xp5OXl1Sti8/Pz4/Ll3xJmsrKy8PHxua9deXk548aNY9KkSYwdO1avvhVApRN6vYAWiqIcqfKaXZffQwiRAewHJgshIoDjwNNCiHxgOLCj2twUpS3QFThUl3HqgtUKG9xz7X7rrbd49dVXGTduHF999ZVBT3cjR7bgzOkgOnd2uxe5NbaFfy2UFtXfXLYxUaz6ir2fppI88jCuXnVj4KAMZs48VJkg8nsqSgPKysq4ceMGNjY2BAcH4+/vz8mTJ7l06ZJBiWMPor4RW8+ePTl37hwXLlygrKyMzz//nFGjRlVrI4Rg5syZhIaGMn/+fP07F6DS6PcCbgghelR5ffj77hRF2a0oyskaXqN/bRIGnPz16w7AmV+/HkoVYVMUxQXYDPzHr8JnEuRtAhg8eDDffvst69evZ9GiRZSU1D889/Cw5YfvO7BooRc2tnZmW/NWXlC7Ya7ZIiO2ajT1iK0qWq0NX8Tb4N92Pzt3Vt+LKikp4eLFi6SmppKRkUHz5s3x8fFBo9Hg6upK9+7d0Wq1HD16lPx8495P79y5U6+IzcbGhtjYWIYOHUpoaCgTJkwgLCwMgMjISK5evcqBAwf49NNP2bNnDxEREURERPD1118/vPO6CdvDuxNisBCiUw2vLYqiOAIOQojbiqK0Bm4KIcoURXECmgkhrgIoimLLPVHbIIRIrPN/WB2wjsc9PfDy8mLbtm289957DBs2jA8//JDg4OB697dsWRtGjy5g9NOXuXmzABTz2uQvk0uRTYKmlBWpL3l5Lox/5ioD+p9n5crWFBbeW6L09vYmPDy82kGlGo2mcg+9wpX/zJkzuLq6EhAQYBST5Nu3b+tl8VUTkZGRNZonV4iXj49PvVaRFKG/aBmBjtzbMwMIrfL148BeAOXe5uFHwGkhxEpTT0hGbFVQqVQsWLCA2NhYpk2bxoYNGwxamuza1ZXzGSE88cSvyxRmlFhSVmSpEZtUtqoIK4rYqiKEiu/2OdN/wGV+SnOmR48etG7d+r7Tt6uWBmi1Wuzt7QkPD8fJyYnU1FRu3qx5364u1DdiMylGjtgeQtVlyGKgm6IoHai+v9YPmAz84dcEkzRFUUx2HIIUthro1asXKSkp7N27l1mzZlFQUFDvvmxt1WzdEkzsP3xwcDSfmjddua05bwE+EKlr1WmK6f51oaTEmZdeKuLRR5PJzi6qsU3V5BIhBDqdjlatWhEeHs61a9dIT0+nrKzmonB9yMvLo3nz5vV+vylQBNiU6PcyFCHE/wkhFvz69T4hRIAQ4megL3Dg15/vF0IoQoguQoiIX196rKnWDylsD8Dd3Z0NGzYwePBghg0bxk8//WRQf9OnP8Kxn9rj39byat7MCSls1bGmPbYHo3Ay3ZWOYcd46+0TD2xVURqgVqsrLbc6duyIt7c3aWlpXL16tV4rNHfu3KlX8ohJadiIreYpCNFNCNEopy1LYasFRVGYMWMGGzZs4KWXXiIuLs6grCo/PwfST3RgytQWqFTqxk8sERaoEhY4ZVNirUuRNVFWZs/y5WV0CN1L+qnbNbap6jupKAparRYPDw+6detGYWEhaWlp3L17t07jmmvE1tjC1phIYdODjh07kpKSwi+//EJUVJRBdlyKovDBqgC+TGyNq5szNM4DDXDvdGNLQ6b7V0entVAzaxOSleVG375nmfvcjw+MwCoETq1Wo9PpEEIQGBhI+/btOXXqFBcuXND7Iba+6f4mRQqbRB8cHR1ZtWoV06dPN4od1+AnmnPuTDDdurk3Ws2b0FmesMmIrTo6jXXvsT0IrdaGf/1LhX/bFJKTr9XY5venBmi1WpydnenWrRsqlYrU1FTu3Lnz0LHu3r2Li4uLsX8Fg5ARm0RvFEVh3LhxbNu2jXfeeYe3337bIDsuV1cb9n3XgSVLHsHW1r7B996EzvL+/HKPrTpCa6Fm1g3ErVuujBqdxdNj9j/wzLqK5BIbG5tK38nWrVsTFhbGhQsXOHPmzAM/5/V19jc5DZg8Yo5Y3p3NDPD392f37t0AjBo1Sj+37Vp45WU/9u9vh5e3a4MuTQqt5f35ze4G0sjoNHKP7WHodCp273akbbvv+eyzXx7YrqI0oCK5xNbWls6dO+Pm5sbRo0fJzc194HvN7bqUEZukXtja2vL2228bzY6rU5gzGWdDiBzRHFAapOZNZ4ERm1yKrI7cY9OfoiInZs2+Rb/+yeTm1uwdWbU0AO6dmebt7U1ERAS5ubkcP368mjNRaWnpfbVzZoEUNokhDB48mN27d7Nu3Tpefvllg+y41GoVCV8E8dEaXxydnEwevQmt4a4LDY2ZPRg3OjqNFLa6oXD8uC1du/2LoqLSB7aqKA2wsbFBq9WiKAodOnTAz8+P48ePk5WVhRCi3gbIpkZGbBKD8fLyYvv27bRr147hw4dz9mzNhyTqQ1lZGf37l/H5xrv4+ZnWa1JXboGOavKKrYaQEZteCFGGEOkI8Q3u7nsZPswTR8faE29+Xxqg0Whwd3ene/fulJaWsn//fg4dOmR+GZG/Ys3CZoF3NvNEpVKxcOFCBg4cyPTp03n++eeJiYnRa+29tLSUnJwccnJy0Ol0tGzZkn79wjjzsyMvLbjA2rUF6LSloBg3wtJpZMRm6ciI7cEIoQF+ATJxcSnnsUGB/OUvM+ja9f6jYWqjQuC0Wm3lyQLt2rWjtLSUhQsX4urqSklJCQ4O5pPIo+iabmKIPsjnXyPTq1cvkpOT2bt3L7Nnz36gHVdJSQmXLl3iyJEjnDhxAiEEYWFh9OzZk7Zt21YeNf/einZ8vb0N7s1cjJ41qdVY4HONFLZq6DTmczM1B4TQIcQFhPgWB4dveGxQKdu2TuR69hK++CKmzqJWQU3RW0hICEuWLMHFxYV+/foZXAJkVKx8KdIC72zmT4Ud17p16xg2bBixsbF07dqV7OxsSkpKyM3NRaVS4eXlRefOnbG3r/2pe8CAZpw/58LIp87zw6HCezVvRqhUtsSlSJtyF+BWY0/DLLiXq2R5UbcpEOIKkIGd3R3Cw3148cURjHm6o9GzFVUqFfb29mi1WsrLy8nJyaFfv348++yzZGRkGHUsQ2hgd3+zw/LubBZChR1Xq1atiIqKwtHRkbZt2xIbG3vf0Rr64Ohow7e7Q3j/H1dY9noeZaVFoBj259OWW15xb0Ghyc4mlFgYQuQCZ7CxuUVISEtmzxrI9OndUKtNuxBVVlbGnj17iI+P59ChQ8yePZs2bdrQpk0bk45bV6SwSYxOYWEhgwcPpkWLFrz22mscPnyY7OxsHBwcDEoP/vOLvkQO92D4iItcu1pgkLhpyyzvzy9XIq0bIW4DZ1Grcmgb4M6UKT144U99sLc37bWs1Wr54YcfiI+P5+DBgzz22GO88MILbNy4EZXK/HZ0ZMQmMQkuLi4kJSXh6uoKwKxZs0hMTGTkyJH893//NwMHDqx330FBTpw704HJU87z7y2FCJ1G/6VJoQUygUx05ZYX/UivyKpYh8wLUQj8jKJcw9fXiQnPdGXRohm4u5t2f1EIwcmTJ4mPj2fXrl1EREQwadIk4uLiKuvc6suOHTv485//jFar5dlnn+Uvf/lLvdo8ePLWLWzKQ4qKLfDELvPm4sWLTJkyhf79+/PKK69gY2PYs8WX/77B7Dk53C0qfHD0JrTABSATO7siOnf2Zc7s3hT0X8mZIjPa8NaDzO9KyDlhxZ/YKgihcPzTbxp7GiZBiBLgDJBFixY2jHqqE4sXP4aPj5uJxxVkZmaSkJDA1q1badOmDdHR0Tz11FM4OTkZZQytVktwcDBJSUn4+fnRs2dPPvvsMzp27FinNrUR5K/w3l/1m89Tc0kVQvSoz+9irsiIrYGpsONatmwZo0aNYs2aNfj6+ta7vzFPt2DgADeGDvuF06cLAeVeTrzQUBGZ2dkVER7uy5zZQ4mK6lK5of7RJcvLqJMRWxUs8HSG2rh3dNc54CLu7oInh3Rg8eLnCAlpafKxr1+/TmJiIomJiTg4OBAVFcXu3btNchzN4cOHCQwMJCAgAICoqCi2bNlSTbT0aVMrVh6xSWFrBCrsuHbv3s24ceN47bXXiIyMrHcGl6enHUd+7MCrr17kH/+8iVabhJ3dXSIi/Jg7dzgTnulUY9+2KgsUNlnIVgXL/78QQsu9WrMLODuX0b1bKyZM7MX4cX0ql/FNRX5+Plu3bmXTpk0UFBQwfvx44uPj8fHxMel1duXKFVq3bl35vZ+fH4cOHapzm9pQkMImaSQq7LimTZtGcnIyb775pkFFnm+95c/TT7txIXME48eFPfTDaWeJwiYjtkqEUH4t/bAsgRNCB1wCzuNgX0TPnv4sWPAMQ4YEAvcSr06dOkXz5s0JCAgwanJGSUkJu3btIiEhgfPnzzN69Gj++c9/Ehwc3GAPTTVt//x+bH3a1D6IFDZJI1Jhx7Vy5UqGDx/O6tWrCQ4Ornd/PXp40KOHfhY/topjvcdpNCzrHm5S1GoVarU9Wu1dg0s/GoKKWjNb2zw6d/bhT/OGMWFC5/tu2C4uLvTo0YNLly7x448/0qG9tXRrAAAVjklEQVRDB9zd3es9rkajYd++fSQkJHDkyBGGDBnC4sWLK89da2j8/Py4fPly5fdZWVn4+PjUuU2tSGGTNDZV7bimTZvGvHnz9LbjMgR7lXE2wxsSCwtOTIqNWuGzz+yY9yc1uTmFgMrs/oPu1ZqdRa2+SXCQJzOf7cfsWT0fWmumUqlo27YtLVu25PTp07i5udG+fXvUav0K0nU6HT/99BNffPEFe/fupW/fvkydOpWPPvpI7z5MRc+ePTl37hwXLlzA19eXzz//nI0bN9a5TW1Yu6WWFDYzolevXqSkpDB37lySk5NZuXKlSfcZ7CxQ2Mztxt2Y2NjYEBkZwi/nBc899wsbP7uLzgyiNyHygDOoVNfx93fjj3/swX/8uQ+OjnWv33R2dqZ79+5kZWXx448/Ehwc/MCEDiEEZ8+eJT4+nq+//pqQkBAmTZrEihUrHuru05DY2NgQGxvL0KFD0Wq1zJgxg7CwMAAiIyNZu3YtPj4+D2yjD9a+xybT/c0QnU7HunXriI2NZdWqVURERJhknJSbH/Pv62+apG9TcfVIGVmHyhp7GmaBs4Mru1b8dnDm4cN3eGbCVW7eKDC6YfbDEKIIOIOiXOGRRxwZPy6CRYsG4OlpvIen4uJiTp8+jaOjI+3atcPBwQEhBFevXmXTpk18+eWXeHp6Eh0dzdNPP42bm2lLA8yZDq0U1k7Tr+2A5TLdX9IAqFQqZs6cSZ8+fZg2bRoTJ05kzpw5Rt8PsFc5G7W/BkEGbJWofpdJ06uXO5kX3Jgx4zybE4tNHr3dqzU7C1zGs7makSM78de/TsDPr/77YbXh6OhI165dOXfuHI8++ijDhg3j+PHjaLVaJk6cyPbt2/Hy8jLJ2BaH3GOTmCsdO3YkJSWFBQsWEBUVRVxcHC1atDBa/xYpbDIrshKV6v6oTFEU1q0LZMaMPGImXeP2rXyjitu9WrMMIBM3N8ETT4Sw+C9zCQszvaAUFRXx9ddfk5CQgKenJ3v37iUoKIg1a9bg6elp8vEtCWu31JK3CTPH0dGRVatWMX36dEaOHMm+ffuM1reDysVofTUUZmjL12jUJGwVDBjQjMwLIYwe3RyVYmvQgbVCaBEiAyF24eS0gyFDBElJU7h29TX+9ekEk4paWVkZO3fuZObMmTzxxBOcPn2av/3tbxw8eJATJ04QExPDkiVLTDa+xSKPrZHUlVu3bjFz5kx27dpFixYteOedd4iJiTHZeIqiMG7cOHr06MGUKVMYMGAAL7/8ssF2XL72Ydje9aDc6baRZtoASGGrRF2LsMG9coANG4LY/e0tpk69zp28ukVvQlwEzmNvX0j3bq156aWxREaGGDjrh6PT6SoNhw8cOMCgQYOYN28effv2vW85fvz48YwfP97kc7I0FGHdWZHyNlEP5s2bh52dHdevX2fDhg0899xzpKenm3zcCjsuIQSjR4/mypUrBvXnZteSd7r9SJvi/gidZeQJyaTI36gtYqvK4Ceak3khmOHDPVAUm1oPrBXiKkKkoFZvISI8mw9XP8nNG8tISnrWpKJWYTi8ZMkS+vTpwyeffMKYMWNIS0sjLi6O/v37m6WLvtli5RGbzIqsI0VFRXh4eHDy5MnKQurJkyfj6+vL8uXLG2weSUlJzJ8/n6VLlzJ8+HCDa96+u/gFm278FbWjzkgzNA056WVkJsusSAAfT38S3jhSp/fs2HGT6TNyKMi/A8o9h3ohbgBnUKtv0L69JzOm92Lu3J7Y2pp2QafCcHjTpk1s3bqV1q1bG91w2FoJ81T4LFK/tuH/klmRVs/Zs2dRq9XV3EHCw8NJSUlp0HkMGTKE3bt3M336dJKTk3njjTcMsuMa6D+RTl6D+PuxcZS4XTXiTI2LtNT6DX0jtqoMG+ZJ5oVmPDMhgz3fZqKoDtKmtSsxMd156aV+ODvX/6xAfanJcDgpKckkhsNWi5Unj0hhqyOFhYX32fu4u7tTUFDQ4HPx9vZm+/btrFixguHDh/Phhx8SFBRU7/6aOz7C33rv55P0xfyk/QKVGR6wLYXtN2zU9fv42tur2bolhIMH3QgK6k/LlqbPjm0sw2FrxdoLtKWw1REXFxfy86sf0Jmfn29yJ/IHoVKpWLRoEYMGDWLq1Kn86U9/Ijo6ut43C0VRmNZpOT1yRrA2cxaKS6mRZ2wY8ib4G+p6ClsFffu2MtJMasYcDIetFiu31JLPv3UkODgYjUbDuXPnKn927NixOtndmIJevXqRnJzMt99+y5w5cwyOIDt5DWB5t1Q8CzvV6DTeaMgrthK1yvyeSzUaDcnJyTz//PMMHDiQQ4cOsXjxYo4ePcrrr79OSEiIFLUGQLHy5BF5m6gjzs7OjB07liVLllBUVMSBAwfYsmULkydPbuyp0axZMzZs2MDjjz/O8OHDSUtLM6g/BxtnXu21jcF2i9CWmsfNSFGZxzzMgfouRRobnU5Hamoqr7zyCn379iUxMZEpU6Zw7Ngx3nvvPXr06CEzGhsaKxc28/hkWBhxcXHMmDEDLy8vPD09+eCDDxo9YqvAFHZcI4Pm0TV/CO+dmojWLc+Is607co/tN2zUjbcJWmE4nJCQwNdff01QUBCTJk3i3XffNSvDYWvF2p1HZLp/E6a4uJgFCxZw+fJl4uLiDLYd0uq0xB2bQ4bNblTqxomc8i5qOLvdijcPqvBo2B9Y8fwXDTaeEIJr166xadMmEhMTad68OdHR0YwZM8aqDYfNkS6uCtv19E733y/T/SUWRIUdV2JiIiNGjODdd9+lf//+9e5PrVLzQte1fJ+1hc+yF6J2avhHQmvfnlFUCv4+ATzVZzLj+s9ukDFv3brFli1b2LRpExqNhgkTJrBt2za8vb0bZHxJ3bH289hkxGYlXLx40ah2XHdKcvn7sfEUuV4y0gz1HDdLw5ktVvaJVcDHy4+hvZ4h+rEXcHYwfQZuUVER33zzDQkJCWRnZzNmzBiio6Np27atTP6wAMKdFHbqaRTTKq3pRWxS2KyI8vJyli1bxsGDB1mzZk3djpp/ABtPLeVQ+f81WM1bwVUNp7+0DmFr0bwlj3cbxZTBC2ju2tLk45WXl7Nnzx7i4+M5efIkI0aMICYmhs6dO0sxszAiHBV2BerX1vukFDZJEyApKYkFCxawZMkSo9hxnblxmP89Pw1ci40zwVooyNZwenPTFTZ3N3f6dXmSaUMW4tsiwOTj1WQ4HBMTU6PhsMRyiHBU2N1Wv7Ytf5bCJmkiXL9+nenTp9O+fXveeOMNgzPZyrWlrDgaQ7ZTqkmf7otyNKQnNC1hc3JyolfHQUx5cgEhvuEmH08IQXp6OvHx8ezcuZOIiAhiYmIYPHgwtrZmaDdTR2JjY1m/fj0nTpwgOjqa9evXN/aUGpwIB4U9rfVr65khhU3ShNDpdKxYsYLNmzezevVqg+y4Ktj5y1q+yvsbagfTXDpFuRrS4y1f2Ozt7QgPfpQ/PvEi3YMGmXy83xsO+/n5ER0dzahRo5qc4XBiYiIqlYqdO3dSXFxslcLW1V5hr547DR6ZTU/YZFakFfN7O64XXniBqKgogyKuoQHPEl7wBCvTn6Hc7aYRZ3sPSy7QtrFV0zEggomPPc+gzk81yL5VTk4OiYmJbN68GXt7e6swHB47diwAR44cISsrq5Fn0zhYe1akFDZJpR3X3LlzSU5OZsWKFbi41P907Udc2/FfvX9k9bEXOK3ebtSaN0sTNpVaIbBNKGP6TWdE78kPPRzUGOTn57Nt2zY2bdrEnTt3pOGwlWLNBdpS2CTAPTuujRs38vHHHzNs2DBWrVpFeHj993sURWFuRCypV0fyf1deROVcbpR5WoLziKKC1q0CGPloDM8MnIudjemdOEpLS9m1axfx8fGcP3+eUaNG8f7770tvRivF2p1HpLBJKlGpVDz77LP06dOH6dOnExUVxezZsw3KjuvuM4wQz8P8PW08+a7nDZ6j2QqbAl6ej9DZrx9//MOfCQroYHJB0Wg0HDhwgPj4eH788UeGDBnC4sWL6datm8xotHaksEkk1QkLCyMlJYX58+cTExPDqlWrDLLjcrFvxuu9d5Pw83L2laxGbcBZluYmbJ4engzqOpIpgxfQ0r0VWq2Ws2fPcuzYMTp27IidnXEP7tTpdKSlpREfH8+ePXvo3bs3kydPZu3atajVpl/mlFgIDShsiqIEAt8DRUAe0Aa4DXQVQuTX9l5TIYVNUiOOjo7ExcWxefNmo9hxATzT4S90vz2c2LN/RLgW1qsPcxA2V1c3+nYezNQhC/H3qp5JqlarCQ0NJTc3l9TUVAIDA2nZ0rDiaiEE586dIz4+vprh8N///ndpOFwDGo0GjUaDVqtFq9VSUlKCjY2NwW47loQiGi55RAiRoSjKfmClEGKfoijJwAuNJWog0/0lepCZmcnUqVMNsuMSQlBYWEh2djbXc6/xbfm73PY8XudkEE2pjqNr79Z5fENxdHSkR+gApj65gNDW3fR6T1lZGadPn8bW1paQkJA6RVTScLj+LFu2jNdff73az5YuXcqyZcsaZ0KNgKIoO4EWejZ3AKrK4IdCiA9/199u4JEa3vufQogtiqKcBXoLIW4ripINtBFClNVn7sZACptEL8rLy1m6dCnff/99ney4ioqKyM7OJjc3FycnJx555BE8PT1Rq9XszdzAlzeXoHbU6T0PbbmO1A8bRtjs7G3pEtiLmCdepHfIH+rVhxCCq1evcvnyZUJDQ3F3d6+1fVXD4fLyciZOnMiECROk4bCkrjRYxpCiKI7AGSFEG0VRWgM7hBCNeo6XFDZJnUhKSmL+/PksXbr0gXZcxcXFZGdnk5OTg729Pd7e3rRs2bLGSO/G3SzePf4MpW7Zeo2v0+g4stp0wqa2UdOhXWeeGTSHwRHjjJYAcvfuXdLT02nevDnt2rWrltxx9+5dvvnmG+Lj47l27Vql4XC7du1kRqOkvjSksHUH/iaEGKooypPAbCHE+IYav8Y5SWGT1JXr168zbdo0AgMDK+24ioqKuHnzJtevX0etVuPt7Y2Xl5deFk1CCD4+sZDjJKLSY5Xz8Kr67c89CJVKIaB1CKP7TWV0n+kmqzXT6XRkZmby3nvvMWPGDHJzc4mPj+fEiRNERkYSExNDly5dpJhJjEFDCtsUIFwIsUBRlAHAJ0CkEOLnhprDfXOSwiapD1qtljfffJP4+Hjs7e3p2rUrr7zyCt7e3vXOBDx+PZmPLs5B5VL70rwxhE1RwLeVP5G9o5g4cB4Odo4G9/kwdDodhw4d4pNPPuGbb76hY8eOvPnmm/Tv31+m50uMjVU/HclPk6TOpKamMnLkSJKSkhgyZAh2dnb069cPPz8/g9Lbu3g/xjtdj+BRGMpDHrjqjVfLR4gZ9hxf/dcZvvjPI0wdvNCkolZhOLx06VL69OnDunXriIqKIiMjg06dOrFy5Up0Ov33GCUSycOREZukzly5coWSkhLat28PQF5eHnPnzsXW1tZgO64K/n32PfYU/QN1DdnsdY3YPJo1Z2DEcKYNWYRXM1+D5/YwhBBcunSJhIQEtm7diq+vL1FRUYwePfo+w+H09HTCwhp1n13SNLHqiE0Km8Qo6HQ6Pv74Y+Li4gy246rg0p3TvP9zFDrX6uUwh+MKH3pluri40qfTH5g6ZCHtHulg8Fz0ocJwODExETs7O6Kiohg/fnyTNhyWmC1S2GpBCpukTqSnpxvNjgtAq9MSmzaTC3bJlTVvDxI2B0cHunfoxx+feIku7XobNK6+1GQ4HBUVha+vr0wCkTQmVn3xSWGTGJ3i4mLmz5/PlStXDLbjqmDfpQQSchajdtJy+INC+HVbytbOls6B3Yl+/E/07TjU4HH0oSbD4ZiYGGk4LDEnrPpClMImMQlCCDZv3swbb7xhFDsugNvFObx7bBwH4jMI9Atj/MBZDO0+oUHERKvVsn///krD4cGDBzNp0iS6d+8uMxol5ogUtlqQwiYxiAo7roEDB7Jo0SKj+PXphA5VA5hG/t5wuFevXkyaNIlBgwZZle+gxCKRwlYLUtgkBlNhx/XDDz/w4Ycf6m3H1RhUGA4nJCTw1VdfERQURHR0NJGRkTg4ODT29CQSfZHCVgtS2CRGIykpiQULFrB06VKGDRtmNvtRQgiys7NJSEjgyy+/xMPDg6ioKMaMGfNQb0eJxEwxjw9XIyGFTdKg1GTH1Vjcvn270nC4rKxMGg5LmhJS2GpBCpvE6Gi1WlauXMnmzZtZvXo1QUFBD3+Tkbh79y47duwgPj6eq1evSsNhSVPFqi9mKWySRuPQoUPMmTOHF198kYkTJ5pMWMrLy0lOTuaLL76QhsMSa8GqL2wpbJJGxRR2XPCb4XB8fDz79+9n4MCBxMTE0K9fP5meL7EGpLDVghQ2ickxlh2XEIJTp04RHx/Pjh07CA8PJyYmhiFDhuh1fI5E0oSQwlYLUtisiNjYWNavX8+JEyeIjo5m/fr1DTp+hR1XdHQ0s2bN0iuy+r3hsI+PD9HR0TUaDkskVoRVC5tck5FU4uPjw6uvvsqMGTMaZfywsDBSUlI4c+YMkyZN4ubNmw9sm5uby+rVqxk6dCjPP/883t7e7Ny5k61btxIdHW2xonbr1i3GjBmDs7Mz/v7+bNy4sbGnJJFYHNI+QVLJ2LFjAThy5AhZWVmNMgdHR0c++OADNm3axIgRI1ixYgX9+vUDoKCggG3btpGQkMCdO3cYN24cn3/+eZMyHJ43bx52dnZcv36dtLQ0RowYQXh4uDzaRiKpA3IpUnIfr776KllZWQ2+FPl7MjMzmTJlCn5+fpSWljZ5w+GioiI8PDw4efIkwcHBAEyePBlfX1+WL1/eyLOTWBhN68NRR2TEJjFb2rZty7fffsu0adNYuHAhvXv3btIZjWfPnkWtVleKGkB4eDgpKSmNOCuJxPKQwiYxa2xtbdmwYUNjT6NBKCwsvM/Cy93dnYKCgkaakURimTTdx1+JxMJwcXEhP7/6aeH5+fm4uro20owkEstECpukEo1GQ0lJCVqtFq1WS0lJCRqNprGnZTUEBwej0Wg4d+5c5c+OHTsmE0ckkjoik0cklSxbtozXX3+92s+WLl3KsmXLGmdCVkhUVBSKorB27VrS0tKIjIzk4MGDUtwkdcWqk0eksEkkZsStW7eYMWMGSUlJeHp6snz5cmJiYhp7WhLLQwpbLUhhk0gkEsvDqoVN7rFJJBKJpEkhhU0ikUgkTQopbBKJRCJpUkhhk0gkEkmTQgqbRCKRSJoUUtgkEolE0qR4mFekVaeMSiQSicTykBGbRCKRSJoUUtgkEolE0qSQwiaRSCSSJoUUNolEIpE0KaSwSSQSiaRJIYVNIpFIJE2K/weAZt4e6Os3ZAAAAABJRU5ErkJggg==\n",
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+       "</svg>\r\n"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 614.4x460.8 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -183,7 +1935,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fb642ac4fa0>"
+       "<matplotlib.legend.Legend at 0x23226ed1fd0>"
       ]
      },
      "execution_count": 6,
@@ -192,14 +1944,1722 @@
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     {
      "data": {
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pfU7TWZq2JezvzTAT6vyWHlgKwMyMEy/3EE7a6v51REUS7Pdrk5WUpJRbgcvbTxw/ivdr+bH2LIPEofCbRTBgelguHVY8mwPiqSBGVkBkQqMuRlsM0aIaISXm6HgAaqQFg9BuVb6MD5+8nYDXfn4N6HqKRKGxcNdCoPMpkraiIyqSHMB/r2QacEQnWTQcVbDqGfj+WTCa4Zz/g6lzteddkQiPIhEV2NzVYIlu3McaQwzVOIUJo8UGgMtv70aJjMEhjViEq3XXzlmnxbKsegbOewrMbVeLRU+emv6U3iIoFO1GR1Qka4HBQogMIBeYBVyliyRuN2z9CL5+TNthNOpyTYnEpuoiTtiI1KyJVFGMATdYYxr3scZgFU6iZA111gg+vvlkXKvWwR7tcDUWHJix4KJWmrEKZ2jXfvUsSBoOhTsgcQic+vs2mpS+JEYkttxJoeik6L39933gR2CoECJHCHGDlLIOuA1YBuwAPpRSbgu7cPtXwCuZ8MlN2m6iaxfD5a91fSUCPoskTXiSMlqDWSSagz1WVGG22JjYvyfJ8fX9XBipxWOxxSS37vqFO7THHYu0x7yf4cuHtaSXnZTunotJ0bXRe9fW7CbalwBLwiyORs56yHoc9n4JcX211Cajr9DSpXcXbHG4hdFPkQSJQvdb7jJbteUnk8nia3NixOFRJCIyAew5ANRKE1ZRF5oclYXasuIb52uZhXd9Abf8BIYgOcI6ON09F5Oia9MRl7b04fBqLTfWvq+1NfoZf9b8IGab3pKFHyGoNcWQ5tKCEpvykXixWLX/kclc/3Z6+sqJyCXvQy0YoxNBC4Bnh+zHOLE/NDmkG45s0JTImFmw5QPYtQQGngU/fwgTru34ucs8/DPzn3qLoGhHuvv97d6KxFkD2z/VUrznroPIRJjxKEy+MbhfoBthtETQs9azdTfo0lb9/8dgsgJg8jwCxEVFImJjoTAXY1T9rq/t7v6MM4SoSFx1kL1ae37O/8GOz7Sa9vtXwNpXNItx0Fmtm5hOxNvULrauTHe/v91TkeSuh62fwOb3oeoYJAyGmU/ChF+DJUpv6ToEFouVvjY7OGjS2e7Do0DM5vpdbAaTCbxLXX7bh/fJ3iHLUF1tp3j7D0RH9CMuOklLv39sH77d4Ds+09LeR8RDVMd2Zn916CsAZvSfobMkivbg072fAnDJoEt0lkQfup8iKdgOr5wJBjMM+YVmfQzI7DRLJGHDaEU4KrTnliCKxF/hGj0WiZ8iwWDGFxLkl7Ryr0wLWQThrCY7NxdBJOPqXFgTBmj1XaI9zvv1b2h/AI+Uduh7+O6OdwGlSLoq/9v7P0Apku6D0QoXPw/Dztd+ySqC4+c4D2qRmCMb9TVb/M4xmkB6Ykj8nPX9Jp3L0ztKuMc5H5c5GqPT3qQINuEkVlaRKxNYvb+YCVH9iShZjMEc0ThqtWA7JI8McXLh57kzn9NbBIWi3ehGW5E8JAyE8dcoJdISRn9FEsRH4q9IvBaJyd8iMWn1RwAs9X3/cul4xvzyXqaZ38d5t19m4CZIFGVUEMn3e4t4ZoMLo3QhC4Oct/erFsfSkxhLDDHBLDuFogvQ/SwSRWh4lAPCEKg0vFj8LRKrp2uDpS23Z5tvg/PPHpHM2SPOC0mMXqIUU0QcS7flMbTaAhYwuIPUMSk9DNUlWrxJ4lA45baQxg8XnTUXk0IRCkqRKILjTf9iiQnuezD5pS7x7tYy+r2dAiySE9vAkJCQyKEDVaQbLE13qjoGB76DDZ6KBKuegV8t0KLkq4p0T+3f3XMxKbo2SpEoguNVDk3luvIP0PRaLwa/t5PR1KRF0lr690mBA/hyegWlqlgLYPS9LoIFF0FUEtjzdM8q/NKMl3S9vqJ96e73t/v5SBSh4fWRhBKQ6XXMGxr4SDyFq4IGNLaCtORkVtyXyV+vmNxkn4qSfM0q8Ue6NCUC8GictuNLJyJMEUSYukYCSkVjuvv9VRaJIjheRRLKhyOYRWIwBzrbE4f4cni1Glsc/ROiwNl0BcWKkgKMpfk0a/tsfh/WVWtBp9Ymluzaic/2fQbAhQMvDNs1FeHjg50fADBr2CydJdEHpUgUwfEtbbXGImnoI/Fb2rptbfBzM6bDgRXNj+/dPtyMUutJBcvWbefi5tJwrf43uJ2w7jUtFuWmbyAu9LiWE+GTPZ8ASpF0VZYdXAZ0X0WilrYUwfE621tjkRiPw0dy7SIqogdqz/ueBBc937iPzatIrI2PebsIJ31EUfNyuv1S2dvzYdHvtQwH2U0ouTZk/jnzmX/O/Ha/jkKhB8oiUQTH2BqLpImlrT4TtPiOFotTeVKenHxL8OUvr0XSxDh1woJJOhgoWplmft/X2h+0uzPebOiiRdAUCpQiUTSFVzmEZJE0sbR1+RtQtDt4QGNT4wRTFjEpgTI1wBGZjKkym3jRdJR8i9SUgb0AEgcf/xjN0N1zMSm6NmppSxEc79LW8VokRrO2JJU2qXXX9CkLP0e4N1dXE0otMnmg73m1rZfvedm0h7nX+bvQrr3gQnhhEkgZuryt4H97/+fLx6RQdDWURaIIjrEVFkkwRSKO4zeKwQwmj+Iy2aCuuoFMTbxd4zOALAAccQOIqNGKn0Rl3sXyL/8DoawqHd2sPf65B9z4desUYAi8MfONNh1P0bHo7ve3U1kkQogBQojXhBAfNdemaAO8Fkko1QiDbf9t1dZajxVgtNQrEms0JA2DS19p+XS/nVfuhCG+5yajgYrmNwQjB53duNEbHQ9a6pXiEOunKBTdlLApEiHE60KIAiHE1gbtM4UQu4QQe4UQDzQ3hpRyv5TyhpbaFG2AyS/XVkt4fSTGE3QoGy31ykgY4NbVMOZXLZ/n51cxJw8JOCRbeIs7I3s1bvSOV5YL80bDc+PBWd24Xyv4aPdHfLRb/dbpqry59U3e3Pqm3mLoRjgtkjeBgERDQggj8CJwLjACmC2EGCGEGC2EWNzgL8gnXtFu+BzozVgkk2/y9PG8jQzHu1Iq6sfx+mTSmo5ib4RfpuKIlMY5tS6o/T9eiL076KmL9rkbN3oVSWVBfVvhrtDlCcLSg0tZenDpCY2h6LisyFnBipwW4qG6MGHzkUgpVwoh0hs0TwH2Sin3AwghPgAullI+DlzQVtcWQswB5gAkJyeTlZXVVkOHBbvdHnaZ++TsYTCQc+Qoe5u6duT5MP088Bw31lUyzXOoNfKOd2kR8OvWb8AeU0bMhH9QGdUXd5AxMj2PLoMNo7sGgJ37DjLM0755534m+MkQbxVsrR3AvRNG8+6+eK7O/lPAeBtKI7m8gSF1ICePQ1lZxJVuY7ynbcfKT8hPKQ15Tv7Y7Xauib7GJ1NXQo/3ZjgJdX6lpdp7o7P9L9rq/untbO8DZPu9zgGmNtVZCJEA/BUYL4R4UEr5eLC2hudJKecD8wEmTZokMzMz23AK7U9WVhZhl/nH7bAX0tL6kRbqtR1VsEp72hp5K9ZpFs2kiROh9zjq1UUQsrQH43074ZM5sGc5w4aNgBlroCKPCRE9YGO9DF9OqqXIXsuwlFjk9Onw50BFkicb16XJyBhAxvRM2FsHm7S2ofFuhvvPSUotbX1ky2lfdLl/YaIrzw1Cn9+CpQuA1r3vOwJtdf/0ViTBPLJN7r+UUh4D5rbUpmgDvAkXW7P76riXtjy0xkEfEV9fC14ILU180tBGS1CJ0VYSoz31UoKMXyCD5O+qOEpddQUmP7/It6tWUZ16hAvGeGrOL30A1r4G1y2Gfie1KG53z8Wk6NrovWsrB+jr9zoNaGV4sqJd8CoSQ/srkmMJHn9IVBu4wfwrOzbH1JsBqCZIkOO618l7Yhx7czUfSVnMIPqKAr7Zqb1es3kbrH5ZS7my8h8hXS4rJ4usnKzQZFN0OqwmK9ZmUvh0dfRWJGuBwUKIDCGEBZgFLNJZJgXU11tvlUVyfG+ng+mz4Z7dEJt6XOcHBBGaWgig9G73PfcJvrhsJymJwZem0kQR2QVaWvpCWwZpooikKE1J/f0DzWkuDWbc9nyoq4V3fwW7l0FpdtDxXp7xMi/PeLkVk1J0Jrr7/Q3b0pYQ4n20xe9EIUQO8IiU8jUhxG3AMsAIvC6l3BYumRTN4FvaCiGO5EQRBohJbpuxWvpVOOs9X6DjuaNTOVY4AFYG71paVg5Atqk/g0QtqeZKAFJFMQC51oGk5W2h5h8jsdUUwh4tAyzXL4X+J5/4XBSKTkI4d23NbqJ9CbAkXHIoQiTJsw8qZZS+cjRk1ntQ7ln9DJbOpCVFYrLUp70HLpo80KdIymQkcaLKd6yiQlMk+0jjDCC6OgdH3URSPIrku4pUZpt2akrEnz3LtBxjQ8+FaG257p3t7wBwzYhrQpyoojPx8mbNGpk7tnu6a/Ve2lJ0VIadDzf/AKMu01uSQIadD1M88Su9x2mP8en1x42tW6eOjYrxPf/INZ1sd5LvdVVlBVIY2ObQrCVbZS7HKmtJFcVUyAiyZRM+nb1fw2e/hw9/42tafXQ1q4+ubpVsis5Dd7+/eu/aUnRkkkfqLUHzTJ0L6dMCraam8nE1hcGAAwsWHBjMNkzeqo7AXPEJNdj4JtcINoiuyqawuIwUUUwBPSmhiazGeVu0x+IDvqbnzwpSZ0Wh6CIoi0TReRGi6aW36NB9LnWeWiFGawQm6gIv4XbhtsbhkEYyc15m0MfnkCqKiU9J59JTxzQeLLZP/XNXbcgyKBSdGaVIFF2P3y6D3zXhQQ+K9jEwWyMx4wo4YhVORvSOo0Ro8SaR9kOkiGJsiX2ZPGJQ46H6TvE9lXW1sPyPRFYers/FdPB7qD6+CHmFoqOilrYUXY8QAgT9qTXFEOmowGyLxNzAIgGwmo3UCqsvVDZVFOOK7xu0CNf72fF4d5UIZxX88DwjI9N4P/JscDlh1R2QMhoufxMSgygiRaekhzVIYGs3Qlkkim6PPUKLVjeaIzA1sEgAHjx3GDYcAW3GHn2g58BGfb8pavyFElWVwzOr3uOZas/vtryf4YWJUOdo1FfROXnmjGd45oxn9BZDN5QiUXR7qqK05ApGnI2WtgCGp8ZiwRnYGNsHInpwY8ZX3BvzJFz0PItM5/CTe0Twi7hqYf2bAU3PvzSPKkdjC0ih6GwoRaJoW/qfChN+03K/DkRVlOYgj3UUYBDa+tXnvwj0sVQbogJPitWsmGiriTWuoTDhNzzG75ospPVqXCyvxsUGtEUWbODQsaqg/RWdi3nr5zFv/Ty9xdAN5SNRtC3Xd77Y0j19r6Ro5w+s63kpB48UcJ1pOUQmBfR5vMefSc37hsuNKxlsyK1XJDYT9lrNqqiocTYa28vmpGHYygMrLfYVBcoi6SJsLtystwi6oiwSRbfHaY3jJue9lFuSeLTuWgbWvI3FbISbvoHb1gNwSPTm364LucrxED9PfdqXPj7KqikSR52b2jotrUyOTOSIDMzhdefIh3jyxm1stY73tQ0QR6muVhaJovOjLBJFt+eCMb1ZujWP284czPtrsnFhxGoyQJ+Jvj4Ol7bk9YcrpzN6fH2N+BirCUedmyF//MLXNr1Wc7reMjGSvYdzsR7byW8Tx2J3G9lXFcEoI+x292GIIZf+H58Md6z3pVJRKDojSpEouj1xEWbeviGwnprFFGisO+o0J3y0NbCcYlJM45QsLrRElwWiF3uFmT3uRGL2v0lCvoW3nL9hn7s30aKaIYZczM5yihb9iU3jHmPGCC2IMre0mtIqB2ajgdySas4YppSMomOjFIlCEQRrA0Xi9FgkMbbAj8z4fo0rLHqprXPh8iSWPFJ5mAqXiWLSec51KSdH5zPWsY/U1DSSd33Eg1tOZ8YTV0NlEV/9Yy5/q7uaPknx7C+s5Jt7ptMr1ka0VX1cOyrJUW2UvbqTot6ZCkUQGlokTpfm/2ioSAYlNZFvC9h2pJz9hVrq+WsGPMiQlBg+Xf4NAPa4IVyZ+zB/HxDJr/K/Yq3tFnjjPTi0imtN8LMcwIHIS4BKznx6BQAHnzi/raanaGOemPaE3iLoinK2KxRBsJoC67D4FEmDpS2DQXBzZuPARIA9BXbf89o6N06PMx7ql8TyTGl84jpNazy0ync8ihriIwOrPcpgafMVig6AUitDba0AACAASURBVCQKRRAaLm05PEog2tbYiL9/5jASPNUTLUbtvJgGy1BLct7grZ3/9r2OizBjMRqodNRxn/N3vFd3ZkD/P5sXcNqx/wS0lVQ1vb1YoS9PrnmSJ9c8qbcYuqEUiUIRhIZLW1dM0qLfm/JTXD21HwDxUZrFYjUHnl9cW0BBdb7vdaTFSITFSHl1HS6MPF53FStdowPOubTi3YDXBRU1xzETRTjYWbyTncU79RZDNzqVIhFCDBBCvCaE+KhBe5QQYr0Q4gK9ZFN0LRpaJA+dN5wdj81spGC83DljCJsfOYdnZ41nRGosg3vFBBw/N+VObhr2oO91tNVElMVIbqlW9reCSB6tuzbgnB0ynWSK+bPpDV4zPwU/B7ztFYoOQ9gUiRDidSFEgRBia4P2mUKIXUKIvUKIB5obQ0q5X0p5Q5BD9wMftqW8iu5NQ4VhMAgiLE3XrzcYBHERZk4akMCSO6YRGxFouTjq3D4/C2iBjBEWIzkl9QGJOTIwml64nSyxPsi1pi85y7iRYd/fRf6iRymyB9Y5WX+ohFe/C4yaVyjCSTgtkjeBmf4NQggj8CJwLjACmC2EGCGEGC2EWNzgL+hmeiHEDGA7kB/suEJxPHh9Hcd9fgNnfVbhAhbuq/eReJe2vLu6AByYuS/274yveZnPXVMYKHJJEBUB4yRveIYrnv82oO2yf/3A/32+44TkVShOhLBt/5VSrhRCpDdongLslVLuBxBCfABcLKV8HAh1meoMIApNEVULIZZIKd3+HYQQc4A5AMnJyWRlZR3vNHTBbrd3OplbQ0ea3+QUI2vzXKz6rjWFsRqTnx/oz8gvyaXKDqBVVcw9uI/deY3TyG929qMENyUyppES8RJdvoesLFujdj3+hx3p3rUHoc7PUqlttuhs/4u2un96x5H0AbL9XucAU5voixAiAfgrMF4I8aCU8nEp5UOeY9cBRQ2VCICUcj4wH2DSpEkyMzOzzSYQDrKysuhsMreGjjS/U05zY6+to2eUpeXOzfB+9jrIqzeSZ/S+l3F9e7B26zoAJowZyRvbNgLw4e9O5lf//hGA1KQEdpcUcgwtU7BbCl9GYi+jDAfIPLAUrDEw4iJGiBK2y3SmT59Okd3BL1/6nudmj2dCM8GSbUVHunftQajzy6TlPh2Rtrp/eisSEaStyc3yUspjwNwmjr3ZRjIpujEWk4GephNTIlAfCe/F4Qr0kdjMRv580Uh25Vcwqb/2hX/phD5UO7RULMVSUyRL3ZM5z7gmYKw/md6BQx4/ye4vWGKF9Jr3cLklW4+UkVNSzd0LN5F13xknPA+FIhT0ViQ5QF+/12nAEZ1kUSjaDH+lAbCufAGHs23AZACMBrj2lHTf8c2PnEOUxcgHa7P5YmseJVLb9eXIOBMOByqSSBHobPfy1PJd/HuF5nQ/eKwKp8tNZW0dPSJPXDEqmufRHx7VHk95VFc59ELv7b9rgcFCiAwhhAWYBSzSWSaF4oSp81gkN2cOxGIAh9tBrateAQgRaIzHRZgxGQ1cPbUf396byUYxjK9c4zmQEGhVvFx3ITe77uWzvv+v0TW9SsTL7e9tZNxjX+J2q4j49uZQ+SEOlR/SWwzdCOf23/eBH4GhQogcIcQNUso64DZgGbAD+FBKuS1cMikU7UWdW7NIpg9JIsYiGGL6Db9Ivtl3PK1HRNDzhBBkJEZhtyZzo/M+XLZAP4dd2lhlnMIf9gwJen6mYRPjxR4GiRyWbssDoNCzXfi/G3M4/e/fIqVk4+ES9uQHd+YrFK0lnLu2ZjfRvgTofGX1FIpm8PpIzEaB2QCHj1UxqnccAB/ffAqDk2OaO51om4mSKmejeBY3ghqnizoa79rqTRFvWv7ue31KzXPUYuZIQRHJsWnc/9HPOFxujlU6+OVLPwAqEaSibdB7aUuh6JIM7qVlBe4RaaHGBT/XvMWTa7VcTIN6NZ0x2Iu37klDRSIx4HRJZJCP7lWmrwNe/2D7PettN7PjzdvZcLjElyfsaGn91uR5X+1WySAVJ4xSJApFO/DYxaN478apDEyKprQ28Is6lGBHb9LHhn2b/sqXjBIHqZHmRkeuMn2D5ZMb6G8uBeBIWbXv2Lyv9lBRq+rGnyjDeg5jWM9heouhG61WJJ68Vk3nilAoFERYjJwyKNH3ujb/QmrzLwS05a6W8FoPFpMBrlsCZ/wRRlxM9Kk3Be1vxckQQzZL3FPJqHnH1+5NUT+q9Gvudr0OaMts/tR4thzvLbBz23sbWLj2cKjTVHi4f8r93D/lfr3F0I0WfSRCCAPabqqr0fYu1gJWIUQhmm9jvpRyT7tKqVB0EYQAoyEERWL1UyTpp2p/gPh2L8GyAZ1tWE9vUcxudxoSA3Mcd5Ene/LYkP2+Oifj6zZjwM3OvEAnu722jl7Ayyv2sXjLUVbuLuTicX2wmdXvRUVohGKRfAsMBB4EUqSUfaWUvYBpwE/AE0KIa9pRRoWiU3PVMAvW5E+xJn+K2WhotPU3GF6LpGEW4sgmEke+YHkegD2yDwDL3ZPZIgcybuTI+jFlJU+ZX2ZXfnnAuWc+vYLFW46wbGseqXE2ymvqyNpVEPoEFTzw3QM88F2zOWe7NKEokhlSyr9IKbf4px+RUhZLKT+WUl4GLGw/ERWKzs056WbiIyJBmkNOBtmUjyTCYyX0jmu8awsgt0EGYSK07cNr3EMBuMy4iqpjWszvSHEAgfaRvu29jVTU1nHnjMEA7M7Xqjv+Y9kuHvtse0gyd2fyK/PJr+y+eWNbfFdLKVssyxZKH4WiO5Ps+hW1BeeH5B+BBktbfnhT2af1jAx63lHZkwvH9q5viE8HYIlrKlfUPgzAROc6fmX8ls+tD3HAdg3fWO5mieVBzNTRu0cESTFWcks0h/wL3+7l9e8PhDxPRfck5DgSIcSzwHC0jSObgfeklJvaSzCFoisR6bEkzCFaJAHOdj+qPI7xtPgI/pPyFhvXrOCYjOHflnkAzP3FRG4+YxDXndKfxGgrJESx4fwlvPlxCVFo236fMs8PGHOAQQtcHCCO0DPKQp8eEWSXVPHPL3f7+uSX17Cv0M59/9nC8rtOJ6qJSpGK7klrdm3tAJ4CngUKgHeEELe1i1QKRRcj3/I21pRPQlckTSxtxUdq23tPHZhISfwo3nOdxQ7Z33c80nPexP496Z8QBUBs/zGAoJIItrn705BrHdpuoyEih8RoKyNiqsg4uJDnvq5XJD/nlPHXz3eQW1rN3gJ7iLNWdBdCViRSypellF9KKZdIKf8BTAJ+136iKRRdB5sxBumKDHlpK6YJi+QXI1P47y2ncOmEPj6llC/r06gEc8Ynx9b7U24z/IknnLN8r3/vuJWf3MNxScEgQy7xkRbmFD3BX82vM1TUV3jYV2jH5cnZ5Y1l+WZnPnaHCmYEGJs0lrFJY/UWQzdabZ8KIeYCg4AYoLyF7gqFAhgdcRXbC7Mx9wrtt9vJAxK57pR0RvSODWgXQjDeU2fE5FEktdRn9w225BRjqw9SHDNsEMd+rs8mnCOTqMXCQZnCYJGLxWSgV81BAJZZH+C9ujP5Q90NFNlrcXsi4O01ddQ4XdywYB2/HGQOuQJdV+bOiXfqLYKuHE9k+xK0Za404PG2FUeh6Jp4neShLm3FRZp59KKRWE1Nx3KY/eJRrnY8yBm1TzdZVz7SYqR3nI0hyTHUyHrFU+NRQkXEEY8dnNXYnKW+41eZviGRcorsDrxJhMtrnDhcbqSEomplkShaoUiEEB8KIYZLKQ9LKV8DLkSrVqhQKFpgU/XL2FL/Q0J029UGMfkppe/dozkgU4m1NU6RAvDjg2fx9T2ZRFtNPuUBUIvWv0paiRQ1UFmEQdbxpWsCX7nGA2DBSWFFrS8dfUWNE5cnKWVxjVIkAHd9exd3fXuX3mLoRmuWtt4BFgotmmo9EA00KmurUCgaE2tOwu00MCAxqs3GDOZviYsIrki87Q0Vidc66dEjnj7OUnBpdeQ/d50EwAzjRgYkWCivKOcyx+e8zQTs1bW4Sw4CUFytvgIASmtLW+7UhQlZkUgpFwGLhBBjgHFo1oxK/65QhEB/w6WsLDpE36nB4z+OB5Oh8YJCbETzH+koqyno0tb4gWmwf49PkTgw+epgj+xlZcLBf/IL+QPlxhqG7/mZhG/m01/8k/yaFKSUIUXrK7ouoeTaEtIvz7SUcguwpbk+CoUikBqnFv8xIKl9LZKmlra8RFmNARbJK789FWt0D9i0Ahx2qNOKYDn9vhoGRtXSz5UDBq3Mb0qpllrvUuN3XGX8mqrtr7IzegrlNU7OGNqrLaam6GSElGtLCHG7EKKff6MQwiKEOFMIsQC4tn3EUyi6BnUJ7zJh4hKmD2m7L9pgjvuWEi3WuWWAIpkwsDcje8eBJQoclX4WidmnTH61dS7DDVpG4Giq2V+qKcXJYhdJohzrZ79j9r9WMPeN7wF468eD7C8MjDWprXNR5VDp6rsqoSxtzQR+C7wvhBgAlAARaEpoOfBMuCLcPdd/CIiTUl7uaZuGlpnYBIyQUp4SDlkUitYwNGEAQxNCy/wbKqYQY1L86R0XQbWfIsHg+QqwRIG7Dmq1zMCOJr4arjZ+hU1oGZGSRYkmR00JP1pvI4oajpTO5OH/bWN0nzg+u/0033mX/+tHfs4t67IVGaemTtVbBF0JRZH8W0p5LfCSEMIMJALVUspWeZeEEK8DFwAFUspRfu0z0aLljcCrUsonmhpDSrkfuEEI8ZFf23fAd0KIS4C1rZFJoQgXc8fObfMxg/lIWmJoSgwLbzkDXvM0eH0bFk/VxmpNOTikCRGkjJZXiQCkiGLf8wShKaC6T+bygjmPd61/9h17culOfs4ta7WsnYn2uL+diVDeiWP8nn8upTzaWiXi4U0068aHp0DWi8C5wAhgthBihBBitBBicYO/ltYErgLePw65FIpOSahR8g3pkxTfuNHi8d14FIkTEw5a8LeIWnJlQkBbv8OfcoHxJ9I9u9NKKh38K2uf77jbLVm+LY/s4sDiWorOTSgWif/PkqQme7U0iJQrhRDpDZqnAHs9lgZCiA+Ai6WUj0PoAbMe/02ZlDJopL0QYg4wByA5OZmsrKxWy68ndru908ncGrrD/K79j+ZGvD7p+jYb1+ur8CeU/6NwO5neoH9SwSFGAge2byADGJJoxShEi7kr8mU8Lkz0E4Ep1LNzcsnKOkZu3hFGi2P8LAcA8MQHXzN/i+bQz0wzcd0oa4vy6kmo782X8l8C4JbkW9pZoralrT57oSiSFCHEdWgZf9t6j18fINvvdQ7Q5GKjECIBLQhyvBDiQY/CAbgBeKOp86SU84H5AJMmTZKZmZknKHZ4ycrKorPJ3Bq6w/ym9Z8GQObozDYbN+lIGfy0KqAtpP+jlLCyQf/dDtgOGclxcBD+8ZsztPYX60+rsfTE5ij2H4kSGcMDzptYZPljwLJXUs8YMqeOhCcv5morpNe8Cwg2lUehFVmFrJw63rj1nA69dTjU9+aCpQuAEP//HYi2+uyFokgeRUvQeD2QJoT4Gdjm+dsupfz4BK4f7B3U5DZiKeUxoNFipJTykROQQaFod24cfWObj+m/a+vPF41kcHJ0aCcG++JusLSFqXEEvi0iGhooEruIZrfsy/mOv/G19T5fe2/7dnjyPN/rty+K59eLSjlaXh1w/l8W7yC/vIYXr54QmuyKDkmLisTza96HECINzW8yGrgEOBFFkgP09XudBhw5gfEUim6DyW8H2LWnpJ/YYD5F4lEUxiCpXGJSoOxwQJPDHAMOONrAV3J1kVYfpVpEEiGrmLb8PJJ5gfyyxIB+3qJZL6LozLR624eUMseTSv5JKeWvT/D6a4HBQogMIYQFmAUsOsExFYoOR3vkYgo1AWRINNi1hdGq/Xm5ZTWYGvsz6sxaduIqbNzsuIO5Di0Lbu+6HBh/Db9O+ogyQw8A+ogiHC434/r2YObIlEZjVdQ4eeR/WymvUQVXOxtt+E5sHiHE+8CPwFAhRI4Q4gYpZR1wG7AMLaPwh1LKbeGSSaEIF+1Rr+J44kiaxOJJ3eJTJGbtz4vZ5gtW9GdXTH3Y1hfuqeySfgsMg2ZQUu3k9VRt5dnq8aHER5p56Pzhvm7e2JpXvjvAgh8P8cGaQKunMzA9bTrT06a33LGLErZ6mVLK2U20L0Hl7FJ0ca4bdV2bj3k8cSQ+DGboVf9ljslT/KrGs02rofVhskH6aZC92td0fu1fmRzbG6hXMMUyxvf81tXx7CusxJqiLZvZPP0iLMaAYltedXikVPOfuDphHsj2uL+dibBZJAqFom1pWIa3VfyxAOasqH9t9lgkNZ4QMYM50E9iskLmH2DidQDsc6eyTWaQGBEoQzmRFBt68mLdRXy+uxKAiAhNkUQKJ2mikAGug1j2LeOkjHiirSbq3BK5fwXjD70OaPXh/bHX1nHXwk0UVtQe/3zbmQNFlZz8+NccLatuuXMXJGwWiULRnbn969sBeP6s59tszBNa2mpozZisgNCWrwzmIMcjwGiCXiMCmuOsgscuHonbLXn0s+1IDDwy6GM+23LU1ycqSlMko825vMg82A/shw9iUlk56Go+3WFHvPUyVwOPkUlJUR4w0nf+2gPF/HdjLtOHJHHu6JRmi33pxW+XXU9pXA2fb8ngxmkD9BYn7ChFolCEgfbIxdSmPhIhwBwBzqrgO7a8S12eY970KUYBV52czvd7i3xdk2JsAadGRmqO/MmGXYEVjCqOcnrFPzjd73IfWh5j7OH9/PLFz7n9rMGMTevBPk8CyDsXbuLlFTEsvfP0E5xsO+AJWmjLXGqdCaVIFIowcM2Ia9p8TPOJ+EiCDuhRJEFiSHyxJw18J97vTZu5XpYIS6BcUVGaIkkSLWdWGmvYD8DR7P3c8GYJFxh/osf4X/qO78yrwO2WGDrYF7Y3+M3UweQKF8pHolB0Utr8y9TrJwlmkXgJYpEAActNDZeeatCUT4I7MJiRwec0eZlhhsOMEft53vwCEzf/Cf845Svn/9jcLHTBW42poym4cKEsEoUiDMz9SkvI8PKMl3WWpBm8O7eMzeS/8vQxeL7YDT5FUv+b1FGnrV9dOLY3B4sqOWVoHwCiZGCNEuLTm7zMMJHNVjIAuMT4A7XSQh7xPFN3BWsPlrArr4IhydEIIdiSU0qU1cTApBAj+9sB6fl/uNzds76fUiQKRRjITMvUW4SWMUdoj8Zmsv56lraas0jstVoBq7FpcTw/e7xnTAu4HBTIHlT1OZX0066EnKarPpxi2MZ+VyoATmnkSlMWAAfNg1hUPYZfzFvJoxeO4LpTM7joBa2glp61TnoZpnK0opQqR+NEmt0BtbSlUISBWcNmMWvYLL3FaB6fIgllaUvDu5Rj9fOReCshRln9fqeatLHLZBSbpzwFIy5u8hKFMpbTDFu5a7w2pn+Q4zPyKX5v+gSArUcapybemlvGI//bSrgrf6eIM3GWnEy1UiQKhaIzMrhXGy3peBVJMGe7F8/SlhCBFonNzyIZ4FliGpDoV5/erJ1XRhSRFo+CGaMp1iIZG3CJpa4pGIRkuGMrAFvd6QHHJ4ndACS5CqDymK99zKPLuOD5VSz48RDFlY2j8NuTCkcVCAfVzu6pSNTSlkIRBm5crmX/ffWcV9t03DV/OCvwl/+JEIqzvYGS8fpILB4fiRBw07QBTMnoyYR+fgW0PAqoTEYR4a0rnzKKpZfvImLhFUw3bqF04MVUJo7l9ZVx/Nr0FZTlAJArAxM9JgjNErl/5+WwE14yT+EW5x2U19TXhK+oqSMhOny1Tna6nyGir5Nqx9/Cds2OhFIkCkUYmJk+s+VOx0GvWFvLnUIlmLN93NX1W5IaHsPPIjEbGNk7llsyB2E0iEAl4jd2GVH099sebDYKqjy7uoQtDsfkueSvWKodLNNKFU0+ZxZ866uuTW9xDOEXkHKecQ09nHZKqU/PUlod3sSPdR4ne3f1kShFolCEgcuHXK63CC3jtUjMfsrpkpcC+zRwtnstEiEEn/9+WjNja2P2SkpmeGr9UpbJaKDUo0gMlghibCaqsFIrzVhry8Fg4vTpZzP2i/lsts0BIFZUcbPxs4Dhk0UJpX55vkqrwru05fIo25puurSlfCQKhULDq0DiM5ru49nR1XD7b4u4tGWn08aPqveRAGaDoFp6FUkkMTYTICjB4/cxa36WMqIChvt/5oUAZLm0jMoLLX/hauNXvuNlDSySKkdduzrgXT6LpK6Fnl0TpUgUijBw/dLruX5p29VrbxccWpJFEgY13UcEfmUYQi2T61mmImloQLPJaMDpWRgxWiN824hLpEeReNPb+xVT/dw1xfd8j9RiVHqISv5qfp1phi3aZXYvhEfjoKqYwvIaRjy8jAU/HATA6XIze/5PrDvYIEDyOCmrclLnSVncXZ3tSpEoFGHg4kEXc/Ggpre8dgjKc7XHhIFN9/FUUlzvHtK6sWs9W3UTA88zGwXSoyRM5nr/i8Pi8bF4l9uAXzseYNvFX3CH8zZf2z7Z2/e8Ulq5xmOVTNj1T63x7xlEvpFJb4p4z1Pn5NCxSn7cf4z/99GW1s2hCXblV+Asm4izbKLa/qtQKNqPSwZdwiWDLtFbjOapKdMe49Ka7hMRD3NXcY9z7vFdo0E0u9lowO1RJEZP7rD1f5zBqEGefpZ6RfKdewyRfcdS5+faPea3dXiDezCJQpuDQdYvMUWV7OAv5jdwuSUfrs2muNKz7NVG2Ux25VdQVzaJkbEzlLNdoVC0H0639uVlNjQTNa43l7wMG9+GxKHN90sZTS2trGJ40q2wa0mjqHmTUeDy/J4VUlseSoi2QrRny6850DcSHxl4fgWRFMtoDstkSoihD1oWYuly+hSFXdrIEEfJKSzh/328hbgIj5+nwbKc260lOmltBt/deRXERNbSN8HNpkPdU5F0KotECHGJEOIVIcT/hBDneNqihBALPO1X6y2jQhGMOcvnMGf5HL3FaJ7kETDz8ca1SNqCmX+DOzY1ajYZDEjv15D0yzHv9dN4/TYe/B31ALaoHkyu/ReXOR6lWMbQU1QAYKb+C323TGOAIY+11psZJfYz1/kWBtzsLbDzyYYcX7/znvuOCX/5stVTO1BUiS3tHba7XqC40hH2qPqOQDhrtr8uhCgQQmxt0D5TCLFLCLFXCPFAc2NIKT+VUt4EXAdc6Wm+FPjI035Re8iuUJwolw6+lEsHX6q3GB0Oi9/SFtLv13yfidpj4Q4AYm2aArGYAr+yZkwYzL9+PQUXRkpkDD1EJRPSYjCI+i/z6h6aXyZWVPNfyyPcbPqMSWIXAHd/uNnXb2deRaPdXqFwoKgSm8mI1WSgyuEKe1R9RyCcFsmbQEBUlhDCCLwInAuMAGYLIUYIIUYLIRY3+Ovld+ofPecBpAGeLSF0T7tS0eG5cOCFXDjwQr3F6HCYjMJPkfj9kk8Zoz26NV/H4tun8eJVExqdb7DFEWHRdnoVewISU62B5W57po/1PTcL7StikmF3i7J9ujGXrw45KatycvfCTfy0/1ijPrV1Lo6UVWMzG335xg4XV7U4dlcjbD4SKeVKIUR6g+YpwF4p5X4AIcQHwMVSyseBCxqOIYQQwBPAF1LKDZ7mHDRlsokmFKMQYg4wByA5OZmsrKyA49HR0RiNHa98p5fY2Fg2btx4wuO4XC7sdnvLHcOM3W5vdE+6Ena7neXfLAfAYmgm/Ugn5ETvXWmtm3fqZpBp2Ex5zUAcfmNl9LuMipjBFHnaooCsrF387bQICtfGkSTK2JudR2Wpto23xBOQ6C7JDrjG7lIjwxtcd7pxM2+5zuZzyx/Y/MkeSnqO8x379ttvqXTCnd9oCiH79W9YkVNHYUE+NaO1nWUHy1zEWQXVdZr+cztrcLg1hbjs+/WU7e8c7ue2+uzpPds+1FsToCmF5mqS3g7MAOKEEIOklC8DnwAvCCHOBz4LdpKUcj4wH2DSpEkyMzMz4PiOHTsYPrzhW63jUFFRQUxMTMsdW2DHjh1MmjSpDSRqW7Kysmh4T7oSWVlZLKhZAMAbM9/QWZo2YunngPYj7ETuXUmlgzu//ZILHH/j4C8apIFvZtwL1v+VwbXbOH30aNITomD1Dz6L5B/VjwT0TZtwJhz+v4C2qYadvGh+jv6GAmr2LeY/NacDlUwQu5k86Xq+3leJ9tsUnNY44BgRcQlkZk6its7F7x5djsVoYOqABKCannExRHoso5iUdDIzm4nF6UC01WdPb0USbHtEk54qKeVzwHMN2iqBDh7ppejuXDn0ypY7dUPMpuNbXc8mma3unpxjMpKRqCWCzBw9DLYHK6CVwbFfLuTZD5fwmHkBH7lOZ7zYw3SjFkeytiKeb3cWYKOWT6yPUvvBUuxj5vtOL6nS/CbrDpWw42g5BiGorXMTbTXx1Y58AK4ffRUGIdj/s4U1B4q59YzjmlanRe9dWzlAX7/XacARnWRRKNqNmRkzmZnRPokbOzPHW+Pc7UlJYjMb6RFpYcdfZjJ05Hh+cg+nwJjC4TF3+PpaTUasw2bwk3sEALvcaZzneJybHVqfHrKMefLvXGjUSvhac38KCCz05u0qrnRw7rPf8eM+bYvx81eNZ2BSFH88fzjnDTiXmRkzuXhsH77bU0heWc1xzauzorciWQsMFkJkCCEswCxgkc4yKRRtToWjggpHhd5idDjMxuP7CnJ7HPP+BbV6xMYyy/EnXhr7CYUT7/S1W00GoixGdss07nfexMeu06nFwhfuqWxyD+Qsw0bOMa7nKXO9FbIjt9T3vKCiNuDaj362HYAJ/eL5+p5Mbpw2gLzKPPIq87hwbG/cElbuLjyueXVWwrn9933gR2CoECJHCHGDlLIOuA1YBuwAPpRSbguXTB2J7OxsMjIyKC72OA5LSsjIyODQoUM6S6ZoC37/ze/5/Te/11uMCoiWeQAAHplJREFUDkdrg/+8eEuj+2qbAGP79uDN6yfz4HnDsPhtnrGYDGj7dAQLXWdQTH00fLGMIUI03q5rLKnf1eVyy4DrAPSMsmDza3vwuwd58LsHGZIcTWK0hR+D7PDqyoRz19bsJtqXAEvCJUdHpW/fvtx888088MADzJ8/nwceeIA5c+bQv39/KirUL9nOztXDu1as7Owp/SipdAD6vDe9FomtwRd85lAtSsBqNrDGPZRSGc0YU9M7MksIvomlX+la4ExONmzjfctf+fikj7Bs+4R7Cs/DgZms+zKDnieE4KQBCazaW4TLLY9bUXY29Ha2K/y46667mDhxIvPmzWPVqlU8//zzeoukaCNm9J+htwhtyuOXjgbQbdt2U4rEi9Vk4FcObffWxmYc+uVSy+W1yT2AcYb9vvYRNZuAM5ljXAzA2TseIrZiFzmmKvqLfGKLUiBtYtAxZ45KYfGWo/y0/xinDkoM2qeroRRJA/782Ta2Hylv0zFH9I7lkQtHttjPbDbz1FNPMXPmTJYvX47F0rViDrozJTUlAMTb4lvo2T2xmVu3yu5d2mrqPP8IeO/zF64aj0EIbnl3g+/Y5EQnlMJ691CfIsl2J9EbbTeWDW3HljsqGcp2cbPJE2Hw0XVw589Brz1jeDIxVhOfbT7SbRSJ3s52RQO++OILUlNT2bp1a8udFZ2Gu7Pu5u6su/UWo0PywZyT+PqezFad4y0kZWti2crq1271KJILxvTmvNGpAf2ie2sJKq+ZdY2vrZgYhhmy2W39Nb2FtkPL4grM+YUzMHreH5vZyKmDElm5u7Db5N1SFkkDQrEc2otNmzbx5Zdf8tNPP3Haaacxa9YsUlNTWz5R0eG5duS1eovQYTlpQMJxn9vU0pa/RWJqZmdYwfg7SD/1Cqx+VSErhOaMtwgX/UWBdp3qvMATaz2+oR9egAMrufaka321WgCmDUlk6bY8/vDfn3n80jGtmlNnRFkkHQQpJTfffDPz5s2jX79+3Hfffdx77716i6VoIzL7ZpLZN1NvMboMY/v2AOqtjYY01Q7w9BV+ubesNi1BpLXe6W439Wh0jqH8iJbS/vYNMO0eqKuBJzNg+UOwZxmZlqSA+3vuqFTiIsx8sDabytquX35XKZIOwiuvvEK/fv04++yzAbjlllvYuXMnK1as0FkyRVtQVF1EUXWR3mJ0Gd66fgqf3noqhiZ2RTUX6HjZxDRG94kD/LYf+9VJqTXHBjlLgjVaqx4Z6fF7VNeX6j1QcZgDuxfD08NgzSv0jLLwzJVjkRK25pa1bnKdELW01UGYM2cOc+bU16swGo2sX78eQG3/7QLct+I+oAvl2tKZuEgz4yIbWw5eRAu15KOs2pKYKUjtFWGOhGCB6RZPHXlrdKNDj+16FyqO8EbFUdizHKbcxJg0Tb4tOWWenFxdF6VIFIowcMPoG/QWQeHHvCvH8//bu/f4qKpz4eO/J5M7CSHcISCgkHCLBASEqjXaKEgtF+E9BawHUz8g8OqLpccjR1uk1BsePL5QLxiLohbwggqiCFXbaO0BBSEqGEQUkJDDRSGQkITc1vljJsnMZJLMMJPZM+H5fj5+mFn7Ms9yT/Yza6+911r32WEGdGv4HEl0bLznx2NqE0h0w0TCMaebY87an2rvmBBDSrs4Pi8oarh+K6OJRKkguDLlSqtDUE66JsVyx7X9PC5LSkwATyOcRDuSTkwTI3FHREJR/TTEl/ZIuiASifaRKBUEtWMxqdCX3NYtUdTOG99Ui6TWqDlQ+iNsnAf7tnBpj3acOFnEqWP25HLTU//kgbe/aoGoraWJRKkgqB2LSYWw6x+En95N+3Zune1d7KMGN9VHAkByL+jmmCDrs1Ww5l9IT0liVfSjJD+dTv7/nGHn90X8+eMDvP5Zged9hCm9tKVUEMy6dFbzKylr/eQOANrvXONa3vVSKNgOkfbZET21SGYVnYZLhkGn/vWF8R3o3DaG1Aj7vPMDnunJCFnIdtOf376Wx6SM7kSc5+jHoaZ11EKpEDe6+2hGdx9tdRgXnI4JMT5vExkd61qQOsb+b6FjumsPfSSjy88xOvFi6OL0QHNUGzqd2Oay3u2RG+knBbwU9TCy5CIozIOaasKdtkhCxJtvvskf/vAHl7IvvviCd955hyuv1I7acHe42D6jdM/Ens2sqQJl7x/H0sxdwJ5F1ieSbZfnMKrP1fY3QxwDmHtokeyNjoLKIvqLQPdhULgTTn9P8ropLutl2XaRZXMkpAog52o+rL6Ukzet5fqBXWkTE56n5PCMuhWaNGkSkyZNqnufk5PD6tWrGTNmDGfPnm1iSxUOFv5zIaDPkQRTY8OnNCvKkUgioiiP62J/v/AU1D5zEtmwlbOkfTIc+4jnWQC3vg3/P93e6e6Fq21f0PuVzxl18WHWzhzV7DMwoUgvbYWgffv2sXjxYl566SUiPDwwpcLP3Iy5zM2Ya3UYyhu1LRLnE7rz36EI3P6PhtuJY53oNtDuIp8+8r7Iv7Dtu5P89atjUFPjY8DW0xaJu3cXwFHPw0Oft67pcMMjXq1aWVnJ9OnTWbp0KRdd5NuXUYWuEV1HWB2C8lZdi6OJlkE3DwMxOicem29TQMyM3ERO1K8oW/8bzOt/RTKmw5iHG79DLMSE1c9dEZkoIs+KyAYRud5RNkBEVojIOhGZY3WM/vr973/PoEGDmDp1qtWhqAA6cPoAB04fsDoM5Q1PLRKvOJ1OI6JcltxacTenrn7IpazYxLm8v+snHbimIhepqYKdL8EzP4UzhT7GYI2gtUhE5DngRuC4MWawU/lYYBlgA/5sjGn0p7sxZj2wXkSSgaXAX40x+cBsEYkAnvU7UC9bDi0hNzeX119/nZ07dza/sgori7cuBrSPJCxExja/jifilEhsronkgOlGbKLrfseee4QHop7jGtvnAAyMPkaSlHJ45EJ69r8MXpwAT/+EfUMW0CtrpsscK6EmmC2SVcBY5wIRsQFPAjcAA4FpIjJQRNJF5G23/zo7bfo7x3a1+xkPfAx80NKVaCmnTp0iOzubF198kcTEJoZgUGFp3rB5zBs2z+owlDe8ubTlZt6pIuZdXH+zjPulraOmPbHx9X/X+bY0jtCJd2tG1pX1OvsFAN9HdIeLM6FNZyg7Req2e/j2ickhfatw0FokxpiPRKS3W/FIYL8x5jsAEXkZmGCMeRh768WF2G9neAR41xhT97PdGPMW8JaIvAOs8bDdLGAWQJcuXRrMM52UlGT5CLvLli3j+PHj3H777S7l8+fPZ+LEiQGJr7y83LI5tptSUlISknEFSklJCThGxcj9KtfSWAKtNR67qIrTXAFU11Q3Wb9Mp9cZ5yr47FAZuafs6w46dZpOTst/1qcNX+zdQ+1MKEuSF8PZakpNfSul/adLAXj/QBWVubmMKiuhdunAor9Dzt/5n65ZfN3/Tv8r6RCo42d1Z3sKcNjpfQFweRPr3wlkAUki0tcYs0JEMoGbgBhgk6eNjDE5QA7A8OHDTWZmpsvy/Px8y1sBixYtYtGiRR6XFRcXByS+2NhYhg4d6vd+Ai03Nxf3Y9Ka5ObmkjIkBYB+yZ4HCgxXrfLYlZ+G/wabLZKEhITG65db/zIvJhpbvw5kDnCse+IFcJp+5qnbr4fvk8De6ODRGZnMW5vHovRBsMV1t0XtBpCZOZwHP5jMfbaXXJZ1O/o+3Yp2wJSV0DfLn1raqxCg42d1IvHUdmx0kmNjzHJguVtZLi6HVKnQ89An9o5W7SMJA7WDNF7/R/DyEa5lye1g/2s8P8DxAKJbHwkA0fF1LzsnxrJ21iioGgo/fgo7ngNgVvfXOH7yHADPVt7AVeTxU5vTXaRRbaC8iO3vvsCHqT35tzFpPlevJVidSAoA50d9ewDhcZuCUj747fDfWh2C8pYtEhY5ZjX05bJPY53t//Ki/d+oeBqIjIEbH69LJB06dmPHnvpRos/hlpDuOUjVmqm03b+TbYVvQ8oe+9D1fX4KcY1P9NXSrE4k24F+ItIHOAJMBaZbG5JSgTe44+DmV1JhzsNzJJdOhYET7K+j2zS7h94d4jl5toLTZZUAVLqdoo+ereF0zEDSIj5gXcxiWOdYkHkvXP3v53HLcmAE7a4tEVkLbAXSRKRARG4zxlQBd2C/SpgPvGqM2ROsmJQKlr0n97L35F6rw1AtyaVF4kgkkU53bzWVSG58HKa/Sq8O9lbLX7YdAqDCLZGMevgDvjzTcD+793xO5aP94Mt1DZYFQzDv2prWSPkmGukkV6q1WPLpEkD7SFo1T5e2bE7jcnm6tFVr+K8BuPiY/e7M/9zyNQCVpv4U/ZsK+/PW7x0oZ4ojP31x9UpSdj5Kx+P/TZScgm1PQbrrQJHBYPWlLaUuCPeMvMfqEFQLuufkKbjp3+oLalskzs+TRDT/QGG/zgkM6dmOzw8X8e68qyh74xU4Ad/WdOPNmqsAKKY+IeV+X8E1dCZd7ImHI5/B15vhH0shoQtMXe133byhiUSpIOjfvn/zK6nwMvuf9iFM1vwf+ldUgnM/WO0QKTbfTrEiwtqZl3Pox1IGdGsLfbrCCeiQVD+P/BlTn0g27C0hzpZIuvPHrP1l3cv5j+Vw/5xskuI93EUWQGE11pZS4Wr3D7vZ/cNuq8NQgdR1MKReD8DW2Bi2Hv2kflltAhG3U6zYYPDkJncbHx1pTyJQd4nMOF0iO0N9H8kZE893plvd+5erMtlb05OjE18DoNvJ7dz0x+c5tmIClJ70qXq+0EQSQg4ePMi4ceNIS0sjNTWVhx9+2OqQVIA8tuMxHtvxmNVhqBaS0y6JnC9yPCxxu4vq/pMw5Tnvd+y4NCZOnfbOLZKsoamsr74CgALTkQVVsxhbsYQ1J3pTJVEkSBnjIj6hy9FceLQP7H/f+8/2gV7aChE1NTVMnjyZ+++/n/Hjx3Pu3DmmT59OTk4O06Z5vE9BhZF7L7/X6hCUFfy9Hdcx7leEU1+Lcx9J24QEyohlRPlTiONZ7tQuCez6/hSlEs+cyI1UGaf2wl8mc7LDUA5OeJNhFyX7F5sTbZGEiC1bttC7d2/Gjx8PQExMDE888QRLly71uP6ZM2cYOnQogwYNIj4+noyMDEaNGkVNGE6KcyHol9yv1Q2PoprQ6PgcPnJc2nLOR9XUd9ontbEnmBO04zj2xDC8d3vyDhdxpsY+Ulek1FCDwCU/A6D9j7t4ZsUyinOXBShITSQeZW/OZv3+9QBU1lSSvTmbjd9uBKCsqozszdlsPrAZgOKKYrI3Z/P+IXuT8VT5KbI3Z5N7OBeAH8p+aPgBHuTn5zNkyBCXsm7dunHmzBkqKioarN+2bVt27drF888/z3XXXUdeXh7btm3TGRVDVN7xPPKO51kdhmoJv3od2vdxK6zNJH62SBwtkYgI+34u7uT6DEm7uIYTaKWnJFFcXsXpmvoBIcsj4jlSUv8j85nox0nMXUhhSWB+eOpZJ0TYbDb7KLFOjDGUlpYSGRnJiBEjmDNnDllZWS7r7d69m0GDBgU7XOWjZTuXsWxn4H4BqhDSNwvapriWmQA1SRyd7G2iI3nmlsvYeMeV3JVV37Jt53Y3VkxkBJ0S7NuUUD9xVpnEs4rxDXZ/78elZD//KX/fe9yvMLWPxAPnh8aiIqJc3sdFxrm8T4xOdHmfHJvs8r5jXEevPjMzM5Obb76ZJUuWII527HvvvcewYcMoLCxk9OjRLF++nFtuuYUTJ06QkGCfgvOrr75i2LBh51dRFTQLRy+0OgTVghoeX0cicb9ry1dOY3aNGdQVgLuyUmFbLES3oV2cfXm0LYKK6hrio220T7C3UpxnYCythGcPdSEpcgKzbRuJFHtLpB0lfPn1GUoKlsBdT0KbTl497+JOWyQhYsiQIQwdOpSFC+1fyGPHjjF//nweeugh8vLy2LdvH9dddx1paWn06VPfjC4sLKRr165Wha281CepD32S3C9/qNaiwfGtbZH429ne2Nzv9xyC33xFYqwjkUTaT+Xt4qNpH2/fpsqpnSBij6fUxNQlEYDrbTv4c/RSflH9PjyWBu+d3w8eTSQh4pFHHmHHjh088MAD/O1vf2POnDkcOnSIuXPnsmHDBh5//HFee+019u51Ha9pzJgx3HbbbXz44YcWRa68sf3odrYf3W51GKqF5B7OresXDaja2RrdE1JULETF1iWQi9rH8+sr+vDsvw4n2dEBb6M+YdS+LiPGZTePRj1LRsS39QVbn+B0WSWPvLuX0ooq78P0ek3VohYsWMCCBQvq3l977bV1r8ePH09qaio2m43y8nLOnj1Lmzb2TrcZM2YwY8aMoMerfPNU3lOAjrXVWr2w5wUAMntmOkoC1dne9BPp/ToncOtPenPL6F5c0sl+uds4WkM26qflrU0kpTQ/H33NYwM4UvpLlplfMfeaviTFNf9UvCaSMLB69WpsNvt1y3XrrBndU/ln8RWLrQ5BBVPaOPhwCaTd4N9+Gru05RARISwa73qzTW0fa6RTIomobZEY1xaJs9sqfsvK6MdIrjrB6IivuPej73hh60G29v0LyalXNB1Hk0uVUgHRM7EnPRN7Nr+iah26Z9gnx+qe4d9+mmmRNLmp06Wt2qRS6nZp64eE+jHgPqi5jCvKl/F1TQ86yBlG925LnCkl+cDbsOU/mvwsTSRKBcHWwq1sLdxqdRgq3Phx19c7NaPqXu+u6Q1AGfUtnDVV11LQbazLNkfoRLEtiTG2Haw9eiNzBlR69Vl6aUupIKgdh2l099EWR6IuBOtmj6a8YiT0foD/fH4tLx1IBFwvbZ0jColJcNmuXXwUGamXwF77/IKDSz/BG5pIlAqCh6/SAThbs5Y/vr512g/v3b7udef0n3HmwB6ezx7BklXf15VXEEl1VGLd+2VTMxjSox2R2zbXlQ09/oZXnxdWiUREJgI/BzoDTxpj/ioiEcAfgbbADmPMC1bGqJQnXdvosz6tWSgf338d3Ytx6d3olBhD+pxrwDH4cAVRVEXWt0gmZDiezo+vT0JxlUX8o3owd1X+X+DmRj8jmHO2Pycix0Vkt1v5WBH5WkT2i8iCxrYHMMasN8bMBG4FamdvmQCkAJVAQQuErpTfPj7yMR8f+djqMFQL2Xxgc934e6FGROiUaL+k1TG5fsTfzu0SqYxK8LSFy7udph8/ktTkZwSzs30V4NKzIyI24EngBmAgME1EBopIuoi87fZfZ6dNf+fYDiAN2GqMmQ/MafFaKHUeVn65kpVfrrQ6DNVCXvn6FV75+pXA77jHSOiaDtcF6Pbx6Poh6H85qi9VkW0arlNZav93wC+ojGrL+uorm91t0C5tGWM+EpHebsUjgf3GmO8ARORlYIIx5mHgRvd9iP0G6UeAd40xOx3FBUDt8LjV7ts4tpsFzALo0qULubm5LsuTkpIoLi72vVJBUl1dHZD4ysvLG9Q9FJSUlIRkXIFSUlLCpLhJAK2unhfCsfOmfkVFRUALHd/+D8C+ItgXgH2bajIdL785eJjidn0pSPk5P3QcTZEj9sRzvbkM+DRhLJ/1z+bA9vJmd2t1H0kKcNjpfQFweRPr3wlkAUki0tcYswJ4A/iTiFwFfORpI2NMDpADMHz4cJOZmemyPD8/n8TERA9bhobi4uKAxBcbG8vQoUMDEFFg5ebm4n5MWpPWXL/WXDfwvn4vbHY82R4O/y8coyn1SxvIkbMJ9Ji5hh4uK2TC+JmMBOIKTrNke/OXZK1+jsTTrQiNjr9sjFlujLnMGDPbkUQwxpQaY24zxtxpjHmysW3DwVtvvcWUKVNcyp5++mnuvvtuj+vr5Fbho8XGYlLKZ47TbmTzw6W0ifFuJGCrWyQFgPPjvj2AQotiqZO9ObtB2ZjeY5jafyplVWXMfX9ug+UT+k5gYt+JnCo/xfzc+S7LvB1f6b777mPt2rUuZZdccgmvvvqqx/VrJ7f69NNPefDBB9mwYYNXn6OCr+FYTEpZJLoNVJTUDwjZhIRY71KE1YlkO9BPRPoAR4CpwHRrQ7LG559/Tk1NDYMHD+bQoUNs2rSJOXPmUFlZWTd2Tq0tW7Zw/PhxbrnlFkAntwoH/5X5X1aHoFpQWB3fqHh7ImlmHC+AxBjvhmgJWiIRkbVAJtBRRAqA+40xK0XkDmALYAOeM8bsCVZMjWmqBeE+sZU794mtvJWXl8dll10G2Ce0+uabbwD7xFWDBw+moqKC+fPn07ZtWz755BOefLL+Kp5ObhX6kmOTm19Jha2wOr7R8XAWr1oksVHe9X4E866taY2UbwI2BSuOUFVTU0NJSQnV1dW88cYbpKSkUFZWxqpVq1ixYgVPP/00M2bMYMSIEVx55ZWkpaXVbVtYWMi4ceMsjF415/1D7wOQ1SvL4khUS1i/fz0AE/tOtDgSL0Q5bgH2okXifjWkMVZ3tiuHcePG8d1335GRkcHs2bPZs2cPw4cPZ9asWWRkZLBr1y7S09MpLi6mY8eOLgdYJ7cKfavzV7M6f7XVYagWsmH/BjbsD5M+yijHFLxeJBKAf/z7NYwd1PST+1b3kSiHLl26kJeXV/d+/Pjxda+Li4sZM2YMs2fPJj4+ntTUVJdtdXKr0Lf82uVWh6CUXW2LpLoCb8bw6tk+nj6dPDy46EQTSZiYNm0a06Z5vDqowkBidOg+p6QuMNGOpFBZBsQ3uWqte8b2p6nxq/TSllJBEMpjMakLzM/utw+70rvpWQ99oS0SpYKgdhymsX3GNrOmUi2sy0CYHdgBRDWRKBUET2U9ZXUIqgVd6MdXE4lSQRAXGWd1CKoFXejHV/tIlAqCjd9uZOO3G60OQ7WQl/e+zMt7X7Y6DMtoiwSIiooiPz/f6jAaVV5eTmxs8wOsNScqyrvhDlTgvfGNfcrSX1zyC4sjUS1hy8EtAEztP9XiSKyhiQTo27ev1SE0KTc3NySHf1fey7k+x+oQlGoxmkiUCoKoCG0NqtZL+0iUCoL1+9fXjcekVGujiUSpIAirsZiU8pEY0+iEhK2SiJwADlkdh486Aj9YHUQL0vqFr9ZcN9D6OetljOnkacEFl0jCkYjsMMYMtzqOlqL1C1+tuW6g9fOWXtpSSinlF00kSiml/KKJJDy09ocQtH7hqzXXDbR+XtE+EqWUUn7RFolSSim/aCJRSinlF00kSiml/KKJJAyJSBsReUFEnhWRm62OJ9BEZKKjbhtE5Hqr4wk0x/H7TERutDqWQBORCBF5UET+JCIzrI4n0ETkIhF5S0SeE5GmpjEPGyJysYisFJF1TmU+nWM0kYQIxxfzuIjsdisfKyJfi8h+py/uTcA6Y8xMYHzQgz0PvtTPGLPeUbdbgV9aEK5PfDx2APcArwY3yvPnY/0mAClAJVAQ7FjPh4/1SwXeMcb8GhgY9GC95OPf23fGmNvcduHTOUYTSehYBbhM6C0iNuBJ4AbsX9ppIjIQ6AEcdqxWHcQY/bEK7+tX63eO5aFuFV7WTUSygK+AY8EO0g+r8P7YpQFbjTHzgTlBjvN8rcL7+u0CporI34C/BzlOX6zC9783Zz6dYzSRhAhjzEfASbfikcB+xy+GCuBl7L/4CrAfaAiTY+hL/cRuCfCuMWZnsGP1lY/H7hpgFDAdmCkiIX/8zuO7ecqxTlj8yPGxftnA/caYa4GfBzdS7/lYJ098OseE/Jf4ApdC/a8CsB/cFOANYLKIPA2E8/ytjdXvTiALmCIis60ILAA81s0Yc58x5i5gDfCsMabGkuj819R3c4yI/An4yIrAAqSx+m0G/p+IrAAOWhCXPzzWSUQ6OOozVET+w7HMp3OMTmwV2sRDmTHGnMX+yyjcNVa/5cDyYAcTYB7rVvfCmFXBC6VFNHbsSgH36+3hqLH67QamBDuYAGmsTj8Cs90KfTrHaIsktBUAPZ3e9wAKLYqlJbTm+rXmuoHWLxy1WJ00kYS27UA/EekjItHAVOAti2MKpNZcv9ZcN9D6haMWq5MmkhAhImuBrUCaiBSIyG3GmCrgDmALkA+8aozZY2Wc56s116811w20foRh/YJdJx20USmllF+0RaKUUsovmkiUUkr5RROJUkopv2giUUop5RdNJEoppfyiiUQppZRfNJEopZTyiyYSpZRSftFEopQFRGS884x0jrI5IrJcRNqKyC4R2SMipSKSJyLbwmHIeXVh0tF/lbLGg8A0t7JvgcnGmDPYh/QeCdxnjGlszgilQoL+wlEqyERkCBBhjNktIr1EpHYmwSichpoHBgNhM76TunBpIlEq+DKAzxyvrwP6OV4PBD53Wm8g4DLntlKhSBOJUsEXASQ45tC+CUgUkTjgVuwzJ9bqDhwNfnhK+UYTiVLBtwm4GMgDVgCDgB1Ajtsc9VuAlSJydfBDVMp7Ooy8Ukopv2iLRCmllF80kSillPKLJhKllFJ+0USilFLKL5pIlFJK+UUTiVJKKb9oIlFKKeUXTSRKKaX88r/i0YXbTl7trgAAAABJRU5ErkJggg==\n",
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+       "    </g>\r\n",
+       "    <g id=\"line2d_38\"/>\r\n",
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+       "    </g>\r\n",
+       "   </g>\r\n",
+       "  </g>\r\n",
+       " </g>\r\n",
+       " <defs>\r\n",
+       "  <clipPath id=\"p7608fd2f7c\">\r\n",
+       "   <rect height=\"266.112\" width=\"357.12\" x=\"55.778125\" y=\"7.2\"/>\r\n",
+       "  </clipPath>\r\n",
+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 614.4x460.8 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -218,7 +3678,7 @@
     "toc.append(time.perf_counter())\n",
     "\n",
     "# Plot \n",
-    "fig, ax, _ = ff.plot_filter_function(ECHO, omega, yscale='log')\n",
+    "fig, ax, _ = plotting.plot_filter_function(ECHO, omega, yscale='log')\n",
     "ax.axvline(Omega_R*ECHO.t[-1], label=r'$\\Omega_R\\tau$', ls=':',\n",
     "           color='tab:green')\n",
     "ax.axvline(omega_0*ECHO.t[-1], label=r'$\\omega_d\\tau$', ls='--',\n",
@@ -249,7 +3709,7 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30b23e89df10479c8c339c7abd7c19ff",
+       "model_id": "c8e33bb2527c47baa85da46dbd5e2c0a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -296,16 +3756,16 @@
      "output_type": "stream",
      "text": [
       "===========================================\n",
-      "ATOMIC initialization\t\t : 0.0013 s\n",
-      "ATOMIC filter function\t\t : 0.0395 s\n",
-      "NOT concatenation (periodic)\t : 0.0837 s\n",
-      "NOT concatenation (standard)\t : 5.5189 s\n",
-      "ECHO concatenation\t\t : 0.0384 s\n",
+      "ATOMIC initialization\t\t : 0.0016 s\n",
+      "ATOMIC filter function\t\t : 0.0299 s\n",
+      "NOT concatenation (periodic)\t : 0.1027 s\n",
+      "NOT concatenation (standard)\t : 4.6181 s\n",
+      "ECHO concatenation\t\t : 0.0549 s\n",
       "-------------------------------------------\n",
-      "Total (periodic)\t\t : 0.1628 s\n",
-      "Total (standard)\t\t : 5.5980 s\n",
+      "Total (periodic)\t\t : 0.1891 s\n",
+      "Total (standard)\t\t : 4.7045 s\n",
       "===========================================\n",
-      "Total (brute force)\t\t : 148.03 s\n",
+      "Total (brute force)\t\t : 134.57 s\n",
       "===========================================\n"
      ]
     }
@@ -353,7 +3813,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.1"
+   "version": "3.8.3"
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@@ -396,6 +3856,19 @@
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+      "state": {
+       "bar_style": "success",
+       "description": "Calculating control matrix: 100%",
+       "layout": "IPY_MODEL_4b53554abbeb472e837ff411adf1fff1",
+       "max": 200002,
+       "style": "IPY_MODEL_c054834b543e4ad88d0c002aa5467f6b",
+       "value": 200002
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@@ -438,6 +3911,36 @@
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+      "model_module": "@jupyter-widgets/base",
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+      "model_module_version": "1.5.0",
+      "model_name": "HBoxModel",
+      "state": {
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+        "IPY_MODEL_f6717f07f6564129b02a078b0e4ecb0f",
+        "IPY_MODEL_664e9e53cf9c4659b21b652f467042a1"
+       ],
+       "layout": "IPY_MODEL_fb2cf1794dc14cdda400391782bda92d"
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@@ -448,6 +3951,16 @@
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+     "664e9e53cf9c4659b21b652f467042a1": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
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+       "style": "IPY_MODEL_cbeafdbd91c249c8a26df3e2e47d8a9d",
+       "value": " 21454/200002 [00:13&lt;01:42, 1733.73it/s]"
+      }
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      "729767462bbb4e29a76309328edf7863": {
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@@ -481,6 +3994,20 @@
        "value": 200002
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+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
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+      "model_module": "@jupyter-widgets/controls",
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@@ -489,6 +4016,16 @@
        "description_width": "initial"
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+      "model_module": "@jupyter-widgets/controls",
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+       "style": "IPY_MODEL_d4e740028c1243cd9ed487c36938298c",
+       "value": " 200002/200002 [02:12&lt;00:00, 1513.73it/s]"
+      }
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      "a7275154b1b842d3a3da2b623bf74521": {
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@@ -527,6 +4064,20 @@
        "value": " 200002/200002 [03:22&lt;00:00, 986.75it/s]"
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+     "c054834b543e4ad88d0c002aa5467f6b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
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@@ -553,17 +4104,63 @@
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+        "IPY_MODEL_a4c8ffec27094a7ea69fbe0d06c5d24c"
+       ],
+       "layout": "IPY_MODEL_45a883d55889455a90d4bbe0ca293807"
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diff --git a/doc/source/examples/quantum_fourier_transform.ipynb b/doc/source/examples/quantum_fourier_transform.ipynb
index 8a66868..ce759af 100644
--- a/doc/source/examples/quantum_fourier_transform.ipynb
+++ b/doc/source/examples/quantum_fourier_transform.ipynb
@@ -135,49 +135,3411 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X_pi2 \t: (True, 3.1415926535897927)\n",
-      "Y_pi2 \t: (True, -3.141592653589793)\n",
-      "CZ_pi16 \t: (True, -1.5217089415825569)\n"
+      "X_pi2 \t: (True, -3.141592653589793)\n",
+      "Y_pi2 \t: (True, 3.141592653589793)\n",
+      "CZ_pi16 \t: (True, -1.5217089415825558)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
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\n",
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+       "   </g>\r\n",
+       "   <g id=\"legend_1\">\r\n",
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+       "    </g>\r\n",
+       "    <g id=\"line2d_39\"/>\r\n",
+       "    <g id=\"text_19\">\r\n",
+       "     <!-- X_0 -->\r\n",
+       "     <defs>\r\n",
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+       "    </g>\r\n",
+       "    <g id=\"line2d_41\"/>\r\n",
+       "    <g id=\"text_20\">\r\n",
+       "     <!-- X_1 -->\r\n",
+       "     <g transform=\"translate(227.025625 38.754687)scale(0.1 -0.1)\">\r\n",
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+       "    </g>\r\n",
+       "    <g id=\"line2d_43\"/>\r\n",
+       "    <g id=\"text_21\">\r\n",
+       "     <!-- Y_0 -->\r\n",
+       "     <defs>\r\n",
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+       "    <g id=\"line2d_47\"/>\r\n",
+       "    <g id=\"text_23\">\r\n",
+       "     <!-- ZZ_01 -->\r\n",
+       "     <defs>\r\n",
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+       "     </g>\r\n",
+       "    </g>\r\n",
+       "   </g>\r\n",
+       "  </g>\r\n",
+       " </g>\r\n",
+       " <defs>\r\n",
+       "  <clipPath id=\"p7703b883d9\">\r\n",
+       "   <rect height=\"266.112\" width=\"357.12\" x=\"50.178125\" y=\"7.2\"/>\r\n",
+       "  </clipPath>\r\n",
+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 614.4x460.8 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
+    "from filter_functions import plotting\n",
+    "\n",
     "identifiers = {\n",
     "    'X_pi2': ['X_0', 'Y_0'],\n",
     "    'Y_pi2': ['X_0', 'Y_0'],\n",
@@ -196,7 +3558,7 @@
     "    print(name, '\\t:', ff.util.oper_equiv(\n",
     "        pulse.total_Q, target_gates[name], eps=1e-10\n",
     "    ))\n",
-    "    _ = ff.plot_pulse_train(pulse)"
+    "    _ = plotting.plot_pulse_train(pulse)"
    ]
   },
   {
@@ -224,7 +3586,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0cad77063fe74c6c9ef874666d1a1990",
+       "model_id": "aaae02874a4a4b8f81f38d86d2a5a8b7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -245,7 +3607,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7ce710f03424c72a5c66f62cd06f840",
+       "model_id": "9463815ab5a841fc8acac311c3516ec5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -266,7 +3628,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1c4412c0cc0441c5a1e43dbcde5b0eb9",
+       "model_id": "6a2c13f4a7514f599cd89d1aba368840",
        "version_major": 2,
        "version_minor": 0
       },
@@ -340,7 +3702,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b40511534141488ab1a614a36452d500",
+       "model_id": "84bd7724e2f8417c9e20e45559548b05",
        "version_major": 2,
        "version_minor": 0
       },
@@ -361,7 +3723,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "646b044110c54f85bf85cec2bcabc8ef",
+       "model_id": "0dceba0d804f46059e8749f29067c0f4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -382,7 +3744,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "678f9e4e0c6447518637bca68a98388e",
+       "model_id": "49cd66a904454222a18ab65114415c42",
        "version_major": 2,
        "version_minor": 0
       },
@@ -399,9 +3761,9 @@
      "text": [
       "\n",
       "Correct action:\n",
-      "X_pi2:  (True, 3.1415926535897927)\n",
-      "Y_pi2:  (True, -3.141592653589793)\n",
-      "CZ_pi16:  (True, -1.5217089415825569)\n"
+      "X_pi2:  (True, -3.141592653589793)\n",
+      "Y_pi2:  (True, 3.141592653589793)\n",
+      "CZ_pi16:  (True, -1.5217089415825558)\n"
      ]
     }
    ],
@@ -491,7 +3853,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "48f2105d5e0140b6b31e62e6a478296d",
+       "model_id": "655635274bc4495b971b690203e215b9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -512,7 +3874,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b2e9e971614d4ff4a66bd222e1807f43",
+       "model_id": "340d7b5efd78443799a5c182010d5a60",
        "version_major": 2,
        "version_minor": 0
       },
@@ -533,7 +3895,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f14aa9e28a4f4199b9fd989eab715d5b",
+       "model_id": "e8cd8da3d2df4ecbbad41fbd063b502b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -554,7 +3916,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9756f7fe793146e9b9a28370f5fe82fe",
+       "model_id": "04094873f4d346eaa49d06ccf11c80c8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -575,7 +3937,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a3069c9be774e34a163b4438464f5c0",
+       "model_id": "a3e3b9e43ebc4a77b9bf25e5535b57fe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -596,7 +3958,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "375ae15197d8414c850d6c11d3128ccd",
+       "model_id": "9e80c69bfc6b4506b6370bf5c3d1c8ee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -613,12 +3975,12 @@
      "text": [
       "\n",
       "Correct action:\n",
-      "hadamard:  (True, -1.5707963267948972)\n",
-      "CZ_pi8:  (True, -3.0434178831651137)\n",
-      "CZ_pi4:  (True, 0.19634954084935896)\n",
-      "CZ_pi2:  (True, 0.3926990816987194)\n",
-      "CZ_pi:  (True, 0.7853981633974423)\n",
-      "CX_pi: (1, 0) (True, -2.356194490192353)\n"
+      "hadamard:  (True, -1.5707963267948961)\n",
+      "CZ_pi8:  (True, -3.0434178831651115)\n",
+      "CZ_pi4:  (True, 0.1963495408493633)\n",
+      "CZ_pi2:  (True, 0.392699081698728)\n",
+      "CZ_pi:  (True, 0.7853981633974593)\n",
+      "CX_pi: (1, 0) (True, -2.3561944901923337)\n"
      ]
     }
    ],
@@ -684,7 +4046,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "22ba10f7267a462fb95d86fbbc933eae",
+       "model_id": "62abc25a5cb8460d8c1ce3e330ea2e9c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -705,7 +4067,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6dc33cbc501a402f9c497452faacc1d9",
+       "model_id": "c3198ed671814f1f932b959301b6dac9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -722,9 +4084,9 @@
      "text": [
       "\n",
       "Correct action:\n",
-      "hadamard: (1) (True, -1.5707963267948972)\n",
-      "CX_pi: (0, 1) (True, -2.3561944901923493)\n",
-      "swap: (0, 1) (True, -0.7853981633976129)\n"
+      "hadamard: (1) (True, -1.5707963267948961)\n",
+      "CX_pi: (0, 1) (True, -2.35619449019233)\n",
+      "swap: (0, 1) (True, -0.7853981633975559)\n"
      ]
     }
    ],
@@ -839,7 +4201,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a9384e54f8844d0aa6591fc4195870ad",
+       "model_id": "4ce70c42484b4da2804ffe1a610aeb7a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -860,7 +4222,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3cee710a62934fa69b032091133be935",
+       "model_id": "b586cd2a83b74a0a90d675e1f89dcff6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -881,7 +4243,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dbe4edd5e1bb479b9490a8c78f6f89dd",
+       "model_id": "386f6783d746450cb405b9bbab585a23",
        "version_major": 2,
        "version_minor": 0
       },
@@ -902,7 +4264,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f2c7d4a1d8e4eb39101f7cefb6c92a4",
+       "model_id": "a4ac0eae73f848cf837b680e569f9720",
        "version_major": 2,
        "version_minor": 0
       },
@@ -964,7 +4326,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a85054157d84925b63e53abaacb132d",
+       "model_id": "24b5b91eb75e4966bfdde6706c1f89be",
        "version_major": 2,
        "version_minor": 0
       },
@@ -980,16 +4342,16 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Correct action: (True, -0.09817477042616429)\n",
-      "Trace fidelity: 0.9951846746729673\n",
+      "Correct action: (True, -0.09817477042654765)\n",
+      "Trace fidelity: 0.9951846746732991\n",
       "Filter function cached: True\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "(<Figure size 432x288 with 2 Axes>,\n",
-       " <mpl_toolkits.mplot3d.axes3d.Axes3D at 0x7f40eca7bf70>)"
+       "(<Figure size 614.4x460.8 with 2 Axes>,\n",
+       " <mpl_toolkits.mplot3d.axes3d.Axes3D at 0x20e812f66a0>)"
       ]
      },
      "execution_count": 10,
@@ -998,14 +4360,10570 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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+      ],
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 614.4x460.8 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1032,20 +14950,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 648x648 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -1056,12 +14961,12 @@
     "    i for i in qft_pulse.n_oper_identifiers if len(i) > 4\n",
     "]\n",
     "fig, ax = plt.subplots(2, 1, sharex=True, sharey=True, figsize=(9, 9))\n",
-    "_ = ff.plot_filter_function(qft_pulse, axes=ax[0],\n",
-    "                            yscale='log', omega_in_units_of_tau=False,\n",
-    "                            n_oper_identifiers=single_qubit_identifiers)\n",
-    "_ = ff.plot_filter_function(qft_pulse, axes=ax[1],\n",
-    "                            yscale='log', omega_in_units_of_tau=False,\n",
-    "                            n_oper_identifiers=two_qubit_identifiers)\n",
+    "_ = plotting.plot_filter_function(qft_pulse, axes=ax[0],\n",
+    "                                  yscale='log', omega_in_units_of_tau=False,\n",
+    "                                  n_oper_identifiers=single_qubit_identifiers)\n",
+    "_ = plotting.plot_filter_function(qft_pulse, axes=ax[1],\n",
+    "                                  yscale='log', omega_in_units_of_tau=False,\n",
+    "                                  n_oper_identifiers=two_qubit_identifiers)\n",
     "\n",
     "for n in (1, 2, 3, 4, 6, 8):\n",
     "    ax[0].axvline(n*2*np.pi/30, color='k', zorder=0, linestyle='--', alpha=0.3)\n",
@@ -1094,11 +14999,19 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.1"
+   "version": "3.8.3"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
+     "0027306f0929465e9c1853e7dc1ff650": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "description_width": "initial"
+      }
+     },
      "002ef5b9f7ab47b8b3c51eab9b63e83c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
@@ -1113,6 +15026,26 @@
        "description_width": "initial"
       }
      },
+     "00c3bf5cf8e1418a83a0c23f45b15699": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "023b100e04504a769dcedaf4ee56c43e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "description_width": "initial"
+      }
+     },
+     "02b364efbe714b02b20988074d0287d1": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "031113faa89643b7b5a3d08051c060c7": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
@@ -1121,6 +15054,34 @@
        "description_width": ""
       }
      },
+     "033360d5cf5d4fda9ac4a5c83c24f57d": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "03b03ffe665347e7bcce5e36d01cd2b9": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_aa565ab1358d4e9da90e821d608bd5da",
+       "style": "IPY_MODEL_a7af6ac48c414d0cb9452c944d3f781f",
+       "value": " 2/2 [00:01&lt;00:00,  1.38it/s]"
+      }
+     },
+     "04094873f4d346eaa49d06ccf11c80c8": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "children": [
+        "IPY_MODEL_bac93702655041a794ebd2ae2e458928",
+        "IPY_MODEL_81828dbd4e6143e6b5d7df29f9af6d92"
+       ],
+       "layout": "IPY_MODEL_af5e9eae1c674c4fb139bc01ba3b9141"
+      }
+     },
      "048cdf07b3ea498489c6755473b2d58a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
@@ -1158,6 +15119,30 @@
        "value": 2
       }
      },
+     "0687c37308ab4eac951cc670f6dc3dec": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_a31796b29a274dd78e65ed83fb51dfaa",
+       "style": "IPY_MODEL_b57df0eeb3b7466b981f27e441538d5b",
+       "value": " 30/30 [00:00&lt;00:00, 137.61it/s]"
+      }
+     },
+     "06a729b9fad74781975ead22066ae9ff": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "07fbe37f743c4217aef3da886f775e2c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "description_width": "initial"
+      }
+     },
      "09d7288e43734e67b1415401d3e001f0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
@@ -1168,6 +15153,12 @@
        "value": " 30/30 [00:00&lt;00:00, 136.41it/s]"
       }
      },
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       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
       "state": {}
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+     "e82c8776023d442ab6776f1ba43cdd14": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "e8cd8da3d2df4ecbbad41fbd063b502b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "children": [
+        "IPY_MODEL_79cd39d49a204fb6a2f272658d977768",
+        "IPY_MODEL_03b03ffe665347e7bcce5e36d01cd2b9"
+       ],
+       "layout": "IPY_MODEL_5b30af6a362f4484a62d3c70a17c686f"
+      }
+     },
+     "e8d1c937473b4b09bcc988408c718069": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "ea93f29675cb41df85dca8ffa3cee3cf": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "bar_style": "success",
+       "description": "Calculating control matrix: 100%",
+       "layout": "IPY_MODEL_84206614e0554091a679a834dbe255e2",
+       "max": 3,
+       "style": "IPY_MODEL_70a56525298c4a6282d7017085d69646",
+       "value": 3
+      }
+     },
      "eaa7613dda404ca69030f674f26dce87": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
@@ -2319,12 +17455,24 @@
        "description_width": "initial"
       }
      },
+     "eb4e730a504c4ad0a3341824e51dcb53": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "ed4786b09b0e4fbfbab9cd21c4c0d3ac": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
       "state": {}
      },
+     "ed51e5a0c53e48dca2d97e9eb19ce96f": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
      "ee1cc0b7dc98435ab7da479286ada155": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
@@ -2337,6 +17485,14 @@
       "model_name": "LayoutModel",
       "state": {}
      },
+     "f050a5196f7b4a9c8701e12b6fce0590": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "description_width": "initial"
+      }
+     },
      "f051accfb7084de1b17f6ded3905d0f0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
@@ -2367,12 +17523,59 @@
        "description_width": ""
       }
      },
+     "f3502f72932842a6ae6f37fe2c9d9cba": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "bar_style": "success",
+       "description": "Calculating control matrix: 100%",
+       "layout": "IPY_MODEL_3b01627771ee4cd688f82a3f427f51df",
+       "max": 30,
+       "style": "IPY_MODEL_60cc1d7d97b8422e8950bc1ba3891165",
+       "value": 30
+      }
+     },
      "f48ebb022f254f859161aa62968ed9ee": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
       "state": {}
      },
+     "f562053ae6064971997985f7033f58d5": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_f59651f288ba41c3b3a4a7fa30142383",
+       "style": "IPY_MODEL_24c5d8cd60bf49caab0181289fce540f",
+       "value": " 30/30 [00:03&lt;00:00,  8.82it/s]"
+      }
+     },
+     "f59651f288ba41c3b3a4a7fa30142383": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "f5dada6b9c44443db7900152bcabc72d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_cf190f99ea4c4eeb981b9ce8e4ef50cc",
+       "style": "IPY_MODEL_3567011792054c1eb232e9533939882d",
+       "value": " 30/30 [00:25&lt;00:00,  1.17it/s]"
+      }
+     },
+     "f7ac0c559b234b47bad50972d7d25abf": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
      "f7ce710f03424c72a5c66f62cd06f840": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
@@ -2385,6 +17588,19 @@
        "layout": "IPY_MODEL_abba682d403949d29949f14426cb40be"
       }
      },
+     "f9614f09e20e4e5699befd358be4fcce": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "bar_style": "success",
+       "description": "Calculating control matrix: 100%",
+       "layout": "IPY_MODEL_3eb0c9bb680c4018a5380914b5b6ed4d",
+       "max": 2,
+       "style": "IPY_MODEL_e4e27776b2074c2e9780b603a2ec6770",
+       "value": 2
+      }
+     },
      "fb09890ce1aa4a1d92942221df8fb85f": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
diff --git a/doc/source/examples/qutip_integration.ipynb b/doc/source/examples/qutip_integration.ipynb
index 0d485e9..ae42053 100644
--- a/doc/source/examples/qutip_integration.ipynb
+++ b/doc/source/examples/qutip_integration.ipynb
@@ -91,6 +91,7 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "from filter_functions import plotting\n",
     "\n",
     "identifiers = ['XI', 'YI', 'IX', 'IY', 'ZZ']\n",
     "pulses = {\n",
@@ -104,12 +105,12 @@
     "fig, ax = plt.subplots(2, 3, figsize=(12, 6))\n",
     "for i, (p_type, pulse) in enumerate(pulses.items()):\n",
     "    # Plot the pulse train\n",
-    "    *_, = ff.plot_pulse_train(pulse, fig=fig, axes=ax[0, i])\n",
+    "    *_, = plotting.plot_pulse_train(pulse, fig=fig, axes=ax[0, i])\n",
     "    ax[0, i].set_title(p_type)\n",
     "    # Plot the filter functions\n",
     "    omega = ff.util.get_sample_frequencies(pulse, spacing='log',\n",
     "                                           symmetric=False)\n",
-    "    *_, = ff.plot_filter_function(pulse, omega, fig=fig, axes=ax[1, i])\n",
+    "    *_, = plotting.plot_filter_function(pulse, omega, fig=fig, axes=ax[1, i])\n",
     "    \n",
     "fig.tight_layout()"
    ]
@@ -138,7 +139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.1"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
diff --git a/examples/qft.py b/examples/qft.py
index 6e5ae26..cda49d5 100644
--- a/examples/qft.py
+++ b/examples/qft.py
@@ -32,6 +32,7 @@
 import numpy as np
 
 import filter_functions as ff
+from filter_functions import plotting
 import qutip as qt
 from qutip import qip
 
@@ -149,6 +150,6 @@ def QFT_pulse(N: int = 4, tau: float = 1):
 print('Correct action: ',
       ff.util.oper_equiv(prop, qip.algorithms.qft.qft(N), eps=1e-14))
 
-fig, ax, _ = ff.plot_filter_function(QFT, omega)
+fig, ax, _ = plotting.plot_filter_function(QFT, omega)
 # Move the legend to the side because of many entries
 ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

From 4eccc441863ceced88c85f94904247d4c9dc523f Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Thu, 25 Jun 2020 17:46:39 +0200
Subject: [PATCH 04/22] Revert bug introduced in
 073c5e0a5d3b37bffd225e0ee52e5d340fb3599d

---
 filter_functions/plotting.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/filter_functions/plotting.py b/filter_functions/plotting.py
index 7f0c3ee..fd29d17 100644
--- a/filter_functions/plotting.py
+++ b/filter_functions/plotting.py
@@ -808,8 +808,8 @@ def plot_error_transfer_matrix(
     cbar = fig.colorbar(im, cax=grid.cbar_axes[0])
     cbar.set_label(cbar_label)
     if colorscale == 'log':
-        labels = cbar.get_ticklabels()
+        labels = cbar.ax.get_yticklabels()
         labels[len(labels) // 2] = ''
-        labels = cbar.set_ticklabels(labels)
+        labels = cbar.ax.set_yticklabels(labels)
 
     return fig, grid

From 296c5a014a6d54aa603a79c0d021ab980221a70d Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Thu, 25 Jun 2020 18:18:11 +0200
Subject: [PATCH 05/22] Revert back to actually needed dependencies for doc

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 0796a23..c049a4d 100644
--- a/setup.py
+++ b/setup.py
@@ -28,8 +28,8 @@ def extract_version(version_file):
 extras_require = {'plotting': ['matplotlib'],
                   'bloch_sphere_visualization': ['qutip', 'matplotlib'],
                   'fancy_progressbar': ['tqdm', 'requests'],
-                  'doc': ['jupyter', 'nbsphinx', 'numpydoc', 'sphinx',
-                          'pandoc', 'sphinx_rtd_theme'],
+                  'doc': ['ipython', 'ipykernel', 'nbsphinx', 'numpydoc', 'sphinx',
+                          'jupyter_client', 'pandoc', 'sphinx_rtd_theme'],
                   'tests': ['pytest', 'coverage', 'coveralls']}
 
 extras_require['all'] = [dep for deps in extras_require.values()

From 264a29cf2d940f818f6bdc8d992c15c00885a6ce Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Thu, 25 Jun 2020 18:19:36 +0200
Subject: [PATCH 06/22] Update README

---
 README.md                       |   9 +++++----
 doc/source/_static/hadamard.png | Bin 12306 -> 11078 bytes
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index bba48f6..ace58e7 100644
--- a/README.md
+++ b/README.md
@@ -27,7 +27,8 @@ hadamard = ff.PulseSequence(H_c, H_n, dt)   # Central object representing a cont
 omega = ff.util.get_sample_frequencies(hadamard)
 F = hadamard.get_filter_function(omega)
 
-ff.plot_filter_function(hadamard)           # Filter function cached from before
+from filter_functions import plotting
+plotting.plot_filter_function(hadamard)     # Filter function cached from before
 ```
 
 ![Hadamard dephasing filter function](./doc/source/_static/hadamard.png)
@@ -59,16 +60,16 @@ infidelity = ff.infidelity(hadamard, spectrum, omega)
 ```
 
 ## Installation
-To install the package from PyPI, run `pip install filter_functions`. It is recommended to install QuTiP before by following the [instructions on their website](http://qutip.org/docs/latest/installation.html) rather than installing it through `pip`. To install the package from source run `python setup.py develop` to install using symlinks or `python setup.py install` without.
+To install the package from PyPI, run `pip install filter_functions`. If you require the optional features provided by QuTiP (visualizing Bloch sphere trajectories), it is recommended to install QuTiP before by following the [instructions on their website](http://qutip.org/docs/latest/installation.html) rather than installing it through `pip`. To install the package from source run `python setup.py develop` to install using symlinks or `python setup.py install` without.
 
-To install the optional dependencies (`tqdm` and `requests` for a fancy progress bar), run `pip install -e .[fancy_progressbar]` from the root directory.
+To install dependencies of optional extras (`tqdm` and `requests` for a fancy progress bar, `matplotlib` for plotting, `QuTiP` for Bloch sphere visualization), run `pip install -e .[extra]` where `extra` is one or more of `fancy_progressbar`, `plotting`, `bloch_sphere_visualization` from the root directory. To install all dependencies, including those needed to build the documentation and run the tests, use the extra `all`.
 
 ## Documentation
 You can find the documentation on [Readthedocs](https://filter-functions.readthedocs.io/en/latest/). It is built from Jupyter notebooks that can also be run interactively and are located [here](doc/source/examples). The notebooks explain how to use the package and thus make sense to follow chronologically as a first step. Furthermore, there are also a few example scripts in the [examples](examples) folder.
 
 The documentation including the example notebooks and an automatically generated API documentation can be built by running `make <format>` inside the *doc* directory where `<format>` is for example `html`.
 
-Building the documentation requires the following additional dependencies: `nbsphinx`, `numpydoc`, `sphinx_rtd_theme`, `jupyter_client`, `ipython`, `ipykernel`, as well as `pandoc`. The last can be installed via conda (`conda install pandoc`) or downloaded from [Github](https://github.com/jgm/pandoc/releases/) and the rest automatically by running `pip install -e .[doc]`.
+Building the documentation requires the following additional dependencies: `nbsphinx`, `numpydoc`, `sphinx_rtd_theme`, `ipython`, `ipykernel`, `jupyter_client`, as well as `pandoc`. The last can be installed via conda (`conda install pandoc`) or downloaded from [Github](https://github.com/jgm/pandoc/releases/) and the rest automatically by running `pip install -e .[doc]`.
 
 ## References
 [1]: Cywinski, L., Lutchyn, R. M., Nave, C. P., & Das Sarma, S. (2008). How to enhance dephasing time in superconducting qubits. Physical Review B - Condensed Matter and Materials Physics, 77(17), 1–11. [https://doi.org/10.1103/PhysRevB.77.174509](https://doi.org/10.1103/PhysRevB.77.174509)
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From b9c586487359825c8d7a0fe82a110e23987e30b4 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Thu, 25 Jun 2020 21:25:47 +0200
Subject: [PATCH 07/22] Install all extras for tests

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index eb8e230..2783876 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -18,7 +18,7 @@ install:
   - conda info -a
   - conda env create -f ./environment.yml
   - source activate filter_functions
-  - pip install .[fancy_progressbar,doc,tests]
+  - pip install .[all]
 
 script:
   - coverage run --rcfile=coverage.ini -m pytest

From 9826e9c6af31bab71a3028d2edf07e13d3c0049c Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 26 Jun 2020 12:09:55 +0200
Subject: [PATCH 08/22] Add jupyter (again) so that notebooks can be run

---
 README.md | 2 +-
 setup.py  | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index ace58e7..3b1d0bc 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ You can find the documentation on [Readthedocs](https://filter-functions.readthe
 
 The documentation including the example notebooks and an automatically generated API documentation can be built by running `make <format>` inside the *doc* directory where `<format>` is for example `html`.
 
-Building the documentation requires the following additional dependencies: `nbsphinx`, `numpydoc`, `sphinx_rtd_theme`, `ipython`, `ipykernel`, `jupyter_client`, as well as `pandoc`. The last can be installed via conda (`conda install pandoc`) or downloaded from [Github](https://github.com/jgm/pandoc/releases/) and the rest automatically by running `pip install -e .[doc]`.
+Interactively using the documentation requires `jupyter`, and building a static version additionally requires `nbsphinx`, `numpydoc`, `sphinx_rtd_theme`,, as well as `pandoc`. The last can be installed via conda (`conda install pandoc`) or downloaded from [Github](https://github.com/jgm/pandoc/releases/) and the rest automatically by running `pip install -e .[doc]`.
 
 ## References
 [1]: Cywinski, L., Lutchyn, R. M., Nave, C. P., & Das Sarma, S. (2008). How to enhance dephasing time in superconducting qubits. Physical Review B - Condensed Matter and Materials Physics, 77(17), 1–11. [https://doi.org/10.1103/PhysRevB.77.174509](https://doi.org/10.1103/PhysRevB.77.174509)
diff --git a/setup.py b/setup.py
index c049a4d..0796a23 100644
--- a/setup.py
+++ b/setup.py
@@ -28,8 +28,8 @@ def extract_version(version_file):
 extras_require = {'plotting': ['matplotlib'],
                   'bloch_sphere_visualization': ['qutip', 'matplotlib'],
                   'fancy_progressbar': ['tqdm', 'requests'],
-                  'doc': ['ipython', 'ipykernel', 'nbsphinx', 'numpydoc', 'sphinx',
-                          'jupyter_client', 'pandoc', 'sphinx_rtd_theme'],
+                  'doc': ['jupyter', 'nbsphinx', 'numpydoc', 'sphinx',
+                          'pandoc', 'sphinx_rtd_theme'],
                   'tests': ['pytest', 'coverage', 'coveralls']}
 
 extras_require['all'] = [dep for deps in extras_require.values()

From 96e42ef66462425c3056f9bab573c271ddcf19f3 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 26 Jun 2020 14:16:19 +0200
Subject: [PATCH 09/22] Update .readthedocs.yml

---
 .readthedocs.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.readthedocs.yml b/.readthedocs.yml
index 720be39..5aad26f 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -13,4 +13,6 @@ python:
       path: .
       extra_requirements:
          - doc
+         - plotting
+         - bloch_sphere_visualization
          - fancy_progressbar

From bda1139c763712ae12f6392ddce4a9bbf7a4b1b8 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Sun, 28 Jun 2020 12:26:44 +0200
Subject: [PATCH 10/22] Remove pandoc from doc dependencies

This is not the pandoc we need but a python package
---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 0796a23..6421ccf 100644
--- a/setup.py
+++ b/setup.py
@@ -29,7 +29,7 @@ def extract_version(version_file):
                   'bloch_sphere_visualization': ['qutip', 'matplotlib'],
                   'fancy_progressbar': ['tqdm', 'requests'],
                   'doc': ['jupyter', 'nbsphinx', 'numpydoc', 'sphinx',
-                          'pandoc', 'sphinx_rtd_theme'],
+                          'sphinx_rtd_theme'],
                   'tests': ['pytest', 'coverage', 'coveralls']}
 
 extras_require['all'] = [dep for deps in extras_require.values()

From 9dbd5eae7b777c0e759b6bc670b2cfdfa7d43806 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Mon, 29 Jun 2020 09:27:42 +0200
Subject: [PATCH 11/22] Use util.parse_optional_parameter in plotting.py

---
 filter_functions/plotting.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/filter_functions/plotting.py b/filter_functions/plotting.py
index fd29d17..7bcb690 100644
--- a/filter_functions/plotting.py
+++ b/filter_functions/plotting.py
@@ -106,6 +106,7 @@ def init_bloch_sphere(**bloch_kwargs) -> qt.Bloch:
     return b
 
 
+@util.parse_optional_parameter('prop', ['total', 'piecewise'])
 def get_states_from_prop(U: Sequence[Operator],
                          psi0: Optional[State] = None,
                          prop: str = 'total') -> ndarray:
@@ -135,7 +136,8 @@ def get_states_from_prop(U: Sequence[Operator],
     if prop == 'total':
         for j in range(len(U)):
             states[j] = U[j] @ psi0
-    elif prop == 'piecewise':
+    else:
+        # prop == 'piecewise'
         states[0] = U[0] @ psi0
         for j in range(1, len(U)):
             states[j] = U[j] @ states[j-1]
@@ -427,12 +429,12 @@ def plot_filter_function(pulse: 'PulseSequence',
     # Set the axis scales
     axes.set_xscale(xscale)
     axes.set_yscale(yscale)
-    if xscale == 'log':
+    if xscale != 'linear':
         z_min_idx = (z > 0).nonzero()[0][0]
     else:
         z_min_idx = (z >= 0).nonzero()[0][0]
 
-    if yscale != 'log':
+    if yscale == 'linear':
         axes.set_ylim(bottom=0)
 
     axes.set_xlim(z[z_min_idx], max(z))
@@ -621,6 +623,7 @@ def plot_infidelity_convergence(n_samples: Sequence[int],
     return fig, ax
 
 
+@util.parse_optional_parameter('colorscale', ['linear', 'log'])
 def plot_error_transfer_matrix(
         pulse: Optional['PulseSequence'] = None,
         S: Optional[ndarray] = None,
@@ -778,7 +781,8 @@ def plot_error_transfer_matrix(
     if colorscale == 'log':
         linthresh = np.abs(U).mean()/10 if linthresh is None else linthresh
         norm = colors.SymLogNorm(linthresh=linthresh, vmin=Umin, vmax=Umax)
-    elif colorscale == 'linear':
+    else:
+        # colorscale == 'linear'
         norm = colors.Normalize(vmin=Umin, vmax=Umax)
 
     imshow_kw = {} if imshow_kw is None else imshow_kw

From fefd8cba1b89dfd26962b986fa02dadb5bec1939 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 12:20:42 +0200
Subject: [PATCH 12/22] Make tqdm hard dependency

Avoids using stackoverflow replacement code, makes life easier, is
widely used.
---
 filter_functions/gradient.py | 605 +++++++++++++++++++++++++++++++++++
 filter_functions/util.py     |  52 +--
 setup.py                     |   4 +-
 tests/test_util.py           |   9 -
 4 files changed, 614 insertions(+), 56 deletions(-)
 create mode 100644 filter_functions/gradient.py

diff --git a/filter_functions/gradient.py b/filter_functions/gradient.py
new file mode 100644
index 0000000..fa2a092
--- /dev/null
+++ b/filter_functions/gradient.py
@@ -0,0 +1,605 @@
+# -*- coding: utf-8 -*-
+# =============================================================================
+#     This module is an extension of the filter_functions package written by
+#     Tobias Hangleiter. Its implementation was center of the Bachelor thesis
+#     'Filter Function Derivative for Quantum Optimal Control' by Isabel Le.
+#
+#     Contact email: isabel.le@rwth-aachen.de
+# =============================================================================
+"""
+This module implements functions to calculate filter function and infidelity
+derivatives.
+
+Throughout this documentation the following notation will be used: n_dt denotes
+the number of time steps, n_cops the number of all control operators, n_ctrl
+the number of time-dependent control operators (if identifiers are provided,
+otherwise n_ctrl=n_cops), n_nops the number of noise operators, n_omega the
+number of frequency samples and d the dimension of the system.
+
+Functions
+---------
+:func:`liouville_derivative`
+    Calculate the derivatives of the control propagators in Liouville space.
+:func:`control_matrix_at_timestep_derivative`
+    Calculate the control matrices and corresponding derivatives.
+:func:`calculate_derivative_of_control_matrix_from_scratch`
+    Calculate the derivative of the control matrix from scratch.
+:func:`calculate_canonical_filter_function_derivative`
+    Compute the filter function derivative from the control matrix.
+:func:`infidelity_derivative`
+    Calculate the infidelity derivative.
+"""
+from typing import Callable, Optional, Sequence, Union
+
+import numpy as np
+from numpy import ndarray
+from scipy.integrate import trapz
+
+from .basis import Basis
+from .types import Coefficients, Operator
+from .util import cexp
+
+__all__ = ['liouville_derivative', 'control_matrix_at_timestep_derivative',
+           'calculate_derivative_of_control_matrix_from_scratch',
+           'calculate_canonical_filter_function_derivative',
+           'infidelity_derivative']
+
+
+def liouville_derivative(
+        t: Coefficients,
+        dt: Coefficients,
+        Q: ndarray,
+        c_coeffs: Sequence[Coefficients],
+        c_opers: Sequence[Operator],
+        basis: Basis,
+        HV: ndarray,
+        HD: ndarray,
+        VHV: ndarray) -> ndarray:
+    r"""
+    Calculate the derivatives of the control propagators in Liouville
+    representation.
+
+    Parameters
+    ----------
+    t : array_like, shape (n_dt+1)
+        The absolute times of the different segments.
+    dt : array_like, shape (n_dt)
+        Sequence duration, i.e. for the :math:`g`-th pulse
+        :math:`t_g - t_{g-1}`.
+    Q : array_like, shape (n_dt+1, d, d)
+        The propagators :math:`Q_g = P_g P_{g-1}\cdots P_0` as a (d, d) array
+        with *d* the dimension of the Hilbert space.
+    c_coeffs : array_like, shape (n_cops, n_dt)
+        The control strengths of the control operators given by *c_opers* for
+        each time step.
+    c_opers : array_like, shape (n_cops, d, d)
+        Control operators :math:`H_k`.
+    basis : Basis, shape (d**2, d, d)
+        The basis elements, in which the pulse control matrix will be expanded.
+    HV : array_like, shape (n_dt, d, d)
+        Eigenvector matrices for each time pulse segment *g* with the first
+        axis counting the pulse segment, i.e.
+        ``HV == array([V_0, V_1, ...])``.
+    HD : array_like, shape (n_dt, d)
+        Eigenvalue vectors for each time pulse segment *g* with the first
+        axis counting the pulse segment, i.e.
+        ``HD == array([D_0, D_1, ...])``.
+    VHV : array_like, shape (n_dt, c_ctrl, d, d)
+        The control operators transformed into the eigenspace of the control
+        Hamiltonian. The drift operators are ignored, if identifiers for time-
+        dependent control operators are provided.
+
+    Returns
+    -------
+    dL: array_like, shape (n_dt, n_ctrl, n_dt, d**2, d**2)
+        The derivative of the control propagators in Liouville representation
+        :math:`\frac{\partial \mathcal{Q}_{jk}^{(g)}}
+        {\partial u_h(t_{g^\prime})}`.
+        The array's indexing has shape :math:`(g,h,g^\prime,j,k)`.
+
+    Notes
+    -----
+    The derivatives of the control propagators in Liouville representation are
+    calculated according to
+
+    .. math::
+
+        \frac{\partial\mathcal{Q}_{jk}^{(g-1)}}{\partial u_h(t_{g^\prime})} &=
+        \Theta_{g-1}(g^\prime) \mathrm{tr}\Big(
+            \frac{\partial U_c^\dagger(t_{g-1},0)}{\partial u_h(t_{g^\prime})}
+            C_j U_c(t_{g-1},0) C_k\Big)\\
+            &+\Theta_{g-1}(g^\prime)\mathrm{tr}\Big(U_c^\dagger(t_{g-1},0)C_j
+                                                \frac{\partial U_c(t_{g-1},0)}
+                                                {\partial u_h(t_{g^\prime})}C_k
+                                                \Big),
+
+    where :math:`\Theta_{g-1}(g^\prime)` being 1 if :math:`g^\prime < g-1` and
+    zero otherwise.
+    """
+    n = len(dt)
+    d = basis.shape[-1]
+
+    # Allocate memory
+    A_mat = np.empty((d, d), dtype=complex)
+    partial_U = np.empty((n, VHV.shape[1], d, d), dtype=complex)
+    deriv_U = np.zeros((n, n, VHV.shape[1], d, d), dtype=complex)
+
+    # intermediate = Q[1:] @ Q[:-1].conj().T @ HV[:-1]
+    for g in (range(n)):
+        omega_diff = np.subtract.outer(HD[g], HD[g])
+        mask = (abs(omega_diff) < 1e-10)
+        A_mat[mask] = dt[g]  # if the integral diverges
+        A_mat[~mask] = (cexp(omega_diff[~mask]*dt[g]) - 1) \
+            / (1j * omega_diff[~mask])
+        # Calculate dU(t_g,t_{g-1})/du_h within one time step
+        partial_U[g] = -1j * np.einsum('ia,ja,jk,hkl,kl,ml->him', Q[g+1],
+                                       Q[g].conj(), HV[g], VHV[g], A_mat,
+                                       HV[g].conj(),
+                                       optimize=['einsum_path', (3, 4), (0, 1),
+                                                 (0, 3), (0, 1), (0, 1)])
+        # partial_U[g] = -1j * np.einsum('ik,hkl,kl,ml->him',
+        #                                intermediate[g],
+        #                                VHV[g], A_mat, HV[g].conj(),
+        #                                optimize=['einsum_path', (3, 4), (0, 1),
+        #                                          (0, 3), (0, 1), (0, 1)])
+        # Calculate the whole propagator derivative
+        for g_prime in range(g+1):  # condition g' <= g-1
+            deriv_U[g, g_prime] = np.einsum('ij,kj,hkl,lm->him', Q[g+1],
+                                            Q[g_prime+1].conj(),
+                                            partial_U[g_prime], Q[g_prime],
+                                            optimize=['einsum_path', (0, 1),
+                                                      (0, 1), (0, 1)])
+            # deriv_U[g, g_prime] = (Q[g+1] @ Q[g_prime+1].conj().T @
+            #                        partial_U[g_prime] @ Q[g_prime])
+
+    # Now calculate derivative of Liouville representation
+    # Calculate traces first to save memory
+    sum1 = np.einsum('tshba,jbc,tcd,kda->thsjk', deriv_U.conj(), basis, Q[1:],
+                     basis, optimize=['einsum_path', (1, 2), (0, 2), (0, 1)])
+    sum2 = np.einsum('tba,jbc,tshcd,kda->thsjk', Q[1:].conj(), basis, deriv_U,
+                     basis, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
+    dL = sum1 + sum2
+
+    return dL
+
+
+def control_matrix_at_timestep_derivative(
+        omega: Coefficients,
+        dt: Coefficients,
+        HD: ndarray,
+        HV: ndarray,
+        basis: Basis,
+        c_opers: Sequence[Operator],
+        c_coeffs: Sequence[Coefficients],
+        n_opers: Sequence[Operator],
+        n_coeffs: Sequence[Coefficients],
+        VHV: ndarray) -> dict:
+    r"""
+    Calculate the control matrices and corresponding derivatives.
+
+    Calculate control matrices at each time step and the corresponding partial
+    derivatives of those with respect to control strength at each time step.
+
+    Parameters
+    ----------
+    omega : array_like, shape (n_omega)
+        Frequencies, at which the pulse control matrix is to be evaluated.
+    dt : array_like, shape (n_dt)
+        Sequence duration, i.e. for the :math:`g`-th pulse
+        :math:`t_g - t_{g-1}`.
+    HD : array_like, shape (n_dt, d)
+        Eigenvalue vectors for each time pulse segment *g* with the first
+        axis counting the pulse segment, i.e.
+        ``HD == array([D_0, D_1, ...])``.
+    HV : array_like, shape (n_dt, d, d)
+        Eigenvector matrices for each time pulse segment *g* with the first
+        axis counting the pulse segment, i.e.
+        ``HV == array([V_0, V_1, ...])``.
+    basis : Basis, shape (d**2, d, d)
+        The basis elements, in which the pulse control matrix will be expanded.
+    c_opers : array_like, shape (n_cops, d, d)
+        Control operators :math:`H_k`.
+    c_coeffs : array_like, shape (n_cops, n_dt)
+        The control strengths of the control operators given by *c_opers* for
+        each time step.
+    n_opers : array_like, shape (n_nops, d, d)
+        Noise operators :math:`B_\alpha`.
+    n_coeffs : array_like, shape (n_nops, n_dt)
+        The sensitivities of the system to the noise operators given by
+        *n_opers* at the given time step.
+    VHV : array_like, shape (n_dt, n_ctrl, d, d)
+        The control operators transformed into the eigenspace of the control
+        Hamiltonian. The drift operators are ignored, if identifiers for time-
+        dependent control operators are provided.
+
+    Returns
+    -------
+    ctrlmat_data : dict {'R_g': R_g, 'dR_g': gradient}
+        * **R_g** *(array_like, shape (n_dt, n_nops, d**2, n_omega))*
+        The control matrix at each time step
+        :math:`\mathcal{R}_{\alpha j}^{(g)}(\omega)` is identified with R_g.
+        The array's indexing has shape :math:`(g,\alpha,j,\omega)`.
+
+        * **dR_g** *(array_like, shape (n_dt, n_nops, d**2, n_ctrl, n_omega))*
+        The corresponding derivative with respect to the control strength
+        :math:`\frac{\partial\mathcal{R}_{\alpha j}^{(g)}(\omega)}
+        {\partial u_h(t_{g^\prime})}` is identified with dR_g. The array's
+        indexing has shape :math:`(g^\prime,\alpha,j,h,\omega)`. Here, only one
+        time axis is needed, since the derivative is zero for
+        :math:`g\neq g^\prime`.
+
+
+    Notes
+    -----
+    The control matrix at each time step is evaluated according to
+
+    .. math::
+
+            \mathcal{R}_{\alpha j}^{(g)}(\omega) = s_\alpha^{(g)}\mathrm{tr}
+            \left([\bar{B}_\alpha \circ I_1^{(g)}(\omega)] \bar{C}_j \right),
+
+    where
+
+    .. math::
+
+        I_{1,nm}^{(g)}(\omega) = \frac{\exp(\mathrm{i}(\omega + \omega_n^{(g)}
+                                            - \omega_m^{(g)}) \Delta t_g) - 1}
+        {\mathrm{i}(\omega + \omega_n^{(g)} - \omega_m^{(g)})}
+
+    The derivative of the control matrix with respect to the control strength
+    at different time steps is calculated according to
+
+    .. math::
+
+        \frac{\partial \mathcal{R}_{\alpha j}^{(g)}(\omega)}
+        {\partial u_h(t_{g^\prime})} =
+        \mathrm{i}\delta_{gg^\prime} s_\alpha^{(g)} \mathrm{tr}
+        \left( \bar{B}_{\alpha} \cdot \mathds{M} \right).
+
+    If denoting :math:`\Delta\omega_{ij} = \omega_i^{(g)} - \omega_j^{(g)}`
+    the integral part is encapsulated in
+
+    .. math::
+
+        \mathds{M}_{mn} = \left[ \bar{C}_j, \mathds{I}^{(mn)}
+                                \circ \bar{H}_h \right]_{mn},
+
+    with
+
+    .. math::
+
+        \mathds{I}_{pq}^{(mn)} &= \delta_{pq} \left(
+            \frac{\Delta t_g \cdot
+                  \exp(\mathrm{i}(\omega + \Delta\omega_{nm})\Delta t_g)}
+            {\mathrm{i}(\omega + \Delta\omega_{nm})}
+            + \frac{\exp(\mathrm{i}(\omega + \Delta\omega_{nm})\Delta t_g) - 1}
+            {(\omega + \Delta\omega_{nm})^2}\right)\\
+            &+  \frac{1-\delta_{pq}}{\mathrm{i}\Delta\omega_{pq}} \cdot
+            \frac{\exp(\mathrm{i}(\omega + \Delta\omega_{nm}
+                                  + \Delta\omega_{pq})\Delta t_g) - 1}
+            {\mathrm{i}(\omega + \Delta\omega_{nm} + \Delta\omega_{pq})}\\
+            &- \frac{1-\delta_{pq}}{\mathrm{i}\Delta\omega_{pq}} \cdot
+            \frac{\exp(\mathrm{i}(\omega + \Delta\omega_{nm})\Delta t_g) - 1}
+            {\mathrm{i}(\omega + \Delta\omega_{nm})}
+    """
+    d = HV.shape[-1]
+    n_dt = len(dt)
+    E = omega
+
+    # Precompute some transformation into eigenspace of control Hamiltonian
+    path = ['einsum_path', (0, 1), (0, 1)]
+    VBV = np.einsum('gji,ajk,gkl->gail', HV.conj(), n_opers, HV, optimize=path)
+    VCV = np.einsum('tnm,jnk,tkl->tjml', HV.conj(), basis, HV, optimize=path)
+
+    # Allocate memory
+    R_g = np.empty((n_dt, len(n_opers), len(basis), len(E)), dtype=complex)
+    # For calculating dR_g: d,d = p,q, d,d = m,n
+    integral_deriv = np.empty((n_dt, len(E), d, d, d, d), dtype=complex)
+
+    for g in range(n_dt):
+        # Any combination of \omega_m-\omega_n (R_g), \omega_p-\omega_q (dR_g)
+        dE = np.subtract.outer(HD[g], HD[g])
+        # Any combination of \omega+\omega_m-\omega_n (R_g) or
+        # \omega-\omega_m+\omega_n (dR_g)
+        EdE = np.add.outer(E, dE)
+
+        # 1) Calculation of the control matrix R_g at each time step
+        integral_Rg = np.empty((len(E), d, d), dtype=complex)
+        # Mask the integral to avoid convergence problems
+        mask_Rg = np.abs(EdE*dt[g]) <= 1e-7
+        integral_Rg[mask_Rg] = dt[g]
+        integral_Rg[~mask_Rg] = (cexp(EdE[~mask_Rg]*dt[g]) - 1) \
+            / (1j*(EdE[~mask_Rg]))
+
+        R_g[g] = np.einsum('a,bcd,adc,hdc->abh', n_coeffs[:, g], VCV[g],
+                           VBV[g], integral_Rg,
+                           optimize=['einsum_path', (0, 2), (0, 2), (0, 1)])
+
+        # 2) Calculation of derivatives of control matrices at each time step
+        mask_deriv = (abs(dE) < 1e-15)  # case: \omega_p-\omega_q = 0
+        # calculation if omega_diff = 0
+        n_case = sum(sum(mask_deriv))
+        a = dt[g]*cexp(EdE*dt[g]) / (1j * EdE) \
+            + (cexp(EdE*dt[g]) - 1) / (EdE)**2
+        integral_deriv[g, :, mask_deriv] = np.concatenate(([[a]*n_case]),
+                                                          axis=0)
+        # calculation if omega_diff != 0
+        b1 = - (cexp(np.add.outer(EdE, dE[~mask_deriv])*dt[g]) - 1) \
+            / (np.add.outer(EdE, dE[~mask_deriv])) / dE[~mask_deriv]
+        b2 = + np.divide.outer(((cexp(EdE*dt[g]) - 1) / EdE), dE[~mask_deriv])
+        integral_deriv[g, :, ~mask_deriv] = (b1 + b2).transpose(3, 0, 1, 2)
+
+    # Computation of the derivative/ gradient
+    I_circ_H = np.einsum('toijnm,thij->tohijnm', integral_deriv, VHV)
+    M_mat = (np.einsum('tjmk,tohknnm->tojhmn', VCV, I_circ_H)
+             - np.einsum('tohmknm,tjkn->tojhmn', I_circ_H, VCV))
+    gradient = 1j * np.einsum('at,tamn,tojhnm->tajho', n_coeffs, VBV, M_mat,
+                              optimize=['einsum_path', (1, 2), (0, 1)])
+
+    ctrlmat_data = {'R_g': R_g, 'dR_g': gradient}
+    return ctrlmat_data
+
+
+def calculate_derivative_of_control_matrix_from_scratch(
+    omega: Coefficients,
+    Q: ndarray,
+    Q_Liou: ndarray,
+    HD: ndarray,
+    HV: ndarray,
+    basis: Basis,
+    t: Coefficients,
+    dt: Coefficients,
+    n_opers: Sequence[Operator],
+    n_coeffs: Sequence[Coefficients],
+    c_opers: Sequence[Operator],
+    c_coeffs: Sequence[Coefficients],
+    all_id: Sequence[str],
+    c_id: Optional[Sequence[str]] = None) -> ndarray:
+    r"""
+    Calculate the derivative of the control matrix from scratch.
+
+    Parameters
+    ----------
+    omega : array_like, shape (n_omega,)
+        Frequencies, at which the pulse control matrix is to be evaluated.
+    Q : array_like, shape (n_dt+1, d, d)
+        The propagators :math:`Q_g = P_g P_{g-1}\cdots P_0` as a (d, d) array
+        with *d* the dimension of the Hilbert space.
+    Q_Liou : ndarray, shape (n_dt+1, d**2, d**2)
+        The Liouville representation of the cumulative control propagators
+        U_c(t_g,0).
+    HD : array_like, shape (n_dt, d)
+        Eigenvalue vectors for each time pulse segment *g* with the first
+        axis counting the pulse segment, i.e.
+        ``HD == array([D_0, D_1, ...])``.
+    HV : array_like, shape (n_dt, d, d)
+        Eigenvector matrices for each time pulse segment *g* with the first
+        axis counting the pulse segment, i.e.
+        ``HV == array([V_0, V_1, ...])``.
+    basis : Basis, shape (d**2, d, d)
+        The basis elements, in which the pulse control matrix will be expanded.
+    t : array_like, shape (n_dt+1), optional
+        The absolute times of the different segments.
+    dt : array_like, shape (n_dt)
+        Sequence duration, i.e. for the :math:`g`-th pulse
+        :math:`t_g - t_{g-1}`.
+    n_opers : array_like, shape (n_nops, d, d)
+        Noise operators :math:`B_\alpha`.
+    n_coeffs : array_like, shape (n_nops, n_dt)
+        The sensitivities of the system to the noise operators given by
+        *n_opers* at the given time step.
+    c_opers : array_like, shape (n_cops, d, d)
+        Control operators :math:`H_k`.
+    c_coeffs : array_like, shape (n_cops, n_dt)
+        The control strengths of the control operators given by *c_opers* for
+        each time step.
+    all_id : array_like, shape (n_cops)
+        Identifiers of all control operators.
+    c_id : Sequence[str], shape (n_ctrl), Optional
+        Sequence of strings with the control identifiern to distinguish between
+        control and drift Hamiltonian. The default is None.
+
+    Raises
+    ------
+    ValueError
+        If the given identifiers *c_id* are not subset of the total identifiers
+        *all_id* of all control operators.
+
+    Returns
+    -------
+    dR : array_like, shape (n_nops, d**2, n_dt, n_ctrl, n_omega)
+        Partial derivatives of the total control matrix with respect to each
+        control direction
+        :math:`\frac{\partial R_{\alpha k}(\omega)}{\partial u_h(t_{g'})}`.
+        The array's indexing has shape :math:`(\alpha,k,g^\prime,h,\omega)`.
+
+    Notes
+    -----
+    The derivative of the control matrix is calculated according to
+
+    .. math ::
+
+        \frac{\partial R_{\alpha k}(\omega)}{\partial u_h(t_{g'})} =
+            \sum_{g=1}^G \mathrm{e}^{\mathrm{i}\omega t_{g-1}}\cdot\left(\sum_j
+                \left[\frac{\partial R_{\alpha j}^{(g)}(\omega)}
+                    {\partial u_h(t_{g'})} \cdot \mathcal{Q}_{jk}^{(g-1)}
+                +   R_{\alpha j}^{(g)}(\omega)
+                \cdot\frac{\partial \mathcal{Q}_{jk}^{(g-1)}}
+                {\partial u_h(t_{g'})} \right] \right)
+
+    See Also
+    --------
+    :func:`liouville_derivative`
+    :func:`control_matrix_at_timestep_derivative`
+    """
+    # Distinction between control and drift operators and only calculate the
+    # derivatives in control direction
+    path = ['einsum_path', (0, 1), (0, 1)]
+    if (c_id is None):
+        VHV = np.einsum('tji,hjk,tkl->thil', HV.conj(), c_opers, HV,
+                        optimize=path)
+    elif (set(c_id) <= set(all_id)):
+        dict_id_oper = dict(zip(all_id, c_opers))
+        control = [dict_id_oper[element] for element in c_id]
+        VHV = np.einsum('tji,hjk,tkl->thil', HV.conj(), control, HV,
+                        optimize=path)
+    else:
+        raise ValueError('Given control identifiers have to be a \
+                         subset of (drift+control) Hamiltonian!')
+
+    # Get derivative of Liouville, control matrix at each time step, derivative
+    # derivative of control matrix at each time step
+    dL = liouville_derivative(t, dt, Q, c_coeffs, c_opers, basis, HV, HD, VHV)
+    ctrlmat_data = control_matrix_at_timestep_derivative(
+        omega, dt, HD, HV, basis, c_opers, c_coeffs, n_opers, n_coeffs, VHV)
+    ctrlmat_g = ctrlmat_data['R_g']
+    ctrlmat_g_deriv = ctrlmat_data['dR_g']
+
+    # Calculate the derivative of the total control matrix
+    exp_factor = cexp(np.multiply.outer(t, omega))
+    summand1 = np.einsum('to,tajho,tjk->aktho', exp_factor[:-1],
+                         ctrlmat_g_deriv, Q_Liou[:-1],
+                         optimize=['einsum_path', (1, 2), (0, 1)])
+    summand2 = np.einsum('to,tajo,thsjk->aksho', exp_factor[1:-1],
+                         ctrlmat_g[1:], dL[:-1],
+                         optimize=['einsum_path', (0, 1), (0, 1)])
+
+    dR = summand1 + summand2
+    return dR  # alpha, k, g', h, omega
+
+
+def calculate_canonical_filter_function_derivative(
+        R: ndarray,
+        deriv_R: ndarray) -> ndarray:
+    r"""
+    Compute the filter function derivative from the control matrix.
+
+    Parameters
+    ----------
+    R : array_like, shape (n_nops, d**2, n_omega)
+        The control matrix.
+    deriv_R: array_like, shape (n_nops, d**2, n_t, n_ctrl, n_omega)
+        The derivative of the control matrix.
+
+    Returns
+    -------
+    deriv_F : ndarray, shape (n_nops, n_dt, n_ctrl, n_omega)
+        The regular filter functions' derivatives for variation in each control
+        contribution :math:`\frac{\partial F_\alpha}{\partial u_h(t_{g'})}`.
+        The array's indexing has shape :math:`(\alpha,g^\prime,h,\omega)`.
+
+    Notes
+    -----
+    The filter function derivative is calculated according to
+
+    .. math ::
+
+        \frac{\partial F_\alpha(\omega)}{\partial u_h(t_{g'})}
+                    = 2 \mathrm{Re} \left( \sum_k R_{\alpha k}^\ast(\omega)
+                    \frac{\partial R_{\alpha k}(\omega)}
+                    {\partial u_h(t_{g'})} \right)
+    """
+    summe = np.einsum('ako,aktho->atho', R.conj(), deriv_R)
+    return 2*summe.real
+
+
+def infidelity_derivative(
+        pulse: 'PulseSequence',
+        S: Union[Coefficients, Callable],
+        omega: Coefficients,
+        c_id: Optional[Sequence[str]] = None) -> ndarray:
+    r"""
+    Calculate the infidelity derivative.
+
+    Calculate the entanglement infidelity derivative of the ``PulseSequence``
+    *pulse*.
+
+    Parameters
+    ----------
+    pulse : PulseSequence
+        The ``PulseSequence`` instance, for which to calculate the infidelity
+        for.
+    S : array_like or callable
+        The two-sided noise power spectral density in units of inverse
+        frequencies as an array of shape (n_omega,) or (n_nops, n_omega). In
+        the first case, the same spectrum is taken for all noise operators, in
+        the second, it is assumed that there are no correlations between
+        different noise sources and thus there is one spectrum for each noise
+        operator.
+    omega : array_like, shape (n_omega)
+        The frequencies at which the integration is to be carried out.
+    c_id : Sequence[str], shape (n_ctrl)
+        Sequence of strings with the control identifiern to distinguish between
+        control and drift Hamiltonian.
+
+    Raises
+    ------
+    ValueError
+        If the provided noise spectral density does not fit expected shape.
+
+    Returns
+    -------
+    deriv_infid : ndarray, shape (n_nops, n_dt, n_ctrl)
+        Array with the derivative of the infidelity for each noise source taken
+        for each control direction at each time step
+        :math:`\frac{\partial I_e}{\partial u_h(t_{g'})}`. The array's indexing
+        has shape :math:`(\alpha,g^\prime,h)`.
+
+    Notes
+    -----
+    The infidelity's derivative is given by
+
+    .. math::
+
+        \frac{\partial I_e}{\partial u_h(t_{g'})} = \frac{1}{d}
+                                            \int_{-\infty}^\infty
+                                            \frac{d\omega}{2\pi}
+                                            S_\alpha(\omega)
+                                            \frac{\partial F_\alpha (\omega)}
+                                            {\partial u_h(t_{g'})}
+
+    with :math:`S_{\alpha}(\omega)` the noise spectral density
+    and :math:`F_{\alpha}(\omega)` the canonical filter function for
+    noise source :math:`\alpha`.
+
+    To convert to the average gate infidelity, use the
+    following relation given by Horodecki et al. [Hor99]_ and
+    Nielsen [Nie02]_:
+
+    .. math::
+
+        \big\langle\mathcal{I}_\mathrm{avg}\big\rangle = \frac{d}{d+1}
+                \big\langle\mathcal{I}_\mathrm{e}\big\rangle.
+
+    References
+    ----------
+    .. [Hor99]
+        Horodecki, M., Horodecki, P., & Horodecki, R. (1999). General
+        teleportation channel, singlet fraction, and quasidistillation.
+        Physical Review A - Atomic, Molecular, and Optical Physics, 60(3),
+        1888–1898. https://doi.org/10.1103/PhysRevA.60.1888
+
+    .. [Nie02]
+        Nielsen, M. A. (2002). A simple formula for the average gate
+        fidelity of a quantum dynamical operation. Physics Letters,
+        Section A: General, Atomic and Solid State Physics, 303(4), 249–252.
+        https://doi.org/10.1016/S0375-9601(02)01272-0
+    """
+    n_ops = len(pulse.n_opers)
+    S = np.asarray(S)
+    if(S.shape[0] == n_ops):
+        S_all = S
+    elif(S.shape[0] == 1):
+        S_all = np.array([S[0]*n_ops])
+    elif(S.shape[0] == len(omega)):
+        S_all = np.array([S[0]*n_ops])
+    else:
+        raise ValueError('Not fitting shape of S.')
+
+    deriv_F = pulse.get_filter_function_derivative(omega, c_id)
+    d = pulse.d
+    integrand = np.einsum('ao,atho->atho', S_all, deriv_F)
+
+    deriv_infid = trapz(integrand, omega)/(2*np.pi*d)
+
+    return deriv_infid
diff --git a/filter_functions/util.py b/filter_functions/util.py
index cab3f1c..a068c37 100644
--- a/filter_functions/util.py
+++ b/filter_functions/util.py
@@ -77,7 +77,6 @@
 import os
 import re
 import string
-import sys
 from itertools import zip_longest
 from typing import (Callable, Generator, Iterable, List, Optional, Sequence,
                     Tuple, Union)
@@ -161,14 +160,11 @@ def _get_notebook_name() -> str:
 except ImportError:
     _NOTEBOOK_NAME = ''
 
-try:
-    if _NOTEBOOK_NAME:
-        from tqdm.notebook import tqdm
-    else:
-        # Either not running notebook or not able to determine
-        from tqdm import tqdm
-except ImportError:
-    tqdm = None
+if _NOTEBOOK_NAME:
+    from tqdm.notebook import tqdm as _tqdm
+else:
+    # Either not running notebook or not able to determine
+    from tqdm import tqdm as _tqdm
 
 __all__ = ['paulis', 'abs2', 'all_array_equal', 'dot_HS',
            'get_sample_frequencies', 'hash_array_along_axis', 'mdot',
@@ -1035,50 +1031,16 @@ def all_array_equal(it: Iterable) -> bool:
     return len(set(hash(i.tobytes()) for i in it)) == 1
 
 
-def _simple_progressbar(iterable: Iterable, desc: str = "Computing",
-                        size: int = 25, count: int = None, file=None):
-    """https://stackoverflow.com/a/34482761"""
-    if count is None:
-        try:
-            count = len(iterable)
-        except TypeError:
-            raise TypeError("Require total number of iterations 'count'.")
-
-    file = sys.stdout if file is None else file
-
-    if desc:
-        # tqdm desc compatibility
-        desc = desc.strip(': ') + ': '
-
-    def show(j):
-        x = int(size*j/count)
-        file.write("\r{}[{}{}] {} %".format(desc, "#"*x, "."*(size - x),
-                                            int(100*j/count)))
-        file.flush()
-
-    show(0)
-    for i, item in enumerate(iterable):
-        yield item
-        show(i + 1)
-
-    file.write("\n")
-    file.flush()
-
-
 def progressbar(iterable: Iterable, *args, **kwargs):
     """
-    Progress bar for loops. Uses tqdm if available or a quick-and-dirty
-    implementation from stackoverflow.
+    Progress bar for loops. Uses tqdm.
 
     Usage::
 
         for i in progressbar(range(10)):
             do_something()
     """
-    if tqdm is not None:
-        return tqdm(iterable, *args, **kwargs)
-
-    return _simple_progressbar(iterable, *args, **kwargs)
+    return _tqdm(iterable, *args, **kwargs)
 
 
 def progressbar_range(*args, show_progressbar: Optional[bool] = True,
diff --git a/setup.py b/setup.py
index 6421ccf..283cd00 100644
--- a/setup.py
+++ b/setup.py
@@ -27,7 +27,7 @@ def extract_version(version_file):
 
 extras_require = {'plotting': ['matplotlib'],
                   'bloch_sphere_visualization': ['qutip', 'matplotlib'],
-                  'fancy_progressbar': ['tqdm', 'requests'],
+                  'fancy_progressbar': ['requests'],
                   'doc': ['jupyter', 'nbsphinx', 'numpydoc', 'sphinx',
                           'sphinx_rtd_theme'],
                   'tests': ['pytest', 'coverage', 'coveralls']}
@@ -45,7 +45,7 @@ def extract_version(version_file):
       author_email='tobias.hangleiter@rwth-aachen.de',
       packages=['filter_functions'],
       package_dir={'filter_functions': 'filter_functions'},
-      install_requires=['numpy', 'scipy', 'opt_einsum', 'sparse'],
+      install_requires=['numpy', 'scipy', 'opt_einsum', 'sparse', 'tqdm'],
       extras_require=extras_require,
       test_suite='tests',
       classifiers=[
diff --git a/tests/test_util.py b/tests/test_util.py
index 6136678..3fa389d 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -561,15 +561,6 @@ def test_symmetrize_spectrum(self):
         self.assertArrayEqual(S_sym, np.abs(np.arange(-9, 10)/2))
         self.assertArrayEqual(omega_sym, np.arange(-9, 10))
 
-    def test_simple_progressbar(self):
-        with self.assertRaises(TypeError):
-            for i in util._simple_progressbar((i for i in range(10))):
-                pass
-
-        for i in util._simple_progressbar(range(10), desc="foo", size=10,
-                                          count=5):
-            pass
-
     def test_progressbar_range(self):
         ii = []
         for i in util.progressbar_range(2, 125, 32, desc=""):

From 37c74097c69932f8758a132e53fd51a73b810650 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 12:22:02 +0200
Subject: [PATCH 13/22] Refactor tests for optional extras

---
 .travis.yml              |  36 ++++++++------
 tests/__init__.py        |  11 ++++
 tests/test_basis.py      |  28 ++++++++---
 tests/test_extras.py     |  72 +++++++++++++++++++++++++++
 tests/test_plotting.py   | 105 +++++++++++++++++++++------------------
 tests/test_precision.py  |  10 ++--
 tests/test_sequencing.py |   2 +-
 tests/test_util.py       |  69 +++++++++++++++++++++----
 tests/testutil.py        |   9 +---
 9 files changed, 248 insertions(+), 94 deletions(-)
 create mode 100644 tests/test_extras.py

diff --git a/.travis.yml b/.travis.yml
index 2783876..ef602cc 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,24 +1,28 @@
-dist: xenial
-
 language: python
 
 python:
-  - '3.5'
-  - '3.6'
-  - '3.7'
-  - '3.8'
+  - 3.5
+  - 3.6
+  - 3.7
+  - 3.8
+env:
+  - INSTALL_EXTRAS=[plotting,bloch_sphere_visualization,fancy_progressbar,tests]
+  - INSTALL_EXTRAS=[bloch_sphere_visualization,fancy_progressbar,tests]
+  - INSTALL_EXTRAS=[plotting,bloch_sphere_visualization,tests]
+  - INSTALL_EXTRAS=[plotting,fancy_progressbar,tests]
+
+#use container based infrastructure
+sudo: false
+
+#these directories are persistent
+cache: pip
+
+#manually install these dependencies so that qutip can be installed via pip
+before_install:
+  - pip install numpy scipy cython
 
 install:
-  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
-  - bash miniconda.sh -b -p $HOME/miniconda
-  - export PATH="$HOME/miniconda/bin:$PATH"
-  - hash -r
-  - conda config --set always_yes yes --set changeps1 no
-  - conda config --append channels conda-forge
-  - conda info -a
-  - conda env create -f ./environment.yml
-  - source activate filter_functions
-  - pip install .[all]
+  - pip install .$INSTALL_EXTRAS
 
 script:
   - coverage run --rcfile=coverage.ini -m pytest
diff --git a/tests/__init__.py b/tests/__init__.py
index 99cf95c..0034098 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -18,3 +18,14 @@
 #
 #     Contact email: tobias.hangleiter@rwth-aachen.de
 # =============================================================================
+
+try:
+    import qutip
+except ImportError:
+    qutip = None
+
+try:
+    import matplotlib
+    matplotlib.use('Agg')
+except ImportError:
+    matplotlib = None
diff --git a/tests/test_basis.py b/tests/test_basis.py
index 204795d..a3391b8 100644
--- a/tests/test_basis.py
+++ b/tests/test_basis.py
@@ -25,12 +25,14 @@
 from itertools import product
 
 import numpy as np
-import qutip as qt
+import pytest
 from sparse import COO
 
 import filter_functions as ff
 from tests import testutil
 
+from . import qutip
+
 
 class BasisTest(testutil.TestCase):
 
@@ -42,14 +44,11 @@ def test_basis_constructor(self):
             _ = ff.Basis(1)
 
         # All elements should be either sparse, Qobj, or ndarray
-        elems = [ff.util.paulis[1], qt.sigmay(), qt.qeye(2).data,
-                 COO.from_numpy(ff.util.paulis[3]), [[0, 1], [1, 0]]]
+        elems = [ff.util.paulis[1], COO.from_numpy(ff.util.paulis[3]),
+                 [[0, 1], [1, 0]]]
         with self.assertRaises(TypeError):
             _ = ff.Basis(elems)
 
-        # Excluding the fast_csr element should work
-        self.assertEqual(ff.Basis.pauli(1), ff.Basis(elems[:-1]))
-
         # Too many elements
         with self.assertRaises(ValueError):
             _ = ff.Basis(testutil.rng.randn(5, 2, 2))
@@ -328,3 +327,20 @@ def test_control_matrix(self):
                 elif i == 1 and j == 1:
                     # base traceless, nopers traceless
                     self.assertTrue(np.allclose(R[:, 0], 0))
+
+
+@pytest.mark.skipif(
+    qutip is None,
+    reason='Skipping qutip compatibility test for build without qutip')
+class QutipCompatibilityTest(testutil.TestCase):
+
+    def test_constructor(self):
+        """Test if can create basis from qutip objects."""
+
+        elems_qutip = [qutip.sigmay(), qutip.qeye(2).data]
+        elems_np = [qutip.sigmay().full(), qutip.qeye(2).full()]
+
+        basis_qutip = ff.Basis(elems_qutip)
+        basis_np = ff.Basis(elems_np)
+
+        self.assertArrayEqual(basis_qutip, basis_np)
diff --git a/tests/test_extras.py b/tests/test_extras.py
new file mode 100644
index 0000000..484417b
--- /dev/null
+++ b/tests/test_extras.py
@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+# =============================================================================
+#     filter_functions
+#     Copyright (C) 2019 Quantum Technology Group, RWTH Aachen University
+#
+#     This program is free software: you can redistribute it and/or modify
+#     it under the terms of the GNU General Public License as published by
+#     the Free Software Foundation, either version 3 of the License, or
+#     (at your option) any later version.
+#
+#     This program is distributed in the hope that it will be useful,
+#     but WITHOUT ANY WARRANTY; without even the implied warranty of
+#     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+#     GNU General Public License for more details.
+#
+#     You should have received a copy of the GNU General Public License
+#     along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+#     Contact email: tobias.hangleiter@rwth-aachen.de
+# =============================================================================
+"""
+This module tests if optional extras are handled correctly.
+"""
+import os
+
+import pytest
+from numpy import ndarray
+
+from tests import testutil
+
+all_extras = ['fancy_progressbar', 'plotting', 'bloch_sphere_visualization']
+
+
+class MissingExtrasTest(testutil.TestCase):
+
+    @pytest.mark.skipif(
+        'fancy_progressbar' in os.environ.get('INSTALL_EXTRAS', all_extras),
+        reason='Skipping tests for missing fancy progressbar extra in build with requests')  # noqa
+    def test_fancy_progressbar_not_available(self):
+        from filter_functions import util
+        from tqdm import tqdm
+        self.assertEqual(util._NOTEBOOK_NAME, '')
+        self.assertIs(tqdm, util._tqdm)
+
+    @pytest.mark.skipif(
+        'plotting' in os.environ.get('INSTALL_EXTRAS', all_extras),
+        reason='Skipping tests for missing plotting extra in build with matplotlib')  # noqa
+    def test_plotting_not_available(self):
+        with self.assertRaises(ModuleNotFoundError):
+            from filter_functions import plotting
+
+    @pytest.mark.skipif(
+        'bloch_sphere_visualization' in os.environ.get('INSTALL_EXTRAS', all_extras),  # noqa
+        reason='Skipping tests for missing bloch sphere visualization tests in build with qutip')  # noqa
+    def test_bloch_sphere_visualization_not_available(self):
+
+        with self.assertWarns(UserWarning):
+            from filter_functions import plotting
+
+        with self.assertRaises(RuntimeError):
+            plotting.get_bloch_vector(testutil.rng.randn(10, 2))
+
+        with self.assertRaises(RuntimeError):
+            plotting.init_bloch_sphere()
+
+        with self.assertRaises(RuntimeError):
+            plotting.plot_bloch_vector_evolution(
+                testutil.rand_pulse_sequence(2))
+
+        from filter_functions import types
+        self.assertIs(types.State, ndarray)
+        self.assertIs(types.Operator, ndarray)
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index 45b8cff..edb7b86 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -21,23 +21,24 @@
 """
 This module tests the plotting functionality of the package.
 """
-import matplotlib
-
-# Needs to be executed before the pyplot import
-matplotlib.use('Agg')
-
 import string
 from random import sample
 
-import matplotlib.pyplot as plt
 import numpy as np
 import pytest
-import qutip as qt
 
 import filter_functions as ff
-from filter_functions import plotting
 from tests import testutil
 
+from . import qutip
+
+plotting = pytest.importorskip(
+    'filter_functions.plotting',
+    reason='Skipping plotting tests for build without matplotlib',
+)
+if plotting is not None:
+    import matplotlib.pyplot as plt
+
 simple_pulse = testutil.rand_pulse_sequence(2, 1, 1, 1, btype='Pauli')
 complicated_pulse = testutil.rand_pulse_sequence(2, 100, 3, 3)
 two_qubit_pulse = testutil.rand_pulse_sequence(4, 1, 1, 6, btype='Pauli')
@@ -45,45 +46,6 @@
 
 class PlottingTest(testutil.TestCase):
 
-    def test_get_bloch_vector(self):
-        states = [qt.rand_ket(2) for _ in range(10)]
-        bloch_vectors_qt = plotting.get_bloch_vector(states)
-        bloch_vectors_np = plotting.get_bloch_vector([state.full()
-                                                      for state in states])
-
-        for bv_qt, bv_np in zip(bloch_vectors_qt, bloch_vectors_np):
-            self.assertArrayAlmostEqual(bv_qt, bv_np)
-
-    def test_get_states_from_prop(self):
-        P = testutil.rand_unit(2, 10)
-        Q = np.empty((11, 2, 2), dtype=complex)
-        Q[0] = np.identity(2)
-        for i in range(10):
-            Q[i+1] = P[i] @ Q[i]
-
-        psi0 = qt.rand_ket(2)
-        states_piecewise = plotting.get_states_from_prop(P, psi0, 'piecewise')
-        states_total = plotting.get_states_from_prop(Q[1:], psi0, 'total')
-        self.assertArrayAlmostEqual(states_piecewise, states_total)
-
-    def test_plot_bloch_vector_evolution(self):
-        # Call with default args
-        b = plotting.plot_bloch_vector_evolution(simple_pulse)
-        # Call with custom args
-        b = plotting.init_bloch_sphere(background=True)
-        b = plotting.plot_bloch_vector_evolution(simple_pulse,
-                                                 psi0=qt.basis(2, 1), b=b,
-                                                 n_samples=50, show=False,
-                                                 return_Bloch=True)
-
-        b = plotting.plot_bloch_vector_evolution(complicated_pulse)
-
-        # Check exceptions being raised
-        with self.assertRaises(ValueError):
-            plotting.plot_bloch_vector_evolution(two_qubit_pulse)
-
-        plt.close('all')
-
     def test_plot_pulse_train(self):
         # Call with default args
         fig, ax, leg = plotting.plot_pulse_train(simple_pulse)
@@ -292,12 +254,12 @@ def test_plot_error_transfer_matrix(self):
         fig, grid = plotting.plot_error_transfer_matrix(
             complicated_pulse, S=S, omega=omega,
             n_oper_identifiers=n_oper_identifiers, basis_labels=basis_labels,
-            basis_labelsize=4, linthresh=1e-4, cmap=matplotlib.cm.jet
+            basis_labelsize=4, linthresh=1e-4, cmap=plt.cm.jet
         )
         fig, grid = plotting.plot_error_transfer_matrix(
             U=U[n_oper_inds], n_oper_identifiers=n_oper_identifiers,
             basis_labels=basis_labels, basis_labelsize=4, linthresh=1e-4,
-            cmap=matplotlib.cm.jet
+            cmap=plt.cm.jet
         )
 
         # neither U nor all of pulse, S, omega given
@@ -338,3 +300,48 @@ def S(omega):
 
         n, infids = ff.infidelity(simple_pulse, S, {}, test_convergence=True)
         fig, ax = plotting.plot_infidelity_convergence(n, infids)
+
+
+@pytest.mark.skipif(
+    qutip is None,
+    reason='Skipping bloch sphere visualization tests for build without qutip')
+class BlochSphereVisualizationTest(testutil.TestCase):
+
+    def test_get_bloch_vector(self):
+        states = [qutip.rand_ket(2) for _ in range(10)]
+        bloch_vectors_qutip = plotting.get_bloch_vector(states)
+        bloch_vectors_np = plotting.get_bloch_vector([state.full()
+                                                      for state in states])
+
+        for bv_qutip, bv_np in zip(bloch_vectors_qutip, bloch_vectors_np):
+            self.assertArrayAlmostEqual(bv_qutip, bv_np)
+
+    def test_get_states_from_prop(self):
+        P = testutil.rand_unit(2, 10)
+        Q = np.empty((11, 2, 2), dtype=complex)
+        Q[0] = np.identity(2)
+        for i in range(10):
+            Q[i+1] = P[i] @ Q[i]
+
+        psi0 = qutip.rand_ket(2)
+        states_piecewise = plotting.get_states_from_prop(P, psi0, 'piecewise')
+        states_total = plotting.get_states_from_prop(Q[1:], psi0, 'total')
+        self.assertArrayAlmostEqual(states_piecewise, states_total)
+
+    def test_plot_bloch_vector_evolution(self):
+        # Call with default args
+        b = plotting.plot_bloch_vector_evolution(simple_pulse)
+        # Call with custom args
+        b = plotting.init_bloch_sphere(background=True)
+        b = plotting.plot_bloch_vector_evolution(simple_pulse,
+                                                 psi0=qutip.basis(2, 1), b=b,
+                                                 n_samples=50, show=False,
+                                                 return_Bloch=True)
+
+        b = plotting.plot_bloch_vector_evolution(complicated_pulse)
+
+        # Check exceptions being raised
+        with self.assertRaises(ValueError):
+            plotting.plot_bloch_vector_evolution(two_qubit_pulse)
+
+        plt.close('all')
diff --git a/tests/test_precision.py b/tests/test_precision.py
index be252f3..c562d33 100644
--- a/tests/test_precision.py
+++ b/tests/test_precision.py
@@ -23,10 +23,9 @@
 """
 
 import numpy as np
-import qutip as qt
 
 import filter_functions as ff
-from filter_functions import analytic, numeric
+from filter_functions import analytic, numeric, util
 from tests import testutil
 
 
@@ -192,6 +191,9 @@ def test_liouville_representation(self):
 
     def test_diagonalization_cnot(self):
         """CNOT"""
+        cnot_mat = np.block([[util.paulis[0], np.zeros((2, 2))],
+                             [np.zeros((2, 2)), util.paulis[1]]])
+
         subspace_c_opers = testutil.subspace_opers
         subspace_n_opers = subspace_c_opers
         c_opers = testutil.opers
@@ -212,12 +214,12 @@ def test_diagonalization_cnot(self):
         cnot_subspace.diagonalize()
 
         phase_eq = ff.util.oper_equiv(cnot_subspace.total_Q[1:5, 1:5],
-                                      qt.cnot(), eps=1e-9)
+                                      cnot_mat, eps=1e-9)
 
         self.assertTrue(phase_eq[0])
 
         phase_eq = ff.util.oper_equiv(
-            cnot.total_Q[np.ix_(*subspace)][1:5, 1:5], qt.cnot(), eps=1e-9)
+            cnot.total_Q[np.ix_(*subspace)][1:5, 1:5], cnot_mat, eps=1e-9)
 
         self.assertTrue(phase_eq[0])
 
diff --git a/tests/test_sequencing.py b/tests/test_sequencing.py
index 208e376..647dc61 100644
--- a/tests/test_sequencing.py
+++ b/tests/test_sequencing.py
@@ -22,8 +22,8 @@
 This module tests the concatenation functionality for PulseSequence's.
 """
 
-from copy import copy
 import string
+from copy import copy
 from itertools import product
 from random import sample
 
diff --git a/tests/test_util.py b/tests/test_util.py
index 3fa389d..46c9e22 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -22,11 +22,13 @@
 This module tests the utility functions in util.py
 """
 import numpy as np
-import qutip as qt
+import pytest
 
 from filter_functions import PulseSequence, util
 from tests import testutil
 
+from . import qutip
+
 
 class UtilTest(testutil.TestCase):
 
@@ -441,8 +443,8 @@ def test_oper_equiv(self):
             util.oper_equiv(*[np.ones((1, 2, 3))]*2)
 
         for d in testutil.rng.randint(2, 10, (5,)):
-            psi = qt.rand_ket(d)
-            U = qt.rand_dm(d)
+            psi = testutil.rng.randn(d, 1)*(1 + 1j)
+            U = testutil.rand_herm(d).squeeze()
             phase = testutil.rng.randn()
 
             result = util.oper_equiv(psi, psi*np.exp(1j*phase))
@@ -453,8 +455,7 @@ def test_oper_equiv(self):
             self.assertTrue(result[0])
             self.assertAlmostEqual(result[1], -phase, places=5)
 
-            psi = psi.full()
-            psi /= np.sqrt(np.linalg.norm(psi, ord=2))
+            psi /= np.linalg.norm(psi, ord=2)
 
             result = util.oper_equiv(psi, psi*np.exp(1j*phase),
                                      normalized=True, eps=1e-13)
@@ -472,7 +473,6 @@ def test_oper_equiv(self):
             self.assertTrue(result[0])
             self.assertAlmostEqual(result[1], -phase, places=5)
 
-            U = U.full()
             U /= np.sqrt(util.dot_HS(U, U))
             result = util.oper_equiv(U, U*np.exp(1j*phase), normalized=True,
                                      eps=1e-10)
@@ -489,13 +489,12 @@ def test_dot_HS(self):
         self.assertArrayEqual(S, T)
 
         for d in testutil.rng.randint(2, 10, (5,)):
-            U = qt.rand_herm(d)
-            V = qt.rand_herm(d)
-            self.assertArrayAlmostEqual(util.dot_HS(U, V), (U.dag()*V).tr())
+            U, V = testutil.rand_herm(d, 2)
+            self.assertArrayAlmostEqual(util.dot_HS(U, V),
+                                        (U.conj().T @ V).trace())
 
-            U = qt.rand_unitary(d)
+            U = testutil.rand_unit(d).squeeze()
             self.assertEqual(util.dot_HS(U, U), d)
-
             self.assertEqual(util.dot_HS(U, U + 1e-14, eps=1e-10), d)
 
     def test_all_array_equal(self):
@@ -598,3 +597,51 @@ def foobar(a, b, foo=None, x=2):
             self.assertEqual(str(err.exception),
                              "Invalid value for foo: {}.".format([1, 2]) +
                              " Should be one of {}".format([1, 'bar', (2, 3)]))
+
+
+@pytest.mark.skipif(
+    qutip is None,
+    reason='Skipping qutip compatibility tests for build without qutip')
+class QutipCompatibilityTest(testutil.TestCase):
+
+    def test_dot_HS(self):
+        for d in testutil.rng.randint(2, 10, (5,)):
+            U = qutip.rand_herm(d)
+            V = qutip.rand_herm(d)
+            self.assertArrayAlmostEqual(util.dot_HS(U, V), (U.dag()*V).tr())
+
+            U = qutip.rand_unitary(d)
+            self.assertEqual(util.dot_HS(U, U), d)
+
+            self.assertEqual(util.dot_HS(U, U + 1e-14, eps=1e-10), d)
+            self.assertArrayAlmostEqual(util.dot_HS(U, V), (U.dag()*V).tr())
+
+            U = qutip.rand_unitary(d)
+            self.assertEqual(util.dot_HS(U, U), d)
+
+            self.assertEqual(util.dot_HS(U, U + 1e-14, eps=1e-10), d)
+
+    def test_oper_equiv(self):
+        self.assertFalse(
+            util.oper_equiv(qutip.rand_ket(2), qutip.rand_dm(2))[0])
+
+        for d in testutil.rng.randint(2, 10, (5,)):
+            psi = qutip.rand_ket(d)
+            U = qutip.rand_dm(d)
+            phase = testutil.rng.randn()
+
+            result = util.oper_equiv(psi, psi*np.exp(1j*phase))
+            self.assertTrue(result[0])
+            self.assertAlmostEqual(result[1], phase, places=5)
+
+            result = util.oper_equiv(psi*np.exp(1j*phase), psi)
+            self.assertTrue(result[0])
+            self.assertAlmostEqual(result[1], -phase, places=5)
+
+            result = util.oper_equiv(U, U*np.exp(1j*phase))
+            self.assertTrue(result[0])
+            self.assertAlmostEqual(result[1], phase, places=5)
+
+            result = util.oper_equiv(U*np.exp(1j*phase), U)
+            self.assertTrue(result[0])
+            self.assertAlmostEqual(result[1], -phase, places=5)
diff --git a/tests/testutil.py b/tests/testutil.py
index f819ed4..1ab2f0a 100644
--- a/tests/testutil.py
+++ b/tests/testutil.py
@@ -21,13 +21,11 @@
 """
 This module defines some testing utilities.
 """
-
 import string
 import unittest
 from pathlib import Path
 
 import numpy as np
-import qutip as qt
 from numpy.random import RandomState
 from numpy.testing import assert_allclose, assert_array_equal
 from scipy import io
@@ -126,7 +124,7 @@ def cdd_even(l, t):
     t = np.append(t, tau)
     s = np.append(s, 0)
 
-    H = [[qt.sigmax()/2, s]]
+    H = [[util.paulis[1]/2, s]]
     return H, np.diff(t)
 
 
@@ -146,10 +144,7 @@ def rand_herm_traceless(d: int, n: int = 1) -> np.ndarray:
 def rand_unit(d: int, n: int = 1) -> np.ndarray:
     """n random unitary matrices of dimension d"""
     H = rand_herm(d, n)
-    if n == 1:
-        return sla.expm(1j*H)
-    else:
-        return np.array([sla.expm(1j*h) for h in H])
+    return np.array([sla.expm(1j*h) for h in H])
 
 
 def rand_pulse_sequence(d: int, n_dt: int, n_cops: int = 3, n_nops: int = 3,

From e689841fc9faf31dfbf6c2211d0b4579f148aa85 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 13:44:11 +0200
Subject: [PATCH 14/22] Clean up travis.yml

---
 .travis.yml | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index ef602cc..ba4a4ed 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,3 +1,7 @@
+os: linux
+
+dist: xenial
+
 language: python
 
 python:
@@ -11,9 +15,6 @@ env:
   - INSTALL_EXTRAS=[plotting,bloch_sphere_visualization,tests]
   - INSTALL_EXTRAS=[plotting,fancy_progressbar,tests]
 
-#use container based infrastructure
-sudo: false
-
 #these directories are persistent
 cache: pip
 

From b12c7c201f4b54d2b725b768a23d9f6c83a9b5a2 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 14:42:44 +0200
Subject: [PATCH 15/22] upgrade pip before install

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index ba4a4ed..71fe5f6 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -20,6 +20,7 @@ cache: pip
 
 #manually install these dependencies so that qutip can be installed via pip
 before_install:
+  - pip install --upgrade pip
   - pip install numpy scipy cython
 
 install:

From 1213e6dbe470a0878b12c65cf5bfac9c93466398 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 15:22:28 +0200
Subject: [PATCH 16/22] Switch to recommended numpy.random methods

---
 tests/test_basis.py      |  46 ++++++-------
 tests/test_core.py       |  75 +++++++++++-----------
 tests/test_extras.py     |   2 +-
 tests/test_plotting.py   |  15 ++---
 tests/test_precision.py  |  16 ++---
 tests/test_sequencing.py |  48 +++++++-------
 tests/test_util.py       | 135 ++++++++++++++++++++-------------------
 tests/testutil.py        |   8 +--
 8 files changed, 176 insertions(+), 169 deletions(-)

diff --git a/tests/test_basis.py b/tests/test_basis.py
index a3391b8..c9efff0 100644
--- a/tests/test_basis.py
+++ b/tests/test_basis.py
@@ -30,6 +30,7 @@
 
 import filter_functions as ff
 from tests import testutil
+from tests.testutil import rng
 
 from . import qutip
 
@@ -51,7 +52,7 @@ def test_basis_constructor(self):
 
         # Too many elements
         with self.assertRaises(ValueError):
-            _ = ff.Basis(testutil.rng.randn(5, 2, 2))
+            _ = ff.Basis(rng.standard_normal((5, 2, 2)))
 
         # Properly normalized
         self.assertEqual(ff.Basis.pauli(1), ff.Basis(ff.util.paulis))
@@ -70,8 +71,8 @@ def test_basis_constructor(self):
 
     def test_basis_properties(self):
         """Basis orthonormal and of correct dimensions"""
-        d = testutil.rng.randint(2, 17)
-        n = testutil.rng.randint(1, 5)
+        d = rng.randint(2, 17)
+        n = rng.randint(1, 5)
 
         ggm_basis = ff.Basis.ggm(d)
         pauli_basis = ff.Basis.pauli(n)
@@ -87,11 +88,11 @@ def test_basis_properties(self):
             if not btype == 'Pauli':
                 self.assertEqual(d, base.d)
                 # Check if __contains__ works as expected
-                self.assertTrue(base[testutil.rng.randint(0, d**2)] in base)
+                self.assertTrue(base[rng.randint(0, d**2)] in base)
             else:
                 self.assertEqual(2**n, base.d)
                 # Check if __contains__ works as expected
-                self.assertTrue(base[testutil.rng.randint(0, (2**n)**2)]
+                self.assertTrue(base[rng.randint(0, (2**n)**2)]
                                 in base)
             # Check if all elements of each basis are orthonormal and hermitian
             self.assertArrayEqual(base.T,
@@ -130,13 +131,13 @@ def test_basis_properties(self):
     def test_basis_expansion_and_normalization(self):
         """Correct expansion of operators and normalization of bases"""
         for _ in range(10):
-            d = testutil.rng.randint(2, 16)
+            d = rng.randint(2, 16)
             ggm_basis = ff.Basis.ggm(d)
             basis = ff.Basis(
-                np.einsum('i,ijk->ijk', testutil.rng.randn(d**2), ggm_basis),
+                np.einsum('i,ijk->ijk', rng.standard_normal(d**2), ggm_basis),
                 skip_check=True
             )
-            M = testutil.rng.randn(d, d) + 1j*testutil.rng.randn(d, d)
+            M = rng.standard_normal((d, d)) + 1j*rng.standard_normal((d, d))
             M -= np.trace(M)/d
             coeffs = ff.basis.expand(M, basis, normalized=False)
             self.assertArrayAlmostEqual(M, np.einsum('i,ijk', coeffs, basis))
@@ -147,8 +148,9 @@ def test_basis_expansion_and_normalization(self):
                                         ff.basis.ggm_expand(M, traceless=True),
                                         atol=1e-14)
 
-            n = testutil.rng.randint(1, 50)
-            M = testutil.rng.randn(n, d, d) + 1j*testutil.rng.randn(n, d, d)
+            n = rng.randint(1, 50)
+            M = (rng.standard_normal((n, d, d)) +
+                 1j*rng.standard_normal((n, d, d)))
             coeffs = ff.basis.expand(M, basis, normalized=False)
             self.assertArrayAlmostEqual(M, np.einsum('li,ijk->ljk', coeffs,
                                                      basis))
@@ -181,11 +183,11 @@ def test_basis_generation_from_partial_ggm(self):
         # Do 100 test runs with random elements from a GGM basis in (2 ... 8)
         # dimensions
         for _ in range(50):
-            d = testutil.rng.randint(2, 9)
+            d = rng.randint(2, 9)
             b = ff.Basis.ggm(d)
             inds = [i for i in range(d**2)]
-            tup = tuple(inds.pop(testutil.rng.randint(0, len(inds)))
-                        for _ in range(testutil.rng.randint(1, d**2)))
+            tup = tuple(inds.pop(rng.randint(0, len(inds)))
+                        for _ in range(rng.randint(1, d**2)))
             elems = b[tup, ...]
             basis = ff.Basis(elems)
             self.assertTrue(basis.isorthonorm)
@@ -199,12 +201,12 @@ def test_basis_generation_from_partial_pauli(self):
         # Do 100 test runs with random elements from a Pauli basis in (2 ... 8)
         # dimensions
         for _ in range(50):
-            n = testutil.rng.randint(1, 4)
+            n = rng.randint(1, 4)
             d = 2**n
             b = ff.Basis.pauli(n)
             inds = [i for i in range(d**2)]
-            tup = tuple(inds.pop(testutil.rng.randint(0, len(inds)))
-                        for _ in range(testutil.rng.randint(1, d**2)))
+            tup = tuple(inds.pop(rng.randint(0, len(inds)))
+                        for _ in range(rng.randint(1, d**2)))
             elems = b[tup, ...]
             basis = ff.Basis(elems)
             self.assertTrue(basis.isorthonorm)
@@ -226,7 +228,7 @@ def test_basis_generation_from_partial_random(self):
         # Do 25 test runs with random elements from a random basis in
         # (2 ... 8) dimensions
         for _ in range(25):
-            d = testutil.rng.randint(2, 7)
+            d = rng.randint(2, 7)
             # Get a random traceless hermitian operator
             oper = testutil.rand_herm_traceless(d)
             # ... and build a basis from it
@@ -238,8 +240,8 @@ def test_basis_generation_from_partial_random(self):
             # Choose random elements from that basis and generate a new basis
             # from it
             inds = [i for i in range(d**2)]
-            tup = tuple(inds.pop(testutil.rng.randint(0, len(inds)))
-                        for _ in range(testutil.rng.randint(1, d**2)))
+            tup = tuple(inds.pop(rng.randint(0, len(inds)))
+                        for _ in range(rng.randint(1, d**2)))
             elems = b[tup, ...]
             basis = ff.Basis(elems)
             self.assertTrue(basis.isorthonorm)
@@ -250,7 +252,7 @@ def test_basis_generation_from_partial_random(self):
 
         # Test runs with non-traceless opers
         for _ in range(25):
-            d = testutil.rng.randint(2, 7)
+            d = rng.randint(2, 7)
             # Get a random hermitian operator
             oper = testutil.rand_herm(d)
             # ... and build a basis from it
@@ -262,8 +264,8 @@ def test_basis_generation_from_partial_random(self):
             # Choose random elements from that basis and generate a new basis
             # from it
             inds = [i for i in range(d**2)]
-            tup = tuple(inds.pop(testutil.rng.randint(0, len(inds)))
-                        for _ in range(testutil.rng.randint(1, d**2)))
+            tup = tuple(inds.pop(rng.randint(0, len(inds)))
+                        for _ in range(rng.randint(1, d**2)))
             elems = b[tup, ...]
             basis = ff.Basis(elems)
             self.assertTrue(basis.isorthonorm)
diff --git a/tests/test_core.py b/tests/test_core.py
index 68eb14f..97ea991 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -30,6 +30,7 @@
 import filter_functions as ff
 from filter_functions import numeric, util
 from tests import testutil
+from tests.testutil import rng
 
 
 class CoreTest(testutil.TestCase):
@@ -55,7 +56,7 @@ def test_pulse_sequence_constructor(self):
             # dt not a sequence
             ff.PulseSequence(H_c, H_n, dt[0])
 
-        idx = testutil.rng.randint(0, 5)
+        idx = rng.randint(0, 5)
         with self.assertRaises(ValueError):
             # negative dt
             dt[idx] *= -1
@@ -94,7 +95,7 @@ def test_pulse_sequence_constructor(self):
             # Element of noise Hamiltonian not list or tuple
             ff.PulseSequence(H_c, [np.array(H_n[0])], dt)
 
-        idx = testutil.rng.randint(0, 3)
+        idx = rng.randint(0, 3)
         with self.assertRaises(TypeError):
             # Control Hamiltonian element not list or tuple
             H_c[idx] = dict(H_c[idx])
@@ -241,19 +242,19 @@ def test_pulse_sequence_constructor(self):
     def test_pulse_sequence_attributes(self):
         """Test attributes of single instance"""
         X, Y, Z = util.paulis[1:]
-        n_dt = testutil.rng.randint(1, 10)
+        n_dt = rng.randint(1, 10)
 
         # trivial case
-        A = ff.PulseSequence([[X, testutil.rng.randn(n_dt), 'X']],
-                             [[Z, testutil.rng.randn(n_dt), 'Z']],
-                             np.abs(testutil.rng.randn(n_dt)))
+        A = ff.PulseSequence([[X, rng.standard_normal(n_dt), 'X']],
+                             [[Z, rng.standard_normal(n_dt), 'Z']],
+                             np.abs(rng.standard_normal(n_dt)))
         self.assertFalse(A == 1)
         self.assertTrue(A != 1)
 
         # different number of time steps
-        B = ff.PulseSequence([[X, testutil.rng.randn(n_dt+1), 'X']],
-                             [[Z, testutil.rng.randn(n_dt+1), 'Z']],
-                             np.abs(testutil.rng.randn(n_dt+1)))
+        B = ff.PulseSequence([[X, rng.standard_normal(n_dt+1), 'X']],
+                             [[Z, rng.standard_normal(n_dt+1), 'Z']],
+                             np.abs(rng.standard_normal(n_dt+1)))
         self.assertFalse(A == B)
         self.assertTrue(A != B)
 
@@ -261,7 +262,7 @@ def test_pulse_sequence_attributes(self):
         B = ff.PulseSequence(
             list(zip(A.c_opers, A.c_coeffs, A.c_oper_identifiers)),
             list(zip(A.n_opers, A.n_coeffs, A.n_oper_identifiers)),
-            np.abs(testutil.rng.randn(n_dt))
+            np.abs(rng.standard_normal(n_dt))
         )
         self.assertFalse(A == B)
         self.assertTrue(A != B)
@@ -277,7 +278,7 @@ def test_pulse_sequence_attributes(self):
 
         # different control coeffs
         B = ff.PulseSequence(
-            list(zip(A.c_opers, [testutil.rng.randn(n_dt)],
+            list(zip(A.c_opers, [rng.standard_normal(n_dt)],
                      A.c_oper_identifiers)),
             list(zip(A.n_opers, A.n_coeffs, A.n_oper_identifiers)),
             A.dt
@@ -297,7 +298,7 @@ def test_pulse_sequence_attributes(self):
         # different noise coeffs
         B = ff.PulseSequence(
             list(zip(A.c_opers, A.c_coeffs, A.c_oper_identifiers)),
-            list(zip(A.n_opers, [testutil.rng.randn(n_dt)],
+            list(zip(A.n_opers, [rng.standard_normal(n_dt)],
                      A.n_oper_identifiers)),
             A.dt
         )
@@ -341,7 +342,7 @@ def test_pulse_sequence_attributes(self):
                     _ = A.is_cached(attr)
             else:
                 # set mock attribute at random
-                if testutil.rng.randint(0, 2):
+                if rng.randint(0, 2):
                     setattr(A, attr, 'foo')
                     assertion = self.assertTrue
                 else:
@@ -381,7 +382,7 @@ def test_pulse_sequence_attributes(self):
 
         for alias, attr in aliases.items():
             # set mock attribute at random
-            if testutil.rng.randint(0, 2):
+            if rng.randint(0, 2):
                 setattr(A, attr, 'foo')
                 assertion = self.assertTrue
             else:
@@ -446,23 +447,23 @@ def test_pulse_sequence_attributes(self):
     def test_pulse_sequence_attributes_concat(self):
         """Test attributes of concatenated sequence."""
         X, Y, Z = util.paulis[1:]
-        n_dt_1 = testutil.rng.randint(5, 11)
-        x_coeff_1 = testutil.rng.randn(n_dt_1)
-        z_coeff_1 = testutil.rng.randn(n_dt_1)
-        dt_1 = np.abs(testutil.rng.randn(n_dt_1))
-        n_dt_2 = testutil.rng.randint(5, 11)
-        y_coeff_2 = testutil.rng.randn(n_dt_2)
-        z_coeff_2 = testutil.rng.randn(n_dt_2)
-        dt_2 = np.abs(testutil.rng.randn(n_dt_2))
+        n_dt_1 = rng.randint(5, 11)
+        x_coeff_1 = rng.standard_normal(n_dt_1)
+        z_coeff_1 = rng.standard_normal(n_dt_1)
+        dt_1 = np.abs(rng.standard_normal(n_dt_1))
+        n_dt_2 = rng.randint(5, 11)
+        y_coeff_2 = rng.standard_normal(n_dt_2)
+        z_coeff_2 = rng.standard_normal(n_dt_2)
+        dt_2 = np.abs(rng.standard_normal(n_dt_2))
         pulse_1 = ff.PulseSequence([[X, x_coeff_1]],
                                    [[Z, z_coeff_1]],
                                    dt_1)
         pulse_2 = ff.PulseSequence([[Y, y_coeff_2]],
                                    [[Z, z_coeff_2]],
                                    dt_2)
-        pulse_3 = ff.PulseSequence([[Y, testutil.rng.randn(2)],
-                                    [X, testutil.rng.randn(2)]],
-                                   [[Z, np.abs(testutil.rng.randn(2))]],
+        pulse_3 = ff.PulseSequence([[Y, rng.standard_normal(2)],
+                                    [X, rng.standard_normal(2)]],
+                                   [[Z, np.abs(rng.standard_normal(2))]],
                                    [1, 1])
 
         # Concatenate with different noise opers
@@ -476,7 +477,7 @@ def test_pulse_sequence_attributes_concat(self):
         pulse_21 = pulse_2 @ pulse_1
 
         with self.assertRaises(TypeError):
-            _ = pulse_1 @ testutil.rng.randn(2, 2)
+            _ = pulse_1 @ rng.standard_normal((2, 2))
 
         # Concatenate pulses with same operators but different labels
         with self.assertRaises(ValueError):
@@ -526,13 +527,13 @@ def test_pulse_sequence_attributes_concat(self):
             self.assertArrayEqual(total_Q_liouville, pulse._total_Q_liouville)
 
         # Test custom identifiers
-        letters = testutil.rng.choice(list(string.ascii_letters), size=(6, 5),
-                                      replace=False)
+        letters = rng.choice(list(string.ascii_letters), size=(6, 5),
+                             replace=False)
         ids = [''.join(l) for l in letters[:3]]
         labels = [''.join(l) for l in letters[3:]]
         pulse = ff.PulseSequence(
-            list(zip([X, Y, Z], testutil.rng.randn(3, 2), ids, labels)),
-            list(zip([X, Y, Z], testutil.rng.randn(3, 2), ids, labels)),
+            list(zip([X, Y, Z], rng.standard_normal((3, 2)), ids, labels)),
+            list(zip([X, Y, Z], rng.standard_normal((3, 2)), ids, labels)),
             [1, 1]
         )
 
@@ -541,8 +542,8 @@ def test_pulse_sequence_attributes_concat(self):
 
     def test_filter_function(self):
         """Test the filter function calculation and related methods"""
-        for d, n_dt in zip(testutil.rng.randint(2, 10, (3,)),
-                           testutil.rng.randint(10, 200, (3,))):
+        for d, n_dt in zip(rng.randint(2, 10, (3,)),
+                           rng.randint(10, 200, (3,))):
             total_pulse = testutil.rand_pulse_sequence(d, n_dt, 4, 6)
             c_opers, c_coeffs = total_pulse.c_opers, total_pulse.c_coeffs
             n_opers, n_coeffs = total_pulse.n_opers, total_pulse.n_coeffs
@@ -756,7 +757,7 @@ def test_pulse_correlation_filter_function(self):
             infid_1 = ff.infidelity(pulse_1, S, omega, which='foobar')
 
         for _ in range(10):
-            n_nops = testutil.rng.randint(1, 4)
+            n_nops = rng.randint(1, 4)
             identifiers = sample(['B_0', 'B_1', 'B_2'], n_nops)
 
             infid_X = ff.infidelity(pulses['X'], S, omega, which='total',
@@ -787,9 +788,9 @@ def test_calculate_error_vector_correlation_functions(self):
         """Test raises of numeric.error_transfer_matrix"""
         pulse = testutil.rand_pulse_sequence(2, 1, 1, 1)
 
-        omega = testutil.rng.randn(43)
+        omega = rng.standard_normal(43)
         # single spectrum
-        S = testutil.rng.randn(78)
+        S = rng.standard_normal(78)
         for i in range(4):
             with self.assertRaises(ValueError):
                 numeric.calculate_error_vector_correlation_functions(
@@ -829,8 +830,8 @@ def S(omega):
         n, infids = ff.infidelity(simple_pulse, S, {}, test_convergence=True)
 
         # Test with non-default args
-        identifiers = testutil.rng.choice(complicated_pulse.n_oper_identifiers,
-                                          testutil.rng.randint(1, 4))
+        identifiers = rng.choice(complicated_pulse.n_oper_identifiers,
+                                 rng.randint(1, 4))
 
         n, infids = ff.infidelity(complicated_pulse, S, omega,
                                   test_convergence=True,
diff --git a/tests/test_extras.py b/tests/test_extras.py
index 484417b..47d6031 100644
--- a/tests/test_extras.py
+++ b/tests/test_extras.py
@@ -58,7 +58,7 @@ def test_bloch_sphere_visualization_not_available(self):
             from filter_functions import plotting
 
         with self.assertRaises(RuntimeError):
-            plotting.get_bloch_vector(testutil.rng.randn(10, 2))
+            plotting.get_bloch_vector(testutil.rng.standard_normal((10, 2)))
 
         with self.assertRaises(RuntimeError):
             plotting.init_bloch_sphere()
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index edb7b86..7ba0b9d 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -29,6 +29,7 @@
 
 import filter_functions as ff
 from tests import testutil
+from tests.testutil import rng
 
 from . import qutip
 
@@ -59,8 +60,7 @@ def test_plot_pulse_train(self):
 
         # Call with custom args
         c_oper_identifiers = sample(
-            complicated_pulse.c_oper_identifiers.tolist(),
-            testutil.rng.randint(2, 4)
+            complicated_pulse.c_oper_identifiers.tolist(), rng.randint(2, 4)
         )
 
         fig, ax = plt.subplots()
@@ -102,8 +102,7 @@ def test_plot_filter_function(self):
 
         # Non-default args
         n_oper_identifiers = sample(
-            complicated_pulse.n_oper_identifiers.tolist(),
-            testutil.rng.randint(2, 4)
+            complicated_pulse.n_oper_identifiers.tolist(), rng.randint(2, 4)
         )
 
         fig, ax = plt.subplots()
@@ -168,8 +167,7 @@ def test_plot_pulse_correlation_filter_function(self):
 
         # Non-default args
         n_oper_identifiers = sample(
-            complicated_pulse.n_oper_identifiers.tolist(),
-            testutil.rng.randint(2, 4)
+            complicated_pulse.n_oper_identifiers.tolist(), rng.randint(2, 4)
         )
 
         fig, ax = plt.subplots()
@@ -239,13 +237,12 @@ def test_plot_error_transfer_matrix(self):
 
         # Non-default args
         n_oper_inds = sample(range(len(complicated_pulse.n_opers)),
-                             testutil.rng.randint(2, 4))
+                             rng.randint(2, 4))
         n_oper_identifiers = complicated_pulse.n_oper_identifiers[n_oper_inds]
 
         basis_labels = []
         for i in range(4):
-            basis_labels.append(
-                string.ascii_uppercase[testutil.rng.randint(0, 26)])
+            basis_labels.append(string.ascii_uppercase[rng.randint(0, 26)])
 
         omega = ff.util.get_sample_frequencies(complicated_pulse, n_samples=50,
                                                spacing='log')
diff --git a/tests/test_precision.py b/tests/test_precision.py
index c562d33..46d8376 100644
--- a/tests/test_precision.py
+++ b/tests/test_precision.py
@@ -27,13 +27,14 @@
 import filter_functions as ff
 from filter_functions import analytic, numeric, util
 from tests import testutil
+from tests.testutil import rng
 
 
 class PrecisionTest(testutil.TestCase):
 
     def test_FID(self):
         """FID"""
-        tau = abs(testutil.rng.randn())
+        tau = abs(rng.standard_normal())
         FID_pulse = ff.PulseSequence([[ff.util.paulis[1]/2, [0]]],
                                      [[ff.util.paulis[3]/2, [1]]],
                                      [tau])
@@ -64,7 +65,7 @@ def test_SE(self):
 
         # Test again with a factor of one between the noise operators and
         # coefficients
-        r = testutil.rng.randn()
+        r = rng.standard_normal()
         H_n = [[ff.util.paulis[3]/2*r, np.ones_like(dt)/r]]
 
         SE_pulse = ff.PulseSequence(H_c, H_n, dt)
@@ -273,7 +274,7 @@ def test_infidelity_cnot(self):
 
     def test_infidelity(self):
         """Benchmark infidelity results against previous version's results"""
-        testutil.rng.seed(123456789)
+        rng.seed(123456789)
 
         spectra = [
             lambda S0, omega: S0*omega**0,
@@ -315,7 +316,7 @@ def test_infidelity(self):
             pulse.n_oper_identifiers = np.array(['B_0', 'B_2'])
 
             omega = np.geomspace(0.1, 10, 51)
-            S0 = np.abs(testutil.rng.randn())
+            S0 = np.abs(rng.standard_normal())
             for spec in spectra:
                 S, omega_t = ff.util.symmetrize_spectrum(spec(S0, omega),
                                                          omega)
@@ -372,7 +373,7 @@ def test_infidelity(self):
 
         with self.assertRaises(ValueError):
             # S wrong dimensions
-            ff.infidelity(pulse, testutil.rng.randn(2, 3, 4, len(omega)),
+            ff.infidelity(pulse, rng.standard_normal((2, 3, 4, len(omega))),
                           omega)
 
         with self.assertRaises(NotImplementedError):
@@ -384,7 +385,7 @@ def test_infidelity(self):
     def test_single_qubit_error_transfer_matrix(self):
         """Test the calculation of the single-qubit transfer matrix"""
         d = 2
-        for n_dt in testutil.rng.randint(1, 11, 10):
+        for n_dt in rng.randint(1, 11, 10):
             pulse = testutil.rand_pulse_sequence(d, n_dt, 3, 2, btype='Pauli')
             omega = ff.util.get_sample_frequencies(pulse, n_samples=51)
             n_oper_identifiers = pulse.n_oper_identifiers
@@ -464,8 +465,7 @@ def test_multi_qubit_error_transfer_matrix(self):
         """Test the calculation of the multi-qubit transfer matrix"""
         n_cops = 4
         n_nops = 2
-        for d, n_dt in zip(testutil.rng.randint(3, 9, 10),
-                           testutil.rng.randint(1, 11, 10)):
+        for d, n_dt in zip(rng.randint(3, 9, 10), rng.randint(1, 11, 10)):
             f, n = np.modf(np.log2(d))
             btype = 'Pauli' if f == 0.0 else 'GGM'
             pulse = testutil.rand_pulse_sequence(d, n_dt, n_cops, n_nops,
diff --git a/tests/test_sequencing.py b/tests/test_sequencing.py
index 647dc61..2d0078a 100644
--- a/tests/test_sequencing.py
+++ b/tests/test_sequencing.py
@@ -32,6 +32,7 @@
 import filter_functions as ff
 from filter_functions import pulse_sequence, util
 from tests import testutil
+from tests.testutil import rng
 
 
 class ConcatenationTest(testutil.TestCase):
@@ -273,7 +274,7 @@ def test_concatenate_4_spin_echos(self):
         H_n_CPMG = [[util.paulis[3], np.ones_like(dt_CPMG)]]
         CPMG = ff.PulseSequence(H_c_CPMG, H_n_CPMG, dt_CPMG)
 
-        SE[testutil.rng.randint(0, len(SE)-1)].cache_filter_function(omega)
+        SE[rng.randint(0, len(SE)-1)].cache_filter_function(omega)
         CPMG.cache_filter_function(omega)
 
         CPMG_concat_1 = ff.concatenate(SE)
@@ -281,7 +282,7 @@ def test_concatenate_4_spin_echos(self):
         for se in SE:
             se.cleanup('all')
 
-        SE[testutil.rng.randint(0, len(SE)-1)].cache_filter_function(omega)
+        SE[rng.randint(0, len(SE)-1)].cache_filter_function(omega)
         CPMG_concat_2 = SE[0] @ SE[1] @ SE[2] @ SE[3]
 
         self.assertEqual(CPMG, CPMG_concat_1)
@@ -368,43 +369,43 @@ def test_concatenate_split_cnot(self):
 
     def test_different_n_opers(self):
         """Test behavior when concatenating with different n_opers."""
-        for d, n_dt in zip(testutil.rng.randint(2, 5, 20),
-                           testutil.rng.randint(1, 11, 20)):
+        for d, n_dt in zip(rng.randint(2, 5, 20),
+                           rng.randint(1, 11, 20)):
             opers = testutil.rand_herm_traceless(d, 10)
             letters = np.array(sample(list(string.ascii_letters), 10))
-            n_idx = sample(range(10), testutil.rng.randint(2, 5))
-            c_idx = sample(range(10), testutil.rng.randint(2, 5))
+            n_idx = sample(range(10), rng.randint(2, 5))
+            c_idx = sample(range(10), rng.randint(2, 5))
             n_opers = opers[n_idx]
             c_opers = opers[c_idx]
             n_coeffs = np.ones((n_opers.shape[0], n_dt))
-            n_coeffs *= np.abs(testutil.rng.randn(n_opers.shape[0], 1))
-            c_coeffs = testutil.rng.randn(c_opers.shape[0], n_dt)
-            dt = np.abs(testutil.rng.randn(n_dt))
+            n_coeffs *= np.abs(rng.standard_normal(n_opers.shape[0] + (1,)))
+            c_coeffs = rng.standard_normal(c_opers.shape[0] + (n_dt,))
+            dt = np.abs(rng.standard_normal(n_dt))
             n_ids = np.array([''.join(l) for l in letters[n_idx]])
             c_ids = np.array([''.join(l) for l in letters[c_idx]])
 
             pulse_1 = ff.PulseSequence(list(zip(c_opers, c_coeffs, c_ids)),
                                        list(zip(n_opers, n_coeffs, n_ids)),
                                        dt)
-            permutation = testutil.rng.permutation(range(n_opers.shape[0]))
+            permutation = rng.permutation(range(n_opers.shape[0]))
             pulse_2 = ff.PulseSequence(list(zip(c_opers, c_coeffs, c_ids)),
                                        list(zip(n_opers[permutation],
                                                 n_coeffs[permutation],
                                                 n_ids[permutation])),
                                        dt)
-            more_n_idx = sample(range(10), testutil.rng.randint(2, 5))
+            more_n_idx = sample(range(10), rng.randint(2, 5))
             more_n_opers = opers[more_n_idx]
             more_n_coeffs = np.ones((more_n_opers.shape[0], n_dt))
-            more_n_coeffs *= np.abs(testutil.rng.randn(
-                more_n_opers.shape[0], 1))
+            more_n_coeffs *= np.abs(rng.standard_normal(
+                more_n_opers.shape[0] + (1,)))
             more_n_ids = np.array([''.join(l) for l in letters[more_n_idx]])
             pulse_3 = ff.PulseSequence(list(zip(c_opers, c_coeffs, c_ids)),
                                        list(zip(more_n_opers, more_n_coeffs,
                                                 more_n_ids)),
                                        dt)
 
-            nontrivial_n_coeffs = np.abs(testutil.rng.randn(
-                n_opers.shape[0], n_dt))
+            nontrivial_n_coeffs = np.abs(rng.standard_normal(
+                n_opers.shape[0] + (n_dt,)))
             pulse_4 = ff.PulseSequence(list(zip(c_opers, c_coeffs, c_ids)),
                                        list(zip(more_n_opers,
                                                 nontrivial_n_coeffs,
@@ -523,7 +524,7 @@ def test_extend_with_identity(self):
         """Test extending a pulse to more qubits"""
         ID, X, Y, Z = util.paulis
         n_dt = 10
-        coeffs = testutil.rng.randn(3, n_dt)
+        coeffs = rng.standard_normal((3, n_dt))
         ids = ['X', 'Y', 'Z']
         pulse = ff.PulseSequence(
             list(zip((X, Y, Z), coeffs, ids)),
@@ -532,8 +533,8 @@ def test_extend_with_identity(self):
         )
 
         omega = util.get_sample_frequencies(pulse, spacing='log', n_samples=50)
-        for N in testutil.rng.randint(2, 5, 4):
-            for target in testutil.rng.randint(0, N-1, 2):
+        for N in rng.randint(2, 5, 4):
+            for target in rng.randint(0, N-1, 2):
                 pulse.cleanup('all')
                 ext_opers = util.tensor(*np.insert(np.tile(ID, (N-1, 3, 1, 1)),
                                                    target, (X, Y, Z), axis=0))
@@ -548,8 +549,7 @@ def test_extend_with_identity(self):
                 )
 
                 # Use custom mapping for identifiers and or labels
-                letters = testutil.rng.choice(list(string.ascii_letters),
-                                              size=(3, 5))
+                letters = rng.choice(list(string.ascii_letters), size=(3, 5))
                 mapped_ids = np.array([''.join(l) for l in letters])
                 mapping = {i: new_id for i, new_id in zip(ids, mapped_ids)}
                 ext_pulse_mapped_identifiers = ff.PulseSequence(
@@ -570,7 +570,7 @@ def test_extend_with_identity(self):
                     np.ones(n_dt), basis=ff.Basis.pauli(N)
                 )
 
-                calc_FF = testutil.rng.randint(0, 2)
+                calc_FF = rng.randint(0, 2)
                 if calc_FF:
                     # Expect things to be cached in extended pulse if original
                     # also was cached
@@ -750,7 +750,7 @@ def test_accuracy(self):
         XXX = util.tensor(X, X, X)
 
         n_dt = 10
-        coeffs = testutil.rng.randn(3, n_dt)
+        coeffs = rng.standard_normal((3, n_dt))
         X_pulse = ff.PulseSequence(
             [[X, coeffs[0], 'X']],
             list(zip((X, Y, Z), np.ones((3, n_dt)), ('X', 'Y', 'Z'))),
@@ -1054,7 +1054,7 @@ def test_caching(self):
     def test_accuracy(self):
         paulis = np.array(util.paulis)
         I, X, Y, Z = paulis
-        amps = testutil.rng.randn(testutil.rng.randint(1, 11))
+        amps = rng.standard_normal(rng.randint(1, 11))
         pulse = ff.PulseSequence(
             [[util.tensor(X, Y, Z), amps]],
             [[util.tensor(X, I, I), np.ones_like(amps), 'XII'],
@@ -1067,7 +1067,7 @@ def test_accuracy(self):
         pulse.cache_filter_function(omega)
 
         for _ in range(100):
-            order = testutil.rng.permutation(range(3))
+            order = rng.permutation(range(3))
             reordered_pulse = ff.PulseSequence(
                 [[util.tensor(*paulis[1:][order]), amps]],
                 [[util.tensor(*paulis[[1, 0, 0]][order]), np.ones_like(amps),
diff --git a/tests/test_util.py b/tests/test_util.py
index 46c9e22..871138d 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -26,6 +26,7 @@
 
 from filter_functions import PulseSequence, util
 from tests import testutil
+from tests.testutil import rng
 
 from . import qutip
 
@@ -33,12 +34,12 @@
 class UtilTest(testutil.TestCase):
 
     def test_abs2(self):
-        x = testutil.rng.randn(20, 100) + 1j*testutil.rng.randn(20, 100)
+        x = rng.standard_normal((20, 100)) + 1j*rng.standard_normal((20, 100))
         self.assertArrayAlmostEqual(np.abs(x)**2, util.abs2(x))
 
     def test_cexp(self):
         """Fast complex exponential."""
-        x = testutil.rng.randn(50, 100)
+        x = rng.standard_normal((50, 100))
         a = util.cexp(x)
         b = np.exp(1j*x)
         self.assertArrayAlmostEqual(a, b)
@@ -71,14 +72,14 @@ def test_get_indices_from_identifiers(self):
 
     def test_tensor(self):
         shapes = [(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)]
-        A = testutil.rng.randn(*shapes[0])
-        B = testutil.rng.randn(*shapes[1])
+        A = rng.standard_normal(shapes[0])
+        B = rng.standard_normal(shapes[1])
         with self.assertRaises(ValueError):
             util.tensor(A, B)
 
         shapes = [(3, 2, 1), (3, 4, 2)]
-        A = testutil.rng.randn(*shapes[0])
-        B = testutil.rng.randn(*shapes[1])
+        A = rng.standard_normal(shapes[0])
+        B = rng.standard_normal(shapes[1])
         with self.assertRaises(ValueError):
             util.tensor(A, B, rank=1)
 
@@ -86,33 +87,35 @@ def test_tensor(self):
         self.assertEqual(util.tensor(A, B, rank=3).shape, (9, 8, 2))
 
         shapes = [(10, 1, 3, 2), (10, 1, 2, 3)]
-        A = testutil.rng.randn(*shapes[0])
-        B = testutil.rng.randn(*shapes[1])
+        A = rng.standard_normal(shapes[0])
+        B = rng.standard_normal(shapes[1])
         self.assertEqual(util.tensor(A, B).shape, (10, 1, 6, 6))
 
         shapes = [(3, 5, 4, 4), (3, 5, 4, 4)]
-        A = testutil.rng.randn(*shapes[0])
-        B = testutil.rng.randn(*shapes[1])
+        A = rng.standard_normal(shapes[0])
+        B = rng.standard_normal(shapes[1])
         self.assertEqual(util.tensor(A, B).shape, (3, 5, 16, 16))
 
-        d = testutil.rng.randint(2, 9)
+        d = rng.randint(2, 9)
         eye = np.eye(d)
         for i in range(d):
             for j in range(d):
                 A, B = eye[i:i+1, :], eye[:, j:j+1]
                 self.assertArrayEqual(util.tensor(A, B, rank=1), np.kron(A, B))
 
-        i, j = testutil.rng.randint(0, 4, (2,))
+        i, j = rng.randint(0, 4, (2,))
         A, B = util.paulis[i], util.paulis[j]
         self.assertArrayEqual(util.tensor(A, B), np.kron(A, B))
 
-        args = [testutil.rng.randn(4, 1, 2), testutil.rng.randn(3, 2),
-                testutil.rng.randn(4, 3, 5)]
+        args = [rng.standard_normal((4, 1, 2)),
+                rng.standard_normal((3, 2)),
+                rng.standard_normal((4, 3, 5))]
         self.assertEqual(util.tensor(*args, rank=1).shape, (4, 3, 20))
         self.assertEqual(util.tensor(*args, rank=2).shape, (4, 9, 20))
         self.assertEqual(util.tensor(*args, rank=3).shape, (16, 9, 20))
 
-        args = [testutil.rng.randn(2, 3, 4), testutil.rng.randn(4, 3)]
+        args = [rng.standard_normal((2, 3, 4)),
+                rng.standard_normal((4, 3))]
         with self.assertRaises(ValueError) as err:
             util.tensor(*args, rank=1)
 
@@ -149,9 +152,9 @@ def test_tensor_insert(self):
         self.assertEqual(msg, str(err.exception))
 
         # Test broadcasting and rank != 2
-        A, B, C = (testutil.rng.randn(2, 3, 1, 2),
-                   testutil.rng.randn(2, 3, 1, 2),
-                   testutil.rng.randn(3, 1, 3))
+        A, B, C = (rng.standard_normal((2, 3, 1, 2)),
+                   rng.standard_normal((2, 3, 1, 2)),
+                   rng.standard_normal((3, 1, 3)))
 
         arr = util.tensor(A, C, rank=1)
         r = util.tensor_insert(arr, B, pos=1, rank=1,
@@ -167,9 +170,9 @@ def test_tensor_insert(self):
             util.tensor_insert(arr, B, pos=1, rank=1,
                                arr_dims=[[2], [2, 1]])
 
-        A, B, C = (testutil.rng.randn(2, 3, 1, 2),
-                   testutil.rng.randn(2, 3, 2, 2),
-                   testutil.rng.randn(3, 2, 1))
+        A, B, C = (rng.standard_normal((2, 3, 1, 2)),
+                   rng.standard_normal((2, 3, 2, 2)),
+                   rng.standard_normal((3, 2, 1)))
         arr = util.tensor(A, C, rank=3)
         r = util.tensor_insert(arr, B, pos=1, rank=3,
                                arr_dims=[[3, 3], [1, 2], [2, 1]])
@@ -184,15 +187,16 @@ def test_tensor_insert(self):
             util.tensor_insert(arr, B, pos=1, rank=2,
                                arr_dims=[[3, 3, 1], [1, 2], [2]])
 
-        A, B, C = (testutil.rng.randn(2, 1),
-                   testutil.rng.randn(1, 2, 3),
-                   testutil.rng.randn(1))
+        A, B, C = (rng.standard_normal((2, 1)),
+                   rng.standard_normal((1, 2, 3)),
+                   rng.standard_normal(1))
 
         arr = util.tensor(A, C, rank=1)
         r = util.tensor_insert(arr, B, pos=0, rank=1, arr_dims=[[1, 1]])
         self.assertArrayAlmostEqual(r, util.tensor(A, B, C, rank=1))
 
-        arrs, args = testutil.rng.randn(2, 2, 2), testutil.rng.randn(2, 2, 2)
+        arrs, args = [rng.standard_normal((2, 2, 2)),
+                      rng.standard_normal((2, 2, 2))]
         arr_dims = [[2, 2], [2, 2]]
 
         r = util.tensor_insert(util.tensor(*arrs), *args, pos=(0, 1),
@@ -217,8 +221,8 @@ def test_tensor_insert(self):
                                arr_dims=arr_dims)
 
         # Test exception for wrong shapes
-        arrs, args = (testutil.rng.randn(2, 4, 3, 2),
-                      testutil.rng.randn(2, 2, 3, 4))
+        arrs, args = (rng.standard_normal((2, 4, 3, 2)),
+                      rng.standard_normal((2, 2, 3, 4)))
         with self.assertRaises(ValueError) as err:
             util.tensor_insert(util.tensor(*arrs), *args, pos=(1, 2),
                                arr_dims=[[3, 3], [2, 2]])
@@ -232,12 +236,13 @@ def test_tensor_insert(self):
         self.assertEqual(cause_msg, str(err.exception.__cause__))
 
         # Do some random tests
-        for rank, n_args, n_broadcast in zip(testutil.rng.randint(1, 4, 10),
-                                             testutil.rng.randint(3, 6, 10),
-                                             testutil.rng.randint(1, 11, 10)):
-            arrs = testutil.rng.randn(n_args, n_broadcast, *[2]*rank)
-            split_idx = testutil.rng.randint(1, n_args-1)
-            ins_idx = testutil.rng.randint(split_idx-n_args, n_args-split_idx)
+        for rank, n_args, n_broadcast in zip(
+                rng.randint(1, 4, 10),
+                rng.randint(3, 6, 10),
+                rng.randint(1, 11, 10)):
+            arrs = rng.randn(n_args, n_broadcast, *[2]*rank)
+            split_idx = rng.randint(1, n_args-1)
+            ins_idx = rng.randint(split_idx-n_args, n_args-split_idx)
             ins_arrs = arrs[:split_idx]
             arr = util.tensor(*arrs[split_idx:], rank=rank)
             sorted_arrs = np.insert(arrs[split_idx:], ins_idx, ins_arrs,
@@ -249,7 +254,7 @@ def test_tensor_insert(self):
             self.assertArrayAlmostEqual(
                 r, util.tensor(*sorted_arrs, rank=rank))
 
-            pos = testutil.rng.randint(-split_idx+1, split_idx, split_idx)
+            pos = rng.randint(-split_idx+1, split_idx, split_idx)
             r = util.tensor_insert(arr, *ins_arrs, pos=pos, rank=rank,
                                    arr_dims=arr_dims)
             sorted_arrs = np.insert(arrs[split_idx:], pos, ins_arrs, axis=0)
@@ -325,8 +330,8 @@ def test_tensor_merge(self):
                               ins_dims=[[2, 3], [2, 2]])
 
         # Incompatible shapes
-        arrs, args = (testutil.rng.randn(2, 4, 3, 2),
-                      testutil.rng.randn(2, 2, 3, 4))
+        arrs, args = (rng.standard_normal((2, 4, 3, 2)),
+                      rng.standard_normal((2, 2, 3, 4)))
         with self.assertRaises(ValueError) as err:
             util.tensor_merge(util.tensor(*arrs), util.tensor(*args),
                               pos=(1, 2), arr_dims=[[3, 3], [2, 2]],
@@ -338,8 +343,8 @@ def test_tensor_merge(self):
         self.assertEqual(msg, str(err.exception))
 
         # Test rank 1 and broadcasting
-        arr = testutil.rng.randn(2, 10, 3, 4)
-        ins = testutil.rng.randn(2, 10, 3, 2)
+        arr = rng.standard_normal((2, 10, 3, 4))
+        ins = rng.standard_normal((2, 10, 3, 2))
         r = util.tensor_merge(util.tensor(*arr, rank=1),
                               util.tensor(*ins, rank=1), pos=[0, 1],
                               arr_dims=[[4, 4]], ins_dims=[[2, 2]], rank=1)
@@ -348,14 +353,15 @@ def test_tensor_merge(self):
         )
 
         # Do some random tests
-        for rank, n_args, n_broadcast in zip(testutil.rng.randint(1, 4, 10),
-                                             testutil.rng.randint(3, 6, 10),
-                                             testutil.rng.randint(1, 11, 10)):
-            arrs = testutil.rng.randn(n_args, n_broadcast, *[2]*rank)
-            split_idx = testutil.rng.randint(1, n_args-1)
+        for rank, n_args, n_broadcast in zip(
+                rng.randint(1, 4, 10),
+                rng.randint(3, 6, 10),
+                rng.randint(1, 11, 10)):
+            arrs = rng.standard_normal((n_args, n_broadcast, *[2]*rank))
+            split_idx = rng.randint(1, n_args-1)
             arr = util.tensor(*arrs[split_idx:], rank=rank)
             ins = util.tensor(*arrs[:split_idx], rank=rank)
-            pos = testutil.rng.randint(0, split_idx, split_idx)
+            pos = rng.randint(0, split_idx, split_idx)
             sorted_arrs = np.insert(arrs[split_idx:], pos, arrs[:split_idx],
                                     axis=0)
 
@@ -375,7 +381,7 @@ def test_tensor_transpose(self):
         order = np.arange(4)
 
         for _ in range(20):
-            order = testutil.rng.permutation(order)
+            order = rng.permutation(order)
             r = util.tensor_transpose(arr, order, arr_dims)
             self.assertArrayAlmostEqual(r, util.tensor(*paulis[order]))
 
@@ -409,11 +415,12 @@ def test_tensor_transpose(self):
             r = util.tensor_transpose(arr, (0., 1., 2., 3.), arr_dims)
 
         # random tests
-        for rank, n_args, n_broadcast in zip(testutil.rng.randint(1, 4, 10),
-                                             testutil.rng.randint(3, 6, 10),
-                                             testutil.rng.randint(1, 11, 10)):
-            arrs = testutil.rng.randn(n_args, n_broadcast, *[2]*rank)
-            order = testutil.rng.permutation(n_args)
+        for rank, n_args, n_broadcast in zip(
+                rng.randint(1, 4, 10),
+                rng.randint(3, 6, 10),
+                rng.randint(1, 11, 10)):
+            arrs = rng.standard_normal((n_args, n_broadcast, *[2]*rank))
+            order = rng.permutation(n_args)
             arr_dims = [[2]*n_args]*rank
 
             r = util.tensor_transpose(util.tensor(*arrs, rank=rank),
@@ -423,7 +430,7 @@ def test_tensor_transpose(self):
                 r, util.tensor(*arrs[order], rank=rank))
 
     def test_mdot(self):
-        arr = testutil.rng.randn(3, 2, 4, 4)
+        arr = rng.standard_normal((3, 2, 4, 4))
         self.assertArrayEqual(util.mdot(arr, 0), arr[0] @ arr[1] @ arr[2])
         self.assertArrayEqual(util.mdot(arr, 1), arr[:, 0] @ arr[:, 1])
 
@@ -433,8 +440,8 @@ def test_remove_float_errors(self):
             for dtype in (float, complex):
                 arr = np.zeros((10, 10), dtype=dtype)
                 arr += scale*np.finfo(arr.dtype).eps *\
-                    testutil.rng.random(arr.shape)
-                arr[testutil.rng.randint(0, 2, arr.shape, dtype=bool)] *= -1
+                    rng.random_sample(arr.shape)
+                arr[rng.randint(0, 2, arr.shape, dtype=bool)] *= -1
                 arr = util.remove_float_errors(arr, eps_scale)
                 self.assertArrayEqual(arr, np.zeros(arr.shape, dtype=dtype))
 
@@ -442,10 +449,10 @@ def test_oper_equiv(self):
         with self.assertRaises(ValueError):
             util.oper_equiv(*[np.ones((1, 2, 3))]*2)
 
-        for d in testutil.rng.randint(2, 10, (5,)):
-            psi = testutil.rng.randn(d, 1)*(1 + 1j)
+        for d in rng.randint(2, 10, (5,)):
+            psi = rng.standard_normal((d, 1)) + 1j*rng.standard_normal((d, 1))
             U = testutil.rand_herm(d).squeeze()
-            phase = testutil.rng.randn()
+            phase = rng.standard_normal()
 
             result = util.oper_equiv(psi, psi*np.exp(1j*phase))
             self.assertTrue(result[0])
@@ -483,12 +490,12 @@ def test_oper_equiv(self):
             self.assertFalse(result[0])
 
     def test_dot_HS(self):
-        U, V = testutil.rng.randint(0, 100, (2, 2, 2))
+        U, V = rng.randint(0, 100, (2, 2, 2))
         S = util.dot_HS(U, V)
         T = util.dot_HS(U, V, eps=0)
         self.assertArrayEqual(S, T)
 
-        for d in testutil.rng.randint(2, 10, (5,)):
+        for d in rng.randint(2, 10, (5,)):
             U, V = testutil.rand_herm(d, 2)
             self.assertArrayAlmostEqual(util.dot_HS(U, V),
                                         (U.conj().T @ V).trace())
@@ -498,7 +505,7 @@ def test_dot_HS(self):
             self.assertEqual(util.dot_HS(U, U + 1e-14, eps=1e-10), d)
 
     def test_all_array_equal(self):
-        for n in testutil.rng.randint(2, 10, (10,)):
+        for n in rng.randint(2, 10, (10,)):
             gen = (np.ones((10, 10)) for _ in range(n))
             lst = [np.ones((10, 10)) for _ in range(n)]
             self.assertTrue(util.all_array_equal(gen))
@@ -513,7 +520,7 @@ def test_get_sample_frequencies(self):
         pulse = PulseSequence(
             [[util.paulis[1], [np.pi/2]]],
             [[util.paulis[1], [1]]],
-            [abs(testutil.rng.randn())]
+            [abs(rng.standard_normal())]
         )
         # Default args
         omega = util.get_sample_frequencies(pulse)
@@ -540,7 +547,7 @@ def test_symmetrize_spectrum(self):
         pulse = PulseSequence(
             [[util.paulis[1], [np.pi/2]]],
             [[util.paulis[1], [1]]],
-            [abs(testutil.rng.randn())]
+            [abs(rng.standard_normal())]
         )
 
         asym_omega = util.get_sample_frequencies(pulse, symmetric=False,
@@ -605,7 +612,7 @@ def foobar(a, b, foo=None, x=2):
 class QutipCompatibilityTest(testutil.TestCase):
 
     def test_dot_HS(self):
-        for d in testutil.rng.randint(2, 10, (5,)):
+        for d in rng.randint(2, 10, (5,)):
             U = qutip.rand_herm(d)
             V = qutip.rand_herm(d)
             self.assertArrayAlmostEqual(util.dot_HS(U, V), (U.dag()*V).tr())
@@ -625,10 +632,10 @@ def test_oper_equiv(self):
         self.assertFalse(
             util.oper_equiv(qutip.rand_ket(2), qutip.rand_dm(2))[0])
 
-        for d in testutil.rng.randint(2, 10, (5,)):
+        for d in rng.randint(2, 10, (5,)):
             psi = qutip.rand_ket(d)
             U = qutip.rand_dm(d)
-            phase = testutil.rng.randn()
+            phase = rng.standard_normal()
 
             result = util.oper_equiv(psi, psi*np.exp(1j*phase))
             self.assertTrue(result[0])
diff --git a/tests/testutil.py b/tests/testutil.py
index 1ab2f0a..2e616a5 100644
--- a/tests/testutil.py
+++ b/tests/testutil.py
@@ -130,7 +130,7 @@ def cdd_even(l, t):
 
 def rand_herm(d: int, n: int = 1) -> np.ndarray:
     """n random Hermitian matrices of dimension d"""
-    A = rng.randn(n, d, d) + 1j*rng.randn(n, d, d)
+    A = rng.standard_normal((n, d, d)) + 1j*rng.standard_normal((n, d, d))
     return (A + A.conj().transpose([0, 2, 1]))/2
 
 
@@ -156,14 +156,14 @@ def rand_pulse_sequence(d: int, n_dt: int, n_cops: int = 3, n_nops: int = 3,
     c_opers = rand_herm_traceless(d, n_cops)
     n_opers = rand_herm_traceless(d, n_nops)
 
-    c_coeffs = rng.randn(n_cops, n_dt)
-    n_coeffs = rng.rand(n_nops, n_dt)
+    c_coeffs = rng.standard_normal((n_cops, n_dt))
+    n_coeffs = rng.random_sample((n_nops, n_dt))
 
     letters = np.array(list(string.ascii_letters))
     c_identifiers = rng.choice(letters, n_cops, replace=False)
     n_identifiers = rng.choice(letters, n_nops, replace=False)
 
-    dt = 1 - rng.rand(n_dt)  # (0, 1] instead of [0, 1)
+    dt = 1 - rng.random_sample(n_dt)  # (0, 1] instead of [0, 1)
     if btype == 'GGM':
         basis = Basis.ggm(d)
     else:

From ddc76bfb8573889d57e49614e0a0a7300731f767 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 16:47:53 +0200
Subject: [PATCH 17/22] Fix bug introduced in 1213e6d

---
 tests/test_sequencing.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/test_sequencing.py b/tests/test_sequencing.py
index 14391ea..1e8bd21 100644
--- a/tests/test_sequencing.py
+++ b/tests/test_sequencing.py
@@ -378,8 +378,8 @@ def test_different_n_opers(self):
             n_opers = opers[n_idx]
             c_opers = opers[c_idx]
             n_coeffs = np.ones((n_opers.shape[0], n_dt))
-            n_coeffs *= np.abs(rng.standard_normal(n_opers.shape[0] + (1,)))
-            c_coeffs = rng.standard_normal(c_opers.shape[0] + (n_dt,))
+            n_coeffs *= np.abs(rng.standard_normal((n_opers.shape[0], 1)))
+            c_coeffs = rng.standard_normal((c_opers.shape[0], n_dt))
             dt = np.abs(rng.standard_normal(n_dt))
             n_ids = np.array([''.join(l) for l in letters[n_idx]])
             c_ids = np.array([''.join(l) for l in letters[c_idx]])
@@ -397,7 +397,7 @@ def test_different_n_opers(self):
             more_n_opers = opers[more_n_idx]
             more_n_coeffs = np.ones((more_n_opers.shape[0], n_dt))
             more_n_coeffs *= np.abs(rng.standard_normal(
-                more_n_opers.shape[0] + (1,)))
+                (more_n_opers.shape[0], 1)))
             more_n_ids = np.array([''.join(l) for l in letters[more_n_idx]])
             pulse_3 = ff.PulseSequence(list(zip(c_opers, c_coeffs, c_ids)),
                                        list(zip(more_n_opers, more_n_coeffs,
@@ -405,7 +405,7 @@ def test_different_n_opers(self):
                                        dt)
 
             nontrivial_n_coeffs = np.abs(rng.standard_normal(
-                n_opers.shape[0] + (n_dt,)))
+                (n_opers.shape[0], n_dt)))
             pulse_4 = ff.PulseSequence(list(zip(c_opers, c_coeffs, c_ids)),
                                        list(zip(more_n_opers,
                                                 nontrivial_n_coeffs,

From 8a6bef1c494552a2a5cafbe8366a5e0cec8b5275 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 17:21:13 +0200
Subject: [PATCH 18/22] Skip test also if bloch_sphere_visualization extra
 installed

---
 tests/test_extras.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/test_extras.py b/tests/test_extras.py
index 47d6031..7450769 100644
--- a/tests/test_extras.py
+++ b/tests/test_extras.py
@@ -43,7 +43,8 @@ def test_fancy_progressbar_not_available(self):
         self.assertIs(tqdm, util._tqdm)
 
     @pytest.mark.skipif(
-        'plotting' in os.environ.get('INSTALL_EXTRAS', all_extras),
+        any(extra in os.environ.get('INSTALL_EXTRAS', all_extras)
+            for extra in ['plotting', 'bloch_sphere_visualization']),
         reason='Skipping tests for missing plotting extra in build with matplotlib')  # noqa
     def test_plotting_not_available(self):
         with self.assertRaises(ModuleNotFoundError):

From a6afbb9b2c14cada3d6b920c866f1aebf5ce1e48 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 17:39:53 +0200
Subject: [PATCH 19/22] Reduce build matrix complexity

---
 .travis.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 23369c4..30798f4 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -10,9 +10,8 @@ python:
   - 3.8
 env:
   - INSTALL_EXTRAS=[plotting,bloch_sphere_visualization,fancy_progressbar,tests]
-  - INSTALL_EXTRAS=[bloch_sphere_visualization,fancy_progressbar,tests]
-  - INSTALL_EXTRAS=[plotting,bloch_sphere_visualization,tests]
   - INSTALL_EXTRAS=[plotting,fancy_progressbar,tests]
+  - INSTALL_EXTRAS=[tests]
 
 #these directories are persistent
 cache: pip

From 0fd65dea3523359be6ba52532f530b83ff331a6b Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 17:40:31 +0200
Subject: [PATCH 20/22] Remove unused import

---
 filter_functions/plotting.py | 2 --
 tests/test_extras.py         | 3 +--
 tests/testutil.py            | 4 ++--
 3 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/filter_functions/plotting.py b/filter_functions/plotting.py
index e4f184b..8db7f64 100644
--- a/filter_functions/plotting.py
+++ b/filter_functions/plotting.py
@@ -192,8 +192,6 @@ def plot_bloch_vector_evolution(pulse: 'PulseSequence',
     --------
     qutip.bloch.Bloch: Qutip's Bloch sphere implementation.
     """
-    from mpl_toolkits.mplot3d import Axes3D
-
     # Raise an exception if not a one-qubit pulse
     if not pulse.d == 2:
         raise ValueError('Plotting Bloch sphere evolution only implemented ' +
diff --git a/tests/test_extras.py b/tests/test_extras.py
index 7450769..8a36486 100644
--- a/tests/test_extras.py
+++ b/tests/test_extras.py
@@ -55,8 +55,7 @@ def test_plotting_not_available(self):
         reason='Skipping tests for missing bloch sphere visualization tests in build with qutip')  # noqa
     def test_bloch_sphere_visualization_not_available(self):
 
-        with self.assertWarns(UserWarning):
-            from filter_functions import plotting
+        from filter_functions import plotting
 
         with self.assertRaises(RuntimeError):
             plotting.get_bloch_vector(testutil.rng.standard_normal((10, 2)))
diff --git a/tests/testutil.py b/tests/testutil.py
index 376bcd1..e0fc1a1 100644
--- a/tests/testutil.py
+++ b/tests/testutil.py
@@ -26,14 +26,14 @@
 from pathlib import Path
 
 import numpy as np
-from numpy.random import RandomState
+from numpy import random
 from numpy.testing import assert_allclose, assert_array_equal
 from scipy import io
 from scipy import linalg as sla
 
 from filter_functions import Basis, PulseSequence, util
 
-rng = RandomState()
+rng = random.RandomState()
 
 
 class TestCase(unittest.TestCase):

From 1bb09ae93eb336d0a5a0c24c79141a32dcbe066c Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 17:43:00 +0200
Subject: [PATCH 21/22] Fix faulty rand_pulse_sequence call

---
 tests/test_extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_extras.py b/tests/test_extras.py
index 8a36486..a04b596 100644
--- a/tests/test_extras.py
+++ b/tests/test_extras.py
@@ -65,7 +65,7 @@ def test_bloch_sphere_visualization_not_available(self):
 
         with self.assertRaises(RuntimeError):
             plotting.plot_bloch_vector_evolution(
-                testutil.rand_pulse_sequence(2))
+                testutil.rand_pulse_sequence(2, 1))
 
         from filter_functions import types
         self.assertIs(types.State, ndarray)

From b65d79dd3e0727c9a809757589becd604e01bc90 Mon Sep 17 00:00:00 2001
From: Tobias Hangleiter <tobias.hangleiter@rwth-aachen.de>
Date: Fri, 3 Jul 2020 18:08:51 +0200
Subject: [PATCH 22/22] Catch exception during test discovery

---
 tests/test_extras.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tests/test_extras.py b/tests/test_extras.py
index a04b596..1d3978d 100644
--- a/tests/test_extras.py
+++ b/tests/test_extras.py
@@ -55,7 +55,10 @@ def test_plotting_not_available(self):
         reason='Skipping tests for missing bloch sphere visualization tests in build with qutip')  # noqa
     def test_bloch_sphere_visualization_not_available(self):
 
-        from filter_functions import plotting
+        try:
+            from filter_functions import plotting
+        except ModuleNotFoundError:
+            plotting = None
 
         with self.assertRaises(RuntimeError):
             plotting.get_bloch_vector(testutil.rng.standard_normal((10, 2)))