From 61bdb5b322f6dab8c1b35b515a5f945bd19935df Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 10:00:55 -0600 Subject: [PATCH 01/15] Remove __eq__ and __hash__ on geometry objects --- openmc/cell.py | 26 -------------------------- openmc/lattice.py | 38 -------------------------------------- openmc/surface.py | 3 --- openmc/universe.py | 18 ------------------ 4 files changed, 85 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index d962c272536..5975d7f70bb 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -108,32 +108,6 @@ def __contains__(self, point): else: return point in self.region - def __eq__(self, other): - if not isinstance(other, Cell): - return False - elif self.id != other.id: - return False - elif self.name != other.name: - return False - elif self.fill != other.fill: - return False - elif self.region != other.region: - return False - elif self.rotation != other.rotation: - return False - elif self.temperature != other.temperature: - return False - elif self.translation != other.translation: - return False - else: - return True - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Cell\n' string += '{: <16}=\t{}\n'.format('\tID', self.id) diff --git a/openmc/lattice.py b/openmc/lattice.py index 3d078bd014c..79f0d43f3b4 100644 --- a/openmc/lattice.py +++ b/openmc/lattice.py @@ -517,24 +517,6 @@ def __init__(self, lattice_id=None, name=''): # Initialize Lattice class attributes self._lower_left = None - def __eq__(self, other): - if not isinstance(other, RectLattice): - return False - elif not super(RectLattice, self).__eq__(other): - return False - elif self.shape != other.shape: - return False - elif np.any(self.lower_left != other.lower_left): - return False - else: - return True - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'RectLattice\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) @@ -864,26 +846,6 @@ def __init__(self, lattice_id=None, name=''): self._num_axial = None self._center = None - def __eq__(self, other): - if not isinstance(other, HexLattice): - return False - elif not super(HexLattice, self).__eq__(other): - return False - elif self.num_rings != other.num_rings: - return False - elif self.num_axial != other.num_axial: - return False - elif self.center != other.center: - return False - else: - return True - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'HexLattice\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) diff --git a/openmc/surface.py b/openmc/surface.py index 961ac4d4592..6c29ed458cf 100644 --- a/openmc/surface.py +++ b/openmc/surface.py @@ -79,9 +79,6 @@ def __neg__(self): def __pos__(self): return Halfspace(self, '+') - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Surface\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) diff --git a/openmc/universe.py b/openmc/universe.py index cb6574f98f2..7d9f5a29f47 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -63,24 +63,6 @@ def __init__(self, universe_id=None, name='', cells=None): if cells is not None: self.add_cells(cells) - def __eq__(self, other): - if not isinstance(other, Universe): - return False - elif self.id != other.id: - return False - elif self.name != other.name: - return False - elif dict.__ne__(self.cells, other.cells): - return False - else: - return True - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Universe\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) From e2dfb5b5fb94cd9624320cf44811ecb2198a87f4 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 10:42:21 -0600 Subject: [PATCH 02/15] Remove more unnecessary eq/hash --- openmc/macroscopic.py | 17 --------------- openmc/tallies.py | 44 ++++++++++++++------------------------ openmc/tally_derivative.py | 3 --- 3 files changed, 16 insertions(+), 48 deletions(-) diff --git a/openmc/macroscopic.py b/openmc/macroscopic.py index f2521c4acfd..3a46b5225e7 100644 --- a/openmc/macroscopic.py +++ b/openmc/macroscopic.py @@ -25,23 +25,6 @@ def __init__(self, name=''): # Set the Macroscopic class attributes self.name = name - def __eq__(self, other): - if isinstance(other, Macroscopic): - if self.name != other.name: - return False - else: - return True - elif isinstance(other, string_types) and other == self.name: - return True - else: - return False - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash((self._name)) - def __repr__(self): string = 'Macroscopic - {0}\n'.format(self._name) return string diff --git a/openmc/tallies.py b/openmc/tallies.py index 3c91da8c971..de9a6657df1 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -1971,10 +1971,8 @@ def _align_tally_data(self, other, filter_product, nuclide_product, """ # Get the set of filters that each tally is missing - other_missing_filters = \ - set(self.filters).difference(set(other.filters)) - self_missing_filters = \ - set(other.filters).difference(set(self.filters)) + other_missing_filters = set(self.filters) - set(other.filters) + self_missing_filters = set(other.filters) - set(self.filters) # Add filters present in self but not in other to other for other_filter in other_missing_filters: @@ -2001,14 +1999,10 @@ def _align_tally_data(self, other, filter_product, nuclide_product, # Repeat and tile the data by nuclide in preparation for performing # the tensor product across nuclides. if nuclide_product == 'tensor': - self._mean = \ - np.repeat(self.mean, other.num_nuclides, axis=1) - self._std_dev = \ - np.repeat(self.std_dev, other.num_nuclides, axis=1) - other._mean = \ - np.tile(other.mean, (1, self.num_nuclides, 1)) - other._std_dev = \ - np.tile(other.std_dev, (1, self.num_nuclides, 1)) + self._mean = np.repeat(self.mean, other.num_nuclides, axis=1) + self._std_dev = np.repeat(self.std_dev, other.num_nuclides, axis=1) + other._mean = np.tile(other.mean, (1, self.num_nuclides, 1)) + other._std_dev = np.tile(other.std_dev, (1, self.num_nuclides, 1)) # Add nuclides to each tally such that each tally contains the complete # set of nuclides necessary to perform an entrywise product. New @@ -2016,25 +2010,21 @@ def _align_tally_data(self, other, filter_product, nuclide_product, else: # Get the set of nuclides that each tally is missing - other_missing_nuclides = \ - set(self.nuclides).difference(set(other.nuclides)) - self_missing_nuclides = \ - set(other.nuclides).difference(set(self.nuclides)) + other_missing_nuclides = set(self.nuclides) - set(other.nuclides) + self_missing_nuclides = set(other.nuclides) - set(self.nuclides) # Add nuclides present in self but not in other to other for nuclide in other_missing_nuclides: - other._mean = \ - np.insert(other.mean, other.num_nuclides, 0, axis=1) - other._std_dev = \ - np.insert(other.std_dev, other.num_nuclides, 0, axis=1) + other._mean = np.insert(other.mean, other.num_nuclides, 0, axis=1) + other._std_dev = np.insert(other.std_dev, other.num_nuclides, 0, + axis=1) other.nuclides.append(nuclide) # Add nuclides present in other but not in self to self for nuclide in self_missing_nuclides: - self._mean = \ - np.insert(self.mean, self.num_nuclides, 0, axis=1) - self._std_dev = \ - np.insert(self.std_dev, self.num_nuclides, 0, axis=1) + self._mean = np.insert(self.mean, self.num_nuclides, 0, axis=1) + self._std_dev = np.insert(self.std_dev, self.num_nuclides, 0, + axis=1) self.nuclides.append(nuclide) # Align other nuclides with self nuclides @@ -2059,10 +2049,8 @@ def _align_tally_data(self, other, filter_product, nuclide_product, else: # Get the set of scores that each tally is missing - other_missing_scores = \ - set(self.scores).difference(set(other.scores)) - self_missing_scores = \ - set(other.scores).difference(set(self.scores)) + other_missing_scores = set(self.scores) - set(other.scores) + self_missing_scores = set(other.scores) - set(self.scores) # Add scores present in self but not in other to other for score in other_missing_scores: diff --git a/openmc/tally_derivative.py b/openmc/tally_derivative.py index d6c2fab028e..facc56f447c 100644 --- a/openmc/tally_derivative.py +++ b/openmc/tally_derivative.py @@ -51,9 +51,6 @@ def __init__(self, derivative_id=None, variable=None, material=None, self.material = material self.nuclide = nuclide - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Tally Derivative\n' string += '{: <16}=\t{}\n'.format('\tID', self.id) From b771fb7ae3bc54cc2c7e4083b71ae63595a1a88f Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 11:05:52 -0600 Subject: [PATCH 03/15] Remove eq/hash on Mesh and Tally --- openmc/mesh.py | 5 +---- openmc/tallies.py | 43 ------------------------------------------- 2 files changed, 1 insertion(+), 47 deletions(-) diff --git a/openmc/mesh.py b/openmc/mesh.py index 76a33d8e2d1..b315b2628bb 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -11,7 +11,7 @@ from openmc.mixin import EqualityMixin, IDManagerMixin -class Mesh(EqualityMixin, IDManagerMixin): +class Mesh(IDManagerMixin): """A structured Cartesian mesh in one, two, or three dimensions Parameters @@ -124,9 +124,6 @@ def width(self, width): cv.check_length('mesh width', width, 1, 3) self._width = width - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Mesh\n' string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) diff --git a/openmc/tallies.py b/openmc/tallies.py index de9a6657df1..b358a14bcce 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -129,49 +129,6 @@ def __init__(self, tally_id=None, name=''): self._sp_filename = None self._results_read = False - def __eq__(self, other): - if not isinstance(other, Tally): - return False - - # Check all filters - if len(self.filters) != len(other.filters): - return False - - for self_filter in self.filters: - if self_filter not in other.filters: - return False - - # Check all nuclides - if len(self.nuclides) != len(other.nuclides): - return False - - for nuclide in self.nuclides: - if nuclide not in other.nuclides: - return False - - # Check derivatives - if self.derivative != other.derivative: - return False - - # Check all scores - if len(self.scores) != len(other.scores): - return False - - for score in self.scores: - if score not in other.scores: - return False - - if self.estimator != other.estimator: - return False - - return True - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Tally\n' string += '{: <16}=\t{}\n'.format('\tID', self.id) From 3c9e259553e8e8cec08f3076cbdc55c0094c3b74 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 12:20:38 -0600 Subject: [PATCH 04/15] Remove __eq__ and __hash__ on Material --- openmc/material.py | 27 --------------------------- 1 file changed, 27 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index 3800d89c67a..f79f7a4a10a 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -115,33 +115,6 @@ def __init__(self, material_id=None, name='', temperature=None): # If specified, this file will be used instead of composition values self._distrib_otf_file = None - def __eq__(self, other): - if not isinstance(other, Material): - return False - elif self.id != other.id: - return False - elif self.name != other.name: - return False - # FIXME: We cannot compare densities since OpenMC outputs densities - # in atom/b-cm in summary.h5 irregardless of input units, and we - # cannot compute the sum percent in Python since we lack AWR - #elif self.density != other.density: - # return False - #elif self._nuclides != other._nuclides: - # return False - #elif self._elements != other._elements: - # return False - elif self._sab != other._sab: - return False - else: - return True - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Material\n' string += '{: <16}=\t{}\n'.format('\tID', self._id) From 4d162335f98ff3d9fc4010aecc7820fc78f97286 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 14:10:05 -0600 Subject: [PATCH 05/15] Remove __eq__ and __hash__ on Element --- openmc/element.py | 23 ----------------------- 1 file changed, 23 deletions(-) diff --git a/openmc/element.py b/openmc/element.py index fc019a5d30d..48c4e664302 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -38,29 +38,6 @@ def __init__(self, name=''): # Set class attributes self.name = name - def __eq__(self, other): - if isinstance(other, Element): - if self.name != other.name: - return False - else: - return True - elif isinstance(other, string_types) and other == self.name: - return True - else: - return False - - def __ne__(self, other): - return not self == other - - def __gt__(self, other): - return repr(self) > repr(other) - - def __lt__(self, other): - return not self > other - - def __hash__(self): - return hash(repr(self)) - def __repr__(self): string = 'Element - {0}\n'.format(self._name) if self.scattering is not None: From f722d57ca352cec14ac83379522f2d265ed22587 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 17:36:59 -0600 Subject: [PATCH 06/15] Change input format for isotropic-in-lab scattering --- docs/source/io_formats/materials.rst | 23 ++- openmc/element.py | 25 +-- openmc/material.py | 32 ++-- openmc/nuclide.py | 22 +-- src/input_xml.F90 | 59 +++---- src/relaxng/materials.rnc | 4 +- src/relaxng/materials.rng | 21 +-- tests/test_iso_in_lab/inputs_true.dat | 214 ++++++++++++++------------ 8 files changed, 187 insertions(+), 213 deletions(-) diff --git a/docs/source/io_formats/materials.rst b/docs/source/io_formats/materials.rst index 5f0aa3a5e68..7875eb330d6 100644 --- a/docs/source/io_formats/materials.rst +++ b/docs/source/io_formats/materials.rst @@ -92,20 +92,8 @@ Each ``material`` element can have the following attributes or sub-elements: .. note:: If one nuclide is specified in atom percent, all others must also be given in atom percent. The same applies for weight percentages. - An optional attribute/sub-element for each nuclide is ``scattering``. This - attribute may be set to "data" to use the scattering laws specified by the - cross section library (default). Alternatively, when set to "iso-in-lab", - the scattering laws are used to sample the outgoing energy but an - isotropic-in-lab distribution is used to sample the outgoing angle at each - scattering interaction. The ``scattering`` attribute may be most useful - when using OpenMC to compute multi-group cross-sections for deterministic - transport codes and to quantify the effects of anisotropic scattering. - *Default*: None - .. note:: The ``scattering`` attribute/sub-element is not used in the - multi-group :ref:`energy_mode`. - :sab: Associates an S(a,b) table with the material. This element has an attribute/sub-element called ``name``. The ``name`` attribute @@ -119,6 +107,17 @@ Each ``material`` element can have the following attributes or sub-elements: .. note:: This element is not used in the multi-group :ref:`energy_mode`. + :isotropic: + The ``isotropic`` element indicates a list of nuclides for which elastic + scattering should be treated as though it were isotropic in the laboratory + system. This element may be most useful when using OpenMC to compute + multi-group cross-sections for deterministic transport codes and to quantify + the effects of anisotropic scattering. + + *Default*: No nuclides are treated as have isotropic elastic scattering. + + .. note:: This element is not used in the multi-group :ref:`energy_mode`. + :macroscopic: The ``macroscopic`` element is similar to the ``nuclide`` element, but, recognizes that some multi-group libraries may be providing material diff --git a/openmc/element.py b/openmc/element.py index 48c4e664302..8543500e988 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -25,51 +25,29 @@ class Element(object): ---------- name : str Chemical symbol of the element, e.g. Pu - scattering : {'data', 'iso-in-lab', None} - The type of angular scattering distribution to use """ def __init__(self, name=''): # Initialize class attributes self._name = '' - self._scattering = None # Set class attributes self.name = name def __repr__(self): - string = 'Element - {0}\n'.format(self._name) - if self.scattering is not None: - string += '{0: <16}{1}{2}\n'.format('\tscattering', '=\t', - self.scattering) - - return string + return 'Element - {0}\n'.format(self._name) @property def name(self): return self._name - @property - def scattering(self): - return self._scattering - @name.setter def name(self, name): cv.check_type('element name', name, string_types) cv.check_length('element name', name, 1, 2) self._name = name - @scattering.setter - def scattering(self, scattering): - - if not scattering in ['data', 'iso-in-lab', None]: - msg = 'Unable to set scattering for Element to {0} which ' \ - 'is not "data", "iso-in-lab", or None'.format(scattering) - raise ValueError(msg) - - self._scattering = scattering - def expand(self, percent, percent_type, enrichment=None, cross_sections=None): """Expand natural element into its naturally-occurring isotopes. @@ -239,7 +217,6 @@ def expand(self, percent, percent_type, enrichment=None, isotopes = [] for nuclide, abundance in abundances.items(): nuc = openmc.Nuclide(nuclide) - nuc.scattering = self.scattering isotopes.append((nuc, percent * abundance, percent_type)) return isotopes diff --git a/openmc/material.py b/openmc/material.py index f79f7a4a10a..00f5981b83d 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -63,6 +63,9 @@ class Material(IDManagerMixin): List in which each item is a 3-tuple consisting of an :class:`openmc.Nuclide` instance, the percent density, and the percent type ('ao' or 'wo'). + isotropic : list of str + Nuclides for which elastic scattering should be treated as though it + were isotropic in the laboratory system. average_molar_mass : float The average molar mass of nuclides in the material in units of grams per mol. For example, UO2 with 3 nuclides will have an average molar mass @@ -95,6 +98,7 @@ def __init__(self, material_id=None, name='', temperature=None): self._num_instances = None self._volume = None self._atoms = {} + self._isotropic = [] # A list of tuples (nuclide, percent, percent type) self._nuclides = [] @@ -194,6 +198,10 @@ def elements(self): def nuclides(self): return self._nuclides + @property + def isotropic(self): + return self._isotropic + @property def convert_to_distrib_comps(self): return self._convert_to_distrib_comps @@ -254,6 +262,12 @@ def volume(self, volume): cv.check_type('material volume', volume, Real) self._volume = volume + @isotropic.setter + def isotropic(self, isotropic): + cv.check_iterable_type('Isotropic scattering nuclides', isotropic, + string_types) + self._isotropic = list(isotropic) + @classmethod def from_hdf5(cls, group): """Create material from HDF5 group @@ -629,10 +643,10 @@ def add_s_alpha_beta(self, name, fraction=1.0): self._sab.append((new_name, fraction)) def make_isotropic_in_lab(self): - for nuclide, percent, percent_type in self._nuclides: - nuclide.scattering = 'iso-in-lab' - for element, percent, percent_type, enrichment in self._elements: - element.scattering = 'iso-in-lab' + self.isotropic = [x[0].name for x in self._nuclides] + if self._elements: + raise NotImplementedError( + 'Isotropic-in-lab scattering on elements is not supported.') def get_nuclides(self): """Returns all nuclides in the material @@ -650,7 +664,6 @@ def get_nuclides(self): nuclides.append(nuclide.name) for ele, ele_pct, ele_pct_type, enr in self._elements: - # Expand natural element into isotopes isotopes = ele.expand(ele_pct, ele_pct_type, enr) for iso, iso_pct, iso_pct_type in isotopes: @@ -808,9 +821,6 @@ def _get_nuclide_xml(self, nuclide, distrib=False): else: xml_element.set("wo", str(nuclide[1])) - if not nuclide[0].scattering is None: - xml_element.set("scattering", nuclide[0].scattering) - return xml_element def _get_macroscopic_xml(self, macroscopic): @@ -951,13 +961,17 @@ def to_xml_element(self, cross_sections=None): subsubelement = self._get_macroscopic_xml(self._macroscopic) subelement.append(subsubelement) - if len(self._sab) > 0: + if self._sab: for sab in self._sab: subelement = ET.SubElement(element, "sab") subelement.set("name", sab[0]) if sab[1] != 1.0: subelement.set("fraction", str(sab[1])) + if self._isotropic: + subelement = ET.SubElement(element, "isotropic") + subelement.text = ' '.join(self._isotropic) + return element diff --git a/openmc/nuclide.py b/openmc/nuclide.py index fc0d7ef07f1..d1f0c1f298b 100644 --- a/openmc/nuclide.py +++ b/openmc/nuclide.py @@ -17,15 +17,12 @@ class Nuclide(object): ---------- name : str Name of the nuclide, e.g. 'U235' - scattering : 'data' or 'iso-in-lab' or None - The type of angular scattering distribution to use """ def __init__(self, name=''): # Initialize class attributes self._name = '' - self._scattering = None # Set the Material class attributes self.name = name @@ -54,20 +51,12 @@ def __hash__(self): return hash(repr(self)) def __repr__(self): - string = 'Nuclide - {0}\n'.format(self._name) - if self.scattering is not None: - string += '{0: <16}{1}{2}\n'.format('\tscattering', '=\t', - self.scattering) - return string + return 'Nuclide - {0}\n'.format(self._name) @property def name(self): return self._name - @property - def scattering(self): - return self._scattering - @name.setter def name(self, name): cv.check_type('name', name, string_types) @@ -82,12 +71,3 @@ def name(self, name): msg = 'OpenMC nuclides follow the GND naming convention. Nuclide ' \ '"{}" is being renamed as "{}".'.format(name, self._name) warnings.warn(msg) - - @scattering.setter - def scattering(self, scattering): - if not scattering in ['data', 'iso-in-lab', None]: - msg = 'Unable to set scattering for Nuclide to {0} which ' \ - 'is not "data", "iso-in-lab", or None'.format(scattering) - raise ValueError(msg) - - self._scattering = scattering diff --git a/src/input_xml.F90 b/src/input_xml.F90 index c76d66df7f4..cf520757213 100644 --- a/src/input_xml.F90 +++ b/src/input_xml.F90 @@ -2025,6 +2025,7 @@ subroutine read_materials_xml(material_temps) integer :: i ! loop index for materials integer :: j ! loop index for nuclides + integer :: k ! loop index integer :: n ! number of nuclides integer :: n_sab ! number of sab tables for a material integer :: i_library ! index in libraries array @@ -2035,11 +2036,12 @@ subroutine read_materials_xml(material_temps) character(MAX_WORD_LEN) :: units ! units on density character(MAX_LINE_LEN) :: filename ! absolute path to materials.xml character(MAX_LINE_LEN) :: temp_str ! temporary string when reading + character(MAX_WORD_LEN), allocatable :: sarray(:) real(8) :: val ! value entered for density real(8) :: temp_dble ! temporary double prec. real logical :: sum_density ! density is sum of nuclide densities type(VectorChar) :: names ! temporary list of nuclide names - type(VectorInt) :: list_iso_lab ! temporary list of isotropic lab scatterers + type(VectorChar) :: list_iso_lab ! temporary list of isotropic lab scatterers type(VectorReal) :: densities ! temporary list of nuclide densities type(Material), pointer :: mat => null() type(XMLDocument) :: doc @@ -2246,23 +2248,6 @@ subroutine read_materials_xml(material_temps) // trim(to_str(mat % id))) end if - ! Check enforced isotropic lab scattering - if (run_CE) then - if (check_for_node(node_nuc, "scattering")) then - call get_node_value(node_nuc, "scattering", temp_str) - if (adjustl(to_lower(temp_str)) == "iso-in-lab") then - call list_iso_lab % push_back(1) - else if (adjustl(to_lower(temp_str)) == "data") then - call list_iso_lab % push_back(0) - else - call fatal_error("Scattering must be isotropic in lab or follow& - & the ACE file data") - end if - else - call list_iso_lab % push_back(0) - end if - end if - ! store nuclide name call get_node_value(node_nuc, "name", name) name = trim(name) @@ -2297,6 +2282,19 @@ subroutine read_materials_xml(material_temps) end do INDIVIDUAL_NUCLIDES end if + ! ======================================================================= + ! READ AND PARSE element + + if (check_for_node(node_mat, "isotropic")) then + n = node_word_count(node_mat, "isotropic") + allocate(sarray(n)) + call get_node_array(node_mat, "isotropic", sarray) + do j = 1, n + call list_iso_lab % push_back(sarray(j)) + end do + deallocate(sarray) + end if + ! ======================================================================== ! COPY NUCLIDES TO ARRAYS IN MATERIAL @@ -2306,7 +2304,6 @@ subroutine read_materials_xml(material_temps) allocate(mat % names(n)) allocate(mat % nuclide(n)) allocate(mat % atom_density(n)) - allocate(mat % p0(n)) ALL_NUCLIDES: do j = 1, mat % n_nuclides ! Check that this nuclide is listed in the cross_sections.xml file @@ -2340,17 +2337,23 @@ subroutine read_materials_xml(material_temps) mat % names(j) = name mat % atom_density(j) = densities % data(j) - ! Cast integer isotropic lab scattering flag to boolean - if (run_CE) then - if (list_iso_lab % data(j) == 1) then - mat % p0(j) = .true. - else - mat % p0(j) = .false. - end if - end if - end do ALL_NUCLIDES + if (run_CE) then + ! By default, isotropic-in-lab is not used + allocate(mat % p0(n)) + mat % p0(:) = .false. + + ! Apply isotropic-in-lab treatment to specified nuclides + do j = 1, list_iso_lab % size() + do k = 1, n + if (names % data(k) == list_iso_lab % data(j)) then + mat % p0(k) = .true. + end if + end do + end do + end if + ! Check to make sure either all atom percents or all weight percents are ! given if (.not. (all(mat % atom_density >= ZERO) .or. & diff --git a/src/relaxng/materials.rnc b/src/relaxng/materials.rnc index d55656536b7..c7e0fbf8633 100644 --- a/src/relaxng/materials.rnc +++ b/src/relaxng/materials.rnc @@ -14,14 +14,14 @@ element materials { element nuclide { (element name { xsd:string } | attribute name { xsd:string }) & - (element scattering { ( "data" | "iso-in-lab" ) } | - attribute scattering { ( "data" | "iso-in-lab" ) })? & ( (element ao { xsd:double } | attribute ao { xsd:double }) | (element wo { xsd:double } | attribute wo { xsd:double }) ) }* & + element isotropic { xsd:string }? & + element macroscopic { (element name { xsd:string } | attribute name { xsd:string }) diff --git a/src/relaxng/materials.rng b/src/relaxng/materials.rng index 0fa81c11b22..342260be8ed 100644 --- a/src/relaxng/materials.rng +++ b/src/relaxng/materials.rng @@ -68,22 +68,6 @@ - - - - - data - iso-in-lab - - - - - data - iso-in-lab - - - - @@ -105,6 +89,11 @@ + + + + + diff --git a/tests/test_iso_in_lab/inputs_true.dat b/tests/test_iso_in_lab/inputs_true.dat index 098056f5b0b..9e30f245f9d 100644 --- a/tests/test_iso_in_lab/inputs_true.dat +++ b/tests/test_iso_in_lab/inputs_true.dat @@ -150,149 +150,161 @@ - - - - - + + + + + + U234 U235 U238 Xe135 O16 - - - - - + + + + + + Zr90 Zr91 Zr92 Zr94 Zr96 - - - - + + + + + H1 O16 B10 B11 - - - - + + + + + H1 O16 B10 B11 - - - - - - - - - - + + + + + + + + + + + Fe54 Fe56 Fe57 Fe58 Ni58 Ni60 Mn55 Cr52 C0 Cu63 - - - - - - - - - - - + + + + + + + + + + + + H1 O16 B10 B11 Fe54 Fe56 Fe57 Fe58 Ni58 Mn55 Cr52 - - - - - - - - - - - + + + + + + + + + + + + H1 O16 B10 B11 Fe54 Fe56 Fe57 Fe58 Ni58 Mn55 Cr52 - - - - - - - - - - - + + + + + + + + + + + + H1 O16 B10 B11 Fe54 Fe56 Fe57 Fe58 Ni58 Mn55 Cr52 - - - - - - - - - - - + + + + + + + + + + + + H1 O16 B10 B11 Fe54 Fe56 Fe57 Fe58 Ni58 Mn55 Cr52 - - - - - - - - - - - + + + + + + + + + + + + H1 O16 B10 B11 Fe54 Fe56 Fe57 Fe58 Ni58 Mn55 Cr52 - - - - - - - - - + + + + + + + + + + H1 O16 B10 B11 Zr90 Zr91 Zr92 Zr94 Zr96 - - - - - - - - - + + + + + + + + + + H1 O16 B10 B11 Zr90 Zr91 Zr92 Zr94 Zr96 From 9744d173b774c710f020724d8faace5ab3f4a691 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 21:53:27 -0600 Subject: [PATCH 07/15] Don't store material % p0 if it isn't being used --- src/input_xml.F90 | 23 +++++++++++++---------- src/material_header.F90 | 9 ++------- src/physics.F90 | 29 ++++++++++++++++------------- 3 files changed, 31 insertions(+), 30 deletions(-) diff --git a/src/input_xml.F90 b/src/input_xml.F90 index cf520757213..91cd57ae299 100644 --- a/src/input_xml.F90 +++ b/src/input_xml.F90 @@ -2341,17 +2341,20 @@ subroutine read_materials_xml(material_temps) if (run_CE) then ! By default, isotropic-in-lab is not used - allocate(mat % p0(n)) - mat % p0(:) = .false. - - ! Apply isotropic-in-lab treatment to specified nuclides - do j = 1, list_iso_lab % size() - do k = 1, n - if (names % data(k) == list_iso_lab % data(j)) then - mat % p0(k) = .true. - end if + if (list_iso_lab % size() > 0) then + mat % has_isotropic_nuclides = .true. + allocate(mat % p0(n)) + mat % p0(:) = .false. + + ! Apply isotropic-in-lab treatment to specified nuclides + do j = 1, list_iso_lab % size() + do k = 1, n + if (names % data(k) == list_iso_lab % data(j)) then + mat % p0(k) = .true. + end if + end do end do - end do + end if end if ! Check to make sure either all atom percents or all weight percents are diff --git a/src/material_header.F90 b/src/material_header.F90 index eda0b423029..a3c14f8cab4 100644 --- a/src/material_header.F90 +++ b/src/material_header.F90 @@ -57,7 +57,8 @@ module material_header logical :: fissionable = .false. logical :: depletable = .false. - ! enforce isotropic scattering in lab + ! enforce isotropic scattering in lab for specific nuclides + logical :: has_isotropic_nuclides = .false. logical, allocatable :: p0(:) contains @@ -302,7 +303,6 @@ function openmc_material_add_nuclide(index, name, density) result(err) bind(C) real(8) :: awr integer, allocatable :: new_nuclide(:) real(8), allocatable :: new_density(:) - logical, allocatable :: new_p0(:) character(:), allocatable :: name_ name_ = to_f_string(name) @@ -339,11 +339,6 @@ function openmc_material_add_nuclide(index, name, density) result(err) bind(C) if (n > 0) new_density(1:n) = m % atom_density call move_alloc(FROM=new_density, TO=m % atom_density) - allocate(new_p0(n + 1)) - if (n > 0) new_p0(1:n) = m % p0 - new_p0(n + 1) = .false. - call move_alloc(FROM=new_p0, TO=m % p0) - ! Append new nuclide/density k = nuclide_dict % get(to_lower(name_)) m % nuclide(n + 1) = k diff --git a/src/physics.F90 b/src/physics.F90 index cb309f93acd..fd6a0087c9d 100644 --- a/src/physics.F90 +++ b/src/physics.F90 @@ -419,19 +419,22 @@ subroutine scatter(p, i_nuclide, i_nuc_mat) ! Set event component p % event = EVENT_SCATTER - ! Sample new outgoing angle for isotropic in lab scattering - if (materials(p % material) % p0(i_nuc_mat)) then - - ! Sample isotropic-in-lab outgoing direction - uvw_new(1) = TWO * prn() - ONE - phi = TWO * PI * prn() - uvw_new(2) = cos(phi) * sqrt(ONE - uvw_new(1)*uvw_new(1)) - uvw_new(3) = sin(phi) * sqrt(ONE - uvw_new(1)*uvw_new(1)) - p % mu = dot_product(uvw_old, uvw_new) - - ! Change direction of particle - p % coord(1) % uvw = uvw_new - end if + ! Sample new outgoing angle for isotropic-in-lab scattering + associate (mat => materials(p % material)) + if (mat % has_isotropic_nuclides) then + if (materials(p % material) % p0(i_nuc_mat)) then + ! Sample isotropic-in-lab outgoing direction + uvw_new(1) = TWO * prn() - ONE + phi = TWO * PI * prn() + uvw_new(2) = cos(phi) * sqrt(ONE - uvw_new(1)*uvw_new(1)) + uvw_new(3) = sin(phi) * sqrt(ONE - uvw_new(1)*uvw_new(1)) + p % mu = dot_product(uvw_old, uvw_new) + + ! Change direction of particle + p % coord(1) % uvw = uvw_new + end if + end if + end associate end subroutine scatter From ee2f7e925f5cd3c9aa792dc0bc17f5448af05440 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 22:23:47 -0600 Subject: [PATCH 08/15] Make Nuclide, Element, and Macroscopic subclasses of str --- openmc/element.py | 31 ++++++++--------------- openmc/macroscopic.py | 22 ++++------------ openmc/nuclide.py | 58 ++++++++++--------------------------------- 3 files changed, 28 insertions(+), 83 deletions(-) diff --git a/openmc/element.py b/openmc/element.py index 8543500e988..386f9ac580b 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -10,7 +10,7 @@ from openmc.data import NATURAL_ABUNDANCE, atomic_mass -class Element(object): +class Element(str): """A natural element that auto-expands to add the isotopes of an element to a material in their natural abundance. Internally, the OpenMC Python API expands the natural element into isotopes only when the materials.xml file @@ -28,25 +28,14 @@ class Element(object): """ - def __init__(self, name=''): - # Initialize class attributes - self._name = '' - - # Set class attributes - self.name = name - - def __repr__(self): - return 'Element - {0}\n'.format(self._name) + def __new__(cls, name): + cv.check_type('element name', name, string_types) + cv.check_length('element name', name, 1, 2) + return super(Element, cls).__new__(cls, name) @property def name(self): - return self._name - - @name.setter - def name(self, name): - cv.check_type('element name', name, string_types) - cv.check_length('element name', name, 1, 2) - self._name = name + return self def expand(self, percent, percent_type, enrichment=None, cross_sections=None): @@ -93,7 +82,7 @@ def expand(self, percent, percent_type, enrichment=None, # Get the nuclides present in nature natural_nuclides = set() for nuclide in sorted(NATURAL_ABUNDANCE.keys()): - if re.match(r'{}\d+'.format(self.name), nuclide): + if re.match(r'{}\d+'.format(self), nuclide): natural_nuclides.add(nuclide) # Create dict to store the expanded nuclides and abundances @@ -113,7 +102,7 @@ def expand(self, percent, percent_type, enrichment=None, root = tree.getroot() for child in root: nuclide = child.attrib['materials'] - if re.match(r'{}\d+'.format(self.name), nuclide) and \ + if re.match(r'{}\d+'.format(self), nuclide) and \ '_m' not in nuclide: library_nuclides.add(nuclide) @@ -134,14 +123,14 @@ def expand(self, percent, percent_type, enrichment=None, # 0 nuclide is present. If so, set the abundance to 1 for this # nuclide. Else, raise an error. elif len(mutual_nuclides) == 0: - nuclide_0 = self.name + '0' + nuclide_0 = self + '0' if nuclide_0 in library_nuclides: abundances[nuclide_0] = 1.0 else: msg = 'Unable to expand element {0} because the cross '\ 'section library provided does not contain any of '\ 'the natural isotopes for that element.'\ - .format(self.name) + .format(self) raise ValueError(msg) # If some, but not all, natural nuclides are in the library, add diff --git a/openmc/macroscopic.py b/openmc/macroscopic.py index 3a46b5225e7..cdb5cbb395d 100644 --- a/openmc/macroscopic.py +++ b/openmc/macroscopic.py @@ -3,7 +3,7 @@ from openmc.checkvalue import check_type -class Macroscopic(object): +class Macroscopic(str): """A Macroscopic object that can be used in a material. Parameters @@ -18,22 +18,10 @@ class Macroscopic(object): """ - def __init__(self, name=''): - # Initialize class attributes - self._name = '' - - # Set the Macroscopic class attributes - self.name = name - - def __repr__(self): - string = 'Macroscopic - {0}\n'.format(self._name) - return string + def __new__(cls, name): + check_type('name', name, string_types) + return super(Macroscopic, cls).__new__(cls, name) @property def name(self): - return self._name - - @name.setter - def name(self, name): - check_type('name', name, string_types) - self._name = name + return self diff --git a/openmc/nuclide.py b/openmc/nuclide.py index d1f0c1f298b..2e5a8e1193c 100644 --- a/openmc/nuclide.py +++ b/openmc/nuclide.py @@ -5,7 +5,7 @@ import openmc.checkvalue as cv -class Nuclide(object): +class Nuclide(str): """A nuclide that can be used in a material. Parameters @@ -20,54 +20,22 @@ class Nuclide(object): """ - def __init__(self, name=''): + def __new__(cls, name): # Initialize class attributes - self._name = '' + orig_name = name - # Set the Material class attributes - self.name = name - - def __eq__(self, other): - if isinstance(other, Nuclide): - if self.name != other.name: - return False - else: - return True - elif isinstance(other, string_types) and other == self.name: - return True - else: - return False - - def __ne__(self, other): - return not self == other - - def __gt__(self, other): - return repr(self) > repr(other) - - def __lt__(self, other): - return not self > other + if '-' in name: + name = name.replace('-', '') + name = name.replace('Nat', '0') + if name.endswith('m'): + name = name[:-1] + '_m1' - def __hash__(self): - return hash(repr(self)) + msg = 'OpenMC nuclides follow the GND naming convention. Nuclide ' \ + '"{}" is being renamed as "{}".'.format(orig_name, name) + warnings.warn(msg) - def __repr__(self): - return 'Nuclide - {0}\n'.format(self._name) + return super(Nuclide, cls).__new__(cls, name) @property def name(self): - return self._name - - @name.setter - def name(self, name): - cv.check_type('name', name, string_types) - self._name = name - - if '-' in name: - self._name = name.replace('-', '') - self._name = self._name.replace('Nat', '0') - if self._name.endswith('m'): - self._name = self._name[:-1] + '_m1' - - msg = 'OpenMC nuclides follow the GND naming convention. Nuclide ' \ - '"{}" is being renamed as "{}".'.format(name, self._name) - warnings.warn(msg) + return self From 9c182ece342da6cb616a92e7dd5bae1c6c2d1ed3 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 22:52:43 -0600 Subject: [PATCH 09/15] Remove various uses of Nuclide.name property --- openmc/material.py | 27 +++++++++++++-------------- openmc/tallies.py | 16 +++------------- 2 files changed, 16 insertions(+), 27 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index 00f5981b83d..f94df830de0 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -136,7 +136,7 @@ def __repr__(self): string += '{: <16}\n'.format('\tNuclides') for nuclide, percent, percent_type in self._nuclides: - string += '{0: <16}'.format('\t{0.name}'.format(nuclide)) + string += '{: <16}'.format('\t{}'.format(nuclide)) string += '=\t{: <12} [{}]\n'.format(percent, percent_type) if self._macroscopic is not None: @@ -146,7 +146,7 @@ def __repr__(self): string += '{: <16}\n'.format('\tElements') for element, percent, percent_type, enr in self._elements: - string += '{0: <16}'.format('\t{0.name}'.format(element)) + string += '{: <16}'.format('\t{}'.format(element)) if enr is None: string += '=\t{: <12} [{}]\n'.format(percent, percent_type) else: @@ -510,7 +510,7 @@ def remove_macroscopic(self, macroscopic): raise ValueError(msg) # If the Material contains the Macroscopic, delete it - if macroscopic.name == self._macroscopic.name: + if macroscopic == self._macroscopic: self._macroscopic = None def add_element(self, element, percent, percent_type='ao', enrichment=None): @@ -565,10 +565,9 @@ def add_element(self, element, percent, percent_type='ao', enrichment=None): .format(self._id, enrichment) raise ValueError(msg) - elif element.name != 'U': + elif element != 'U': msg = 'Unable to use enrichment for element {} which is not ' \ - 'uranium for Material ID="{}"'.format(element.name, - self._id) + 'uranium for Material ID="{}"'.format(element, self._id) raise ValueError(msg) # Check that the enrichment is in the valid range @@ -643,7 +642,7 @@ def add_s_alpha_beta(self, name, fraction=1.0): self._sab.append((new_name, fraction)) def make_isotropic_in_lab(self): - self.isotropic = [x[0].name for x in self._nuclides] + self.isotropic = [x[0] for x in self._nuclides] if self._elements: raise NotImplementedError( 'Isotropic-in-lab scattering on elements is not supported.') @@ -661,13 +660,13 @@ def get_nuclides(self): nuclides = [] for nuclide, percent, percent_type in self._nuclides: - nuclides.append(nuclide.name) + nuclides.append(nuclide) for ele, ele_pct, ele_pct_type, enr in self._elements: # Expand natural element into isotopes isotopes = ele.expand(ele_pct, ele_pct_type, enr) for iso, iso_pct, iso_pct_type in isotopes: - nuclides.append(iso.name) + nuclides.append(iso) return nuclides @@ -685,14 +684,14 @@ def get_nuclide_densities(self): nuclides = OrderedDict() for nuclide, density, density_type in self._nuclides: - nuclides[nuclide.name] = (nuclide, density, density_type) + nuclides[nuclide] = (nuclide, density, density_type) for ele, ele_pct, ele_pct_type, enr in self._elements: # Expand natural element into isotopes isotopes = ele.expand(ele_pct, ele_pct_type, enr) for iso, iso_pct, iso_pct_type in isotopes: - nuclides[iso.name] = (iso, iso_pct, iso_pct_type) + nuclides[iso] = (iso, iso_pct, iso_pct_type) return nuclides @@ -753,7 +752,7 @@ def get_nuclide_atom_densities(self): if not percent_in_atom: for n, nuc in enumerate(nucs): nuc_densities[n] *= self.average_molar_mass / \ - openmc.data.atomic_mass(nuc.name) + openmc.data.atomic_mass(nuc) # Now that we have the atomic amounts, lets finish calculating densities sum_percent = np.sum(nuc_densities) @@ -813,7 +812,7 @@ def clone(self, memo=None): def _get_nuclide_xml(self, nuclide, distrib=False): xml_element = ET.Element("nuclide") - xml_element.set("name", nuclide[0].name) + xml_element.set("name", nuclide[0]) if not distrib: if nuclide[2] == 'ao': @@ -825,7 +824,7 @@ def _get_nuclide_xml(self, nuclide, distrib=False): def _get_macroscopic_xml(self, macroscopic): xml_element = ET.Element("macroscopic") - xml_element.set("name", macroscopic.name) + xml_element.set("name", macroscopic) return xml_element diff --git a/openmc/tallies.py b/openmc/tallies.py index b358a14bcce..2cf3589871d 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -144,10 +144,7 @@ def __repr__(self): string += '{: <16}=\t'.format('\tNuclides') for nuclide in self.nuclides: - if isinstance(nuclide, openmc.Nuclide): - string += nuclide.name + ' ' - else: - string += str(nuclide) + ' ' + string += str(nuclide) + ' ' string += '\n' @@ -1012,16 +1009,9 @@ def to_xml_element(self): subelement.text = ' '.join(str(f.id) for f in self.filters) # Optional Nuclides - if len(self.nuclides) > 0: - nuclides = '' - for nuclide in self.nuclides: - if isinstance(nuclide, openmc.Nuclide): - nuclides += '{0} '.format(nuclide.name) - else: - nuclides += '{0} '.format(nuclide) - + if self.nuclides: subelement = ET.SubElement(element, "nuclides") - subelement.text = nuclides.rstrip(' ') + subelement.text = ' '.join(str(n) for n in self.nuclides) # Scores if len(self.scores) == 0: From 2b6cd880f1d4d76446ee764433a831059fc73f92 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 29 Nov 2017 23:23:38 -0600 Subject: [PATCH 10/15] Auto-expand elements at the time they are added to a material --- openmc/element.py | 7 +- openmc/material.py | 173 +++++---------------------------- tests/test_triso/test_triso.py | 2 +- 3 files changed, 29 insertions(+), 153 deletions(-) diff --git a/openmc/element.py b/openmc/element.py index 386f9ac580b..5ba19c63c4d 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -65,8 +65,8 @@ def expand(self, percent, percent_type, enrichment=None, ------- isotopes : list Naturally-occurring isotopes of the element. Each item of the list - is a tuple consisting of an openmc.Nuclide instance and the natural - abundance of the isotope. + is a tuple consisting of a nuclide string, the atom/weight percent, + and the string 'ao' or 'wo'. Notes ----- @@ -205,7 +205,6 @@ def expand(self, percent, percent_type, enrichment=None, # Create a list of the isotopes in this element isotopes = [] for nuclide, abundance in abundances.items(): - nuc = openmc.Nuclide(nuclide) - isotopes.append((nuc, percent * abundance, percent_type)) + isotopes.append((nuclide, percent * abundance, percent_type)) return isotopes diff --git a/openmc/material.py b/openmc/material.py index f94df830de0..e22fe0b8ba6 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -55,14 +55,9 @@ class Material(IDManagerMixin): depletable : bool Indicate whether the material is depletable. This attribute can be used by downstream depletion applications. - elements : list of tuple - List in which each item is a 4-tuple consisting of an - :class:`openmc.Element` instance, the percent density, the percent - type ('ao' or 'wo'), and enrichment. nuclides : list of tuple - List in which each item is a 3-tuple consisting of an - :class:`openmc.Nuclide` instance, the percent density, and the percent - type ('ao' or 'wo'). + List in which each item is a 3-tuple consisting of a nuclide string, the + percent density, and the percent type ('ao' or 'wo'). isotropic : list of str Nuclides for which elastic scattering should be treated as though it were isotropic in the laboratory system. @@ -107,9 +102,6 @@ def __init__(self, material_id=None, name='', temperature=None): # (only one is allowed, hence this is different than _nuclides, etc) self._macroscopic = None - # A list of tuples (element, percent, percent type, enrichment) - self._elements = [] - # If specified, a list of table names self._sab = [] @@ -143,16 +135,6 @@ def __repr__(self): string += '{: <16}\n'.format('\tMacroscopic Data') string += '{: <16}'.format('\t{}'.format(self._macroscopic)) - string += '{: <16}\n'.format('\tElements') - - for element, percent, percent_type, enr in self._elements: - string += '{: <16}'.format('\t{}'.format(element)) - if enr is None: - string += '=\t{: <12} [{}]\n'.format(percent, percent_type) - else: - string += '=\t{: <12} [{}] @ {} w/o enrichment\n'\ - .format(percent, percent_type, enr) - return string @property @@ -190,10 +172,6 @@ def num_instances(self): 'the Geometry.determine_paths() method.') return self._num_instances - @property - def elements(self): - return self._elements - @property def nuclides(self): return self._nuclides @@ -387,7 +365,7 @@ def add_nuclide(self, nuclide, percent, percent_type='ao'): Parameters ---------- - nuclide : str or openmc.Nuclide + nuclide : str Nuclide to add percent : float Atom or weight percent @@ -401,9 +379,9 @@ def add_nuclide(self, nuclide, percent, percent_type='ao'): 'macroscopic data-set has already been added'.format(self._id) raise ValueError(msg) - if not isinstance(nuclide, string_types + (openmc.Nuclide,)): + if not isinstance(nuclide, string_types): msg = 'Unable to add a Nuclide to Material ID="{}" with a ' \ - 'non-Nuclide value "{}"'.format(self._id, nuclide) + 'non-string value "{}"'.format(self._id, nuclide) raise ValueError(msg) elif not isinstance(percent, Real): @@ -411,18 +389,11 @@ def add_nuclide(self, nuclide, percent, percent_type='ao'): 'non-floating point value "{}"'.format(self._id, percent) raise ValueError(msg) - elif percent_type not in ['ao', 'wo', 'at/g-cm']: + elif percent_type not in ('ao', 'wo'): msg = 'Unable to add a Nuclide to Material ID="{}" with a ' \ 'percent type "{}"'.format(self._id, percent_type) raise ValueError(msg) - if isinstance(nuclide, openmc.Nuclide): - # Copy this Nuclide to separate it from the Nuclide in - # other Materials - nuclide = deepcopy(nuclide) - else: - nuclide = openmc.Nuclide(nuclide) - self._nuclides.append((nuclide, percent, percent_type)) def remove_nuclide(self, nuclide): @@ -430,14 +401,11 @@ def remove_nuclide(self, nuclide): Parameters ---------- - nuclide : openmc.Nuclide + nuclide : str Nuclide to remove """ - cv.check_type('nuclide', nuclide, string_types + (openmc.Nuclide,)) - - if isinstance(nuclide, string_types): - nuclide = openmc.Nuclide(nuclide) + cv.check_type('nuclide', nuclide, string_types) # If the Material contains the Nuclide, delete it for nuc in self._nuclides: @@ -452,32 +420,25 @@ def add_macroscopic(self, macroscopic): Parameters ---------- - macroscopic : str or openmc.Macroscopic + macroscopic : str Macroscopic to add """ # Ensure no nuclides, elements, or sab are added since these would be # incompatible with macroscopics - if self._nuclides or self._elements or self._sab: + if self._nuclides or self._sab: msg = 'Unable to add a Macroscopic data set to Material ID="{}" ' \ 'with a macroscopic value "{}" as an incompatible data ' \ - 'member (i.e., nuclide, element, or S(a,b) table) ' \ + 'member (i.e., nuclide or S(a,b) table) ' \ 'has already been added'.format(self._id, macroscopic) raise ValueError(msg) - if not isinstance(macroscopic, string_types + (openmc.Macroscopic,)): + if not isinstance(macroscopic, string_types): msg = 'Unable to add a Macroscopic to Material ID="{}" with a ' \ - 'non-Macroscopic value "{}"'.format(self._id, macroscopic) + 'non-string value "{}"'.format(self._id, macroscopic) raise ValueError(msg) - if isinstance(macroscopic, openmc.Macroscopic): - # Copy this Macroscopic to separate it from the Macroscopic in - # other Materials - macroscopic = deepcopy(macroscopic) - else: - macroscopic = openmc.Macroscopic(macroscopic) - if self._macroscopic is None: self._macroscopic = macroscopic else: @@ -499,14 +460,14 @@ def remove_macroscopic(self, macroscopic): Parameters ---------- - macroscopic : openmc.Macroscopic + macroscopic : str Macroscopic to remove """ - if not isinstance(macroscopic, openmc.Macroscopic): + if not isinstance(macroscopic, string_types): msg = 'Unable to remove a Macroscopic "{}" in Material ID="{}" ' \ - 'since it is not a Macroscopic'.format(self._id, macroscopic) + 'since it is not a string'.format(self._id, macroscopic) raise ValueError(msg) # If the Material contains the Macroscopic, delete it @@ -518,7 +479,7 @@ def add_element(self, element, percent, percent_type='ao', enrichment=None): Parameters ---------- - element : openmc.Element or str + element : str Element to add percent : float Atom or weight percent @@ -537,9 +498,9 @@ def add_element(self, element, percent, percent_type='ao', enrichment=None): 'macroscopic data-set has already been added'.format(self._id) raise ValueError(msg) - if not isinstance(element, string_types + (openmc.Element,)): + if not isinstance(element, string_types): msg = 'Unable to add an Element to Material ID="{}" with a ' \ - 'non-Element value "{}"'.format(self._id, element) + 'non-string value "{}"'.format(self._id, element) raise ValueError(msg) if not isinstance(percent, Real): @@ -552,12 +513,6 @@ def add_element(self, element, percent, percent_type='ao', enrichment=None): 'percent type "{}"'.format(self._id, percent_type) raise ValueError(msg) - # Copy this Element to separate it from same Element in other Materials - if isinstance(element, openmc.Element): - element = deepcopy(element) - else: - element = openmc.Element(element) - if enrichment is not None: if not isinstance(enrichment, Real): msg = 'Unable to add an Element to Material ID="{}" with a ' \ @@ -583,26 +538,10 @@ def add_element(self, element, percent, percent_type='ao', enrichment=None): format(enrichment, self._id) warnings.warn(msg) - self._elements.append((element, percent, percent_type, enrichment)) - - def remove_element(self, element): - """Remove a natural element from the material - - Parameters - ---------- - element : openmc.Element - Element to remove - - """ - cv.check_type('element', element, string_types + (openmc.Element,)) - - if isinstance(element, string_types): - element = openmc.Element(element) - - # If the Material contains the Element, delete it - for elm in self._elements: - if element == elm[0]: - self._elements.remove(elm) + # Add naturally-occuring isotopes + element = openmc.Element(element) + for nuclide in element.expand(percent, percent_type, enrichment): + self._nuclides.append(nuclide) def add_s_alpha_beta(self, name, fraction=1.0): r"""Add an :math:`S(\alpha,\beta)` table to the material @@ -643,9 +582,6 @@ def add_s_alpha_beta(self, name, fraction=1.0): def make_isotropic_in_lab(self): self.isotropic = [x[0] for x in self._nuclides] - if self._elements: - raise NotImplementedError( - 'Isotropic-in-lab scattering on elements is not supported.') def get_nuclides(self): """Returns all nuclides in the material @@ -656,19 +592,7 @@ def get_nuclides(self): List of nuclide names """ - - nuclides = [] - - for nuclide, percent, percent_type in self._nuclides: - nuclides.append(nuclide) - - for ele, ele_pct, ele_pct_type, enr in self._elements: - # Expand natural element into isotopes - isotopes = ele.expand(ele_pct, ele_pct_type, enr) - for iso, iso_pct, iso_pct_type in isotopes: - nuclides.append(iso) - - return nuclides + return [x[0] for x in self._nuclides] def get_nuclide_densities(self): """Returns all nuclides in the material and their densities @@ -686,13 +610,6 @@ def get_nuclide_densities(self): for nuclide, density, density_type in self._nuclides: nuclides[nuclide] = (nuclide, density, density_type) - for ele, ele_pct, ele_pct_type, enr in self._elements: - - # Expand natural element into isotopes - isotopes = ele.expand(ele_pct, ele_pct_type, enr) - for iso, iso_pct, iso_pct_type in isotopes: - nuclides[iso] = (iso, iso_pct, iso_pct_type) - return nuclides def get_nuclide_atom_densities(self): @@ -828,37 +745,10 @@ def _get_macroscopic_xml(self, macroscopic): return xml_element - def _get_element_xml(self, element, cross_sections, distrib=False): - - # Get the nuclides in this element - nuclides = element[0].expand(element[1], element[2], element[3], - cross_sections) - - xml_elements = [] - for nuclide in nuclides: - xml_elements.append(self._get_nuclide_xml(nuclide, distrib)) - - return xml_elements - def _get_nuclides_xml(self, nuclides, distrib=False): - xml_elements = [] - for nuclide in nuclides: xml_elements.append(self._get_nuclide_xml(nuclide, distrib)) - - return xml_elements - - def _get_elements_xml(self, elements, cross_sections, distrib=False): - - xml_elements = [] - - for element in elements: - nuclide_elements = self._get_element_xml(element, cross_sections, - distrib) - for nuclide_element in nuclide_elements: - xml_elements.append(nuclide_element) - return xml_elements def to_xml_element(self, cross_sections=None): @@ -907,12 +797,6 @@ def to_xml_element(self, cross_sections=None): subelements = self._get_nuclides_xml(self._nuclides) for subelement in subelements: element.append(subelement) - - # Create element XML subelements - subelements = self._get_elements_xml(self._elements, - cross_sections) - for subelement in subelements: - element.append(subelement) else: # Create macroscopic XML subelements subelement = self._get_macroscopic_xml(self._macroscopic) @@ -922,7 +806,7 @@ def to_xml_element(self, cross_sections=None): subelement = ET.SubElement(element, "compositions") comps = [] - allnucs = self._nuclides + self._elements + allnucs = self._nuclides dist_per_type = allnucs[0][2] for nuc in allnucs: if nuc[2] != dist_per_type: @@ -948,13 +832,6 @@ def to_xml_element(self, cross_sections=None): distrib=True) for subelement_nuc in subelements: subelement.append(subelement_nuc) - - # Create element XML subelements - subelements = self._get_elements_xml(self._elements, - cross_sections, - distrib=True) - for subsubelement in subelements: - subelement.append(subsubelement) else: # Create macroscopic XML subelements subsubelement = self._get_macroscopic_xml(self._macroscopic) diff --git a/tests/test_triso/test_triso.py b/tests/test_triso/test_triso.py index 685034491f0..e5c823c01fe 100644 --- a/tests/test_triso/test_triso.py +++ b/tests/test_triso/test_triso.py @@ -36,8 +36,8 @@ def _build_inputs(self): sic = openmc.Material() sic.set_density('g/cm3', 3.20) - sic.add_element('Si', 1.0) sic.add_nuclide('C0', 1.0) + sic.add_element('Si', 1.0) opyc = openmc.Material() opyc.set_density('g/cm3', 1.87) From 9aaa9272222881ac821f2aab10ccb69007647e89 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 4 Dec 2017 21:05:58 -0600 Subject: [PATCH 11/15] Update Jupyter notebooks to remove use of openmc.Nuclide --- docs/source/conf.py | 4 +- examples/jupyter/mg-mode-part-i.ipynb | 6 +- examples/jupyter/mg-mode-part-iii.ipynb | 117 +- examples/jupyter/mgxs-part-i.ipynb | 189 ++- examples/jupyter/mgxs-part-ii.ipynb | 1505 +++++++++++----------- examples/jupyter/mgxs-part-iii.ipynb | 45 +- examples/jupyter/pincell.ipynb | 439 +++---- examples/jupyter/post-processing.ipynb | 459 +++---- examples/jupyter/tally-arithmetic.ipynb | 405 ++---- openmc/examples.py | 8 +- openmc/plotter.py | 104 +- openmc/settings.py | 2 +- openmc/tallies.py | 4 +- openmc/universe.py | 3 +- tests/test_mg_tallies/test_mg_tallies.py | 6 +- 15 files changed, 1529 insertions(+), 1767 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 5f993b1a3be..ae8bfe6b5cd 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -27,8 +27,8 @@ MOCK_MODULES = ['numpy', 'numpy.polynomial', 'numpy.polynomial.polynomial', 'numpy.ctypeslib', 'scipy', 'scipy.sparse', 'scipy.interpolate', 'scipy.integrate', 'scipy.optimize', 'scipy.special', - 'scipy.stats','h5py', 'pandas', 'uncertainties', 'openmoc', - 'openmc.data.reconstruct'] + 'scipy.stats', 'h5py', 'pandas', 'uncertainties', + 'matplotlib.pyplot','openmoc', 'openmc.data.reconstruct'] sys.modules.update((mod_name, MagicMock()) for mod_name in MOCK_MODULES) import numpy as np diff --git a/examples/jupyter/mg-mode-part-i.ipynb b/examples/jupyter/mg-mode-part-i.ipynb index cdd56db038d..7fcb3d079a2 100644 --- a/examples/jupyter/mg-mode-part-i.ipynb +++ b/examples/jupyter/mg-mode-part-i.ipynb @@ -148,11 +148,9 @@ "source": [ "To build the actual 2-D model, we will first begin by creating the `materials.xml` file.\n", "\n", - "First we need to define materials that will be used in the problem. In other notebooks, either `openmc.Nuclide`s or `openmc.Element`s were created at the equivalent stage. We can do that in multi-group mode as well. However, multi-group cross-sections are sometimes provided as macroscopic cross-sections; the C5G7 benchmark data are macroscopic. In this case, we can instead use `openmc.Macroscopic` objects to in-place of `openmc.Nuclide` or `openmc.Element` objects.\n", + "First we need to define materials that will be used in the problem. In other notebooks, either nuclides or elements were added to materials at the equivalent stage. We can do that in multi-group mode as well. However, multi-group cross-sections are sometimes provided as macroscopic cross-sections; the C5G7 benchmark data are macroscopic. In this case, we can instead use the `Material.add_macroscopic` method to specific a macroscopic object. Unlike for nuclides and elements, we do not need provide information on atom/weight percents as no number densities are needed.\n", "\n", - "`openmc.Macroscopic`, unlike `openmc.Nuclide` and `openmc.Element` objects, do not need to be provided enough information to calculate number densities, as no number densities are needed.\n", - "\n", - "When assigning `openmc.Macroscopic` objects to `openmc.Material` objects, the density can still be scaled by setting the density to a value that is not 1.0. This would be useful, for example, when slightly perturbing the density of water due to a small change in temperature (while of course ignoring any resultant spectral shift). The density of a macroscopic dataset is set to 1.0 in the `openmc.Material` object by default when an `openmc.Macroscopic` dataset is used; so we will show its use the first time and then afterwards it will not be required.\n", + "When assigning macroscopic objects to a material, the density can still be scaled by setting the density to a value that is not 1.0. This would be useful, for example, when slightly perturbing the density of water due to a small change in temperature (while of course ignoring any resultant spectral shift). The density of a macroscopic dataset is set to 1.0 in the `openmc.Material` object by default when a macroscopic dataset is used; so we will show its use the first time and then afterwards it will not be required.\n", "\n", "Aside from these differences, the following code is very similar to similar code in other OpenMC example Notebooks." ] diff --git a/examples/jupyter/mg-mode-part-iii.ipynb b/examples/jupyter/mg-mode-part-iii.ipynb index 0553891316b..c36ecd54afb 100644 --- a/examples/jupyter/mg-mode-part-iii.ipynb +++ b/examples/jupyter/mg-mode-part-iii.ipynb @@ -402,9 +402,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -692,7 +692,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mgxs/mgxs.py:3801: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", + "/home/romano/openmc/openmc/mgxs/mgxs.py:4106: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", " warnings.warn(msg)\n" ] } @@ -735,9 +735,30 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another MeshFilter instance already exists with id=1.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyoutFilter instance already exists with id=11.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another PolarFilter instance already exists with id=21.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another AzimuthalFilter instance already exists with id=22.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another MuFilter instance already exists with id=12.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=18.\n", + " warn(msg, IDWarning)\n" + ] + } + ], "source": [ "# Instantiate tally Filter\n", "mesh_filter = openmc.MeshFilter(mesh)\n", @@ -802,10 +823,10 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 966169de084fcfda3a5aaca3edc0065c8caf6bbc\n", - " Date/Time | 2017-03-09 09:18:01\n", - " OpenMP Threads | 8\n", + " Version | 0.9.0\n", + " Git SHA1 | da61fb4a55e1feaa127799ad9293a766161fbb3e\n", + " Date/Time | 2017-12-11 16:57:27\n", + " OpenMP Threads | 4\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -813,10 +834,10 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83694 +/- 0.00098\n", - " k-effective (Track-length) = 0.83663 +/- 0.00116\n", - " k-effective (Absorption) = 0.83775 +/- 0.00102\n", - " Combined k-effective = 0.83724 +/- 0.00083\n", + " k-effective (Collision) = 0.83880 +/- 0.00101\n", + " k-effective (Track-length) = 0.83917 +/- 0.00118\n", + " k-effective (Absorption) = 0.83859 +/- 0.00104\n", + " Combined k-effective = 0.83879 +/- 0.00086\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -957,12 +978,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1799: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", - " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" + "/home/romano/openmc/openmc/tallies.py:1800: RuntimeWarning: invalid value encountered in true_divide\n", + " new_tally._mean = data['self']['mean'] / data['other']['mean']\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Universe instance already exists with id=0.\n", + " warn(msg, IDWarning)\n" ] } ], @@ -1043,9 +1066,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1106,10 +1129,10 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 966169de084fcfda3a5aaca3edc0065c8caf6bbc\n", - " Date/Time | 2017-03-09 09:18:56\n", - " OpenMP Threads | 8\n", + " Version | 0.9.0\n", + " Git SHA1 | da61fb4a55e1feaa127799ad9293a766161fbb3e\n", + " Date/Time | 2017-12-11 17:00:35\n", + " OpenMP Threads | 4\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1117,10 +1140,10 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.82605 +/- 0.00103\n", - " k-effective (Track-length) = 0.82596 +/- 0.00103\n", - " k-effective (Absorption) = 0.82503 +/- 0.00075\n", - " Combined k-effective = 0.82528 +/- 0.00067\n", + " k-effective (Collision) = 0.82313 +/- 0.00100\n", + " k-effective (Track-length) = 0.82363 +/- 0.00102\n", + " k-effective (Absorption) = 0.82532 +/- 0.00076\n", + " Combined k-effective = 0.82484 +/- 0.00067\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1186,12 +1209,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1799: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", - " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" + "/home/romano/openmc/openmc/tallies.py:1800: RuntimeWarning: invalid value encountered in true_divide\n", + " new_tally._mean = data['self']['mean'] / data['other']['mean']\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Universe instance already exists with id=0.\n", + " warn(msg, IDWarning)\n" ] } ], @@ -1248,10 +1273,10 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 966169de084fcfda3a5aaca3edc0065c8caf6bbc\n", - " Date/Time | 2017-03-09 09:19:06\n", - " OpenMP Threads | 8\n", + " Version | 0.9.0\n", + " Git SHA1 | da61fb4a55e1feaa127799ad9293a766161fbb3e\n", + " Date/Time | 2017-12-11 17:00:59\n", + " OpenMP Threads | 4\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1259,10 +1284,10 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83752 +/- 0.00102\n", - " k-effective (Track-length) = 0.83708 +/- 0.00104\n", - " k-effective (Absorption) = 0.83678 +/- 0.00077\n", - " Combined k-effective = 0.83684 +/- 0.00068\n", + " k-effective (Collision) = 0.83745 +/- 0.00108\n", + " k-effective (Track-length) = 0.83712 +/- 0.00108\n", + " k-effective (Absorption) = 0.83694 +/- 0.00078\n", + " Combined k-effective = 0.83695 +/- 0.00070\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1353,8 +1378,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isotropic to CE Bias [pcm]: 1195.9\n", - "Angle to CE Bias [pcm]: 40.4\n" + "Isotropic to CE Bias [pcm]: 1394.6\n", + "Angle to CE Bias [pcm]: 183.4\n" ] } ], @@ -1389,9 +1414,9 @@ "outputs": [ { "data": { - "image/png": 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D8gPDo6PH8LkmB54FvI2SGwdT9od3+2FlczTviZT90JWUXPl7Zv6CBrBvk9MbKVdH+QPK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\nawYlaW7MV2mymLP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qZfdg+5XAjj3vdwBW9bx/AXBqVf1hbEZP6cltST4GvHGQA/lESkmSJK01j2q6\nB3AacFiSk2lupLx+XD33gYy7mX6s5jtJgP2BCUdGGc+kW5IkSf3mSdKd5CRgL5oylJXAEcAGAFX1\nYeB04Kk0o5PcArysZ9tdaHrBvzlutycm2YamNOV84DWDxGLSLUmSpLXmUU93VR04xfICXruOZZdx\n15sqqaq9704sJt2SJEnqN0+S7tnEMypJkiSNmD3dkiRJ6mdP99CZdEuSJGmteVTTPZuYdEuSJKmf\nSffQmXRLkiRpLXu6R8KkW5IkSf1MuoduJEn3llvCPvuMYs+D2/4uoyrOvG237TqCxi67dB0BXHNN\n1xHAkiVdRwCXr7dz1yEAsOPGP+02gPnYmCewaFG3MbzgBd0eH+AJT+g6gsYFAz0gbqSu2PrhXYfA\nJpt0HQH85CddR9B43OM6DmDTTTsOQF2zp1uSJElrWV4yEibdkiRJ6mfSPXQm3ZIkSepn0j10Jt2S\nJElay/KSkTDpliRJUj+T7qEz6ZYkSdJa9nSPhGdUkiRJGjF7uiVJktTPnu6hM+mWJElSP5PuoTPp\nliRJ0lrWdI+ESbckSZL6mXQPnUm3JEmS1rKneyQ8o5IkSdKIDdTTnWQJ8BHg4UABL6+q744yMEnq\nku2epAXNnu6hG7S85P3AV6rqeUk2BDYZYUySNBvY7klauEy6h27KpDvJ5sATgEMAqur3wO9HG5Yk\ndcd2T9KCZk33SAzS031/4GrgY0keCZwHvK6qbh5pZJLUHds9SQubSffQDXJG1wceDXyoqh4F3Az8\n/fiVkhyaZEWSFbfddvWQw5SkGTXtdu/qW2+d6RglaTTGerrnwjSHDBLtSmBlVX2vfX8KzZdRn6pa\nXlXLqmrZRhttM8wYJWmmTbvd22bjjWc0QEkaqa6T6YWYdFfVlcDlSR7cznoS8JORRiVJHbLdkyQN\n26Cjl/w1cGJ7B/+lwMtGF5IkzQq2e5IWrjnWizwXDJR0V9X5wLIRxyJJs4btnqQFax6NXpLkWODp\nwFVV9fAJlodmiNinArcAh1TVD9pldwA/blf936p6Zjv/fsDJwFbAD4CD2lGuJjU/zqgkSZKGp+ta\n7eHVdB8H7DvJ8v2AXdvpUOBDPct+V1W7t9Mze+a/C3hvVe0KXAe8YqBTOshKkiRJWiDm0eglVXU2\ncO0kqzwLOL4a5wBLkmy77lOTAHvT3GAP8HFg/0FO66A13ZIkSVoo5kl5yQC2By7veb+ynXcFsHGS\nFcDtwL+8WLmqAAAdlUlEQVRW1eeBrYHVVXX7uPWnZNItSZKkfnMn6V7aJsZjllfV8mlsnwnmVfvn\nTlW1Ksn9gW8k+TFwwyTrT8qkW5IkSXPVNVV1T256Xwns2PN+B2AVQFWN/XlpkrOARwGfpSlBWb/t\n7V6z/lTmzM8YSZIkzYB5VNM9gNOAg9N4HHB9VV2RZMskGzWnI0uBxwM/qaoCzgSe127/UuALgxzI\nnm5JkiT1mzvlJZNKchKwF00ZykrgCGADgKr6MHA6zXCBl9AMGTj2TIaHAv+R5E6aTup/raqxh6S9\nBTg5yb8A/wN8dJBYTLolSZK01jwap7uqDpxieQGvnWD+fwOPWMc2lwJ7TDcWk25JkiT1mydJ92xi\n0i1JkqR+Jt1D5xmVJEmSRmwkPd3rrw9bbTWKPQ/ukY/s9vgAK1ZMvc5MeNOLft11CJxw5kDjxo/U\n81e9v+sQYLvDuo6g8atNuz3+fOxBWbwY/uzPOg3h+j/Zr9PjA3zu011H0HjZVld2HQI/vaXrCOCJ\nvz+j6xDY7iFP6ToEAHLnHV2HMHfMo5ru2cTyEkmSJPUz6R46k25JkiStZU/3SJh0S5IkqZ9J99CZ\ndEuSJKmfSffQmXRLkiRpLctLRsIzKkmSJI2YPd2SJEnqZ0/30Jl0S5IkaS3LS0bCpFuSJEn9TLqH\nzqRbkiRJ/Uy6h86kW5IkSWtZXjISnlFJkiRpxAbq6U5yGXAjcAdwe1UtG2VQktQ12z1JC5o93UM3\nnfKSJ1bVNSOLRJJmH9s9SQuP5SUjYU23JEmS+pl0D92gSXcBX01SwH9U1fIRxiRJs4HtnqSFyZ7u\nkRg06X58Va1Kcm/ga0l+WlVn966Q5FDgUIDFi3cacpiSNOOm1e7ttNVWXcQoSaNh0j10A53RqlrV\n/nkVcCqwxwTrLK+qZVW17F732ma4UUrSDJtuu7fN4sUzHaIkjc56682NaQ6ZMtokmybZbOw18GTg\nglEHJkldsd2TJA3bIOUl9wFOTTK2/ier6isjjUqSumW7J2nhsqZ7JKZMuqvqUuCRMxCLJM0KtnuS\nFjyT7qFzyEBJkiStZU/3SJh0S5IkqZ9J99CZdEuSJKmfSffQeUYlSZKkETPpliRJ0lpjNd1zYZry\no+TYJFclmXDY1zSOTnJJkh8leXQ7f/ck301yYTv/hT3bHJfkl0nOb6fdBzmtlpdIkiSp3/wpLzkO\nOAY4fh3L9wN2bac9gQ+1f94CHFxVP0+yHXBekjOqanW73Zuq6pTpBGLSLUmSpLXm0eglVXV2kl0m\nWeVZwPFVVcA5SZYk2baqftazj1VJrgK2AVava0dTmR9nVJIkScPTddnIzD0Gfnvg8p73K9t5ayTZ\nA9gQ+EXP7KPaspP3JtlokAPZ0y1JkqR+c6ene2mSFT3vl1fV8mlsnwnm1ZqFybbAJ4CXVtWd7ezD\ngStpEvHlwFuAI6c6kEm3JEmS1ppb5SXXVNWye7D9SmDHnvc7AKsAkmwOfAl4W1WdM7ZCVV3Rvrwt\nyceANw5yoDlzRiVJkqQhOw04uB3F5HHA9VV1RZINgVNp6r0/07tB2/tNkgD7AxOOjDLeSHq6N9gA\ntttuFHse3IYbdnt8gH9//S+mXmkmbP+AriPgJS/pOgK4+urXdR0C27zq5V2H0PjAB7o9/iabdHv8\nUVi0CDbbrNMQbr+908MD8LLFn5l6pZnwrOd3HQFP7DoA4IQTntJ1CLzkD1/sOgQAfnzD0zs9/u9+\n1+nhp2/u9HRPKslJwF40ZSgrgSOADQCq6sPA6cBTgUtoRix5WbvpC4AnAFsnOaSdd0hVnQ+cmGQb\nmtKU84HXDBKL5SWSJElaa26Vl0yqqg6cYnkBr51g/gnACevYZu+7E4tJtyRJkvrNk6R7NjHpliRJ\nUj+T7qEz6ZYkSdJa86i8ZDbxjEqSJEkjZk+3JEmS+tnTPXQm3ZIkSVrL8pKRMOmWJElSP5PuoTPp\nliRJUj+T7qEz6ZYkSdJalpeMhEm3JEmS+pl0D51nVJIkSRqxgXu6kywCVgC/rqqnjy4kSZodbPck\nLUiWl4zEdMpLXgdcBGw+olgkabax3ZO0MJl0D91AZzTJDsDTgI+MNhxJmh1s9yQtaOutNzemOWTQ\nnu73AW8GNhthLJI0m9juSVqYLC8ZiSmT7iRPB66qqvOS7DXJeocChwJsscVOQwtQkmba3Wn3dtp6\n6xmKTpJmgEn30A1yRh8PPDPJZcDJwN5JThi/UlUtr6plVbVs0023GXKYkjSjpt3ubbO5Zd+SpHWb\nMumuqsOraoeq2gU4APhGVb1k5JFJUkds9yQtaGPlJXNhmkN8OI4kSZL6zbGEdi6YVtJdVWcBZ40k\nEkmahWz3JC1IJt1DZ0+3JEmS1nL0kpEw6ZYkSVI/k+6hM+mWJEnSWvZ0j4RnVJIkSRoxe7olSZLU\nz57uoTPpliRJ0lqWl4yESbckSZL6mXQPnUm3JEmS+pl0D51JtyRJktayvGQkPKOSJEmal5Icm+Sq\nJBesY3mSHJ3kkiQ/SvLonmUvTfLzdnppz/zHJPlxu83RSTJILCbdkiRJ6rfeenNjmtpxwL6TLN8P\n2LWdDgU+BJBkK+AIYE9gD+CIJFu223yoXXdsu8n2v8ZIyktuuw0uvXQUex7c/e7X7fEBvn7ZA7oO\nAYAnzY4wOrcNV3cdAr/9/47tOgQAtv7FhD/4Z85tt3V7/FHYdFPYc89OQ9hoo04PD8CZS5/fdQgA\nPLHrAGaJrbfuOgL4z5VP7zoEAPbbvdvjL1rU7fGnZR6Vl1TV2Ul2mWSVZwHHV1UB5yRZkmRbYC/g\na1V1LUCSrwH7JjkL2LyqvtvOPx7YH/jyVLFY0y1JkqR+8yTpHsD2wOU971e28yabv3KC+VMy6ZYk\nSVKfYqAy5dlgaZIVPe+XV9XyaWw/0QetuzF/SibdkiRJ6nPnnV1HMLBrqmrZPdh+JbBjz/sdgFXt\n/L3GzT+rnb/DBOtPacFcO5AkSdLUqpqkey5MQ3AacHA7isnjgOur6grgDODJSbZsb6B8MnBGu+zG\nJI9rRy05GPjCIAeyp1uSJEnzUpKTaHqslyZZSTMiyQYAVfVh4HTgqcAlwC3Ay9pl1yb5Z+DcdldH\njt1UCfwlzago96K5gXLKmyjBpFuSJEnjzKHykklV1YFTLC/gtetYdixwl2HHqmoF8PDpxmLSLUmS\npDXGyks0XCbdkiRJ6mPSPXwm3ZIkSepj0j18Jt2SJElaw/KS0XDIQEmSJGnE7OmWJElSH3u6h2/K\npDvJxsDZwEbt+qdU1RGjDkySumK7J2khs7xkNAbp6b4N2LuqbkqyAfDtJF+uqnNGHJskdcV2T9KC\nZtI9fFMm3e2g4Te1bzdopxplUJLUJds9SQuZPd2jMVBNd5JFwHnAA4EPVNX3RhqVJHXMdk/SQmbS\nPXwDJd1VdQewe5IlwKlJHl5VF/Suk+RQ4FCATTfdaeiBStJMmm67t9N223UQpSSNhkn38E1ryMCq\nWg2cBew7wbLlVbWsqpZtvPE2QwpPkro1aLu3zVZbzXhskqS5Y8qkO8k2bU8PSe4F/AXw01EHJkld\nsd2TtJCN1XTPhWkuGaS8ZFvg421943rAp6vqi6MNS5I6ZbsnaUGbawntXDDI6CU/Ah41A7FI0qxg\nuydpIXP0ktHwiZSSJEnqY9I9fCbdkiRJ6mPSPXzTGr1EkiRJ0vTZ0y1JkqQ1rOkeDZNuSZIk9THp\nHj6TbkmSJK1hT/domHRLkiSpj0n38Jl0S5IkqY9J9/CZdEuSJGkNy0tGwyEDJUmSpBGzp1uSJEl9\n7OkevpEk3VtsAU996ij2PLglS7o9PsCTLvmPrkMA4KY9X911CCy+7vKuQ4D73rfrCNj6Oc/oOoTG\n+97X7fHXm4cX2f7wB/jNbzoNYfEuu3R6fIAnXnta1yEAcOqpz+06BNafBd1aV17ZdQTwqj1+2HUI\nAFx04yM7Pf5cSmItLxmNWdAkSJIkaTYx6R4+k25JkiT1Mekevnl4jVeSJEl311h5yVyYBpFk3yQX\nJ7kkyd9PsHznJF9P8qMkZyXZoZ3/xCTn90y3Jtm/XXZckl/2LNt9qjjs6ZYkSdK8lGQR8AFgH2Al\ncG6S06rqJz2rvRs4vqo+nmRv4J3AQVV1JrB7u5+tgEuAr/Zs96aqOmXQWEy6JUmS1GcelZfsAVxS\nVZcCJDkZeBbQm3TvBvxt+/pM4PMT7Od5wJer6pa7G4jlJZIkSVpjjpWXLE2yomc6dNzH2R7oHUJt\nZTuv1w+BsSGPng1slmTrcescAJw0bt5RbUnKe5NsNNV5tadbkiRJfeZQT/c1VbVskuWZYF6Ne/9G\n4JgkhwBnA78Gbl+zg2Rb4BHAGT3bHA5cCWwILAfeAhw5WaAm3ZIkSeozh5LuqawEdux5vwOwqneF\nqloFPAcgyWLguVV1fc8qLwBOrao/9GxzRfvytiQfo0ncJ2XSLUmSpDXm2cNxzgV2TXI/mh7sA4AX\n9a6QZClwbVXdSdODfey4fRzYzu/dZtuquiJJgP2BC6YKxKRbkiRJfeZL0l1Vtyc5jKY0ZBFwbFVd\nmORIYEVVnQbsBbwzSdGUl7x2bPsku9D0lH9z3K5PTLINTfnK+cBrporFpFuSJEnzVlWdDpw+bt4/\n9rw+BZhw6L+quoy73nhJVe093ThMuiVJkrTGPCsvmTWmTLqT7AgcD9wXuBNYXlXvH3VgktQV2z1J\nC51J9/AN0tN9O/CGqvpBks2A85J8bdyTfCRpPrHdk7SgmXQP35RJdzskyhXt6xuTXERT2+KXj6R5\nyXZP0kJmecloTKumu72D81HA90YRjCTNNrZ7khYik+7hG/gx8O1g4Z8FXl9VN0yw/NCxR3DecMPV\nw4xRkjoxnXbv6tWrZz5ASdKcMVBPd5INaL54Tqyqz020TlUtp3kMJg94wLLxj9eUpDlluu3esoc8\nxHZP0rxgecloDDJ6SYCPAhdV1XtGH5Ikdct2T9JCZ9I9fIP0dD8eOAj4cZLz23lvbQcal6T5yHZP\n0oJm0j18g4xe8m2aR1xK0oJguydpIbO8ZDR8IqUkSZL6mHQPn0m3JEmS1rCnezQGHjJQkiRJ0t1j\nT7ckSZL62NM9fCbdkiRJWsPyktEw6ZYkSVIfk+7hM+mWJElSH5Pu4TPpliRJ0hqWl4yGo5dIkiRJ\nI2ZPtyRJkvrY0z18Jt2SJElaw/KS0TDpliRJUh+T7uEbSdK95Zbw/OfVKHY9sOtWp9PjA/xhr1d3\nHQIAi1/50q5DgA9+sOsI4DnP6ToCWL686wga55zT7fF/97tujz8KVXDHHd3GcPvt3R4fOG+X53Yd\nAgDPvurLXYfAMb/Yr+sQOOyR3+o6BM79/Z91HQIA227d7fHXm2N30Zl0D5893ZIkSVrD8pLRMOmW\nJElSH5Pu4ZtjFzskSZKkuceebkmSJK1heclomHRLkiSpj0n38FleIkmSpD533jk3pkEk2TfJxUku\nSfL3EyzfOcnXk/woyVlJduhZdkeS89vptJ7590vyvSQ/T/KpJBtOFYdJtyRJktYYKy+ZC9NUkiwC\nPgDsB+wGHJhkt3GrvRs4vqr+CDgSeGfPst9V1e7t9Mye+e8C3ltVuwLXAa+YKhaTbkmSJPXpOpke\nYk/3HsAlVXVpVf0eOBl41rh1dgO+3r4+c4LlfZIE2Bs4pZ31cWD/qQIx6ZYkSdJ8tT1wec/7le28\nXj8Exp7s9WxgsyRjj1PaOMmKJOckGUustwZWV9XYE8km2uddeCOlJEmS1phjo5csTbKi5/3yqup9\n/PNEjygf/9j0NwLHJDkEOBv4NTCWUO9UVauS3B/4RpIfAzcMsM+7MOmWJElSnzmUdF9TVcsmWb4S\n2LHn/Q7Aqt4VqmoV8ByAJIuB51bV9T3LqKpLk5wFPAr4LLAkyfptb/dd9jkRy0skSZLUp+ta7SHW\ndJ8L7NqONrIhcABwWu8KSZYmGcuJDweObedvmWSjsXWAxwM/qaqiqf1+XrvNS4EvTBXIlEl3kmOT\nXJXkgoE+miTNcbZ7khay+TR6SdsTfRhwBnAR8OmqujDJkUnGRiPZC7g4yc+A+wBHtfMfCqxI8kOa\nJPtfq+on7bK3AH+X5BKaGu+PThXLIOUlxwHHAMcPsK4kzQfHYbsnaQGbQ+UlU6qq04HTx837x57X\np7B2JJLedf4beMQ69nkpzcgoA5sy6a6qs5PsMp2dStJcZrsnaSGbYzdSzhlDq+lOcmg7pMqKq6++\neli7laRZq6/du/76rsORJM1iQxu9pB2eZTnAsmXLphw2RZLmur5278EPtt2TNG/Y0z18DhkoSZKk\nPibdw2fSLUmSpDWs6R6NQYYMPAn4LvDgJCuTvGL0YUlSd2z3JC10XQ8FOMRxumeNQUYvOXAmApGk\n2cJ2T9JCZk/3aPhESkmSJGnErOmWJElSH3u6h8+kW5IkSX1MuofPpFuSJElrWNM9GibdkiRJ6mPS\nPXwm3ZIkSVrDnu7RMOmWJElSH5Pu4XPIQEmSJGnE7OmWJElSH3u6h8+kW5IkSWtY0z0aJt2SJEnq\nY9I9fKNJum+5Bc4/fyS7HtSWt9/e6fEBuPe9u46g8fa3dx0BXHpp1xHA0Ud3HQGsWNF1BI1rr+32\n+LPh/+ewJbBoUbcxXHNNt8cHHvPw2dHu1Yb7dR0Ch1Fdh8B1q/+s6xBYtqTrCBpZfV2nx99w0R2d\nHn867OkeDXu6JUmS1Meke/gcvUSSJEkaMXu6JUmS1Mee7uEz6ZYkSdIa1nSPhkm3JEmS+ph0D59J\ntyRJktawp3s0TLolSZLUx6R7+Ey6JUmStIY93aPhkIGSJEnSiNnTLUmSpD72dA+fPd2SJEnqc+ed\nc2MaRJJ9k1yc5JIkfz/B8p2TfD3Jj5KclWSHdv7uSb6b5MJ22Qt7tjkuyS+TnN9Ou08Vhz3dkiRJ\nWmM+1XQnWQR8ANgHWAmcm+S0qvpJz2rvBo6vqo8n2Rt4J3AQcAtwcFX9PMl2wHlJzqiq1e12b6qq\nUwaNZaCe7ql+IUjSfGO7J2kh67oHe4g93XsAl1TVpVX1e+Bk4Fnj1tkN+Hr7+syx5VX1s6r6eft6\nFXAVsM3dPadTJt09vxD2a4M6MMlud/eAkjTb2e5JWsjGerrnwjSA7YHLe96vbOf1+iHw3Pb1s4HN\nkmzdu0KSPYANgV/0zD6qLTt5b5KNpgpkkJ7uQX4hSNJ8YrsnaUHrOpmeRtK9NMmKnunQcR8lE3y8\nGvf+jcCfJ/kf4M+BXwO3r9lBsi3wCeBlVTWW6h8OPAR4LLAV8JapzukgNd0T/ULYc/xK7Yc8FGCn\n+953gN1K0qw1/XbvPveZmcgkSb2uqaplkyxfCezY834HYFXvCm3pyHMAkiwGnltV17fvNwe+BLyt\nqs7p2eaK9uVtST5Gk7hPapCe7kF+IVBVy6tqWVUt22bLLQfYrSTNWtNv95YsmYGwJGlmdN2DPcTy\nknOBXZPcL8mGwAHAab0rJFmaZCwnPhw4tp2/IXAqzU2Wnxm3zbbtnwH2By6YKpBBerqn/IUgSfOM\n7Z6kBWs+jV5SVbcnOQw4A1gEHFtVFyY5ElhRVacBewHvTFLA2cBr281fADwB2DrJIe28Q6rqfODE\nJNvQdNKcD7xmqlgGSbrX/EKgqXE5AHjRQJ9UkuYm2z1JC9p8SboBqup04PRx8/6x5/UpwF2G/quq\nE4AT1rHPvacbx5RJ97p+IUz3QJI0V9juSVrI5lNP92wy0MNxJvqFIEnzme2epIXMpHv4fAy8JEmS\nNGI+Bl6SJEl97OkePpNuSZIkrWFN92iYdEuSJKmPSffwmXRLkiRpDXu6R8OkW5IkSX1MuofPpFuS\nJEl9TLqHzyEDJUmSpBGzp1uSJElrWNM9GibdkiRJ6mPSPXwm3ZIkSVrDnu7RSFUNf6fJ1cCv7sEu\nlgLXDCkcY7jnZkMcxjC/Yti5qrYZRjCzhe3eUM2GOIzBGIYdw5xp9zbaaFltt92KrsMYyGWX5byq\nWtZ1HIMYSU/3Pf1HlWRF1yfQGGZXHMZgDLOd7d78isMYjGG2xTDT7OkePkcvkSRJkkbMmm5JkiSt\nYU33aMzWpHt51wFgDL1mQxzG0DCG+Ws2nNfZEAPMjjiMoWEMjdkQw4wy6R6+kdxIKUmSpLlpgw2W\n1dKlc+NGyiuvXOA3UkqSJGnusqd7+GbdjZRJ9k1ycZJLkvx9B8c/NslVSS6Y6WP3xLBjkjOTXJTk\nwiSv6yCGjZN8P8kP2xj+aaZj6IllUZL/SfLFjo5/WZIfJzk/SWc//ZMsSXJKkp+2/zb+eIaP/+D2\nHIxNNyR5/UzGMF/Z7tnuTRBLp+1eG0PnbZ/tXnfuvHNuTHPJrCovSbII+BmwD7ASOBc4sKp+MoMx\nPAG4CTi+qh4+U8cdF8O2wLZV9YMkmwHnAfvP8HkIsGlV3ZRkA+DbwOuq6pyZiqEnlr8DlgGbV9XT\nOzj+ZcCyqup0nNgkHwe+VVUfSbIhsEl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KDHvfJ/kQ8PlBduQTKSVJkrTeAqrpHsCpwBFJPklzI+UNE+q571ZmOKHm+/nA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\nSNKcYLsnaTFz9JLR8ImUkiR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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/examples/jupyter/mgxs-part-i.ipynb b/examples/jupyter/mgxs-part-i.ipynb index 6fa0c02b14d..d09aeaa464e 100644 --- a/examples/jupyter/mgxs-part-i.ipynb +++ b/examples/jupyter/mgxs-part-i.ipynb @@ -151,7 +151,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." + "We being by creating a material for the homogeneous medium." ] }, { @@ -161,38 +161,15 @@ "collapsed": true }, "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H1')\n", - "o16 = openmc.Nuclide('O16')\n", - "u235 = openmc.Nuclide('U235')\n", - "u238 = openmc.Nuclide('U238')\n", - "zr90 = openmc.Nuclide('Zr90')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the nuclides we defined, we will now create a material for the homogeneous medium." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], "source": [ "# Instantiate a Material and register the Nuclides\n", "inf_medium = openmc.Material(name='moderator')\n", "inf_medium.set_density('g/cc', 5.)\n", - "inf_medium.add_nuclide(h1, 0.028999667)\n", - "inf_medium.add_nuclide(o16, 0.01450188)\n", - "inf_medium.add_nuclide(u235, 0.000114142)\n", - "inf_medium.add_nuclide(u238, 0.006886019)\n", - "inf_medium.add_nuclide(zr90, 0.002116053)" + "inf_medium.add_nuclide('H1', 0.028999667)\n", + "inf_medium.add_nuclide('O16', 0.01450188)\n", + "inf_medium.add_nuclide('U235', 0.000114142)\n", + "inf_medium.add_nuclide('U238', 0.006886019)\n", + "inf_medium.add_nuclide('Zr90', 0.002116053)" ] }, { @@ -204,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -224,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -246,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -271,15 +248,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Instantiate Universe\n", - "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", - "root_universe.add_cell(cell)" + "# Create root universe\n", + "root_universe = openmc.Universe(name='root universe', cells=[cell])" ] }, { @@ -291,15 +267,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry()\n", - "openmc_geometry.root_universe = root_universe\n", + "openmc_geometry = openmc.Geometry(root_universe)\n", "\n", "# Export to \"geometry.xml\"\n", "openmc_geometry.export_to_xml()" @@ -314,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -350,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -389,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -414,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -423,13 +398,13 @@ "data": { "text/plain": [ "OrderedDict([('flux', Tally\n", - " \tID =\t10000\n", + " \tID =\t1\n", " \tName =\t\n", " \tFilters =\tCellFilter, EnergyFilter\n", " \tNuclides =\ttotal \n", " \tScores =\t['flux']\n", " \tEstimator =\ttracklength), ('absorption', Tally\n", - " \tID =\t10001\n", + " \tID =\t2\n", " \tName =\t\n", " \tFilters =\tCellFilter, EnergyFilter\n", " \tNuclides =\ttotal \n", @@ -437,7 +412,7 @@ " \tEstimator =\ttracklength)])" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -455,11 +430,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n" + ] + } + ], "source": [ "# Instantiate an empty Tallies object\n", "tallies_file = openmc.Tallies()\n", @@ -486,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -523,37 +509,27 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 43b141e9ba542da8b28c078cf2df8a6777cfb2ad\n", - " Date/Time | 2017-02-28 11:52:00\n", + " Version | 0.9.0\n", + " Git SHA1 | 9b7cebf7bc34d60e0f1750c3d6cb103df11e8dc4\n", + " Date/Time | 2017-12-04 20:56:46\n", " OpenMP Threads | 4\n", "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading H1 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/H1.h5\n", - " Reading O16 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/O16.h5\n", - " Reading U235 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/U235.h5\n", - " Reading U238 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/U238.h5\n", - " Reading Zr90 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/Zr90.h5\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", + " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", + " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", + " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for H1\n", " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", - " ===========================================================================\n", " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", @@ -609,27 +585,22 @@ " 50/1 1.15798 1.16146 +/- 0.00457\n", " Creating state point statepoint.50.h5...\n", "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.0114E-01 seconds\n", - " Reading cross sections = 1.8743E-01 seconds\n", - " Total time in simulation = 9.7641E+00 seconds\n", - " Time in transport only = 9.5168E+00 seconds\n", - " Time in inactive batches = 1.2602E+00 seconds\n", - " Time in active batches = 8.5039E+00 seconds\n", - " Time synchronizing fission bank = 5.4293E-03 seconds\n", - " Sampling source sites = 4.3508E-03 seconds\n", - " SEND/RECV source sites = 9.9399E-04 seconds\n", - " Time accumulating tallies = 1.2758E-04 seconds\n", - " Total time for finalization = 3.6982E-04 seconds\n", - " Total time elapsed = 1.0075E+01 seconds\n", - " Calculation Rate (inactive) = 19838.7 neutrons/second\n", - " Calculation Rate (active) = 11759.3 neutrons/second\n", + " Total time for initialization = 4.0504E-01 seconds\n", + " Reading cross sections = 3.6457E-01 seconds\n", + " Total time in simulation = 6.3478E+00 seconds\n", + " Time in transport only = 6.0079E+00 seconds\n", + " Time in inactive batches = 8.1713E-01 seconds\n", + " Time in active batches = 5.5307E+00 seconds\n", + " Time synchronizing fission bank = 5.4640E-03 seconds\n", + " Sampling source sites = 4.0981E-03 seconds\n", + " SEND/RECV source sites = 1.2606E-03 seconds\n", + " Time accumulating tallies = 1.2030E-04 seconds\n", + " Total time for finalization = 9.6554E-04 seconds\n", + " Total time elapsed = 6.7713E+00 seconds\n", + " Calculation Rate (inactive) = 30594.8 neutrons/second\n", + " Calculation Rate (active) = 18080.8 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -647,7 +618,7 @@ "0" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -673,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -699,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -734,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -769,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -816,7 +787,7 @@ "0 1 2 total 1.292013 0.007642" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -835,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -853,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -880,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -920,7 +891,7 @@ " 2.000000e+07\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " 1.776357e-15\n", + " 7.771561e-16\n", " 0.002570\n", " \n", " \n", @@ -934,10 +905,10 @@ "\n", " score mean std. dev. \n", "0 (((total / flux) - (absorption / flux)) - (sca... -1.11e-15 1.13e-02 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... 1.78e-15 2.57e-03 " + "1 (((total / flux) - (absorption / flux)) - (sca... 7.77e-16 2.57e-03 " ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -959,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -1016,7 +987,7 @@ "1 ((absorption / flux) / (total / flux)) 1.93e-02 9.46e-05 " ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1031,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -1088,7 +1059,7 @@ "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.74e-03 " ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +1081,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1167,7 +1138,7 @@ "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.74e-03 " ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1197,7 +1168,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/examples/jupyter/mgxs-part-ii.ipynb b/examples/jupyter/mgxs-part-ii.ipynb index b038384771d..c10f55956b7 100644 --- a/examples/jupyter/mgxs-part-ii.ipynb +++ b/examples/jupyter/mgxs-part-ii.ipynb @@ -34,7 +34,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wbinventor/miniconda3/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", + "/home/romano/miniconda3/envs/python3/lib/python3.6/site-packages/matplotlib/__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", @@ -62,35 +62,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." + "First we need to define materials that will be used in the problem. We'll create three distinct materials for water, clad and fuel." ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H1')\n", - "o16 = openmc.Nuclide('O16')\n", - "u235 = openmc.Nuclide('U235')\n", - "u238 = openmc.Nuclide('U238')\n", - "zr90 = openmc.Nuclide('Zr90')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the nuclides we defined, we will now create three distinct materials for water, clad and fuel." - ] - }, - { - "cell_type": "code", - "execution_count": 3, "metadata": { "collapsed": false }, @@ -99,20 +76,20 @@ "# 1.6% enriched fuel\n", "fuel = openmc.Material(name='1.6% Fuel')\n", "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide(u235, 3.7503e-4)\n", - "fuel.add_nuclide(u238, 2.2625e-2)\n", - "fuel.add_nuclide(o16, 4.6007e-2)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", "\n", "# borated water\n", "water = openmc.Material(name='Borated Water')\n", "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide(h1, 4.9457e-2)\n", - "water.add_nuclide(o16, 2.4732e-2)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", "\n", "# zircaloy\n", "zircaloy = openmc.Material(name='Zircaloy')\n", "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide(zr90, 7.2758e-3)" + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" ] }, { @@ -124,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -146,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -174,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -211,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -236,15 +213,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry()\n", - "openmc_geometry.root_universe = root_universe\n", + "openmc_geometry = openmc.Geometry(root_universe)\n", "\n", "# Export to \"geometry.xml\"\n", "openmc_geometry.export_to_xml()" @@ -259,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -299,20 +275,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate a \"coarse\" 2-group EnergyGroups object\n", - "coarse_groups = mgxs.EnergyGroups()\n", - "coarse_groups.group_edges = np.array([0., 0.625, 20.0e6])\n", + "coarse_groups = mgxs.EnergyGroups([0., 0.625, 20.0e6])\n", "\n", "# Instantiate a \"fine\" 8-group EnergyGroups object\n", - "fine_groups = mgxs.EnergyGroups()\n", - "fine_groups.group_edges = np.array([0., 0.058, 0.14, 0.28,\n", - " 0.625, 4.0, 5.53e3, 821.0e3, 20.0e6])" + "fine_groups = mgxs.EnergyGroups([0., 0.058, 0.14, 0.28,\n", + " 0.625, 4.0, 5.53e3, 821.0e3, 20.0e6])" ] }, { @@ -324,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -355,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -379,11 +353,32 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=48.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=18.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyoutFilter instance already exists with id=3.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=40.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=43.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=13.\n", + " warn(msg, IDWarning)\n" + ] + } + ], "source": [ "# Instantiate an empty Tallies object\n", "tallies_file = openmc.Tallies()\n", @@ -415,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -452,37 +447,27 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 647bf77a57a3cc5cce24b39cb192e1b99f52e499\n", - " Date/Time | 2017-02-27 13:35:52\n", + " Version | 0.9.0\n", + " Git SHA1 | da61fb4a55e1feaa127799ad9293a766161fbb3e\n", + " Date/Time | 2017-12-11 16:37:11\n", " OpenMP Threads | 4\n", "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading U235 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/U235.h5\n", - " Reading U238 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/U238.h5\n", - " Reading O16 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/O16.h5\n", - " Reading H1 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/H1.h5\n", - " Reading Zr90 from\n", - " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/Zr90.h5\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", + " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", - " ===========================================================================\n", " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", @@ -536,7 +521,7 @@ " 48/1 1.22204 1.22140 +/- 0.00239\n", " 49/1 1.22077 1.22139 +/- 0.00232\n", " 50/1 1.23166 1.22164 +/- 0.00228\n", - " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 10052\n", + " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 58\n", " The estimated number of batches is 66\n", " Creating state point statepoint.050.h5...\n", " 51/1 1.20071 1.22113 +/- 0.00228\n", @@ -555,7 +540,7 @@ " 64/1 1.23955 1.22122 +/- 0.00200\n", " 65/1 1.21143 1.22104 +/- 0.00197\n", " 66/1 1.21791 1.22099 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 10052\n", + " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 58\n", " The estimated number of batches is 82\n", " 67/1 1.24897 1.22148 +/- 0.00196\n", " 68/1 1.22221 1.22149 +/- 0.00193\n", @@ -576,27 +561,22 @@ " Triggers satisfied for batch 82\n", " Creating state point statepoint.082.h5...\n", "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.8359E-01 seconds\n", - " Reading cross sections = 3.0552E-01 seconds\n", - " Total time in simulation = 1.4019E+02 seconds\n", - " Time in transport only = 1.3995E+02 seconds\n", - " Time in inactive batches = 8.5036E+00 seconds\n", - " Time in active batches = 1.3169E+02 seconds\n", - " Time synchronizing fission bank = 2.6010E-02 seconds\n", - " Sampling source sites = 1.8806E-02 seconds\n", - " SEND/RECV source sites = 7.0698E-03 seconds\n", - " Time accumulating tallies = 2.8324E-03 seconds\n", - " Total time for finalization = 2.2540E-02 seconds\n", - " Total time elapsed = 1.4077E+02 seconds\n", - " Calculation Rate (inactive) = 11759.7 neutrons/second\n", - " Calculation Rate (active) = 3037.46 neutrons/second\n", + " Total time for initialization = 4.1610E-01 seconds\n", + " Reading cross sections = 3.7942E-01 seconds\n", + " Total time in simulation = 1.1100E+02 seconds\n", + " Time in transport only = 1.1076E+02 seconds\n", + " Time in inactive batches = 5.8101E+00 seconds\n", + " Time in active batches = 1.0519E+02 seconds\n", + " Time synchronizing fission bank = 3.8707E-02 seconds\n", + " Sampling source sites = 2.7232E-02 seconds\n", + " SEND/RECV source sites = 1.1284E-02 seconds\n", + " Time accumulating tallies = 1.0514E-03 seconds\n", + " Total time for finalization = 1.4526E-02 seconds\n", + " Total time elapsed = 1.1150E+02 seconds\n", + " Calculation Rate (inactive) = 17211.3 neutrons/second\n", + " Calculation Rate (active) = 6844.60 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -614,7 +594,7 @@ "0" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -640,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -659,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -694,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -706,7 +686,7 @@ "Multi-Group XS\n", "\tReaction Type =\tnu-fission\n", "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", + "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [barns]:\n", " Group 1 [821000.0 - 20000000.0eV]:\t3.30e+00 +/- 2.14e-01%\n", @@ -737,7 +717,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] } @@ -756,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -768,7 +748,7 @@ "Multi-Group XS\n", "\tReaction Type =\tnu-fission\n", "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", + "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\n", " Group 1 [821000.0 - 20000000.0eV]:\t2.52e-02 +/- 2.41e-01%\n", " Group 2 [5530.0 - 821000.0 eV]:\t1.51e-03 +/- 1.31e-01%\n", @@ -798,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -807,7 +787,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, @@ -830,7 +810,7 @@ " \n", " \n", " 126\n", - " 10002\n", + " 3\n", " 1\n", " 1\n", " H1\n", @@ -839,7 +819,7 @@ " \n", " \n", " 127\n", - " 10002\n", + " 3\n", " 1\n", " 1\n", " O16\n", @@ -848,7 +828,7 @@ " \n", " \n", " 124\n", - " 10002\n", + " 3\n", " 1\n", " 2\n", " H1\n", @@ -857,7 +837,7 @@ " \n", " \n", " 125\n", - " 10002\n", + " 3\n", " 1\n", " 2\n", " O16\n", @@ -866,7 +846,7 @@ " \n", " \n", " 122\n", - " 10002\n", + " 3\n", " 1\n", " 3\n", " H1\n", @@ -875,7 +855,7 @@ " \n", " \n", " 123\n", - " 10002\n", + " 3\n", " 1\n", " 3\n", " O16\n", @@ -884,7 +864,7 @@ " \n", " \n", " 120\n", - " 10002\n", + " 3\n", " 1\n", " 4\n", " H1\n", @@ -893,7 +873,7 @@ " \n", " \n", " 121\n", - " 10002\n", + " 3\n", " 1\n", " 4\n", " O16\n", @@ -902,7 +882,7 @@ " \n", " \n", " 118\n", - " 10002\n", + " 3\n", " 1\n", " 5\n", " H1\n", @@ -911,7 +891,7 @@ " \n", " \n", " 119\n", - " 10002\n", + " 3\n", " 1\n", " 5\n", " O16\n", @@ -923,20 +903,20 @@ "" ], "text/plain": [ - " cell group in group out nuclide mean std. dev.\n", - "126 10002 1 1 H1 0.233991 0.003752\n", - "127 10002 1 1 O16 1.569288 0.006360\n", - "124 10002 1 2 H1 1.587279 0.003098\n", - "125 10002 1 2 O16 0.285599 0.001422\n", - "122 10002 1 3 H1 0.010482 0.000220\n", - "123 10002 1 3 O16 0.000000 0.000000\n", - "120 10002 1 4 H1 0.000009 0.000006\n", - "121 10002 1 4 O16 0.000000 0.000000\n", - "118 10002 1 5 H1 0.000005 0.000005\n", - "119 10002 1 5 O16 0.000000 0.000000" + " cell group in group out nuclide mean std. dev.\n", + "126 3 1 1 H1 0.233991 0.003752\n", + "127 3 1 1 O16 1.569288 0.006360\n", + "124 3 1 2 H1 1.587279 0.003098\n", + "125 3 1 2 O16 0.285599 0.001422\n", + "122 3 1 3 H1 0.010482 0.000220\n", + "123 3 1 3 O16 0.000000 0.000000\n", + "120 3 1 4 H1 0.000009 0.000006\n", + "121 3 1 4 O16 0.000000 0.000000\n", + "118 3 1 5 H1 0.000005 0.000005\n", + "119 3 1 5 O16 0.000000 0.000000" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -956,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -978,7 +958,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -990,21 +970,21 @@ "Multi-Group XS\n", "\tReaction Type =\ttransport\n", "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", + "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t7.84e-03 +/- 4.34e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t1.82e-01 +/- 1.91e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.79e-03 +/- 2.12e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.82e-01 +/- 1.92e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t2.17e-01 +/- 1.38e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t2.53e-01 +/- 2.27e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t2.17e-01 +/- 1.12e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t2.53e-01 +/- 1.89e-01%\n", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t1.45e-01 +/- 1.54e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t1.74e-01 +/- 2.60e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t1.45e-01 +/- 1.12e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.74e-01 +/- 2.03e-01%\n", "\n", "\n", "\n" @@ -1017,7 +997,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -1040,67 +1020,67 @@ " \n", " \n", " 3\n", - " 10000\n", + " 1\n", " 1\n", " U235\n", - " 20.912730\n", - " 0.090857\n", + " 20.763062\n", + " 0.044093\n", " \n", " \n", " 4\n", - " 10000\n", + " 1\n", " 1\n", " U238\n", - " 9.577234\n", - " 0.013248\n", + " 9.579086\n", + " 0.010757\n", " \n", " \n", " 5\n", - " 10000\n", + " 1\n", " 1\n", " O16\n", - " 3.158619\n", - " 0.004864\n", + " 3.157274\n", + " 0.003531\n", " \n", " \n", " 0\n", - " 10000\n", + " 1\n", " 2\n", " U235\n", - " 485.364898\n", - " 0.925632\n", + " 485.349036\n", + " 0.930937\n", " \n", " \n", " 1\n", - " 10000\n", + " 1\n", " 2\n", " U238\n", - " 11.196946\n", - " 0.025466\n", + " 11.199167\n", + " 0.021167\n", " \n", " \n", " 2\n", - " 10000\n", + " 1\n", " 2\n", " O16\n", - " 3.788841\n", - " 0.009855\n", + " 3.788383\n", + " 0.007676\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell group in nuclide mean std. dev.\n", - "3 10000 1 U235 20.912730 0.090857\n", - "4 10000 1 U238 9.577234 0.013248\n", - "5 10000 1 O16 3.158619 0.004864\n", - "0 10000 2 U235 485.364898 0.925632\n", - "1 10000 2 U238 11.196946 0.025466\n", - "2 10000 2 O16 3.788841 0.009855" + " cell group in nuclide mean std. dev.\n", + "3 1 1 U235 20.763062 0.044093\n", + "4 1 1 U238 9.579086 0.010757\n", + "5 1 1 O16 3.157274 0.003531\n", + "0 1 2 U235 485.349036 0.930937\n", + "1 1 2 U238 11.199167 0.021167\n", + "2 1 2 O16 3.788383 0.007676" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1126,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -1145,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -1154,11 +1134,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1799: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1800: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1203,7 +1183,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1214,239 +1194,239 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.423140\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.475976\tres = 5.769E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.491509\tres = 1.249E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.487503\tres = 3.263E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.484002\tres = 8.150E-03\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.477365\tres = 7.181E-03\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.469036\tres = 1.371E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.460429\tres = 1.745E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.450711\tres = 1.835E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.441506\tres = 2.111E-02\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.432127\tres = 2.042E-02\n", - "[ NORMAL ] 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@@ -1479,8 +1459,8 @@ "output_type": "stream", "text": [ "openmc keff = 1.224484\n", - "openmoc keff = 1.220510\n", - "bias [pcm]: -397.4\n" + "openmoc keff = 1.220675\n", + "bias [pcm]: -380.9\n" ] } ], @@ -1504,7 +1484,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -1513,11 +1493,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1799: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1800: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1557,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1568,347 +1548,346 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.366890\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.391200\tres = 6.331E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.393015\tres = 6.626E-02\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.381131\tres = 4.640E-03\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.375055\tres = 3.024E-02\n", - "[ NORMAL ] Iteration 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NORMAL ] Iteration 327:\tk_eff = 1.225040\tres = 1.403E-05\n", + "[ NORMAL ] Iteration 328:\tk_eff = 1.225057\tres = 1.356E-05\n", + "[ NORMAL ] Iteration 329:\tk_eff = 1.225073\tres = 1.327E-05\n", + "[ NORMAL ] Iteration 330:\tk_eff = 1.225088\tres = 1.311E-05\n", + "[ NORMAL ] Iteration 331:\tk_eff = 1.225103\tres = 1.278E-05\n", + "[ NORMAL ] Iteration 332:\tk_eff = 1.225118\tres = 1.230E-05\n", + "[ NORMAL ] Iteration 333:\tk_eff = 1.225132\tres = 1.199E-05\n", + "[ NORMAL ] Iteration 334:\tk_eff = 1.225146\tres = 1.169E-05\n", + "[ NORMAL ] Iteration 335:\tk_eff = 1.225160\tres = 1.134E-05\n", + "[ NORMAL ] Iteration 336:\tk_eff = 1.225173\tres = 1.111E-05\n", + "[ NORMAL ] Iteration 337:\tk_eff = 1.225186\tres = 1.073E-05\n", + "[ NORMAL ] Iteration 338:\tk_eff = 1.225199\tres = 1.067E-05\n", + "[ NORMAL ] Iteration 339:\tk_eff = 1.225211\tres = 1.050E-05\n" ] } ], @@ -1924,7 +1903,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1934,8 +1913,8 @@ "output_type": "stream", "text": [ "openmc keff = 1.224484\n", - "openmoc keff = 1.223146\n", - "bias [pcm]: -133.8\n" + "openmoc keff = 1.225211\n", + "bias [pcm]: 72.7\n" ] } ], @@ -1983,7 +1962,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1992,7 +1971,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wbinventor/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, @@ -2002,15 +1981,15 @@ "(1.0000000000000001e-05, 20000000.0)" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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no0Elqs/AmKYi0p/01oDdyfbHaSJCVb1A8K5lpoULvlNeujS1S8vg92vNPaa5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\n3oExzcIOO3iZOLGMceNyWb3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qukFE6gO/AQ8HOc4kCJfLxbUDO5TbVlRcEt+rnnmqhtxP/v0DzEJrD7CSgIlF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\n8qEZU309WjQAYO66itVDngf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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2019,7 +1998,7 @@ ], "source": [ "# Create a figure of the U-235 continuous-energy fission cross section \n", - "fig = openmc.plot_xs(u235, ['fission'])\n", + "fig = openmc.plot_xs('U235', ['fission'])\n", "\n", "# Get the axis to use for plotting the MGXS\n", "ax = fig.gca()\n", @@ -2054,7 +2033,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -2086,16 +2065,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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gG8CWkmaW3Z5ZJzivrarKvvPRYsCJwKaSZpTZllmnOK+tysoeqX8BWBK4KO9I6gb2kPR0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\n", + "image/png": 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6esYVH9u/ZHxlbe3EryprK7mp4vaap7d3XL+x7QrdzKwQTuhmZoWY72GLEXEkcAKwAtCT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\nK8T/B194ISi0+XToAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2139,7 +2118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/examples/jupyter/mgxs-part-iii.ipynb b/examples/jupyter/mgxs-part-iii.ipynb index addca822f82..450276c89f6 100644 --- a/examples/jupyter/mgxs-part-iii.ipynb +++ b/examples/jupyter/mgxs-part-iii.ipynb @@ -63,31 +63,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H1')\n", - "b10 = openmc.Nuclide('B10')\n", - "o16 = openmc.Nuclide('O16')\n", - "u235 = openmc.Nuclide('U235')\n", - "u238 = openmc.Nuclide('U238')\n", - "zr90 = openmc.Nuclide('Zr90')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pins." + "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pins." ] }, { @@ -101,21 +77,21 @@ "# 1.6 enriched fuel\n", "fuel = openmc.Material(name='1.6% Fuel')\n", "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide(u235, 3.7503e-4)\n", - "fuel.add_nuclide(u238, 2.2625e-2)\n", - "fuel.add_nuclide(o16, 4.6007e-2)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", "\n", "# borated water\n", "water = openmc.Material(name='Borated Water')\n", "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide(h1, 4.9457e-2)\n", - "water.add_nuclide(o16, 2.4732e-2)\n", - "water.add_nuclide(b10, 8.0042e-6)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", "\n", "# zircaloy\n", "zircaloy = openmc.Material(name='Zircaloy')\n", "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide(zr90, 7.2758e-3)" + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" ] }, { @@ -338,8 +314,7 @@ "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "geometry = openmc.Geometry()\n", - "geometry.root_universe = root_universe" + "geometry = openmc.Geometry(root_universe)" ] }, { @@ -1679,7 +1654,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/examples/jupyter/pincell.ipynb b/examples/jupyter/pincell.ipynb index 163429688b9..72b65a26532 100644 --- a/examples/jupyter/pincell.ipynb +++ b/examples/jupyter/pincell.ipynb @@ -25,7 +25,7 @@ "source": [ "## Defining Materials\n", "\n", - "Materials in OpenMC are defined as a set of nuclides or elements with specified atom/weight fractions. There are two ways we can go about adding nuclides or elements to materials. The first way involves creating `Nuclide` or `Element` objects explicitly." + "Materials in OpenMC are defined as a set of nuclides with specified atom/weight fractions. To begin, we will create a material by making an instance of the `Material` class. In OpenMC, many objects, including materials, are identified by a \"unique ID\" that is simply just a positive integer. These IDs are used when exporting XML files that the solver reads in. They also appear in the output and can be used for identification. Since an integer ID is not very useful by itself, you can also give a material a `name` as well." ] }, { @@ -34,28 +34,6 @@ "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "u235 = openmc.Nuclide('U235')\n", - "u238 = openmc.Nuclide('U238')\n", - "o16 = openmc.Nuclide('O16')\n", - "zr = openmc.Element('Zr')\n", - "h1 = openmc.Nuclide('H1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have all the nuclides/elements that we need, we can start creating materials. In OpenMC, many objects are identified by a \"unique ID\" that is simply just a positive integer. These IDs are used when exporting XML files that the solver reads in. They also appear in the output and can be used for identification. Assigning an ID is required -- we can also give a `name` as well." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, "outputs": [ { "name": "stdout", @@ -68,7 +46,6 @@ "\tDensity =\tNone []\n", "\tS(a,b) Tables \n", "\tNuclides \n", - "\tElements \n", "\n" ] } @@ -87,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -97,13 +74,12 @@ "output_type": "stream", "text": [ "Material\n", - "\tID =\t10000\n", + "\tID =\t2\n", "\tName =\t\n", "\tTemperature =\tNone\n", "\tDensity =\tNone []\n", "\tS(a,b) Tables \n", "\tNuclides \n", - "\tElements \n", "\n" ] } @@ -117,12 +93,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We see that an ID of 10000 was automatically assigned. Let's now move on to adding nuclides to our `uo2` material. The `Material` object has a method `add_nuclide()` whose first argument is the nuclide and second argument is the atom or weight fraction." + "We see that an ID of 2 was automatically assigned. Let's now move on to adding nuclides to our `uo2` material. The `Material` object has a method `add_nuclide()` whose first argument is the name of the nuclide and second argument is the atom or weight fraction." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -138,7 +114,7 @@ " \n", " Parameters\n", " ----------\n", - " nuclide : str or openmc.Nuclide\n", + " nuclide : str\n", " Nuclide to add\n", " percent : float\n", " Atom or weight percent\n", @@ -161,16 +137,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Add nuclides to uo2\n", - "uo2.add_nuclide(u235, 0.03)\n", - "uo2.add_nuclide(u238, 0.97)\n", - "uo2.add_nuclide(o16, 2.0)" + "uo2.add_nuclide('U235', 0.03)\n", + "uo2.add_nuclide('U238', 0.97)\n", + "uo2.add_nuclide('O16', 2.0)" ] }, { @@ -182,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -202,19 +178,28 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Material instance already exists with id=2.\n", + " warn(msg, IDWarning)\n" + ] + } + ], "source": [ "zirconium = openmc.Material(2, \"zirconium\")\n", - "zirconium.add_element(zr, 1.0)\n", + "zirconium.add_element('Zr', 1.0)\n", "zirconium.set_density('g/cm3', 6.6)\n", "\n", "water = openmc.Material(3, \"h2o\")\n", - "water.add_nuclide(h1, 2.0)\n", - "water.add_nuclide(o16, 1.0)\n", + "water.add_nuclide('H1', 2.0)\n", + "water.add_nuclide('O16', 1.0)\n", "water.set_density('g/cm3', 1.0)" ] }, @@ -227,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -236,28 +221,6 @@ "water.add_s_alpha_beta('c_H_in_H2O')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far you've seen the \"hard\" way to create a material. The \"easy\" way is to just pass strings to `add_nuclide()` and `add_element()` -- they are implicitly converted to `Nuclide` and `Element` objects. For example, we could have created our UO2 material as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "uo2 = openmc.Material(1, \"uo2\")\n", - "uo2.add_nuclide('U235', 0.03)\n", - "uo2.add_nuclide('U238', 0.97)\n", - "uo2.add_nuclide('O16', 2.0)\n", - "uo2.set_density('g/cm3', 10.0)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -267,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -285,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -296,7 +259,7 @@ "True" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -328,26 +291,26 @@ "text": [ "\r\n", "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", "\r\n" ] } @@ -374,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -385,27 +348,27 @@ "text": [ "\r\n", "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", "\r\n" ] } @@ -438,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -464,7 +427,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "\n" @@ -488,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -521,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -541,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -560,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -588,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -607,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -618,7 +581,7 @@ "(array([-1., -1., 0.]), array([ 1., 1., 1.]))" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -636,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -658,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -683,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": { "collapsed": true }, @@ -705,16 +668,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -734,16 +697,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -763,16 +726,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -801,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": { "collapsed": true }, @@ -821,7 +784,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": { "collapsed": true }, @@ -841,11 +804,22 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Cell instance already exists with id=1.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Cell instance already exists with id=2.\n", + " warn(msg, IDWarning)\n" + ] + } + ], "source": [ "fuel = openmc.Cell(1, 'fuel')\n", "fuel.fill = uo2\n", @@ -868,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -890,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -912,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -923,7 +897,7 @@ "openmc.region.Intersection" ] }, - "execution_count": 33, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -943,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -961,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -972,17 +946,17 @@ "text": [ "\r\n", "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", "\r\n" ] } @@ -1010,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -1029,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": { "collapsed": true }, @@ -1044,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -1055,15 +1029,15 @@ "text": [ "\r\n", "\r\n", - " eigenvalue\r\n", - " 1000\r\n", - " 100\r\n", - " 10\r\n", - " \r\n", - " \r\n", - " 0 0 0\r\n", - " \r\n", - " \r\n", + " eigenvalue\r\n", + " 1000\r\n", + " 100\r\n", + " 10\r\n", + " \r\n", + " \r\n", + " 0 0 0\r\n", + " \r\n", + " \r\n", "\r\n" ] } @@ -1088,7 +1062,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 37, "metadata": { "collapsed": true }, @@ -1109,7 +1083,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -1128,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 39, "metadata": { "collapsed": false }, @@ -1139,11 +1113,14 @@ "text": [ "\r\n", "\r\n", - " \r\n", - " \r\n", - " U235\r\n", - " total fission absorption (n,gamma)\r\n", - " \r\n", + " \r\n", + " 1\r\n", + " \r\n", + " \r\n", + " 1\r\n", + " U235\r\n", + " total fission absorption (n,gamma)\r\n", + " \r\n", "\r\n" ] } @@ -1165,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 40, "metadata": { "collapsed": false, "scrolled": true @@ -1203,30 +1180,29 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | bc4683be1c853fe6d0e31bccad416ef219e3efaf\n", - " Date/Time | 2017-04-13 17:17:32\n", - " MPI Processes | 1\n", + " Version | 0.9.0\n", + " Git SHA1 | 168f202e3ecf48cdd15b541dc396b24465832986\n", + " Date/Time | 2017-12-12 15:10:36\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading U235 from /home/smharper/openmc/data/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/smharper/openmc/data/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/smharper/openmc/data/nndc_hdf5/O16.h5\n", - " Reading Zr90 from /home/smharper/openmc/data/nndc_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/smharper/openmc/data/nndc_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/smharper/openmc/data/nndc_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/smharper/openmc/data/nndc_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/smharper/openmc/data/nndc_hdf5/Zr96.h5\n", - " Reading H1 from /home/smharper/openmc/data/nndc_hdf5/H1.h5\n", - " Reading O17 from /home/smharper/openmc/data/nndc_hdf5/O17.h5\n", - " Reading c_H_in_H2O from /home/smharper/openmc/data/nndc_hdf5/c_H_in_H2O.h5\n", + " Reading geometry XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", + " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/romano/openmc/scripts/nndc_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/romano/openmc/scripts/nndc_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/romano/openmc/scripts/nndc_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/romano/openmc/scripts/nndc_hdf5/Zr96.h5\n", + " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", + " Reading O17 from /home/romano/openmc/scripts/nndc_hdf5/O17.h5\n", + " Reading c_H_in_H2O from /home/romano/openmc/scripts/nndc_hdf5/c_H_in_H2O.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1337,20 +1313,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.3000E-01 seconds\n", - " Reading cross sections = 4.7425E-01 seconds\n", - " Total time in simulation = 5.9503E+00 seconds\n", - " Time in transport only = 5.6693E+00 seconds\n", - " Time in inactive batches = 5.9508E-01 seconds\n", - " Time in active batches = 5.3552E+00 seconds\n", - " Time synchronizing fission bank = 4.5385E-03 seconds\n", - " Sampling source sites = 2.4558E-03 seconds\n", - " SEND/RECV source sites = 1.0922E-03 seconds\n", - " Time accumulating tallies = 4.2963E-04 seconds\n", - " Total time for finalization = 4.4542E-04 seconds\n", - " Total time elapsed = 6.4846E+00 seconds\n", - " Calculation Rate (inactive) = 16804.5 neutrons/second\n", - " Calculation Rate (active) = 16806.1 neutrons/second\n", + " Total time for initialization = 1.5898E+00 seconds\n", + " Reading cross sections = 1.4448E+00 seconds\n", + " Total time in simulation = 8.3458E+00 seconds\n", + " Time in transport only = 7.7626E+00 seconds\n", + " Time in inactive batches = 6.6138E-01 seconds\n", + " Time in active batches = 7.6845E+00 seconds\n", + " Time synchronizing fission bank = 7.1921E-03 seconds\n", + " Sampling source sites = 4.8562E-03 seconds\n", + " SEND/RECV source sites = 2.0519E-03 seconds\n", + " Time accumulating tallies = 2.3785E-04 seconds\n", + " Total time for finalization = 3.0973E-03 seconds\n", + " Total time elapsed = 9.9670E+00 seconds\n", + " Calculation Rate (inactive) = 15120.0 neutrons/second\n", + " Calculation Rate (active) = 11711.9 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1368,7 +1344,7 @@ "0" ] }, - "execution_count": 42, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1386,7 +1362,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -1422,7 +1398,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 42, "metadata": { "collapsed": true }, @@ -1445,7 +1421,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 43, "metadata": { "collapsed": false }, @@ -1456,13 +1432,13 @@ "text": [ "\r\n", "\r\n", - " \r\n", - " 0.0 0.0 0.0\r\n", - " 1.26 1.26\r\n", - " 200 200\r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " 0.0 0.0 0.0\r\n", + " 1.26 1.26\r\n", + " 200 200\r\n", + " \r\n", + " \r\n", + " \r\n", "\r\n" ] } @@ -1482,7 +1458,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -1519,23 +1495,22 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | bc4683be1c853fe6d0e31bccad416ef219e3efaf\n", - " Date/Time | 2017-04-13 17:18:01\n", - " MPI Processes | 1\n", + " Version | 0.9.0\n", + " Git SHA1 | 168f202e3ecf48cdd15b541dc396b24465832986\n", + " Date/Time | 2017-12-12 15:10:46\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Building neighboring cells lists for each surface...\n", " Reading tallies XML file...\n", " Reading plot XML file...\n", - " Building neighboring cells lists for each surface...\n", "\n", " =======================> PLOTTING SUMMARY <========================\n", "\n", - " Plot ID: 10000\n", + " Plot ID: 1\n", " Plot file: pinplot.ppm\n", " Universe depth: -1\n", " Plot Type: Slice\n", @@ -1545,7 +1520,7 @@ " Basis: xy\n", " Pixels: 200 200\n", "\n", - " Processing plot 10000: pinplot.ppm ...\n" + " Processing plot 1: pinplot.ppm ...\n" ] }, { @@ -1554,7 +1529,7 @@ "0" ] }, - "execution_count": 46, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1572,7 +1547,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 45, "metadata": { "collapsed": false }, @@ -1590,7 +1565,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": { "collapsed": false, "scrolled": false @@ -1598,12 +1573,12 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] }, - "execution_count": 48, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1622,14 +1597,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -1660,7 +1635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb index b0e9552983f..4c0f4ef0042 100644 --- a/examples/jupyter/post-processing.ipynb +++ b/examples/jupyter/post-processing.ipynb @@ -34,36 +34,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." + "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin." ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H1')\n", - "b10 = openmc.Nuclide('B10')\n", - "o16 = openmc.Nuclide('O16')\n", - "u235 = openmc.Nuclide('U235')\n", - "u238 = openmc.Nuclide('U238')\n", - "zr90 = openmc.Nuclide('Zr90')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pin." - ] - }, - { - "cell_type": "code", - "execution_count": 3, "metadata": { "collapsed": false }, @@ -72,21 +48,21 @@ "# 1.6 enriched fuel\n", "fuel = openmc.Material(name='1.6% Fuel')\n", "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide(u235, 3.7503e-4)\n", - "fuel.add_nuclide(u238, 2.2625e-2)\n", - "fuel.add_nuclide(o16, 4.6007e-2)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", "\n", "# borated water\n", "water = openmc.Material(name='Borated Water')\n", "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide(h1, 4.9457e-2)\n", - "water.add_nuclide(o16, 2.4732e-2)\n", - "water.add_nuclide(b10, 8.0042e-6)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", "\n", "# zircaloy\n", "zircaloy = openmc.Material(name='Zircaloy')\n", "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide(zr90, 7.2758e-3)" + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" ] }, { @@ -98,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -120,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -148,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -185,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -212,20 +188,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "geometry = openmc.Geometry()\n", - "geometry.root_universe = root_universe" + "geometry = openmc.Geometry(root_universe)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -244,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -279,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -307,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -318,7 +293,7 @@ "0" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -330,19 +305,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -364,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -376,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -400,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -419,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true @@ -455,21 +430,18 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2016 Massachusetts Institute of Technology\n", + " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n", - " Date/Time | 2016-10-31 12:47:14\n", + " Version | 0.9.0\n", + " Git SHA1 | 9b7cebf7bc34d60e0f1750c3d6cb103df11e8dc4\n", + " Date/Time | 2017-12-04 20:47:36\n", " OpenMP Threads | 4\n", "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Building neighboring cells lists for each surface...\n", " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", @@ -478,145 +450,138 @@ " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", - " ===========================================================================\n", " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", " 1/1 1.04359 \n", - " 2/1 1.04244 \n", - " 3/1 1.03020 \n", - " 4/1 1.03630 \n", - " 5/1 1.06478 \n", - " 6/1 1.05450 \n", - " 7/1 1.02369 \n", - " 8/1 1.03454 \n", - " 9/1 1.05309 \n", - " 10/1 1.01741 \n", - " 11/1 1.04344 \n", - " 12/1 1.01457 1.02901 +/- 0.01444\n", - " 13/1 1.01643 1.02481 +/- 0.00933\n", - " 14/1 1.04657 1.03025 +/- 0.00855\n", - " 15/1 1.06420 1.03704 +/- 0.00949\n", - " 16/1 1.06048 1.04095 +/- 0.00867\n", - " 17/1 1.01627 1.03742 +/- 0.00813\n", - " 18/1 1.03667 1.03733 +/- 0.00705\n", - " 19/1 1.03977 1.03760 +/- 0.00622\n", - " 20/1 1.03996 1.03784 +/- 0.00557\n", - " 21/1 1.05663 1.03954 +/- 0.00532\n", - " 22/1 1.03944 1.03954 +/- 0.00485\n", - " 23/1 1.07702 1.04242 +/- 0.00532\n", - " 24/1 1.02378 1.04109 +/- 0.00510\n", - " 25/1 1.02817 1.04023 +/- 0.00482\n", - " 26/1 1.08000 1.04271 +/- 0.00515\n", - " 27/1 1.02733 1.04181 +/- 0.00492\n", - " 28/1 1.02993 1.04115 +/- 0.00469\n", - " 29/1 1.01755 1.03991 +/- 0.00461\n", - " 30/1 1.05836 1.04083 +/- 0.00447\n", - " 31/1 1.05936 1.04171 +/- 0.00434\n", - " 32/1 1.03683 1.04149 +/- 0.00414\n", - " 33/1 1.05112 1.04191 +/- 0.00398\n", - " 34/1 1.02927 1.04138 +/- 0.00385\n", - " 35/1 1.05326 1.04186 +/- 0.00372\n", - " 36/1 1.06014 1.04256 +/- 0.00364\n", - " 37/1 1.02320 1.04184 +/- 0.00358\n", - " 38/1 1.04297 1.04188 +/- 0.00345\n", - " 39/1 1.04544 1.04201 +/- 0.00333\n", - " 40/1 1.05178 1.04233 +/- 0.00323\n", - " 41/1 1.01744 1.04153 +/- 0.00323\n", - " 42/1 1.02376 1.04097 +/- 0.00317\n", - " 43/1 1.02344 1.04044 +/- 0.00312\n", - " 44/1 1.05813 1.04096 +/- 0.00307\n", - " 45/1 1.02370 1.04047 +/- 0.00303\n", - " 46/1 1.03536 1.04033 +/- 0.00294\n", - " 47/1 1.04344 1.04041 +/- 0.00286\n", - " 48/1 1.03879 1.04037 +/- 0.00279\n", - " 49/1 1.07122 1.04116 +/- 0.00283\n", - " 50/1 1.03861 1.04110 +/- 0.00276\n", - " 51/1 1.00812 1.04029 +/- 0.00281\n", - " 52/1 1.04620 1.04043 +/- 0.00274\n", - " 53/1 1.07050 1.04113 +/- 0.00277\n", - " 54/1 1.04038 1.04111 +/- 0.00270\n", - " 55/1 1.03770 1.04104 +/- 0.00264\n", - " 56/1 1.05627 1.04137 +/- 0.00261\n", - " 57/1 1.04191 1.04138 +/- 0.00255\n", - " 58/1 1.03633 1.04128 +/- 0.00250\n", - " 59/1 1.02002 1.04084 +/- 0.00249\n", - " 60/1 1.04293 1.04088 +/- 0.00244\n", - " 61/1 1.00919 1.04026 +/- 0.00247\n", - " 62/1 1.09274 1.04127 +/- 0.00262\n", - " 63/1 1.05344 1.04150 +/- 0.00258\n", - " 64/1 1.07892 1.04219 +/- 0.00263\n", - " 65/1 1.09293 1.04312 +/- 0.00274\n", - " 66/1 1.04485 1.04315 +/- 0.00269\n", - " 67/1 1.04794 1.04323 +/- 0.00264\n", - " 68/1 1.04183 1.04321 +/- 0.00260\n", - " 69/1 1.03052 1.04299 +/- 0.00256\n", - " 70/1 1.03630 1.04288 +/- 0.00252\n", - " 71/1 1.04611 1.04293 +/- 0.00248\n", - " 72/1 1.00214 1.04228 +/- 0.00253\n", - " 73/1 1.02488 1.04200 +/- 0.00250\n", - " 74/1 1.03607 1.04191 +/- 0.00246\n", - " 75/1 1.05488 1.04211 +/- 0.00243\n", - " 76/1 1.02252 1.04181 +/- 0.00242\n", - " 77/1 1.03196 1.04166 +/- 0.00238\n", - " 78/1 1.05769 1.04190 +/- 0.00236\n", - " 79/1 1.03101 1.04174 +/- 0.00233\n", - " 80/1 1.04693 1.04182 +/- 0.00230\n", - " 81/1 1.06110 1.04209 +/- 0.00228\n", - " 82/1 1.06273 1.04237 +/- 0.00227\n", - " 83/1 1.07699 1.04285 +/- 0.00229\n", - " 84/1 1.05740 1.04304 +/- 0.00226\n", - " 85/1 1.04040 1.04301 +/- 0.00223\n", - " 86/1 1.02147 1.04273 +/- 0.00222\n", - " 87/1 1.00842 1.04228 +/- 0.00224\n", - " 88/1 1.06173 1.04253 +/- 0.00222\n", - " 89/1 1.08649 1.04309 +/- 0.00227\n", - " 90/1 1.05826 1.04328 +/- 0.00224\n", - " 91/1 1.07673 1.04369 +/- 0.00225\n", - " 92/1 1.02425 1.04345 +/- 0.00224\n", - " 93/1 1.05359 1.04357 +/- 0.00222\n", - " 94/1 1.06085 1.04378 +/- 0.00220\n", - " 95/1 1.05319 1.04389 +/- 0.00218\n", - " 96/1 1.02321 1.04365 +/- 0.00216\n", - " 97/1 1.04124 1.04362 +/- 0.00214\n", - " 98/1 1.06307 1.04384 +/- 0.00213\n", - " 99/1 1.04616 1.04387 +/- 0.00210\n", - " 100/1 1.03278 1.04375 +/- 0.00208\n", + " 2/1 1.04323 \n", + " 3/1 1.04711 \n", + " 4/1 1.03892 \n", + " 5/1 1.02442 \n", + " 6/1 1.02046 \n", + " 7/1 1.05998 \n", + " 8/1 1.04184 \n", + " 9/1 1.04786 \n", + " 10/1 1.06636 \n", + " 11/1 1.07229 \n", + " 12/1 1.04413 1.05821 +/- 0.01408\n", + " 13/1 1.06376 1.06006 +/- 0.00834\n", + " 14/1 1.06898 1.06229 +/- 0.00630\n", + " 15/1 1.05095 1.06002 +/- 0.00538\n", + " 16/1 1.04453 1.05744 +/- 0.00510\n", + " 17/1 1.05626 1.05727 +/- 0.00431\n", + " 18/1 1.03423 1.05439 +/- 0.00472\n", + " 19/1 1.04240 1.05306 +/- 0.00437\n", + " 20/1 1.03719 1.05147 +/- 0.00422\n", + " 21/1 1.04308 1.05071 +/- 0.00389\n", + " 22/1 1.03956 1.04978 +/- 0.00367\n", + " 23/1 1.05824 1.05043 +/- 0.00344\n", + " 24/1 1.03151 1.04908 +/- 0.00346\n", + " 25/1 1.02695 1.04760 +/- 0.00354\n", + " 26/1 1.02581 1.04624 +/- 0.00358\n", + " 27/1 1.09932 1.04936 +/- 0.00459\n", + " 28/1 1.05983 1.04995 +/- 0.00437\n", + " 29/1 1.03381 1.04910 +/- 0.00422\n", + " 30/1 1.06727 1.05001 +/- 0.00410\n", + " 31/1 1.03180 1.04914 +/- 0.00400\n", + " 32/1 1.04520 1.04896 +/- 0.00382\n", + " 33/1 1.07158 1.04994 +/- 0.00378\n", + " 34/1 1.04283 1.04965 +/- 0.00363\n", + " 35/1 1.03272 1.04897 +/- 0.00354\n", + " 36/1 1.02730 1.04814 +/- 0.00351\n", + " 37/1 1.01975 1.04709 +/- 0.00353\n", + " 38/1 1.04815 1.04712 +/- 0.00341\n", + " 39/1 1.02642 1.04641 +/- 0.00336\n", + " 40/1 1.04063 1.04622 +/- 0.00325\n", + " 41/1 0.97384 1.04388 +/- 0.00392\n", + " 42/1 1.06049 1.04440 +/- 0.00383\n", + " 43/1 1.04467 1.04441 +/- 0.00371\n", + " 44/1 1.04454 1.04441 +/- 0.00360\n", + " 45/1 1.06529 1.04501 +/- 0.00355\n", + " 46/1 1.05496 1.04529 +/- 0.00346\n", + " 47/1 1.03717 1.04507 +/- 0.00337\n", + " 48/1 1.03874 1.04490 +/- 0.00328\n", + " 49/1 1.02083 1.04428 +/- 0.00326\n", + " 50/1 1.04847 1.04439 +/- 0.00318\n", + " 51/1 1.03789 1.04423 +/- 0.00310\n", + " 52/1 1.04447 1.04423 +/- 0.00303\n", + " 53/1 1.01942 1.04366 +/- 0.00301\n", + " 54/1 1.04639 1.04372 +/- 0.00294\n", + " 55/1 1.02539 1.04331 +/- 0.00291\n", + " 56/1 1.06312 1.04374 +/- 0.00288\n", + " 57/1 1.05854 1.04406 +/- 0.00283\n", + " 58/1 1.05150 1.04421 +/- 0.00278\n", + " 59/1 1.04321 1.04419 +/- 0.00272\n", + " 60/1 1.04762 1.04426 +/- 0.00266\n", + " 61/1 0.99442 1.04328 +/- 0.00279\n", + " 62/1 1.06907 1.04378 +/- 0.00278\n", + " 63/1 1.03170 1.04355 +/- 0.00274\n", + " 64/1 1.04308 1.04354 +/- 0.00268\n", + " 65/1 1.01439 1.04301 +/- 0.00269\n", + " 66/1 1.04581 1.04306 +/- 0.00264\n", + " 67/1 1.04404 1.04308 +/- 0.00259\n", + " 68/1 1.03158 1.04288 +/- 0.00256\n", + " 69/1 1.04953 1.04299 +/- 0.00251\n", + " 70/1 1.06338 1.04333 +/- 0.00250\n", + " 71/1 1.03768 1.04324 +/- 0.00246\n", + " 72/1 1.02531 1.04295 +/- 0.00243\n", + " 73/1 1.04552 1.04299 +/- 0.00240\n", + " 74/1 1.04293 1.04299 +/- 0.00236\n", + " 75/1 1.05928 1.04324 +/- 0.00233\n", + " 76/1 1.05057 1.04335 +/- 0.00230\n", + " 77/1 1.01846 1.04298 +/- 0.00230\n", + " 78/1 1.05755 1.04320 +/- 0.00227\n", + " 79/1 1.05222 1.04333 +/- 0.00224\n", + " 80/1 1.04860 1.04340 +/- 0.00221\n", + " 81/1 1.03026 1.04322 +/- 0.00219\n", + " 82/1 1.02360 1.04294 +/- 0.00218\n", + " 83/1 1.06679 1.04327 +/- 0.00217\n", + " 84/1 1.06297 1.04354 +/- 0.00216\n", + " 85/1 1.04426 1.04355 +/- 0.00213\n", + " 86/1 1.00337 1.04302 +/- 0.00217\n", + " 87/1 1.04787 1.04308 +/- 0.00214\n", + " 88/1 1.04332 1.04308 +/- 0.00211\n", + " 89/1 1.04369 1.04309 +/- 0.00208\n", + " 90/1 1.05006 1.04318 +/- 0.00206\n", + " 91/1 1.05394 1.04331 +/- 0.00204\n", + " 92/1 1.06017 1.04352 +/- 0.00202\n", + " 93/1 1.02032 1.04324 +/- 0.00202\n", + " 94/1 1.04816 1.04330 +/- 0.00200\n", + " 95/1 1.06601 1.04356 +/- 0.00199\n", + " 96/1 1.02876 1.04339 +/- 0.00197\n", + " 97/1 1.03929 1.04334 +/- 0.00195\n", + " 98/1 1.01958 1.04307 +/- 0.00195\n", + " 99/1 1.01899 1.04280 +/- 0.00195\n", + " 100/1 1.05150 1.04290 +/- 0.00193\n", " Creating state point statepoint.100.h5...\n", "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.3314E-01 seconds\n", - " Reading cross sections = 3.6117E-01 seconds\n", - " Total time in simulation = 3.5193E+02 seconds\n", - " Time in transport only = 3.5172E+02 seconds\n", - " Time in inactive batches = 4.7990E+00 seconds\n", - " Time in active batches = 3.4714E+02 seconds\n", - " Time synchronizing fission bank = 2.3264E-02 seconds\n", - " Sampling source sites = 1.6661E-02 seconds\n", - " SEND/RECV source sites = 6.3975E-03 seconds\n", - " Time accumulating tallies = 2.3345E-02 seconds\n", - " Total time for finalization = 1.7400E-01 seconds\n", - " Total time elapsed = 3.5269E+02 seconds\n", - " Calculation Rate (inactive) = 10418.8 neutrons/second\n", - " Calculation Rate (active) = 1296.32 neutrons/second\n", + " Total time for initialization = 6.5927E-01 seconds\n", + " Reading cross sections = 6.1679E-01 seconds\n", + " Total time in simulation = 1.2768E+02 seconds\n", + " Time in transport only = 1.2740E+02 seconds\n", + " Time in inactive batches = 2.7221E+00 seconds\n", + " Time in active batches = 1.2496E+02 seconds\n", + " Time synchronizing fission bank = 2.4179E-02 seconds\n", + " Sampling source sites = 1.7261E-02 seconds\n", + " SEND/RECV source sites = 6.7268E-03 seconds\n", + " Time accumulating tallies = 1.5442E-02 seconds\n", + " Total time for finalization = 1.6664E-01 seconds\n", + " Total time elapsed = 1.2854E+02 seconds\n", + " Calculation Rate (inactive) = 18368.1 neutrons/second\n", + " Calculation Rate (active) = 3601.09 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.04220 +/- 0.00169\n", - " k-effective (Track-length) = 1.04375 +/- 0.00208\n", - " k-effective (Absorption) = 1.04213 +/- 0.00151\n", - " Combined k-effective = 1.04229 +/- 0.00127\n", + " k-effective (Collision) = 1.04331 +/- 0.00192\n", + " k-effective (Track-length) = 1.04290 +/- 0.00193\n", + " k-effective (Absorption) = 1.04136 +/- 0.00151\n", + " Combined k-effective = 1.04183 +/- 0.00122\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -627,7 +592,7 @@ "0" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -653,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true @@ -673,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -683,9 +648,9 @@ "output_type": "stream", "text": [ "Tally\n", - "\tID =\t10000\n", + "\tID =\t1\n", "\tName =\tflux\n", - "\tFilters =\tMeshFilter\t[10000]\n", + "\tFilters =\tMeshFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['flux', 'fission']\n", "\tEstimator =\ttracklength\n", @@ -707,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -715,21 +680,21 @@ { "data": { "text/plain": [ - "array([[[ 0.41167874, 0. ]],\n", + "array([[[ 0.40981574, 0. ]],\n", "\n", - " [[ 0.40853332, 0. ]],\n", + " [[ 0.40963388, 0. ]],\n", "\n", - " [[ 0.41140779, 0. ]],\n", + " [[ 0.41117481, 0. ]],\n", "\n", " ..., \n", - " [[ 0.40957563, 0. ]],\n", + " [[ 0.41179009, 0. ]],\n", "\n", - " [[ 0.40983338, 0. ]],\n", + " [[ 0.41329412, 0. ]],\n", "\n", - " [[ 0.40877195, 0. ]]])" + " [[ 0.41494587, 0. ]]])" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -747,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -762,33 +727,33 @@ { "data": { "text/plain": [ - "(array([[[ 0.00457421, 0. ]],\n", + "(array([[[ 0.00455351, 0. ]],\n", " \n", - " [[ 0.00453926, 0. ]],\n", + " [[ 0.00455149, 0. ]],\n", " \n", - " [[ 0.0045712 , 0. ]],\n", + " [[ 0.00456861, 0. ]],\n", " \n", " ..., \n", - " [[ 0.00455084, 0. ]],\n", + " [[ 0.00457545, 0. ]],\n", " \n", - " [[ 0.0045537 , 0. ]],\n", + " [[ 0.00459216, 0. ]],\n", " \n", - " [[ 0.00454191, 0. ]]]),\n", - " array([[[ 1.84557765e-05, 0.00000000e+00]],\n", + " [[ 0.00461051, 0. ]]]),\n", + " array([[[ 2.00748004e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.71073587e-05, 0.00000000e+00]],\n", + " [[ 1.75039529e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.76546641e-05, 0.00000000e+00]],\n", + " [[ 1.96093103e-05, 0.00000000e+00]],\n", " \n", " ..., \n", - " [[ 1.82859458e-05, 0.00000000e+00]],\n", + " [[ 1.69721143e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.80961726e-05, 0.00000000e+00]],\n", + " [[ 1.58964240e-05, 0.00000000e+00]],\n", " \n", - " [[ 2.01200499e-05, 0.00000000e+00]]]))" + " [[ 1.81009205e-05, 0.00000000e+00]]]))" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -807,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -817,9 +782,9 @@ "output_type": "stream", "text": [ "Tally\n", - "\tID =\t10001\n", + "\tID =\t2\n", "\tName =\tflux\n", - "\tFilters =\tMeshFilter\t[10000]\n", + "\tFilters =\tMeshFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['flux']\n", "\tEstimator =\ttracklength\n", @@ -842,7 +807,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -856,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -864,18 +829,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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OpDBDJmXZhTZr0i+v8xzfYYt+EvRTwMd73EeUfSR0Hmidx21UyKtuMkKIBjYArnOYLEFU\nmuwSJ6VE+cjgV+nIMpeFFh/yfJM+YZs+dlhhlK3SEPqyDdfkDlZvEzFu8DfSx5iXhzkuXGSeKao4\n+QDfxUWFXCvEV1KfIflWHNtWHdcvVvlu+Bm+YP1Zqoqdk7zPCGusMoqvWuL+wnu86T/LhlTgNBfo\nYGGxMsWFxAPU4lYamo39qwP0TexyxH+dk8tX6fWleDX4KK+sPUMglGI0tkScXVYdBcyQQF2xc5nj\nbDJIiDQqLXQksgQxFYFxzzI1yYEpmUS9O9RzJaopLwveKVJSGJoin0p+nTd8D3LDP8ODvI3ibJJR\nveRlHw5qHGAeE4HShp/kqwrsi4xE1vg4f8v52hk2lkdovuHi1s8cxNLXZOLjN6m5HcRdCZxiBUvM\ngvpoGVEx2Q728m0+yFs8yATLnNAucyC/TL91j9B0ivuE97i8fYI/Xf5l3A9UsMfrNFGR0SjhYYkJ\nDubn+XjnWxzxXKMg+BBb8M/zv0/SFaWkuImaOb4U/Fm+ejcC3NV1D7krpS2jMWjZpG8ogYhBremk\nLNrJ6z6qTSd6ScGmNoixxwZDSOhMsohChzp2HNSwCi3sQp0CLrIESREhRAZ7vkGonmM/HCFZ7yVZ\n6WEqtEBE3WNQ2OCkNYeFNjUcFPAhygbT9lt0mjJGSyLgTLOwOUlNszM2tExDtqFxZ4Fyp97HfGKW\nuZcOUbjlJyik6LQVtsxBrutHmDTnSRHlfU6yTR9erUxvfQ+nr4bHKHO6fIlXbI+xJfbTkG3U5hzU\na3ZaopWa5CAnBFlTRpAdbfpdm+wV47RFK1XDxXxzmordRXAgRV71oSOQJoyJQE13sN3uR8qZCJKJ\nEDEYZh07dXasKQTXGu5Whbpox0QgYBSYac2zrI2xwDgKHYoWDx2LjJc8QbIEOjmaSQe1dRdS1iQ+\ntEFgKE1WD7LQmGG9M4Jqb2OTyww617lv/DwKbfzk8VDC7rIRGd7DLZaoVjzcWj+CnhMJR7JEe/bZ\nFvtI0EdLsmJaQHZ0sLqbGLJICwuYJp5OBR9lUkoEq9AiIu3jkbO8ID5LWogwLq4TEVJ4pTx99j38\nQuZuxLer655yV0p7kC2e4QLvcR+DQ+ucHXybL/EZsq3jNIp29HestP0OCjM+8vjxUuQ5vsM+UXIE\naGNhzTIIgEqTJSZYY4QTXOK57ReZ3Fnm2/c/zebuCDtrw7x85kk+pn6NY1xlmH6um4d5mwcRMej1\nb3PC/z5f3/o0qWyUCfstchdilCsB8j8fICrdWZy8IJzmQuFBFi4fhN8DBkD8sIFNbaBVLdRLHiZj\nSxRlL3/EP2WKeRTxBqKi86j4BsVOm/tSbf46+nOkXSEenHyFt3/7CbLZMN7fSrPvCvMCz/CXB4Mc\nFa4yySKnJt4lb/rZ6gzw7cJRwvY0E/E5UoRwmVUOMI+DGsudcTbKQ2jX7Mg2DUeowEfEbzIobvIl\nQWJg8jIuyngo0WcmiIt7WF1Nei07TLBEE5UNcwgTgVnhFh6zBE1YvniARGIItafBqQ+co90v80ft\nX6HR9GDraxE+mcAtFOkhyaO8xpi5ikqTLQZwSk6GLeuMmKtc3T/F5pUJuAzKIyb0m/x+6L/hWuok\nzR0nF4eOI0zouCZyFPHQQiHKPuFGnimW8So58EOSME1T5YpwjHnrFPWojRAZbGaDdf8gm9WeuxHf\nrq57yl0p7UPcAEIsMvmDKzvKLJsTyLLGpH+BxrN2HJYSu8Sp4KKOnVvMssYIu8Qp4+JD8y8wU5tj\n/1AIq7VFGwsZQswNTqKFRfz2PI/0vsaAdwOHq8IIq+yiskeMxdQB3t94ACFiMhRYJeDOEQ4lUakS\nFtLsDAyTLMf5Sv5nUW01HNYKfnsO7bIM3wMawApY3mjT92yCnqE9YtYkdYuKnRpP8yJ17MzZp/jD\niJ9p6y0a8jytYIe4NcE6A2wwRPkRF0ZNpInKzsYAUstAHGlTsPjYJ0oLK7utOCuZcRoXndT7a+zP\nRMlc6wGHgXuswJB1g4Od2/x87Su8M32WFccIRdHNnhCjiIciCZ5giT52yOPnlVtPo+QN4rNJfPYM\nw6yzS5xeY4dRc5VtqR+fVsRlVpk7+Qq9hzYxBYFOWKZXTPA/K7+Jz1FjWZjgSzxPA5UsQeaZpm9/\nj1gnQ60nS6s4wfz1o2xWxhnzL/G5B/4DzRkbA+F1ajg4yUV8nhIZawSnWsIEGtj/v/8eBGDVPoCL\nAhIdooUszlwdPSMRHU6zG+3BQY0CPubaM8xnDrJuDt6N+HZ13VPuSmmHjCxGJ8JQZptVdYR9XwwZ\nDVunhdiAqcF5gpY0C0zRwoqNOikiAHRqFq6njvF46W3cUpm86UZCp9lWWSuNE7LlCPRkcFNm0LKG\nzVMlTwBbu4XSMJgvzXAjd5TdvT6cnjJ1004D253rqzULuXqQltVKRXEzlzkENg2/K8Oseh2LtUUs\nnsD3dIHsUph20cpmexC7VEOUOpRENxIaPrNAqhPBFASaTit+MmSkDBedEzQkK+iw2RmiOuACHQTZ\npFZ3o5Us2Jxlyn4PVYcTCZ3qspvshQjmJZnqYRf79l6yWxE0m0Re9hHqz+IQa4xZV6j6rXhsOTKE\nKeMkRZg2KVSqhLQMrmadd1p2NoQYS7ZRJItOteNksXSAWt1No+NkzxLDrZYJqWmm47dQxCZL+jiq\n1MAlVPBLeeK2NJl2iGrBQ8CRx640COlZ7EaDBjZWGCdv+MhWgmTWo0y555gdvI59sIGNBgV8SBgM\nq6tMqgtUcGGlhZ06lzjBbqmXQirASmwMyaVhpYXbvITPLNExFTxmCT8Fmlgp4COh9XGjcoSaYrsb\n8e3quqfcldLudCxoDYVfvfyHXI8e5BsnnkMTZBZLM9zYOsYHxr+LxdLkRZ5mhtv4yQNwlnNEM2m+\n/upnWLtvmMnpGLUfzNPW6w4W5g/TH09gGX0HCR0BgToOrnAMT72Gr1Tmm4lHeL94FslqEPdvE/Sk\nqOBiNzXARn0YwaVh5BQoABpgk+lodsohL54ncgw/uswx8wrvfuERXn/3cf5C/BxSS0MVmkxb52hI\nNpbMCeZr00xLczymvIaGzJowzIb8CWo4yLaDbJUH0DQbFqWD3VpH89po1e1U5/00xxwYw+KdfTpe\nBfP3FCjoVHbcVBUvpipAHfSsDae7xl5vhC/aPs6YsMIB5mmxxss8QQEfVtqkiNLf2uWJ/bdYGR7n\nLe9ZrojH6KCw0Rjm5aUPUNr3IlZNTIeAZaRO38Q6/5Pwv+DrBLjSOsZn7F9CF2X+is8SUHPs1+Nk\n13oYH1lmyr3AJxtfQwhqXFUO8+fC59h1bqBEGrQXHNTaTprYOMRNqjhZYIqrHOUQN3iKl7jFLFH2\nOcQNOsikEzEWXznA4rNrVFwOKriJevexeWpsjQxgFZsEyLFLnCYqGgogIAgm3R2bun7a3JXSthWa\nRIQUdrHOsLDOo8LrbDCE4tHwDRW4yAkadZWmTeVi5gxCE2RF47L3JJ2AjOPBImqkRluykKTnzl4Z\n9nlGxtcZdqxi0TsMNRLE5X2caoUMIVZtQ2y4nyYRjeDzpwjoOfo9mwwKG4yZKwyGttnTYiwpE8x7\nD1K1uImNbJOtRLGKTcZZYlq6zURzhdGNTfaDg7x6SqJ+1YOwZdLuaZEY7cfhqmIVWnzU/nXaWRtf\nXP4cz498maCwTlSYx04dXZF42vV93rI/woI2TTkXoGVYEfxtJK9G0hbBXi3zrO0FfE+W8FtyXP7q\nKUoRL6YbpKEmDlcVj1wi4eulJqgEhRxtLIRJEyDHfr6X5fo0enae777wMLe1o8yfnkZ0ahyVrtLA\nhoRGj7rL2dE3ycZD1Dt2LHKHIfcqM5ZbtEULmqwwISyzI/ZR3PWzsDjD5Owcfe5NfnXo35Bz+OiU\nLVgWddIjfizhDr+i/RHDpQKBvTL/j/3XWDSmcBZLHHNewSfniZDCSYUrWyfY2Rjik4G/5bB6HbtY\nw4xKtAMW9MMiCW8fday0sfDH5i8TZZ+gmKEp2CgaXm4aB1HEDpqsgKeDw9r6wS4pXV0/Pe5KaWuG\nhClBIeKh45VwU7ozPWKr41WyvLX8MJKic3zkEs1NN+WGl+xQgG2jH4urSf/kBl4KwJ150Bj7BCw5\najEnPnJUdBcbxjAes0CQLINscs58gEv6cbRUCIu1jRzt0BYsyJpOv7RNn7JLXglgs9YwwjI5LUCk\nJ8nw7ia+RpEZbnOqfJGDqTn862Ui3hRKvI2+ICE32li1Nh1ToYOCU6jSZ02wKY5wvXmEx42XcBpV\nTrYuY5FbtCWZos2LgYgl2+bd+cfQvDLOYJFh9wrpVJTEziApXww5rhF6fp/J1jz79h4Kg17MIQ2r\no4mgGSSzcQqFABVvGlMVaUlW9o0oeS2AoUlIhkmuFqDY8pKo9XCf4zwxJcmGPohDrOG1lBiKrOGh\nQB4/GjJDrDHDLRL0kTf9KEYHq9nCrtXx1Mv069scly5y1HKdvxU/zrbZzzn9DE1TxgD85AmYeYYt\n64wOrLDmHmJBn+IaR+hnGxMIkaGghdiqD6K5ZKx6B0enyZiyzoi6TmJygJLLjYaAlRZzTLNNP+Ms\n46JCHTu7RhyXUMErFzngvkUh42f1bgS4q+secldKOxPxs2EbZO9wjKwQZJNBdomToI/tTj/F8yGO\nOq/wqeG/YfbWMlVcvHr6QdqKQgMbZdw/2I2ug5MqkyxipcVbPEgTlWVxjNecj3FEuMaTvEyAHHLB\nIJeIwFdDCEHIPxFlw11HdTV42vF94oUUbrNBKJbhTP/blEw3WSnEp42vc8i4yRxj9G8nCS4WESsm\n1ngd53Seer8dnyVP1LmHQ6php45KiyscIxsO4wlkyMgBrJrMg+X32XLHuCUd4AJneJTXGc+sceX7\nZ2gf9hI7keXnwl/ktaWneOW9Z/izyD9DPVkjdmibj//Tr7Ivxjgnn6EiuSgV/OS2ejAvCgh2k8zx\nHob71ijZ3bzRehSvt8ihwEUsgffwfNDHWmqM29cOw4yAdyDLrdosfmueAdsmg2xiItDCyhb9OKgS\nJUUePxutYW5XZnne93WivXvcjszyoPIWk+UVAptlvjHwMc75T3PhvlOclC4SEjKsySMsBG/Qd/ws\np3kLh5jntjjDvxf+Kw5yk0PcIM4u/QMJ6BW4Lh5ALrb5YPL7fGb7b3H5q2xO9FIVnRiIOKkyJG3g\npIKMjoiBKBiIkoEgmMSEPZ62v8hL7z3bLe2unzp3pbQlSScq7JNUelhgkpsc5BQXGWGNPSPGd3c/\nhuTWsQkN3NNlWlgpyh4UoYOORB4/lzhBkh7CpNminw4K/WwT0/aRDQ1kUIUmdd3OZGUVXXqZ5XCA\nndkH8diLnAhdZMk6Tlux8BYP4XHV8JNjjBWuSUdooRJjj9cdD/GdyrOk34jglqrMOub4fO7PiOlJ\nDlpukPGGGBeXOKpcxUobA4E6dlYY44A0x2eELzJRXuWVtsGfOD6LIYE7X+aDK9/HGDJRIm0++9yf\n8Fr7SZKFOC/4n2WrdxhjXKBxzY514M50ygXraaqCkzYKkyyiOtrUe5xcHTlBbiNM6zsCcx+eQRxt\n05YtTEtzDEnrLEggqAaWYBPXbIGDgWuESXFbn8Fv5gk3s9zYOU6fZ4tR1xor61Mk3IOs9O7hpIbT\nUsHrKnBDOkhO8iNJGquMUrJ5CcYLrBoj7CwNYFxTOHLiBsaoyIo5RkdcIG7Z5TTnSGejXK/ZCEUT\n2K11qjhp4UeVm9jlBinCFPDS6Nj4M+HzbEq9fFL8MoFcGUMQyfh9XCjfT9LoZ9izTEzYg7ZIK+9E\ndbXJqUG+Vf0oq8Lk3YhvV9c95a6UttVsEzFSJIR+qoKLtmllUl8iLKRJCL3krVGs1hY5IcD1nsPs\nmnEW9Cns2QYd3ULCF2dfiZKs9zCxuUI54kQNNnhGf4lhfR2r2UKRO2zTT8H0Ma0tEyaNzyZQO5Jl\n0jrP87av8DqPkSJCGQ/rzgEaWJDpYKGNv11gsrrEt6XneM92GjEvUg56WXJN8bz5NWLGPtPiHAvW\nSUJkibGPmxKOVgOhYWKXW8QsSR6VX6OhucnqcV43f5ZxFnmk/jZnt97ntdADVIcdPHX/C2wsDzG/\nN8W7Cw/qSOlLAAAgAElEQVRhuCWECR3rdhOnrYJdqtPCSgcFRdNRM238lhyBUIaNiWEszRbhnTQF\n3QWCQZ+cIC7u4qOIjoSVFj2OXdwjJc5wDpdW4ZvSR+gRkvToe5yrP0xAzeGxLxOpZmgKdhbq00xZ\n55ElDVVtsivGcVBjgiX2ibKhDiGoJrliAKlgUp7zYBnVcFNGNjVclBmhwTgrBNo5bM0mveYOYt5k\nv9yDL5rFQwVvq4TmkDElgXX7IC/Yn0Z3ivwCl4h30rRR0Bmi0PGzqQ/i0Mv0soPVaOLoNIjqKbxG\ngYXODI2oejfi29V1T7k7u/wZTRyagSkLDAkbjJmrHG9cpyo5WFFG+e8f/L/JWf18z3yCC/mH2NL6\naXtBP6+i1xS0B0wGgpsUN/y8/n8+w+zHrvHEB15ipJrAJ5VoWmWi7LNLnG1pgOu+Gud3H+D8rp2Y\noTBqXeUQN6jhoI6dXna5wSHmmMZCm0E2GS+vMXNzkcqIG3FAxxWtcKVw3537lSgQlDIMsMVNZrnK\nERL0coAFHsm9w8Ob73Dae5Vy0EEqFOFl31Ocl2XWdk4wHlvBaykihEz21Sj7RJhkEVukhpjV0L9k\nhWMi1rMNIr+8g8tVoockP8dfs0ucV+pPcvn1+yBiEnhsH2tPjbPBt/jw2W/xF67PUhS9nOASOfys\nMYxEggG2CJNmgC36SLAnxehxJYkIKYKkiU9sUpOs7Ii9PHrwJW6UjnJx7zT+WIGS4iLbCXLaeoGT\n0kWOcpV3uZ93uZ/znGHMtcyRgcu8dd+TuMIVhtjgYfFNNtiihzubP/nCWXqDa3jkAqvnJslci/Ev\nP/VbnBbex58ssT0eZcvTz0vOR5BoUBM9fJOPUAs56aAAJhmPn7Yucb1zCJtQJ2ZNciB2ndPSeQaE\nLS75NymfcvOFuxHgrq57yF0p7WLLh2cjwdFbt7D2ayyfGOVty/3ookhZcrM4OsqeFGOZcbbb/aQ3\nY4g3O0xGF5C9OrdvzZAK9iLrGoWHfDSHrORFHy+qj4No0pZlVKFJGwsuocwF6T6yHj/j/usct7UJ\niDlu6QcZXN2hLVnYGY2hIaMjUcCHnzwJe5z9wRiX2ydY3JnFYm2RIYgrVOF7x5+gHHFyg1nKePBS\nJEyGCi6WXGM4+mrYrXXWHEO8K5xGl2T8lltMB77BAett6rLKt8ae5TXrI6wVR1jsHOTG8hGMeQVk\nkZhvh8nwHPe73iIlRdgyB3hJe4qGYKNjVeiZTWBz1PEKeZJKD7oioDtEfobvsckgt5ilgotGwU5m\nLcPxTIeeUBIHNV4rPcl6Z5j7vecYktZxU+Ip64voSNip41fzZM0QzvoBFv5khnLQSeUJD+vyMDbp\nzvXyKg3GWKGFhYdvvou13GbowBYPlN5h+to8A74kX27qtJkgQR99coIP8XcEyTI+tMa+GmPVPUJP\nLUWffIG64ODm9mFenn+G/YkIrY5KYmOI2JEEI7FVppnjm/Xn2S0OYORFbgUP3dl/3AYr4hgaMm6p\nTMyWvBvx7eq6p9yV0m7qNoyyjH+liKJo7AsRtqz92Gjgo8Ct8DSbzUHWs6NUNSdmSkJ/U8L/CwVs\nwTqr++M0RCf2cIUDz97C781SlLxcsR2mgQ0DkTi7BMih0GGfKF5PkZngDQ7YGtRw8L5xiofy5+ko\nCnPmARrYqZgu8qYfVWxSt9vpDCssnD/AQnIWxgwc/hKBUIrloWGSUowFY4p600WvvEuvZYccAQpW\nLxv+AepWlQV5ivfNU/QZO6jSRQ4HLmOhybI5ymJ8ivXaMOl6hERjhFLOg9ps4ZvaZ2roNke973OK\n97nOYRa0KV4oPofPmmfSvshs+DYuSxnNlEgbYZq6imDASeUiXqnIJU7c+ZybKulShFq9gIlAES9L\n9SlyzSBPul8kSBqxY9Bf3sVQRNp2hZzkx2Mr4LNlWbsxga2nxYGHFpEMgz1iNLAzzhIRUgTI8cjS\nOzhbNfynM8zeWKA/lWRATPCS1o+IQQUXlkabvs4uvY4dxoZX2B+O8k0+SqyZ5n7lAlpHoZj1s7U2\nRM4WRG5rhNdTBMdzjLDGIW7w8t6ztJJOaEDd4sThquC1FSjiJUEvMfbp03fuRny7uu4pd6W0/fYs\nt6YnWB8YZsE6SYYgcZK4qCCj3bljyb7K5qVxOmEL2IA0XD1/ElWo4Xy0gKjqzMq3+HXld7kuHWGV\nUQ4wj0IHAZMAObbpY4tBTnOBEBnep8wSE2QIosptNg4O0xKs1LCzo/dS1Lx0DIWmReWs/C4f4u9Y\nvDDL1eUTiNNNRrzLnLGf42nxRa5wjGS7l73EIGW3D2IQIMeR4k2O713jj4c+R9tt4Sle5pXGE2x3\nDtHgCGXdTc200xEtPKC+wzPq92kZKu947qfY9vOk9BIh+539wV/lMbYZoNJy01h2Mxlc5uHomzz6\nvXdoxixceuowrYYVd63Ow83zbIbieO1Ffo0/IEkPq8FRzKkCzVgvc0xjo8FH1W8wKG5hih1kNKyV\nDu5zDaSwQWnSzRvOHmqyA9GvI/3zFk+J3+M31N9lTexHR8BDidd4jDwBznAeu9LANAQ0JMxBkXaP\nTM7rQrnVYogNvBT5QuIXeS97ll85+O9wOKskiaMjIWoG1mqb48kbEBSRnu/wzflP4LPn+finvkTD\nZaOJyqs8zv6tGKSBk/BU6CUec72ITyiSJcg+EfL4CTSKdyO+XV33lLtS2khQtdup2e0MdLY50Fhm\n09LHhjnIbjvOmHWFUfcKnx/9I14Sfob1njF4HPzTGVRPg/TVELYDNfR+kT1i7NBLhhAid24k4GzX\nGMxtY9ok9rw9JOlh3Rjmhu6mx/RRwc26HqJS8tGuWUEwGQ6tMmjfpCmqVEUH1ziCkxrV43YGBlcZ\nCq1yXL3MAWmOJir7WoRUOUJz1Y4QN7HFGshoeKQSfkuBXnEXDREBE0nRaWFltTGKJOs4pSoRYYeW\naGVNHybTCjNlWyDmTqIKLeLCDlrTwsupn0F0acw4bnFf7BJTznmOmZcZaG3TaUm0NZFpaY4BtvBW\nysQUC7ops2H3MrW0zKSxQs2i8XTTj9kQ2XFFWbCNs24dJCCm2V4bpLnj4InIy3giJWoWBwgwxgp2\nsU7eHcYu1umxb9OQJHbpYcUc41rjCGXcFG1eFg7M0K9v0yMlsMl1wERQjTsLmDSx0Oaw9yqSrLOm\nDJMxQiSNGA3JBqqJ4DBxrNRp9qisHB9BbLbwyHlswToWWpSMfi5op0lXwoQ6KQ7GrjLuWqQlWHm9\n/QhRKUVM3qOPBCXlR3In9q6ue9pdKe0ybjRDwdcpMtrcYLi5zb8zfoUFaYoF6xROpcr9/nd5xvcC\n81szbAgjiL06fX2bKMk2m385iuzSKPZ7eZcH2CeKiYCJgI6ErstYixqq2Ub3SNzQD7PRGSLdtuPX\nVXRdZq0yhplR0CoKHUHhA95vMyysstIe44pwjBviYfIE0B6SmeEGJ7jENHO4qHCNwyx1JknWezAr\n4GxV8ZNHwETtNFGaHSaNRUQ67Ao9RKwpNjtNsgthQtE0YV+G47ZLZIQg+60Y+5kePuT/Nscsl3id\nxxAAWdOoZj2MNJd5WHyTw703cMoVxLqJ1KNhd1aZKc/zUOsdPM0yHUHG3yqy0+zhsu0YH0y+yJi+\nwoShcbZpUtI91JwqL6jPsEeMx3mN91L3s5vqJ/jEPgOuLUTDwCq0GGCLgJnjrfYT6KpEweZGQyRD\nkAucZqU9Tl7wk7GFCE1lOMElfpZFRHQMXcRWa6FoEorZRjJ1joUuEQkn+TrPM6dPUzS99JoJRIdB\nJWBHvy6zWhjlfPsM1lCLtiSxrg/jFYsUDS8b2hC4DMZsyzwX+hZaS+Zy4STfaHyUB5zv8KjzdfqU\nBFflo3cjvl1d95S7UtoLTHFM0zi2e5NoK0215eTS+TNs9I3hfyZPUfJyi1ly+EmLYWR3C0e4CFaD\ntm7BbNwpNQORfaLESDLAFnF22WCI96zD3B6cYUMa4LpxhM3yAOWSD3IqYtPELEg0F13Mjl+DIYNN\nYYgxdQm5bPCd1eeRhxrYQnd2kJtigTFWCJNmjxhzHGCFcVLtGKYs4Xs4TY8zQT/b6Eh45kvIL2oM\nf36NgDtNFQcWOqwmZFLft5F9vIfxM+s8fPBNHFIVtd7GsdrBP5am5HagCB1uchDNJvGRia9wfOk6\nh1Zuoh6psxCcYNE2wakHLhIv7eFaa/HB11+k0WMh+fEgiHBbnOLvtA8jHTEZE4a58t0U4z4Dl1lB\nFDWcVOkjwSO8TnA2x+2JGTouGXelynBti+XQMDess9yQDhGIpXCKJVYZpYNCDScNbIScGUKkibNL\nkCx+8iwzge5S6NWSBOZLOCp1gmYWd7vCNekI15XDuClzSnwfq9DCIdSwKVWuBWfIPR3gevkQxcUI\nYsWk6vWSGOnFr+ZRpSbD6jqxR/eYNuc5oMzzhXc/x0s3n6XU9vP24OPcGDqBJdrA9JnA/3Y3ItzV\ndc+4K6W9VRlkVTQZd66RUoOsa0OkRwJYg02G5XVUmogYd36LOkZFor1qY8c1cOcFTuvU7XZSG71U\nCz4KET9ySONB5R0sQhtZ1Ni1xfGT52HjDZYsE5ScPlL2dVR5lHTDChmR6ak5DLdJgl7WGcZuaRAN\n7OKxFujrbDNdXySnetkVelksTzNiX2HQsklvPcWkucKme4AN+wCD0gYRI8U54SxyC6ZKK4iaiYFI\nC5UwaeIeA/dD50mOx9B8IhvCEH0k8AtbTCsrkNYxiRDpTWNXatikBqPONULhHHVBZdvWQ0LspSla\nkRwGkqmjdQQaUxb2QhH27SEETNBNnte+RtCdpSGpGKJIUXFTw06aCBMsESZDjH1s9TZDhW3stRpB\nJQc2A7+YI0yaXnGHCXUJJxV2ibNHlBYqM8JtZuTbdFCoY6OfBF6KdFCoyg5ydj/VgJuapUILlYoI\nmihjo4mfPD4hT097D99mmZQzzIX4fRCCmmojJKYYta3isedBMlhuj9EWLcxab3N/6TxT+iJeX46a\nw0VaicIqqH1NRG+bTaEfIXE30tv1DyMBKhAE/NxZqBIBHWgCuR/8NADjx3SOP5n+s6UtCEIv8FdA\nhDuf7h+bpvlvBUHwAV8BBoBN4BOmaZb+vteoNNxsyl6Ww8NoyMyZBzBjOiP6CsdqV6lYXYSlFJPG\nIiE5w2rVoL7kYdvrxRJt4H0yS7PoJLcTpripk9DitJ0KPy99gQPSPH7y7BNlkA0OcpNz8v3sO2Jc\nd26iKP2YooBXzjMobNJEwURggSn6nAnOON9G0xQmGit8tvIl/kr6NG/yCNfTJ/m8+kcctX+TCW0d\n3QVrrgH+lF/CZjRoGDZuSzNYrAaGW6Qoe0ib4Tt3x8Eg2JPlyGde4ypH0AyFFWMcXZNxiE0a4W06\naQvNXTs90SQBOUuINBba7PT1crNvmk0G6aDgNUq02yplq5v6oIVbozPsinHKuDERGDeW+Zed3+G6\ndJhdevBRQELl/2XvvYMku64zz99z+dL7zKrK8tVl2/sGutENRwAESJAgIYoUzcjvSoodTWhC0kqz\nEbOrmJjZ1WjkJja0OzsrcbUSR5RIgiRIwns00A7tu8t3uSyXlZmV3j+zf2Q9VgKElmCAbIEUT8SL\nevny3vuybp363pffPefcsu6iXHexT77KDmWWCg5i6QRji7cwAiIb3UHWghFCxiaDxi0cYoVulsjh\nY4KdzLEDP1lO8RptrFPEw1X20sEqbkqUcSBgUHC6SAy4KJ1ZplpTqTYc2NQGYTFFUXcjCzq+ao6B\nuSVSbVGmO4cJsono0ej0LHCKF+kw1yjgYbnQRRknPjXHzvgUY/Upsn1O3P0FXLkC5esuuoPzRLvX\nSK0GyS8E3pfz/zB8+5+vCWBzIDpFFF8dLwXsWhWxZGKWQasr1BAxcAGdCPgBGRMNgSxQRWQVlQKS\nrYHgBMMlUpHtFPDQyNkwygbUm+smP7Vtey9MWwP+tWmaVwRBcAMXBUF4DvhF4AXTNP+jIAj/I/D7\nwO+92wBH/WfpZ5Sb7GKWQWYYol+a42TiTR649QoTu3dg91UYa0xwzPcmqfYQNzf3Y04IdKTWeHDn\nt7gV20EqHCE8lOZWeZhCJsiMY5h1qYM43VxhPyVcmHWJr0//LIv5fqqLT6AWwvTHZun3zrHhC7NK\nBxIaB7nEEDMkaOOF9IdZrfdyPPQmuk3AV8sjigaxVzboz8bJf9pJ3SWTIcCkNspr9VN8R/8Iu503\n6FMX0H0yC3IfoqFxQL/M38hfYBEnfjwEyLKjMc8XCl/GUyhgAvHudp6KPcKGEOFnbf9AxEyiUqMq\n2DnLnTzFw0gY1FFAEJhz7CAgZDAFWBJ68FBglEk2CWBWJewJg862VRyeEpOU6cLAltc4OXWOyZ4h\nnol9mDxe9vReZ7hthqLs4ZZ9gJzu45HCc6i2BjWXjQxBnJQ4yCXcFMniY4ludCRs1OhkhWmGcVLh\nbl7BThVVa+AtVVjIVNhxQ4Q4VMecrA528o3NT1FW7MTUOKcOnyZi3+AY58gQYJUYBTzMMsg0w8wY\nQ2QdfgTB5C0O49xXYdPwckS6wJHAGdY62nk9cC82tY49VYMnFYSu9/3v/L59+5+niYAC/cfx3uOm\n/7PTfEL5Okfjb+F+sUbtNZPElMA1U6aCExM7CjIGAjomIhoiFZxU2CdrhAdNlOMC2QdcnOs+wpP1\njzP/pSHyr+Zh+gxNZq7/E//OHxz7vqBtmuY6sL51XhQEYQLoAj4O3L3V7K+BV/hHHFux1ekmzovc\nTw2V/cJl9grX6LMvkQn4qCs2HEIZm1jnDvlN1GCVkR3TnE/fgaw08MgFPPY8ZacDmTrcMCluerkR\n2Ut51Uk83UNp1M28q48NMcqqu4NkPUxdjzAzOUpv+xydfXFKOHFQ4Q7OMsgMQgUuZY9SN1RcjjXK\ndjvDV2eQU9+hfXgDb0+GM647uF7bTbVio+6QiQhJdEnCNAQOZq8S8GaYODaERyxilmHF0dx9x2Us\ncqd+huviHjoq6wzGb5FzeZjz93HBdYCSZKensUh7PklB9RG3d+Mng4YMCIjohMlja9S5vHiIsuBE\nCWo4PQV8SpaK6eBw4xKDyQXE6xDKZzFjArqh4q/mCRczhAsZEo0IMg1MBBKuNlS5RkcqiWFKpD0h\nsoqPqqhSw84sg9ipNNl3bo6K5GTF005Q30RHYlWO4SOPSo1lulmjnYag0i2vcVVJ0W4PcY/xOm16\nklFjige1F3ilcQ83tb3s918hbEsAAov0skIn5bqT8XPNRCbxsI5droLQ9JklfzcBxoiSYNMWwNFe\n4tjJNzjeeZqgLc1i/wD1bpmr78P5fxi+/c/DFOiOIO9tZ7R4liPqJYznTBKlIsKyg46Ly/RINwgk\nl3As12mUm19beoE6TYgX+e6fFrZe24F2E3xFEFdAvm5nYF3lsOame2USoVwmzDg8BEtdvbx0qR8z\n2QHLKaBx22fhg2I/kKYtCEIfsB84C7SZppmApvMLghD9x/qVcBNjjRRhuonzOF+ljQSJUBtnQkeo\notKlLVOoezkkXWSnZ5w1z4v8qfa7TDdGyMteNBQEoIaKvipTXHJzafggS+P9bEx3cGfnq6RcES7b\nunEMlPEHN1l3i8zMjFJKu9F9Ap3uVQaVWfqZJ8YKVyqHeHr5UU71v8C+8Fso1Nl/7jrH4pc4ceg1\nLn7oIE+UP84riw9Qz0t0qQt8Rvx7TBsUBQ/H189TCji4PjrGPWtvkC36edF9EjtV+pnnE43zaIqM\nXlUoJ5zM7urjfPQQr3APH+XbfKjxEtH0JnPuAWaFPg6Jl4hKG4zJE+iIDDBHuL7Jn4wfYloeRR2u\nckw9TV7xcoEjfKz+NLs2x6nN2HDW6qjoW8ktBby1MmWXk4CyyagxSbYeICMFSdciHFq8wUZblAV/\nD2vuCKJhgi4wL/bTEBTajQSPbD5Ph7KB4NHo1ZdYo51b0g6OCeew0eACR3idu0jI7fS757nmHGcu\nup/h6iwdnjj36K9wv+01lJLG/13/ZXyePKJpsC60M8koS2Y31Zqdqae66QrEuePI69xiBzVNJahl\nKCsObokD2KkywRhGBD76yDe4Qz+Lzayz9PFuRMF4X6D9w/Dtn0wTAAXZYaC6NNS8Cf0hpE/t4c61\nNL/jG0d7rsyV5SdJLIP0naYyfY3tbz4GTViVaarbJmDbOvSWdvM6GEvNg6er6FzlEFcRgXZgFyB8\nwskrxx/m3P9xCuVmCGEjSdUL9ZKMVhFpPhr++Zhgmu/tC+bW18dXgH9nmuY3BUHYNE0z2PJ+2jTN\n0Lv0M0cOO+nokZlhEPdYJ3t2auxkHJ+eQ9dlEnIbjvUqA+NLVPfIlNqcZPGzXu2gYHoQVIO42I2B\nyF6ucu6N41yf349zuETdoaI4GuzquoJpF8jRjN0taF42T0/j2bsbY17EnJI5dfJleroXm/HVZFnV\nYjxfe4hBdZYBeZZu4lRSLup1G2q0Sl72UDGcOOoVRNHAJtZpq6ZAgpLNTrXhQKWGR8wjmQYGIpog\nUZNtjL+R50NHSyTkNtJGiHzFy6Y9SM2mYqOGgwphY5MdjVsICQEjK+FUK0yEh5kKDeElh5c8Dr3K\nerGDBgp2e5WQkiIudnPBPMJjxjfoqcXJF720iQkE1eT5qy523eGjotvZ1IIElU3CjTTupTKGX0AP\nisgNjXmlj7jaRYhN2qsJArU8t1x9SHKDsJFisxGiJqiIioHLLLOJn+vCHlxCCR/N7b/idJEijIHE\nra8kcXXu556Rl9kt3SBiplh1tnNROMR1cw+yqNEnzTEozpLDx6wxyM3abqoXXHgcBToOx0k1wlQK\nLoSEiC+2ieqrgClw1LhAP/NUJRvT4waLExVMBDRBYeqJKUzTFN7pdz/QP8H78G1orTQY2Tpuh8WB\n7h/R2ArQQ/vhMsN3LbLzqWkaGzUWPQ6qjUX2SlVYManRBGcLmNl6LW6dN9gWNeStUaWta8JWO4Om\nRqW1jKFvXbeWMs1OgYLLw+WUmyOaghR1MPnRIWZe7yVx0b41Fz9K5v2jnOtWS24dlk2+q2+/J6Yt\nCIIMfBX4G9M0v7l1OSEIQptpmglBENpp5q+9q93/W2M89NkAU4xQwYFsagj1IfqMaQ6LbxFXbJQX\nA9i62xCPNxC6m39qjTq6kcXUJL4mjZGTfHyetyi7d3P10r+gOAjScB1vf4qQP0xds1OvRXC7c0Rk\ncMlFDnw2zMqFHq48c4hTj03RGTM4u3GcHW0X6HcLiHjpJkoHOj6CTDCGiMopXiOPB7MicCI+gatU\nwRAExDYQ7TolucELjiMoQp0RbQofRdb1Nq4Zu1HtVUpc5cOfSlGTTTYkgRnaeSH5EIpu5+ORr+Oo\nKrh0Dz1uH/aFBvU1ByuOMSqx3cjtHRzkEmGK2A0Jub7ObGOIy5U7cc+USBUHkex34dtVw6YkSa3s\nJNA2xYB3jsP/cIPg504wyQgSLu5ovMCd2QS+hRo2qU7dJ7PW1cYVtZswQ4RJsausMVrKsKoXqTsU\n6t4IT+ofpyCEiEmrDDONhoyPru/u3RlhmaLej2o66ZGWMOckvEcP4T8hEzWixGprFPyDdMk70M1e\n0nqYQSHIfVIOAThjdLJRup8NuRNdNag+lKCWCFLNutCrCqGxW/RFZhjWpjliBukQq6TlEBGhmzGC\n2KhTQ2VK+LfvxYV/ZL4Nn35f939/tueHOJYMhOnal2VgNIX9ZQ9dvipjPTWOONPUy2nGs3ARGIOt\nsl5NYFW2eotb1yxQqdAEY4Em+Cpb5/WtfhJQowm3Gk0WLm21KbENw8aKiUkeyPOzMkjOCG/19jJ1\nVWIl6qV6bzfz42FWrntpRqRoP8R5seyHOdfv1f7gXa++V3nkr4Bx0zT/vOXak8AvAH8I/DzwzXfp\nB8BF8zA92HiEp1BoMGGM8e+K/5ZuIY7fnUVCJ97fyev9J+ljgUFuMcokom7galSI1FM8b3+AZbGL\nouFB75OQBA1dlVBDZWzBMmtCB5lMhEwmzKBtggFxFr8xzf3mMpd2HmK8awxvIMfqejf/5eL/wM/e\n+SUOu89xnDNUsZMhwCyDnDGOI9PghPAGDUGhUVARL0g44g0Mu0Dy0z4cYh0pJ7KhtLFp95MRgnw4\n/yJTjZ38T+K/J2ZbpV38M9bVq9ipEmKTNk7zTPxjbFRj9ASXGM7PYa/WWVXDrPa1sdDfx4vcj2bK\ndBtxuoQ4YSGFaJqEiwXeKN7HHyd/D/PLImLcQG2rcvrXTqJ7ZM6fvovQfWvc73+O3dplpvRhzktH\n6WIFb6lE1Ngkt8cB48CsSD1iw1RFbDSwU6XqtJGU/HRPrbJq7+CS/Siv6afYlIKMSRP0skgfCwRJ\nc5q7mGAnMwyyrnXQaa7wM9JXKfYk6TzlIG0L86Z4lJpbpai7UY0abeIGnfIq7axTNl2oZhU3BbrF\nJQqJEBkhRGXDhTyuYVMaCAfLKK4ao9ok/6bxv/Id5SN8XfoEbrOInSoGIjeNXfiEH0pAx/vy7Z8I\ns4mIkhO11suh+ys89ksThObPUnlxk/SLTbcx2QZnlWYAX50mK7bA1bb1niWHaO/oY11rNZMmSNu2\n2tq32hq8XQO3HgbzGhjXkrh+61ke5Fnsd4RJ/c8n+Ob/1UX6Zjd1exlDK0H9JzeM8L2E/J0APgdc\nFwThMs35+zc0HfofBEH4JWAR+Nl/bIy6qaBSQ0PCTYGokUTOmpxfPMG/TwR46NR3sHVWm1uQUcVB\nM0U8Mplhtd7JF0d/Eb+8yaHqJf58/Xeo+RUOOM5zc2YfkUaSqLnKcqmTAdsCD8e+Q8MmsT9znUzi\nIoXyRzFUgQcCz+FTsoSjKX7jxJ+xEoixRA8/x99RwMMmQQJkuJC7kw0txlKwh35pHtmn8dyp+9gd\nn2C4MsuGsw23o4AsG4wroyRoo6y5OLlwlns2Xucv9V9n4UAnE8zhI4eNGhI6BiK/2v1/UtGdeKQC\n1TSsFbIAACAASURBVICCVNFoS20SUvO4HTXWHDE6cgkGi3Okoz7Oq8eYFEfxe/LMOoeIqstk2qP0\ndi1y4sFXUPsrCDKMPDCJP7LJnuUb1M7kOXTnU+wevomASfd4HHnGwOutEO/pZG1/G4ZDoISLBgqB\nrW3Hbsi7EXthYGaBu584w8pd3azHorSRYI0OyjiJkmim0JOjhIuC7CFMilEmWbuU5GBa4Gs//3HK\nQRfFsoeJ6T24QgW6ehbpYI0kEdaMGLObo0hKgy51mbmeUQJqmv7YDMc8ZxkQ5nD7C/z90md51fwQ\n2oDCRGUn8VoPEhpj7nE8ZpFrS4fwBjPvy/l/GL79428Srk/10H9c5fN/9FeEnpyicnWD1Ewz0smS\nOixwbqrdTWB2bF2vt7Sx9Grren2rj59t+cMygbdLK/JW3/rWPaAJ3gbboG0taNqAIlCYymP8y/M8\ntjDH8b5RvvTbn2Lh9RKVv1t4/1PzAbX3Ej3yBt8735Z96L3cpIZK3nAgVSErBZmURnDYq1QkJ6dr\nd+NoFPBnM6ylO6k4PRhuhXb3OrHlJHLVYGNnmGFxEq2ucjF1lK7OeaLBVbzFDMKcSWnCQ24oTCR2\ngRP+11klRkRMUhTrSIJOp7zCfvkKTsr4lDyP+b7Jl2w/xyZB1mmnQXOvR5UaqlhDEZpFqFaNGAm5\njSux/VQcDoSywavqSbSyjFLQSUXCBMgwWLuFa7NMZ2WVoC/J89K9XMXGNfYQIk2klKYtmaTHt0jD\npuBcrCKGDBpOEUetgadQpV7K4O4o4RTKOMQyThQ8egGnUWVWGUAVK3xG/ntyXWHsoRJtB5bJ4ies\npTnUdplJ2zBZyU9G6eSu3DqeZJELwYOU7E7qSNimNWx2DSWmYWDSXk4QKOTpSy8z7+9jNRZjydeD\nUtG47/prDB2YISCliZBkhkGmtWEma2PE1BVixTW65lbJdnsxIlDFTkl2U7B7cApbcbWCQEV2IIga\nFdNBvNGFQ6wgCiZrQgduIU9QTjIyMkFe86HnJdqC6ww6mntCqrYq8XI3T2c+Sk1UQAAPRdaTMdYq\nAmkjRNF0/ACu/qPx7R9fixJ0i5wYPY/ZlsFVkhg13kCYXWNltgnQItus2eKtIttAK7AtjxjvaGcB\nvtjSzgJls2Vsqx9b7SyNeyuoEJ3tB4e8dehbPytAPVNHfnGNDtYI9W5yoDTASKxG41iGN2/eQaZo\n8P+rbv0Y2m3JiKyIThb0dtSswXV1jC8HP4M7tkmvc44bHQd42XMf5opI9ZwHuuHOvtMM7ZikK7tB\noLzJSfN1uomzpPchlAwqdQc1t0rwwDrrX+tm4cvD8Afg9xXpCywAkPO7mW3r47gzwyDTdLJCkgj2\nWp39yXEuRI9wUd7PN3gML3l8ZAmQwenL07kVk/wV7VM8oX0SQTCR/RrlkMoXjV9g5tYY0rzAfcee\n4UHPC3yq8gSSBvUOicIRlXn6mDTrpM372MkEd6bPM3hmmWtH91FSndz7xmnqh0RKYyr1NgXvVAU9\nLZMPeUj7AuR9Lk4Yb3CgfoVc4zn+zPkvGazM86u5v4Y+OBc4xJfNx9kkSF89zoczL/Fc4EHOdR1C\nvbPG/dIUyrTGtw8/iu1AnR7/AqG/KdA9tUpUTrJ+LMhIcY7gdB4uQGOPiq8jxwqdrDfaEQomLq2E\nZDboZ54NolysHeGp1KN8JPwtPhn/Oie/dI7FxzuYjgxwyTzImQNFFj93kmGm6aZM2hlics8INrOG\nYtS5Uj5AVNlgt+sGI+HrlHGSNKPcs/9FFuID/M2lX2b40DSC3UAx65T7VMgYLE/30913i4H2aQa5\nxflX72J8fRfeD6ep2W5PvbOfRBPYyY42kT/+hT8i/socb/wJ3AKcNBm0BdYWexZpslsLbK1FRBlw\n0wT5Gk0gtQDXCuuTABfbmrcF+ELL+LS8brCtb7P1mSx2bQE/bD8UDGAJEBZXOPE7f8jhz4H3V4b4\n/P/2K1wqNjB/Cto/uPWywIJ0jLlANwExxae0r9CzsEZCjHKtYw9dtiX0Lok1Ryc4we/KUBac/O+H\n/ntKDRf98i08FDBcAmM7r+Fx5fCQw0AgdypMRgVESBdCXNEO8MzGR/DZc+gkucZeKjgQMImygU/N\ncz5yFE0VOcglAmTI4qOKnQpO9nAdB5VmmnbJR7ie5Rd9/xW7WGGVDvrEBUoRN0uVPi4+f4zh7lnu\n2neaSDWLWTExdZEHrrzM8pKdIH7uWHmL4coMlTsl1sJRrot7OHPyToYDk4zVJxjMLJDz+sh6PHwo\n/QpxLcayL8Yz4oeJ2pL45BxeMU/FofJy2wlc3hI2rcbPr/0dNYdKIJeFaejbu8CCs4tZQrzafwKP\nVkKR63yj+hg3Xbv5/Cf/G222dSSfju6QWFC6mRm1Meqfpre+yMPXX8DeXyO8K03c106wO41rrUL3\n9CpXdtXYFbjJsfB5/OomYneDpz7/Ia5272HCGGWl0UnKeBGRdmqo362rXcDDw403eaj+PM/Z72dF\njtFAYYgZ7NQoGF7eXDtJuhFi/8Hz3HL3M3NrEP2cjbVQjFrEga2zyKhnnF3cQEZD8dfRszLFCwEO\nD5zlzdvhwD9JFg3DyWN8Zvo1Prr8FBNfTJBKNIHaQRNkLX1ZowmIakt3S6O2zKApZ1jatCWdWOBu\njVHeGkdpaWOBsqWNW0Dfytp13q6NWw8US1qxs83OrYfI2mmozKzxe7n/hSf2PcLfDTwCp89BMv1+\nZ+8DYbeNqlREOxuOMF0s09OIYzc0/LYsw54J+lggp/lJyVF83gw+ZxYBk/O+YyT1MBExQQEPkqpx\nLPomOiJFw8N8w0ZwLEU4mEGtVBkUZ1CLGgtaPzuqt4hVsgiaybrcTsYM0GYmQDK55e6jlwVirBEh\nyRz95PDjokQ/86jUyONBE2QCQpZd4k02xAgrRifHtbN0O5eZbh/GNmUQ1TeoqDYWI10YTgF0nd7r\ncfpXbHRzg9GNadobCapjMiEpQ1DOsNrbjpESMdIy60o7Zb+duk0mtJHDNEUyQuC7O8uEtRS7NiYR\nJJOGV2Y9FCFQzdKfXaaCnZqgctm2l0SqnQZ2BMNE94noNDc6Lpge0s4g47tGSIkB3BRwU2RZiZFW\nQ3R6l2mfS7JzcQrTD1pYxNxn0mGs49soEkrlGFyex0EFNVzBK+Qp+V2MHxghgx/dkMjhQ0dCRkNG\no4aNOjZ85PCYBRRDQyxAUfRSVDz0OhcJypvYqeI0KvikefY4r/CydDdTxhgFLYhfz+CRcxSDDgTV\noI4NE+iIrDBY97GY7EevKN/P7X5qLebY58Y/qBJzLXFMfI3h0ktcvQRlswmGKtvSh8ViJZogLrAN\nzGbLOXyvlCK1jCGwvfjYKoNY71nnbN3HAmyhZSy5pY0F+PB2vbvOdqRKbhG0pSJD6oscFj1c9/SS\nOqWSm3FRuVZ6X3P4QbDbAtpz7GA3Gco4SREmIwd4fegUNWzEWGWddqYSu3ji7KfZe9db3NFzmvt5\nESEhotWdaCGFkugiTIoHeZZFermgH2WusINDgYs8GHuOdnOd3ZuTOJM1nuh8jN2la/Slr+GsdmG4\nBRQa3Ku/TIAMZ8U78FDASRk7VRSaG9QOM42bIkXczNOP7KnhYZNb7KCCnaCR5dPFJ7DZqmy2e4h+\nJI0i1yjZnLxw9DgVHPTWFglOF3EkCgwzjSdbQCqYuAIN7nW9wR2uC2S9Trw3KuRSfp556H6i7uau\nMGc6DzMnDFDAwx2cZbc2Tn92EfGMgOCC+i4bpyNHWXJ2sepoZ0XoZMMXZbMzyKsvP4iwbNJvfIW7\n9TkUGlyV9/GA43n6WOA0J5lmmDApdnKTBFHWhXZKqgMdEbVUZ//6zSazCZrYaiBKJkIX3HPrNG/l\n9vP/3P05HuJZfOSooXI3ryILGk/bHmZcXGUHHkaYasbY0047ayzYurmq/QYX5o6T0cKowQpKT40h\neYpuMc7jXV9mLDXNyK15lAENaVBnpb+Tw8JbyILGefEok4wSp5udjLOv/TKj/gm+mPplLmhHb4f7\n/sRY6JdiHNiZ4RO/9K9QVxKMG00wdLOtO1vgaAGtlRBjAaPFrGG7BFSdbRkEtnVvS06x5A4L5C1m\nDNss3YowoeWzWO0txm5p2e9k6NbnrrKd1LNpwltVcF/9Nl/IXOTVv/xdblzrYOm3Zn/gefug2W0B\n7cdvfZNTT15g+UQ7qVAYWdBwCkWcCATZxAQ6w0t87ugXGQjO4iPDAn0MxSZQtTIva/cyJM7QJ83j\nJ0ecLhb0fqoFDxkhRNzdTLzJe32YqsR+tZlZOBUYRFMPkyJMlA10SULEoI8FXJTwZ3O0LWyi9jRI\nB/2UcFHEjYDJbq4TE1ZooNDLIsbXQTyrE/pECkXQUHM1Mvu9OCYVAqfzHN9zgdKIA6PLYPrxARLP\nrNBWSbEy1M5GWmfH4iJyVcfhqyLs00jsaGO1N4bXkSUr+KgV7OyZnGB3YYqqakfdW6LkdDDuGyV2\naA1fqQh5WA90MC0M0BAUqtiRBY1OcZXfjP5nKoaDb6ccPCk9ipfmprtBYRMHFUaZILyZIVTJUIuI\ndNviBNlkWezCppr0epfJRV0kghESapionCYobxJQssindXa8NM8Xnvl7uh5YprDXRXtonXPiMeZK\nOxiP7yX9rIY0v4/hX5tm3t3PmfJx6gUHdk8Zv3eTB/ufZnZ2hMs3DrEe7OBw8iIfnXqO4gE7K94O\nJnrH2CPdQK3X+Vv1M0iCTi3lIHGjm5LkpN1/g892f4W0y8/ZxjH0ZYU9oWs/tIzIn2SL7DY58Gsm\nsZXn6HxmEj2Vom5ob5MgLBnEYsBOtuvxWYuLFuu1wBq2QwHFlsNixu8EfuterSntDt5eP6aVwVs/\nrYgUi/VbY1vM3tLeha3xrAdLDagbGsJGkpH/9LcED4yQ+M+9XP4vAqmb7ysf65/Ubgto7y9d4b5b\nM7zsPEl5WMXWUyNEmhp2FKNBKLuJQ1xEGtToZAUdkXkGcNeKNCoK1419VLxOCk4PXvLUsKEho0gN\nsmKASXOUJBFUtYbNXqeddYqyk1nXAE4l0pQ9hHlygg9nuUJfLo7mFzF1kXQ1RFFr1o2u4GSx0E9D\nU9jlvcawOIOHPOt0EF1IEnlrg8qHPWg2BbMkMiMMEammia2n6O9dorqoUFh0MNvppRJxkDZl0h0B\nXHIZcwXIQUlwMa93c6ujn5QSasZJ40AwBNyVEh1TG6jFGpmoh7XuKFmHj0ZMxpwRmgGzIRNVbeCs\n1Cg53Xj0IgcK1zjsvEJe8XCWYTLCYTJCkGGmAZA0nbHSFLHkBmLN5MXgSZyU6BHi5PBR8ajkY27e\nCu8n7/LgNCsU5TKSzUNdkXEGayhLDUYTk7jnyjhVDyPhGS53HGRc2EmtZkcu6cgpjaQeoW7acOpl\nhLpMpeQGGe4MniVv90EBDE1Eb8jUinZSeoRxZZTL3v08UHgVXVeQVJ22XBJ7qsZq6Qaz8iCqvUbU\nSJI0gxQED14xT7d98aeg/X0svMtk+HiZgz0J2p4+i/PpGRpsZxy2SgywrUvb2A7xaz0sHVlim3m3\nSiGWZg3bLNhi0f+YTPLOqBPrXibfG7kitvSzDotxw9sjWRrWeOUqsafPEZY3iR01KR5vB5ykbv6g\ns/nBsNsC2vU2BXdXiQe/9BKV4zZSv+pjnn6W6CGjB3hk4gU8tjw3jowQIo2PHIPc4oUzD3N67T4a\nBxXmB3eQc3qJkGSEKQbVGVZjHeRFH1fYj2CYDAkzDAkzXGUfDRTWqXAPcYaZoZ95brAbPWnj8IUb\nTB3rZ7xzlOsH95CWgjiosJNxXlp8gJvF3Rzaf4bP2b7EkDnDl8VPc/fom9xXe4Wbg2PY28o49Apv\n2u5g9OAMBwauUwyoiE+aRP9DltDnLvOGEeVbjo9xQLhC1JlE6DYhBEvubv6r+5cpyU5s1PGTZYA5\ngp4048eG0CdFRs7PERrLobrqlKJJgvkitssNGt9RGBqaYacywUA8zl/1fwF7tcqD11/GFm4QjKS5\nz1ghZmpcF/YwTx8+cnRXVtgxt4QzX2FaHeJvjc+zjyt8iq8wzDRKpEE81M4XpV9gyJjhX+l/ji5L\nbAhRZux7CH4mg/x4g6phZ/TFOUKvZLirfoGrH99P9oCPnl1LpB95kcM/c4uX3fdwQLzMJ21PsBLo\n4ttrH+fM/F3c3LGLdX8HUq9OzL7KrbY+/qDr9wnaNllrdHAhf5SXsg/jtecJB1Y4NvsWx2tn+Njd\nX+cv5N/gmriXJ+RHKQsOTFlg+OBNEH9a+e372cFfNznQuU7gN7+Ne62ASjPb0GLXrZEeFpNtXXi0\nQvqscDzrNWxnRVpmjWWBiiWJWEkzFmhbiTPWmNZ9LEbdyuLfmSLTGg7YGm1itWuNRLE+33dDBp+b\nwxxPc+KPH8W5p48Xf/Pd5+yDbrcFtBO2Nr566BB+b5ZZdYgb07s53HmW3Y1x3OsVem1x0r4ASSKM\nsxM3RfZyFX2PSXRgFW80R9qIIG0KHPe9iSbJLNV7ySQjFG0uBLeGojZYKO0gXWlD8tcZtk3RyxIG\nMRJEcVBhhiEmA2Os7e3A4S+xKnbwhu04/czjocAEYzjbC+xpXKFfnufV9L08W/4IUkeNym6VpVgX\n8WAXPUKcqLlJBSdpZ5CU4sdRqaL66kjHTSQaOEoVosIaYSOJp1BoFuy/Dqq3TrR/g7itCwOREGk2\nCTIv9pNUIxw/cQa1q0ZgdBO8TdFxyrUDv7dAl3uNbjGOomn4KzkeuPASkqZjF2qc9R6m4rchipcI\nCWmiJMjiJ1ZI0FZOkWgPMxEZ5aq8D4etQs/6Cp3pDezBKpteH0lXmDYSdGVXsad1zrUfwCiKjN2a\nRRsBSTWwpxq4E2WkuImUqTN69xQpMcC82M9MeTdLK48yH+4l6MnQ61hEwMDlL2BTy8wpA4ghg2H1\nJr2uBdKNMFeLh3CKJcrLTipXPdgOZrG3l5obPnfVWDfCfMf5EdJiEJUaC/QSIYm7UOD61H5o/8nN\nenu/5tjnJvSLMTpWnyX4zDmU1QJGXUdjO97ZAkoLsFuB1GK8rTo3vJ0FtwoMVjuNt6e3W6zYAtfW\n960+reD8zr+odR/rG4H1sGkWj9vWxK0FytbPZsk0tq2xqjUdfTlH6C/P0LkHev70QyS/uPZjtzh5\nW0C7KqjcHBihNqDy1toxJtd2cUp/iQONywQKBcw1g4TeLGi/TjtOvcxIfZqe/kUE0aBXj3Mjtw+x\nYbCPq6wSY8Nox1mpUkdB1Bu0s05Vd5GtBdlhTLGzMkG9OIu3MUhRcbNk9mCraCQVP88N38dubiDp\nBoF6jpHcLCE9xZK3hz2+qyhKHQmd1+r3sVAd4G7zBerdMqmYH6XawKxI5AU/pilgVqCRkfHWdGxh\nHfNhEOLgSFYZYI5IOY1jvQY3gBnwdOXZrV/HblYoCS6CpFmngwRtLNBL964lEv0+KlMFvOUaStSg\n6HZBTMC7P4/gNZFrOlLaYP/UDSpOlfXDUc76j5B3uzHEVToFPxIGYVLECuuEChkWQ51MBEYYV0bY\nU7pBe26DXC6AW15DlsAmaBxuXCJWWCdf8zFpjOKqldmXvolRNhExsJV00u4gq20qgmqiOwRE02BW\nH2QyW6M09QCUdCZ7xvA6cshoiB6NdvcKG3qUsDvFgG8GhTqFjJeNzRhOZwGppOPKVHDkKnirWbo9\ny4htDeYqfTy59hiqr4LNXmep2kvYlsKv5ShkfQi+nzLtd7VoGO+wyq59Wdr/cArHMzPfDaVrsA2Y\nzSLAbwfmVtnBAlfLWkGxNc3cWiBsBfd3jiXwdpBvlTis8VplmtYxYDtCpTVixSpIZQG0lYxjjdH6\nexlb76s1Hflb07RrAXb/9mEuDfuorNth48cnHPC2gHa7maCP8/wFv8FcpBdfIIluEyk6ncg0aPxJ\nDTmSZdcdN9nNDXzVAr0bazgjFao2B7sLk9xwjLGkxlDEOmOM06Uu4+vNMC2MUJHsnBRep+Jxknd5\neUh6mgNL13lzcZn9+Ze4GtrDTWMXn1z7JiXJxT/0fYIgGXZVJ/n1jS/ifLOMVNJpHFXI9TiYDQ7w\nDA8RatsgEEljygJlnNgbNe6Nn2bKM8SzHfciCw265lZpO7uJNKpjxEDrBjkDNqNOJyt4EyWUcRPe\nAI5D8ESGE8ob7DTHWRfamaefHhaJsUKUUfpZQFotMvH7NfrCdfZ9ooF33w2SA0EmhgbZdAVpn9gg\nfPM6bMD6aJTTe45yw7aTNCHKxMmwHx85hpnGV8jhXKkyuL7I4MA8ekDm8dlvctW3h7/e81l+Jfv/\nEiutEcjl2JeeIB3wM72jn03FT87u5a3gfvbVbyKKdaZGerjQe5TlRieyobHuaeeWPsB8pZ9q5Vaz\nMnVBYsK5k1RbECdluomznyucNu7CI+aJkuQGu5lrDCOWDHoj87gO5CkPu1h9rhchIXH0sfO4hDIr\niR5mn9/JyPEb2HvqTC/sZqRthu5QnM7jC7iVIsu3w4F/nEwQ4O5jRFwL3PMLv4Uzmf6uzqy/47CA\nzgJu2JYyWmOsrbbWQqUF/tbr1iiO1igSraVfa3y29eCQ2ZZoLCB6p4at0GTU5tY4zq1xqy2fu7U6\nYKtZAG5utWfr8xWAyKtXuXtilaX7/5T1e7rhH576/nP7AbHbAtplhx03RQ5yCbtcoSh7qGEjLnax\n5u3A93gSp71Mdz3OLXkHhQo4VuaxO+tsOKK84TjGmtLOktDFfKUft1LApZTQbDJB0giYBMjQKa2g\nSA1irOLJFrCv1+ifjSPrOuFAih0rc2iKzKO9EhE9RWwlQezVVTajfqrDdpxtJdYc/VxOHuTMuVOU\nYm66O5f4SONZRq9O0nYzQVDJEjy6SVtnAidlgsEM8rDe9JwlEPOQ73ORzLi5yD7ag0li3et07Vhn\nau8OykMqO5jBpm3gzpQIXchhDMHmiJ8VurjOHiYD/Qz83Ov0F2eRtQ1kvUbW5ueyZz8mAg5HDa1D\n5NLYfib7BonbY8zX+2k0FEJmhflyPxtz7Vx5/gjLu/q4Y/gMe7jBmGOC7sIynatrLCsx7M4KV9hF\n47TEjjcXcRyscdZ9jL8q/TyCW2OvcpUu4rgulDAlAfvJCnsyN+kzlii3q+wUxjleP0Ou9jRfDplo\nx0YJypss2XtYTfbg9meJKat4zTxokJECLMtdSGg4khWEayZKpI4rWMJmq7Pe1U1DaMZ4l3Ah++rs\nPHCNvOxlLdlFyXA3HaoE2akQ9vZ/XnWUv7+1ITDCx8ZfZ7/wGq6lNWym8V2gtQDZkg8sFmoxVQsE\nWyMyrMQWC5wtXbs1Ld3Ssd9Z7U9sGcdK2KHlJ2yzeatI1DvvSct9WqNTXGzHZrcuYpot51ZFwRrv\nwuzLVWxLa3z6wt8yYpzia9wH3AQS722q/wnttoC2Jsu4GmUOSRexiXXmjAHs5Tp50U/aGST2My66\ntGXC9RUaBRskZEiBEm1Q9Li47tiNJkhsaiGmtWFESSeyVajISRkACZ0gm4RIU8ZFRgjQEDapFJ2o\n5To9/iVETccp1tjDdWRDx15pUFmzs34kQnG/gxBpJhnm0sYhJmZ2YYoSYf8mo7VpRidncLxegw5Q\nB+t4KFDFTjnqYNXVBmsC5pyAtiKzsi/KcsJAEfrJBANo/TKdexMsDnaRd7nYMTeL01bBtVql61sp\nSh+xsTDUjU2oMy/0kw356fn5BPpcic25Gp5aiUrFQdLT3My35HZh9ousxNpJOcK4FyuE7ZvoThEn\nBRq1Ctn1IDfP7ac6ZEPoq+Mix0A6zlB6nophxyMUiJLgjHAMIyUxMLEIQxCv9fBy5T5GnOPs5CZ2\nvYq0bCAKOsFKlp78OiZQMB1IaKimhh2d66EjmAcvsZNxXk/eS77sJ+pNoVKjQrOthkwRN52soNVU\nljJ9NBoy9aqKVlAw/SJF1c0cA6CbNOw2In3rxBd6WcoNgB+yuSArqW5SC20otp+CdqsF3CI7ogqP\nLHyb/tLLTLMNVhYQWtX2WuOxLRnBAm5LcrBA0AIJq5pfa8JN60Jia2y1dc0C7dZFT4thW0cr67b6\nW4uIta3XrYuT1jgWa7fAvlUascwq/Wrj7fKNAeiGxolr36DTVWR+4DjzGxKZ4jtn9YNnt0ceqSTp\ny1VI+iIoYgNvI8/Y9AyaU2J+tI8qKg1JxmMrsO/1mzjW6ggBk5H4HEpDozjiwi9nUKU6d7rOkBLC\nyGgc4QLrtLNAHwbCd0us3mQXw8PTpI6k+dLRT7Fii+FSipw6chq3UGSZTuqyDe9wgYH/bo5VX4wq\nKnVsbBKk1qHg+lyWPvsiO9UrjJuDOB8uMjI4B2lYaevkDHeSJsS0kmHGO4DpkKh1qRTrLla8naR5\njo9xnXn6SdrDmG0CdkcNfUHE9hcmtjBgmnAdbIcbOOtlnLYyA8IcJgIRkqx3Rki4Itxx8SKRWpI9\n0evcZBdluxM5oHHv1dOcWDyPsGbysUefZm5fP3+Bl1/iv/HYyLf5w//wrwmGktSxcYbjyPGzqKu3\nGD84RDHkxFZr8OLkh1EG4aO/+xTSLMRKyxyPvoogmSzTxZPyo3xs57PsKM8TXc6yHgxTd8t4xSxp\nQpRsbmS/QVlWCVNiiBmqATsBX5Kj8nkmGeWccIyAPUs76wwwxxDTqL11zj10jGzET3Y9SPatCDXB\nTq3LxrPGQwh1g/qqnfzFMOVbruZ/4wF4/fo92LQqxTtUFkO3ozD9j4/dNfYm//Ff/Cfm/mqd1ctv\n/+e283YGbAF1a6SIpRlbQGwBrMWGnXwvA7bGsBYYrb7vZPKWVGIBrdkyhsC2Zm0BslXLpMS2hAPb\nurf1e1gSTZGmBGItOsLb5RL9Hf2s2iYzQPvIWb70y1/gt794ku+81cv3Lod+sOy2gHajpLBk72Jc\nHCNNEIdUYTo8SLzQwwvXPkSob4M27zqT4ij7em4w3LiFO14it9vNLc8ALxXu55PyNwhVkzyzt6nV\npAAAIABJREFU+ijpdh+etjx2qsytDDKTHcbbn8XhLCNgkqANzSFTdU0zr92Nwyxzn/o8Nzw78ZNl\nmBlygo8lsZen5Y8SFjbw0qzr3VZM8enGVznpO01Q3MQtFkiJYXIlH2SAHugur3Dojat8Y/ejNHwK\nHcIag415anaVGd8AFezUsSFgNJmmx86V0V14xQKd+irKLp1quw1DFnEqNaR2k2A6z53yW9TddqZc\nQ+TxoqllzIDI/EAPCWeESUZwUSQkphEU8Gv5pqfWILKaZi3azry5l2+Xh6lV7aQ87dyjvMohLiFg\nYruSJXVZYXVvlLQjwka9jUA0TXBjEzMrstnrxozp7FBuESKNlzwusUS1R6aYd+CplEnIbcTtMUyg\nbzaOWmswOzxAdHWDe998Bc/+Agfsl9krXKVTWCVEmlAhTfsbqeYOOm0FMqNeXJ4SHcoK1YabTmGF\nB3c8z5w0QMIfJSe6Kcz4qVxyYZyxQVpo1vbsg/yiH6dZYNA3Tcbh58eAGP3oTRWxP96L1JYl//os\nhVRTFrAyGVsr81mg2Fp0qTUtvZUpW320lveklv5W+F1rAanWkDtazq0xLGC3Mhxb9e/W0MJ3ix83\neDuAs3Wt1vKelcTTKuVYLNsa00rosbT5fLJI8rUZ5FOP4BrqpvTVJWi8tx29/instoB2oeFi0d5J\nKhVBUk1C3jTLoQ6m8qPEZwYgZFJyO5kSRxDHDFS5hnqrwUy4n7eiB3g1cy8npPPYNzW+dfUTiEKV\nzrZF0oSYXRtjaamPgY4pRKdGHRsKDbzk0Y0Y5ZSXHnWZfd6rPMHjaIbCx/VvMS/1cUU7yNcKn+YB\n21MMKDMkxQh7q+PctfkGZrmO4IdksI2nXQ/T2FBgDuiDSCHF0MYcDAhUfXYk06CvEccUoabKbBCh\natQRGybhUpqqYGeqfwcHrl6kI76ENgCVHhXTLeGI1pAM8C+XOKJeYSnSy6w8CIKATaojyTqZPh9z\nYj8zDHMPrxATVpte6ARCNM9LUEh6WDVjfEn7LFrZhtss4bUX6FHjhGspjPkS6UkP+aqbJBEKopuj\nnjP0LC+QWI+Svt+HEYKR4jSD9hlscp284MEINbdXs2/UWRR7GGcUA4H+1VUChU02BwKE0xMcmFzn\nrZ378OTyxAobyD06u203OVC6SvulNI5qlcqQnae7P4ToM+iVlphY30tYTfOxA1/jTU5wuXGQQmEM\n44KC8YrapEJOEIIGkqzhtJUIN5J0FVcQVJO12+HAH2iTkWQnvafcOHJ2Lvzp22WIVv26deGuVQtu\nTZ5pTV5pBbzWiAxrcdAC1dbokNZwwXfKEVrLNQswW+uMtEo2rck875RqWpOCdJqVBeHdk4KUlv6t\nBbCs5BsDyMRh5e/B8Ufy/0fem0dJdld3np+3xb7vmZH7WpVZ+75oByFLgGQbGtlgjGzcbs8Yt7t7\nztjTnjN/9JzpsXFP99Cnme4xuD3GxqixLQshEAgJoa2qJNVelZVZue+ZkREZ+7689+aPrKd8lQgb\nYyjJzT0nTkS+/UX+4nvv+/6+9156BmxMfM2F1jT67rz37M5I/iJW4tk1nvizv6Der1B+xEZkLINe\neZ5i9xcQlRrrrQhjlj3s5TqNDon/9rGPUPHa2bQEGQhOMSYMc70yQqVs567mmwwxzhlOk3YEUbxN\nYlICK1WqbNVYFtDRdJHfSX8WrzNHAQ/dLNJeT+DNV5n2DZOwRTjZ9go/33qantoCZx1HqXotzEx3\nEPzflvDe1cL5WIPYrgTuUBEiwDU4s+sEX/3QR8j53VhockMYIe0IEhUSBEnTRgK1tc6h7A20cyIF\nxcXqB6Jk/rJM+WnwCBC6q4Z3r4Co6Fsjzwr0w33iqxyoXcVlKdH0iNTtMp6FCk5HFb0LeljAK+W3\nnneDt/6DMUABu7OKX8jijq3iCRW5W3yNVaWN/1b+BX5r5o8ID7cQezWc/vpWinspS/d315jz9fLl\nhx4n7l2lL7XA4YUxmrthIjDMG5zk56rPYhfrJDoCXLPspYiLX+RJuvuX0JoScWWV6QEbr33wCF9z\nPcbalyJIZ5rs//1xHo6/wN3Ws+R+yU1FkxGtGpWgbavJhZBkVimzLLXxHI+wSYhMKsjmpXZaz1m2\n5oViwAdAubuObzDFsdNvEVjN8vJ33o/t0D8ufe1PxkJYq1088Yd/yrB6lgW2fLnxwzaA2wAyc1W+\nnUWYDJA0A+XOMqlm3ttcrc/go809IM2NDIz3nUWgzAk15mQZA5wtbKtHjDomBqgb9U3MzYLNWm6j\nNCy39jPAHbafPszO4LHPP8mwtMgf1D9FnWXeq5OSdwS0J9jNSanMkG8BJdmk8rwNb7qEJdRC7RBI\nWIJIlRbBtTzecIY1Z4yc3UNvbQl3vcyMbYCi4EL3ifTsn6E3Mks/s2iIqC4bNwMjrLXakRdbNJI2\n9KZAOJYmIGSxRMFqqRKgSg4vFcnFVdsarxfv4WL1MGpJwq40CLs2sdiaLCsdNL0id/WsY4m2KDkU\nNsQIlo4WwWaG8FyGFXcHNwIj3Cu9jIciZcEJsk4BD2iwOzOFXF7Co5XgOthWaygbTarjZfQiOBpg\nK2koFbZ+QUGohxU2Y35wg1fP4U0VKehOaoob51qVfnEeq1YnGEoiyS0S3iCIYMvX8eZLTHn7yQa8\n7Bu/StzqwUORewpn2LBEQAfNq6O7JcSiTF7wEyRJl7KI2AGNgEw9rFDFSsnuJBdyo1hqiGgoNGnJ\nEmpVxFGsYfPXkDSVvsQSbqFMS1UYujbPX7V6eMb3GBfSx3G3Z/Ef3eSN6mkaFScFm4cDjsuIYouq\naMMtFRlkmoiQZNnZuUVVad0kJtvZSLfR9EhI9zYQR6HltbD3xDX8I2nmXN045SIeW45ywkbD94+3\nfsSPyzr25zl0/xSxr0+gLK6+XV7VnEpu1KI2Ny4w3s0V/HZqtHe+DFA1ZzYa72YNtVlbbaZVzCVU\n3wnsd6pFjHVGtGzcgwHaxrmMezVfpzlhyJgMNUfdZnXL2/e9uEa4f4r7fnuB69+tsHbtb/ni30W7\nI6A90xwk7VimfsKK41IFzysqsk8FK2g1kXWxDUupyZGZq+R0B0Wvi7bWOieLb5FTfLwcvwcksPjq\n9J2aIso6UTYIk2LZ2cuV1iGmm4NYV5vIN6BccHFg/1Vi0jrzPcdo02UONK9QUV0siUE83hzXl/Zz\nY20/6oZEzutHjOkovhZJSxQhLOD4OZlK3MtSrJMFuQctJBGTEnhrBSoOB7WWjZPiGwRJs6h3oyNQ\nVp2U6y4G0vMUyklQQcsKWK43aZtJIcjAHrZcfpytkCgDdECzW2GjI0TDIeOsVHBM1WjIFsouJ2oi\nS6iVxSsW0DWVfMhFwhum5rXhdNfQ2eBMxwnKVhuH81/neCWDpMFoehKbq07LK5DvdVCbVtAyEiXV\nSRgd2dHk6uEDFEQP3foiTq1KzushHdhDF8sAhIUUGasXX8VHX2KZbtsS9boVz1gVS1hFEVS6p1fI\nFQ7yVuMk+c0QQ/un6Ty6yDOZoyzk+pkJ9/K/tn6fLkuLosWFiyIBNYNNrTNhH2FaHEBrSWRmImQb\nQTjVRBmtgyrQKkkcCZ2j07XESuuXkCvqFn12T5m86rsTw/c9bDb6RlI89muTNK+m2JjbksKZ61Ib\n7b6c3J4paNAnMtuJKjvTxw1KxJhkNCJjA7QNxYYBrOYqfwZtYdApZp24sc1OB2JOxDEKVRmOxYjO\nDT7dHKGblxnHNiSGBg3DrWW2W8c2Ox4D4BOA2JPm0V9/jeJaL2vXfGwrvN87dkdAe8Q2hsciUOlV\nKIeiVA456bm2iitRgVXQfl5A8whgA9fZGoOri8QSWfy2PNqIxOhHxxiXdrNAD2WcHOMtFJr8FR/l\nmroPUdOwOaoc2n2JwbZZnpt7DG8kTzCRxkoduaoTzJb49OafUXC6aA3ATHQQ2VcjMxDAs5gjmt7k\nEfFF5qMdaHURy1qDP/V9ku+ID3CQS+yemWTw+jz2jTqBAxm6LYs0RYUWMn6y9DXmcW+WUdetLLW3\ns2pth6llyh+1oX8IPGtVWGdrBIXZAutFtjoh6WCjxqC8yEpHlILoQtVEAsU8rkQFe7LGZnuAlcEY\n/a8v4vA3cL2/xE12seGMInXrvGh5H+1X1znw1a9QHXWxfHecdGeAqLSxVbtaqCAGddz1IqeVs9Sw\ncK25ny8m/kdGHdf5hP/PCGdzLCpdXPAeQEWmgWVr3oABdtkn6ez4CnHHCmpBRtjUoAWtNpHKfRY6\nXlyi3f43zHUPsPZkB2Pn91P4mA+9CDdmD/C5Q7/No65nuJeXucZeIvk0+5M3+XDHN5hwDbMhRZFP\ntpitDDDTHKS+5oKKAIqE7pSwuWv0yAvcffEsPcklzt5zipuTbn56CRIZ2If9pfME595gc6rwdslU\nA4jM0WZzx99GjRFz3Q5jX3MquGhab+a1DTMnxpjNANE628BrOArJ9FJNxzBH4VZul/A12EqK0dgC\nXoMPf6daJIYzMiSDjR3nsbKt4TaidMNpOC5tEviVl3HN7WWrA/uVd7i7d9fuCGj3avN0Fy3g1Ei6\noqwG42hI+EM5lGITn17YUn20B/FNF/HUK3hiFaiCRasTZhMBKOChhItFunFRwkkZjy2HjzSjtmuc\nVs4w5JqiKVvYr1zGWi+z6+I0kqyRi3uxumo4bAJpvBRxUbNa8HqyLDQ6ea10krQjhFUuE3Oso/aD\nEq5hsdTJCn7qTgvNmMQF3wHGbSMUVv0QEQiuZAjdzLF8sBPZqhJ2Z5DcLQoOJ98MPcSwdJP2cmKr\nl1MUip1OlvvaiV5PEazkoAKtNgE9pONaKTMn93MxdoBQPEeXsIKbJvigFrKS83tRgzJOKgRWC3RZ\nV5FtGlmnh5CeIqKksDgadAhrZNQAZxwn6RXmGWKKDlZQ2jWsjibdjWUq0w7krMAp15vI9jrTDFFX\nVklLAfK6l/HqKJlykErJhRbRSVai5CcDRIfX6VdmEdyACuWWk8loL6JNpUNeYtnVSS7qY93XDpMC\nZATS+TBvpk8h7NPZHAwyJ/XSLy8QcmToEefRdZ20EEQIaUQb6/SX5sg3fKSsEVYtcVSbiFMo84Dw\nEnHvCi1dxGUtoqjNv2Pk/fdrokWn/4NF2nNpqt/bvE21YVZcmBUfZiCH7cjUAHJzcowBivo77GdE\n65iWG8fa2Und0Fkb6g5zurvBb+90MIaqxPykYAZwA3yNZWZnYCTSGNdpbGvw+OqO/Y3tDE15K1un\n8kaS+AMphjwF5r6t03qPBdt3BLTb6xv0ZGDB2s6qHGfKMkR5r4PQ3k38Wp6+jWV0TWC1PYo10MTm\nbMBx4CaIkoZV3OpoLqCj0GCeXuxajbvUM6gOBdHZ4v3iC+wVrhNRkgQ603TU1xirNjl45jrJWIib\nB/oohV2UVBeFhpfJ8jCbYphB6xTX4qNcU0eZbfZzXH6Tux2vUblL5oj4FqLU4gUeZLknTrxnmed5\ngHOzJ0jOtmOz1+m8vob0tMAznR8mvd/PntAYB7lM1W7n88O/we9e+Pf0n1mEvwI+AYUTbq507uFg\n+Tq+Zp6mV6F2SqbVI2D5HryZPM7XIh/i3qGXCTZS2LIalsEGQkBHsKhohwWkDQ3/dImT/ovkQ05W\nlAguuYyzvUbukM5oZJJq2cFXvR9FbwoEtQwhOY3eJmINNmhLbOK9USa8kmX/fWO8JN/Lc+qDdLqX\nsYgNNFXkbPkuZtaHENZFRh2XSWWjfPWNX+afBT9PR3QV4qDnoKw7mdR2UdGXENAp4UK7B5xtBeSv\najRu2qgVbKQW2nih/hBv9BzDJZRYtk1TCdr5Gflb2LQam0KIjBCgU1zmE7avsBjv5pJ4iO9ID6KJ\n4KLIac6QHgkyzm5sVAn6Mv+ddf/74U2xqZz61BWG5iZJfe/2Cn3mYk+wnV5uVlS0br2MbjVGmrgR\neRqSOfPkpBm0DTkhbNMpEltyOkOTbbQuMzhoI4KG25saCKb9jXXGMRvc7lSMbc3qlJ3ZnMaxDUdg\nOCyD2jEchYXthg4GsJeBAjD06E3osrHymv0fL2gLgiACF4AVXdcfFQTBD3wV6AYWgI/pup5/p30l\nQWU9HOWLyj9ljTZclFinDS95gkKaVX8HvblFBm7O42je+oY22PqWHQKaIBIgw36u0s0CMTborK6w\nd3ECW6BJPLrCPrZmDWbpR6ZJTbFQc1mpn2yxZI/zXd5HERer2S6mZkd4sPtbfDz4FQDm6cGWbfDL\n177KzGA3qx1xCrKXoLbJaOsGM/IAEiolXHSwwkOx5xDsAiPLkwjtkP1XTh7u/SZlHLRQWKKLmaqL\nyzePs9IWp3TKhnuxBlkIvp7l3o2zeB1FcsM+LhzdTzVspWa3kronStHm4F7XK3jFHAvWLhL+do6q\nl/HZc/QKKkWHDUQv4VwOLQr2eo3uSwnWBzsQ8pBf0uEadNWW+Kejf0xsIkV7KoFrsEwxaKdmt1CJ\nyMyf6GOmNoAjWMW3medTk09yefcenN4SR8XzlD0ublgXKbe5uMfzCoJTJ/jzKeYiPbxevovjuctI\nYZVAOMsDpdc41+yigJuP8xWWnF1sDobZ/embvFq4l2c3H6V+yUmXb5lhZYxrtX1cvnmIxWsDJN7f\nRiVm42ztFLvtE5xYe4vjr19mv3OcQEeB8/uP4BEK2KhTxUYVGw6qnOIcH1Gf5Ykfbdz/WMb1u2cK\nSkXkff/+DF2lCabZBlI72xyumboossVrGz0Vd9ISBtCZ+XD4/ixH5dY5DGmdWW9t8Npmbtl662U0\n5cV0bCPqNo5vAG6NbUfh4vYJQ2N/c7TeNH02qBKzHLCCqWAU28BvftKw3zqv4awOf+kS7c4qXyt+\ngMpthNC7b3+fSPu32SrD77n19/8CvKjr+h8KgvC7wL++tez77KqwH4cjzlVhHwU8hLRNClUfI/UJ\n9jQnWPW1gx26bCvkuz3ogN1dw1MsURJdTDGEnywdLKMjbPWXEUvoVoEhdZr24joxdZ2rtn1cse/H\nTQlR1Cgoy1zu7eS6sI9ZtZ/l9W6UjMYR9RJD8jQhJUkWP0PLs8RnEpy48SYuqUjCFqYasONplOhp\nLnPaeo41pY0L0hEibBBzrtMQrLgWKii+JtJAk6HrM6i6TL7dS0Jsw12DsDVJMehgMxfEZV+jHpXR\n3RrtSxtsjvhZ64pRDVi5UjvIRGU3uZAXl1wkLq5yjX24KWKz1WkEJew08DWKXFAO4amUCc9cBh/I\nqorlYpVu5wpaVSSxqiMsgk/Mczx1AWe6hkOvQwjKLht1l4WSw0HZYadRkwnM14gvrtOW26AZkbBp\nVbrUJerKC/Rb51hxtlMfs5EWQjRHZSqinVzdh+4SqIRtaFaB9psJYhkbexI32Dt/nVz4Jsn2MNJw\nk4ubh7EoDXqOLHK08012S2NsSFGWbd2UfQ6uK3sobbpZmegm4C5QbHmRPE0i9hwHG5f52PhTDMan\n6PPM42vkWbfEyVu8W23TAtf/IWP/Hzyu3zXrDIOjDWamkdKbb0fLxoSfEVUaZoAh3K65NnPXZnme\nOUo3g6M5UcdMixiKVYtpHyMyN+u/zccxA7X5Wsx/70zckU2fG6ZtzIk45sQhI4W9xbY00Fy32zyB\nak7cUQH9xiatYAmO74YbC1tzUO8R+6FAWxCEDuAR4N8C/+rW4seAe299/hLwMj9gcH+DR2iIPlq3\nTrephZnN72JvepJHSi/w/w1/gkV/J4uerRKlGgIhIU1XIcF6o43XOc3P8jV6WOBZPkwFJ7JdpdDv\nZjg7y8jmNNThXOA0V+wHsVFDQqUm3OR5y0Ms0UW9YWPh5gAPNF7hC7v+Gd+0PcgV9pImyMdv/DVH\nL12CLBy2XaVodTDviqNWLYQqWe53vcqf8CleFN/HJ7U/pyZYmRf6qFrs+OU0zmoZxzcayFoN3/0V\nupV1RppdBIZepIHCcquTzmKC7GE3rW6J9mfTzNm6WQ63EWeNudwgf1N4HEc8R49jnoZoYZkO3qe/\nxId4FkFpojYkpKrIdXE/wWyG42OXEd1AHfQ3BAb2zSOocHEd9DQooorvWglpSEMbEWjVZWpNG1Xd\njqCBhwJ7ijcYfH0RR7YKPoETmxcQqiDUdO51n2FXYJIJzwD/xwv/O2+JR3EO5dgtTeC3ZVF7JDIB\nF61NCfuFBIOJeT4yOQ9PAccWSbwvzF8rjzK/0o84LXDy/jPcFXqFDnGFMfsebHtqKHuaZPGTv+BD\n+o7IddchwvvSPPzhZ2ljna65BX7v2/8O9ZQOAzrWnMYl32GWLR04qFDsdvyo4/7HMq7fLZP2tyG2\nVxg/exXWtyNs48dsAJMxAWdwx+aEFQPojMxJc3RqgJ8ZFM1OwAA2c0KNoZk21xIx89RGBGs+huFM\ndnLkRkKPOTnHiKAVtiLiKttAjOm6jYJY5qcH4ynEoIGMazGuz1hmfHcAUw0YD3mRPn0U4XNn0f+x\ngTbwfwP/M+A1LYvqur4BoOt6QhCEyA/aud26yhGm0YFphkiKUY77zxB1rnChtZ8+1yx+NUN3fZmQ\nJU1BcpPFz3/yfYYrrYOIgsZZTvEGJ5ijFxV5S6ONxHnnMSSLhq6JyEqD9/HdrSpyVFkkym4KHOU8\nfjnD0L5p+nML6Fko+t1sEmKDKPn9ThqyiOUFDZbALtbpDqyzGoxxPTjCihwnKKb4F7X/yMDqAuPu\nXdS9dqxynVmpn4uOfRz/2HkkXWUl3AkijN/wcCH/Aa44DpIYaKPvt2b5cvsnqFSc/I7zc0wqw1xk\nP3sZox6QibpXCNhT3Cu+zBEusEY7fc0Fwpt5pOc0am1Wsg+62S9dxh8pIJwEfS+UPTZSx/yEo1mc\nV2sIdWj1QvLeINfv3ctwaQZFafFszyN4vFn6K3OEZtdRxBaaXUC7RyMleCkqbjSvhLdYxFsu85L3\nbupumS55kT2PXsEp5GlXVrh/5jXijTVe6LkH3Q4RzybxfSlIaltp/h2AFdScRMHnoRq0U6q5+W72\nIUqym/2BixTw0ERBQyRMikP9lxn+5BTX5X24PEVWhA5WiW8l1jws0hecod2+RlNu4FGyRElSxsE4\no8CbP9LA/3GM63fL7o99l96es/SNLb1d5tQAwRrbdUbMZqYhzOBsALhRDc+QxRlmAN5OLbcBegZo\nmluQGdGrwW0bkb+hVLFyO2j/IAXKzkjacBwW03ojEjdoEiNT08yjGwBupnzgdsdmJOQY0b4KDHon\n+MND/4L/4K0wxol3uMJ3x/5O0BYE4YPAhq7rVwRBuO9v2fQHJusv/Kdn+ZOvaazoN9FHduEeieNX\nxyhlE7ycKuFuK+J0VJiiSk1qkhQtzAhubpQ2SVTH4Dmd2YZCQ1NoKlmaviWWbWlyBT9OWwmfksWd\nLBNVNwhZ0uQDbupWCzNnMqjcIKCm6VJXEOUxEpU6T6Z0Jl3XSDgqzDj6eUpvMJ7xEMzlEUs6+rqK\nli2x5K+yaq9QZ5mu8jL1/BKraZ2kpUXasca3a1kmPUHORdycJ0BAz6DrG2iCwMbFFFH181iqTVbk\nZZ4K1Lk8PkmjZOXJhMrVyhzz1zWS5JnnDVr6KjmtyaJwGa9wlaZ2nRtqlmS5SfO6hXzATj5rx1ua\ngorIc9Uemldk8qqHjXQEfyBHKJ9h0bdCZaFFXa+yal1jrFxH0wSuz80TVDOsV1aZKJTQJag7rFTd\ndjRZRKdGGSfugog/p/O9sI4iVThYWaXhfAqrJUjhfJOV1auk1DrXOkbRRBFPo8WlopMbM0XQVKhC\nqWhnfc3OBd8i+dY3cVYuk9MFJu1zNOxzTNbsVEUbLlsJiSQl1lnV13FUzqOjc8ZRISlEqLJVbz2A\nlwAabgpcH59kYmKKIm6EHzzk/k77cYzrLfuq6XP41usnaSKtt+apTl3hxqL2dsRpTPDtBHEz3WBu\nBfZO8j0DlM0TmMYkn7H+GtvRtdkMYDcid4NyMCfaGKBt5rZ3TioaZqZHDBA1xHfm+zLMAGbztZq7\n5WBaZyhHDIrFOJ6hmDHUM475JP2f+watuThb7PpPuh5J6tbrb7cfJtI+DTwqCMIjbD2FuQVB+HMg\nIQhCVNf1DUEQYvCDJ/J/61/C3Y938M+rn0OVZN5v+S6/Wphj8LlZhOeBz0D9pEjJZ2WDGK9zF8/z\nBKPCLD1zHr75zZ+lsWHdCiPCYDlwGUt/iuVXT/HBgWf4VOhPOPWnF/DlihQ6Xbzw4CD1uIKPPD0f\n76GvLPKBwg2K/hpCS8eTBJLTnHX4+Xd7fomI6GKo5uSB7GtYZppoDagdERl3+kjJLoaYIjKWwT2m\nbxUuqmyCvgl74AtDR/lq3++wT/wGx1rP8f7Gd2lYFZ5SNX7lQ1PwLWhKIqWPKnxAnkBFokPTOSbk\nmRHKLDBAUzhBRj1Aph7AIT/PYbnC8fpbuIUCdcXGmtjOLP2sN9p55PyLLDs6+NLBXyQtBJk7O8jV\nzx8l9Jk1jp86S1//7/Pg0SRdhVUITrIaDJNzOfl5JgitFHDny9Q7BZKOMOtyG1n8xPQEHfoK42If\ngXyB3nSVjdg+Apkcv3bjda4fyDIeizDBKA/nlwjoGWKeAAkhiiSoDOFCkK/x8XvyUIOL7QOcix0l\nLuzBRpB+7LSQ2UuWoYaHqZlfRLA7GOw9z1HOY8dBWjvGp689RYewzOLeGF8XDrIodNPJMln8NGnQ\nwTWOsUYeL1/hF/GRZ1r47R9iCP9kxvWWPf6jnv9HMAFwMHityR4u0sPWBKON7ei1xRZ1ILIFMwYA\nm7MWzZI4A+CNlO8W26nrdra7spvldg+zDYi6aVvh1rndt/42ztvi+wHU4JuN4k3GZKIx0WkAtuE4\njEj5A2xTMEbizDu1MePWtRjnMfPYxmRt4da1imwBeP7WcYz1nlWdvX/S5G/wc5PDbAsH75T9m3dc\n+neCtq7rvwf8HoAgCPcC/5Ou658UBOEPgSeAzwKfAp75QcdYp423xGMcsF5luj7EC7nED1xEAAAg\nAElEQVSHiNmSPDD6MvsenYAyaAsi6gEZv5qlk2V6pXmq2ElYomghkdjQChZPjbVGJ13xBY653+DI\n0Uscz73F/qUbyA/UaDXArtU4lr/MrLWHl/ESIEvSGuKL3ie4X/0ePZWlrf/IMgRdaU6OnmOTEGPK\nCJ2+ZTor6zRzCueFfZwTT1LGSYx1wt4sxX4Xb7iP4BOydLPIuquNLs8c/6f4r/FQoHtzGcssSJ0t\n5NqtmiJlEK5qSN9qovxaA+620BAtxAop6i0nF31H6JBWGBBn8FuyTFZ38YXSb7Ds7OK0fo626gZ/\naX+cwEaWBxe+x1h0N6947ub18l3022aJDy7R+oxMZCiBkzLj7OZLsQfpDK4yaJ0m/tIaHVeTSIqK\nNdJEVMDy5zqh3jyuAw0a7nUIq2gBkcHCPM6ZOspUk/33XsVpqSB4dELKJnGc5PBia1QJ1HOcbF3g\nVdcplm1xVuigVp+CVB5S0C0vUfNbWLZ00RAV7FQo4sFHjh5pgePx12lJMkNMkCLCKnE2iPGg9xWG\npqfpejnB0Ycu4tlVIE0QO1UiJBlhHBWRCg4OcxEvhb/vr+DHOq7vuFkc0HOKjXIW1+ozhPh+PbQB\nKRpb5UqNOiQGaIpsA+zfZmbKwUxBwDadYFAXZbapFiOhxqyPNmR2Bm3SYMvJGDJEc7S9s6+jcd3G\n+YxlsB1dm1PSzdSPmb/HtGxnar1hxvWXb60vsvVkUQh0QuguWDgHjcrf8c395O0fotP+A+AvBUH4\nVbZy+z72gza0pFu4Fyrc532NoJDjinoAsaiiOiXUAyJL5Q7Sko8KFgYKc3Swxj7/NSbYDU6dYF+K\nQHcKKdAiU/CjOOqELClOdLzBruYskcomhWE7LUHGUlRx14oEixk8mwq9l2useWMsdnehzUsoOW3r\nP1UDvy3LES7wEg8wI/bzvO39nPSfJyhlKMgeSqKThm5FVlWaLplap5Oq14JLlGiisKq04RWzHOQi\nN9mFVpQRFgXyAT8VoU5DqdHosFBNWCimLGy2gog1gXAqjSqI6HYRhSZhknQJSwTkDDf13VxsHGbY\nNYG9VUOuwWvWe+hWl+hXF5kNdjNlHWQ+P0C3tEQ0nMAWruKgiorEJiFeytxPpJGi2SsQ2kjju1ze\nGoW3+Gb5PMiyjnW4gT9ZoLEpUYtYcDXSKEsajSWZUHUTMaKRi7ppWBXc5TIjm1N4G0UUsYnPksNH\nnkLZRWA9x0LDw5jTT7S+gTNVpl9dYPfwBB2qG6ECiUCEDssyQWmTI963kPUmHdoKzwqPsimEaAoy\nK944s+4+qINHLdDGOnm8eMkTZQMBnTXa2STMCBNYfzIpxj/0uL7TJjol3Pd5UJcdZFa3wNFIZjEi\nWDM1YgAa3F5DBG6PuI31Zl32zrT0d6JEhB37GmBuOAcjEjbXGjEmSWVAEaCh384/G+Burhlirhxo\nnrDUd6wzp7LD93PYZtWK4SDMyUjGRKrhqBrcKhnVacV7zENxQ0R7D/Td+HuBtq7rrwCv3PqcAd7/\nw+w3MjXJ48/PIhzSuavnLAl3iP5Ly3gbRRoRhed3PcCEexhR1/jUypN0ssy9vldoCjJlnwPb4Sol\n0UlJcOMO5NgQIszTx/u1F4mEN2j6ZJKuMJLUwuaukSGId7rEvvE1Dl8X6DqwQvuvrRK9mIJNtup+\neMHblmevcI3r7OUih/mW9jDpg1/mYeFb7JYmsFOlptloryap2a0U3HbuqZ7BWa9S0xzUfVbqlq3m\nCRc5QqyVYrQ8yQ37MMvOFAXfOpkPBEi/L0RKC7FuaSOQyLH3lSnGDvUzHe+lV5jDTxY7VTaIUpDd\n2KwV2sR1Xmvdzd/UP4JPyzAX6+G14CketX4dW6GBmrejumWclImRIEWYOlYsNFl6qY9kJs6BX79E\nPWqFQbbCrg22wog2yB10kx3x0P3UGvZLdWzWJkSBKlt1VHBQdttRXSJZwUdgIc9dr7yF2K1R7Haw\nGGjHIRU5sHiNA98Y5/n6Af7s0EM8rH+L3X89Q+CVHA/9xvNYcy3kRZ25Ex20AhIyTfZzBa+WJ9DK\n8pLyABEhSQ8LTPr7mDnaA4dgSJ7ESh07FXpYwEOBa+zjTY7TROE3+X9o/phyw37UcX2nzeKt0/NL\n04TeXEX91u0TfQZQGioQwbTO+GyAllmfbJgRVRv0hJ3bqZOdumbYpkcMGsY8YWhEwXC7ioRb57BJ\nYBVBbYGq386jG0kvBuAbckSB2ydZjYlHo06JAb7GqMiy/QRg3KNZgmgxLYctB2hO8DH05/49acRP\nTDL+fIP6e0Cxf0cyIrMeP7q1yFKwnXH3LiaVIa705+nRlojbV5h19mHXGjxa/SbVqI01LUZfepkB\n9zx5mxeLvMV4ZTU/l5sH2ZTDrItt1AUbT1U/wvXSAT7seIq+5Xk8SxUc8SbWZhMppKPeLeD0l+hL\nLyPsbfGWfpBvOh/BLlaJOtYZFm7Sxxy/XP8yatbCkDBFh7KM4qiTUGKkxDBjtt20pxO0pTdx1qvI\nDQ1btcahjesocgNPuMR9I6/gbNaQKhpDz84xtlDFdX8d5eUs3maF9lNJ5JiGUNARr2lEujYoinbm\n6GONdso4mWSYjCVAWEqxIPawJHaApHFIuIgstSiIni0ljP1FTrado2y1s0wHZzhNFTsRknSwQvup\nyyyvdvP1cx+lx7/CrntuYnlFQ3BDLaqQ2BVCamrEvrOJxdJCGAVCbP0iSiDlVfpt87QSEvaFBqmh\nCDeDQ6yejBNxJ9HcAotSJw1BISamEO0a7ZU1RqU3iZBk4WgnV/v34vLliVvXsdtrXHEcYIZ+cvhQ\naHJYuMAp+RxpIcQM/WwIUQJksMtVkCGDnxgJulgkTJIKTi5whAoO4qwionJBP8mWxPqnw9xCkccs\nT9MtX+fMrWVmYDRnRML3NycwS/TMKd1GROpgG1jNlIY5WcYwY2LTDH7mJBfYfgowtjdHtS0NBFPo\nbzgdo1aIEdAa0a+Z4zabAcDGfZknJM2OwByZm0HbTAMZ9IhB8XBr+0FxhpjlGyzRQf223jjvjt0R\n0F4JtnGpq5/Ljn1sigHqsoXNWJA6ChoaWXz0rSxy4tp5zh08RNbvpSe3RLXmQNLhkHSFpiwzr/dw\nXd9LRXewLrRxXdjLDWGEFS2OtdaEkki25Mdar4O9QTMkMnu6B4vWpLOwRqHXzpK9ndc5iZ8MI00r\n/YU5BsR5gtUMoXQerDqaAloGwrYMBaePTU8An15ArifRWyJ1XaalyfgLWSylFrbNOqPxCYSWjtiE\n9twGzoJCshkltJRBb7RYPRSmqtqxV6owC6HVDNW8jUVXN+tSjEW1hwvVY9SLNmz1GmO+/Uhii7vs\nr7FPvIZLL6OrIu3iGh2WZUZt17jIYRp1C6liEs0l0GZZQ9M3CA5fBR9ce/MQuV4vhV4H9Q0XVbeD\nXK+Hld0xBi7M0zmxAVFodMhUBm1k9ACUBByFMv5WDutGHWleJOjLku/0sDIUQ9Fq2Is15JsaQqyJ\nxdpEiOl4y3nCWoopdYjxjlFWO9qJKyv02uaxO2t8r3A/c/U+Sm4nWk3CJjY4bLtEgAxiA25U97LL\nMY5fyb4tB9yqL1NBRSaPhzxefGRpY50ibub03jsxfN8zZm9VObF8nkBqkTfZBipzXQ/jB22W9+k7\nXuZoU9+xvZlaMSYB3ynpRTCtN9LijcjUvN7sNMxUTUsHTd+mP3Z2sTGA13A2ZjMcjrlutvneDEdh\nROhmUDe/zKnvxvkNZ2Q8LehAWzHBkeUL2Foh+GkB7elwP//XsY9xIXWSB/Iv8quRP2KJbhpY2CCK\ngwreKwX4N+D8gwqlB+ysh0N8O/UItbyT33L/D0w6+ylZnPisOYq4WaOd/8qneSzwDJ92/jHtyRQT\n4SGuDu4hKiXo1RaoeBN83f4h/EKWn3c+TUl04aTMUd4iRoLR0iTH5q8gKy1ETUMo6jQ7BTQNLOd0\nRi1TdHQkmDzYSzViZTkYw0+Ghm6hoHtonrAQupql82ICQdURjayE90FmzM9z8Qd58IGXSapRPjv0\nL4kqCY43zqNtijima/ini/j25JiXekjU21la7qc05kFI6AjHNJ7o+WN+PfyfuSQcJF7b4GjlCi+7\nT5KUIvQxi47I6ew5fmvsCzR2CaSjPv4UG33MYQvVCD+YJKaskRQiXPnYAabEIdalGLos8KHub7O3\nPgkrUNCdzPo7eZV7Efww2Jjm+PhlXMkU2DQOr16jl0WWdkXpaKzjnSohfFUk+xEX+j6Qd7VoJhRu\naKN8vvwZ1modgEA4uIFPziA1NaYv7sEbzRDfu8TExn5WrN3o7QIf5JtoBYWrS8dw9tbw+7NkCNDH\nHN0sUsFBkjBlnAxzkzApfOSZpw9RuJMz+e++SSUN33fKOJe3uXwziBpmgFjDtI3E9oTeznochvrE\nADOD6jC38TJrn83ND8zAbtQ4McDa3MygZtp+Z9lXA5iNBCFDjldnSwFiJMwYk5pwe8q5kQVp/PyM\n+7SalhuAfGs66zYlC2w/TZhHlAHm1vkGgRdKSOWdrP67Y3cEtO1SBasjz6HgeXbLN/Cx1UmmgJcG\nFg5whf7oIpzWaT6ToLzgI/VEkJB7g6bDQkoJMCENM66NkK4HqSy6sVdqhEc2uWg/TM7qZ9g/TdMi\nU7HZ2SAKmk5S0VBEkRWhg6dbP8up628yoMzDbh0BHd0OZ+LHuZQ/imuhwhNv/SmNB6G82452WMZX\nKqFZRebEProaK/iaK2QcQWz1Ou5ajVc8hxEHNAZs8ww1p/ElClu6oasgp1r4pQz1PhlFr3DCco41\noZ3rvaOo/1yhs2MZe7xCTbZhp8qgMslwZIrSPjfVfjv+WJa73K8SExMc5ArTyiD/1frLHFy9Qvfq\nEt61PK1TFqy+Oo6+AjlPlDWxjXVsxPFRkRzM2vuYZBAVkUvOg4TY5B79VdxqkV1LM6hTIsX9diZ6\nh3hLOkyCGDZqJInQCFqYd3bzhuUooqwhuFR0NF6V70PvFOn64DKJ7gh+McdHlGexinViYoK9tusM\nK1M4KWMXq1QFG0WrG+9wiaZDpiVK7PKPMSxO4GkVSIoRhpw3+c34fyRoT+Imj40aKjLecpG9iXFS\nIT812UZwLY+rWaJuszDRMYhg+ekCba0MxVd1xPJ2lGkAkcrt0SfcnlVo1kwbad3m6njmyUQDXM3b\n69xOLxiFoMyTfYYj2DnJuTPaN1fXM1+jAdS6aZ2h/jCoHPM1GNdoTk03tjVTKoappu3MFIlg2tcA\nfnNqfG4ZyjUdzdz65l20OwLaIS3NoH4ZyaMyoM3grReJtVLokkjKFmaIKbo6Vmk+ImH9Dzn0nI2V\nXx7F79ykjpXL7OWt5jEmGqPUmjbkgoqrWCaopkkSISWEsdibtCfWiGZTtPpFWi6FumihQ9hghU7G\n9D305ZcZsEwzyg2W6SKlhKn4XLyoPoBTrvJ47UlSmp+0y4d/II+c08g1/czI/bjrFaSGTssmI7Tq\nKPUWRc1NNWLF4a3Qe2GJWsFOPuDBv5JDyGlYhAaFkAMrDU7wBq9yNyuBThbu6UR2NAjZUoiSRowN\nwsom7mARIbgFRE7KhNikjBM7VVbkOE9bP0y8vs7Q5iz+pTz5gy5qcQtr3RFWxTaSQoSa0MCm1gip\nGQLNLKuWTnKyn2U6CQmbdFWXGJ2fwDNfRq3I5HudLLV1MKsPENU38AgFBFFnPRQlLQa56hzFThWv\nlifc2uQ16S6ybX7ubXuZFTrx1Qrstk6TETP0ik3us71MRgtQrjnRV0SsrjpKsEG9z8omIXK6jz7P\nHIfr53GXyjScVtrsaxywX2aKISRajDBOotmOo1qnt7SIx5ujLliIFjPYNuokxBhrnk5Ez08TaIu0\nGgqJSeE2qgFup0LMnO1OesKcnWhMTpoB24iyzUoOQ/sNt4OF2Rmw4zhm0DarO8wUDKZlBnDX2AZ0\ns/TPfEwzP29Wtxjb7KRxzNmZO5swsOMYhhMynIVx7moGkhkB9bYWwe+e3RHQ7mit8XhlkaTDj6de\noi2fIZLNg0disbMbGzWUcJ3CCTs9+2uosoc3hBEqOCjg4Vs8zNXCETaaUcLBdbr3LdClLROxbyCh\nEVCzPJr7FpG/TqG/qFP5rEJrr8g8TQ5ymTbWWZE7uXh4P0khwL28zAwDSA34UPZ5xt2j5I57aIzC\nRfcBUo0on0j/JWWnjelAN2tyG26pSJtthS5tCdUuknH62C9dwUKDQCuDe67AorOLs48f5f0vvErt\nosoUQ7SQiZDETpU4a3Rm1vjA2e/hGihR7bMy5+zEItQp4OFr/ByDTLOHMSbYzSpxvORZJ8Y0Q0iy\nxhd7n2Au3slv3PNH1FwWEkS5Ku7DS4EAWXpJcn9jnGAxw+Ppp/lc+DO84H+QEXGcSYYpJdz0/78r\nBOJl6vcJ1Dx2VCS85HhIfR6vkCchxXjVeRIQOMEbxFgn0koTquR503EC2dLiJG+wwgoLlh7+S/TX\nmLe+iUCED/JNnmz8An+x+kmaTzn49IEv8vGf+RJnOU2YFDHWGW2M055PYs3qqB0yitIkTIpneIwm\nCkNMs68wjkstUx6yIFsa6OgkRgKE1vNszMX4i/ATfLjj6TsxfN8jZqeBwDTybdyxWSJnAJG5jKo5\nCjd3rDH4YiN5xph4M+upzfy2UWrVAIwGt0fJxnuN7TofZrmeWcFhNDjYWc9a4XZnYKZizKoU85OE\nWcJndjTvpBIxeHqzGRSLQb2Yr9lwYllgGpkG3ltHe3dbb9wR0C6LDlIWiaQYZkIZoeDyIUoac5Ze\nJupD7F+9gUVpUYw7KH3YwzodrIntaIhYaNDLPNPFERo1K1pAoMO+QjcLzDDAMJMcUK8QKSTxOEro\nQbD/lYg6JuGe1el+Zo1qm5uxYz42HBEclGnqCiNrk9jzDbxqgdP2sxRdDpzWGntuTJKvJKjtVpix\n97GidLCXMYaXpgls5lkbbGfTFSAjBmlgIYuPkuLi6P7LYAMtKLJ0JE5uI0eEGhUc5PEi08JPBpur\njnW4gt1RRW+A6pCYp5dlOiniZppByjhxUMFKnRpWqjjQEPFqBU7l3+CAdJVc0HtLblUCBBSaVHCQ\nJoS6uo69WKcQ9XC6fpb+zTkCwRTX5L1UNSd6WUAYAxENV0eRAfc0Ei3OiKfRBQGbUCMhxAiSxkmZ\nJBFEVaSvsorPkmNcH+Evcp+i3bNCm2WVvc0JntaqlOnlBqO45DKHfRc5d+g0SqRJd34NKuep2K1o\nXliSu8k4Q0SkTRRLgxYSSS3C8dUL2IQawXgar5CnLLm4YN2HQ6zgoIIoaUzvGmIiMoo7liVeWbsT\nw/c9YiE07JSxv61ONysyzHI+Y7nB9ZqBS2ObGjE37DXA3FhnNkOVYhdA1rejUoMrNjIWjdKuxnXs\npEFgmx4xPymYdeYi7/wU8U6fzROkOydYzduYi0cZ9Iehwzbva97PcA4aW7x6Bjvq29rZnwLQVssS\nG4S5wQizln5WlA4Kbg9l1YlcUxFuCigOjXqnldm7+hlnmBIuGljwUKCHBWz1GmJVx6I3aWeNXn2e\nBa2XCCkG1WlszRqtfolWU0L+ShP5ZhO7F0KVAuwVWT7aiatSxtMqIgqwKzONo1hDkHV2qxM0WzKO\nYoO9y+NUKlY2R/yUGi5aDYWoI0k8t4ZnvcxbfUfIil4EXUfVZJaFbuYt3bBPwEeOJgpjw7tZubiA\nEydNFBq3fjICOrK7SXqPF6Ggo7UkBHQW6eYmu5FQqTdtbGhtxJVlXGIRq14n0kyh5ST8yTz/ZO1p\nYs511vvDSD4Nt62ElwJ1rKQJskyUhaINsaYzF+winlllpDJBOWBhkyAZOYQcUinNOyguOtHqOjES\naIj8Bb9EFj+79JvEWht4hQIVycGV2kHC1Qx9+jIBsggtnW+UH+Ow9CY/o6W4t/Uar+p95PFyiUNE\n5Q2OBt5i5nQ/WkugXPfgL+axCwppp5czy6dxWUsci79Ji61EpQwBjuUvEGKTakxGU2QSRLkm7MNH\njjYtQXtznQvthznXdgqHXsK1VroTw/c9YkF0YrSw3dYizMzFwjZomzXbsAW65uxBo861GUxhm5Yw\nOwJjW0nYkukZBIGZ6zYm/8zVAs2p8zspkp2Aa76fnWYsM0fWOwH8nRJ/zAlGZtA2jmPm383zA2bH\nYtxLAzs6/cAKsPQOV3nn7M60G7u4ROfPqHy57ZNoNtjHNb7R+hDdwiKfFP6c/plZdJdA6z6Zl3iA\nWfppZ41xRphmkBkGWFR6sFuq9Alz9DHLUe08J2rn8WgFPFoROdgiG/KQDbvxPpnClW9sFRbYC7kh\nHwtqD7859UWOZc/jspYodttptTvw50s0rApNTYGmQP2YhIZOLJXmvtwZJqUhvrD3V8gNeTnUc5lL\nzgP0Ms/9+vfwVivUJAsJe5hznOASh1ikGwcVFniTK3yUfuaIs/p21BogjY5A3WnDoxeIiglCbOKm\ngJ0axwqX6Cqu8GTbP6FmtXFUPc/x1GUcz9dR/0oi6MggtzfpHl5j+eEYxX7n299zDRtJIjw/vJ9O\nfQhJafF6KEJW84Os08US+xzXsI9WuXnfEFf2jhKOJPGSo6w7ydZ9rIntuJUSj+eexi0VeM13gu8s\nPYJTL+Pty9Iur/CA/iKFuJvZ5CDfLkqMtN9gVopS4AC7mUBEoykqhOybLOtxvux4nLzHQ5e0RDS7\nwcuffZCuvgWO/u6bLNGJQos+YRatV6PVErBVm8xYupiVu9EQKeIiWLfQn1ykUA9zpnUftOCE//yd\nGL7vEbMBPrRb9IiRubdTGWJQJ0YDAIPGMHeoad1aLrOl2KibtjMiTAPQNdN7Vdteb7C7RlS9M3JW\nTZ938swOcSsTsqLfLrmDbaA0UyU7o2/juOYa4obSBLapozq3R8/meiXGd2VE3Ra26RdD9WI4JgVw\nIiPiYys77921OyP5ax/A6d7FktRJsewhU44Qc2/Qa5kHi87coW6ClQzhqznunjnHHu0m0Z4kh5zX\nmPX2cSW2F1FQqek2lmpd5AUfitQkIm3SEmUKggu7pY6Y1bDVmiz/Qg//P3nvHSRHep55/tJUVZb3\n1VXV3qPh7QCDAcY7mqGnKJGURPF0p9VpY/ekVcStVnF3Id1qN/YudrUrKaTdlY5GEhUaiqRoh+M4\nBmOAgTcNoA3Q3pb3Jqsy8/5o5HR2c3iijiJmQnojMroqzZdfZn/15JvP97zvG6KEPrtGccxNpHOd\np/gOY/IEttkCi6+B93MqlQNhrob34rfncNBkPLCDWGUdR0tlztfJDfsY0+IwLrnGor2bitNDnDVG\nq9MEa2UuOg+CpOMnTxOFzvYKh1uXydoD6CyQ5CxRMthoUcGDhIZsaHRoKZqinYwUJkoKGy38jTJH\n1i6yQ5jA5mnhEquEjSxdLKO5ReqjduQn2xgtkBs6YkrF3mwhoaEhoSOSqK1xLLvGkL4TxVlnqD7L\npH0Yw2EgojMycYudCxMIY20cXVXiiVU62uvomkhF9PKw/BIrQhJDEFlSElTFQc4bh5k3+sCAr+of\np8+YR5dEfFIRwSsgaG1elh9kvl6jtDaIERY4KF8kJqSwCypTxVFS1QQHIufw2/I45TqVbi9THTt4\nmYdI0YGq2bigHeL9yvfoMFKstLu4Ku+mISmMMsltBpmSRujxLCMoLXbq43Rpi+yw37wbw/c9YhtE\nhA1hy492u1LDut70tk3u2fTQt+erNovdtt/heCtPbAVgs23YpEm2J3narmQx/1r3MwHZvAZr/m8T\n0K3XaY3StAYCWc9jPpjMvm1X1cAPh8tvlxRu76sN4c6d386K3327K6B9c8cIteA9pItRFir93GqO\n8RnPl+iSlliQelk5kWRgcYHY+SxPvv4ikqahtSSMEEx0jqJ2iKRtSWqym6waJm2PkpeDBOQijbaD\nquGm4XAQKFYI5ipMfmYEIWVD/6/rVCMK3f4Fxpo3sFdb5GZh5UUYO9GkscPFmY5D7DWu4qLG9eBu\nTt4+Q7yY4tbxPr6vPMa0Mcyj4otUyh5KNT9Pub7DQG2eci3AOfchZJvKDiZoI7O7fpNP5L7J9cgw\nJUNiP21ENuomrpIg2CoQ1nLEhBSrlQQNzYXNoyHLGu5WlZ2pCaL+FJlwALdYIWJkCAh5rvt3YRwV\niR5I48w0MCYF2uMSstxC1jWKoh9Fa9Bfmuf+1UWGi1Vy9gB9zUWQDPxyDhc1dt+YIjmxzvznE9gi\nKvvUa7j1CqtSnJQU5bj9TZb0bib0Hcw6eynoAcqqD5urRVqL8Fz1SUY9kySllQ3Bpr+AhsQ4uyk2\nptALNor+AEjgooaqOVgud5Mtqhz3v0bQnsfjqpJ4ZJlFbxenOY6ITtHwk2rHGBanKMl+Tsn3c5tB\nulpLfKT+bVaUTpbkTuY83YSlFA/KL7CT6/QXZ+/G8H2PmI5IGzvG26BrnSy0mgmCpidqAp4JXmYo\nuAlwVk8TtlIQ1v2s3K9oObGVBrFOgJoPCYMfpjVMzt1Kl2Bpx3wgmG2Y+1kfCpvUxVbVjAm+1vqT\nVmrEysVbAd68P9s17xv3z9zz3ddq3xXQDpPBVmpT+m4YPS4RPbmMz15AwKCIDzsqt2P95O4PcmT3\nOQJGnmwwjC6LNOwSB6WL6FGZkfANOuVFXFKNcWM3Xa1lQmslXNU2cwOdCC6RZDDFmO0G0vkWV541\nCD5YRNwDQk0j+8cGZGHfr4ErBmqxzEhsmueFx5mjDwcqIxNzDC7PMnboBmWnh6CQ5yZjPHTmNZ48\n/wKhI3muDu7kcnw3j4gv4NeKNGSFHCF8q0XEszpdJ5ewGUnOc4QAeTpZ4X5O0ZlOIbV0ppKjJN9c\nZ2RpAeXBBoFEhXkXXBjby57VG7gnG6RHY9jdKvNCL6e4n+HyDPdmLpCL+Jk70klxZ4Bh1zRNVeE1\nx0neX3+BzrV1hCVQ8g1WE3G+7fsQPeI8Y9xgl36DWDPLai3B7+u/zn2lM3ys/LEiyYYAACAASURB\nVE3SsQAF2U8FL9fYw6XGQeYqffwuv8tB9TKVmoc/D3+GF4VHuZbfj9PeJO5YI8Eq54wjVHFzXHiT\nhu82Az3f5KDjAmtCnDfaJ5gujNDhTDEQus0px0kaOHjE/gN+cff/w7zUyyx9uKlgSAJNh8KQeOvt\nt4a9XGV37gaDFxao7zpNOeJl39J1bkdWmAwPskqSSfcONrMs/2O3OlDARvttusLkn82EoVbAtELL\ndq8bNsDQVHpYKQOrasIEcWspMglwimCXoNLeiGw0AdTKIcMmMMJmoI4BqLqFLxfuqDeMzWO2l0xr\nWNo2wdjUiVsTVFnle1YzA3fMazDBuc5WXt9UxzR5J2sDBTZJmHfP7gpod6qrRO3zuHoqZOwhSqkg\nZxvHGfVN0JuYQcBAsdWJ+de44t+DN19h58wkxQEPUrBNH/PMOgYo4sNGizn6WNUT7JRuMqTMoBo2\nzor3MOSbIUqatl1CjGkYPQLLsSTOtErsQor5/kHWj8bRj/vxuirU/E7mhV46Z9eItbKogzKBdJ7m\nsp11rQOXUKWXeZbpJFAtMpCahxTEA2n6XQsIPmjJNgQM6jg56zzCjdguHI4a60xwlBvM0ccCPXgp\nE1HyuG1VDAnEhIbUaiGvarRdMuveDma8A8gNjROc5r7qGfy1IjEtxyHxChEjS9sjkHaFmHP0sO6L\n07O6hN6QWYvHach2GiE72S4XpaAHRawzIk7go4S7VSVSKVDs9TMVGKTudtKSJJouG3XZiSZKeLQq\nu6s3MTQZwWGgIZCWQpREH0nXEkeEt/C3SwzYpkmwipcy9YaLgh5CdOlgA5ejxtH6BSZtwyyJXTjt\ndWS7isNZx08emTbzYi94oYaT0p10rREhg08s0V1awVZr85j6MqWIB7ejQjoeRnJptCWJU96TJMQV\ndjUnuWaTMO7K6H2vWJaNErWNLaHfsFVzvd1z1d/hszUfiAl8JvBvV1FY27JSJlj+ysIG6Fp56e0T\nhdvVI+ZnFTCMrRpx2AR5E7ytObPNflsrultD1bfXgtw+2WqllKzzA+9Eo5jXaaOBwAwb6pF31+7K\nsPc2K4SULF0PzJFdDZOeTfByoYNq3EN3ZB4kA59RYky7yRfkz+NItXno2dPoHxTRAyIuuUZbkMkQ\nBmCKEURRZ97egytco6a5OGs/QssmM+q6QV4PEhwqIx6tspDsxjteIXItx+yv7uHc/kPM00MPi7j0\nOhXNw2dufpWR+jRTvT0kpTQFOcA19uCiSsjI0WMs4vWWaXbYMGSBrsoyvkyJU55jNAU7Hq1CSfBy\nMbyfCwcPEfZmSYr/iQd4lRQdLNCDjRa+UIluY5EgeVr7RXLdXjrO5qnVnCzTyU3GGHFP87jxAh+s\nfp923Ua7aaNf+j6NgEw6GiAv+8kQYU3voDzjQEfD5a3TUGykO4PkRkQqHU6S7RUeVl9mxZagoTkR\nyiLzO3u4FehjkFsEyVD0uGliQwD8WpET6bPsVCaIJNeYa3VzRdhN1hZmgBn2cYkhZQqvXkbWNeqC\ngrdZod5203AqFA0/GS1KtJbDcN1izR1jwd9DA4UWNu7lNA6jwToxMkRYFZKkiZJglWCrwEB9hsRK\nmkCxRJe2zkvOkyzH4lw+uAuFOot6F1+LfJJPq0/ziPoyeXmZBP+UJH8ZBBqIdzw9c8LMSoOYIGoF\nWxOorTSDCYYmz20tfitva8fK4FrBrq2BdGciURLulO0yth5jkgmmx20FRpNdadxBUmvelDYbjyfY\nqCBvXocZRm++XZi0iDVbn8BmlKOZ59sqUdwe2GM+EFps0ipWVQt37q2HGiJT/JPJPfK89Cjt4md5\nn/cZ4qF1vqV8BK9WZq3ZwRcWf4WdHVepuDxoNomYkKKrtIpwXSc6nIeowGTnIEtSFwWC9DFHJ8vY\nUQmT5bx8iHFpD0lhhbrg5OX2Q5y4+RYdU1nkyTb7/2gcuVvD+CWBUo+fJg4iZBhghuHmbfoKyyRY\nQ3aqhIQ8pY+6WGskKPr8VHGR0Nf4TP2rOHdVuTXYS83pJG8PkrcFaCkyF9UDXKod4tfkP+ZB+TXm\nfD2k5Bg3WGGZTnqZY4hbjDCFHRXFaODTSiyLnZR8ftyHa+hu8FJmJzfourqCbUKn+oiDGz1jzGoD\nPNJ4lUg6S3S2SG53Hj08S6K1xPxX8/hzNf6nT36J8mEFVXGQnF3mwFoJwaYTuFmlMVgk0ykyHetj\nSd7Qvg8zTYw0bWR0JOo4aWoK7XUZt69KsKPIVxc+zZy9F1dPCRU7fcwRJsuh5obXn3H62em5Sc4I\nsi528JruYpU4475RDHlDP36C18kToIaLa+zhiH6O9+vf56x0BEnQcVJnjJvsWR5n8OwCbq3Gakec\n0wcO8z33+1klzjDTnOQ17PU2M6s7+Fbgw2QDAQbEW0hbsif/Y7cGNor00sbH1ug9a5xem82cG3U2\nf+AmwFv12WbCfzMdqwmuAlsL3Vp5aR1o6ZuUhMjGSvM/YbfsZ4aemyBuevRWLt4qVXynNwIzZ4mT\nH11t3uohWykiK89uzettpU9M8Ldy2aYO3uxfABgU2tiMIhuJaN9duyugXZK9OGSJvBAk4kjxoPES\nk9d2strupBrxEDbWEdXdTFXH+LDwbUaVaYR7DCaTI0w4hlgSEtRw4bgTrBImi5cyi3SzKiZoIZMg\nT2whQ3ImTVAtUfF5WI3bsAfLeEI16j0ONJdIFTcFI8BD1VOMTUwQe36NxqiL2ohCUC2xnoxiyAbD\nTDPNMLcZZEScwR4SMDBwZeoU8dO2S/QsLtOQnKgBB33SHBE5DXadCBmyRoqALuIQVATBwI6KR63h\nbVTxVKukfS1yrhDXIrtAMDhYu4R3ocrO6UmkVQM5o7Pg6+El7wNE7Bn2ZcaJZnIkbqdoNBVSoV46\nHDaCnjbucAlJV3HkVVz5OlLdTluQkPMa9kaLpuhgQtnBtfJ+lipdRNQsj/ADhuyzCGFo22UykoPn\nIo9QdnrJCX5czioD8gxRVglQQKFBhAyS2Ga5lOTUzEniyVWMkMEtBgkL1zgkvklR8pISYhvVbFDQ\n7vyMCgSYE3qJCBliQpoSflJECVDA4WiyHo2SkSIsxTq5HehDEtr05ebZNzVOvi/MaiDOLuUqY9pN\n+lKL9C8vItn/KYG2hmxvkeg1cNZAWt4KPlYeGTZB0/TATY/TOvkHm6C2fVLTqlG2rjOjJ63JoEyQ\n3U5DWJUcVu2zCeamB2ya+WAx2AB/EzRNoLUGA5nXul3VYtVkt9n6ZiG8w3aT/zc/a5ZjzEUJQyhq\nIM2oG4T8u2x3BbR9thJjvktcYS/D3OLJxrNMfWcPRV8I+ZMNNFFktjHA9dR+Pig9T0c4A5+DV7wn\neNV+H4IOoq4T0vPUWi5GjGmCYo6L9gMExCIjTGFDZWxyiiOvXoFjMD62g9m0l9yhFRS1iVA2cPoa\nNAyFcWM3ntKf03F+ndp/gNwfehH7ZXrzq6QQsHlVDnCJOfq4auwhqq6xW7jOoDZLx1wBZ6CJPdZk\n7+UJdnfc4Hj36wjAut5BoR3EJxUJGTmGtQI5KURaiDBHL7ubUwQKVaR1HV9PlRW3yBnhGJ0sc7R6\nlsHLC3jSNUS7gbLaJheIctF7kBFxmoicJS5mSN5Ms1zvYvrYDvbdO0XU3mbmRCcjmTnCmSKaJpES\nYqg2mYBSpSK4Wdc6mBB38FbxXq4v74OKQY+6ykeV7yAfbCHYdYo2H18Y+gUqgpe9wlUOdb5Frz5P\nor3KLXEIRAiRI90KcmH1CH/8xr9k7P6rhP0pFltdjBjP8gn5MtfZzW0GuckYDRR8d1KDaYhcEfYz\nKe7gc3wRNxVSxGigsJaIMZfo4wY7aWgKHc0UB2yXGE7PcPzFc/ynJ/85lzr28Vjn97kvfZax+Wns\nb6gY7r9r5P3jMskFwZMCziVwLG+ConXC0EwragVSE+ysXqYJhqaHzba2rBOWVq65zQZob8+Ktz0X\nibnOBEQrv2xqya2TfqY3bD5EzIlDa7Fda/i9GdloeuPW85t0hzU5lFmLUt22HTZB2qRbZMuxOiB2\ngeOogLDK5ivIu2h3x9M2/CRZ4QjnmGKYv1Q/y+p4ArwCwgkRj6+K5C0Q71rka8JT1FQ7n698GZ+z\nhIHEueIRDFlgb+Eqv3n+P9NVWyIf9rP+QBy3r0IH64TJEo2nYRBYhN7KIiPzDlKPdqGuyvS+ssSO\nJya4qYzwQvNR0v4A1VEX3oeqOMQCwhUB8brO5eMHOLP7MEHyRMjw0Zm/xv/vJlEe02h+woGRFInP\np/GfK+Ger3N9/ygvcZJJRtlZmeQTmb/lRscI1bYXZz5L3hdmVUlSwc2ss5t2SaI/vYQrUsNHCTsq\n19nFpG+UffePs29xnO7SMvOjnZQjLpL1VY68fokhdW6jUEEFhqu3+XTu6ywcSrLk7CBEhqZfYske\nZWIsSF/Ch1uuMn8oyVdqP8/Eyg4+FP8GQ+Hb5N0hnFqdY2+dp3nNwa3RQfJ+P+tGB4V6kD5xlo86\nv0EDhVgpy9D6HKeT97HsTdBAYc8fnuP949/g2O5LFNpuyBrEzuX4WqbBizzGBDsYYIYjnCNPkPMc\n5ip7CZMh3whRrAdIelco2zzMMMBf8PP4KSCjESPFoZVLnDx7hsXDCdY6o3zlU5/kanQ3Mwzy10aU\nM7572bPjOvfHXqUl24A378YQfk+Y7hEoPelEv+RAf34D7qyaZ9gKjtaAGmtKVrNCi84GaJrUgzWz\nHfzwxKOVCzfPYw22kdjq1cImaFpzVFvPbe3/9glWUyVievYqW6kL08yHg2FpQ2BDDWLNWWLy1+Y6\nK31kvo1YE0uZDwa130b+EQ/ad8WNLJ7vst2dyjVqiBIix3kTR0slrSXIH47RLso0rrkoJAPEQyvs\nly8w2+7nGeMJYkqKS2sHWTG6aPodGzmybbMMeG6TyKxj1xLIbW1jgq9aovvmMqFGEX1YoFG0YZ9r\nErpYx9fnp+LzMB4a49LEQaZTO6jG/LwWOYF/oMR9Hz9DedCLJOr0CMuUBQ8zuQHylyL8bOdfc9C4\nSi7WQvFK6LKdW6E+mmUnjYKCLouc8x3m+6knmFH6cK81iU+myTcDiHWDaXmIZaGTNeLkCaLKdhSb\nSi/L5IwgBQKEyOKoq0htA6WjStWhsFRJMN0xwKq9g0rNzayrD6+3TCSRRZXtpB1RFuQetJBO02Fj\nhn48jRrhSh57Q0VoG1TcLooxL9fm9zBe2cth/QwjzknGnDc2kle510EwXxlFRAz6hRm6xGVk2lRx\nM6HGmMrvoRzx4aBJHSd6SCTWk2LP6CwpT4R21UbXyhpvVDrpKq9iUw1GnBP0uOZYI06BAHWc9DLH\nmhBnQezFTxEBAz9FcoQwEOhkmToKBZufosfLmq2D255+FoZ7KOPBbqhU8JByRFlQOpkODhIgfzeG\n73vG6jYnZ3sOk1x2oDH+Nj9sgq7phW5Xa8DWeohWLtkacm5VlcAmoFojEq2RhVaAN89lAu72EJTt\n5zT3saZGtbZr7mMXNiY41W3rYWsAjRWwzcW8D9Y+Wq/NmsTKvNbtVIsBrHiTtLsP05AV3gt2V0A7\nV41wox3nXvE0J+qneYg3+Z3f/C2ee+NJZp8eYf5oL+HeFA/yCgu1Ht4w7mUplGT+0jCtpo2+x6e4\n33aKe72nKXcp+F9XMNYENGkjd4cjrxL+2xLOoQbqB2RyAS/Kl1Tk18qM3J7l2qd28MyvP8Zf/NH/\nwM36bsT36XzT/XFy3SFin13jljCIs9gk5sjgi5VxLLUY/28HqD/6fSJP1Sn/XhKnXEFA56znALd2\nDZHa00ETBzeX9nBh6ihSvEFlyYN2VWSnOsVz5Siv+t+HIQrkCTJDP21korYczaCD244hZvQeRoUJ\nRsqzxGspanaZ5VAnE6FB5uhjyehiVunn6RMf47bQy34uU+r3cVa/h+eNx/lX4n/Ebqh8Q/8Yu1LT\n7JmdZmQyg2/Nway/mwYKTRwUDT8v8iguqvQZc4iGhhTUsfW26HQsYzOaCILBA8qrlPFyzriHEl4u\n64c4pT3Krxh/xB4u00Ch9ishcloCf3uWSDODvGwgSdDXWuTnMk9DEfJxD2lXEAdN9nOZPVyjjzmW\nlU6mlWGOcYYVkrSRKeInRordjHOK+3kzfi/peIQMEcp4UbHjoYqHCoIAnSwTI8U6HXSydDeG73vG\nSvj4Zusj7NO86IxTZwNsvLxzJjszNF1iawFgkzO2es2mB2sqUWCrl70dtGGzNqPJP5uKFitImgBq\neq6mksWkcky9dZOt2QG1OyezCXeq3LzD/bC2afXQrR69eY2CZTE9aAcbAr6WZZ3ptcMmjTKpD3Gm\n9WEqTPGjVNx30+4KaJdv+Dn37H2kxxI8Hn6WJ4PP8pj8HPLuFt8JfIgH+l6mg3XmjV7KM0FKpQi3\nwy68fUUi3jLI8Fr6IVJqkkRijdCOAlqfjt9ZwEuFctjD33z6I3jcVUK+HHnZx1BgDjovwwegJ7HM\n+179Adfu248nUqS7Y5ERzwRRIcUFDlHGi+Jq8kLfg7idJZ6yf4uef7XIFX03v5P7P/i8978ii20a\nLSf3LZ1D9bq40rGPMj6yC2GUVxs88NEX8ewu8iexX+aDN58juLDMA2++wvqOMPPRbtrILNPJoruH\nCyOHOVy9yL0r51mIJ5jx99JQHCQba0SNHLK9zXB9loBUotuxyD2cJckyLuroCHQJixwTTiNi4MnX\n+J3Z32MwMo06LEJSpxzwUiCIhMZY9Brx4Ar75YvsZpxII4t7oYnrQgN5RiOcLOEUGghO+FLql7nO\nLlzuCp/2fQVv4BSFHUEkT4sWNsJkeZmH8ItFPid/kehMAft6C8aAFWjVJYqdbk57jzLNEAPMECaD\nkwYlvHgoc4zTxEhjR+UYp3mdE7SwIaGRYBUNiR4WcFNFQyJIntsMMtHewXRlBEnRSDpXCFDgFA8A\nr9+NIfyeMLVgZ+avRokuTBJi00s1lcPWKEGrRyluW29jgzoQ2KzraJUOWosAm8BnnbyzVls3PeS3\nuWgBdGPz3E3LOcxjrXyzSdtYsxCafRGMDf23amnD7I8JrCbAb69EY16Dbvlu7YOV6oENqsQ6QWv1\n6ovXQ8x9ZZhWcZZ/MqAt19oIks4NYSduuYzHVkJKG5SaAYQY+O0b1UpWiTNov43T0WRR6MYIGth9\nTTrEFGkhzpoQ5xL7McJgR6UieGgj03Q6WNvVgZcKNRzkCBHuKWLsBu1h8Chlhsu3eCL5AsPxKRRP\njR3cRKHJMp0EG0WklSa1Sy36tSW6YgtIRzXOlY5Rr7gpiT4cegOH3sQvVFGEBiAQIUNJCpFzdHBQ\nuUAitkwq2EFz3YZdVukWFlkhhqxpHGpd5pptF1k9jE1t4Tq/TjA3R/1eG+UuLwueTly1BhlC5Aiw\nV7jOEe08HdV1xko3aSkyK6E4YBAUCkRJkyZGqFbgyPJVlmMxZsM9pGMlFE+MVRIUCFB0+ZBQaeAg\nQ5hAu0S4OIPhEFnp6uCGbSeg0dDtTFbGWJa66fdMAwYOpYFXKeCjiB2VOk6mGUYXREakSY7YLtHl\nW6WZsFP0Csy6Y8wGunil+RBTuVHwvUhALuCnSIEABgISddrI1HBS1n2sZ5LIUptseJY+5pBpoyNS\n1P1oSHQIaziFOn6KJFhBFtoU8dPCxiSjd2P4vmdMr+kUXikjVGoE2NAwm2DcYFPrbNVjm0ALW6Mm\nrZSAKcWzqlGsCg/YSrm0eedMeWa7Js+9Pae2aVYdt6lAsfLmZvvm5KDZV2tY/XYpojVYxnofrKoW\ns3/baRTzPpkPH6unHgJYUCnWSxsZs94D9mOBtiAIfuDPgN1sXOPngSngaaAXmAN+xjCMd6Tpg9Es\nh588zQwDrBPla41PMnltD8VMEJujxcS9u0g6FxBFnad2foMCQb7Ox5nL9+MoN9gVGkeNTpEjxPf4\nAAUhQLexyC1hiCYOwmTZxxX8FGnf+fcbuwWMk6AeF0AEuany6bWnmUt1c9pzGIUmMVLs4RqBQo32\nKyoLvwGBOqj3R3D8eYOPxL+OO1ZlklGUVp0Rplns66IiOomS5jDn8AzUWXQPMBidYSfXyIsBQoM5\n9AGoHZO5JO6Dlsg/K32RQe9t1LKdsSu3uf2FFplbcPBXLnLpsUNM7+rH4WnyFkeZZpiQ808Yqcww\nkp6Fm3AjPsr10C5U7DipEyPFKBMk9BQ04ZYxyKyzh4nAHJqrmzn6eJmHULHjoMkV9rGDm9wrvEWX\nvI56QuJc1z7+ffu3cEp1duiTZKUIva45Hgy9RIooK3SSJ0gH6wQovP1WkiXMl/glHIMqboobKWEj\nIq/13MM5jvDG4gOs55L4x3Ik5BW6WMJGi1USzNJPL3NMMcp3209x+eY9+J0FwuEUn+JpnDR4iYd5\nUztOGS8N2YGNFsPyNI8GXmSWfmbYiI7Vfog5/fvbTzq276o1a3DzNeJcZwAIshFYbSovDDZ/0Cbg\nWb1SK21ijaI0J/i2B5zIluPNoBWTI26wtSgC3AmKMTaB1nwTMPtjDWu3ArvZL8ed85rqFKv3bw0e\n4s53s9KNNfGTtY6lKfQw+2yFXPO76cWbDw5zncqGPnsX4M0tQu413hPSEX58T/u/AM8YhvFJQRBk\nNh7y/wZ40TCM/0sQhP8V+C3gX7/TwYm+ZXoEkR7mmbyyk8sXDuHYV8PrEKhO+5hv9aABHcYaA5VF\nutOnObAwzjMDT2D4BX5m5ZtIhkbF4WItHCUqpWgKDhw0kWkTJkuSFeyotNs2xvK38IoVJpUAWqqO\nUm4iNkEoGnSUMxx//Tzz93dS7XbjatdIecOsPRBh/r9H2FeYosuT4WjtMu0q6C64p3me8+IhXpIf\n4cOV73HMfg6/s8g6HQS9OT5g+1tCSoZgvkjv6ip+pUzOEeLb4sMsCt1ossyXfT/HAe0K3RMzFP6o\nTVCAwIdk8g96sXc1SAhrSGiEyNHBGtMMsax0YcQk/I4iLUXGT5ELHKKDdQ5xgfMcwRZus/eecToC\nq8RLqxTWyxwoCQT9eULkuMQBptdHmX91iIw3yUpnH624QtsvcrM+ysrlXsLhDM2RWZ5KfoO6pDAn\n9CGhIaGxx7jGaPY2uiCwFoqjCnZirPMgrzAv9DJHLyoOjMZzHC1cxOmrczx1DmFRoD4oUXT7OcVJ\nOkjhp4iXMkEKJFhlj3SV8I4cFcnDGgm+zidwUqeOkwFphgAFDnOeCUa5LQxwjT3ESBElTQU3Jyqn\neekn/w38RGP77toGIyw9AVLchnZWQ7upb5HymaAlsQGq1oIHVs/YpCnMVk2P21R0mGHi5oPAGiZu\n9XytASnWqEtrKTPYVKcYlm2C5S/8cFZAnQ3awvS8zf6Z+5rt2tlazNcMc7e2ax5vTmiaZpURmv01\nvXdll4z+ay6MPxHg2na2/t2zvxO0BUHwAScNw/gcgGEYbaAoCMKHgQfu7PZl4BV+xMDuDC3hV32U\nFgPUl1yUVR++UBZRaaHPi6wVkmiSjKopLNp76NDSjLammZIHadtluo0FHLpKXVfwGTlK+CjjJckK\nMu23PUEbLXQkvEaZliRTkLy0FzTkSnMjkKkCnlwNV26Bel5Bcwg411pc6ulldrAPcVBHuLWAM68S\nldKkjSB5w49m2LnUOMTl9gE+aDxHp7xCGRcXOISgaNwnniJ5dZVQsUjMlocQVPUYy+IemjhwSnXK\nkotMI4RNqoLXSXNnmMKBBAtGJ42WHTcVdnKDIHlSRpRzxj0EpRwdnhQ3PCO0sdE2ZMa1Xay1EoTU\nIhlXEL+7QM7tI1eNEkiXiOTn6ast4HTVGKjOITs1VF1hoTFAzhHmirEP0dtEVtpU624S2ioJfZmA\nWMDhbVAlybLRSduQGWaaY5zBp5dYErpYp4Oh9G36jVl2Rm5wRdzLFCOU8BPlEmWjTgs7LmcVp7dJ\nQ4xSw0NTVxhWZ/CIZVS7jQIB7LrKUf0sk9ECE+IOphkmSxipriPlDeKBFTqFFfZlxrG5NQSXyJyj\nh6SwQpQUbioMGbd/osH/DzG2777pLPb0IQ3sxT69CKS2eNdWr9FKPViB3QpaVt7a6o2aAGsFWZNy\nsO5jzZBn5cG3lzzbDndWmsOqdLGGmlvXWYNg3tZPW/ptVbWY261BMtb+bs+pYp2QtXLspWCEUydP\nkn26vO2uvbv243ja/UBGEIQvAvuA88D/AnQYhrEOYBjGmiAIsR/VQBeL6OU9fO+Zj5DqjuD6xQLl\npgdVd2L0CxSXIpQmw8w1hqmedPPWwEH29l1lQerELVSZ7uxFMKAuOCkIgY1UoPg5wetvg7VEGzcV\nbFKbi5H9aEjUtXHa05WNqxxkY8YmAsIY7BBuYVwF6Q2D6x/dw0K4k6f4DsGZPK2MRG7Uw4onzi2G\nuOLcx6uZB6lmfVT7HDjtG1ViLnGAfmYZLU8y+IcLhJTCxst1AagKtJGJkGGESU7wOs85nuDc0YPs\n2jfOG9IJ3li4n8m/2o38QIMDD5/j9/ht/BSpG06+334fj4nP85D4Mr/Pb3CFfVQMD5Wmm1Ze4XvZ\nj/O/9/02h/wXkND476VfRcm3eVL9eeztJvFaio7ZHCQFPB1lpj41whX2MqMP8lbrHkKtHEPuW3zw\n+F+RFFeo4ubP9V9gQehBEjRqmovdXOMR+UXmIn2Ms4MsYf7Z9S+wv3WZqQf7cYp1dERmGGBV2cOX\nAsMsCD2kdsVo75QZkac4yAWOaOfZlZtCVFoshuK8wKP0tJf4QONZ5p295MQQGSJESVHJBpg4u5eB\nw5N0C8sMn5pj18AUT/a/yFK0g7a0IU8U0Znw7PgJh/9PPrbfDXtp/TFcUj/28t8yROqHXto9bHqP\n1hSnpldrJvY3gdWkPcwJRiuFobLh7Sp32rWCoBkMY57HmpXDGmmps1mgywqkIpsUixn4YvX6zaAY\na/InE3BNBQps8vkmgFuDeKy6bfOzqXax5s+2A6U77Sp3tk+XRvnTi/83EZh75gAAIABJREFUmeIf\n8F4ywTD+v11+QRAOAWeAew3DOC8Iwu8DZeCfG4YRsuyXNQwj/A7HG9HDXeidvdQrTtw7uokejbBQ\n6qWiepGNFvvEq7j1MkXdT7oQoy3Y8IbKNEsKHrnMYPckUTmDhzIybcp4aWEnTIaImkPRmqw44gTa\nRfxaiUv2fRuTW69N8kh/A3e6jpw3aHTbwAZKqUUjZkOVZfSiyK3oEC2bzFh2CkVrkLUHeSN6nPVs\nnHrNhdJZI6tHkJsavyh/maiaoVL3sFjsxumsE/evwYIBdjDi4B2vcWpcpvdQElukjUupEmgVuBze\nS8oTJUSWm4xxqzZMZdXLXuc4I74pak4HbqmK06hRNAIk2muE9Rxft3+UyZUdNGecPNDxKs5AjZQz\nxv3aa0Rt65TdHpbUbjRVJv3mLcR9x9CQOK68TtHpJ+MIU8PFipEgbcRo6TKGKOAUN3j6Jg6W9C5u\n14eoC07ctjLHjdPsKY8zkJtjJt5L2enD1tDoX5yjInp5cegBVrKd5PIRyhUv4vXXCRwawzbUpOlw\nINNmlElCZHHqdVytBhktwjw9lBw+XEKNDj1FUMyxXO3irfS9JDsW0Qoy8xeG6D1wi8Pu83xg+jkq\nSTfVmILd1uTqhMz1azZStRhOe53JZyYxDEPYPu5+rMH/DzC2wfrgiN5ZfsoWDuJwLPEL9ZuEKwus\nt7YGtMiWBbZSFJJln3dKMGX9bgU8Mz/JBWA/Pxzybq16bqU4rOHjumV/81iFrSH0mmUxQdoEY/Pc\n1nOaVI7Vw7YCt1UKaOXLrVpusz0rvRN3QMrby19EPkJ18QpU3x4OP0VL31lMm3jHsf3jeNpLwKJh\nGOfvfP86G6+K64IgdBiGsS4IQhxI/agGRn79Cbyfej/hWp64vIrfVuTNTA/zeg+a0+B/lF+jX5pl\nQejh6dOPc7F6mIUBLywbaM40jWNXCdkuM8okMVLYUdERKTHEruoESXWdl739JNV1+pptXJ4eJFuL\nOVZ4/NMOguMC7teb5J7wIlc1os8UmXt/lFqngnetSt7TwN5uM7hSI9/j5bXYYf6Sf036ZhxHvoVr\n/wJdnibRdoae7EmOTp5naOI2CDm0YYHmURtLgQ5akg1nrUH4+xpits4vhGZhCFpxibrdSc+Qm7WY\nmyBNoiSIa504VJWnqmn69CJ/FvwcO+VxHjReRRdqRGsF9KqNt5T7mb7yMI62jf/5nm/TO3qb851O\n4ukuwg0XHrmIHkpRUdz8jSfM/IlPUFLDdPYb9MsqNVwUCFAgQI4Qq2zka/FR5gBN5uhlXTtGX9FF\nS5BxKxV+mRe4f24Z78U2r52MIsZ07itcIJXp4BXbUapDn8c57SIw66Wc6oYiCIcfx/ZwmYQvTY+w\nwF7yKPhQsVHHxXLtAIuNI3i8ZbKag9Wawi94vognG+H05GeJ7TqLlhdZsB/Hc981ElEvo8spplxD\niCEne5OXGJM9XMjewx+89Zvs6X+TyWc+9uP/Jn4KYxs+9ZOc//+fZW3Y5CL339tDZ6XGjUuZLXk5\n7JalyaY36WQzEMekJayIYHqnJnCZIGqWJTMrt7/Pss0EVvedNq2gaHrtkqVtkyvXLccpbHLepiRv\nO3BzZ9sHLH0zIzutMkIrX29GYZpgbL41mNps+OFCCCbIj+4PM+fp5quvR6g2o8DOH/Xf+Cna77zj\n2r8TtO8M3EVBEEYMw5gCHgGu31k+B/wH4BeBb/2oNvZxhZ9rXaZ3YQXZ26LS7eDe6GmusYcJY5TD\n65fZ2ZpAVS6ROxiiZYer4l7EAR2HUKEqu8gSJk0UJzV6WATgCvsIKTnC9hSy1KaiuEgpQbqERYr4\nSBFjDQ1jRETvLVJzKjifaWF8WWBlbxJdExj+6gLxnVmMUdAHNW55+7nKXvLNIF0DC4SFLDfEMXZr\n48RaKf5t6n/js6f+it9+8d/BflDHJMoRhbLowVOt011Yp/KoA3VWhkgLNKiKTub3J/E6ioRJESFN\nhAyPqC8zmFok7/UxGRxEFNuEtSwBvcAl+QCa4SDUKrNa6SUbj+D/ZJZiTKHtFvALRb4c+Qw9c8v8\n7mv/J2dOHKLRa6PfuMBA8kUucpDXpRN0s4CIwRmO0cUiPsos0EOUNH3M3fGGc7jEGt3+JWqCizUh\nTgE/C/YedslTaIKIZhNo+kVeDhzntHQPMXGd+wbfxOgV+VP9l7Fpsww+fpHL0j4e0y/xhPQcqySw\no+KitlGoWbEjOTR0UWQ2NcjUTA+Xdh5Ci4p4/Hkc9jqq047woQZT2VFKxQCz+3u4/d1RlHyTT3z6\nK3R4U7Q1GapwVd339/0V/IOP7XfHWrRcOj/4jfsYmXGhXHoe2KQIrDlIrMEwsDVnh0mZ2CxLg00q\nwwRdU51h7i/f2ceq+zZB2noua65rE5CbbOYKsd1p06RnrGBuFmcwqZ/t0jwzPavZrp0N8DfBu2nZ\nbk16ZZ2UxbLNKm0EOP+zB7nWe4DG5TY031uJyX5c9ci/AL4iCIINmAF+iY1791VBED4PzAM/8yOP\nbgnsaE7jDNW44tzDa8J92CWVdaJUdQ+zvh58Wp6wnGNQucUT0rPcZ7xBRgiTEmKskqSTZca4iZsK\nXsqI6Axym2viHp41nmS2MkC/bYZDygVipIiRItLK0HlWJVAvgt/g5f6H8fTV+NlPfYM+cQljCmwL\nbeYPdaEGZXoLC9htbbq8S3zC9je4xSq6KKHpIm6hCjbYnzjP6iNh/qT3l/mQ9j06tHXkm3C9dzdZ\nR5hIIMdh+1kkZQHDB4VeD8tdcW4pgwyrM/ibZdacCaaEUVZsnZwP3EPWEWJFirNCkoPFK9grBuW4\nj5pWor+9yC95/5SwfZVT4gn+svWLHKhfYth7E6dURwq3WNwfJ/ZGBuXNBiupBp5GlhH3FCOtKS5J\nB7gu7aKFjYnV3dQqbko9XgYdtwmTZZ5eGjhwCE2mpSGaKDh0la7aGn5XkZUDUU75T1LDiWDTeVl7\nkHPlo1SyfnbHbtLln2cvV1Cal/lIdorR2CROocaF9iEuVg/T1BzYBRVNFtEdApJ9I4im7nOx2pfA\n7aowJtzgw+K3mBd6mFRG6epYIulawytWqChuij0BCkGBcXkPeZYpOQK0umxUnf8gr6w/2dh+l6zV\nkHjzL/YjFmo8zvNbVBaw6dVuT7SkslXfDT9Mjzgs660UhzVBlXXS0OStzUk+E+i366u589eaI1tl\n64PB7Cds5b6tHLnV89YsbZsPIut6632wXqOVSrKaHfAB5787yhu+vai1Wd5r9mOBtmEYV4Aj77Dp\n0R/veBFvq4LWgGU5yRmOESJLqeVntdXJWdchbEKTo/oZusUFwkIGp1Ansxrlhr6TF+KPEJVSxFlF\nwEBAR0PEQ4VxdQ/PNZ5AaTWoi04kXadHXaBDXEfTc2gZmVbZQUuzsdzZRTBeRHgYOptraDWBZsJG\nptNP2y3Tf1XE46jS7V8gKOaQ0CjhJy1FaSMhiBp90dssRbt4Ye9DnDj9Jp2lVeyrGpW4j1veQSZt\nI3Rrc5RtOSYTHQgDLephBUMX8dZreBt1lvNJlgNdXHQfYNWfQMWGjTZB8pRaAW41hpE0DRGNtk1k\n2H2TIWWYs8YhXs+fpNlW6GeaUSZxBmpMBobY/7XrJBfT2IOQyK4j29oMGjP8QHyESWOEIW7TqHvQ\nyzJD2i06WUbTJS62DoFooNga3GAnLWwkWEVoQd3lZD7eSUYPUyDAaeleKlUf3lIVuSQiBTQ8VDjC\nOcqteUZrEmkhyJzYx5Q2wm11kEbbiShqCGj45QIx0hgIiJ42TncFUdCIammOGmco4UUUdVxyjUR0\niZCYp4yX+oCPmuqiaPPTUVkn2Ciws2ucZVuc+b/PaP8pjO13y3RVYOobfgZiEdxHIqjTJbSCugWA\nTa/VBE1rRKOxbT9rfm4TFLYHz1iDUaRt+1ijI62SPpMOES1tW0PXzXOrbC1rZvbNeh3m8VY1idln\nU1Vi5sY229keUr+9GIJ5DW+DeNCBfTjAwrUYUyn/tj3fG3ZXIiJdcpmKoeB9pkFX/yr73n9lg7qo\nJLmSOkK104XPUeLxxosklVXyop8SPnY/P0G0WmDll5LoTolVkgQoUMJPniBTjDCTGUbMSzzV921U\np52L2kG+mfokISWD0/ZnxO630akv45EqPOB4mcRCCmaBDmjusZF51IffV8Cx3kK8qeELl3B317jF\nMDHWcVNBwMBLGRc1LnIQmTY7WpM4Z+qggGNM5ZhyhgBZ5uilKPm56R3j6qHP8in70+zTrjDQmMPT\naCAv6+y/coOFo71M7B6lhUyQAqNM8jAvcSFymD8I/Cr/QvgvxOR1rjrH+I/qrzOjDhBzpPAHSiSF\nBZKssp8rFPFzmX0MBhZhOQ1LcGDxGtPeQb7p/zBXhb2ougO3WOXRrhfZnbyO215hgh28rp3kpdwT\n6ApEgqvUcNNAYV3o4FXfccJkWSHJh/VvUcHD18RP8HOlv2FUnyQ35qNtlzCAAWb4asDJ1/vfx7P2\nJ4iQoU+e4wPBb+OlgpM6LUGmLjjJE+QK+1nSu6hqbubkPsqSl1fFBygKftbUOHPlPioeD3uUazzB\ncxwOnadieChLXh6de4VduZs8vu9ZfuB+mH9/Nwbwe9LawBUqD1dZ+jcnUf/lGcSXV9/WKVuL2poe\nsAk/ZTY4aitNAFtBzQRfEyh1NmgNsy0nm2oP81jYnIw0aZg6m4E4pmLFGkpu1mg0qRfeob0mm6Bt\n0j5Ny7lN79padUa27GN9+7By2SIbFXJMflwH8gdjzPzBg2R+twFPj1uOeO/YXQHteDqF89UW0oJO\nNJphlAnKePA4ynSF5jBsAmcqx/md5TAj3ddJ+JdxUyW1K4yu6jwmPk/DUEAAHRH3HdbNjsrjrRcQ\nmy/QFEQmxBHyRoAu3zyD8i1q4gpFz25K7EChwVHeIqAWMAqgjoroEfBpdYyZOraChjBoIAc3ntPF\nO0n6AbyU8FClZdhIaVEEERSlwVcPfYwx+yT94VlC1wuM+qYxxmCJTjKyjS5XiSWSeLUSg/YZ0p4g\nYodBeEcOLSTSQEHFQR+zDHGLMxwjJ4dwSxW+p3+Ae4oXGCzP0B1aZtVIkK1H6HQsk5BWCGp5EpdS\ndJRzKME2wdE8mf4Aty4EeKV7iOvs4tmlp8gEg8S9K9zH67TtNt7kXsJkyBFGEHWG3RMstnuYzw/i\n8RQJ2bLEhXWKkp9oOcNDmdfpj8yx5EoSJE+yukpnewXD0SYrhajiBgRysp11cYT53CADzjl2uG4y\nIw9SwcBJnThrlPFRwYOAjiI0sIsqNqFFRo2yVkugI9ISZESHjkPaCJxqoNCWZSQ04qyhRmQW3Umy\nzhBrWvxuDN/3sDWYnQjz3S8meGphmiirpNkAIhOUrEEosDVC0fRkTbrDqu+GTUXFdmC2pmWFrZ52\nla1h5NZJzbbls3mc2SdrfhPrJCRszXVi9ZTNtsxozu0qFCuFY51gtT7MrNcSAxbnQ3zvC8eZm6jw\nXqgH+U52V0A7ks7iKhiQBV+rRJexxFscxeZs0u+a2kimVOzkzfIDvK/9He7jFHu5QvZwEIfRYLc+\nTkaPUGn4cOSb/L/svXeQZflV5/m55t37vDeZL70v1+W7qrraVLfU6pZFYpCQhECC1axgEDDLLsMM\nMcQuEzGzE0DsDAODWSDYASQkISFL092SWu3Kdbkul1mV3r/M572/Zv/Ivl23C7EjzBatZk7Ei3iZ\n77q89avvPe97vud7nP4Gol/HTZ1djuv0Obd4WngcAxGn1OL+4Hn2MMMM6xjsJ0cMGY0iQWoODx5/\nh07cRHboBGbqaFUJVBBGoBFw0zTc+LQ621IPTcHFofaryLJGWk4QMQvUDC95V4QXjz5EuevDXa3R\nt7mFu9GgPeXgau0QRlMi8FqL96o0SFAqUlc9qGqXgKeI5NZw0EXAxP3aRLyXeJhxFhkXFvia9H6E\njsju8jwjkRUWxDHWW4OMSsvs6swRqpVoL7vxlBsc7XuVistgcyhBthzizNBxrlUPslIaIele577A\nDfZxk+d4Oy/zMKMsETOzDAsrjLsWOVd/iM36E3RpE3KV2eOcoYYXZ7vDyeJ5KkEnHVFFNAxqeMgI\nMTboZ7PTT8304lbq5DGoaUlqlQARoUi/a5OXzFMYgkhTcANQw0sVPxEKeMU6piBQ1z10OireZp0q\nfgyHjKzoeIU6hi4x29lNV5bwyHUmmGe9p49V+mmjkmm8qeTT/yixedVH8Xo/Dw5NER/Io61vve6b\nYRXzLCC2S+jsniR2T2x7EdP+ssKerdppC4sjb7OTVcMbHxgWVWK1rttpF6uA2bZtY/cssd5bNAq8\nkdbpcMch0H5eK+zFSIuXt/azPxjag0m2tAle/s8DtIw1/kmDtmyYMAHMgFzQkDCY1yZZEwbpkzfY\nxzRaaI6e+7YxnDsWlGEKBKjQxMWcOMlwc4PxxTXEr5gsPTbExiP91PByPbaXVLiHQ+qruKkzzV5G\n2ZnwniOKjBM3dQJUWGeQdr+T/vdsEvLliSyX4Fyb8qMe9D6R2HqZ2/5dpJw9vK/wDLPecbJyhIfX\nz7EZ7MUZa7FHnmGLXtYZwE2DI5WrnNi6hHt3Ey0oMtZdZOTKBnPrg5zlkzzJswSokCWOnwqBdhl1\ny2SyZ4G08wpZopznAS5xP/HXFB1BSnipsRoZ4K/87+CqeoC64GFEXOZD7a9wMnUedbHNn+//IQjr\nvFd5ivO/3EDr5pk8XEbUD5L29PCuya9z3PEKIyyyTS/d12Y9HuIqe4wZhjprqA2DgFxjKxBndnkf\nVW8Qx0iXNirbwTgz7gk8So01fZC/7LwXb3+dMWmRdbGP09nHaHddvLv3q5SpUhCj6KpBW5YpmiGW\nOqNokkzZEeAsJ+niwE2DR3mBCHkqpp+vVT/AAOv8ZPTf8QKnONt8mEvpE5RiJVqmiwsbD6HEG/SE\nNukKMgpdApQ5xKscc17gqXuxgN/UkaftavIn/+ojHKiMcfzf/NobPKTtRUj7eDC7I56d47UXKK1C\noX2f5msvN28ESQtw7UXHFju0g2I7rvVAsYDbLge05IIWDWJdgx2cm7zxG4B1TDttYr8GC6ytzN9O\ndljZuXW8z/3Mj3HRc4TOL9yGZvO73Os3R9wT0C5F/Ji7K+gaOEba+PQqomDgEhrEtSzLc+OIDoPE\n+BaH09eYEmYx4yI1wcuiMMZZTvK4/DwPuF8hFiugunf+aUoESSsJAlqVQ+VrxDrn6TPTbIXjZJUY\nVeqIJIiRZdycJ9IpEWqUCdQrbLp6qPkDRPbcRN3ukq1HuDm4nwueo5RFP3FXnoBcICqm0fwiG64k\nm0Ifj/E8I7VVis15igE/vZUtfKk61d1uwCR4u47cqNIjqAQ4h0qbFEl0JPrYpO7wshYe5ax4nPnW\nGFPKLCviCJrm4N2NZ5nUFpCFDk2fmy1HD68aB5iZu4+WWyUxmKJs+KhpHoL5ClONOcrDPpYPDxFM\nrCK3NOYlD7uETfxUiVHASxlVaxGs11HVLmvOflYZQhSMHUBVw+TkMCPyEmJYIK7ueKCUCLIu68zJ\n43ipUTc8TMmzxOU0frFECDfHXeepKzsuiAkWeVD6Bku+UVSlxZaZ5P3G13ELdRxGd8eDRZDwUWE3\nt3HSoiiEmFDnUIUOm0oSEQN3qYGxKmO4ZMSggTtYpWL4WK8Pong6RIQ8AiYN3HjFN2cmdG9DQ9dq\nrJxtMhnrcv8PwsIFKG/emYAOO+Bp8cf2cWFWCNwxYLKA1aIPBNs2FuDagdGeDVsdjHZJncVft7nD\nMyu241hZswXEdgrFTpPYG2AsmsTa3k53WD/bp9HYpYRN7jwMTCAwCP0n4WtpjZXtFoZW583Utn53\n3BPQzsaibI+rNHYpmBK0dRWxbRCQy/TI20yvHqTrcHBf/1Wm8vMMiJssRkdpiU5WhSEucxi/UsEb\nr3LggRmavU6aupvN9gBr8o7mWK1pDNQ28NJkyTdEQQnTZRMdCS81xswlxvLrBPNVzIrAkjxMy+XB\n3C/g/WaTjbSLb598lFXXICYCl4P7OW68QtjMcSsxwbSwm7weQW10mMwv4ajOcdsxhtLqUm76KCg+\nHC0NcR0kX5ugr8gpnuEqB1lmBB0JGY28HGHau49v629H68j8L9JvEKBC3fDxaP00fe1NNEnG465x\nWj7JijZCbc2PEQZzSGBZHaDXPYxPrXHs9hUyzQiXDu/n6K4MSqvLU40gMSPHLnOOgdY2a1ofja6T\nvduzjMaWuOHcxWkeIiPGSCm9bCp9yGjEzQxyUkPWdQrtCFtCkqrkIyGlkdFAhGPKBXZzCx9Vavg4\nGHiaLg6e4Z30ssW7xb/imuMQt6VJ0kKcT4u/TR+bdHSFVLePjqwgOjQEU9iZ0i64OK5eYFUf4huN\nH8Cpt2lXnDgqbXxambCSwxet0Fgcpdryk96TQFXatNmxEWiZb45JIv/o0TZofWYFjpaIf2yMreVt\n6pv117XVdhmewB1awN4RaIGmpZu2WtIF/jqNYee87QVOgTsNL/aORMtVsMsd/tmiY+xGVHaYtADV\nDs52lYuds4bv3klpbyCy9N0O27EMwCmAO+HF+7YE2h8WaF1Y5s0M2HAPp7H/vudDLAojqEIbT6vB\n9OWDhP155EMajx9/hvnqLr698i6EqIDLXeNq5wD/zPFl9svXmWSWszzI590fJjXxMttKguuN/ZyZ\nfZShxDJ7+qbpxuFqZA832UdddROkRJQs7+YbRMjh7LaQzurQAuGAyf2pq1ACKWXA+A4g6orAAOv0\nkuIIl9kU+rkkHGWbHlw02VufIXGhgLvbRvDDeHaFlcAgLz90goRvC9MQmPNOsL8zgz7bpUyALFHq\nuOkhzSBrpIu9fO3aB8kMRzjY+ypHm9cIqDXmHGPMR4boGuAR6mw74vSwxQ85v8TUsVlm5UmyROll\nm1qPmy+fei/vmHwRr7NCLylcW018G3VGjAZ6ej8LyTE0n0x4rczA0hbKYpf2/TLBeIlTvEiKXtqo\n7GGGNiop+pgx95CuJOnk3dScLnp8KbzBGgYiMhoe6nRxsE2CP+Hj/ABfZ5Ql3DQo4OZKcx+fWf1x\n3JEaB+OXKChhQnoJb7PGyOIGmWCE3FCYoeYmGbGHS+r9fKD2FK6izufyn0AsGjg8HZInV3nQdxo6\n8OXSB6n8Nx+BXIFdv3obX6SCSgsHXa5qB//7i++fTOicvn2CH/vNT/KJzP/OMN9hgTsSu50tdsIO\nwJLtsxZ3CpJwxyXQrn1us+PRYW/IsXPU9mKgFVYDjsodqsI+XMG6DrsToEXf2K/P7kyI7Zqsc9j9\nS+zXYj1ULHrFaroRgSkHzC4e45d+7VdY2p4Gtr/bzX1TxT0BbcE0GDZWKMs+dFHCIXeIRHM0TRdX\nM0f5geBXCagVFovjpHw9OJxtXFqTgFBm0Fgn1C2yIE9Qlz2EvHn8xQpiSWDTO0iva5OAWCarRtCR\nCHdzDKQ22HL2cpEgXir0bqQJzVRQjS5CDPBAaKH8+r+P2YaOppA3o/SyRVLfYrS1humQWeqOcm3p\nMA+LpznquIoeEGksuPDMNpAf75Lq7eE572MMscJIaZXR3CpKvYtRk2jies1utcgYO450piqwL3ad\niFzgSP0yA80Ur5ROcN08hHuqQdEVxMFO63mftsVebRZvoEaPmKJIiHEW8LbruIsd3HoTT7bFwOo2\n7mwLRdDwaBp9L2/jigQwHoGuV6IbETGrUHN76BoKh7WrlMQQc/Ikh3mVFk6yxFHo0BYUtklARWAX\ntzkZPIuORAU/aRK4tQYh2uyXriMJOlV8BClRpoJbqlPwhhDVDh1B4aKwI3/eI9+iHXCRcidZYhi/\nVMMnVpgSZnHIbWoFD8XzEZTxJsF4nlAkT6rUTy3vp5wNo8UE2mEXm/VBXK4GGSVO3oxSMoP3Yvl+\n30ShZnCxrtOz5z0cwkNs5ikk03idVoA7Wae9ccbKUO1ZqQWI9k5Ey2DJ8tq2stqubX+7csPeJm/3\nuTZtx7ybs4Y7RVP7dlaGbX1z+JsGG9jpnDZ3Hgj2v8cC8rYo8/zud3PJeIRLN+zGsG/uuDc6ba3J\n+2vfIOnbpGL46JgKtb1eLhQe4JXNB/mw63Ps9s2wT7lKWoqjiB0OKNcYZI2AVibcKTHJPAGhzB5h\nGndOY6C8TXWvB1nt4terVMwAPZ1tJquLJBbzfDP6OCn6qWKibGqETldpP6miD8m49SZsgJ4X0PbJ\nCDWTbtpBe8SJU28R6ZQIV2s43HOU60G++upHiKplpobmWdo7gLYm0TNjkHunn3nvKK9yiE36CJQa\nfGD6GepdD62KGw0ZHzW81JhkjhltL1XVy49O/TH7KzMM1FK0Wyrz81NcbDxAeDDHkmuULg6GWKFX\ny+Jut+mRM8TEDG4aqHQIlqscnp/GFAVaGSfCtETX5UAYMumkdAIXaoiYlI+5KCW8NHxOav4Ay6F+\nyqaXA52baA6ZFXkEH1UCZoWomWNIWKXi9JPzRSDtoL+9yeN8GzBZNMb4tvEOZE2nT0jxPukbLBmj\nbJm9+MQqLr1JSCyQSG7gFyt0kbnI/TjFFn5nha3eXqbFvayIwwScJYZY5RQvsOQZZaU9gDBrEn0w\nTWxsmwBlrhSPUkxF8Rdq1B9RqYYDXO8cxtmo4xCa6LrEPuXmvVi+30exjSmk+fre97DqHuAnKpcg\nV8Bs7rhQWxSBnfe1VB2WV4cFlhp3OG64Q23Ys1jr91ax0mqasbJsu+2qfQalvQHGLvWzpH3Wy+LB\n7QoWO6zauzHtHLV1Xosjt45jV9JobpViNMafHPg4N2uDcOP7p6R9T0A7WCsjrYI0YdBX3yaZTuMc\naSP5dbqqxIYrSam2j+ezTzKYWCTgLVPDS5EQaTFB0+Xivs4M49oKc64pvi29i3l5F7u5xsPNyxyu\nX0PpdlBm26jLHRwTGp5AnSBl3DRo7FJZivRzJXqIcLnMO6e/A0XNuP+GAAAgAElEQVSohr2snkzi\nlFq4lTKflP+QRDVPTz2HQodgU+NE9zK/NfRzmBGD6YEJVlxDBB8qs707zrmBE5Tx83aeo4Ift6tB\nN+ng6djjTFfKjGKg0MFFEydNXs49Sjkb5leqv0I8niUfDnLRcZhwZJtH9W+x4hva8d+miYcaf6U8\nyefkj9AnrbOL24yzwEXuJxbP8cjx09QFD690jvOZkx/j/eLX2a3eYu2ZIv9x4tMsaWPsc17mFC8g\nd03+be4XqKlO4uEUc84pNsU+whRYYYj7ujO8p/sMqrNDSQmyGUri9HRYl5L8R36JKDlynRhnSg+T\n8GfodaZ4nsdYqo8h6AZH/Jd5peLj2vqTPNL/MqKkv66nX2aEi41jzF3cixJt0XvfOi/zEJskGWWZ\nLHHqQx7EH9B5V+/T9LPKImOUe4KMhBZ4Z/cZ/nL5/VzeOoprTxnRqWGYIp22Qkb8H5K/vxaGCS+e\nJ/uIwvP/7ReZ+LU/I/HsxdeBzy6/s2uWrbDMpOyZrt1r25ovCXcA2a4GuVu9YddVWxnx3cBsV7rY\nW9LtVI11Puv6LbC2zm8VOO3cuNXI07btb7KjY18+dZBL/+uPkf29Apze+pvv55sw7glotxWFZV+E\njBQj7sjicHcYlxZZUYa4rtyHjwo1yU9ddZFtJnDSIuwpkBHiCKKJJOoM6psEOhUipTIDyjp6VGRI\nXmawuk6skWfT2YPXDZ5gk0wiileosjtTZnipjuzXyYxHWaePlu6iHPXg6m/RDTooJoIkahmi7SLJ\nQhrV7ODQNcSciVQxiDXzxMQ8OS3ERqsHj7tBIREiE4kRyFcJdioYIRMdCSmjY14SKL/LR9Yjc4Up\nPDRw0aSKn7Ajj8fdQMCg5nGx6unjReUR+n3rPMAZnuVJIuQZYKfjMS3GyQhxLneP4BC6TDlus8EA\nNaeX3c5bLDLGNLsoEGSZIXQEljwphH0BVK1OyFEk2K7Q1txkQ2HKLi8IGkU5SA9beIwalzr3s7I+\nTiyV58bUHswwHJCusZYeoeHwUBoK0kGhLATBYbImDVIW/aRIskE/bho7ShAjTKa7l31cpSwESLd7\nkDMGDY+LrBzldms/7lqDTsXBbscsPkeNvBxhi16K7RAUQe9KVNt+Vitj+HxVYqEMHSQm6rN4W1VM\nj44gmWi6TEkJ4pRavPmcId4Ekc5RmvdwbboH3+EpAnIR6VvLSJ07A9rsYGpXhMAdcLXe2+dO2k2h\n7AVJC2wtGsbeSWmB792Uh3UswbaP/QFhbwCyf47td9b1W5I/u/ba/rdY19JWJWqPj7Kxfxc3Z0JU\nFlKQ+f5SId0b9YgvwpXxKVL0UXN5cIUa9Ha26OmmCTlKjLCCx9NgwL1EYaOHUiuK6rlOgTAlgtTw\nsqWus0e7zYncFcYiCxQjfrboxatVKes+rgX30L9/C+d4ixVnP6HVMkeXltj7KqyM9pGNxJA0A8Gr\nk9vnJ+4w0EURTZDwFppEi2VEyaDdI9KVRKR1HWEeKAP94GvW6NPStD0KBXk3uW6MD218jbLPx+nQ\ncQRMpBUd4esG4UMFuvh5jrdzhMuEjCJ5LcoDodMIEZNNYrjwssYA19jPaGeJA93rTAt7GXcscNxx\nHhOREkGSxhZ/0PoU61I/OKCGBx2JTfq4wDHSJLifS7QMF9PsZdWUeSJ6iwnmmWSWZDVH1ohzbOws\ny/IwoqHj61YZENdpmi5+o/EvmZ/bQ/eKE3xdjrpf4TH5BdLXB3F6O+wbukmBMKYsMOGdoyL7aOIk\nQBmPo4ZHrtMvbOAkSNn0MNvYxZaRYKk+Tn0+SCKZIjKYQUzo5ImyWJxiv/fXSbo3uCrvJ0WSfCaC\n43yXm+P70JwSV9eO847hp/A4a5zhIR4efpkneJo8ERCgKznIKnFKBLhwLxbw92E0rtZZ+blFpn57\nhP5joNzI4tyuIXT0N8jwLHC1T7+xqARLRWLRJhZY2x3z7NmyXcFhV4RYwM9d2wnsmEdZQH03B25l\nzMZdn1v72zXeFj1Ts53X/i1CALqqRCXpJ/+pE6TWBtn42bm/5V19c8Q9AW0TgSo+ttlxsttu9fKh\ns1/hgP8m2v0SJYIImPwLfpeEs4iHOiYaBUKkSJIhRg0vG2of30r6WFRGyRIlSYqtwBZhTwHV0aaO\ni6XmIAPf2iKwWOHKOlCBOWOSL+kf4sMzf8HhjauEywVa+x1UBtzogkQ6EQUfJJsZHE0DOgJCkh23\n5QVgDc48+QDPjr+dFecQR42LPCE+S21U4bLjABc4xiO8yMTBWfhF8E7UiKxqDHODEVYYLq2xf/4W\nZ0eOsRFPEqbAAOsMsM4n+GP2nptFv6JwQXkY7ZjK3vtv4qWOjyrD4gofd/0xOSHK5/koBUIEKHOT\nfbRRMRDZopeF6iQBs0Qf36FDgg36GWKFVU8fdcPDg9JpvFS5UdnPFy/9CEZQwDdc5qj7Cn1Ht7kx\nfh/loI9lY5ROR+Xk/S/S49xGfE2zXcpEuHH9ILH7tognt4mRBR2KRogr5mHKc3kq834uhk7SHlPR\nxiWSY2uEIzk8apXJ0RnyRKnJHn7X8ZO8TX6O45zjeR6DSYMHf+J5VtURMvne19OvfjY5yVkOtKeJ\nGnkyzgibQpK2ofJo+2Wek992L5bv93Vc+T2B2skeHvzN9yP/wTnEp+b/mszPbsZk0QmWL4nJjuLC\nAka7JtpSflggapficdd7y4bVzmdbXth2SoS73t/t+X23x4ilepFs21uNPpZO3ZIcNt4xRuaTJzjz\nVIK5s28+I6jvNe4JaC/rI1Tbh3A5WsiiRltQybqiRNQck8xxnfuQ0DnEFUyXgrvaJHY7w2JyhEbA\nhZcaydo2Hr3BJd8RLqePUGkEeKT/ReKZLEqxQ2fcgyCbOLU2YaOEu9SiW5C46N/P+fAxVo0hwrkS\nA9spjJbAGedxyk4/k/kFnFIbydShZWLKAh23g6rXh7xHwym3cWXb5JMhloLDpEkg6ToJKc1KcIjF\nxhjX0ocZCS4T7SnSCHnxduuMlNMcSn0HJdQiIuYIKCWS+jZaRwaHgXe7QXwtQ2QlR2SuQq7Uy/jU\nPC61SZEwQcoUCLMsjKA4OuhIr3dhShg0TRfrxSHagkpvcJOL3WPIpkbSTDFEZMfHA4W2Q6GNio8K\n4yxQ1fycKb6NjBEnECkQ9WYZS8wzFFvmaucggm4w1l3iHfnnkF1drsb246bBGIsMGtssMEi+FaVd\nclOTfDtTeSjh0Lp02yoZoRfWwK8UGds1j89bRkeiFtQZ6KwT6+TISBEWhDFMDRa0cbJGDMljoovy\nzqi2FTAiImJYJ0AZUTAwBBE3dfJE2KQfRdAoC/57sXy/ryN7Q8DEg/vACP37BZJaiP6XriE322/w\n77CA9O6Cnz1btY/pslrHJe4UKO3dlPBGq1a7rtoOzlYR8+4JN3bDK4M3UiZ2v5C7s2pLyWL3Oem4\nnaQeOUh6/y5Sm0PMnoHc9N/2Tr554p6A9gX9GFLznXxM+ixj4iIuZ5OVk31Ucb5m1SkioKEKbZ71\nPkpgu8annv9jco9F8fuqJIQ0B/M3MNsSX3R/kIWZXYibAsb7REaurbPnxm2+84mHSHjzTLWX4CCY\nOWi3VT4/9kNcHjmMu1PfAWYXGDGRr7p+EL0u8b7lb6K4uwi6CVlojYqU4h6W5CE8vXWiDxRIFLv4\n3FUGWSNGloiYo4aHOl7yhTgLq7t5bs87WAiP0Stu8fHtL7AnM8sHZ2a5tW+MdkKiuV/mYO0ak40F\nFvyDJG+miX4tT/OLoOyDxPu2+Oc/+LusxIbImVF2mbfZoJ+nhPcQpIRTaBGmgIyGnzJho8jtjX2o\nUov3Bb/GFfEIfrNCQshwwjxPw/TsmDkJAggC+de+mTjlDl8LfoicFKXV8HDOOMmPm3/Ejwqf5bPK\njxAR8ry79jSJZ4vcCu2iOuXDR5UHA+f5wL6n+D+Cv8wXax9kbvY+hP42g+5VHuElzsXHWBvQMbwi\npMGVabKrPYtqNMkIcTJmgne3vslP1f6Qpx1v56vCD/Dvu79Ms+ZG21BZWZpi/Ngt4kKKyqUgnSGF\nfDLKjLGXnBwlKubxU+ESRzktPsQz6pOEhNK9WL7f95G7Ad/6aej/zSfZ/6/uJzr9H1A3M0im/obW\ndvuMSLsMEO7wzwY7mbflyie+9rNdTw13wNTehQlvzKqtjN1q/LG3mlvH6/DGLkgLsK0MW+fOhB47\nn/16oVOQ6MaiXPvFH+fm9QCpn7n9d7qHb6a4J6DtkptMuKfRRel1VcEqQ0jojLKMkxYV/NxmN+Ms\nEOipcOvJMf7U+Dg3tvYR70lxO7aHgF7hEeklflD6S/xyFY9Qon9oAxwgekxM2aTjkSg6Ashv76Jv\n6niHqsTI4KSNSmunuaZpcrR9iVwszJWx+wjIJcLrZeJX8yiXDEI9NXadXEIO6CipDvL/Y+B7tErf\nD24CkDRS+I0KMSnLicgZFFeLEe8SDjo0xR0P6u14Fm0sx0XvEUoEQHyRsLsEJgiCibBhIm6CaxSE\nYaiGFFJSH26ajLcWiS6UOCWeozec42z4GKgmSTbRkAlQplfc4iNDn6ErOHDQ5Z97/wAJnSskERp1\nRkqb6FmJ1WQfhbjMEKtkiLPiHmTv/muMC7fxOGsElQIHWjfpr6f5aOMvEDwaklfj1x/8BW45d2Og\n7XifKwFeiRwnr4Q4Il3i2J4LrLoGaahO/kT4OJ3keQ6/7RxL0hjVW0FKqyGePfNepPEO7UGZaj1E\nSr3AdijMkjxCUQzhU6p4/DXEEZCjBqVOmEbXCw/ArdZ9bJ4eRJ1vo+5tE5tIszd0narkw9eqsZ4a\nJR/4/iog/WNH/o82uTIRYPPx/8JHLv4pD05/gwXuDCJo8cYmmA47JR2RHcWImzeqMyzAtVu8WpSH\nvThpb4KBN3Y3whszY/m189unrlsgbQG4FXb/EmvWZIM7XZ0TwMt738ufHf0Ymd8tU57b/NvftDdh\n3BPQDrSqPKCcI8eOWsAwRW7o+xAM8HGapuyiJTpx00BGI+eJcmn4KBcyR9kykoBGwR0mTIF9XGd/\n/Cp9jk3akky9x8e0fzfLzmG83RqaLpHxRaiM+FgbrzEUSKEjUhJDrMX78VMh0U0z5lrAofRzM7yb\nvYVbhLMlWAUzKCDrOpFCiYbipGs4UNtdotoOlaMhEzNzKEYXSdJJuLc5ol5kMrtASQ5wI7qPitdD\n261AADaUAUp6ALljIm6ZyDmdUL2Cc7GDqIL4KHTGJRoTKi1VxU8Lj17HWelSUUXagopCd+c/igmj\n2ipOoUlXljkcuESGONPsZUBZx0GXLaGXPEU8NMkLEbJCDKMFPekMhUCUWtCLL1GilxoxsmjIVDQf\nC50JxmcWUcMNtg9GmJ8c54a8l5CZJd+M4qSN4mqRENL0yCnkmM52O05ei5CSkvh8FXaNvErBDOLR\n6iDKrKWH8DcKBM08Lc3JvGuMl10nyRNm0FhjyFyl7vBQVz1U/T6urx2h63IQ3ZumUXdTuB3GOCuD\nIBAPpPD4yySkbXbTYYsBSvnQvVi+b5loXq3R3HKyfWqEcU4R91SJTlykka1T2fzr48nszSpWY4z1\nO4sLt6gMO09tAbulGLEDvd2TROONfLVFn9hnTd5tZGUd97t5ZFvnCvSDN+JlfuEYl81T3KgPw4uv\nQKb6d7ltb7q4J6Ady+V4khv8Hv+CNQbxmHXOtR7AqzXxmy22PH2oSpMTnOebPME3jSd4VnuSZCzF\noLSEhP46oK8wTGh3EYfRpFfb4rLrMN+KvoNFxghU6pwsXSLrjXPBcZTzUoHHhQX62eR56TGe23eK\ndXp5F08TFrKU8HGNAxxcnKZnJg81aLxPRj8i4J01yIkhqhMexj+1Rl9sEw9lckTxU8FApEyABm6U\nTpeh65uYXpntaIJhllHNNpIGHUPFoelMFlZRvtWBM8BqAcLAAeCHoT7gpOp14hGqdJHJSjH6fRle\nCj7IHyY+ziFeRcIgZSZ5oHkFUzJ5QX6Ik5xlg34+x0dx0SRKDp0vMucOseWOcr73BH6hylR6nsiZ\nKqG9JZRgmxJBPNRxU9+ZCq+OcU3Zz8+e+78ZSy7g3ttkIjzDthRh2+xhOzfIsLjCP+v7/A7I42eF\nfpZrY3Q1hSPRSwissAeFy8JREqPzBOMVvnXrPUzG5tkbvMqzyhNck/dRxssRrvAe/a94pPMS8+o4\nM+IergkHWHWPYrhNdkenWekMk8320Mr5YE1AHDZQRjoc4BpuZ4PV0UFmzuy/F8v3rRXpHHzpKb5s\nPsL68FE+84mfIPvSMpe/cqfd3epMtEvprO5CK+u1ipOW8sTax8UdOgPucNV3dzJa2bdV3LQKmho7\nQxpats/tmbxF31jbt9hRjNgtVseOQ+hkLz/2W/+eyzMdmPkrMO1CwO/vuCegrUaanOEh7uMGydo2\nfbUt3u57nlV1iJ/XfpWYmGGS23RMhXOlh7hknkAJdMjUe+jVMnza8ft81fk+bjmmKBLCVeigtAye\nib6LZ1ffxVxhFw/tfoGB5+fpfqPN1A/NkZjK0ym12btVpej2Uw1e4qpwALluECuW+a/On2LDleSE\n+zyRegGhChhwS9xD1htm9/gs33E/yi1lN8dGLnHfpWkGn17H82CXer+TkjvEYC1FUY5yUT3G5r5+\nBrsbfDj9FVYDfaTlONt+eHfnGdQbXeQvaAgOYBJ4hDvTU2cABWp+L5e4Hy9VphxzrA0kCSgFHhJO\nU8GPQpcRYZlp1xTbQoIL3E8VH20UppjlCJdptD18tjrFs+0ejskXeH/7aWqKC1VtI/dpNPweangJ\nUSROhshr9rdRIc+4soh7oo6ogLAiszwzhRAT+cCpr1COhKni5wqHKbTDlDIhqrdDVIc8uJJ1toUe\n+jEYFNYYY5E6HtLOOPJIE8HZpdVx0V3wIAW6qKMd4mTYlhJ8Vv0RPGKdNuoOb6/WaeaTzC7cR83h\noXvbBTcF2IDSWphXyg9TPRwgNpwG4MnJp/nsvVjAb7UwTExus5AV+IXPPoTw8Pvx/qrCx3//TzFW\ntljR78jwTO40tLS4IxO0e47cbQ5l99G2c+J2720LfK1j312otNrPrY5JKyzu2sr6raaafgEYSvK5\nn/4Y30l10f+szGL2JqZp17C8NeLeNNe4VM5rJ3in9AxxM4vLaHNKfIlz8jGumfs5lrvAVGkeR0kn\nHsgzEV5EEnd47n2FGU6tnCY11YuQ1BnrLBPpFDENkZSQJGvEqdb81Fb9zCzvpbgZINRo4tWaOIwU\nZT1A3fSg0kbCoGMqFPUQ68Yg22YMHYmuX6I06COtxLkUOUJWCeOJ1WnVXEhZg07NASkB12YHtdlh\nzezntjjBfnOGohlmRtpNMpkiUs/TU0mzTQxDEGkrEnva03jKbYRFYAi0XSKtx1TKRT9azkGgW6Ik\n+NmmhzkmcRhdmoIbLSjjEDpMMsccU3RxUMXHS91H2BJ72JbjiBgMGBu8Q/sOQTlP1ojTo8mENQVZ\n0tBNCcXsYDphfnCUrtdBspOmJvtJkGbEXKYtqvjbVYaba2xOJllujbGZ7mf2+T3IY216Hk0T8RQo\nEGaNQdYZoK2rJNspkHVEZxdBMGniokQQA5EmLuqyBzGkUakGWE2N0Fj14XC1KTYjrPpGWPRMsOoc\nok9ax202aRtO3HIDr1EjvTkAaQFuszNldbNLq+FgNTpKI+IiHMwhN7qMeZfuxfJ9i0aaQg2+fmkU\n565RRifd3C8vEhqfw0zmUV/NYZY6tHlj8dCS3Nnb4O0yQKuYaadJ7MoQu8OH3eHP3sxzt3ufxW9b\nft52WkYKKQgHQ2TXYmSFXbzqv5/lq02al1eAtwaHfXfcE9BO6b0sNg9y2H2FFe8wi+5xfl7/TzzK\ni+ySZhm9uU7oTBXxksG//vSvk+6PMs8wphdiMwWc/7XFO//nZ3gk9gJ9uQw1n4ttX5QRcYmjo+cR\nvBpffuaHqSp+1B9vETuVoj+xjnbtS+jJCA6hS+W1UVeSR+dF10nGhDn8QpGXeZjk/hT1vU6eMd7J\nDcc+BExGWObU5ssM39xAntWRYhrm26EzJHDJc5AviT/Ee31PsSCMs8wIJzmLx13lnOsIPcI2PWwT\nMiuIioY2CI4ngRK0ZIVUT5SrQweomn4OmVfIyxEWmCBPhBVtmMv6ERqqmzFhAT8Vhllmmn28oD/K\nSm4S2dGlv3cJHYkRbYWP1f+c3/B8mpas8knlN/mwADfNPfyO+6c5IFxjwLHGdwYf41TjDO+qf5tt\nXw89Rpoj+mV6lS38hQbqts5vT3yKb1WeYPrVgzRvuBgyl7jCYZy06GeDj/B5nlWexBgQeV/yG2Sl\nKBtiP2sMMUeMZ3icaxzAQx2X2cQ0BJaWx+lcd6O3ZMhD6fkw07sPwRgYfQKSs4VggtCWORF7mSnP\nLBlfP+bnBMgAHwa+3oRlHZYC5FZ6KHjisAKF3VHg0/diCb+lo/XFFWa/4uXftj7Bo/9ymXf/+MsE\nPvkijYtZSuwAp8qduYt17gC5zB1KxKI0LE9tu3c23Ckk2jN1C+jtnZR3DyG2BiK02Sk0Wlm4D5Am\n/TR/636+/AeP89xvTdD+326ja1aLzVszvifQFgTh54FPsnMnbgA/wU4z0xeAIWAF+GHTNMvfbf+2\n5KRX3aZf2KAlOGmLKhI6piAgiTo3pvYg+MFzpE5oTxHdKeCmQWIjh4cm65/sYWtvgpbspBtS2VIS\nZKUIDjRyRJk3JiiXgrSqbjpeBadYJSZnCDHNox2BkhhgWt2NmwYD5U2ObVxhfaCX24EJMsRoyi4M\nWSDJBjXc6Ei4qaPHBZqjDoKpNvn+ENtTMRo+Jz6pwruEZwgJRU7MXeDIzHUaD6jUEx4CQpkQBZw0\n6QgqWUecbp8D9fEu8aUCstrF7WgQcJTZoJ8/4n8irSXodBQmHXM83v0Ow/V1hrMrBMQyoqKzGUzg\nULp4hDpfcoXZyA3A8ihHpq4SCJbIuMI8UjyNaBpcV3S2lV5yYpSEsM2V2aNMVw5w4r6XWXP2cUvf\nRUEMMStMoootJsQ5agE314wJXll9gKbDzf0Hz7L8CyNoIZhngt3cIkyBJCk+eO0rODSN3oNr3JA+\nyCLjTDBPnRST3MBHldube1lKTVBr+OgsOtE3HBADfCA72xyauohjsEPGHWczO0DddCP5NNalPqSA\nhm9fnuY+H911JySA9zt3VOd7azjHm/gjFZLBLXK3on+vxf/3XddvmWgb6O0GdTa4+p0WpdQYntX7\n6H9bhqn33OLQn17DnMmz9BpxbSlLrPfWZBqRO12UdmrDkuLd3XRjl/HZM3L71BrB9rkBTCkg741y\n7ccOcebrU6zditL8D1UWp9s0jDWoN3grAzZ8D6AtCEIS+Flgl2maHUEQvgB8FNgDfNs0zV8TBOFf\nA78E/JvvdgyX2GCfcpM4Gep4KAohWpLzdevSyoCH9oCKx1RxFNu4cw2CYgWjJFP2B6gdVdmSe3a+\nonsHyBKli8IIyzs+z3Kd/p41ul4FwiaKXGdIX6VXm+eA3maJYVbNQcKtApPFJaa2FyjEAmgBmS4O\nbmm7aODGL1UYExYxEFHpYHhENL+MqQm0XCqZRIQ1BomZWR7tvEAt4yNxPU/PlSxf2/0u2gGFZDVN\ntFLAme0glJxomkJBCVHaHUDp3iLcKtISVEoEWGWIF3iUrulg2Fjh/u4ljhuXGDVX0ToSsqAhmCaO\nZgcNmawcw+8uInT7yS8nqA94KURCzEljjBXWcJlNXpL83HLsYpExDESyjTiuSoup5gKXvIc54ziJ\nW2jgEDusMcgYi2iySFn1UWqFMBSBUF+WrWSMshggRxQ3dSLkcNMgVi/h6jRxalWyYpwVcZiDXCVA\nmUHWMBGYvbWP7MUkxMCl1XFFSnT6FbqSjFNo0Tu+QTiaI6mvEy6V2Gj1s9WJUzb9ONoacq6LOKGB\nV4e8iLhfRBoREH06squLU23i8xfJXYn9nRf+P8S6fmuFBqTZvAqbV4PAAXYFi4ijTvo8bbRIjdsR\nP2L7KlG5RueWSZU7HZSW0sPeSWkHbauoaJ8mY1d+2L1JrJcCBABjj0guEGV7LcyGriJ7fdwYPcTp\n4CHm0n743HV2cvDG/+936c0Q3ys9IgEeQRAMdr79bLKzmE+99vkfAy/wNyzuYVZ5hBUU2nda0ulH\noU0fKWJk6KLQNF30Xs/hTTUxXQJfO/AeMv0RjkiX6KCwSZJlRnYmrZBBQueY+Apj0QXS7+ulbnpo\niG7Wnf0kW2lCzQqGrNBUVDDgUOYmo+UVTL/AVccBLnCcAiE+0/5R4kaWj3g+z6Cw9prBkw9XpU1s\ntYR43SCQLBOmwDkewGvWGKhuoD5j4pjT6JoOVLNDJFfk0LWbyFc0XDMG4YMagWoTLary4rFT9LUy\nKM0ut8zdPMuTXOA4JgKH5Fd5mJd5ovE8TkeD7WiYStiH36wQ13L0F7Z4pXuCL0Q+jO6SCPvypHw+\nLsjHaOGggZtG3I2bJimhRoMDO6oQxhnfu8BDzbMcqE1z2niYG577GFWXGBMW6WeDLg4ShTwPb5/n\nufEbvKye5NvVx2kbKk61hc9XpZ9NEmRo4WTuyBSebp2HOmcxRZGa4mGbBE2c1PGwxiDlMwH4CvCz\n0PPwJkMDi2TlGCUzSNtUWHEO4aPCCfEcb+9/jlfSJ/mjpZ/E627AHGz952GEj3aQxjro/8WJMtBA\n9Om0Zr20XD5KwShr/kG0EfXvtfj/vuv6rRtt4FUWnzHYeNnDVytPwvFdyD9xmA9sfYij/hm6P9Pm\nBpBiZyiClSFbtAbccQO0O/UZtu0sOsUaptDlTqFRB3qAg4D5swrPHz/J2d85xaVbSYQLs7Q+pdOq\nLXJHm/JPJ/67oG2aZkoQhP8LWGPnUfZN0zS/LQhCwjTN9GvbbAuC8Df6ZO6v3mAUFy2cDJY2GU+v\nES7mCWol/HId0WmwFU2wkBwn6GighDVywyFG9GVG08skQ/hLoT0AACAASURBVCmCt+v4Wk0qRwMY\nTpGm6eYvzfcSF9K4aXBZOIzi6NCnbnKCczQUJ+ecT9J1BDFFga4hczl4kC1XnEFznZrbQ4AyQ6yy\nXe2npXvoc28yuriK1DSYnfSw4h5kfWyA9A8nEEY0JDR62CYtJPhz94d49OjLuMablM0gY81lwp0S\nzmgH/CB2QLpqImkayfo2bxt6CTMuMC1PMidPsDwzQaUaZu+BazzYPcdj1dNE6wWyoSCz7glekk7R\nMp0kxDQPB86ykh9hbWOMsdE5BiKr+PZVecL/DCEK3GI314QD9LOBlz9nszjIij7GcGgFQTVYlEY4\nLT3AsjgIEvioYiKwTQ9FQuTMXpaNcdJKnKgrh2q0WX91GMPrwDwusMA4RSNE3ogwpiySVFI8232C\ntqTg6rR5vvwElfUuK6ffTlpIkI4nET+o4TlcYap/hkP+y8wyxa2Vvawsj7I+KaBEOnicdY7L53nC\n8U32KAtcEA+yNthP8KMVVgeGaaOS+NQKkYNZOrLCdOsQfeF1ErEUmiqSEvr4u5pq/kOs67du7BAg\n3QZ0GwI1RFguIv/FDGdqUf5P9R0YCGT692LudxF72yYPSOfZlZ4ndKmJNgvlLVjT3phx2wuXViad\nBCID4L0PqodUbkcnOas9wNbz/Qg3GiTWb8LXBVYuDVK7tIGZd0NbhIw9T/+nFd8LPRIE3s8Ox1cG\nvigIwsf46zqav1FX89TvLHHlaYm66Gb3EDwYLLO8ZSLWX9srBqneLreSLea2wYWDTM5JOL+Gu9vm\nlYCEtFCn2G6wOS9SUz3UTS8pM0m/tklAL3NRi+JzVOg4byMzz4oxzOmLXrKKSFzI4GSDIiHcuBhG\nJcU1GszToE272AW9w3ToJpvrVYymRGZYoC55KBFgRXbgLnaJXyzQ07hFWkuwRQ9rXhm3JqFV2/TW\n1nCLTXBBdcvHxXwHzrQxFWClCGuXmAlPMu/qZ8tsk5t/EW/9BZwzMxQa17laWeGqDjlHmyVXmRcC\nFUoOAQ8OrhBjI1WmuP4tclMLeII1HJi0OUsOjWX2kNXjrFIkeL7MRuUWq+0WDudtmk4X84rBOgJb\nxhVUVmgKq0wLTaaBFk4yVYlMtUHo+nlktYPZUXBc70V3itQWq9xkk3ZTZa4cZ7+7RdgBVaMPXV2g\nbmyRrfZQOJ9lIbWOU5yn61FwBAzC1zZRb1xF687jbPbhzUziLozTGRFZC9doqWlKlBlvLNJf+Sbq\nzRO4XH07Ke+L+2nrXqS+VZxXtpHrCvLG/XSKL5HeuIaDLl3j795c8w+xrnfiC7b3sdde9yLW79F5\n7pxOW4cblLjBICCBruBuO4hXVMqSj7laEG/bhaGbVE2BVUSM13wCdSQbaO8AroMOPRgENBO1JdCs\nOVhQfFzSnGTaDpqaArjhaZ0d97Z1+Ecx471X9zr72uv/O74XeuRxYMk0zQKAIAhfAU4CaSsrEQSh\nh51a/3eND/1Ukp97Z55bvYM45QYDlTbel9s4rhiwCjz5/7L3pkGS3vd93+e5+77vue/ZmZ2dPbCL\n3QUWIECABECCJEhRFGmLkunIdimVxEo5TsovUuVUpRxbLqtklUsuSTQVXZRokgp4gAcOArsAFnvf\nc98zfU3f9/UceTGQnURWxNjKEkXO52V3dT/9dH372//r9/3B1imVayEvI90wEgZJNYGzF8GwZDJy\njFCzQMKS6biOclc8hmG5+ALv8WRmHVuxy1b4MWLuFC/YkwQYptt7AknX+egvbDMrpbCQWSKBhcgA\nDp4lSxYv3+dF+npDTJorfJEOdT1It6cx0M0g1fZJWTG+PXCCkKZztFxh5K0cYjpHRd1h+yNx/EmJ\nsXe3EV7qIQcErJrM17wv4PrqGp8/fgMzDNhB1OFfRp9j2/kpdlqDfNb4Os9L36ffuUtiu0gwDTig\ne7dOMlXC9xkb+T4XAk6WeILUyhF4MIx+bh1bLE2YPCHsTLPE51ikaGXJEeYH4kl8n/sIyf05lm47\niU/vMTm6wDlMepZCD4M4IjvCEZY4QoUwc/ouk8YKu8oAW+IQu+YAw/UmkmiAy+KTvEzhQYQH3/y7\nLE3VUaI9jJZMdHyPsegKnzRu8g1HkOanznNKvsG6MUZNcPHL7t/n2cIeRze26dzKcPOszpuPBdiw\njdBVgtjFMBbHUIxF5vVXeVROUpBaPGCW6usXWO+O4//wfS6oryLrOrutJ+nTxrmgXOTnza/xbZ7g\nf5Ev/xgS/v9H1wd87j/3+n8DzP2ErnsUEKDgoH1TJLMR5xJPcL13GqluQgt0U6GNl4Ni8lEsfFjI\nCOgcpIJtILCKSgW50EO8BeaqQFO2U7M8dCsKNCQOthj+r/+bP6l7/klc95/+Jx/9cUx7BzgrCIKN\ngyWnDwPXOChE+mXgnwO/BLz8V72BaYO8K0CkVWDZNs5F55N8ou/7DBpJGACS4HTU6X9ilxVtgh0G\n2aOfJ9U3cdJkiSmOue8ypO/wocbbHMmv0mupjEdWGW7tYlgSP699jRB5jrfuUtFcxMQMYdEkI8QI\nkmeIbSxEesgYSNxjjh0GyRNEUnTceg2t0+OKOssdbR5nu828eBfZ7HJdP8W0tMiksooa6+GWm7ho\nILZ0bI0ODqONkYOcLchmcISsO0zDlyQ5FUFy6nQklXInQNHmB1HAUkUicpawlGWHQe74TiDJFkfV\n+4imSb4/iNtVY6s1wnZ7BL8rx1RoAd9MmaZLI1+KUk6HGezfYdszzDLTuIQ6uU6UO9U2c70yH/K8\nwc7oCC5vBScNskQP0vIQqTHJysI0q7vTSGe64IO66SKV6QcV+kN7uD01mjjIEGWDUUrOEOawhB6X\nEMI69AzaTo10K8GVpI3i6iLdy35WH5lg0r3MlLJMv7hHzhHkXuIIo+YGsUiSCe8Ka4y9P8HK0UOh\nJdm5Lc1zgltU6n5+kPs4aXcM2dbGITZoYici5fiM8+vUBDcCFm3Bhizof5Xkfhz+i3X9s40F3QZm\nF9olaP/ferjDf0wEaXHQjPUvUkz+YjW79f7zFnTNg4Xs0l+8tsN/jJg65P/Jj7OmfVUQhK8DtzhY\nRLoF/A4HxyS/JgjClzgYL//8X/UeNd3Frt/F0coS+V6E7zme43z4GoNa8uAnchFsaocgBa5zmjXG\nyRDj/Pvx6xliTLGMz6zg7yQ5UbiLtt9FyfTQPTLVoItnbK8RqpSIF/dZGRxmwLFDXJbIiFMEewWO\ndh+gaR16skwLO7c4wSYj9JARdGjrdiqWjzVrnLeVxyi7fPRsIuO9ddJGlJCxT0EJsjwwjTdawW1W\niZJHVXoQBzoCjaKLpBjHrVZR5R4bsUGC7SIVvNz0n0AQDAZ7O2TbcdxCA0U22LYNseKfwPKK+Lt5\npIBBUfHjokq5EOBG8VFeEF7GJyQRXBabrRFqJS9mRoGwSAk/VzmDq96gWA6zWt7maFpi3LuEbbKF\nJnawLIF1c4ygUMQtVskTZjM1xva9EUaOrpDS+riTP0nzloexyCrTofuo9OgaClXDy115jk7AgfNM\nDU+8iCx36O7b0Pdl9kqD3F0+g7lcQbL72J4QeNr9Ok+rb5Cijy33IHvuBNrQwVqYw2ySb4bRJZkB\n2x5y26TYDpLq9GPztdlqjfG9vRcREh0Gw5sMSDs0cFKmxywP2GSEquDhgTBLOpf4zxb+34SuD/l/\nw+DgRHeDg6/xkL8pfqzTI5Zl/VP+8li9yMEU86/l8vJjnJByDHh30YQGA+YuNrMN68ANYAy6MypV\nvJzkJnPcw0BCRidJHxYCBYIsytO0vHYSR9IMmrv0/fo++ef9bP+tBGkljnZLJ3E5i/zLBr7hCgEE\nXNQJlktM727SGHWx7JtgnTEkdFQ6bDJCveahZ2i85n0KS4Lj3KaOi/nKfebb95BDPUxFIN8J868r\n/wifs8gT0Tf5VPPb9MtprJBAw2/Df7PIh3/vEvoXJfI9OxucI7aVx7A0Lk4/wYeF14hWcrxx53kk\nSSIezHFkcpEpbQlvt8ZIKknPI1IKuckT4nrrHI28k1dbLyBumnTvKnQHNUJTWY48cp1x+woBirip\n8cr9T3I/M49V2+VHP3qWO67jBF/MctJxA5vZ5kF7lk8qL/O8+n3SxGjMutiN99Pv3yG3E2frohfz\nqxL6BZnGOSdNLAqdMJlKH6LPwussMzt4C0MVqdwPkP2dCFZexNKFgzhWCdzzNY5HrqPZOgd9HvFi\np4WXCvuEaWMn2e0ju9jHvitMbiJEfcNPZ9UOW5D7WJh20I5lE7C72ww5t3me7/Ee57hsnuOV7gt4\nlQo+ucwe/ax9Z/r/i9b/xnV9yCE/CR5KRaQ7WKPYCcKKiM3TRR7Q2fQO4RhtEBJzbA0Ns50YZJcE\nMTJ4qKLSZYVJ6rqbj3TeIKDmcQh1pKaAf7OMeLXJym0TJdAmMlHAOdbFE6xQO+kg44riMJuMGymG\nrSAjnV1cuQaTmU2UuI52ossUy6yyT5oElU6QnBFjUZwhISYZ5aDasKj5eFs8z5bcjyzqSLLBMf9N\ndLtESfNQEVzktKOsmFOcMa6SsKfRBnWaHg27ZBEnjcdWByvHaa6RI8JKdZrWPSdCRMDlajJg7dJD\nQRV72BwNttRJ7jFLB418OYS5KlNUQwgYyJMdIvEs/liBlsvOu/sX8AoV1EgLf7jAlLZAan2Ppiqy\nWxmmtuNGSfRwOBtURS8VwcuuPsiVxjk2OhN0BTu7tVFqZS9mSYK9FrWCQpoEc9yl03bQy9twO+q4\n7TXqdhcR9tG6OpuFKVzDFWRJp/ReCEd/hSNz9znrvEwFL6/3PoxXruAU6lTwECNzUIovuGnbNSo5\nP+20hhrqwSAYmsSD28foGSqWJhCK5RmSthhihwoHn/2K9Cht4eAgWR9JcuOxhyHfQw75QPFQTHsi\nsozcnEdes3CHmgSCZfaFEO6xOOYxnfc4xTpjtHBQxnfQCYUe68Y44V6BZ9pvYloG6BaBdB0eQ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kI+xtDGEtSSTPD9ByaVgKZM0oWSNKQQgiYhEli4cqeKBtszFo30DXJHRLpLgWoiXasfwiXluF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\n", 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dFOPivZOX+IeFlwA4EWtgm0AyqwMYCJF3IUBzw5Pn/Ic7D/PxW/LOBS/nGX1K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\n85Ix5rb+X/xFx/r/t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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -898,16 +863,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1064,7 +1029,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1075,15 +1040,15 @@ "(-0.5, 0.5)" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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M860k759PorMVWlZgGjkash7DE11Eg5xPZHMyWuuT+AYq2LY0oOrvwvOXEjWtieb6FKyh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\nyUFjJq3Zkzk+fA7nHMyk5+G3iZoSxDrax8h3y4i1HUBJuhLn5A48UT7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tB+T2EaNdSmd6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9AxFzL1uyTzPVnknc0Mmw+lm8WgsdkbW4kzWMLglsyYjCB7G1pyMd2Ia+ZjsFi3YSTBiL6UQs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\nA/9etN2hNUdlToJuaw4/VpoDX04An2qUQmBGKnX6pwjma8R/bodrVkLDCgZ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JwZmMxXQxcuPttNdMocXtZpdtGM0pViy6E/DG5hEXM4YKW5CKgiI63bFE0m2QGAsz5yBE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\nkj6gH+Hf/oba4z6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XogaiwNMAjXfhiKtB7qzjN5Pf4gT/Es57eTZrTjifs2s7YOMqAmou\nygXHg6eStn5n0dF9O55wX0a/N5eQNQam/5Nw514aC7LIl5cRClyJDO9D9+IWIgM9eGxW0EWIGA3o\n9WbY68X8Rjf+O0wYUkwoKfuo8Z1IRiSZ7oT72MXtZJTtw5dpItKSi2oNYTC6EOaRmGrexV4cwTjP\ng/SaCLut6O9MRVb6aR/sIqEkGdlvLCTWgzkGrJMR7sGw8g9Ext9EUDcX3bx/oZyyBpE05AerqiSC\n+B89qD4i042mCLnpMG8RKf5x6OlGhRA5wHwp5cAfs+2btBb0z61pB6z4O5z1Yk/fLT1dHSCI4yY8\nrKY+fCNLv3iaTItCcudHJBu8jG7/iM+KTmN9vGRMaxfxmfdxzK5rqPXdTZx/PaJiJdLqQznnZWxb\n34Rts2HMG6Do8YYWYVZzaBz4Z5oN68gnB1zNyC1PERm3GaW8DeEPYbq+lcBwG0aTC3fiRaQ9+wki\nmE1o3yZCuyWqxYr1hH7MyX2OKd3DsMyPQvSLJhSRFC0rJefDp/GoBlxDp5Ca/nsouxT6PAb6uJ73\nXnwdpE0moi7HG/8cxioVtauOC9Tfc06Sgebx/Yl/s4yauCwK/lKJOGYtSp6AOklkuB59oQu7sy+C\nYyBmEPaifJz6akZyOoXBbeDfg77xYpKn6mFXkJBxCEkJ1xEx1+If9DJd0kK6V+LhMTAqWFyZKMnn\nEePYgaz/B++W3EXdwCtZe+VvOe6Rl9iSlsysex6mwFHFlH0LqRp1M41sYFTERuG2PShCwVjvR6EQ\n2boCdwEEAvejNM9C19EH0TeXSGkFxq0+9O3AuJNQ4gTOluUoQYk+JoxvZTYeMZnE0S5E1g1UNc8m\nekcz0q0jwakSFW6kZdBMhHsvIXcJdX2GYrHWkrrHjXV3J56JXqL8jSgj78Jb9CWh2M2E7R9jUJqB\nwVA1GiofhzPeRzHYMK7cCOpISBwMIT8oOlAO3kz08CZWLv/ZfxK91s98qbcQIlVK+dUJqDOBnT+U\nHrQA/fNw10PrOkg5Hgqmwb8vhsRCmPT77yS1cBx6NRdL1BaeumQD/m0bWWDJpjhvItOMcTxCNkre\nPVD1BimllRRZ7idkLqT59HuwBoqJde6A+M8h2wiiHZyfYomeDroTSQcquZ8usolylBI53YKyS0FM\nvYTQ5nvxeQTWdAeUGTGfPJOdL+9jwLMgKnQoN+cQ+kyPWrSO2xadhOPCKGIbG/C6vbiGjCPxjS+R\n/QswVxXTvvZlWmcsJlptQyqrMbQUIZIKkKlnEmi6CndiN+FKPW19/0B+jJFQ8J8YzToyUu9Geq7E\n9Rsrnc/nEhtfgVBU6BdAqdRjGXUs5N4A2SeBTo+RWoKUIIJN2HefCx3dhI1JyPSzEOEQutu3Ebnw\ndYRzKL4OC3H9cxBtAWzh+wn5G2HZZBg6C0IKwmAlMaY/lpXxZK+fS2xGKonbSxjeuRyqtrF++Iss\nEdswhydy7O63UJr0qLETCeQKDJ9fij9oxjG4goamGrIWDsdnG0r9gB0YBo4hq+oDxOA4SF4FaW7c\nU3LQV3ipzzyPvv1fRxcqxtg4knLn8wTdyZjtA0ia9ylqnIJOr5LesgXGP4js+gfJy59BlusxuD0E\noi2Iy4PITjvS/STJla2stJ/E4tizuTRgod+GrT03XTj1Xz3znHjaoWkLXLL06yO4eTfBqc+CcmBL\nOUwjXTyKhcv2NyD+P3QEx0ELId4DJtLTFVIHPABMFEIMpaeLowq49lD5aAH652BNhz2bYfk5MPZZ\nuGoxkTVPoEQi3/lhAEScAe7Ieoqovcvp3JNEqk3lj8s/R+iWINNz8XsrUfSlqOkegtl76cg5jhj9\nFKy6a6DyIlDHQosdOj+E+k14E06lNuN4pGqiAJUdsozkuPmo22xIq55NigvX+KcYVbOKQMEGRJoO\n132DiFITUdYMhswQwt6K/vx8cAZY0P9ibsw7GVk/HUvrACyJS5FJgzBFT0EOLCRh+Ar0TQre6MH4\nGv6AwdlFzN4++PpvQ6T6cfuKyGxLIGH0Fbj862iMmUwosomilx9CTAgzYnYtIr8db7qN2hGx5G9y\noqQPhPUu2HwGeOLwWs14C3IJTrRC09PgMMDyZpSkIrhyMQych//q29FtnYfoWo+tIB7xQTky4ERa\nZxKpXY7i7Ma/81y8OTbUxCj8paupyTISd+PlxJcsZPFFtzNq650kjqwmNXQN13flEl3yJXR1otvi\nYs/MWAJdLgo2G9EXlhDnycebGMWe8x3E62aT4fNhIh5xzSjorKXKVkBH/8vo8H3CmPtvo2/U1RCJ\nYNw+F6mLwpWeTWuagzTuZvZgPePcq0kp9WLY+RLytWpk/UoMugCkRsDWF/WKKFTdZhDdEHsPrFvA\nxJLFvHblBXjXvk1X/8ux1wQhInv6UlfcDxMf/rrFrDNC9ZfI+TMJn/oEOvH1uOkunkXiJUIrKkm/\n0A+llzmCAVpKecFBVr/6Y/P53+xw6o3ayiH8jdPpw/4Io/4Bjl2EHLNp62+CDS8e+JqgA0ruoGP1\n3RQZtqMLJOK+6kwGFpUhwu8TsayitnApHmUOSnwxhu2CjRkvEev+ElvraQjnBmh0gicEuVPA90cY\nEcSU9REWJZdGlhEJbmesdwe7dDkEur3sTrOQUreCScHp2JJuw080+kwTTc05JExzIcsWQxNAPBiL\nad6UzbQdbyKLz0YZeQfoEmDV+4i4vuhGXIl++svYutwY2/cQPX8RyW/uJKp7DN6RTUhpRe+5DIO3\nkr2FbZQ0Hkt76xWkNi4i780dsKENccZziN+nwzFgmu0i7y0VxZ8OjaXgWQ2FGVDUjakgD116DFVj\nEikfcz6RLSnQlQ6BZGjdDu0R1AueoGu0gcgxEjnByZoLx9N1ajT7TnPhHuLFlzwM372bsBtuwLIp\nnQSlH8MbVpKz/FZkdCETqj9gzoDp+I33kxrx4LH6KR0UT3v/wSjVYbI/DKHP6ouS6YZ4Sb/qvWS1\n7ia7uwKdwYUnMIrO+LOhuoEGYz/WDsjAFdxIVjATvWkUzHsAXjwTun2I4gKGVnzG1E1JuD3zicGL\nwRCmNd2E9OiRg4qRtxQSPsNKJCYKnHtQ68pR64wo6mOIpNvgxH/gOXE6r75zHcmDr+OevOF84ihH\nqgpUrQBfB3TuOuCScP/gE2hxrkFEIkhChCkjRCsu1mJjJgqHHFjw66XNB/1rJuHx/jD8Uhh3c8/F\nGwNuA6DZ/RfagxtI+vBNyMyG1BPY0/oUufVfot/gQS0vI/p0N9JrIXXRdvT5pxGYOoTAoCmklN9H\nJF1PYLGe+luiSLLVstpyHVO6VoD7HjD6IPkyqJsNSxbD0MkIIEOcSnJkOMI3lIg6kYpAG8lRFvJs\nBkwlG8C2ELJuwmR7hKB/F7EXVmF0vkz3bwdj7/BBbDLC14xusPv/sffe0XFVV8P+c+6dPqORZqSR\nRr3bkmzLcpF7NzbuYJrBprfQQkKoAUINNUAggYRqWgzGgAFjMO42tuVuy0W2XNR7HWl6vff7Q8n7\nS7K+9/vlXR8JfCTPWnetuffcdc6Ze+/e56x99tmbNK0fSRMBfRrMfAKWXwptekjMHMgO7uqHUAy1\n9HFCuS8gkvfg7RrDqXQXCeZWorFMBu+0YCw34HfnoN8WhMNnEUoMKjcNDDBZ8WDyIe3qhIIZ0Ckg\nMQGmZEJzHMIbh62+n7L17ZhDlTRPCpPQImNOMSDVhFAbZ4HagTasIuJBrtJTpB9FfOpgrA1PE+0T\naHwyprtvg/lXULMyjdy7P0CcugCfs4dA4EO0vh6W1fTQlTSOnqwZNGpPYqYcT8JQWNiJM1CIMxxB\nq21A3a0ncsnrmKwOoonxGMmhSXMbmVsfR9HFodpsLPZfiMG7iePaoUSeOhdDnRcKZkK0Ckqrwe5D\nl5BIvd5GYWwJUvQ08Y6fE7zhGAROoz26g0g0DlnNQCq7FoaPQWq+HnH2G6jaibb4CmrKEujJXsaE\nzX/g5cPlrCqZwM8jp3nukwVo4xLgnN/8l3nDTQ9N9q2keCWk7uP4Up7GwN0EqSHCSeAiBFrw1oMl\n5/sSpO+XH1hGlf8o6O8Kex6MvQl2vwyD5qBmlCCEGYCo2YpOnQyjE+Dj8zkyZyFVcVkMPhJCdR2j\n3VBMsjlM27h70GjtOELDYe816A7fBmnP4g4ewdL/Fqm2N+Dw4+wcMoQmi4Mk/fkYui9FdGbCpevh\nvjGwfjLYJLAWoKYJItpfEjZezLwjz3KyqJSU0GrQzkVNvYoYUag7jOSKkZBiQvZF0fSWwZBrUE9d\nR9Q2He2QzRh7gojsqyDxyoH/6vZBV+OAO5csg2xDcSYRtH2EYbcXKrQELptCrkjBLpIJRC9BKahD\n1E8k5rTRNd1P0tY0VJ0WMW05bJ4F6ZcTsmxBb9kEnnboqwPnz8A+C7R3g2sN5L+E2/MxGUEP8dpE\nXNo2vGP2owtKeK1JdDtmYG5RMHUsR4hckldugesmgsaBf0gUa38dyi8+prv7EzxZ2+nufI6QdJQz\n/UW0lSWTwHQUfSWWUC5yuJXEgJtyfTf66CAko4FQ9T7MzWFiYgk0fALnXUWkZA7aZ19CZNmp2hol\n3dSPJlZNuvE2cL0J9lcZ/PEsNAvUgcE0NQ+yn4QYcGoxPXl3oYovyPYb6YmL0iM/hRxOwXl/NXRH\nkB55FemLFfDSk8SkL5HUoTD8A/C1I6rfI/fQNkhLx5t+MdY1j3OpppG5B48hFV8EE34JcRn/9Yl2\n0ETIMZGkhpfx9y0lktKLmQ8x4sFACXqfA/afB9mX/nsq6B9gLI4fWHf+H0NVoGk91K2G9h1QeCVM\nWQKbr0fNdIP5AoQKicpxdIoNfDuJmWI4N1egz2yFiXfQFZrI0eoSSuNWEFO7se/9HfiOgWMZDFmJ\nKoGv833ifrEKc/xMsJxkiFeDv+cQXvObdMXn4mzajc59CxQMgfgjA4uU7bvpjWUSTqwiS3sj+Dop\n9KfiTzWxIUvLac3zDGMEE9etIpYEmiNNuMdn4+joQC0fiSffiBLYjKZJQdGnI3l3Q8wHshlmPwob\nfgYooAqIjERUVWNM7UYE82H4VSRoQphCrWiD7xHmAjQNH0L9Oqwj7kWTUoNQXYgsHeq2HKJpFhTd\ny0R6HUj+a9EVzQPzCUiZAlvmQXImWMaAtRbSM1COH8SX0U/nEBvJri66lBSMXa0EknTEOxegnNQg\nB47DRUHYeRx1cBK6fREihjrUuO1YuqehvyyDXucKhDSD5P7jOIJzcdTWoDedwlM0jaCrkoK+h1Aa\nA8Ti7yV6tBGx34fSMhYxfjxItehmH0I89h7CZOV4RxvvVN3P3AmzQdJD912gmwW7y9HE9aMOegRh\nGYGaMBUPK4jKrUhFE9jMa5hUE1WaccSJDNTeWvL+aEYk6uGaVxCeVCgdA1otSrQajfJnE4TZCaPu\nwasGibQ2IDa/AnYXdB3CW5BA/KBzwGQdMG/EwvRG6jnV9y7zuyejFthRrG9g6X4KkaRFxg6qB+3+\nP4K3E7Iv+V7F6nvjPwH7f2QICRJLwf9nz5ns+SDpYPDVqB1zUPkMKX09Xbr3yBQ/Ae9ROrNfQd1z\nmuz0VGi7H73i47zMNOhWSG2/DSkhDKWVYBzI/+5iI5rRlyOMMwfayLyUIad/zabhizjv4MeoR04R\nO9gL5bNg8ZugewB04/AeLCfq8NJRdw5Zp6+Dwqswb/+EXr9EfqaWeGUYOXIJHaPjae0xErlxMH3d\nMChNIdlTgEcpIetQPf16HebaJkIjH0TXdxphLwNnLvTq4ODr0F0FciOiSAvrfPCTbyD6Alr3O8Tk\nUWiVV4mvKqAzwc7+xeNI7YujIPVjXE++R6zvYxK3bEETXwxtu9Fs70K5zIW6fhuNc7eQrm9DU/Qw\nat+vUFosxHZmkZOrR7LMxrShDfMsMCpR9MaLccU+RhPR0Cvvx5SXA94OLLEa6mYWkXPwBJL1SeTw\nW3jV14j2LSV9t4w+bOF0p5svG3/OFVMOY0jUIOkf4bhrFaEDZnqDm0m1FuK0vonm6htQZxxBrkhF\nXH8TfHgGDh6FrQ+gzvs9974VZcXMBxAFX4PrRdi1krBjHw1xVl4sfQabdTFG+Qh5oV+SrdlPn5LJ\nxtANDNa8RXXwSs54MliyPobeakZkWOC8HWArhnuugl++AIAaO4CQ/9ZtVi9KSfJ3cqIknrHhcmJy\nhIQzh+HYzZDjBM8YIjot2yeamPOnrxBJtYSnFaKJ5qPd+QlcfAMAsqcbKek8GHHJf+uG96PnPzPo\nHyHmdCi6duD4C33rEDovquiBzy9EWlSGJj4VVapki8PO4iFDMO7rAcs0njo+nCdL34LcAjqGjsFu\nvQuDXAgMbGjpYQ35xoFAPSgK7N2AHKmiJ1zEkSQ9wy88iub4SFjzUxgxF3K+Qul5l6he5mzfYHSx\nPGj8FVgmoHZVYM4o5myuRIbaQZvrTxwaOY6lPScwnMghkP4WcsyMdn8SnoljqMtPQ9+4k7DkJvrV\n84RNu7D4nAhFhTMd8NRtRBYZaChOpcGQSbniIq52LCS1EutP4aySCJFFuIpHofFBgfsotrRBENyK\nzVGOv/VZ+soNJOw5CJkWItlWqrxzSajeQqdYSralgh22n1IWl4hb1WIv3gYVMc7ceD8pra+Q4p2J\nbD2N1PMK+rgCrF2D0BDEHCiFzk+hV0OBsZZofAKahOFozowCZSrdhtfJVHvR226kaqfCkJzRpLOM\nXv9CXsxyMrYnxpCZLfTpkqmSTvKt1EAstojhhhDZ6n46Ot+ma8k55PXvJKNuN+teXc69I7/BmnM9\n0bbNqIFGtGdkot82s/f+t1mqWUVpwz1INhOSYyIS89FqbsavfZpS5WryKk4iH3iRxksySG2/BJGd\nOKCcO9vAZAZb4sD3EKzgDtMMXmz/DBKHQ6QNQ6gK05rlSAuTiZ67FH9PO32lD2M+tR36v0adPI/9\nhlrG6y/H+NOnUA0yYa7EHF0LptFQtxXUFkRCBFWajzjdCeUF/2Ih+oHwHwX9b0LCXCRrM7G+i2Di\ncJwfvQblC+nnFBMkMNXkQnwm5BTx6MRPkJSRkPMCfZo3SWYgjKQaq6Ox904sSQuR/uIOJUlQMgXd\na3ciFWbRFJfJ8PgECAyF3lpo3oJ6pou+czKIc5mZfGQHstoLNcVE2l4mPKcLzYkOCvbqWT2ukUmn\n6lk8ahNyp45gYQUJb3mQjitEb7yagt1t+GY+S1XxXSQ3Sgj9aiz6RxAJU4ieXUNj2mHqp5SB3U9W\nQz+jGvRUXHsRmVoXnbjRC5ncdxpoWzIItzbItL2ddE/IprZ/C8Wh98F0Dcb20+jzBGJeGPXTIJqx\nEecB3swAACAASURBVEbsfgtG63BYokTiMhib9CEioqHFnEGbWSZ3RBtxG35PbXw5Jdu+QCocg0jc\ngGKqQaN0IPpjRBQTbmsSgdETMNRXkqBtRTjjUWpjNCor0LYlIxmOQv5PqW76jEtn+BHo+NQxkje5\nip90NiBKGjCEvOQd2E/88BWoRhlv5buow0rRdK3jW0cJ266ZSVlFC0MrnscxzI3oDXMqUyHjxCG0\nlxiQekJc8tnVRH86Bdm5AHp2oTmzAxGXTGPaDqxNrQxa9VsonUPnXUuI84YQ+zfDxe8PvO/kVLj/\ntwPfg6sGIn4iaoya8Frya26AmJuYTkVuiyJqFJSu3+EuTSGxZSW0JUNiHErnjVgK1+IUOWCAAA+h\n5xcITTyMegW2L0MpOJdoTio88QKMmAjlE/71MvND4H+21ftfwn8U9D8BVY1B06+RrE8R01xD6+hC\nstcdZ8ficcyzzwfb2YGA68dvR+d2gZRLQPLQpe4gIdJNamAjim4RXqmGweLv7IHJuYgbtnP+4RvZ\nFXHjdS3Coiioqg3F1UDn9HiCkpOes1YytO0YwscJ+JMInp+GUe3ihGY4e2yDWLJyL3ELTiMkFS16\ntNtzidm70cxOQ7f9CXqnDaM5chkGbSlqxkPE9p3gbMp1tHgnELWYyUoqZ9LGTnTmY4THWzmSkowa\nn0S8dC1D6sNEn5rByaeWYm7sZbbj9+xdVMs6XQXXRWSiihdNbQpKYRnuzHOJdL5NYqwVmmQEUWJa\nO1J3H2KXimYhCKUY4/Q30K29HuuI5YRXzCUxuh25uQ/aNxAY58DQBVJyN5IhinJWhUGDiMQ7UQbN\nRtfwId6k24lMBTmmoDE10jnKgrFiPsakKaQceZvGfgPe1BHM8FeQsaqHvgUNxB9pItDbjNr1GH2z\nr8bS6MF4aA/2kIF7bXVoyvO4Yv2NTD9vNdo2I53jLDTJLSQUWknocaE3TUeUZqN770sY0oWqvRQx\n+n763auJnPwFEw6PgyufR3n5Hvqn6il4ZR1ctQwCvwe3HXQzwP7nuM8HHkTkxLGobQ07Ss8h3/p7\ncL2ETjsV34i3CHnMRGQf/qRCTMY0QtZqzMZW/EY7Wep63KICFT8BVmIggBEJnaIgTH5ipl3IkQKo\n2AaODP5t+QHOoP/jB/1dE/QgPrgFqp6A9nOJGOIwlp7DrruXM3XfPmSPBc7WoCJQq6pAmQHmSzB8\neReaoA9r5DBImdQbvKioyH+3aqHG+mlzrsU9UqVoVi7SGR3Rg9tRO/bRO9KGaplMwrc+9jrPJTCy\nnIg3D31eD+xoo9mYTI8BrtvxJaklTYi4CLoWGePabqJmHWTnwdKVMPJhdMwmqgEXXXwsPcKO7GFE\njwvKKz/jHPN5DOrMQmd1gyYPnXQr5Z0nmBtMJeOsDL99nEO/XYAxKDH4yAbUl0dzquULBrk9pNRG\niPh3QPMHkDKZ+JoYXU4bNbfmE1GDxISM7O/EZEtCYy9AuBOg10G2djCxeS/h2XUfEWs6AZ0NZaqD\nwKKpeDPPQ1I0aNojiD4VqUqD0phM+oljOGO56L7IJO3EAVJXtpNzdDa2P+lx7PdhtUNYH4+uNB5b\nShpHM6byduWjiAsmY2s6C84szFkXoTt5AMPHD6FqomDuQQSb0abF+KD7XiZMnEKf9Wo03hJM++JJ\nDOWSuCeCuTYeoTVCrh4yQrD+MCJ9NWrvp+yKP83G0on0X3UP7N6JL7qTzFeOIlJHgHkutP0R3roZ\nQmtAjUL7IZRoI9LBNmboZrM9vhSffw2kvYXkeAiNtYyEvma64hLo8gSx39VN3KFJBCNJmFtvJu7N\nSvR7PQRiR9ExEw15iN6z0PQbKOgn6utCklJQRpfBHf/GKbMEYPgHj38R/1HQ3zV9LTBqCRS9BSkR\ngvFWTJFNNOn6sA7Nhx1Xo2TrUHeOgXYPXUMfp1Z0QPAAIz7Yi9n7COh+j19UI2NG/GVIr9pGROnH\nU1eG1/sqnUaIdnRy4MEhuEZZ6R1jRns0TOoz32ALjCTklbB920JLgsw3Ixbx4ZKFpIWaGZe6H4Zn\nYvRqkfuGYD/pQagW8PWhO71twK/5goex7GxAe8qPQpTx7rGUb9hB8eFOxKT5hDuXsT9hP9HkGeCy\nwO4/gWccbFkBp86DXy9n6JkDFOwNEFzwNR/csox5MZklvln0pFahP6AlJh9CPbEaJW0wdu040Otw\n59kJ5xgRajlS4UJUhwO1pw9C6+HokxQc20yPRWDtqyG630N08NWYwjuxJhaC1Ydqhpgkwyw9VVmD\n0PZfiWbdTjTeZtSDEqKrHmPdeyTeWIVmWBK63krumfAI5n3rqTTLjPW2os//GeqxX6NWW5De2wSD\nFsGwXHwX3E6g7HJEBHjCQ8f4N1i938o1RZdgEuuQ0huRj39A/r7NGOQoGGXwroOuNbAmCiTCgaOE\n6m/C4tnPuS0h0g/9hFjHOtoXJ6PPHIdqbkHd8zP4JAN+sh5sRnBfCBvORzEFkdQ4NNpssl2H+Nw7\nDFY9Bp/dgrH3OOa+EBp3EUGrkdivP6RrfhMax4PosvMRN3xEX9EREj9QsG0YhLmnHN3JlYiUFrCP\nob7oCkxnjkO0H/Q/MDeGfyX/yUn4b4CzCJxFCCAadBJTltIjSpgdy4BwCHXw1ai2BpQuJx9fcw1V\n7re55fQKKBqPMSQQXy9BjYtSkFdCU+pfRRo8+CVizzuoS98jveO32GICR9V2BtXbaP6VBk2vDvu7\nKmq3irpvM/mD+uia1cfrBb8AYvzc/Qw6OYrm6Qibni4hI76K3N27OOvIpWXpMka+/SIkxcNHcyCx\nBGw+nJvrSc26FvuB54k6dNClx2h4iT9ILxAsjDJajIfEMthwF3Rkwar1cOtYgl2TiQgNUb2HDxLb\nmaPeh5LxM/rknxKT/fSMyMMoEpATzsXLk/QHg2RtidE5NQfj2z4YHoVYDdHxOUR278co9SHefQCR\nnkIePbRn2YmlOtF+YqH2+sso3vs6pGcjfN1E1W6iRYtxKieh/RAiqkWa+DAe9mCq/RyqmlFliWhu\nF2ptAG2WQCnQ8r5jFC+sf5FQtIle5wXYOj5CTktEmzsJrCdIrFyBJnQe9Nk5eiLA8xtG8czN0O28\nGcv+y4im3k5D0nsMrq+HTCP0doBrCmjDEGwAx+Uoq1agv76HCYca0XQcBoMgVGgju11AwXrQtiG2\nxiCpBfZfOrDmEDNDyIVmd9vAbLrxMu5It3B0XAn9Zgfxe09CaR8YM2i7fBIhpZHWIzfgtE9Fm7QY\nteEJupzbsMU/j3ZpApG+mch9zyMyTGD+OfsTp9IuehhsyEAEXkD13A7mBxHSv+F27x+gieM76Y4Q\nYg7wEgNjy5uqqj79d+XLgHsZeAQe4GZVVY98F23/kFHVZtxaM7XqUGb0X4TSHkCdlUfVLnh/9p0s\ncDeyMHEFzByMqn0JiUQYFYY1szA0qxj0XVSmfMFwaRFiyhVofjmGeKsK9hhmpwXSE/DlOEB2krTe\nTmy+BTVjEMqu5ym3tvCtcQbDAt2kqofoNycSb2ynbXQxJaEN6FxWPJbx5Hed5KizkZOlgxgfsIPZ\nBDnZULQfi7ge3TEN9PajHd8Cg/PZd+QJDjjm84h4ikPxVQzZHYehyw+2CnjpXRgyHWHqRVc9jrZk\nmfkH1iOGBfAaC+hTarC1u4jaKwnLKmFpI7FYAoldYVzz4rC5aumel4SSdRnxNRuRvQfoNOjpz3bg\nlMYjGr9C0gr64lNIbelFf+wJ0j82IIrt4LkQxfw43hIj8ZqbqOyrpjD5Xij1EjpkoWNMBKc9DkN/\nP3KLBuN6C0G3RI9son7kKHL8jegjtYRSnDgLRtE7WMFQUY3W2wk5d6BrGgolz0H9pTz0mY2mbpV0\nWxR/9yES+vtQsyqJ+m1InkwYsQl22aH/DESdUDQU2k4jxQ9HteWiGbIcVJmoZw+tkXvJDdSAxQkH\nIzDuMnj5NSgbAwsvg/VXE0sahcvRgk8o2Btc6E96GLf7IEqiIJaiRw6FEa2t2Nf+kQTZSry5A+3n\njaiFfYSGbiVeWYku6ILAPDRKH4qxDKrGUWM10hS3h8X9UYRJg/ro5ajL/4BIWI46eSRkrkRo0/7/\nP/QfEz82BS2EkIFXgFlAM7BfCLFGVdUTf3VbHTBVVVWXEGIu8Dow9v+27R86it5DLGRiojIG0fox\nriEW3uxyYsl08FD0WSwZtxHRv0qs9nJU4zuQdifIOsTi7biP3oGx9giNaS+T4a4jqb0GLp4NVbVw\n32bQaGDPBOS2l0k9PppI6i6C02OYmMMe1zNM2H0nIywHCCcfxeF8HEVag07qo2fRuSTpPsC/MY7M\nrl10GJIp6z2IvbgEKishXQv2eghXYrRPgvzFRPwPETIsxd1+lCcyprDSdxOmlOUkJedzNPctssZO\nwPntJ2DXgNmK2ttCY81gCpIrQdvIYd0+HFyEWS3B5q9G0itozfcQf/gUcns34VlP45E/I2IHk6mK\ndv07BIt8OA5LhEcLap2DsLXsxDf1QTabwriEi+BgmfOOnkA3tgq983aONGynzBiH3BBGc+oNpnsP\ncHrBOFLlnegmHCP5lIzsUugsnAhlNqzuLgxr95NU7ePxiTO5ecfrNA29jdyh56DUP4+1ZhtSSibK\n/mWI+XsQSSPhyHMEJRvlutd4/8JtGHXvIKkOyB5GVDqITlsKE24EZE44pqL2BxlSewiybHA6EXRu\nhN4EmgQA2m0nSfm2HbUY2KpBDNdA1oXwQDfRlpEETj5DtMBMv6MJ47oQGYPLkI+qqEbBJ6O1xJIl\nxph9pDXuQm8LkNLnx+xuoz8pD9VTC5XL0TdrEPseHPBXH3czSnEXsUAN6nNv0vDU3ZzfsQnRMwtq\nzsK8m1GXbUE6Mh111R4YNA9VyoQ5F4L+KoT4kUe5+5F6cYwBzqqqWgsghFgJnAf8l4JWVbXir+7f\nA/woloqjHEBm1H8bnjGm7iV/bx4h9TbWamZQMWomN27eReaQOnzuMEp4A1JyAyJhAXLFXahhPSLn\nNgD0uRfQZNvEgve3Ekk6DLM3wbgyyN8AHz8MS5+CIR9g/PQcVM0hZEMKho7JqMkXUD1kJRmFaZis\nHixeP7H2nyMFSlF8WoZaAricDgKLBuHffoIU0UbicD/aSC0UWaG2HYz6gcHC/gh0vIFs6qBfquDW\n7ApetnRgSv4cfC9g9sYzIjiP2txmtPsasW24kZBrOLVtLpKGXUssOhWf9x0SIkH0WifG0BtY/eV4\nnU0Ymocg9j4Os95Fb5yFlqlIR1ajNGqxydXUzWxBY/RBXARbiwmNZMUwYiyTut7FvPMbmn2pHH1x\nIckrXTTOe4+UYU00nMwkOVpHiE8JLIyj0TScTF8Gug2n0RXMRQwdQ9KZzXRlBzDtOcj2inLylrSg\nExGEVkv+S8/BsH1w2WO0FFSTeUILkbO4Dw5HGXIjCU1vEtWmcX/RiwjLbSBk1M7N1DpNeMwCjWsQ\nxNtACHZlF9KeK7ErkMucym84XRRHuNFCeaSF3sA+7MZC2sLdJGYEkVbnIJZuQHH9hG79WXyZZ9Hk\nOHC+14nqugTLF6+iybUjUm3w8Byiv1tD3YILqTL145ajXFH4c3yrfkJIF0GfIhGXupDWX35O4kE7\nhv4eUOKhwwK730WNpLExfRCTTTJTjdORHJPB/BW0FyJ2fQsTi2DGc4hpUfjgfvjyOdSOtbDsOKr5\nNz9uJf0DNHF8F4uE6UDTX503//naf8d1wLrvoN3vnTCfEmXr30QL+wsx9QTdlLJNn82jiQ8gLEYu\nOnMamxX87fUYq/3IxkeRTfcjdNuJ5GiItd2PWrEQTj2B3uMiURmMtOATpJ4Qwf1PDVQ8fPbADsYD\na+DVS0A7DDG5GG9uN72aJpo88yhvf5fEr72krDER11qMuVPG0HySaGeAWNc72LtPoFc2EZsiowZN\nBOITqUopoTcpFXwGSCxH1VtQm/Kh448E9RYe897NnQYDGVErxApx12hp9n9Kv+FuBvXvJ5KQTV+i\n4MsiB9nGCCniALqmTRhDkFN3CnfgRZKCjYS1h9A3B1BP/wrGjwHv7bB+JJ2ddxE2g1T5MfrK4xQ8\nX0+PU8XU5iP9mI+uhDh0u5+kv+cYUYOCNd5DoWszOQtUcgI9iCYdZl07vUMSaJiXjMdkQlT6MH9j\nQ5l0JZK/k47kJqpKI+hrz4LXSdm0Oh4deQ9X9XyEM78X/1XjQK4gUvcyciiGKFmMuKALq+YOorXH\n8WZ0E3U2IKbtHYhc+O1iDLEmks3n0WzKohMjHykHeY9DzAhOQz1sYMzufpzuXoa0HyQ8OEZLei8H\n9ftYFfkcKfAF76cuZOM1vyVi7CXGCUxiGDkVw8h8cSuaP3aiWbUCzdUFiD/VE5qXSGvTI7iXVXOl\ncpxh8inO9kX41qHDcs23xJuM9IQz8VS+S8qKRiSPFgbdDNYDkFcIV47i8ORf09Plx31OGbLBBcbx\nEG2EWZeBuxO66waCK538FVz+NOryXpi2GnzFoNT8iyXsX8xftnr/I8e/iH+pF4cQYjoDCvre/8M9\nNwohDgghDnR1df3rOvc/pJsqFOoJqk9A82sQdMHpP0LIBUCP+jH3SVN5ZsQClvRtIq3dT178Jmya\nVDoK8jm5aBKkTkNIOWjiatAMDaEkjyGmb4aeCjh8HSnVhyGxEHX++7j9VcRqvxpo/OJHYM39EGyE\nnD48trk0ZSfh0VWTdbKGBm8268dPQ57wNVrNUkRPH1E1jpPO4ajuCJ/GFvOc6ad0pB0nNH8nnoph\nNGlvwRLKAIOKKpqIffQ8KI3EYqfZZj2XOZ0HGfPVVDZp76YhcjeB4Dp0/Rkkxh9GxN1N0ugcasoK\nyVDqcY8dDPnvw9Y89I5leJInENJa8Jtvpz/Nhm/QM7jmvgh5dw8E5Rn2BHGnzhKovg3FEITxtyHp\nkkhd34M7z4Gx/igNmRoiHafJ/CpEV10JhAVqShi9vgxnVR853zTR0zWS1MppJNZegCeWztDCb+ku\n6QbdZCIZI0jz3M/Ij+NxHOpg7+KbeeWmK/FZ4sizXku87ESfk0Z0iANXcRtWs4ra/Ts4NQcxqAzH\n3k8wn/AR1cPpuBUEk/Og6hswdNNsjFDATKZJg1kSK+JcCqkzJXDtqXd4NXsqkhwhpdXF+btPk9PT\nzdAjx7lo6yP4hZ44VU+xLg3Jdyca3VwsJ/rgvTdQjlcRHbcQcUcBSqGRjtADuGorcLQNIbHsCFbL\nRAoidWxPKaOm4yv6LFrU8Q6SJ/yGyHm/RDZGaFW78AnDQN7Irq9RN7eS+8W9LN3xBRnpqbDuWvB1\ngO1B6P01XPA4eHvh0BfgPgqVtyDMCaiFc+hPOYcWuZs6tqEQ+97k7p/KjzTcaAuQ+VfnGX++9jcI\nIUqBN4G5qqr2/HeV/Tnx4uswkJPwO+jfd06AHk7wPuNi5xPtegC1ehuivQjMflidjjJuOTstPVzR\n9hpFYht3Fj1Phl7PVbXPcTpxHzVKMSM1hzjIBgzkYMLIEXGCoXl/JPvQVJShzyHVf4XQmKHlQ3Se\nk4SLJ3HK6qVEVSG8H39xL9p4L7LqRgnWka9/B52+BFwzSdPX4dMkE2q/C3365TD2Cz51DmejtJUl\niSYGn6zCPqiXU5Yqjtkn40k0U3riHZTjVWCIElvlJ/JsP7KunMuG3sPCvs1c1LkabbePNMWPtE/G\nUjwNxa7g53rQgOqQKdQ10mqyYJfuQz25D5Gdg9Dn0e9ZTY5cRIU1j+HRVLRSPi51H4me/ZC7CvQF\naLwapBcvhT4g+Bx90/WYpCvJ+WgVUW8EncVMJAGkWBeuienEJQeI9Nv4WjsZUZTJokFriWj7aOnR\n0aHpx7G/kbNKIUNSqvHGnieQ2Ypl51dYu9pg2SsYU7qZzgTOr/4tuyfcik1XBhhxlmaRFt1FdJ9E\ncPgyDAdTEO6nUG35SN39JMldxB+y0ToySpZTIRoZgkucpJSnkOQ9EPWRUvcljuaPCWp76YtL4GRJ\nMYXe07x20W9ZVnErRfXLaSsrZsLaDrjsDkT0EMTSEfoyYrs3EemzoJlkR7r393S3zuMMGXj2nmJi\n/TkE8zejUWLUsoVZneMImnMxRTL4puEBLgo14eZ3tMY8WGN6HK0S9XMHU1J2Ejofg84DJPZqUKpj\nKAYLkqOUiM6GVpsCQgehPZAzFnXlZwPmlP4NEPPjcv+aHbZ2tMLEVPVXSOIHZqj9rvgBmji+i+7s\nBwqFELkMKOZLgaV/fYMQIgtYDVyhqurp76DN75UzfIqHJrSehURibXRu3UHKr5aj6iehBE4Srvkp\n0wsG4eqCNtdcPt5+K58UXMSKxPO4XbMOl72LWrWIoyJMCWES6eMoJwlFT2JxZJBUdREi6w2EpRy8\nbigeSqrnOK0tT6HuexKvKuipSCIrexjS+mPEu7+BcQGYl43adART0QhCiTPZNERilJiMkzwuQeUU\ng9lnG4O1oIdlZz4j0G+mx3yUg0NGkPjNaVZeOY2RTVC4biOaC3XUBm1c2/Y6cXIEbzpIHhX9RjOO\nojvRpZ2HhGXggbQcQrVpaDf34pPrUaNFiDsnw7QCYik3E+t/DguLGcE8jpu9ZPMmJlc7qvkc6P4a\nVnyErlmB9El0XduN/Ww33sIY1gM7kWN+NNEY6aaf0df6AKmj68gecRTLFxH0XW10D+1gXPYiquOT\nMfavxxrYTaO+CFNxLi3W+xj70KX4f9qOretutP4tBC4QBPIeo5Dbsajz8ae+xDDpYlTZTLDxGiJ2\nFeWQRGNSDs3OOoZNWo1Oq0dvLkGT+QnU3Yh2wztkHRxNv82KpnIHsZKxeMKfYz70KiLigQm/QZqw\nEnlLHuXBStwWI3prgJu23oErTkWeHMGwNUYsT0LjXo4ifYocuxE1ZiDw8lsY5hkITHQSbDgfHaPQ\nHDiCwSnz7oR+iJ/GxNAGCLViTriMJVIGHbk3c2bvL6hxOYhNayRrWw+ttlzahjopeP8mKoePx+IP\nYkk5TEJ9BMomo5tzF4HqLRw/vZ47lIWMN97P476ZaG3pMLYY9fMvCIzK54z7PiRlL+PD95KgOwf9\nns9h/FXfrwD+s/gxKmhVVaNCiNuA9QysgS5XVbVKCHHTn8tfBR4CEoE//HmRIfp/m4H3e0GJgqQh\nkaFoMUPre8Rw8NXPF3Gt0QSoBEcupSuwk6jciCkSoSCjDc/gJBb4D1Ea6+HRzPu5ibvIUKaSL3/F\nRh5mQ8xD+RHBIv03GE0KUW0/ke770J29k77OE+hatPgbT5Hf00zQ5cJfE8Nx8TIkowVy/JCdBTNl\n1PrXODz0XIxBA67UUnpZSztncZKHjCCsJpKj+rnC9BVGo4YuLXjibSz0HceQEc+SFWuJ1tuQDUGY\nrCOYoWHIoDgMgW8xrTJBXJjcI230n3qQ/jnHSBnyJEgSqhqB381GO16DeYwZ1fUCRIPgaKLHcACb\nV4+qmHCQi45MvPV/Im1jHcqR3UgGH7ELxyDLbkR4JJZ+M61XNmOq70OO+VHH34h6ZjNJb9xH5ZRk\n0uPKMXTvwj3zItLf/wMzP3sM0rYzquMQ7hE6jo3NYWhHPVJclPNPP4CcmUDCyjaiJc8T08tEC4ah\n7+rDHEwF3Qp6NB3oqy9EsYzhgH0h+cd70Tu0BDPsNEZjdJrSmd6bglrSTUQ6B0sggMYeQlR8hOvW\nDEKFaZQcPsmZ0U0EJ6STU5uPyLkUwp1EM6dx+5Hf8emgxaj+PXSkGvEXm6iUH2R44W+oGZqA8+jr\nSOZCrPJThHXj0d0exlWWTV9uAQXvf0jQ3YVl+gXEO1WmStNxxzlYLVbTLYpoSlCYhB+nMJOUfg1/\nsodZ3LoRg+NSbN0d5DgvI1z0Lua+k3RMHUuH+wak029z/AZBJPYq0UHJtCg7uS3WwwtVIzmkTWSs\ncQNeRwzf9TYSVpxhmBpFymkF2Q3b/gjt1T9eBQ3fmReHEGI5sADo/PvM3UKIO4HnAIeqqt3/p3q+\nk/FCVdWvga//7tqrf/X7euD676Kt75X9r8DYn+GjFafHhOhw4R5mpDtR4KuqIHbkAxrb29Dn6DGV\nP0Xa0UeImS2E4xsJjbkFh+jjcc+zHLSkEVaqcdLBDUoDeu0Y9g9dxqHuvWijMfpjNzA28UNCvsfw\nZ2dwfPAc9hTmERdOJ+/peiyDSxkvHYRNe6FUhvQkGHQfaslDfBq/nLueX8uJ8SHOi83jpKV3oO+9\nh7lp7zVkBBtRMw0o+5bScuU6kv2d2GOL8He8h+GMn0hKDrLSQc+4MkTBCZpFGvlrtRgtXryJeqzj\nXsK+ZRXHhsVwEEVChyvDBeUq2lMBNGUlmIKj4OrtUCTRH3sHZ9z5nNYeRNO7n7K9lRg/OYWqVdl5\nyxgSim8nXnRiPfAMdt+7qM5ikrZ1gAVE2UWQ/UsiQxV0PINomY/LvAO9UkZ6oBZR4gBNI+g2gtWE\nHPCTX63BZNYQVUPI/noku49olx7foAAiXosU6MF4pAMRt5r6hEZc8SmYki4iTjOP+/QHudlXxeWB\nekpqN1IyKhs0T6NsuJvITbMwtq7DlzkYi3wQVT8VETmDtWcoicRj7ZyK19BA0NGI8cRVIHSYUn+C\nr3InS9q/JaZKkKXhCOMYqiTgaFaIj6VhbKgkKHcRKQgQbN2JpFFQ/cVki4cJxVWgGuJwjv8ZZyNb\nYO9aDNlPMrxvH2VH7NRMvoovOIyfMEWZTURjeUR2hQiMvIKUUy+B8Qi6hNPo+grJq59Nz9vPYpqZ\nyBS5imhAi9fyB/pO1pEYrGBB1a1oxoGiSHSovTiT1mK42QLPj4ZGKywZCxXnQ/my71cG/5l8tzPo\nd4CXgff+pgkhMoHZQOM/UskPbEL/A0aJwrePwpAleMyN5J3ZhLrNh320YIxQePGTOtYcvoHJV23j\nnqnFaOz9BKsVdK2HMYbtqL63sepWIqy/pEZ5kdZ6mbOWg8zZOo+knrkU3fAUVsskgroS1mcNISn3\nfwAAIABJREFU5+H++dziuI2EQC0jv36OlLNO+k4ksrXnZs6/LQaeIGScJaZXcC/9PTaRgYdjlFFK\nQu9ahrz7Iol5NUxQVdC8AgnleEwOJOFAOdyFeH8FxmlJZNY20qz9CPeoJKSyRIpWVePOTQatGdO2\nDsLJekyD5yLtex9LTQA+WYIwO0g7lk1L4Xs4DQtx8Xu6pt2OaVIj5vqDdLZ9gGbOrWj9ZzGFNmL2\neMjvPoOmspdwQEPVr6YQjgsjm2W0wUdIbKnHM8xEf7sR0+nD4NOgoEH1ZAMOVLWDgPb32EUEb0sm\nDn8JlM2DjCVw1gLBCOGQkV1LxjGxfzQu84ck7Z1Nx5zPsUg56A5JxH96ltis+fQXbUWkq1j2H0E7\ncj5yRKHHXUFij5e8VBPnr/8T6rhsSO2G8JsIOQnZXI50Zjh7HAs5bJ7ADfVHkKJH0XXIWLfthGGF\naBtfw5Y9DpIWQNqDoE1AdO/HEOknnDITbexzTP4G6kxLuHDrMiRJj0l7IaqhklDWeCLZKrq4QuKK\nZxDnCsLql4j1pNI4bTDdVQ/QPrSMQLCJptC75DR4EV2VFCjxFMiTCBLhbX7GHmkETaXXM1v7Himm\nSmiejqieABnbYMMVWKNB3DnxaO1W9J4dOOR6kpKvhL0LUKYNZ2+1gbGDd5BlPMpFB3Vclm/h0pIL\nEV8/A5MroWAynPOL71sS/3l8hwH7VVX9VgiR878p+i1wD/DFP1LPfxT039PXCWt+CxMvhrwR/1+6\n+vbKgazIzXuIZZ5Ac1CgXpeAotZRQhmGy/O4tOgBvlo/kWc3TYfkRh6c1ohn9lzstc3o9h4jVHwp\nkv9ckrO1TE04wBfmNA4uGsmG1incvOkhagwdjLTqOL8ki/PXP4ivIB1JDhCa2ELL8Ty2t9zGA8+M\n4lXbYZy7WvEnqzRmpjIsFqNK3I+hpZ3zK/Yi+uvIbRSQUow0cwV41kLXeoyn+uCLSlpuK6Lvg2xC\ncT78Byw4XRayfW1EE7yImIq5xoP0xiGS89xE7XDsUQvDBhejtteAN4Q0O0LioVc5rhuKZe2vSNJB\nV0kOOSULCOZOI+mzXxFsf4+2eUGcnT2op3xICMJDivHEd5OeYsfsTqbevRVXu4mNzimEZS35jjoG\nESBOlRFaBYWnkfzNqFo7GkaScPogtXEnyP12E7y5GkrOhbGDoW038tq1TOrahXplKm45g8BUI44W\nD6LJhDm4CHQn0bRkYB9aQyjlcZRyJ4asiSCO4VGOwtdf8lNjBPMi68A7PzsZ0fQSpFpxzZ/Gqu4d\nFGhzuElzLZJuL77iGownDhNM1WDZfhCGGEEeCelXQawH2j6Fs9/iy55Cl/UIiXIyETmG3lSEHMxA\nEf1AGFLGkTB25YCrpiMGkgYywjBiHrKqkgtkbbyCPxXWsjkXkjybGFSnhcLpoI0DRaFdWkOJepRz\nT9TTk6jHbZIg9W7EgW2Q4wLFCJlW5LoAMY8GnSQRTD4HfcfLqP2ncOclUBfaxE6/kXG985DTNvJZ\n2yO8tb+EC7Iu5tEFVZSufwEW/xEMcd+jcP6T+SfboIUQ5wEtqqoe+Uf9yf+joP+ehGRILYBfjILb\n3oRZ1+EhwPq0GGNyyujLywBXI/QPBYedNkKk08G4/OsgtIjb+37CJ2fu57JfP8ayhG2UjVkAJVuI\n9mWiO7kLyfcbjAlPg/EmSsMPc9qcTntuIpucMwhbDpK7qxfb6nI4LmHKvo9Y9W/osTkh00/8lCK2\nJ+ymIOjB5TlAS1ExJS1hutUt9Ei7GXPYhbatCTRaWlOTyanrQbw1B9rHg7DTPNtB1t3VZJhsOB3P\n0ijuIekcM2TsJur/EM4uQ9Ub6JulQbnZRGLnHWj3PU9J70j6dcewRhXCg3UYFp1EKJWkuD+g9mdB\nUutDjDi+mdi6JhIa25FONKDtCpLb6aKjYBY7hiXRlaZiiDiYEV6N1LYJfb3Anusk76yO5vyrGNLZ\nRpu1ny+zSkhvC1Ia6MYQbsCgi2DQy2A00zBZw9Df9IIjDwblw/SlMHwGsaCXHZbzmLTLgHvtalJU\nFcMtl2IKvEQkPYL6yQuIRhtoahH+EAb7M2AHo9pLsPEdpEg9Skki5dGjkPkiQjsd0t2oFbeyo+RG\njke+4pI1a0mafTdYtBCJoDtbRdAo0z1bQ8I3bjSOkVCzHDSrQGuDlEdg0tuYGr5E13EnrkFpGBNq\nKBNW2gYX0OLoR4rrJ6epgEQYGBTEn8XxwGrw1sI59wMgT36UZP/Pic/KJ33LNyhtAaTBA7Eygl0V\naI6+QlFhMsn7d5Mz9DEixrdQzZ8h2APDtoI5H068hmT/HLHbDCUuzIbHQPkASXyOPz8XueY6ynI/\nQJSuhMYbkJK+4sZJd7PkyTt4YtR0zo37lpnZ/+8tG/2P+J8p6CQhxIG/On/9zx5o//uqhTAB9zNg\n3viH+fdV0IoC1esgdRjYsv627JxrQatHOfgZ/bq9mKc+zkQG04mHYMNDdKUPo+PGm0luuozmtPlk\nnPoI9r0JMQH6PC5Kf5rFr65GHdeE6v0AcWwOWreGWFhG8QvM+9bB/LvwqXXoxHZe9vyG2v4x1Mt+\ndP+LvfcOruo6978/u5xeJR313iWQEEIgejHdGGODcYltjHE3jnt3HCdxr3Gw44Z7XDEGY2wwmN47\nCCQkJIF6L0dHR6e3/fuD3Jub933ve5P7m0mciT8za+acWfusWWf2er577bWe9TyFW84nZS0fCZWv\nIzm76e2NQzA/xJSrLkcMzsPUsY2EhE5STnThSlqMI3KQctUqtIkPgH48HenV2M+6kKZOI635R7jn\nT+A7xeTuF1CcLgalswT6bsEo2EGdBIKApJ+LzxOPVu3FmPUUDt3TMPgyg7YRfGPop+3iy7nlg49x\n2zQc5AcwuPFrgsQKMbRGa5HnDcOs7iBpewKh1OFoqt3sv+FS0ioqGCktIyq0GpdmNgy2Yjr8HUq6\nGmvgBhxzmlhY3cVHSe3M9LWQ5mrH57OxP3MGsZFMDGd3U5h7C3LbZKITFuD4hRbbrn2Ic1dCeiFI\nEhV9uxjKTiE49zXqKy6k7Otq5F3NcNFTqAH//v14mncQrjmM97MbSVn2OcLG36CxN+CbDgkxsQx1\nrMWc/eR5cQb61PDV8AKKwhFud3cinKoF+3pYFAZHJWK/gmwJ4LWFkIarYOgIJJRARxvYJqGIRQiC\niEpOIOakxNlMP41COTGRDtqGDRFyqhnTM5OnRozmScDLBkSioLmTQeNK4t7aBUENzJoPg6ORLRfT\nq6phuDYTMUEPYRdK5+ucTBxidKMf5dw2BOtC2PVHSK1EkUbC2QgEW0EuAZcHUi4kOuZ7HK19GPNT\nofYUyrhKNO6bMOiOUpIcRhAskPYx6Lrg/TuxLH+MFxN64cw26D8EtnH/aEv9x/H3CXTf3+nokA1k\nAv8xe04BjguCUK4oStd/96N/33Cjogg6K7xYAG9Og3DoL3WCANOuRbx3NVJfLwPvDEcXOExxQM/w\nYBpjjmk4Gj6GMnQUmroQHcMhMAS28WDNQEktR7wApF4VgupdhGnrYMHXIJfTU27DlFEMgkC+eDMa\nIReb40nGnQkz+7v1KD8cRKnzo1Qegr5mfEYtQ8kSxb4bST3VR2zb+9jqGul4M4PeIzK1phryVtWg\n9YZA0sHMN0mc+wGhTBUxtWvosAUZ6FqK4l6PIPYjRmQ0Sa9wOrmcHu1o3D497WffxN39B6QDeoQY\nPfqabmICX+Pr0GLBxHSxmNltrQxOXIJBcjH9yHvMPruOifJhsoQGksztDFPVkYqexglxNMzNQSf6\nWdQgM8mylISYmXiFKKKVmZgOtxCI1RCKlhH6XsG2+ThSTCFXCEXovB4y7ZOR49zM8vdi0RjZUaTl\nG9WPhD2JJG89iRByQ7wZ+oEXp8IjSbRs/JySI0fYKHyCJj0P+foHITEZd9DNGvdxfjd7JpXF5QwW\nTyX1yrcR/rQE9ryBNOF2IqnlRFcdod9UBps7OBk6x1aqWMNhfiHPZUogEUE9D/weUK9HcXWgJFyI\nMBTEfMpDdEcswoiJEFUK31lBdQ2sfh/fmYtAUVBkNTpPHRHMHFXGYFP6GKUkM+aIAVX+kv8cchom\nYGcZLv3LeLXncE8Yy6GsU/QPLWVIfwVqTQlx9gwscjG4W0ETR4OwjUTGIk39NRGtDsy1CLIdebUA\nXRFIHwHRf56w2esg8w6kQBUq+xD+2psh/07Q5RE2qVHbvUS3vvTn4a+DuEy4bxWh7R/CiT2QOhlP\ny5t4CPzjbPSfgCL9beXvbldRKhVFiVMUJUNRlAzOn7ge9f8nzvDvLNAAmRPh+m8gfRx8cjm0Hv3r\nelHENPdGpPGzkF+8BZzHMea9RHx7Dxd5C1EkDaGCxXxWeBHvXvggFfOfIRJvhxGdkL4RITAd4eBa\naNwAK3+BlLWUcE4aGm8DACoMDOc2xLTZMOsNxCmpbJ8yg4AawhnABSCNUpAK49lgnYb6hET8Cj0B\nmw3brWp6nlvOsPy3EU5X0Oe9mPbhQUJ9v0RwvkpMnIzWMx19+vN8aErhWUuI3lAlhMPo/E0kBWTS\n18XS872H/Ul7aHduJWAOodT1ED62AjoW05WRhHvoHEH3Z2SdqyX74RWEjTa6C0R68kME5GhULXoE\ndxqu2nTSlbeYGCpmxv6NxA240bW0IxTdRp8mzGb1bIQd9xH2V6BkealKTULUumHe/SjWaEzV6xHc\nOrZkDyer0UeDwUt2z1rm2uvIHPya1u0eRG07+h4vkVwv7HoL3BF8gxFSvt9Ja71CXPeXjHjhB5oG\natkU283vVY14ur7iYXWYMbevJDg9RDjQCxfcC3e9Ca3Xg3M3gt5Jc66RyjKJ18UfaaCbW5hBVFQZ\nqAxw/AmYoBAsnkOf9BXCsRcQ2oeIRMWSLN8J4++B29eAzQE1p1Auuhu55hyRz+KItEyEiSoKXFWk\ner3EeX+FEmlCDNUT8l4PoYN0dM0i8mMZ5q2xhH1+QtEm9IuWk9XaQK1O5JAxmYgoQPQswq5WGGpB\nyVyBIOlI9cdD+I+Iw1wQqIdL5yFmZSF6zsDIg9Blgs55IA6Cygi2segiEdyD+yBpNgIC5vCzVLyz\nAKfwIex+8fzbJXBc8rFj2RvgF2HrG/Rp5/Ech3iIHbzOHtZwkuO0YceNwl/OlP3Xz/9KKCIEtH9b\n+Z8QBOEL4ACQLwhCmyAIN/5v+vTvLdAA+XPgoufhyg+hYhV8fRs4//JQExCxiVvRTRhPxO5h4PBv\nqFo4Ec/+X2GPLWBiuIgbPn6aZXu2k185Ad/QWb5RbsErJsPsNZD/OGy6BwoTYMR84lW/JmJUYOg0\nKBEsrjA0vQ1hH5bOPiIqI6FcLb6sfAI6mcaMQrqGjaVk+E3I51wcWjqX6M1nafeE0Z3dw4D9GeyX\nF6E62odX7qE+JguMC8nWtyFdPBdr+qXcpPklo/ua2C4uYJtlMt73d4K9E/OiX5FxzyE0+mKOZOXw\n5rXP4itIIGJ0EZF6GYwTcCcPoLQ4sb55GmUsMG4Aq2U4GcJKjCygI2o6/rATR+4pOgenEzFoUDUl\nI/pCUPYQEUFhI1uYX90H577BN34cQ+p4AhqJcPwDMPAByPejDFQQU99Ptm4bbq+L1K+rCD+1i1SV\nQrqphOiLr4TStehzF+AxxoHvGGRl8upv76I5LYWGi/KxWvt4+o7lHI4qZ9pgG78aNDOmdi39Vjc9\n0oMIcZU02N5HyR4H6ddDwhiIuNFGz8alD9KYl8Dd/vUsUyYjKAqc/QAOLIPEGHwpxbSOO4i6txns\nKSh6GcosyIeWQs0TcO4VmHchivco7FtBYGos4bJriJwpQKgspVNTwiVSIZL8GoLqDiLTcnAH7+Bo\nKJkjajNet4C+cAEdZ2I5qcli0PASMQWdxEdCXCA8jkAsJ9hM59g4QhmDNLfOIMt1AOHsRND04ioo\ngQv+ABWnEKSZCBXZsCcImwT49Ci0NcOehVCxH7klA1W+BYUIhMMEFz3KpM2rEfOvIpAArL4GPP08\n56nlVCgCY64Gs460FQ/zmKsQHTEsZyJjSMOFn++p5jVlF5/3vUf1gev4yrGK76giQOi/MbqfJooA\nIUn8m8r/2Jai/EJRlERFUVSKoqQoivL+/6M+43/ygYafBfov6Kxw8Usw5V5Yfy/sfBlCARCNoLMh\nNm9BsPpQNe1COPweGxap6Aia0X+4ACXFj1R6Bm3mV+jtufxRuYv7WlVw8FPY/ke48SSkTYetVyJ7\nRhFI9KO0fwk9P8CxxaCLI1hzBZ7GBMpbFFyxpbha9NTHluC0Kkxw1xHT+RKVN2WT5d1A08g0NClT\nyLJ9RNL+qWjWOBjYH8FeFU1mn51I++V4orS0R+fCsc2Ynp3MlM8GmLkX4hwya6/PRp18OSRkICCQ\ni56ILKLR7KZxvpFgsoiwMZ6kJguu0kkIPSF8U2Lx3xBHyBiPsekEg6F1uCnAbx5Fob0PtaSCrgSM\nd65BPOYCJUwgbhRf9f2KcdVnMOz+HVx9gIhKQerykRm8A8l2HTi8EOmBknTEkIYc580wFMT4+gkc\nc/QMqZKI8dQTTDnEkK0Hwd6Bvr4Tf+ZEWP4DDrNESzAWvc6PvyqRew/s5ZLq/fi0AZRvZnGg7BJ8\nkSTSzw4iaCYQUmpp5l46Bq/BE2+DqIXoGUV2p8zc3Z9TGKxF7l0Luxae96jIewAOHiYgDKDrNmOs\nLsCVnEIgPxYlPAZCqYTPChxyRNjZdozP5s5lwK7FvclMX/c46q67ho4Fl6Bt6UF3+k7EmmeRHd8j\ntdZhdEzkOfcjlKn2IY73IQhfkR9XyVzPdrSBPjzmRLQBO0LP/Yxuf52o4BAJnhaGonXE99WAtwzy\nuwhlf4XLbATLtTD5Czi+DYzZIFwKVWNh8uOQOAzafgSniGBTUEUtJRw6grJzM63tTo7e8SpaqRhH\nbj0bZj7Le4ffYa8njsawF8yZMPoCeHYfup5W4tDS2ldPGlFMIZvrGMPdrR6uXnMz6ZpMOq2JVNDO\n15zES/Cfbdl/M4ogEJblv6n8o/j33ST874jLh2u/gJqN8PFlMP5W6E5FmHgbHPkDocvPklJlxN3o\nw6nSEpxwDtk2DuK+wueoZ0dyGQZJzy3Hfw+aENzw8fk17cxLcdnMuMW9WM1zUeq/RQj0g6iFE1ci\n7srCOehB8/paAkcvpHb2NbTFtlEgVOAOV6Pp6Wa75WaWsJlAlETh5gCu6tGcvmocbQ9nY6vso+SN\nrRDtJ2j0c/DyDLq8K0j2NRJ1YxSaRBFR0qIP3sMvXn4L8f7fA+CjDRdrmKxcR4uyGZsjAfb1UrMk\nk+S9DlqnJJO/tZfOqxLojQe9OgdtusxA6D0GhRsYETqKUT2c4sFLaAp+QeK2HUSWRCM1WfnCsw7L\nUDd5Gz6CC34PPRtR+U6DLGKsXYt4Yg1MHY2iWgbi71CiJyC/fTfRp+z4bjMQmbOUNtV+BiQPUqSH\nSOBxXPosEjQKA6N6ier+Ffl9IXLPHWBMsxXFvJSGaV8T33uSqGMupM4AE/t1JPS/CXnryHQv45xO\nIUN6ncC6m3AszscvnqQtczip3tvxWx5F0wFh/RE0BQ/Bvl9CyIN/4q14rYdIeKkJwVNP3Z0lmOIS\nyTF/Cn1vIbX/kjLrXPwfHUIuH4/9wiQMr7QzoD9GHbEM2M6hRI8l+3Am5V/sRSfsZu/y6YTiS0kV\ndHQIG4g50oji9+EJKZxOL2W4sxantpWwPkxleD9mZwLFZxVeFXP4pX0XuqRYkHPgxyfwJVQjZQYg\nCvC4QUoG50l4pAZEFay5Euw7YJoCpgJwrUD96j0EYp9AeTOGt2+7j3uXPcJgxIOmNZUJvbfx1piF\nSJ4wBvtODurmUu7rQrQCMaUs3PsRqxJV3GvLP28zDWvh3Bcw43MMWVdwz08tqPLfQVj6afX9Z4H+\nryi+87M5KQ0K50HuTNj5HJytgpw8JP1EzPKLdMa+Q5d4juHuPRxSZzOuYjOYZnFv4UOMs4jcd+i3\njCoaDcPn/MWPGug3tRFNOSplJmHjx4ht20ClB0c+4vFaTtw9jYQDl2DRupGjMnGLpUShRf3NMYIm\nCdOwQQYCIdL79By+bQKRvlTCDfsp/KSOuOYIapVCKO8qNN4QU74d4tDVPQwbIxFUG9FQiIUnEVRa\nmG8ksvZZxKueREUUsZ15WPSP09uThXxYjTZqGHkVPZwriWHKa+sIJUHc8R6M4gWcmRBPo6Yfkz+N\nqPA3tPZImFQ3Yug9SOHzh3F+cD9iy0e4FSMe1z5m1+7CnxwL4bdQNzrQqKw4BT/mqmEw8xpItCC0\nvABVR+FsJ5yKIC26Ck1WE6LwFS7ndHr7hjG3cw+BHAF3zXa8sog18Sw9gTXMtodRXx9AsvpRRT4g\nvT8G2R2gfUQ6cd2N5Bx+hkOT72X8QA8qbSyyoAZAnXkh0ccCeKfY6XO4kd0fYR/hQx8Kktp4CJr2\nQ9lviWTMol+5nfhTBlpNGmImZ2FRJ1NrGsKqbOUT043ckjkcY93ryEN9TDJMJKy+EfeeS4mZtJ28\nnsc5Z6tGo+wjKTkf9ZWXohg6cMWNwqaeQDTR+JU2pOyDBAIxDOi1NJqH4zFameavoUUbQ6rhOTxR\nVrYHTjGq7zski48eKZ1wSz0x9ZUY897GGbX+/CA7uhaMJ2BUPmx8BBqqwVwJl34MnssgfjWkZCB6\nVxB2L2AgIZfDVRfgenUN8YOnUG88ReSGGh7evIe9S7/iroHHaFM9ywYpl4urXoW64ySt28nQH97F\n1b4FY8M6MCTCjC9B/GmJ29+LgkD4J/Zw+Vmg/yuCFgZvA+3VoLsGZDWIfSgXfkGbIcCxwu20CxUk\npMyl7MgmUtWz0dW/RGfahQz6R3HInMDCtnqmDXbAgdPw6W0w9XbInQBuO66yetK4BkGQCCQWIA/U\nIGzvggo3PPc1KcldDHRuJVoIk6MezxkaSNgbRG/M4vUCPYs+Wo1V9OLOiyLpvZW0jUwh1R0mbfgi\nuGQuPDuTiK0N55ggEVUXSdt8WK88gEIYiWgAXPjYVKyhy6jF4FrDkMFOvEVm4cCtJMnvcOLSCUw4\nWc3uuGxsB7rBkI0YrkPoMRBfo6Z+eDUGdRhP2EKZdwnWr28hPFNGaTiL+MDHWFt/gS8rldDZNpbu\n/QaNXsJx6bsMdq0klBhFf2os2Ws/xlf7KSy+Bu1Hv4GoNUAs1Ahw20so4Q8h3IumRcULffP5estz\niGIU2k/3oukbIJKpRZrsxlzpxCOqUcIh/ONM+DOm4qvZQfQ5EyaLBpUhgj0xi6TWVdBSS2SsQiTz\nzymciichffok6glGMva8iTnhdmKq3UScxxE6DhG0JHG2aR37g/vIjsklrG7BMUZmSc6b1AaL+K3/\nUT5XvNiIYPSbwXMGUpMhaR+CR0a4YTjS9asR2n5NUsO3BDKuRopRiNRtwlv8W5K0q8gO/B5ZGI4i\nyShOCSW+k66oEeSHz7BLmskM/R+wCk8Sph1X/yDzut4i55yE0leAZtZj9BZZ6R65kyJHDwElg4D/\nPdT5n0DSAtiXhRJ6nUB2CrJxFpJ6JYh3g5wO9WcQfnU/yn02omPr2LugHFFSQBcL1z6KL8aJuHsr\nRq+Z5JFVxG15DHn3C4SXvQtSFlKgmHkRCxv713GFuxGC/bB/OUQVgf0UlD0J+sR/mhn/b1EQCP0s\n0D9Rjm2BslmgvRL/4E2oVcNp9AQ4nmWjO81Jir+Z0XXbuWR7A4K3B6QgzJlC0oV7cA3cy/HoK/hV\n6zekpzvwj3wdbWsITnwDvbWw723CWg2CJRth93WgKKh9HSDXwmYR7lmKYP6A4W1WOg6dJnZCBuda\n11GWMpOvJyRTvvdd4vuLiA/1ECpVIVoUvNF21LmxpLU3g78BGt6B6RqErpMYAw8gN6xFv7aC4FUa\nNH/OE+8jwE6qGMBFdPJoFn7wKp6rKzA3zUc+8zmJBjUD6gFOFxfQY7dQVFFB/VOTqdWMYso3A7hd\nHSQfkwkXudmlvZmoitVAEdK3ESITBYTwUjBLuMVuOqNTKarrBIuDmEKISfkITt9Es0ck5v1Bjlw5\niYK1c1CpZyIVLoN334dcHcqwZBRHCKG+g99IK/l1bjKuHC9ivYGBwuXYmlNRx00g7B5F+ygjOUeD\n+FtC6LoyCZzbiUZR8F1QinXrD0T8EAh4GSox4YqXiFj9GOu7UY4uond4GqrObyk8a8PcHUCauoQe\ncxcnfU+xt2sTOn0Gk4d2c5nnFNbqsSiVp0gZsrPWdzc7JpXQE1GTFK5iopyKoovFG+hA79ND4jsE\nX7kDYb4TweqgX9+A+UwKtGxDVIK4NYl8p91BdMRPt7IIVdCEy+DFLLmQTjVhHuXCa1HjFuCE8Ayx\nrlqM9u+we0Yz2vAsLJiNsO9pLIYiLEIBWMZD16OkOOtoVOvJFt5B/O2DULoKYV4JkdZu6tIPEaNa\nSmxvJULzfNi6A2VpLsIRB5qibHwVThgRQjP1OCib0IYqcV3chtQZRNm4nKa5y/GNHkVRbRvOvKfR\npF3CqJOr+Xr6/SweWY6IcD4Oet0H0LMf9twIZU+DbdQ/1aT/XhQEAv/IaPx/Az8L9H+w4naCr+9n\nnTmDWu0KokMbyejpY5I5TEL352Bedj6zhP0Q7LsMAgmw5QGQYtBGutg0Yz+vbdzOwOX5aKKGQaoA\nqSXn245EcA6swxIDZMyDvc8hDuhgRxcMi4PL3oHBdqTji5A0QcShvQyvaSTi2UxT/mi8Z0Yw48g2\nXI9qCGVINHQmkne8lrRj+xmKGkGkcB6asYvR6HIQWo4jy7+Bs2MQpdN00EJibRWajNkE8DBSSWG+\ndjQEtuGPtBI5pmZw5HB0+dcTUj1JrvgY3yurWfDbTfTdr0GrChMW3AT3HiIuLNA4dzJGyzCm73oG\n3/CpaPs6ECprETNSUbpP8UXmE/htvSzZ9D6kXAHCNqj7EjIvRumIYDy3HkkME4pN4qnIKNM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MPFoWvLCbpuIau9kOTmAwhlI9ClPkO3fSl6ehBDNnyHv8WbqWA9E4aWBEIhO75IL7p8DxrnEYTK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\nqo1SA7RKpVK1UWqAVqlUqjbq/wF79oRnTvb9OAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1116,7 +1081,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/examples/jupyter/tally-arithmetic.ipynb b/examples/jupyter/tally-arithmetic.ipynb index de5917d7b76..391dc809a7f 100644 --- a/examples/jupyter/tally-arithmetic.ipynb +++ b/examples/jupyter/tally-arithmetic.ipynb @@ -33,7 +33,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." + "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin." ] }, { @@ -43,49 +43,25 @@ "collapsed": true }, "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H1')\n", - "b10 = openmc.Nuclide('B10')\n", - "o16 = openmc.Nuclide('O16')\n", - "u235 = openmc.Nuclide('U235')\n", - "u238 = openmc.Nuclide('U238')\n", - "zr90 = openmc.Nuclide('Zr90')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pin." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], "source": [ "# 1.6 enriched fuel\n", "fuel = openmc.Material(name='1.6% Fuel')\n", "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide(u235, 3.7503e-4)\n", - "fuel.add_nuclide(u238, 2.2625e-2)\n", - "fuel.add_nuclide(o16, 4.6007e-2)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", "\n", "# borated water\n", "water = openmc.Material(name='Borated Water')\n", "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide(h1, 4.9457e-2)\n", - "water.add_nuclide(o16, 2.4732e-2)\n", - "water.add_nuclide(b10, 8.0042e-6)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", "\n", "# zircaloy\n", "zircaloy = openmc.Material(name='Zircaloy')\n", "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide(zr90, 7.2758e-3)" + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" ] }, { @@ -97,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -119,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -148,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -185,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -212,20 +188,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "geometry = openmc.Geometry()\n", - "geometry.root_universe = root_universe" + "geometry = openmc.Geometry(root_universe)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -244,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -280,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -308,8 +283,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 11, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -317,7 +294,7 @@ "0" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -329,17 +306,19 @@ }, { "cell_type": "code", - 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"execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -361,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -373,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -396,7 +375,7 @@ "tally.filters = [openmc.CellFilter(fuel_cell)]\n", "tally.filters.append(energy_filter)\n", "tally.scores = ['nu-fission', 'scatter']\n", - "tally.nuclides = [u238, u235]\n", + "tally.nuclides = ['U238', 'U235']\n", "tallies_file.append(tally)\n", "\n", "# Instantiate reaction rate Tally in moderator\n", @@ -404,7 +383,7 @@ "tally.filters = [openmc.CellFilter(moderator_cell)]\n", "tally.filters.append(energy_filter)\n", "tally.scores = ['absorption', 'total']\n", - "tally.nuclides = [o16, h1]\n", + "tally.nuclides = ['O16', 'H1']\n", "tallies_file.append(tally)\n", "\n", "# Instantiate a tally mesh\n", @@ -434,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -450,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -465,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -481,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -496,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -510,23 +489,21 @@ "tally.filters = [openmc.CellFilter([fuel_cell, moderator_cell])]\n", "tally.filters.append(fine_energy_filter)\n", "tally.scores = ['nu-fission', 'scatter']\n", - "tally.nuclides = [h1, u238]\n", + "tally.nuclides = ['H1', 'U238']\n", "tallies_file.append(tally)" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": 20, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=1.\n", - " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another MeshFilter instance already exists with id=5.\n", - " warn(msg, IDWarning)\n", "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=3.\n", @@ -550,8 +527,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ @@ -588,13 +566,13 @@ " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.9.0\n", - " Git SHA1 | 5ca1d06b0c6ac3b56060ef289b7e5215210e7332\n", - " Date/Time | 2017-10-24 13:35:19\n", + " Git SHA1 | 9b7cebf7bc34d60e0f1750c3d6cb103df11e8dc4\n", + " Date/Time | 2017-12-04 20:43:15\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", + " Reading materials XML file...\n", " Reading geometry XML file...\n", " Building neighboring cells lists for each surface...\n", " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", @@ -605,6 +583,7 @@ " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -635,20 +614,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.1497E-01 seconds\n", - " Reading cross sections = 3.6232E-01 seconds\n", - " Total time in simulation = 3.6447E+00 seconds\n", - " Time in transport only = 3.5939E+00 seconds\n", - " Time in inactive batches = 4.4241E-01 seconds\n", - " Time in active batches = 3.2022E+00 seconds\n", - " Time synchronizing fission bank = 2.7734E-03 seconds\n", - " Sampling source sites = 1.1981E-03 seconds\n", - " SEND/RECV source sites = 1.5506E-03 seconds\n", - " Time accumulating tallies = 1.2237E-04 seconds\n", - " Total time for finalization = 1.4924E-03 seconds\n", - " Total time elapsed = 4.0823E+00 seconds\n", - " Calculation Rate (inactive) = 28254.0 neutrons/second\n", - " Calculation Rate (active) = 11710.5 neutrons/second\n", + " Total time for initialization = 5.6782E-01 seconds\n", + " Reading cross sections = 5.3276E-01 seconds\n", + " Total time in simulation = 6.4149E+00 seconds\n", + " Time in transport only = 6.2767E+00 seconds\n", + " Time in inactive batches = 6.8747E-01 seconds\n", + " Time in active batches = 5.7274E+00 seconds\n", + " Time synchronizing fission bank = 2.7492E-03 seconds\n", + " Sampling source sites = 1.9584E-03 seconds\n", + " SEND/RECV source sites = 7.4113E-04 seconds\n", + " Time accumulating tallies = 1.0576E-04 seconds\n", + " Total time for finalization = 2.2075E-03 seconds\n", + " Total time elapsed = 7.0056E+00 seconds\n", + " Calculation Rate (inactive) = 18182.5 neutrons/second\n", + " Calculation Rate (active) = 6547.45 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -666,15 +645,12 @@ "0" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Remove old HDF5 (summary, statepoint) files\n", - "!rm statepoint.*\n", - "\n", "# Run OpenMC!\n", "openmc.run()" ] @@ -695,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": true, "scrolled": true @@ -717,26 +693,15 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, + "execution_count": 23, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", " \n", @@ -764,7 +729,7 @@ "0 total (nu-fission / (absorption + current)) 1.02e+00 6.65e-03" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -795,26 +760,15 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, + "execution_count": 24, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -849,7 +803,7 @@ "0 ((absorption + current) / (absorption + current)) 6.94e-01 4.61e-03 " ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -874,26 +828,15 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 25, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -928,7 +871,7 @@ "0 1.20e+00 9.61e-03 " ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -951,26 +894,15 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "execution_count": 26, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1007,7 +939,7 @@ "0 7.49e-01 6.09e-03 " ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1028,26 +960,15 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 27, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1084,7 +1005,7 @@ "0 1.66e+00 1.44e-02 " ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1104,26 +1025,15 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, + "execution_count": 28, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1158,7 +1068,7 @@ "0 ((absorption + current) / (absorption + current)) 9.85e-01 5.51e-03 " ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1177,26 +1087,15 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, + "execution_count": 29, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1231,7 +1130,7 @@ "0 (absorption / (absorption + current)) 9.97e-01 7.55e-03 " ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1250,26 +1149,15 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, + "execution_count": 30, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1306,7 +1194,7 @@ "0 (((((((absorption + current) / (absorption + c... 1.02e+00 1.88e-02 " ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1327,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "collapsed": true, "scrolled": true @@ -1343,26 +1231,15 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, + "execution_count": 32, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1483,7 +1360,7 @@ "7 (scatter / flux) 3.36e-03 1.34e-05 " ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1502,8 +1379,10 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, + "execution_count": 33, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -1532,8 +1411,10 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, + "execution_count": 34, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -1554,8 +1435,10 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, + "execution_count": 35, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -1583,26 +1466,15 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, + "execution_count": 36, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1675,7 +1547,7 @@ "3 5.98e-04 " ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1688,26 +1560,15 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, + "execution_count": 37, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", " \n", @@ -1840,7 +1701,7 @@ "8 2.90e-03 " ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1870,7 +1731,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/openmc/examples.py b/openmc/examples.py index 4eeae7799e8..3d6a0682733 100644 --- a/openmc/examples.py +++ b/openmc/examples.py @@ -573,16 +573,16 @@ def slab_mg(reps=None, as_macro=True): for mat in mat_names: for rep in reps: i += 1 + name = mat + '_' + rep + xs.append(name) if as_macro: - xs.append(openmc.Macroscopic(mat + '_' + rep)) m = openmc.Material(name=str(i)) m.set_density('macro', 1.) - m.add_macroscopic(xs[-1]) + m.add_macroscopic(name) else: - xs.append(openmc.Nuclide(mat + '_' + rep)) m = openmc.Material(name=str(i)) m.set_density('atom/b-cm', 1.) - m.add_nuclide(xs[-1].name, 1.0, 'ao') + m.add_nuclide(name, 1.0, 'ao') model.materials.append(m) # Define the materials file diff --git a/openmc/plotter.py b/openmc/plotter.py index 9e356b1eda0..810ee5d27cc 100644 --- a/openmc/plotter.py +++ b/openmc/plotter.py @@ -1,7 +1,9 @@ from numbers import Integral, Real -from six import string_types from itertools import chain +import string +from six import string_types +import matplotlib.pyplot as plt import numpy as np import openmc.checkvalue as cv @@ -63,15 +65,15 @@ _MAX_E = 20.e6 -def plot_xs(this, types, divisor_types=None, temperature=294., axis=None, - sab_name=None, ce_cross_sections=None, mg_cross_sections=None, - enrichment=None, plot_CE=True, orders=None, divisor_orders=None, - **kwargs): +def plot_xs(this, types, divisor_types=None, temperature=294., data_type=None, + axis=None, sab_name=None, ce_cross_sections=None, + mg_cross_sections=None, enrichment=None, plot_CE=True, orders=None, + divisor_orders=None, **kwargs): """Creates a figure of continuous-energy cross sections for this item. Parameters ---------- - this : openmc.Element, openmc.Nuclide, or openmc.Material + this : str or openmc.Material Object to source data from types : Iterable of values of PLOT_TYPES The type of cross sections to include in the plot. @@ -84,6 +86,9 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None, temperature of 294K will be plotted. Note that the nearest temperature in the library for each nuclide will be used as opposed to using any interpolation. + data_type : {'nuclide', 'element', 'material', 'macroscopic'}, optional + Type of object to plot. If not specified, a guess is made based on the + `this` argument. axis : matplotlib.axes, optional A previously generated axis to use for plotting. If not specified, a new axis and figure will be generated. @@ -121,25 +126,28 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None, generated. """ - - from matplotlib import pyplot as plt - cv.check_type("plot_CE", plot_CE, bool) - if isinstance(this, openmc.Nuclide): - data_type = 'nuclide' - elif isinstance(this, openmc.Element): - data_type = 'element' - elif isinstance(this, openmc.Material): - data_type = 'material' - elif isinstance(this, openmc.Macroscopic): - data_type = 'macroscopic' - else: - raise TypeError("Invalid type for plotting") + if data_type is None: + if isinstance(this, openmc.Nuclide): + data_type = 'nuclide' + elif isinstance(this, openmc.Element): + data_type = 'element' + elif isinstance(this, openmc.Material): + data_type = 'material' + elif isinstance(this, openmc.Macroscopic): + data_type = 'macroscopic' + elif isinstance(this, string_types): + if this[-1] in string.digits: + data_type = 'nuclide' + else: + data_type = 'element' + else: + raise TypeError("Invalid type for plotting") if plot_CE: # Calculate for the CE cross sections - E, data = calculate_cexs(this, types, temperature, sab_name, + E, data = calculate_cexs(this, data_type, types, temperature, sab_name, ce_cross_sections, enrichment) if divisor_types: cv.check_length('divisor types', divisor_types, len(types)) @@ -220,23 +228,25 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None, ylabel = 'Macroscopic Cross Section [1/cm]' ax.set_ylabel(ylabel) ax.legend(loc='best') - if this.name is not None and this.name != '': - if len(types) > 1: - ax.set_title('Cross Sections for ' + this.name) - else: - ax.set_title('Cross Section for ' + this.name) + name = this.name if data_type == 'material' else this + if len(types) > 1: + ax.set_title('Cross Sections for ' + name) + else: + ax.set_title('Cross Section for ' + name) return fig -def calculate_cexs(this, types, temperature=294., sab_name=None, +def calculate_cexs(this, data_type, types, temperature=294., sab_name=None, cross_sections=None, enrichment=None): """Calculates continuous-energy cross sections of a requested type. Parameters ---------- - this : openmc.Element, openmc.Nuclide, or openmc.Material + this : str or openmc.Material Object to source data from + data_type : {'nuclide', 'element', material'} + Type of object to plot types : Iterable of values of PLOT_TYPES The type of cross sections to calculate temperature : float, optional @@ -269,7 +279,7 @@ def calculate_cexs(this, types, temperature=294., sab_name=None, if enrichment: cv.check_type('enrichment', enrichment, Real) - if isinstance(this, openmc.Nuclide): + if data_type == 'nuclide': energy_grid, xs = _calculate_cexs_nuclide(this, types, temperature, sab_name, cross_sections) # Convert xs (Iterable of Callable) to a grid of cross section values @@ -278,11 +288,11 @@ def calculate_cexs(this, types, temperature=294., sab_name=None, data = np.zeros((len(types), len(energy_grid))) for line in range(len(types)): data[line, :] = xs[line](energy_grid) - elif isinstance(this, openmc.Element): + elif data_type == 'element': energy_grid, data = _calculate_cexs_elem_mat(this, types, temperature, cross_sections, sab_name, enrichment) - elif isinstance(this, openmc.Material): + elif data_type == 'material': energy_grid, data = _calculate_cexs_elem_mat(this, types, temperature, cross_sections) else: @@ -352,7 +362,7 @@ def _calculate_cexs_nuclide(this, types, temperature=294., sab_name=None, # Now we can create the data sets to be plotted energy_grid = [] xs = [] - lib = library.get_by_material(this.name) + lib = library.get_by_material(this) if lib is not None: nuc = openmc.data.IncidentNeutron.from_hdf5(lib['path']) # Obtain the nearest temperature @@ -464,7 +474,7 @@ def _calculate_cexs_nuclide(this, types, temperature=294., sab_name=None, funcs.append(lambda x: 0.) xs.append(openmc.data.Combination(funcs, op)) else: - raise ValueError(this.name + " not in library") + raise ValueError(this + " not in library") return energy_grid, xs @@ -476,7 +486,7 @@ def _calculate_cexs_elem_mat(this, types, temperature=294., Parameters ---------- - this : {openmc.Material, openmc.Element} + this : openmc.Material or openmc.Element Object to source data from types : Iterable of values of PLOT_TYPES The type of cross sections to calculate @@ -508,8 +518,10 @@ def _calculate_cexs_elem_mat(this, types, temperature=294., T = this.temperature else: T = temperature + data_type = 'material' else: T = temperature + data_type = 'element' # Load the library library = openmc.data.DataLibrary.from_xml(cross_sections) @@ -518,23 +530,23 @@ def _calculate_cexs_elem_mat(this, types, temperature=294., # Expand elements in to nuclides with atomic densities nuclides = this.get_nuclide_atom_densities() # For ease of processing split out the nuclide and its fraction - nuc_fractions = {nuclide[1][0].name: nuclide[1][1] + nuc_fractions = {nuclide[1][0]: nuclide[1][1] for nuclide in nuclides.items()} # Create a dict of [nuclide name] = nuclide object to carry forward # with a common nuclides format between openmc.Material and # openmc.Element objects - nuclides = {nuclide[1][0].name: nuclide[1][0] + nuclides = {nuclide[1][0]: nuclide[1][0] for nuclide in nuclides.items()} else: # Expand elements in to nuclides with atomic densities nuclides = this.expand(1., 'ao', enrichment=enrichment, cross_sections=cross_sections) # For ease of processing split out the nuclide and its fraction - nuc_fractions = {nuclide[0].name: nuclide[1] for nuclide in nuclides} + nuc_fractions = {nuclide[0]: nuclide[1] for nuclide in nuclides} # Create a dict of [nuclide name] = nuclide object to carry forward # with a common nuclides format between openmc.Material and # openmc.Element objects - nuclides = {nuclide[0].name: nuclide[0] for nuclide in nuclides} + nuclides = {nuclide[0]: nuclide[0] for nuclide in nuclides} # Identify the nuclides which have S(a,b) data sabs = {} @@ -559,7 +571,7 @@ def _calculate_cexs_elem_mat(this, types, temperature=294., name = nuclide[0] nuc = nuclide[1] sab_tab = sabs[name] - temp_E, temp_xs = calculate_cexs(nuc, types, T, sab_tab, + temp_E, temp_xs = calculate_cexs(nuc, data_type, types, T, sab_tab, cross_sections) E.append(temp_E) # Since the energy grids are different, store the cross sections as @@ -587,10 +599,10 @@ def _calculate_cexs_elem_mat(this, types, temperature=294., return energy_grid, data -def calculate_mgxs(this, types, orders=None, temperature=294., +def calculate_mgxs(this, data_type, types, orders=None, temperature=294., cross_sections=None, ce_cross_sections=None, enrichment=None): - """Calculates continuous-energy cross sections of a requested type. + """Calculates multi-group cross sections of a requested type. If the data for the nuclide or macroscopic object in the library is represented as angle-dependent data then this method will return the @@ -598,8 +610,10 @@ def calculate_mgxs(this, types, orders=None, temperature=294., Parameters ---------- - this : openmc.Element, openmc.Nuclide, openmc.Material, or openmc.Macroscopic + this : str or openmc.Material Object to source data from + data_type : {'nuclide', 'element', material', 'macroscopic'} + Type of object to plot types : Iterable of values of PLOT_TYPES_MGXS The type of cross sections to calculate orders : Iterable of Integral, optional @@ -639,10 +653,10 @@ def calculate_mgxs(this, types, orders=None, temperature=294., cv.check_type("cross_sections", cross_sections, str) library = openmc.MGXSLibrary.from_hdf5(cross_sections) - if isinstance(this, (openmc.Nuclide, openmc.Macroscopic)): + if data_type in ('nuclide', 'macroscopic'): mgxs = _calculate_mgxs_nuc_macro(this, types, library, orders, temperature) - elif isinstance(this, (openmc.Element, openmc.Material)): + elif data_type in ('element', 'material'): mgxs = _calculate_mgxs_elem_mat(this, types, library, orders, temperature, ce_cross_sections, enrichment) @@ -713,7 +727,7 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None, if orders[i]: cv.check_greater_than("order value", orders[i], 0, equality=True) - xsdata = library.get_by_name(this.name) + xsdata = library.get_by_name(this) if xsdata is not None: # Obtain the nearest temperature @@ -799,7 +813,7 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None, data[i, :] = temp_data[:, order] else: raise ValueError("{} not present in provided MGXS " - "library".format(this.name)) + "library".format(this)) return data diff --git a/openmc/settings.py b/openmc/settings.py index c608994fa7c..9ead82ec611 100644 --- a/openmc/settings.py +++ b/openmc/settings.py @@ -9,7 +9,7 @@ from openmc.clean_xml import clean_xml_indentation import openmc.checkvalue as cv -from openmc import Nuclide, VolumeCalculation, Source, Mesh +from openmc import VolumeCalculation, Source, Mesh _RUN_MODES = ['eigenvalue', 'fixed source', 'plot', 'volume', 'particle restart'] _RES_SCAT_METHODS = ['dbrc', 'wcm', 'ares'] diff --git a/openmc/tallies.py b/openmc/tallies.py index 2cf3589871d..e049d698372 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -3437,12 +3437,12 @@ def _create_mesh_subelements(self, root_element): for tally in self: for f in tally.filters: if isinstance(f, openmc.MeshFilter): - if f.mesh not in already_written: + if f.mesh.id not in already_written: if len(f.mesh.name) > 0: root_element.append(ET.Comment(f.mesh.name)) root_element.append(f.mesh.to_xml_element()) - already_written.add(f.mesh) + already_written.add(f.mesh.id) def _create_filter_subelements(self, root_element): already_written = dict() diff --git a/openmc/universe.py b/openmc/universe.py index 7d9f5a29f47..92b152ca09e 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -6,6 +6,7 @@ import sys from six import string_types +import matplotlib.pyplot as plt import numpy as np import openmc @@ -225,8 +226,6 @@ def plot(self, origin=(0., 0., 0.), width=(1., 1.), pixels=(200, 200), :func:`matplotlib.pyplot.imshow`. """ - import matplotlib.pyplot as plt - # Seed the random number generator if seed is not None: random.seed(seed) diff --git a/tests/test_mg_tallies/test_mg_tallies.py b/tests/test_mg_tallies/test_mg_tallies.py index 57767fd6b2f..aeafae31823 100644 --- a/tests/test_mg_tallies/test_mg_tallies.py +++ b/tests/test_mg_tallies/test_mg_tallies.py @@ -28,10 +28,10 @@ mat_filter = openmc.MaterialFilter(model.materials) - nuclides = [xs.name for xs in model.xs_data] + nuclides = model.xs_data - scores= {False: ['total', 'absorption', 'flux', 'fission', 'nu-fission'], - True: ['total', 'absorption', 'fission', 'nu-fission']} + scores = {False: ['total', 'absorption', 'flux', 'fission', 'nu-fission'], + True: ['total', 'absorption', 'fission', 'nu-fission']} for do_nuclides in [False, True]: t = openmc.Tally() From e698403076f8ed6476b15e314285a05b9bcd7c79 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 18 Dec 2017 10:33:30 +0700 Subject: [PATCH 12/15] Don't allocate/deallocate p0 from openmc_material_set_densities --- src/material_header.F90 | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/material_header.F90 b/src/material_header.F90 index a3c14f8cab4..9d93131acfd 100644 --- a/src/material_header.F90 +++ b/src/material_header.F90 @@ -455,8 +455,8 @@ function openmc_material_set_densities(index, n, name, density) result(err) bind associate (m => materials(index)) ! If nuclide/density arrays are not correct size, reallocate if (n /= m % n_nuclides) then - deallocate(m % nuclide, m % atom_density, m % p0, STAT=stat) - allocate(m % nuclide(n), m % atom_density(n), m % p0(n)) + deallocate(m % nuclide, m % atom_density, STAT=stat) + allocate(m % nuclide(n), m % atom_density(n)) end if do i = 1, n @@ -474,9 +474,6 @@ function openmc_material_set_densities(index, n, name, density) result(err) bind end do m % n_nuclides = n - ! Set isotropic flags to flags - m % p0(:) = .false. - ! Set total density to the sum of the vector err = m % set_density(sum(density)) From e6b5803f709f43e2b06cfa6917cff51a001898f4 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 19 Dec 2017 15:58:14 +0700 Subject: [PATCH 13/15] Make filter_strides a property of Tally --- openmc/arithmetic.py | 32 ++----- openmc/filter.py | 210 ++++++++++++------------------------------- openmc/mgxs/mgxs.py | 2 - openmc/statepoint.py | 3 - openmc/tallies.py | 49 +++------- 5 files changed, 71 insertions(+), 225 deletions(-) diff --git a/openmc/arithmetic.py b/openmc/arithmetic.py index 6a781cb5a23..fe002f1e31d 100644 --- a/openmc/arithmetic.py +++ b/openmc/arithmetic.py @@ -237,9 +237,6 @@ class CrossFilter(object): left / right filters num_bins : Integral The number of filter bins (always 1 if aggregate_filter is defined) - stride : Integral - The number of filter, nuclide and score bins within each of this - crossfilter's bins. """ @@ -250,7 +247,6 @@ def __init__(self, left_filter=None, right_filter=None, binary_op=None): self._type = '({0} {1} {2})'.format(left_type, binary_op, right_type) self._bins = {} - self._stride = None self._left_filter = None self._right_filter = None @@ -314,10 +310,6 @@ def num_bins(self): else: return 0 - @property - def stride(self): - return self._stride - @type.setter def type(self, filter_type): if filter_type not in _FILTER_TYPES: @@ -347,10 +339,6 @@ def binary_op(self, binary_op): cv.check_value('binary_op', binary_op, _TALLY_ARITHMETIC_OPS) self._binary_op = binary_op - @stride.setter - def stride(self, stride): - self._stride = stride - def get_bin_index(self, filter_bin): """Returns the index in the CrossFilter for some bin. @@ -611,9 +599,6 @@ class AggregateFilter(object): The filter bins included in the aggregation num_bins : Integral The number of filter bins (always 1 if aggregate_filter is defined) - stride : Integral - The number of filter, nuclide and score bins within each of this - aggregatefilter's bins. """ @@ -622,7 +607,6 @@ def __init__(self, aggregate_filter=None, bins=None, aggregate_op=None): self._type = '{0}({1})'.format(aggregate_op, aggregate_filter.short_name.lower()) self._bins = None - self._stride = None self._aggregate_filter = None self._aggregate_op = None @@ -684,10 +668,6 @@ def bins(self): def num_bins(self): return len(self.bins) if self.aggregate_filter else 0 - @property - def stride(self): - return self._stride - @type.setter def type(self, filter_type): if filter_type not in _FILTER_TYPES: @@ -714,10 +694,6 @@ def aggregate_op(self, aggregate_op): cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS) self._aggregate_op = aggregate_op - @stride.setter - def stride(self, stride): - self._stride = stride - def get_bin_index(self, filter_bin): """Returns the index in the AggregateFilter for some bin. @@ -753,7 +729,7 @@ def get_bin_index(self, filter_bin): else: return self.bins.index(filter_bin) - def get_pandas_dataframe(self, data_size, summary=None, **kwargs): + def get_pandas_dataframe(self, data_size, stride, summary=None, **kwargs): """Builds a Pandas DataFrame for the AggregateFilter's bins. This method constructs a Pandas DataFrame object for the AggregateFilter @@ -762,8 +738,10 @@ def get_pandas_dataframe(self, data_size, summary=None, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter summary : None or Summary An optional Summary object to be used to construct columns for distribcell tally filters (default is None). NOTE: This parameter @@ -793,7 +771,7 @@ def get_pandas_dataframe(self, data_size, summary=None, **kwargs): filter_bins[i] = bin # Repeat and tile bins as needed for DataFrame - filter_bins = np.repeat(filter_bins, self.stride) + filter_bins = np.repeat(filter_bins, stride) tile_factor = data_size / len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) diff --git a/openmc/filter.py b/openmc/filter.py index 2b329ef0e0f..fc133168d2d 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -86,9 +86,6 @@ class Filter(IDManagerMixin): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -99,7 +96,6 @@ def __init__(self, bins, filter_id=None): self.bins = bins self.id = filter_id self._num_bins = 0 - self._stride = None def __eq__(self, other): if type(self) is not type(other): @@ -194,10 +190,6 @@ def bins(self): def num_bins(self): return self._num_bins - @property - def stride(self): - return self._stride - @bins.setter def bins(self, bins): # Format the bins as a 1D numpy array. @@ -214,16 +206,6 @@ def num_bins(self, num_bins): cv.check_greater_than('filter num_bins', num_bins, 0, equality=True) self._num_bins = num_bins - @stride.setter - def stride(self, stride): - cv.check_type('filter stride', stride, Integral) - if stride < 0: - msg = 'Unable to set stride "{0}" for a "{1}" since it ' \ - 'is a negative value'.format(stride, type(self)) - raise ValueError(msg) - - self._stride = stride - def check_bins(self, bins): """Make sure given bins are valid for this filter. @@ -270,11 +252,7 @@ def can_merge(self, other): Whether the filter can be merged """ - - if type(self) is not type(other): - return False - - return True + return type(self) is type(other) def merge(self, other): """Merge this filter with another. @@ -402,7 +380,7 @@ def get_bin(self, bin_index): # Return a 1-tuple of the bin. return (self.bins[bin_index],) - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -411,13 +389,15 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Keyword arguments ----------------- paths : bool - Only used for DistirbcellFilter. If True (default), expand + Only used for DistribcellFilter. If True (default), expand distribcell indices into multi-index columns describing the path to that distribcell through the CSG tree. NOTE: This option assumes that all distribcell paths are of the same length and do not have @@ -431,11 +411,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): the total number of bins in the corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -444,7 +419,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Initialize Pandas DataFrame df = pd.DataFrame() - filter_bins = np.repeat(self.bins, self.stride) + filter_bins = np.repeat(self.bins, stride) tile_factor = data_size // len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) df = pd.concat([df, pd.DataFrame( @@ -502,9 +477,6 @@ class UniverseFilter(WithIDFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @property @@ -535,9 +507,6 @@ class MaterialFilter(WithIDFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @property @@ -568,15 +537,12 @@ class CellFilter(WithIDFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @property def bins(self): return self._bins - + @bins.setter def bins(self, bins): self._smart_set_bins(bins, openmc.Cell) @@ -601,15 +567,12 @@ class CellFromFilter(WithIDFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @property def bins(self): return self._bins - + @bins.setter def bins(self, bins): self._smart_set_bins(bins, openmc.Cell) @@ -634,9 +597,6 @@ class CellbornFilter(WithIDFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @property @@ -668,9 +628,6 @@ class SurfaceFilter(Filter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @property @@ -696,7 +653,7 @@ def bins(self, bins): @num_bins.setter def num_bins(self, num_bins): pass - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -705,8 +662,10 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Returns ------- @@ -717,11 +676,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): the corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -730,7 +684,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Initialize Pandas DataFrame df = pd.DataFrame() - filter_bins = np.repeat(self.bins, self.stride) + filter_bins = np.repeat(self.bins, stride) tile_factor = data_size // len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) filter_bins = [_CURRENT_NAMES[x] for x in filter_bins] @@ -760,9 +714,6 @@ class MeshFilter(Filter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -854,7 +805,7 @@ def get_bin(self, bin_index): y = bin_index - (x * ny) return (x, y) - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -863,8 +814,10 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Returns ------- @@ -875,11 +828,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -906,7 +854,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Generate multi-index sub-column for x-axis filter_bins = np.arange(1, nx + 1) - repeat_factor = ny * nz * self.stride + repeat_factor = ny * nz * stride filter_bins = np.repeat(filter_bins, repeat_factor) tile_factor = data_size // len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) @@ -914,7 +862,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Generate multi-index sub-column for y-axis filter_bins = np.arange(1, ny + 1) - repeat_factor = nz * self.stride + repeat_factor = nz * stride filter_bins = np.repeat(filter_bins, repeat_factor) tile_factor = data_size // len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) @@ -922,7 +870,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Generate multi-index sub-column for z-axis filter_bins = np.arange(1, nz + 1) - repeat_factor = self.stride + repeat_factor = stride filter_bins = np.repeat(filter_bins, repeat_factor) tile_factor = data_size // len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) @@ -952,9 +900,6 @@ class RealFilter(Filter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -1063,9 +1008,6 @@ class EnergyFilter(RealFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -1100,7 +1042,7 @@ def check_bins(self, bins): 'increasing'.format(bins, type(self)) raise ValueError(msg) - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -1109,8 +1051,10 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Returns ------- @@ -1121,11 +1065,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -1136,8 +1075,8 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Extract the lower and upper energy bounds, then repeat and tile # them as necessary to account for other filters. - lo_bins = np.repeat(self.bins[:-1], self.stride) - hi_bins = np.repeat(self.bins[1:], self.stride) + lo_bins = np.repeat(self.bins[:-1], stride) + hi_bins = np.repeat(self.bins[1:], stride) tile_factor = data_size // len(lo_bins) lo_bins = np.tile(lo_bins, tile_factor) hi_bins = np.tile(hi_bins, tile_factor) @@ -1167,9 +1106,6 @@ class EnergyoutFilter(EnergyFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -1229,9 +1165,6 @@ class DistribcellFilter(Filter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. paths : list of str The paths traversed through the CSG tree to reach each distribcell instance (for 'distribcell' filters only) @@ -1296,7 +1229,7 @@ def get_bin_index(self, filter_bin): # the Cell in the Geometry (consecutive integers starting at 0). return filter_bin - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -1305,8 +1238,10 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Keyword arguments ----------------- @@ -1331,11 +1266,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): of bins in the corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -1418,7 +1348,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Tile the Multi-index columns for level_key, level_bins in level_dict.items(): - level_bins = np.repeat(level_bins, self.stride) + level_bins = np.repeat(level_bins, stride) tile_factor = data_size // len(level_bins) level_bins = np.tile(level_bins, tile_factor) level_dict[level_key] = level_bins @@ -1434,7 +1364,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # NOTE: This is performed regardless of whether the user # requests Summary geometric information filter_bins = np.arange(self.num_bins) - filter_bins = np.repeat(filter_bins, self.stride) + filter_bins = np.repeat(filter_bins, stride) tile_factor = data_size // len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) df = pd.DataFrame({self.short_name.lower() : filter_bins}) @@ -1474,9 +1404,6 @@ class MuFilter(RealFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -1504,7 +1431,7 @@ def check_bins(self, bins): 'increasing'.format(bins, type(self)) raise ValueError(msg) - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -1513,8 +1440,10 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Returns ------- @@ -1525,11 +1454,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -1540,8 +1464,8 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Extract the lower and upper energy bounds, then repeat and tile # them as necessary to account for other filters. - lo_bins = np.repeat(self.bins[:-1], self.stride) - hi_bins = np.repeat(self.bins[1:], self.stride) + lo_bins = np.repeat(self.bins[:-1], stride) + hi_bins = np.repeat(self.bins[1:], stride) tile_factor = data_size // len(lo_bins) lo_bins = np.tile(lo_bins, tile_factor) hi_bins = np.tile(hi_bins, tile_factor) @@ -1579,9 +1503,6 @@ class PolarFilter(RealFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -1609,7 +1530,7 @@ def check_bins(self, bins): 'increasing'.format(bins, type(self)) raise ValueError(msg) - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -1618,8 +1539,10 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Returns ------- @@ -1630,11 +1553,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -1645,8 +1563,8 @@ def get_pandas_dataframe(self, data_size, **kwargs): # Extract the lower and upper angle bounds, then repeat and tile # them as necessary to account for other filters. - lo_bins = np.repeat(self.bins[:-1], self.stride) - hi_bins = np.repeat(self.bins[1:], self.stride) + lo_bins = np.repeat(self.bins[:-1], stride) + hi_bins = np.repeat(self.bins[1:], stride) tile_factor = data_size // len(lo_bins) lo_bins = np.tile(lo_bins, tile_factor) hi_bins = np.tile(hi_bins, tile_factor) @@ -1684,9 +1602,6 @@ class AzimuthalFilter(RealFilter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -1714,7 +1629,7 @@ def check_bins(self, bins): 'increasing'.format(bins, type(self)) raise ValueError(msg) - def get_pandas_dataframe(self, data_size, paths=True): + def get_pandas_dataframe(self, data_size, stride, paths=True): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -1723,8 +1638,10 @@ def get_pandas_dataframe(self, data_size, paths=True): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Returns ------- @@ -1735,11 +1652,6 @@ def get_pandas_dataframe(self, data_size, paths=True): corresponding tally, with the filter bin appropriately tiled to map to the corresponding tally bins. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -1750,8 +1662,8 @@ def get_pandas_dataframe(self, data_size, paths=True): # Extract the lower and upper angle bounds, then repeat and tile # them as necessary to account for other filters. - lo_bins = np.repeat(self.bins[:-1], self.stride) - hi_bins = np.repeat(self.bins[1:], self.stride) + lo_bins = np.repeat(self.bins[:-1], stride) + hi_bins = np.repeat(self.bins[1:], stride) tile_factor = data_size // len(lo_bins) lo_bins = np.tile(lo_bins, tile_factor) hi_bins = np.tile(hi_bins, tile_factor) @@ -1785,9 +1697,6 @@ class DelayedGroupFilter(Filter): Unique identifier for the filter num_bins : Integral The number of filter bins - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @property @@ -1840,9 +1749,6 @@ class EnergyFunctionFilter(Filter): Unique identifier for the filter num_bins : Integral The number of filter bins (always 1 for this filter) - stride : Integral - The number of filter, nuclide and score bins within each of this - filter's bins. """ @@ -1850,7 +1756,6 @@ def __init__(self, energy, y, filter_id=None): self.energy = energy self.y = y self.id = filter_id - self._stride = None def __eq__(self, other): if type(self) is not type(other): @@ -2006,7 +1911,7 @@ def get_bin(self, bin_index): """This function is invalid for EnergyFunctionFilters.""" raise RuntimeError('EnergyFunctionFilters have no get_bin() method') - def get_pandas_dataframe(self, data_size, **kwargs): + def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -2015,8 +1920,10 @@ def get_pandas_dataframe(self, data_size, **kwargs): Parameters ---------- - data_size : Integral + data_size : int The total number of bins in the tally corresponding to this filter + stride : int + Stride in memory for the filter Returns ------- @@ -2027,11 +1934,6 @@ def get_pandas_dataframe(self, data_size, **kwargs): EnergyFunctionFilters. The number of rows in the DataFrame is the same as the total number of bins in the corresponding tally. - Raises - ------ - ImportError - When Pandas is not installed - See also -------- Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe() @@ -2052,7 +1954,7 @@ def get_pandas_dataframe(self, data_size, **kwargs): # hex characters) of the digest are probably sufficient. out = out[:14] - filter_bins = np.repeat(out, self.stride) + filter_bins = np.repeat(out, stride) tile_factor = data_size // len(filter_bins) filter_bins = np.tile(filter_bins, tile_factor) df = pd.concat([df, pd.DataFrame( diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 192f484a5ef..6a74ee2313e 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -2751,7 +2751,6 @@ def rxn_rate_tally(self): # Switch EnergyoutFilter to EnergyFilter. old_filt = self.tallies['scatter-1'].filters[-1] new_filt = openmc.EnergyFilter(old_filt.bins) - new_filt.stride = old_filt.stride self.tallies['scatter-1'].filters[-1] = new_filt self._rxn_rate_tally = \ @@ -2771,7 +2770,6 @@ def xs_tally(self): # Switch EnergyoutFilter to EnergyFilter. old_filt = self.tallies['scatter-1'].filters[-1] new_filt = openmc.EnergyFilter(old_filt.bins) - new_filt.stride = old_filt.stride self.tallies['scatter-1'].filters[-1] = new_filt # Compute total cross section diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 16d5b5d8483..4656e085609 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -415,9 +415,6 @@ def tallies(self): tally.scores.append(score) - # Compute and set the filter strides - tally._update_filter_strides() - # Add Tally to the global dictionary of all Tallies tally.sparse = self.sparse self._tallies[tally_id] = tally diff --git a/openmc/tallies.py b/openmc/tallies.py index e049d698372..9f8f7e505ea 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -78,6 +78,8 @@ class Tally(IDManagerMixin): shape : 3-tuple of int The shape of the tally data array ordered as the number of filter bins, nuclide bins and score bins + filter_strides : list of int + Stride in memory for each filter num_realizations : int Total number of realizations with_summary : bool @@ -818,10 +820,6 @@ def merge(self, other): # Differentiate Tally with a new auto-generated Tally ID merged_tally.id = None - # If the two tallies are equal, simply return copy - if self == other: - return merged_tally - # Create deep copy of other tally to use for array concatenation other_copy = copy.deepcopy(other) @@ -877,9 +875,6 @@ def merge(self, other): else: self._derived = True - # Update filter strides in merged tally - merged_tally._update_filter_strides() - # Concatenate sum arrays if present in both tallies if self.sum is not None and other_copy.sum is not None: self_sum = self.get_reshaped_data(value='sum') @@ -1538,11 +1533,10 @@ def get_pandas_dataframe(self, filters=True, nuclides=True, scores=True, # Build DataFrame columns for filters if user requested them if filters: - # Append each Filter's DataFrame to the overall DataFrame - for self_filter in self.filters: - filter_df = self_filter.get_pandas_dataframe( - data_size, paths=paths) + for f, stride in zip(self.filters, self.filter_strides): + filter_df = f.get_pandas_dataframe( + data_size, stride, paths=paths) df = pd.concat([df, filter_df], axis=1) # Include DataFrame column for nuclides if user requested it @@ -1867,20 +1861,16 @@ def cross_score(score1, score2, binary_op): new_score = cross_score(self_score, other_score, binary_op) new_tally.scores.append(new_score) - # Update the new tally's filter strides - new_tally._update_filter_strides() - return new_tally - def _update_filter_strides(self): - """Update each filter's stride based on the tally's nuclides and scores - for derived tallies created by tally arithmetic. - """ - + @property + def filter_strides(self): + all_strides = [] stride = self.num_nuclides * self.num_scores for self_filter in reversed(self.filters): - self_filter.stride = stride + all_strides.append(stride) stride *= self_filter.num_bins + return all_strides[::-1] def _align_tally_data(self, other, filter_product, nuclide_product, score_product): @@ -2019,10 +2009,6 @@ def _align_tally_data(self, other, filter_product, nuclide_product, if other_index != i: other._swap_scores(score, other.scores[i]) - # Update the tallies' filter strides - other._update_filter_strides() - self._update_filter_strides() - data = {} data['self'] = {} data['other'] = {} @@ -2107,9 +2093,6 @@ def _swap_filters(self, filter1, filter2): self.filters[filter1_index] = filter2 self.filters[filter2_index] = filter1 - # Update the tally's filter strides - self._update_filter_strides() - # Realign the data for i, (bin1, bin2) in enumerate(product(filter1_bins, filter2_bins)): filter_bins = [(bin1,), (bin2,)] @@ -2861,9 +2844,6 @@ def get_slice(self, scores=[], filters=[], filter_bins=[], nuclides=[]): find_filter.bins = np.unique(find_filter.bins[bin_indices]) find_filter.num_bins = num_bins - # Update the new tally's filter strides - new_tally._update_filter_strides() - # If original tally was sparse, sparsify the sliced tally new_tally.sparse = self.sparse return new_tally @@ -3010,9 +2990,6 @@ def summation(self, scores=[], filter_type=None, else: tally_sum._scores = copy.deepcopy(self.scores) - # Update the tally sum's filter strides - tally_sum._update_filter_strides() - # Reshape condensed data arrays with one dimension for all filters mean = np.reshape(mean, tally_sum.shape) std_dev = np.reshape(std_dev, tally_sum.shape) @@ -3170,9 +3147,6 @@ def average(self, scores=[], filter_type=None, else: tally_avg._scores = copy.deepcopy(self.scores) - # Update the tally avg's filter strides - tally_avg._update_filter_strides() - # Reshape condensed data arrays with one dimension for all filters mean = np.reshape(mean, tally_avg.shape) std_dev = np.reshape(std_dev, tally_avg.shape) @@ -3246,9 +3220,6 @@ def diagonalize_filter(self, new_filter): new_tally._std_dev = np.zeros(new_tally.shape, dtype=np.float64) new_tally._std_dev[diag_indices, :, :] = self.std_dev - # Update the new tally's filter strides - new_tally._update_filter_strides() - # If original tally was sparse, sparsify the diagonalized tally new_tally.sparse = self.sparse return new_tally From 2ef1c23d5a5d04ee733d28c1d7540c5dace8aad1 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 19 Dec 2017 21:54:08 +0700 Subject: [PATCH 14/15] Make Filter.num_bins a (usually) computed property --- openmc/filter.py | 74 +++++++++++++++++++---------------------------- openmc/tallies.py | 17 ++++------- 2 files changed, 34 insertions(+), 57 deletions(-) diff --git a/openmc/filter.py b/openmc/filter.py index fc133168d2d..260354d1900 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -2,8 +2,10 @@ from abc import ABCMeta from collections import Iterable, OrderedDict import copy +from functools import reduce import hashlib from numbers import Real, Integral +import operator from xml.etree import ElementTree as ET from six import add_metaclass @@ -95,7 +97,6 @@ class Filter(IDManagerMixin): def __init__(self, bins, filter_id=None): self.bins = bins self.id = filter_id - self._num_bins = 0 def __eq__(self, other): if type(self) is not type(other): @@ -170,7 +171,7 @@ def from_hdf5(cls, group, **kwargs): # there is no overriden from_hdf5 method. Pass the bins to __init__. if group['type'].value.decode() == cls.short_name.lower(): out = cls(group['bins'].value, filter_id) - out.num_bins = group['n_bins'].value + out._num_bins = group['n_bins'].value return out # Search through all subclasses and find the one matching the HDF5 @@ -188,7 +189,7 @@ def bins(self): @property def num_bins(self): - return self._num_bins + return len(self.bins) @bins.setter def bins(self, bins): @@ -200,12 +201,6 @@ def bins(self, bins): self._bins = bins - @num_bins.setter - def num_bins(self, num_bins): - cv.check_type('filter num_bins', num_bins, Integral) - cv.check_greater_than('filter num_bins', num_bins, 0, equality=True) - self._num_bins = num_bins - def check_bins(self, bins): """Make sure given bins are valid for this filter. @@ -430,17 +425,6 @@ def get_pandas_dataframe(self, data_size, stride, **kwargs): class WithIDFilter(Filter): """Abstract parent for filters of types with ids (Cell, Material, etc.).""" - @property - def num_bins(self): - return len(self.bins) - - # Since num_bins property is declared, also need a num_bins.setter, but - # we don't want it to do anything since num_bins is completely determined - # by len(self.bins). We also don't want to raise an error because that - # makes importing from HDF5 more complicated. - @num_bins.setter - def num_bins(self, num_bins): pass - def _smart_set_bins(self, bins, bin_type): # Format the bins as a 1D numpy array. bins = np.atleast_1d(bins) @@ -634,10 +618,6 @@ class SurfaceFilter(Filter): def bins(self): return self._bins - @property - def num_bins(self): - return len(self.bins) - @bins.setter def bins(self, bins): # Format the bins as a 1D numpy array. @@ -650,8 +630,12 @@ def bins(self, bins): self._bins = bins - @num_bins.setter - def num_bins(self, num_bins): pass + @property + def num_bins(self): + # Need to handle number of bins carefully -- for surface current + # tallies, the number of bins depends on the mesh, which we don't have a + # reference to in this filter + return self._num_bins def get_pandas_dataframe(self, data_size, stride, **kwargs): """Builds a Pandas DataFrame for the Filter's bins. @@ -737,7 +721,7 @@ def from_hdf5(cls, group, **kwargs): filter_id = int(group.name.split('/')[-1].lstrip('filter ')) out = cls(mesh_obj, filter_id) - out.num_bins = group['n_bins'].value + out._num_bins = group['n_bins'].value return out @@ -751,6 +735,13 @@ def mesh(self, mesh): self._mesh = mesh self.bins = mesh.id + @property + def num_bins(self): + try: + return self._num_bins + except AttributeError: + return reduce(operator.mul, self.mesh.dimension) + def check_bins(self, bins): if not len(bins) == 1: msg = 'Unable to add bins "{0}" to a MeshFilter since ' \ @@ -898,7 +889,7 @@ class RealFilter(Filter): A grid of bin values. id : int Unique identifier for the filter - num_bins : Integral + num_bins : int The number of filter bins """ @@ -915,12 +906,6 @@ def __gt__(self, other): def num_bins(self): return len(self.bins) - 1 - @num_bins.setter - def num_bins(self, num_bins): - cv.check_type('filter num_bins', num_bins, Integral) - cv.check_greater_than('filter num_bins', num_bins, 0, equality=True) - self._num_bins = num_bins - def can_merge(self, other): if type(self) is not type(other): return False @@ -1006,7 +991,7 @@ class EnergyFilter(RealFilter): A grid of energy values in eV. id : int Unique identifier for the filter - num_bins : Integral + num_bins : int The number of filter bins """ @@ -1104,7 +1089,7 @@ class EnergyoutFilter(EnergyFilter): A grid of energy values in eV. id : int Unique identifier for the filter - num_bins : Integral + num_bins : int The number of filter bins """ @@ -1163,7 +1148,7 @@ class DistribcellFilter(Filter): An iterable with one element---the ID of the distributed Cell. id : int Unique identifier for the filter - num_bins : Integral + num_bins : int The number of filter bins paths : list of str The paths traversed through the CSG tree to reach each distribcell @@ -1185,7 +1170,7 @@ def from_hdf5(cls, group, **kwargs): filter_id = int(group.name.split('/')[-1].lstrip('filter ')) out = cls(group['bins'].value, filter_id) - out.num_bins = group['n_bins'].value + out._num_bins = group['n_bins'].value return out @@ -1193,6 +1178,12 @@ def from_hdf5(cls, group, **kwargs): def bins(self): return self._bins + @property + def num_bins(self): + # Need to handle number of bins carefully -- for distribcell tallies, we + # need to know how many instances of the cell there are + return self._num_bins + @property def paths(self): return self._paths @@ -1703,10 +1694,6 @@ class DelayedGroupFilter(Filter): def bins(self): return self._bins - @property - def num_bins(self): - return len(self.bins) - @bins.setter def bins(self, bins): # Format the bins as a 1D numpy array. @@ -1719,9 +1706,6 @@ def bins(self, bins): self._bins = bins - @num_bins.setter - def num_bins(self, num_bins): pass - class EnergyFunctionFilter(Filter): """Multiplies tally scores by an arbitrary function of incident energy. diff --git a/openmc/tallies.py b/openmc/tallies.py index 9f8f7e505ea..52e7a03c528 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -3,9 +3,10 @@ from collections import Iterable, MutableSequence import copy import re -from functools import partial +from functools import partial, reduce from itertools import product from numbers import Integral, Real +import operator import warnings from xml.etree import ElementTree as ET @@ -185,19 +186,11 @@ def num_filters(self): @property def num_filter_bins(self): - num_bins = 1 - - for self_filter in self.filters: - num_bins *= self_filter.num_bins - - return num_bins + return reduce(operator.mul, (f.num_bins for f in self.filters), 1) @property def num_bins(self): - num_bins = self.num_filter_bins - num_bins *= self.num_nuclides - num_bins *= self.num_scores - return num_bins + return self.num_filter_bins * self.num_nuclides * self.num_scores @property def shape(self): @@ -2842,7 +2835,7 @@ def get_slice(self, scores=[], filters=[], filter_bins=[], nuclides=[]): num_bins += 1 find_filter.bins = np.unique(find_filter.bins[bin_indices]) - find_filter.num_bins = num_bins + find_filter._num_bins = num_bins # If original tally was sparse, sparsify the sliced tally new_tally.sparse = self.sparse From 55f7260b2a2fe3b2a3e03786bd65f6da52298410 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 20 Dec 2017 10:12:07 +0700 Subject: [PATCH 15/15] Remove _smart_set_bins in favor of overriding Filter.bins.setter --- openmc/filter.py | 78 +++++++++++++---------------------------------- openmc/tallies.py | 40 ++++++++++++++---------- 2 files changed, 45 insertions(+), 73 deletions(-) diff --git a/openmc/filter.py b/openmc/filter.py index 260354d1900..21bb64f8d55 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -14,7 +14,10 @@ import openmc import openmc.checkvalue as cv +from .cell import Cell +from .material import Material from .mixin import IDManagerMixin +from .universe import Universe _FILTER_TYPES = ['universe', 'material', 'cell', 'cellborn', 'surface', @@ -170,7 +173,7 @@ def from_hdf5(cls, group, **kwargs): # If the HDF5 'type' variable matches this class's short_name, then # there is no overriden from_hdf5 method. Pass the bins to __init__. if group['type'].value.decode() == cls.short_name.lower(): - out = cls(group['bins'].value, filter_id) + out = cls(group['bins'].value, filter_id=filter_id) out._num_bins = group['n_bins'].value return out @@ -425,12 +428,15 @@ def get_pandas_dataframe(self, data_size, stride, **kwargs): class WithIDFilter(Filter): """Abstract parent for filters of types with ids (Cell, Material, etc.).""" - def _smart_set_bins(self, bins, bin_type): + + @Filter.bins.setter + def bins(self, bins): # Format the bins as a 1D numpy array. bins = np.atleast_1d(bins) # Check the bin values. - cv.check_iterable_type('filter bins', bins, (Integral, bin_type)) + cv.check_iterable_type('filter bins', bins, + (Integral, self.expected_type)) for edge in bins: if isinstance(edge, Integral): cv.check_greater_than('filter bin', edge, 0, equality=True) @@ -463,13 +469,7 @@ class UniverseFilter(WithIDFilter): The number of filter bins """ - @property - def bins(self): - return self._bins - - @bins.setter - def bins(self, bins): - self._smart_set_bins(bins, openmc.Universe) + expected_type = Universe class MaterialFilter(WithIDFilter): @@ -493,13 +493,7 @@ class MaterialFilter(WithIDFilter): The number of filter bins """ - @property - def bins(self): - return self._bins - - @bins.setter - def bins(self, bins): - self._smart_set_bins(bins, openmc.Material) + expected_type = Material class CellFilter(WithIDFilter): @@ -523,13 +517,7 @@ class CellFilter(WithIDFilter): The number of filter bins """ - @property - def bins(self): - return self._bins - - @bins.setter - def bins(self, bins): - self._smart_set_bins(bins, openmc.Cell) + expected_type = Cell class CellFromFilter(WithIDFilter): @@ -553,13 +541,7 @@ class CellFromFilter(WithIDFilter): The number of filter bins """ - @property - def bins(self): - return self._bins - - @bins.setter - def bins(self, bins): - self._smart_set_bins(bins, openmc.Cell) + expected_type = Cell class CellbornFilter(WithIDFilter): @@ -583,13 +565,7 @@ class CellbornFilter(WithIDFilter): The number of filter bins """ - @property - def bins(self): - return self._bins - - @bins.setter - def bins(self, bins): - self._smart_set_bins(bins, openmc.Cell) + expected_type = Cell class SurfaceFilter(Filter): @@ -614,11 +590,7 @@ class SurfaceFilter(Filter): The number of filter bins """ - @property - def bins(self): - return self._bins - - @bins.setter + @Filter.bins.setter def bins(self, bins): # Format the bins as a 1D numpy array. bins = np.atleast_1d(bins) @@ -720,7 +692,7 @@ def from_hdf5(cls, group, **kwargs): mesh_obj = kwargs['meshes'][mesh_id] filter_id = int(group.name.split('/')[-1].lstrip('filter ')) - out = cls(mesh_obj, filter_id) + out = cls(mesh_obj, filter_id=filter_id) out._num_bins = group['n_bins'].value return out @@ -1169,15 +1141,11 @@ def from_hdf5(cls, group, **kwargs): filter_id = int(group.name.split('/')[-1].lstrip('filter ')) - out = cls(group['bins'].value, filter_id) + out = cls(group['bins'].value, filter_id=filter_id) out._num_bins = group['n_bins'].value return out - @property - def bins(self): - return self._bins - @property def num_bins(self): # Need to handle number of bins carefully -- for distribcell tallies, we @@ -1188,7 +1156,7 @@ def num_bins(self): def paths(self): return self._paths - @bins.setter + @Filter.bins.setter def bins(self, bins): # Format the bins as a 1D numpy array. bins = np.atleast_1d(bins) @@ -1690,11 +1658,7 @@ class DelayedGroupFilter(Filter): The number of filter bins """ - @property - def bins(self): - return self._bins - - @bins.setter + @Filter.bins.setter def bins(self, bins): # Format the bins as a 1D numpy array. bins = np.atleast_1d(bins) @@ -1799,7 +1763,7 @@ def from_hdf5(cls, group, **kwargs): y = group['y'].value filter_id = int(group.name.split('/')[-1].lstrip('filter ')) - return cls(energy, y, filter_id) + return cls(energy, y, filter_id=filter_id) @classmethod def from_tabulated1d(cls, tab1d): @@ -1836,7 +1800,7 @@ def y(self): @property def bins(self): - raise RuntimeError('EnergyFunctionFilters have no bins.') + raise AttributeError('EnergyFunctionFilters have no bins.') @property def num_bins(self): diff --git a/openmc/tallies.py b/openmc/tallies.py index 52e7a03c528..235ad2472e8 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -237,10 +237,10 @@ def sum(self): # Convert NumPy arrays to SciPy sparse LIL matrices if self.sparse: - self._sum = \ - sps.lil_matrix(self._sum.flatten(), self._sum.shape) - self._sum_sq = \ - sps.lil_matrix(self._sum_sq.flatten(), self._sum_sq.shape) + self._sum = sps.lil_matrix(self._sum.flatten(), + self._sum.shape) + self._sum_sq = sps.lil_matrix(self._sum_sq.flatten(), + self._sum_sq.shape) # Indicate that Tally results have been read self._results_read = True @@ -2817,25 +2817,33 @@ def get_slice(self, scores=[], filters=[], filter_bins=[], nuclides=[]): # Remove and/or reorder filter bins to user specifications bin_indices = [] - num_bins = 0 for filter_bin in filter_bins[i]: bin_index = find_filter.get_bin_index(filter_bin) - if filter_type in [openmc.EnergyFilter, - openmc.EnergyoutFilter]: - bin_indices.extend([bin_index]) + if issubclass(filter_type, openmc.RealFilter): bin_indices.extend([bin_index, bin_index+1]) - num_bins += 1 - elif filter_type in [openmc.DistribcellFilter, - openmc.MeshFilter]: - bin_indices = [0] - num_bins = find_filter.num_bins else: bin_indices.append(bin_index) - num_bins += 1 - find_filter.bins = np.unique(find_filter.bins[bin_indices]) - find_filter._num_bins = num_bins + # Set bins for mesh/distribcell filters apart from others + if filter_type is openmc.MeshFilter: + bins = find_filter.mesh + elif filter_type is openmc.DistribcellFilter: + bins = find_filter.bins + else: + bins = np.unique(find_filter.bins[bin_indices]) + + # Create new filter + new_filter = filter_type(bins) + + # Set number of bins manually for mesh/distribcell filters + if filter_type in (openmc.DistribcellFilter, openmc.MeshFilter): + new_filter._num_bins = find_filter._num_bins + + # Replace existing filter with new one + for j, test_filter in enumerate(new_tally.filters): + if isinstance(test_filter, filter_type): + new_tally.filters[j] = new_filter # If original tally was sparse, sparsify the sliced tally new_tally.sparse = self.sparse