From 7dd7af6dfa87676190bd7043b050a9f79f0652a8 Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Mon, 2 Mar 2020 18:54:03 +0000 Subject: [PATCH 001/205] Checks for -ve volume and adds it to Cell.__repl_ --- openmc/cell.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/openmc/cell.py b/openmc/cell.py index e01cb86137..792f152bef 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -4,6 +4,7 @@ from copy import deepcopy from math import cos, sin, pi from numbers import Real from xml.etree import ElementTree as ET +from uncertainties import UFloat import sys import warnings @@ -134,6 +135,7 @@ class Cell(IDManagerMixin): string += '\t{0: <15}=\t{1}\n'.format('Temperature', self.temperature) string += '{: <16}=\t{}\n'.format('\tTranslation', self.translation) + string += '{: <16}=\t{}\n'.format('\tVolume', self.volume) return string @@ -285,9 +287,18 @@ class Cell(IDManagerMixin): @volume.setter def volume(self, volume): if volume is not None: - cv.check_type('cell volume', volume, Real) + try: + cv.check_type('cell volume', volume, Real) + except TypeError: + cv.check_type('cell volume', volume, UFloat) + cv.check_greater_than('cell volume', volume, 0.0) self._volume = volume + # Forget now invalid info about atoms content + # (sice volume has just changed) + if self._atoms is not None: + self._atoms = None + def add_volume_information(self, volume_calc): """Add volume information to a cell. From c0f7459ff39c5735dd873cfb658a8885b363a603 Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Mon, 2 Mar 2020 19:41:56 +0000 Subject: [PATCH 002/205] Implement getter for Cell.atoms Make type checking in Cell.volume more compact --- openmc/cell.py | 36 +++++++++++++++++++++++++++++++----- 1 file changed, 31 insertions(+), 5 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index 792f152bef..3ecab7ba19 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -184,6 +184,35 @@ class Cell(IDManagerMixin): def volume(self): return self._volume + @property + def atoms(self): + if self._atoms is None: + if self._volume is None: + msg = 'Cannot calculate atoms content becouse no volume '\ + 'is set. Use Cell.volume to provide it or perform '\ + 'stochastic volume calculation.' + raise ValueError(msg) + + elif self._fill is None: + msg = 'Cell is filled with void. It contains no atoms.' + raise ValueError(msg) + + elif isinstance(self._fill, (openmc.Universe, openmc.Lattice)): + msg = 'Universe and Lattice cells can contain multiple '\ + 'materials. Atoms content must be calculated with '\ + 'stochastic volume calculation' + raise ValueError(msg) + + elif isinstance(self._fill, openmc.Material): + # Get atomic Densities + self._atoms = self._fill.get_nuclide_atom_densities() + + # Convert to total number of atoms + for key, nuclide in self._atoms.items(): + self._atoms[key] = (nuclide[0], nuclide[1] * self._volume) + + return self._atoms + @property def paths(self): if self._paths is None: @@ -287,14 +316,11 @@ class Cell(IDManagerMixin): @volume.setter def volume(self, volume): if volume is not None: - try: - cv.check_type('cell volume', volume, Real) - except TypeError: - cv.check_type('cell volume', volume, UFloat) + cv.check_type('cell volume', volume, (Real, UFloat)) cv.check_greater_than('cell volume', volume, 0.0) self._volume = volume - # Forget now invalid info about atoms content + # Info about atoms content can now be invalid # (sice volume has just changed) if self._atoms is not None: self._atoms = None From 36512e30968f1c46f29736c20b59719a26f5b10c Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Tue, 3 Mar 2020 15:54:28 +0000 Subject: [PATCH 003/205] Add tests for Cell.atoms and Cell.volume Remove unnecessary 'if' blocks Print volume in __repr__ only if its set to preserve regression Make shure that _atoms is forgotten for volume/fill changes --- openmc/cell.py | 52 +++++++++++------ tests/unit_tests/test_cell.py | 102 +++++++++++++++++++++++++++++++++- 2 files changed, 136 insertions(+), 18 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index 3ecab7ba19..67c224de5b 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -135,7 +135,10 @@ class Cell(IDManagerMixin): string += '\t{0: <15}=\t{1}\n'.format('Temperature', self.temperature) string += '{: <16}=\t{}\n'.format('\tTranslation', self.translation) - string += '{: <16}=\t{}\n'.format('\tVolume', self.volume) + + # Print Volume only when its set to avoid breaking regression + if self._volume is not None: + string += '{: <16}=\t{}\n'.format('\tVolume', self.volume) return string @@ -188,28 +191,33 @@ class Cell(IDManagerMixin): def atoms(self): if self._atoms is None: if self._volume is None: - msg = 'Cannot calculate atoms content becouse no volume '\ - 'is set. Use Cell.volume to provide it or perform '\ - 'stochastic volume calculation.' + msg = ('Cannot calculate atoms content becouse no volume ' + 'is set. Use Cell.volume to provide it or perform ' + 'stochastic volume calculation.') raise ValueError(msg) - elif self._fill is None: - msg = 'Cell is filled with void. It contains no atoms.' + elif self.fill_type == 'void': + msg = ('Cell is filled with void. It contains no atoms. ' + 'Material must be set to calculate atoms content.') raise ValueError(msg) - elif isinstance(self._fill, (openmc.Universe, openmc.Lattice)): - msg = 'Universe and Lattice cells can contain multiple '\ - 'materials. Atoms content must be calculated with '\ - 'stochastic volume calculation' + elif self.fill_type != 'material': + msg = ('Universe, Lattice and Distributed Material cells can ' + 'contain multiple materials. Atoms content must be ' + 'calculated with stochastic volume calculation') raise ValueError(msg) - elif isinstance(self._fill, openmc.Material): + elif self.fill_type == 'material': # Get atomic Densities self._atoms = self._fill.get_nuclide_atom_densities() # Convert to total number of atoms for key, nuclide in self._atoms.items(): - self._atoms[key] = (nuclide[0], nuclide[1] * self._volume) + atom_num = nuclide[1] * self._volume * 1.0E+24 + self._atoms[key] = (nuclide[0], atom_num) + else: + msg = 'Unrecognised fill_type:{}'.format(self.fill_type) + raise ValueError(msg) return self._atoms @@ -254,12 +262,16 @@ class Cell(IDManagerMixin): elif not isinstance(fill, (openmc.Material, openmc.Lattice, openmc.Universe)): - msg = 'Unable to set Cell ID="{0}" to use a non-Material or ' \ - 'Universe fill "{1}"'.format(self._id, fill) + msg = ('Unable to set Cell ID="{0}" to use a non-Material or ' + 'Universe fill "{1}"'.format(self._id, fill)) raise ValueError(msg) self._fill = fill + # Info about atoms content can now be invalid + # (sice fill has just changed) + self._atoms = None + @rotation.setter def rotation(self, rotation): cv.check_length('cell rotation', rotation, 3) @@ -317,13 +329,19 @@ class Cell(IDManagerMixin): def volume(self, volume): if volume is not None: cv.check_type('cell volume', volume, (Real, UFloat)) - cv.check_greater_than('cell volume', volume, 0.0) + + # Note that ufloat(0.0, 0.1) >= 0.0 is False + # we need special treatment for UFloat input + val = volume + if isinstance(val, UFloat): + val = volume.nominal_value + cv.check_greater_than('cell volume', val, 0.0) + self._volume = volume # Info about atoms content can now be invalid # (sice volume has just changed) - if self._atoms is not None: - self._atoms = None + self._atoms = None def add_volume_information(self, volume_calc): """Add volume information to a cell. diff --git a/tests/unit_tests/test_cell.py b/tests/unit_tests/test_cell.py index 6ac4ae9c07..03b03c6e98 100644 --- a/tests/unit_tests/test_cell.py +++ b/tests/unit_tests/test_cell.py @@ -1,10 +1,16 @@ import xml.etree. ElementTree as ET import numpy as np +import uncertainties as u import openmc import pytest + from tests.unit_tests import assert_unbounded +from openmc.data import atomic_mass, AVOGADRO + +# Relative tolerance for float comparison +TOL = 1e-9 def test_contains(): @@ -29,6 +35,14 @@ def test_repr(cell_with_lattice): c = openmc.Cell() repr(c) + # Empty cell with Volume + c.volume = 3.0 + repr(c) + + # Empty cell with Uncertain Volume + c.volume = u.ufloat(3.0, 0.2) + repr(c) + def test_bounding_box(): zcyl = openmc.ZCylinder() @@ -112,6 +126,92 @@ def test_get_nuclides(uo2): assert nucs == ['U235', 'O16'] +def test_volume_setting(): + c = openmc.Cell() + + # Test ordinary volume and uncertain volume + c.volume = 3 + c.volume = u.ufloat(3, 0.7) + + # Test errors for -ve and 0 volume + with pytest.raises(ValueError): + c.volume = 0.0 + with pytest.raises(ValueError): + c.volume = -1.0 + with pytest.raises(ValueError): + c.volume = u.ufloat(0.0, 0.1) + with pytest.raises(ValueError): + c.volume = u.ufloat(-0.05, 0.1) + + +def test_atoms_of_material_cell(uo2, water): + """ Test if correct number of atoms is returned. + Also check if Cell.atoms still works after volume/material was changed + """ + c = openmc.Cell(fill=uo2) + c.volume = 2.0 + expected_nucs = ['U235', 'O16'] + + # Precalculate the expected number of atoms + molarMass = ((atomic_mass('U235') + 2 * atomic_mass('O16'))/3) + expected_atoms = list() + expected_atoms.append(1/3 * uo2.density/molarMass * AVOGADRO * 2.0) # U235 + expected_atoms.append(2/3 * uo2.density/molarMass * AVOGADRO * 2.0) # O16 + + tuples = list(c.atoms.values()) + for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): + assert nuc == t[0] + assert atom_num == t[1] + + # Change volume and check if OK + c.volume = 3.0 + expected_atoms = list() + expected_atoms.append(1/3 * uo2.density/molarMass * AVOGADRO * 3.0) # U235 + expected_atoms.append(2/3 * uo2.density/molarMass * AVOGADRO * 3.0) # O16 + + tuples = list(c.atoms.values()) + for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): + assert nuc == t[0] + assert atom_num == pytest.approx(t[1], rel=TOL) + + # Change material and check if OK + c.fill = water + expected_nucs = ['H1', 'O16'] + molarMass = ((2 * atomic_mass('H1') + atomic_mass('O16'))/3) + expected_atoms = list() + expected_atoms.append(2/3 * water.density/molarMass * AVOGADRO * 3.0) # H1 + expected_atoms.append(1/3 * water.density/molarMass * AVOGADRO * 3.0) # O16 + + tuples = list(c.atoms.values()) + for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): + assert nuc == t[0] + assert atom_num == pytest.approx(t[1], rel=TOL) + + +def test_atoms_errors(cell_with_lattice): + cells, mats, univ, lattice = cell_with_lattice + + # Distributed Material + with pytest.raises(ValueError): + cells[0].volume = 2 + cells[0].atoms + + # Material Cell with no Volume + with pytest.raises(ValueError): + cells[1].atoms + + # Cell with lattice + with pytest.raises(ValueError): + cells[2].volume = 3 + cells[2].atoms + + # Cell with volume but with Void fill + with pytest.raises(ValueError): + cells[1].volume = 2 + cells[1].fill = None + cells[1].atoms + + def test_nuclide_densities(uo2): c = openmc.Cell(fill=uo2) expected_nucs = ['U235', 'O16'] @@ -119,7 +219,7 @@ def test_nuclide_densities(uo2): tuples = list(c.get_nuclide_densities().values()) for nuc, density, t in zip(expected_nucs, expected_density, tuples): assert nuc == t[0] - assert density == t[1] + assert density == pytest.approx(t[1], rel=TOL) # Empty cell c = openmc.Cell() From 023f9e46900629ff36d6b13e33b39dda60846072 Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Tue, 3 Mar 2020 19:02:54 +0000 Subject: [PATCH 004/205] Add support for 'distribmat' cells to Cell.atoms Provide missing atoms entry in Cell Attributes Create docstring for Cell.atoms --- openmc/cell.py | 49 ++++++++++++++++++++++++++++----- tests/unit_tests/test_cell.py | 52 +++++++++++++++++++++++------------ 2 files changed, 77 insertions(+), 24 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index 67c224de5b..f2884e494b 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -86,7 +86,12 @@ class Cell(IDManagerMixin): volume : float Volume of the cell in cm^3. This can either be set manually or calculated in a stochastic volume calculation and added via the - :meth:`Cell.add_volume_information` method. + :meth:`Cell.add_volume_information` method. For 'distribmat' cells + it is a total volume of all instances. + atoms : dict + Mapping of nuclides to total number of atoms for each nuclide present + in the cell, or all its instances for 'sdistribmat' fill. For example, + {'U235': 1.0e22, 'U238': 5.0e22, ...}. """ @@ -189,6 +194,21 @@ class Cell(IDManagerMixin): @property def atoms(self): + """ Get total number of atoms of each nuclide in the cell + + Returns + ------- + atoms: collections.OrderedDict + Dictionary which keys are nuclides and values the number of atoms. + For example, {'H1':1.0e22, 'O16':0.5e22, ...} + + Raises + ------ + ValueError + If total volume of the cell is not set + ValueError + If cell is filled with Universe, Lattice or Void + """ if self._atoms is None: if self._volume is None: msg = ('Cannot calculate atoms content becouse no volume ' @@ -201,10 +221,10 @@ class Cell(IDManagerMixin): 'Material must be set to calculate atoms content.') raise ValueError(msg) - elif self.fill_type != 'material': - msg = ('Universe, Lattice and Distributed Material cells can ' - 'contain multiple materials. Atoms content must be ' - 'calculated with stochastic volume calculation') + elif self.fill_type in ['lattice', 'universe']: + msg = ('Universe and Lattice cells can contain multiple ' + 'materials in diffrent proportions. Atoms content must ' + 'be calculated with stochastic volume calculation') raise ValueError(msg) elif self.fill_type == 'material': @@ -213,8 +233,23 @@ class Cell(IDManagerMixin): # Convert to total number of atoms for key, nuclide in self._atoms.items(): - atom_num = nuclide[1] * self._volume * 1.0E+24 - self._atoms[key] = (nuclide[0], atom_num) + atom = nuclide[1] * self._volume * 1.0e+24 + self._atoms[key] = atom + + elif self.fill_type == 'distribmat': + # Assumes that volume is total volume of all instances + # Also assumes that all instances have the same volume + partial_volume = self.volume / len(self.fill) + self._atoms = OrderedDict() + for mat in self.fill: + for key, nuclide in mat.get_nuclide_atom_densities().items(): + # To account for overlap of nuclides between distribmat + # we need to append new atoms # to any existing value + # hence it is necessary to ask for default. + atom = self._atoms.setdefault(key, 0) + atom += nuclide[1] * partial_volume * 1.0e+24 + self._atoms[key] = atom + else: msg = 'Unrecognised fill_type:{}'.format(self.fill_type) raise ValueError(msg) diff --git a/tests/unit_tests/test_cell.py b/tests/unit_tests/test_cell.py index 03b03c6e98..d682c29c79 100644 --- a/tests/unit_tests/test_cell.py +++ b/tests/unit_tests/test_cell.py @@ -144,7 +144,7 @@ def test_volume_setting(): c.volume = u.ufloat(-0.05, 0.1) -def test_atoms_of_material_cell(uo2, water): +def test_atoms_material_cell(uo2, water): """ Test if correct number of atoms is returned. Also check if Cell.atoms still works after volume/material was changed """ @@ -153,12 +153,12 @@ def test_atoms_of_material_cell(uo2, water): expected_nucs = ['U235', 'O16'] # Precalculate the expected number of atoms - molarMass = ((atomic_mass('U235') + 2 * atomic_mass('O16'))/3) + M = ((atomic_mass('U235') + 2 * atomic_mass('O16'))/3) expected_atoms = list() - expected_atoms.append(1/3 * uo2.density/molarMass * AVOGADRO * 2.0) # U235 - expected_atoms.append(2/3 * uo2.density/molarMass * AVOGADRO * 2.0) # O16 + expected_atoms.append(1/3 * uo2.density/M * AVOGADRO * 2.0) # U235 + expected_atoms.append(2/3 * uo2.density/M * AVOGADRO * 2.0) # O16 - tuples = list(c.atoms.values()) + tuples = list(c.atoms.items()) for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] assert atom_num == t[1] @@ -166,10 +166,10 @@ def test_atoms_of_material_cell(uo2, water): # Change volume and check if OK c.volume = 3.0 expected_atoms = list() - expected_atoms.append(1/3 * uo2.density/molarMass * AVOGADRO * 3.0) # U235 - expected_atoms.append(2/3 * uo2.density/molarMass * AVOGADRO * 3.0) # O16 + expected_atoms.append(1/3 * uo2.density/M * AVOGADRO * 3.0) # U235 + expected_atoms.append(2/3 * uo2.density/M * AVOGADRO * 3.0) # O16 - tuples = list(c.atoms.values()) + tuples = list(c.atoms.items()) for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] assert atom_num == pytest.approx(t[1], rel=TOL) @@ -177,12 +177,35 @@ def test_atoms_of_material_cell(uo2, water): # Change material and check if OK c.fill = water expected_nucs = ['H1', 'O16'] - molarMass = ((2 * atomic_mass('H1') + atomic_mass('O16'))/3) + M = ((2 * atomic_mass('H1') + atomic_mass('O16'))/3) expected_atoms = list() - expected_atoms.append(2/3 * water.density/molarMass * AVOGADRO * 3.0) # H1 - expected_atoms.append(1/3 * water.density/molarMass * AVOGADRO * 3.0) # O16 + expected_atoms.append(2/3 * water.density/M * AVOGADRO * 3.0) # H1 + expected_atoms.append(1/3 * water.density/M * AVOGADRO * 3.0) # O16 - tuples = list(c.atoms.values()) + tuples = list(c.atoms.items()) + for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): + assert nuc == t[0] + assert atom_num == pytest.approx(t[1], rel=TOL) + + +def test_atoms_distribmat_cell(uo2, water): + """ Test if correct number of atoms is returned for a cell with + 'distribmat' fill + """ + c = openmc.Cell(fill=[uo2, water]) + c.volume = 6.0 + + # Calculate the expected number of atoms + expected_nucs = ['U235', 'O16', 'H1'] + M_uo2 = ((atomic_mass('U235') + 2 * atomic_mass('O16'))/3) + M_water = ((2 * atomic_mass('H1') + atomic_mass('O16'))/3) + expected_atoms = list() + expected_atoms.append(1/3 * uo2.density/M_uo2 * AVOGADRO * 3.0) # U235 + expected_atoms.append(2/3 * uo2.density/M_uo2 * AVOGADRO * 3.0 + + 1/3 * water.density/M_water * AVOGADRO * 3.0) # O16 + expected_atoms.append(2/3 * water.density/M_water * AVOGADRO * 3.0) # H1 + + tuples = list(c.atoms.items()) for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] assert atom_num == pytest.approx(t[1], rel=TOL) @@ -191,11 +214,6 @@ def test_atoms_of_material_cell(uo2, water): def test_atoms_errors(cell_with_lattice): cells, mats, univ, lattice = cell_with_lattice - # Distributed Material - with pytest.raises(ValueError): - cells[0].volume = 2 - cells[0].atoms - # Material Cell with no Volume with pytest.raises(ValueError): cells[1].atoms From a9dcbc657fa23c741f9af3d790d0c14db04e4693 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Fri, 6 Mar 2020 22:14:19 +0300 Subject: [PATCH 005/205] Fix the bug on MGXS mode with Interpolation method This PR closed the Issue #1512. The problem related with incorrect initialization 'xt::xarray available_temps(num_temps)' which have been lead to available_temps array elements to have the same address. I fixed it according to https://xtensor.readthedocs.io/en/latest/container.html with shape vector. In addition I found interpolation procedure themselves didn't work right and I repaired it too. --- src/mgxs.cpp | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/src/mgxs.cpp b/src/mgxs.cpp index 163954caae..d6f1f45abb 100644 --- a/src/mgxs.cpp +++ b/src/mgxs.cpp @@ -82,13 +82,14 @@ Mgxs::metadata_from_hdf5(hid_t xs_id, const std::vector& temperature, // Determine the available temperatures hid_t kT_group = open_group(xs_id, "kTs"); - int num_temps = get_num_datasets(kT_group); + size_t num_temps = get_num_datasets(kT_group); char** dset_names = new char*[num_temps]; for (int i = 0; i < num_temps; i++) { dset_names[i] = new char[151]; } get_datasets(kT_group, dset_names); - xt::xarray available_temps(num_temps); + std::vector shape = {num_temps}; + xt::xarray available_temps(shape); for (int i = 0; i < num_temps; i++) { read_double(kT_group, dset_names[i], &available_temps[i], true); @@ -131,7 +132,12 @@ Mgxs::metadata_from_hdf5(hid_t xs_id, const std::vector& temperature, case TemperatureMethod::INTERPOLATION: for (int i = 0; i < temperature.size(); i++) { - for (int j = 0; j < num_temps - 1; j++) { + for (int j = 0; j < num_temps; j++) { + if (j == (num_temps - 1)) { + fatal_error("MGXS Library does not contain cross sections for " + + in_name + " at temperatures that bound " + + std::to_string(std::round(temperature[i]))); + } if ((available_temps[j] <= temperature[i]) && (temperature[i] < available_temps[j + 1])) { if (std::find(temps_to_read.begin(), @@ -144,13 +150,10 @@ Mgxs::metadata_from_hdf5(hid_t xs_id, const std::vector& temperature, std::round(available_temps[j + 1])) == temps_to_read.end()) { temps_to_read.push_back(std::round((int) available_temps[j + 1])); } - continue; + break; } } - fatal_error("MGXS Library does not contain cross sections for " + - in_name + " at temperatures that bound " + - std::to_string(std::round(temperature[i]))); } } std::sort(temps_to_read.begin(), temps_to_read.end()); @@ -380,16 +383,17 @@ Mgxs::Mgxs(const std::string& in_name, const std::vector& mat_kTs, // If we are doing nearest temperature interpolation, then we don't need // to do the 2nd temperature int num_interp_points = 2; + std::vector interp(micros.size()); + std::vector temp_indices(micros.size()); if (settings::temperature_method == TemperatureMethod::NEAREST) num_interp_points = 1; for (int interp_point = 0; interp_point < num_interp_points; interp_point++) { - std::vector interp(micros.size()); - std::vector temp_indices(micros.size()); for (int m = 0; m < micros.size(); m++) { interp[m] = (1. - micro_t_interp[m]) * atom_densities[m]; temp_indices[m] = micro_t[m] + interp_point; + micro_t_interp[m] = 1. - micro_t_interp[m]; } - combine(micros, interp, micro_t, t); + combine(micros, interp, temp_indices, t); } // end loop to sum all micros across the temperatures } // end temperature (t) loop } From 51581341f19c2c5a6b23d3d933062a9e3307d783 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Sun, 8 Mar 2020 00:34:11 +0300 Subject: [PATCH 006/205] some corrections --- src/mgxs.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mgxs.cpp b/src/mgxs.cpp index d6f1f45abb..d5c144658d 100644 --- a/src/mgxs.cpp +++ b/src/mgxs.cpp @@ -383,9 +383,9 @@ Mgxs::Mgxs(const std::string& in_name, const std::vector& mat_kTs, // If we are doing nearest temperature interpolation, then we don't need // to do the 2nd temperature int num_interp_points = 2; + if (settings::temperature_method == TemperatureMethod::NEAREST) num_interp_points = 1; std::vector interp(micros.size()); std::vector temp_indices(micros.size()); - if (settings::temperature_method == TemperatureMethod::NEAREST) num_interp_points = 1; for (int interp_point = 0; interp_point < num_interp_points; interp_point++) { for (int m = 0; m < micros.size(); m++) { interp[m] = (1. - micro_t_interp[m]) * atom_densities[m]; From 275512bb0e964a6f6c385e4df18699aae26f637a Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Wed, 11 Mar 2020 18:40:51 +0000 Subject: [PATCH 007/205] Add documentation for atoms calculations via cell --- docs/source/usersguide/geometry.rst | 33 ++++++++++++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index dc7dd978eb..be2c92a12d 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -183,9 +183,13 @@ the :class:`openmc.Cell` class:: fuel.fill = uo2 fuel.region = pellet + # This cell will be filled with void on export to XML + gap = openmc.Cell(region=pellet_gap) + In this example, an instance of :class:`openmc.Material` is assigned to the :attr:`Cell.fill` attribute. One can also fill a cell with a :ref:`universe -` or :ref:`lattice `. +` or :ref:`lattice `. If you provide +no fill to a cell, it will be filled with void on export to XML. The classes :class:`Halfspace`, :class:`Intersection`, :class:`Union`, and :class:`Complement` and all instances of :class:`openmc.Region` and can be @@ -434,3 +438,30 @@ named ``dagmc.h5m``) when initializing a simulation. If a `geometry.xml `_. Future implementations of DAGMC geometry will support small volume overlaps and un-merged surfaces. + +------------------------- +Calculating Atoms Content +------------------------- + +If the total volume occupied by all instances of a cell in a geometry is known +by a user, it is possible to assign it to a cell without a stochastic volume +calculation:: + + from uncertainties import ufloat + + # Set known total volume in [cc] + cell = openmc.Cell() + cell.volume = 17.0 + + # Set volume if it is known with some uncertainty + cell.volume = ufloat(17.0, 0.1) + +Once a volume is set and a cell is filled with a material or distributed +materials. It is possible to use :func:`~openmc.Cell.atoms` method to obtain +a dictionary that maps nuclides to a total number of atoms in all instances +of a cell (e.g. ``{'H1':1.0e22, 'O16':0.5e22, ...}``):: + + cell = openmc.Cell(fill = u02) + cell.volume = 17.0 + + O16_atoms = cell.atoms['O16'] From e98c50fcef416b484930d2b6925985d4dab10151 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Fri, 13 Mar 2020 01:03:51 +0300 Subject: [PATCH 008/205] start with initial value read a current this->matrix before combined --- src/scattdata.cpp | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/src/scattdata.cpp b/src/scattdata.cpp index 4fd7a7b7c4..e11b085048 100644 --- a/src/scattdata.cpp +++ b/src/scattdata.cpp @@ -65,7 +65,10 @@ ScattData::base_combine(size_t max_order, xt::xtensor this_matrix({groups, groups, max_order}, 0.); xt::xtensor mult_numer({groups, groups}, 0.); xt::xtensor mult_denom({groups, groups}, 0.); - + // TODO: Need to review this: + if (this->scattxs.size() > 0) { + this_matrix = this->get_matrix(max_order); + } // Build the dense scattering and multiplicity matrices // Get the multiplicity_matrix // To combine from nuclidic data we need to use the final relationship @@ -110,7 +113,19 @@ ScattData::base_combine(size_t max_order, // Combine mult_numer and mult_denom into the combined multiplicity matrix xt::xtensor this_mult({groups, groups}, 1.); - this_mult = xt::nan_to_num(mult_numer / mult_denom); + // TODO: Need to check this too + xt::xtensor this_mult({groups, groups}, 1.); + for (int gin = 0; gin < groups; gin++) { + for (int gout = 0; gout < groups; gout++) { + if (std::abs(mult_denom(gin, gout)) > 0.0) { + this_mult(gin, gout) = mult_numer(gin, gout) / mult_denom(gin, gout); + } else { + if (mult_numer(gin, gout) == 0.0) { + this_mult(gin, gout) = 1.0; + } + } + } + } // We have the data, now we need to convert to a jagged array and then use // the initialize function to store it on the object. From 4a0c8ee2b7211618dc6fd7c0a1191fb1b8c46ac7 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Fri, 13 Mar 2020 01:24:42 +0300 Subject: [PATCH 009/205] delete a line doubling --- src/scattdata.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/scattdata.cpp b/src/scattdata.cpp index e11b085048..e3c97512c3 100644 --- a/src/scattdata.cpp +++ b/src/scattdata.cpp @@ -114,7 +114,6 @@ ScattData::base_combine(size_t max_order, // Combine mult_numer and mult_denom into the combined multiplicity matrix xt::xtensor this_mult({groups, groups}, 1.); // TODO: Need to check this too - xt::xtensor this_mult({groups, groups}, 1.); for (int gin = 0; gin < groups; gin++) { for (int gout = 0; gout < groups; gout++) { if (std::abs(mult_denom(gin, gout)) > 0.0) { From 24f704849419e4544531f3a7d5dcc9c52975bfd7 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 4 Sep 2019 16:32:50 -0500 Subject: [PATCH 010/205] Update pincell python example --- examples/python/pincell/build-xml.py | 99 +++++++++++----------------- openmc/settings.py | 4 -- 2 files changed, 38 insertions(+), 65 deletions(-) diff --git a/examples/python/pincell/build-xml.py b/examples/python/pincell/build-xml.py index 072cc911f0..dc6df91631 100644 --- a/examples/python/pincell/build-xml.py +++ b/examples/python/pincell/build-xml.py @@ -16,23 +16,23 @@ particles = 1000 # Instantiate some Materials and register the appropriate Nuclides -uo2 = openmc.Material(material_id=1, name='UO2 fuel at 2.4% wt enrichment') +uo2 = openmc.Material(name='UO2 fuel at 2.4% wt enrichment') uo2.set_density('g/cm3', 10.29769) uo2.add_element('U', 1., enrichment=2.4) uo2.add_element('O', 2.) -helium = openmc.Material(material_id=2, name='Helium for gap') +helium = openmc.Material(name='Helium for gap') helium.set_density('g/cm3', 0.001598) helium.add_element('He', 2.4044e-4) -zircaloy = openmc.Material(material_id=3, name='Zircaloy 4') +zircaloy = openmc.Material(name='Zircaloy 4') zircaloy.set_density('g/cm3', 6.55) zircaloy.add_element('Sn', 0.014 , 'wo') zircaloy.add_element('Fe', 0.00165, 'wo') zircaloy.add_element('Cr', 0.001 , 'wo') zircaloy.add_element('Zr', 0.98335, 'wo') -borated_water = openmc.Material(material_id=4, name='Borated water') +borated_water = openmc.Material(name='Borated water') borated_water.set_density('g/cm3', 0.740582) borated_water.add_element('B', 4.0e-5) borated_water.add_element('H', 5.0e-2) @@ -40,54 +40,31 @@ borated_water.add_element('O', 2.4e-2) borated_water.add_s_alpha_beta('c_H_in_H2O') # Instantiate a Materials collection and export to XML -materials_file = openmc.Materials([uo2, helium, zircaloy, borated_water]) -materials_file.export_to_xml() +materials = openmc.Materials([uo2, helium, zircaloy, borated_water]) +materials.export_to_xml() ############################################################################### # Exporting to OpenMC geometry.xml file ############################################################################### -# Instantiate ZCylinder surfaces -fuel_or = openmc.ZCylinder(surface_id=1, x0=0, y0=0, r=0.39218, name='Fuel OR') -clad_ir = openmc.ZCylinder(surface_id=2, x0=0, y0=0, r=0.40005, name='Clad IR') -clad_or = openmc.ZCylinder(surface_id=3, x0=0, y0=0, r=0.45720, name='Clad OR') -left = openmc.XPlane(surface_id=4, x0=-0.62992, name='left') -right = openmc.XPlane(surface_id=5, x0=0.62992, name='right') -bottom = openmc.YPlane(surface_id=6, y0=-0.62992, name='bottom') -top = openmc.YPlane(surface_id=7, y0=0.62992, name='top') +# Create cylindrical surfaces +fuel_or = openmc.ZCylinder(r=0.39218, name='Fuel OR') +clad_ir = openmc.ZCylinder(r=0.40005, name='Clad IR') +clad_or = openmc.ZCylinder(r=0.45720, name='Clad OR') -left.boundary_type = 'reflective' -right.boundary_type = 'reflective' -top.boundary_type = 'reflective' -bottom.boundary_type = 'reflective' +# Create a region represented as the inside of a rectangular prism +pitch = 1.25984 +box = openmc.rectangular_prism(pitch, pitch, boundary_type='reflective') -# Instantiate Cells -fuel = openmc.Cell(cell_id=1, name='cell 1') -gap = openmc.Cell(cell_id=2, name='cell 2') -clad = openmc.Cell(cell_id=3, name='cell 3') -water = openmc.Cell(cell_id=4, name='cell 4') +# Create cells, mapping materials to regions +fuel = openmc.Cell(fill=uo2, region=-fuel_or) +gap = openmc.Cell(fill=helium, region=+fuel_or & -clad_ir) +clad = openmc.Cell(fill=zircaloy, region=+clad_ir & -clad_or) +water = openmc.Cell(fill=borated_water, region=+clad_or & box) -# Use surface half-spaces to define regions -fuel.region = -fuel_or -gap.region = +fuel_or & -clad_ir -clad.region = +clad_ir & -clad_or -water.region = +clad_or & +left & -right & +bottom & -top - -# Register Materials with Cells -fuel.fill = uo2 -gap.fill = helium -clad.fill = zircaloy -water.fill = borated_water - -# Instantiate Universe -root = openmc.Universe(universe_id=0, name='root universe') - -# Register Cells with Universe -root.add_cells([fuel, gap, clad, water]) - -# Instantiate a Geometry, register the root Universe, and export to XML -geometry = openmc.Geometry(root) +# Create a geometry and export to XML +geometry = openmc.Geometry([fuel, gap, clad, water]) geometry.export_to_xml() @@ -96,22 +73,22 @@ geometry.export_to_xml() ############################################################################### # Instantiate a Settings object, set all runtime parameters, and export to XML -settings_file = openmc.Settings() -settings_file.batches = batches -settings_file.inactive = inactive -settings_file.particles = particles +settings = openmc.Settings() +settings.batches = batches +settings.inactive = inactive +settings.particles = particles # Create an initial uniform spatial source distribution over fissionable zones -bounds = [-0.62992, -0.62992, -1, 0.62992, 0.62992, 1] +bounds = [-pitch/2, -pitch/2, -1, pitch/2, pitch/2, 1] uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) -settings_file.source = openmc.source.Source(space=uniform_dist) +settings.source = openmc.source.Source(space=uniform_dist) entropy_mesh = openmc.RegularMesh() -entropy_mesh.lower_left = [-0.39218, -0.39218, -1.e50] -entropy_mesh.upper_right = [0.39218, 0.39218, 1.e50] -entropy_mesh.dimension = [10, 10, 1] -settings_file.entropy_mesh = entropy_mesh -settings_file.export_to_xml() +entropy_mesh.lower_left = (-fuel_or.r, -fuel_or.r) +entropy_mesh.upper_right = (fuel_or.r, fuel_or.r) +entropy_mesh.dimension = (10, 10) +settings.entropy_mesh = entropy_mesh +settings.export_to_xml() ############################################################################### @@ -120,19 +97,19 @@ settings_file.export_to_xml() # Instantiate a tally mesh mesh = openmc.RegularMesh() -mesh.dimension = [100, 100, 1] -mesh.lower_left = [-0.62992, -0.62992, -1.e50] -mesh.upper_right = [0.62992, 0.62992, 1.e50] +mesh.dimension = (100, 100) +mesh.lower_left = (-pitch/2, -pitch/2) +mesh.upper_right = (pitch/2, pitch/2) # Instantiate some tally Filters -energy_filter = openmc.EnergyFilter([0., 4., 20.e6]) +energy_filter = openmc.EnergyFilter((0., 4., 20.e6)) mesh_filter = openmc.MeshFilter(mesh) # Instantiate the Tally -tally = openmc.Tally(tally_id=1, name='tally 1') +tally = openmc.Tally() tally.filters = [energy_filter, mesh_filter] tally.scores = ['flux', 'fission', 'nu-fission'] # Instantiate a Tallies collection and export to XML -tallies_file = openmc.Tallies([tally]) -tallies_file.export_to_xml() +tallies = openmc.Tallies([tally]) +tallies.export_to_xml() diff --git a/openmc/settings.py b/openmc/settings.py index 794c97b618..cddef9255f 100644 --- a/openmc/settings.py +++ b/openmc/settings.py @@ -612,10 +612,6 @@ class Settings(object): @entropy_mesh.setter def entropy_mesh(self, entropy): cv.check_type('entropy mesh', entropy, RegularMesh) - if entropy.dimension: - cv.check_length('entropy mesh dimension', entropy.dimension, 3) - cv.check_length('entropy mesh lower-left corner', entropy.lower_left, 3) - cv.check_length('entropy mesh upper-right corner', entropy.upper_right, 3) self._entropy_mesh = entropy @trigger_active.setter From eeb7925376dda670e2ed2e301c63c7bce9014a8e Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Sat, 15 Feb 2020 11:40:25 -0600 Subject: [PATCH 011/205] Remove basic and boxes examples --- examples/python/basic/build-xml.py | 121 ---------------------------- examples/python/boxes/build-xml.py | 125 ----------------------------- examples/xml/basic/geometry.xml | 15 ---- examples/xml/basic/materials.xml | 16 ---- examples/xml/basic/settings.xml | 16 ---- examples/xml/basic/tallies.xml | 31 ------- examples/xml/boxes/geometry.xml | 39 --------- examples/xml/boxes/materials.xml | 21 ----- examples/xml/boxes/plots.xml | 9 --- examples/xml/boxes/settings.xml | 15 ---- 10 files changed, 408 deletions(-) delete mode 100644 examples/python/basic/build-xml.py delete mode 100644 examples/python/boxes/build-xml.py delete mode 100644 examples/xml/basic/geometry.xml delete mode 100644 examples/xml/basic/materials.xml delete mode 100644 examples/xml/basic/settings.xml delete mode 100644 examples/xml/basic/tallies.xml delete mode 100644 examples/xml/boxes/geometry.xml delete mode 100644 examples/xml/boxes/materials.xml delete mode 100644 examples/xml/boxes/plots.xml delete mode 100644 examples/xml/boxes/settings.xml diff --git a/examples/python/basic/build-xml.py b/examples/python/basic/build-xml.py deleted file mode 100644 index 5caed28104..0000000000 --- a/examples/python/basic/build-xml.py +++ /dev/null @@ -1,121 +0,0 @@ -import openmc - - -############################################################################### -# Simulation Input File Parameters -############################################################################### - -# OpenMC simulation parameters -batches = 15 -inactive = 5 -particles = 10000 - - -############################################################################### -# Exporting to OpenMC materials.xml file -############################################################################### - -# Instantiate some Materials and register the appropriate Nuclides -moderator = openmc.Material(material_id=41, name='moderator') -moderator.set_density('g/cc', 1.0) -moderator.add_element('H', 2.) -moderator.add_element('O', 1.) -moderator.add_s_alpha_beta('c_H_in_H2O') - -fuel = openmc.Material(material_id=40, name='fuel') -fuel.set_density('g/cc', 4.5) -fuel.add_nuclide('U235', 1.) - -# Instantiate a Materials collection and export to XML -materials_file = openmc.Materials([moderator, fuel]) -materials_file.export_to_xml() - - -############################################################################### -# Exporting to OpenMC geometry.xml file -############################################################################### - -# Instantiate ZCylinder surfaces -surf1 = openmc.ZCylinder(surface_id=1, x0=0, y0=0, r=7, name='surf 1') -surf2 = openmc.ZCylinder(surface_id=2, x0=0, y0=0, r=9, name='surf 2') -surf3 = openmc.ZCylinder(surface_id=3, x0=0, y0=0, r=11, name='surf 3') -surf3.boundary_type = 'vacuum' - -# Instantiate Cells -cell1 = openmc.Cell(cell_id=1, name='cell 1') -cell2 = openmc.Cell(cell_id=100, name='cell 2') -cell3 = openmc.Cell(cell_id=101, name='cell 3') -cell4 = openmc.Cell(cell_id=2, name='cell 4') - -# Use surface half-spaces to define regions -cell1.region = -surf2 -cell2.region = -surf1 -cell3.region = +surf1 -cell4.region = +surf2 & -surf3 - -# Register Materials with Cells -cell2.fill = fuel -cell3.fill = moderator -cell4.fill = moderator - -# Instantiate Universes -universe1 = openmc.Universe(universe_id=37) -root = openmc.Universe(universe_id=0, name='root universe') -cell1.fill = universe1 - -# Register Cells with Universes -universe1.add_cells([cell2, cell3]) -root.add_cells([cell1, cell4]) - -# Instantiate a Geometry, register the root Universe, and export to XML -geometry = openmc.Geometry(root) -geometry.export_to_xml() - - -############################################################################### -# Exporting to OpenMC settings.xml file -############################################################################### - -# Instantiate a Settings object, set all runtime parameters, and export to XML -settings_file = openmc.Settings() -settings_file.batches = batches -settings_file.inactive = inactive -settings_file.particles = particles - -# Create an initial uniform spatial source distribution over fissionable zones -bounds = [-4., -4., -4., 4., 4., 4.] -uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) -settings_file.source = openmc.source.Source(space=uniform_dist) - -settings_file.export_to_xml() - - -############################################################################### -# Exporting to OpenMC tallies.xml file -############################################################################### - -# Instantiate some tally Filters -cell_filter = openmc.CellFilter(cell2) -energy_filter = openmc.EnergyFilter([0., 20.e6]) -energyout_filter = openmc.EnergyoutFilter([0., 20.e6]) - -# Instantiate the first Tally -first_tally = openmc.Tally(tally_id=1, name='first tally') -first_tally.filters = [cell_filter] -scores = ['total', 'scatter', 'nu-scatter', - 'absorption', 'fission', 'nu-fission'] -first_tally.scores = scores - -# Instantiate the second Tally -second_tally = openmc.Tally(tally_id=2, name='second tally') -second_tally.filters = [cell_filter, energy_filter] -second_tally.scores = scores - -# Instantiate the third Tally -third_tally = openmc.Tally(tally_id=3, name='third tally') -third_tally.filters = [cell_filter, energy_filter, energyout_filter] -third_tally.scores = ['scatter', 'nu-scatter', 'nu-fission'] - -# Instantiate a Tallies collection and export to XML -tallies_file = openmc.Tallies((first_tally, second_tally, third_tally)) -tallies_file.export_to_xml() diff --git a/examples/python/boxes/build-xml.py b/examples/python/boxes/build-xml.py deleted file mode 100644 index d04f0f67a3..0000000000 --- a/examples/python/boxes/build-xml.py +++ /dev/null @@ -1,125 +0,0 @@ -import numpy as np -import openmc - -############################################################################### -# Simulation Input File Parameters -############################################################################### - -# OpenMC simulation parameters -batches = 15 -inactive = 5 -particles = 10000 - - -############################################################################### -# Exporting to OpenMC materials.xml File -############################################################################### - -# Instantiate some Materials and register the appropriate Nuclides -fuel1 = openmc.Material(material_id=1, name='fuel') -fuel1.set_density('g/cc', 4.5) -fuel1.add_nuclide('U235', 1.) - -fuel2 = openmc.Material(material_id=2, name='depleted fuel') -fuel2.set_density('g/cc', 4.5) -fuel2.add_nuclide('U238', 1.) - -moderator = openmc.Material(material_id=3, name='moderator') -moderator.set_density('g/cc', 1.0) -moderator.add_element('H', 2.) -moderator.add_element('O', 1.) -moderator.add_s_alpha_beta('c_H_in_H2O') - -# Instantiate a Materials collection and export to XML -materials_file = openmc.Materials([fuel1, fuel2, moderator]) -materials_file.export_to_xml() - - -############################################################################### -# Exporting to OpenMC geometry.xml file -############################################################################### - -# Instantiate planar surfaces -x1 = openmc.XPlane(surface_id=1, x0=-10) -x2 = openmc.XPlane(surface_id=2, x0=-7) -x3 = openmc.XPlane(surface_id=3, x0=-4) -x4 = openmc.XPlane(surface_id=4, x0=4) -x5 = openmc.XPlane(surface_id=5, x0=7) -x6 = openmc.XPlane(surface_id=6, x0=10) -y1 = openmc.YPlane(surface_id=11, y0=-10) -y2 = openmc.YPlane(surface_id=12, y0=-7) -y3 = openmc.YPlane(surface_id=13, y0=-4) -y4 = openmc.YPlane(surface_id=14, y0=4) -y5 = openmc.YPlane(surface_id=15, y0=7) -y6 = openmc.YPlane(surface_id=16, y0=10) -z1 = openmc.ZPlane(surface_id=21, z0=-10) -z2 = openmc.ZPlane(surface_id=22, z0=-7) -z3 = openmc.ZPlane(surface_id=23, z0=-4) -z4 = openmc.ZPlane(surface_id=24, z0=4) -z5 = openmc.ZPlane(surface_id=25, z0=7) -z6 = openmc.ZPlane(surface_id=26, z0=10) - -# Set vacuum boundary conditions on outside -for surface in [x1, x6, y1, y6, z1, z6]: - surface.boundary_type = 'vacuum' - -# Instantiate Cells -inner_box = openmc.Cell(cell_id=1, name='inner box') -middle_box = openmc.Cell(cell_id=2, name='middle box') -outer_box = openmc.Cell(cell_id=3, name='outer box') - -# Use each set of six planes to create solid cube regions. We can then use these -# to create cubic shells. -inner_cube = +x3 & -x4 & +y3 & -y4 & +z3 & -z4 -middle_cube = +x2 & -x5 & +y2 & -y5 & +z2 & -z5 -outer_cube = +x1 & -x6 & +y1 & -y6 & +z1 & -z6 -outside_inner_cube = -x3 | +x4 | -y3 | +y4 | -z3 | +z4 - -# Use surface half-spaces to define regions -inner_box.region = inner_cube -middle_box.region = middle_cube & outside_inner_cube -outer_box.region = outer_cube & ~middle_cube - -# Register Materials with Cells -inner_box.fill = fuel1 -middle_box.fill = fuel2 -outer_box.fill = moderator - -# Instantiate root universe -root = openmc.Universe(universe_id=0, name='root universe') -root.add_cells([inner_box, middle_box, outer_box]) - -# Instantiate a Geometry, register the root Universe, and export to XML -geometry = openmc.Geometry(root) -geometry.export_to_xml() - - -############################################################################### -# Exporting to OpenMC settings.xml File -############################################################################### - -# Instantiate a Settings object, set all runtime parameters, and export to XML -settings_file = openmc.Settings() -settings_file.batches = batches -settings_file.inactive = inactive -settings_file.particles = particles - -# Create an initial uniform spatial source distribution over fissionable zones -uniform_dist = openmc.stats.Box(*outer_cube.bounding_box, only_fissionable=True) -settings_file.source = openmc.source.Source(space=uniform_dist) - -settings_file.export_to_xml() - -############################################################################### -# Exporting to OpenMC plots.xml File -############################################################################### - -plot = openmc.Plot(plot_id=1) -plot.origin = [0, 0, 0] -plot.width = [20, 20] -plot.pixels = [200, 200] -plot.color_by = 'cell' - -# Instantiate a Plots collection and export to XML -plot_file = openmc.Plots([plot]) -plot_file.export_to_xml() diff --git a/examples/xml/basic/geometry.xml b/examples/xml/basic/geometry.xml deleted file mode 100644 index b30884f8ca..0000000000 --- a/examples/xml/basic/geometry.xml +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/examples/xml/basic/materials.xml b/examples/xml/basic/materials.xml deleted file mode 100644 index 606c676df8..0000000000 --- a/examples/xml/basic/materials.xml +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - - - - - - - - - - - diff --git a/examples/xml/basic/settings.xml b/examples/xml/basic/settings.xml deleted file mode 100644 index 6e622b6cef..0000000000 --- a/examples/xml/basic/settings.xml +++ /dev/null @@ -1,16 +0,0 @@ - - - - eigenvalue - 15 - 5 - 10000 - - - - - -4 -4 -4 4 4 4 - - - - diff --git a/examples/xml/basic/tallies.xml b/examples/xml/basic/tallies.xml deleted file mode 100644 index a125e9ca2a..0000000000 --- a/examples/xml/basic/tallies.xml +++ /dev/null @@ -1,31 +0,0 @@ - - - - - 100 - - - - 0 20.0e6 - - - - 0 20.0e6 - - - - 1 - total scatter nu-scatter absorption fission nu-fission - - - - 1 2 - total scatter nu-scatter absorption fission nu-fission - - - - 1 2 3 - scatter nu-scatter nu-fission - - - diff --git a/examples/xml/boxes/geometry.xml b/examples/xml/boxes/geometry.xml deleted file mode 100644 index abe4924e66..0000000000 --- a/examples/xml/boxes/geometry.xml +++ /dev/null @@ -1,39 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/examples/xml/boxes/materials.xml b/examples/xml/boxes/materials.xml deleted file mode 100644 index 1d0ab4a1ca..0000000000 --- a/examples/xml/boxes/materials.xml +++ /dev/null @@ -1,21 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - diff --git a/examples/xml/boxes/plots.xml b/examples/xml/boxes/plots.xml deleted file mode 100644 index 0a75f574eb..0000000000 --- a/examples/xml/boxes/plots.xml +++ /dev/null @@ -1,9 +0,0 @@ - - - - cell - 0. 0. 0. - 20. 20. - 200 200 - - diff --git a/examples/xml/boxes/settings.xml b/examples/xml/boxes/settings.xml deleted file mode 100644 index 9007a12c59..0000000000 --- a/examples/xml/boxes/settings.xml +++ /dev/null @@ -1,15 +0,0 @@ - - - - - eigenvalue - 15 - 5 - 10000 - - - - - - - From e77ce2c0df97100afe59a04c6dc57820316f1925 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Sat, 15 Feb 2020 16:36:33 -0600 Subject: [PATCH 012/205] Add jezebel example --- examples/python/jezebel/jezebel.py | 33 ++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 examples/python/jezebel/jezebel.py diff --git a/examples/python/jezebel/jezebel.py b/examples/python/jezebel/jezebel.py new file mode 100644 index 0000000000..5114ac714d --- /dev/null +++ b/examples/python/jezebel/jezebel.py @@ -0,0 +1,33 @@ +import openmc + +# Create plutonium metal material +pu = openmc.Material() +pu.set_density('sum') +pu.add_nuclide('Pu239', 3.7047e-02) +pu.add_nuclide('Pu240', 1.7512e-03) +pu.add_nuclide('Pu241', 1.1674e-04) +pu.add_element('Ga', 1.3752e-03) +mats = openmc.Materials([pu]) +mats.export_to_xml() + +# Create a single cell filled with the Pu metal +sphere = openmc.Sphere(r=6.3849, boundary_type='vacuum') +cell = openmc.Cell(fill=pu, region=-sphere) +geom = openmc.Geometry([cell]) +geom.export_to_xml() + +# Finally, define some run settings +settings = openmc.Settings() +settings.batches = 200 +settings.inactive = 10 +settings.particles = 10000 +settings.export_to_xml() + +# Run the simulation +openmc.run() + +# Get the resulting k-effective value +n = settings.batches +with openmc.StatePoint(f'statepoint.{n}.h5') as sp: + keff = sp.k_combined + print(f'Final k-effective = {keff}') From 241c359f80498d334eefdf8b6cbb647c6fee0f3a Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 13 Mar 2020 13:59:15 -0500 Subject: [PATCH 013/205] Update pincell example adding flux spectrum tally and associated plotting script --- examples/python/pincell/build-xml.py | 73 ++++++++++++------------ examples/python/pincell/plot_spectrum.py | 23 ++++++++ 2 files changed, 58 insertions(+), 38 deletions(-) create mode 100644 examples/python/pincell/plot_spectrum.py diff --git a/examples/python/pincell/build-xml.py b/examples/python/pincell/build-xml.py index dc6df91631..c5d614ea90 100644 --- a/examples/python/pincell/build-xml.py +++ b/examples/python/pincell/build-xml.py @@ -1,21 +1,11 @@ +from math import log10 + +import numpy as np import openmc ############################################################################### -# Simulation Input File Parameters -############################################################################### +# Create materials for the problem -# OpenMC simulation parameters -batches = 100 -inactive = 10 -particles = 1000 - - -############################################################################### -# Exporting to OpenMC materials.xml file -############################################################################### - - -# Instantiate some Materials and register the appropriate Nuclides uo2 = openmc.Material(name='UO2 fuel at 2.4% wt enrichment') uo2.set_density('g/cm3', 10.29769) uo2.add_element('U', 1., enrichment=2.4) @@ -39,14 +29,12 @@ borated_water.add_element('H', 5.0e-2) borated_water.add_element('O', 2.4e-2) borated_water.add_s_alpha_beta('c_H_in_H2O') -# Instantiate a Materials collection and export to XML +# Collect the materials together and export to XML materials = openmc.Materials([uo2, helium, zircaloy, borated_water]) materials.export_to_xml() - -############################################################################### -# Exporting to OpenMC geometry.xml file ############################################################################### +# Define problem geometry # Create cylindrical surfaces fuel_or = openmc.ZCylinder(r=0.39218, name='Fuel OR') @@ -67,22 +55,23 @@ water = openmc.Cell(fill=borated_water, region=+clad_or & box) geometry = openmc.Geometry([fuel, gap, clad, water]) geometry.export_to_xml() - -############################################################################### -# Exporting to OpenMC settings.xml file ############################################################################### +# Define problem settings -# Instantiate a Settings object, set all runtime parameters, and export to XML +# Indicate how many particles to run settings = openmc.Settings() -settings.batches = batches -settings.inactive = inactive -settings.particles = particles +settings.batches = 100 +settings.inactive = 10 +settings.particles = 1000 # Create an initial uniform spatial source distribution over fissionable zones -bounds = [-pitch/2, -pitch/2, -1, pitch/2, pitch/2, 1] -uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) +lower_left = (-pitch/2, -pitch/2, -1) +upper_right = (pitch/2, pitch/2, 1) +uniform_dist = openmc.stats.Box(lower_left, upper_right, only_fissionable=True) settings.source = openmc.source.Source(space=uniform_dist) +# For source convergence checks, add a mesh that can be used to calculate the +# Shannon entropy entropy_mesh = openmc.RegularMesh() entropy_mesh.lower_left = (-fuel_or.r, -fuel_or.r) entropy_mesh.upper_right = (fuel_or.r, fuel_or.r) @@ -90,26 +79,34 @@ entropy_mesh.dimension = (10, 10) settings.entropy_mesh = entropy_mesh settings.export_to_xml() - -############################################################################### -# Exporting to OpenMC tallies.xml file ############################################################################### +# Define tallies -# Instantiate a tally mesh +# Create a mesh that will be used for tallying mesh = openmc.RegularMesh() mesh.dimension = (100, 100) mesh.lower_left = (-pitch/2, -pitch/2) mesh.upper_right = (pitch/2, pitch/2) -# Instantiate some tally Filters -energy_filter = openmc.EnergyFilter((0., 4., 20.e6)) +# Create a mesh filter that can be used in a tally mesh_filter = openmc.MeshFilter(mesh) -# Instantiate the Tally -tally = openmc.Tally() -tally.filters = [energy_filter, mesh_filter] -tally.scores = ['flux', 'fission', 'nu-fission'] +# Now use the mesh filter in a tally and indicate what scores are desired +mesh_tally = openmc.Tally(name="Mesh tally") +mesh_tally.filters = [mesh_filter] +mesh_tally.scores = ['flux', 'fission', 'nu-fission'] + +# Let's also create a tally to get the flux energy spectrum. We start by +# creating an energy filter +e_min, e_max = 1e-5, 20.0e6 +groups = 500 +energies = np.logspace(log10(e_min), log10(e_max), groups + 1) +energy_filter = openmc.EnergyFilter(energies) + +spectrum_tally = openmc.Tally(name="Flux spectrum") +spectrum_tally.filters = [energy_filter] +spectrum_tally.scores = ['flux'] # Instantiate a Tallies collection and export to XML -tallies = openmc.Tallies([tally]) +tallies = openmc.Tallies([mesh_tally, spectrum_tally]) tallies.export_to_xml() diff --git a/examples/python/pincell/plot_spectrum.py b/examples/python/pincell/plot_spectrum.py new file mode 100644 index 0000000000..d88a6de433 --- /dev/null +++ b/examples/python/pincell/plot_spectrum.py @@ -0,0 +1,23 @@ +import matplotlib.pyplot as plt +import openmc + + +# Get results from statepoint +with openmc.StatePoint('statepoint.100.h5') as sp: + t = sp.get_tally(name="Flux spectrum") + + # Get the energies from the energy filter + energy_filter = t.filters[0] + energies = energy_filter.bins[:, 0] + + # Get the flux values + mean = t.get_values(value='mean').ravel() + uncertainty = t.get_values(value='std_dev').ravel() + +# Plot flux spectrum +fix, ax = plt.subplots() +ax.loglog(energies, mean, drawstyle='steps-post') +ax.set_xlabel('Energy [eV]') +ax.set_ylabel('Flux') +ax.grid(True, which='both') +plt.show() From 03a349916de7267a69936266737c58578cc31256 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 13 Mar 2020 14:01:07 -0500 Subject: [PATCH 014/205] Remove reflective example --- examples/python/reflective/build-xml.py | 82 ------------------------- examples/xml/reflective/geometry.xml | 19 ------ examples/xml/reflective/materials.xml | 9 --- examples/xml/reflective/settings.xml | 17 ----- 4 files changed, 127 deletions(-) delete mode 100644 examples/python/reflective/build-xml.py delete mode 100644 examples/xml/reflective/geometry.xml delete mode 100644 examples/xml/reflective/materials.xml delete mode 100644 examples/xml/reflective/settings.xml diff --git a/examples/python/reflective/build-xml.py b/examples/python/reflective/build-xml.py deleted file mode 100644 index 09fa026263..0000000000 --- a/examples/python/reflective/build-xml.py +++ /dev/null @@ -1,82 +0,0 @@ -import numpy as np -import openmc - -############################################################################### -# Simulation Input File Parameters -############################################################################### - -# OpenMC simulation parameters -batches = 500 -inactive = 10 -particles = 10000 - - -############################################################################### -# Exporting to OpenMC materials.xml file -############################################################################### - -# Instantiate a Material and register the Nuclide -fuel = openmc.Material(material_id=1, name='fuel') -fuel.set_density('g/cc', 4.5) -fuel.add_nuclide('U235', 1.) - -# Instantiate a Materials collection and export to XML -materials_file = openmc.Materials([fuel]) -materials_file.export_to_xml() - - -############################################################################### -# Exporting to OpenMC geometry.xml file -############################################################################### - -# Instantiate Surfaces -surf1 = openmc.XPlane(surface_id=1, x0=-1, name='surf 1') -surf2 = openmc.XPlane(surface_id=2, x0=+1, name='surf 2') -surf3 = openmc.YPlane(surface_id=3, y0=-1, name='surf 3') -surf4 = openmc.YPlane(surface_id=4, y0=+1, name='surf 4') -surf5 = openmc.ZPlane(surface_id=5, z0=-1, name='surf 5') -surf6 = openmc.ZPlane(surface_id=6, z0=+1, name='surf 6') - -surf1.boundary_type = 'vacuum' -surf2.boundary_type = 'vacuum' -surf3.boundary_type = 'reflective' -surf4.boundary_type = 'reflective' -surf5.boundary_type = 'reflective' -surf6.boundary_type = 'reflective' - -# Instantiate Cell -cell = openmc.Cell(cell_id=1, name='cell 1') - -# Use surface half-spaces to define region -cell.region = +surf1 & -surf2 & +surf3 & -surf4 & +surf5 & -surf6 - -# Register Material with Cell -cell.fill = fuel - -# Instantiate Universes -root = openmc.Universe(universe_id=0, name='root universe') - -# Register Cell with Universe -root.add_cell(cell) - -# Instantiate a Geometry, register the root Universe, and export to XML -geometry = openmc.Geometry(root) -geometry.export_to_xml() - - -############################################################################### -# Exporting to OpenMC settings.xml file -############################################################################### - -# Instantiate a Settings object, set all runtime parameters, and export to XML -settings_file = openmc.Settings() -settings_file.batches = batches -settings_file.inactive = inactive -settings_file.particles = particles - -# Create an initial uniform spatial source distribution over fissionable zones -uniform_dist = openmc.stats.Box(*cell.region.bounding_box, - only_fissionable=True) -settings_file.source = openmc.source.Source(space=uniform_dist) - -settings_file.export_to_xml() diff --git a/examples/xml/reflective/geometry.xml b/examples/xml/reflective/geometry.xml deleted file mode 100644 index 51cdf1ecb0..0000000000 --- a/examples/xml/reflective/geometry.xml +++ /dev/null @@ -1,19 +0,0 @@ - - - - - - 0 - 1 - 1 -2 3 -4 5 -6 - - - - - - - - - - - diff --git a/examples/xml/reflective/materials.xml b/examples/xml/reflective/materials.xml deleted file mode 100644 index 2472a74717..0000000000 --- a/examples/xml/reflective/materials.xml +++ /dev/null @@ -1,9 +0,0 @@ - - - - - - - - - diff --git a/examples/xml/reflective/settings.xml b/examples/xml/reflective/settings.xml deleted file mode 100644 index 0ae6e5623b..0000000000 --- a/examples/xml/reflective/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - - - eigenvalue - 500 - 10 - 10000 - - - - - -1 -1 -1 1 1 1 - - - - From ad269469ac5ff77e075e2ab0c599a4e219a4e783 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 13 Mar 2020 14:10:59 -0500 Subject: [PATCH 015/205] Update multigroup example --- .../python/pincell_multigroup/build-xml.py | 131 +++++++----------- 1 file changed, 53 insertions(+), 78 deletions(-) diff --git a/examples/python/pincell_multigroup/build-xml.py b/examples/python/pincell_multigroup/build-xml.py index bb9fc78606..1f34683919 100644 --- a/examples/python/pincell_multigroup/build-xml.py +++ b/examples/python/pincell_multigroup/build-xml.py @@ -1,20 +1,12 @@ +from math import log10 + import numpy as np import openmc import openmc.mgxs ############################################################################### -# Simulation Input File Parameters -############################################################################### - -# OpenMC simulation parameters -batches = 100 -inactive = 10 -particles = 1000 - -############################################################################### -# Exporting to OpenMC mgxs.h5 file -############################################################################### +# Create multigroup data # Instantiate the energy group data groups = openmc.mgxs.EnergyGroups(group_edges=[ @@ -69,107 +61,90 @@ mg_cross_sections_file = openmc.MGXSLibrary(groups) mg_cross_sections_file.add_xsdatas([uo2_xsdata, h2o_xsdata]) mg_cross_sections_file.export_to_hdf5() - -############################################################################### -# Exporting to OpenMC materials.xml file ############################################################################### +# Create materials for the problem # Instantiate some Macroscopic Data uo2_data = openmc.Macroscopic('UO2') h2o_data = openmc.Macroscopic('LWTR') # Instantiate some Materials and register the appropriate Macroscopic objects -uo2 = openmc.Material(material_id=1, name='UO2 fuel') +uo2 = openmc.Material(name='UO2 fuel') uo2.set_density('macro', 1.0) uo2.add_macroscopic(uo2_data) -water = openmc.Material(material_id=2, name='Water') +water = openmc.Material(name='Water') water.set_density('macro', 1.0) water.add_macroscopic(h2o_data) # Instantiate a Materials collection and export to XML materials_file = openmc.Materials([uo2, water]) -materials_file.cross_sections = "./mgxs.h5" +materials_file.cross_sections = "mgxs.h5" materials_file.export_to_xml() - -############################################################################### -# Exporting to OpenMC geometry.xml file ############################################################################### +# Define problem geometry -# Instantiate ZCylinder surfaces -fuel_or = openmc.ZCylinder(surface_id=1, x0=0, y0=0, r=0.54, name='Fuel OR') -left = openmc.XPlane(surface_id=4, x0=-0.63, name='left') -right = openmc.XPlane(surface_id=5, x0=0.63, name='right') -bottom = openmc.YPlane(surface_id=6, y0=-0.63, name='bottom') -top = openmc.YPlane(surface_id=7, y0=0.63, name='top') +# Create a surface for the fuel outer radius +fuel_or = openmc.ZCylinder(r=0.54, name='Fuel OR') -left.boundary_type = 'reflective' -right.boundary_type = 'reflective' -top.boundary_type = 'reflective' -bottom.boundary_type = 'reflective' +# Create a region represented as the inside of a rectangular prism +pitch = 1.26 +box = openmc.rectangular_prism(pitch, pitch, boundary_type='reflective') # Instantiate Cells -fuel = openmc.Cell(cell_id=1, name='cell 1') -moderator = openmc.Cell(cell_id=2, name='cell 2') +fuel = openmc.Cell(fill=uo2, region=-fuel_or, name='fuel') +moderator = openmc.Cell(fill=water, region=+fuel_or & box, name='moderator') -# Use surface half-spaces to define regions -fuel.region = -fuel_or -moderator.region = +fuel_or & +left & -right & +bottom & -top - -# Register Materials with Cells -fuel.fill = uo2 -moderator.fill = water - -# Instantiate Universe -root = openmc.Universe(universe_id=0, name='root universe') - -# Register Cells with Universe -root.add_cells([fuel, moderator]) - -# Instantiate a Geometry, register the root Universe, and export to XML -geometry = openmc.Geometry(root) +# Create a geometry with the two cells and export to XML +geometry = openmc.Geometry([fuel, moderator]) geometry.export_to_xml() - -############################################################################### -# Exporting to OpenMC settings.xml file ############################################################################### +# Define problem settings # Instantiate a Settings object, set all runtime parameters, and export to XML -settings_file = openmc.Settings() -settings_file.energy_mode = "multi-group" -settings_file.batches = batches -settings_file.inactive = inactive -settings_file.particles = particles +settings = openmc.Settings() +settings.energy_mode = "multi-group" +settings.batches = 100 +settings.inactive = 10 +settings.particles = 1000 # Create an initial uniform spatial source distribution over fissionable zones -bounds = [-0.63, -0.63, -1, 0.63, 0.63, 1] -uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:]) -settings_file.source = openmc.source.Source(space=uniform_dist) - -settings_file.export_to_xml() +lower_left = (-pitch/2, -pitch/2, -1) +upper_right = (pitch/2, pitch/2, 1) +uniform_dist = openmc.stats.Box(lower_left, upper_right, only_fissionable=True) +settings.source = openmc.source.Source(space=uniform_dist) +settings.export_to_xml() ############################################################################### -# Exporting to OpenMC tallies.xml file -############################################################################### +# Define tallies -# Instantiate a tally mesh -mesh = openmc.RegularMesh(mesh_id=1) -mesh.dimension = [100, 100, 1] -mesh.lower_left = [-0.63, -0.63, -1.e50] -mesh.upper_right = [0.63, 0.63, 1.e50] +# Create a mesh that will be used for tallying +mesh = openmc.RegularMesh() +mesh.dimension = (100, 100) +mesh.lower_left = (-pitch/2, -pitch/2) +mesh.upper_right = (pitch/2, pitch/2) -# Instantiate some tally Filters -energy_filter = openmc.EnergyFilter([1e-5, 0.0635, 10.0, 1.0e2, 1.0e3, 0.5e6, - 1.0e6, 20.0e6]) +# Create a mesh filter that can be used in a tally mesh_filter = openmc.MeshFilter(mesh) -# Instantiate the Tally -tally = openmc.Tally(tally_id=1, name='tally 1') -tally.filters = [energy_filter, mesh_filter] -tally.scores = ['flux', 'fission', 'nu-fission'] +# Now use the mesh filter in a tally and indicate what scores are desired +mesh_tally = openmc.Tally(name="Mesh tally") +mesh_tally.filters = [mesh_filter] +mesh_tally.scores = ['flux', 'fission', 'nu-fission'] -# Instantiate a Tallies collection, register all Tallies, and export to XML -tallies_file = openmc.Tallies([tally]) -tallies_file.export_to_xml() +# Let's also create a tally to get the flux energy spectrum. We start by +# creating an energy filter +e_min, e_max = 1e-5, 20.0e6 +groups = 500 +energies = np.logspace(log10(e_min), log10(e_max), groups + 1) +energy_filter = openmc.EnergyFilter(energies) + +spectrum_tally = openmc.Tally(name="Flux spectrum") +spectrum_tally.filters = [energy_filter] +spectrum_tally.scores = ['flux'] + +# Instantiate a Tallies collection and export to XML +tallies = openmc.Tallies([mesh_tally, spectrum_tally]) +tallies.export_to_xml() From aba1c4a16d7d9df1ec454c6aa8cc357727524fd3 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 13 Mar 2020 16:13:53 -0500 Subject: [PATCH 016/205] Add Python version of custom_source --- examples/python/custom_source/CMakeLists.txt | 8 +++++ examples/python/custom_source/build_xml.py | 36 +++++++++++++++++++ examples/python/custom_source/show_flux.py | 18 ++++++++++ examples/python/custom_source/source_ring.cpp | 26 ++++++++++++++ 4 files changed, 88 insertions(+) create mode 100644 examples/python/custom_source/CMakeLists.txt create mode 100644 examples/python/custom_source/build_xml.py create mode 100644 examples/python/custom_source/show_flux.py create mode 100644 examples/python/custom_source/source_ring.cpp diff --git a/examples/python/custom_source/CMakeLists.txt b/examples/python/custom_source/CMakeLists.txt new file mode 100644 index 0000000000..9498176944 --- /dev/null +++ b/examples/python/custom_source/CMakeLists.txt @@ -0,0 +1,8 @@ +cmake_minimum_required(VERSION 3.3 FATAL_ERROR) +project(openmc_sources CXX) +add_library(source SHARED source_ring.cpp) +find_package(OpenMC REQUIRED) +if (OpenMC_FOUND) + message(STATUS "Found OpenMC: ${OpenMC_DIR}") +endif() +target_link_libraries(source OpenMC::libopenmc) diff --git a/examples/python/custom_source/build_xml.py b/examples/python/custom_source/build_xml.py new file mode 100644 index 0000000000..a0817d4223 --- /dev/null +++ b/examples/python/custom_source/build_xml.py @@ -0,0 +1,36 @@ +import openmc + +# Create a single material +iron = openmc.Material() +iron.set_density('g/cm3', 5.0) +iron.add_element('Fe', 1.0) +mats = openmc.Materials([iron]) +mats.export_to_xml() + +# Create a 5 cm x 5 cm box filled with iron +box = openmc.model.rectangular_prism(10.0, 10.0, boundary_type='vacuum') +cell = openmc.Cell(fill=iron, region=box) +geometry = openmc.Geometry([cell]) +geometry.export_to_xml() + +# Tell OpenMC we're going to use our custom source +settings = openmc.Settings() +settings.run_mode = 'fixed source' +settings.batches = 10 +settings.particles = 1000 +source = openmc.Source() +source.library = 'build/libsource.so' +settings.source = source +settings.export_to_xml() + +# Finally, define a mesh tally so that we can see the resulting flux +mesh = openmc.RegularMesh() +mesh.lower_left = (-5.0, -5.0) +mesh.upper_right = (5.0, 5.0) +mesh.dimension = (50, 50) + +tally = openmc.Tally() +tally.filters = [openmc.MeshFilter(mesh)] +tally.scores = ['flux'] +tallies = openmc.Tallies([tally]) +tallies.export_to_xml() diff --git a/examples/python/custom_source/show_flux.py b/examples/python/custom_source/show_flux.py new file mode 100644 index 0000000000..9c49e1978c --- /dev/null +++ b/examples/python/custom_source/show_flux.py @@ -0,0 +1,18 @@ +import matplotlib.pyplot as plt +import openmc + +# Get the flux from the statepoint +with openmc.StatePoint('statepoint.10.h5') as sp: + flux = sp.tallies[1].mean + flux.shape = (50, 50) + +# Plot the flux +fig, ax = plt.subplots() +ax.imshow(flux, origin='lower', extent=(-5.0, 5.0, -5.0, 5.0)) +ax.set_xlabel('x [cm]') +ax.set_ylabel('y [cm]') +plt.show() + +# If all worked well, you should see a ring "imprint" as well as a higher flux +# to the right side (since the custom source has all particles moving in the +# positive x direction)) diff --git a/examples/python/custom_source/source_ring.cpp b/examples/python/custom_source/source_ring.cpp new file mode 100644 index 0000000000..d68122dd75 --- /dev/null +++ b/examples/python/custom_source/source_ring.cpp @@ -0,0 +1,26 @@ +#include // for M_PI + +#include "openmc/random_lcg.h" +#include "openmc/source.h" +#include "openmc/particle.h" + +// you must have external C linkage here otherwise +// dlopen will not find the file +extern "C" openmc::Particle::Bank sample_source(uint64_t* seed) +{ + openmc::Particle::Bank particle; + // wgt + particle.particle = openmc::Particle::Type::neutron; + particle.wgt = 1.0; + // position + double angle = 2. * M_PI * openmc::prn(seed); + double radius = 3.0; + particle.r.x = radius * std::cos(angle); + particle.r.y = radius * std::sin(angle); + particle.r.z = 0.0; + // angle + particle.u = {1.0, 0.0, 0.0}; + particle.E = 14.08e6; + particle.delayed_group = 0; + return particle; +} From 58c5130eb5e60f5b46fd4a3c30aed2da11da6236 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 13 Mar 2020 16:14:40 -0500 Subject: [PATCH 017/205] Remove examples/xml/ --- examples/xml/custom_source/CMakeLists.txt | 8 --- examples/xml/custom_source/geometry.xml | 15 ---- examples/xml/custom_source/materials.xml | 16 ----- examples/xml/custom_source/settings.xml | 14 ---- examples/xml/custom_source/source_ring.cpp | 26 ------- examples/xml/custom_source/tallies.xml | 17 ----- examples/xml/lattice/nested/geometry.xml | 43 ----------- examples/xml/lattice/nested/materials.xml | 17 ----- examples/xml/lattice/nested/plots.xml | 10 --- examples/xml/lattice/nested/settings.xml | 17 ----- examples/xml/lattice/nested/tallies.xml | 20 ------ examples/xml/lattice/simple/geometry.xml | 32 --------- examples/xml/lattice/simple/materials.xml | 17 ----- examples/xml/lattice/simple/plots.xml | 10 --- examples/xml/lattice/simple/settings.xml | 17 ----- examples/xml/lattice/simple/tallies.xml | 20 ------ examples/xml/pincell/geometry.xml | 27 ------- examples/xml/pincell/materials.xml | 67 ------------------ examples/xml/pincell/settings.xml | 32 --------- examples/xml/pincell/tallies.xml | 23 ------ examples/xml/pincell_multigroup/geometry.xml | 10 --- examples/xml/pincell_multigroup/materials.xml | 12 ---- examples/xml/pincell_multigroup/mgxs.h5 | Bin 16832 -> 0 bytes examples/xml/pincell_multigroup/plots.xml | 28 -------- examples/xml/pincell_multigroup/settings.xml | 13 ---- examples/xml/pincell_multigroup/tallies.xml | 18 ----- 26 files changed, 529 deletions(-) delete mode 100644 examples/xml/custom_source/CMakeLists.txt delete mode 100644 examples/xml/custom_source/geometry.xml delete mode 100644 examples/xml/custom_source/materials.xml delete mode 100644 examples/xml/custom_source/settings.xml delete mode 100644 examples/xml/custom_source/source_ring.cpp delete mode 100644 examples/xml/custom_source/tallies.xml delete mode 100644 examples/xml/lattice/nested/geometry.xml delete mode 100644 examples/xml/lattice/nested/materials.xml delete mode 100644 examples/xml/lattice/nested/plots.xml delete mode 100644 examples/xml/lattice/nested/settings.xml delete mode 100644 examples/xml/lattice/nested/tallies.xml delete mode 100644 examples/xml/lattice/simple/geometry.xml delete mode 100644 examples/xml/lattice/simple/materials.xml delete mode 100644 examples/xml/lattice/simple/plots.xml delete mode 100644 examples/xml/lattice/simple/settings.xml delete mode 100644 examples/xml/lattice/simple/tallies.xml delete mode 100644 examples/xml/pincell/geometry.xml delete mode 100644 examples/xml/pincell/materials.xml delete mode 100644 examples/xml/pincell/settings.xml delete mode 100644 examples/xml/pincell/tallies.xml delete mode 100644 examples/xml/pincell_multigroup/geometry.xml delete mode 100644 examples/xml/pincell_multigroup/materials.xml delete mode 100644 examples/xml/pincell_multigroup/mgxs.h5 delete mode 100644 examples/xml/pincell_multigroup/plots.xml delete mode 100644 examples/xml/pincell_multigroup/settings.xml delete mode 100644 examples/xml/pincell_multigroup/tallies.xml diff --git a/examples/xml/custom_source/CMakeLists.txt b/examples/xml/custom_source/CMakeLists.txt deleted file mode 100644 index 9498176944..0000000000 --- a/examples/xml/custom_source/CMakeLists.txt +++ /dev/null @@ -1,8 +0,0 @@ -cmake_minimum_required(VERSION 3.3 FATAL_ERROR) -project(openmc_sources CXX) -add_library(source SHARED source_ring.cpp) -find_package(OpenMC REQUIRED) -if (OpenMC_FOUND) - message(STATUS "Found OpenMC: ${OpenMC_DIR}") -endif() -target_link_libraries(source OpenMC::libopenmc) diff --git a/examples/xml/custom_source/geometry.xml b/examples/xml/custom_source/geometry.xml deleted file mode 100644 index b30884f8ca..0000000000 --- a/examples/xml/custom_source/geometry.xml +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/examples/xml/custom_source/materials.xml b/examples/xml/custom_source/materials.xml deleted file mode 100644 index 606c676df8..0000000000 --- a/examples/xml/custom_source/materials.xml +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - - - - - - - - - - - diff --git a/examples/xml/custom_source/settings.xml b/examples/xml/custom_source/settings.xml deleted file mode 100644 index f935ed685e..0000000000 --- a/examples/xml/custom_source/settings.xml +++ /dev/null @@ -1,14 +0,0 @@ - - - - fixed source - 10 - 0 - 100000 - - - - build/libsource.so - - - diff --git a/examples/xml/custom_source/source_ring.cpp b/examples/xml/custom_source/source_ring.cpp deleted file mode 100644 index d68122dd75..0000000000 --- a/examples/xml/custom_source/source_ring.cpp +++ /dev/null @@ -1,26 +0,0 @@ -#include // for M_PI - -#include "openmc/random_lcg.h" -#include "openmc/source.h" -#include "openmc/particle.h" - -// you must have external C linkage here otherwise -// dlopen will not find the file -extern "C" openmc::Particle::Bank sample_source(uint64_t* seed) -{ - openmc::Particle::Bank particle; - // wgt - particle.particle = openmc::Particle::Type::neutron; - particle.wgt = 1.0; - // position - double angle = 2. * M_PI * openmc::prn(seed); - double radius = 3.0; - particle.r.x = radius * std::cos(angle); - particle.r.y = radius * std::sin(angle); - particle.r.z = 0.0; - // angle - particle.u = {1.0, 0.0, 0.0}; - particle.E = 14.08e6; - particle.delayed_group = 0; - return particle; -} diff --git a/examples/xml/custom_source/tallies.xml b/examples/xml/custom_source/tallies.xml deleted file mode 100644 index 7f6f299261..0000000000 --- a/examples/xml/custom_source/tallies.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - - - 100 - - - - 0 20.0e6 - - - - 1 2 - flux - - - diff --git a/examples/xml/lattice/nested/geometry.xml b/examples/xml/lattice/nested/geometry.xml deleted file mode 100644 index 324f71cb36..0000000000 --- a/examples/xml/lattice/nested/geometry.xml +++ /dev/null @@ -1,43 +0,0 @@ - - - - - - - - - - - - - - - 2 2 - -1.0 -1.0 - 1.0 1.0 - - 1 2 - 2 3 - - - - - - 2 2 - -2.0 -2.0 - 2.0 2.0 - - 5 5 - 5 5 - - - - - - - - - - - - diff --git a/examples/xml/lattice/nested/materials.xml b/examples/xml/lattice/nested/materials.xml deleted file mode 100644 index 2222721959..0000000000 --- a/examples/xml/lattice/nested/materials.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - - - - - - - - diff --git a/examples/xml/lattice/nested/plots.xml b/examples/xml/lattice/nested/plots.xml deleted file mode 100644 index 0f92e06211..0000000000 --- a/examples/xml/lattice/nested/plots.xml +++ /dev/null @@ -1,10 +0,0 @@ - - - - - 0. 0. 0. - 4.0 4.0 - 400 400 - - - diff --git a/examples/xml/lattice/nested/settings.xml b/examples/xml/lattice/nested/settings.xml deleted file mode 100644 index 879173d1b3..0000000000 --- a/examples/xml/lattice/nested/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - - - eigenvalue - 20 - 10 - 10000 - - - - - -1 -1 -1 1 1 1 - - - - diff --git a/examples/xml/lattice/nested/tallies.xml b/examples/xml/lattice/nested/tallies.xml deleted file mode 100644 index d342174a99..0000000000 --- a/examples/xml/lattice/nested/tallies.xml +++ /dev/null @@ -1,20 +0,0 @@ - - - - - regular - 4 4 - -2.0 -2.0 - 1.0 1.0 - - - - 1 - - - - 1 - total - - - diff --git a/examples/xml/lattice/simple/geometry.xml b/examples/xml/lattice/simple/geometry.xml deleted file mode 100644 index bda7246c79..0000000000 --- a/examples/xml/lattice/simple/geometry.xml +++ /dev/null @@ -1,32 +0,0 @@ - - - - - - - - - - - - - 4 4 - -2.0 -2.0 - 1.0 1.0 - - 1 2 1 2 - 2 3 2 3 - 1 2 1 2 - 2 3 2 3 - - - - - - - - - - - - diff --git a/examples/xml/lattice/simple/materials.xml b/examples/xml/lattice/simple/materials.xml deleted file mode 100644 index 2222721959..0000000000 --- a/examples/xml/lattice/simple/materials.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - - - - - - - - diff --git a/examples/xml/lattice/simple/plots.xml b/examples/xml/lattice/simple/plots.xml deleted file mode 100644 index 0f92e06211..0000000000 --- a/examples/xml/lattice/simple/plots.xml +++ /dev/null @@ -1,10 +0,0 @@ - - - - - 0. 0. 0. - 4.0 4.0 - 400 400 - - - diff --git a/examples/xml/lattice/simple/settings.xml b/examples/xml/lattice/simple/settings.xml deleted file mode 100644 index 879173d1b3..0000000000 --- a/examples/xml/lattice/simple/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - - - eigenvalue - 20 - 10 - 10000 - - - - - -1 -1 -1 1 1 1 - - - - diff --git a/examples/xml/lattice/simple/tallies.xml b/examples/xml/lattice/simple/tallies.xml deleted file mode 100644 index d342174a99..0000000000 --- a/examples/xml/lattice/simple/tallies.xml +++ /dev/null @@ -1,20 +0,0 @@ - - - - - regular - 4 4 - -2.0 -2.0 - 1.0 1.0 - - - - 1 - - - - 1 - total - - - diff --git a/examples/xml/pincell/geometry.xml b/examples/xml/pincell/geometry.xml deleted file mode 100644 index f67f9e74c2..0000000000 --- a/examples/xml/pincell/geometry.xml +++ /dev/null @@ -1,27 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - diff --git a/examples/xml/pincell/materials.xml b/examples/xml/pincell/materials.xml deleted file mode 100644 index 9f9afa3843..0000000000 --- a/examples/xml/pincell/materials.xml +++ /dev/null @@ -1,67 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/examples/xml/pincell/settings.xml b/examples/xml/pincell/settings.xml deleted file mode 100644 index 5845898c8d..0000000000 --- a/examples/xml/pincell/settings.xml +++ /dev/null @@ -1,32 +0,0 @@ - - - - - eigenvalue - 100 - 10 - 1000 - - - - - - -0.62992 -0.62992 -1. - 0.62992 0.62992 1. - - - - - - - -0.39218 -0.39218 -1.e50 - 0.39218 0.39218 1.e50 - 10 10 1 - - 1 - - diff --git a/examples/xml/pincell/tallies.xml b/examples/xml/pincell/tallies.xml deleted file mode 100644 index 7e1e0dafe4..0000000000 --- a/examples/xml/pincell/tallies.xml +++ /dev/null @@ -1,23 +0,0 @@ - - - - - 100 100 1 - -0.62992 -0.62992 -1.e50 - 0.62992 0.62992 1.e50 - - - - 2 - - - - 0. 4. 20.0e6 - - - - 1 2 - flux fission nu-fission - - - diff --git a/examples/xml/pincell_multigroup/geometry.xml b/examples/xml/pincell_multigroup/geometry.xml deleted file mode 100644 index a1df07a9ec..0000000000 --- a/examples/xml/pincell_multigroup/geometry.xml +++ 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mode 100644 index 6966d02b7b..0000000000 --- a/examples/xml/pincell_multigroup/settings.xml +++ /dev/null @@ -1,13 +0,0 @@ - - - eigenvalue - 1000 - 100 - 10 - - - -0.63 -0.63 -1 0.63 0.63 1 - - - multi-group - diff --git a/examples/xml/pincell_multigroup/tallies.xml b/examples/xml/pincell_multigroup/tallies.xml deleted file mode 100644 index d84e129f2f..0000000000 --- a/examples/xml/pincell_multigroup/tallies.xml +++ /dev/null @@ -1,18 +0,0 @@ - - - - 100 100 1 - -0.63 -0.63 -1e+50 - 0.63 0.63 1e+50 - - - 1e-05 0.0635 10.0 100.0 1000.0 500000.0 1000000.0 20000000.0 - - - 1 - - - 1 2 - flux fission nu-fission - - From 4525e3879712264b8af12e0f99ffb41e704a9d95 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 13 Mar 2020 16:15:35 -0500 Subject: [PATCH 018/205] Move Python examples into subdirectory --- examples/{python => }/custom_source/CMakeLists.txt | 0 examples/{python => }/custom_source/build_xml.py | 0 examples/{python => }/custom_source/show_flux.py | 0 examples/{python => }/custom_source/source_ring.cpp | 0 examples/{python => }/jezebel/jezebel.py | 0 examples/{python => }/lattice/hexagonal/build-xml.py | 0 examples/{python => }/lattice/nested/build-xml.py | 0 examples/{python => }/lattice/simple/build-xml.py | 0 examples/{python => }/pincell/build-xml.py | 0 examples/{python => }/pincell/plot_spectrum.py | 0 examples/{python => }/pincell_depletion/chain_simple.xml | 0 examples/{python => }/pincell_depletion/restart_depletion.py | 0 examples/{python => }/pincell_depletion/run_depletion.py | 0 examples/{python => }/pincell_multigroup/build-xml.py | 0 14 files changed, 0 insertions(+), 0 deletions(-) rename examples/{python => }/custom_source/CMakeLists.txt (100%) rename examples/{python => }/custom_source/build_xml.py (100%) rename examples/{python => }/custom_source/show_flux.py (100%) rename examples/{python => }/custom_source/source_ring.cpp (100%) rename examples/{python => }/jezebel/jezebel.py (100%) rename examples/{python => }/lattice/hexagonal/build-xml.py (100%) rename examples/{python => }/lattice/nested/build-xml.py (100%) rename examples/{python => }/lattice/simple/build-xml.py (100%) rename examples/{python => }/pincell/build-xml.py (100%) rename examples/{python => }/pincell/plot_spectrum.py (100%) rename examples/{python => }/pincell_depletion/chain_simple.xml (100%) rename examples/{python => }/pincell_depletion/restart_depletion.py (100%) rename examples/{python => }/pincell_depletion/run_depletion.py (100%) rename examples/{python => }/pincell_multigroup/build-xml.py (100%) diff --git a/examples/python/custom_source/CMakeLists.txt b/examples/custom_source/CMakeLists.txt similarity index 100% rename from examples/python/custom_source/CMakeLists.txt rename to examples/custom_source/CMakeLists.txt diff --git a/examples/python/custom_source/build_xml.py b/examples/custom_source/build_xml.py similarity index 100% rename from examples/python/custom_source/build_xml.py rename to examples/custom_source/build_xml.py diff --git a/examples/python/custom_source/show_flux.py b/examples/custom_source/show_flux.py similarity index 100% rename from examples/python/custom_source/show_flux.py rename to examples/custom_source/show_flux.py diff --git a/examples/python/custom_source/source_ring.cpp b/examples/custom_source/source_ring.cpp similarity index 100% rename from examples/python/custom_source/source_ring.cpp rename to examples/custom_source/source_ring.cpp diff --git a/examples/python/jezebel/jezebel.py b/examples/jezebel/jezebel.py similarity index 100% rename from examples/python/jezebel/jezebel.py rename to examples/jezebel/jezebel.py diff --git a/examples/python/lattice/hexagonal/build-xml.py b/examples/lattice/hexagonal/build-xml.py similarity index 100% rename from examples/python/lattice/hexagonal/build-xml.py rename to examples/lattice/hexagonal/build-xml.py diff --git a/examples/python/lattice/nested/build-xml.py b/examples/lattice/nested/build-xml.py similarity index 100% rename from examples/python/lattice/nested/build-xml.py rename to examples/lattice/nested/build-xml.py diff --git a/examples/python/lattice/simple/build-xml.py b/examples/lattice/simple/build-xml.py similarity index 100% rename from examples/python/lattice/simple/build-xml.py rename to examples/lattice/simple/build-xml.py diff --git a/examples/python/pincell/build-xml.py b/examples/pincell/build-xml.py similarity index 100% rename from examples/python/pincell/build-xml.py rename to examples/pincell/build-xml.py diff --git a/examples/python/pincell/plot_spectrum.py b/examples/pincell/plot_spectrum.py similarity index 100% rename from examples/python/pincell/plot_spectrum.py rename to examples/pincell/plot_spectrum.py diff --git a/examples/python/pincell_depletion/chain_simple.xml b/examples/pincell_depletion/chain_simple.xml similarity index 100% rename from examples/python/pincell_depletion/chain_simple.xml rename to examples/pincell_depletion/chain_simple.xml diff --git a/examples/python/pincell_depletion/restart_depletion.py b/examples/pincell_depletion/restart_depletion.py similarity index 100% rename from examples/python/pincell_depletion/restart_depletion.py rename to examples/pincell_depletion/restart_depletion.py diff --git a/examples/python/pincell_depletion/run_depletion.py b/examples/pincell_depletion/run_depletion.py similarity index 100% rename from examples/python/pincell_depletion/run_depletion.py rename to examples/pincell_depletion/run_depletion.py diff --git a/examples/python/pincell_multigroup/build-xml.py b/examples/pincell_multigroup/build-xml.py similarity index 100% rename from examples/python/pincell_multigroup/build-xml.py rename to examples/pincell_multigroup/build-xml.py From 282001803b750ffb9008ee79dce7d18f36870565 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 13 Mar 2020 16:17:55 -0500 Subject: [PATCH 019/205] Use consistent naming on example scripts --- examples/lattice/hexagonal/{build-xml.py => build_xml.py} | 0 examples/lattice/nested/{build-xml.py => build_xml.py} | 0 examples/lattice/simple/{build-xml.py => build_xml.py} | 0 examples/pincell/{build-xml.py => build_xml.py} | 0 examples/pincell_multigroup/{build-xml.py => build_xml.py} | 0 5 files changed, 0 insertions(+), 0 deletions(-) rename examples/lattice/hexagonal/{build-xml.py => build_xml.py} (100%) rename examples/lattice/nested/{build-xml.py => build_xml.py} (100%) rename examples/lattice/simple/{build-xml.py => build_xml.py} (100%) rename examples/pincell/{build-xml.py => build_xml.py} (100%) rename examples/pincell_multigroup/{build-xml.py => build_xml.py} (100%) diff --git a/examples/lattice/hexagonal/build-xml.py b/examples/lattice/hexagonal/build_xml.py similarity index 100% rename from examples/lattice/hexagonal/build-xml.py rename to examples/lattice/hexagonal/build_xml.py diff --git a/examples/lattice/nested/build-xml.py b/examples/lattice/nested/build_xml.py similarity index 100% rename from examples/lattice/nested/build-xml.py rename to examples/lattice/nested/build_xml.py diff --git a/examples/lattice/simple/build-xml.py b/examples/lattice/simple/build_xml.py similarity index 100% rename from examples/lattice/simple/build-xml.py rename to examples/lattice/simple/build_xml.py diff --git a/examples/pincell/build-xml.py b/examples/pincell/build_xml.py similarity index 100% rename from examples/pincell/build-xml.py rename to examples/pincell/build_xml.py diff --git a/examples/pincell_multigroup/build-xml.py b/examples/pincell_multigroup/build_xml.py similarity index 100% rename from examples/pincell_multigroup/build-xml.py rename to examples/pincell_multigroup/build_xml.py From c3fff76bea597199729eca8030971bc8e28720c6 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Sun, 15 Mar 2020 19:41:16 +0300 Subject: [PATCH 020/205] upload test new test for mg-temperature mode --- .../mg_temperature/build_2g.py | 292 ++++++++++++++++++ tests/regression_tests/mg_temperature/test.py | 106 +++++++ 2 files changed, 398 insertions(+) create mode 100644 tests/regression_tests/mg_temperature/build_2g.py create mode 100644 tests/regression_tests/mg_temperature/test.py diff --git a/tests/regression_tests/mg_temperature/build_2g.py b/tests/regression_tests/mg_temperature/build_2g.py new file mode 100644 index 0000000000..bfdd7f282f --- /dev/null +++ b/tests/regression_tests/mg_temperature/build_2g.py @@ -0,0 +1,292 @@ +import openmc +import numpy as np + +names = ['H', 'O', 'Zr', 'U235', 'U238'] + + +def build_openmc_xs_lib(name, groups, temperatures, xsdict, micro=True): + """Build an Openm XSdata based on dictonary values""" + xsdata = openmc.XSdata(name, groups, temperatures=temperatures) + xsdata.order = 0 + for tt in temperatures: + xsdata.set_absorption(xsdict[tt]['absorption'][name], temperature=tt) + xsdata.set_scatter_matrix(xsdict[tt]['scatter'][name], temperature=tt) + xsdata.set_total(xsdict[tt]['total'][name], temperature=tt) + if (name in xsdict[tt]['nu-fission'].keys()): + xsdata.set_nu_fission(xsdict[tt]['nu-fission'][name], + temperature=tt) + xsdata.set_chi(np.array([1., 0.]), temperature=tt) + return xsdata + + +def create_micro_xs_dict(): + """Returns micro xs library""" + xs_micro = {} + reactions = ['absorption', 'total', 'scatter', 'nu-fission'] + # chi is unnecessary when energy bound is in thermal region + # Temperature 300K + # absorption + xs_micro[300] = {r: {} for r in reactions} + xs_micro[300]['absorption']['H'] = np.array([1.0285E-4, 0.0057]) + xs_micro[300]['absorption']['O'] = np.array([7.1654E-5, 3.0283E-6]) + xs_micro[300]['absorption']['Zr'] = np.array([4.5918E-5, 3.6303E-5]) + xs_micro[300]['absorption']['U235'] = np.array([0.0035, 0.1040]) + xs_micro[300]['absorption']['U238'] = np.array([0.0056, 0.0094]) + # nu-scatter matrix + xs_micro[300]['scatter']['H'] = np.array([[[0.0910, 0.01469], + [2.1545E-8, 0.3316]]]) + xs_micro[300]['scatter']['O'] = np.array([[[0.0814, 3.3235E-4], + [1.4152E-8, 0.0960]]]) + xs_micro[300]['scatter']['Zr'] = np.array([[[0.0311, 2.6373E-5], + [6.1273E-8, 0.0315]]]) + xs_micro[300]['scatter']['U235'] = np.array([[[0.0311, 2.6373E-5], + [6.1273E-8, 0.0315]]]) + xs_micro[300]['scatter']['U238'] = np.array([[[0.0551, 2.2341E-5], + [8.7247E-8, 0.0526]]]) + # nu-fission + xs_micro[300]['nu-fission']['U235'] = np.array([0.0059, 0.2160]) + xs_micro[300]['nu-fission']['U238'] = np.array([0.0019, 1.4627E-7]) + # total + xs_micro[300]['total']['H'] = xs_micro[300]['absorption']['H'] + \ + np.sum(xs_micro[300]['scatter']['H'][0], 1) + xs_micro[300]['total']['O'] = xs_micro[300]['absorption']['O'] + \ + np.sum(xs_micro[300]['scatter']['O'][0], 1) + + xs_micro[300]['total']['Zr'] = xs_micro[300]['absorption']['Zr'] + \ + np.sum(xs_micro[300]['scatter']['Zr'][0], 1) + + xs_micro[300]['total']['U235'] = xs_micro[300]['absorption']['U235'] + \ + np.sum(xs_micro[300]['scatter']['U235'][0], 1) + + xs_micro[300]['total']['U238'] = xs_micro[300]['absorption']['U238'] + \ + np.sum(xs_micro[300]['scatter']['U238'][0], 1) + + # Temperature 600K + xs_micro[600] = {r: {} for r in reactions} + # absorption + xs_micro[600]['absorption']['H'] = np.array([1.0356E-4, 0.0046]) + xs_micro[600]['absorption']['O'] = np.array([7.2678E-5, 2.4963E-6]) + xs_micro[600]['absorption']['Zr'] = np.array([4.7256E-5, 2.9757E-5]) + xs_micro[600]['absorption']['U235'] = np.array([0.0035, 0.0853]) + xs_micro[600]['absorption']['U238'] = np.array([0.0058, 0.0079]) + # nu-scatter matrix + xs_micro[600]['scatter']['H'] = np.array([[[0.0910, 0.0138], + [8.9e-08, 0.3316]]]) + xs_micro[600]['scatter']['O'] = np.array([[[0.0814, 3.5367E-4], + [3.4404E-8, 0.0959]]]) + xs_micro[600]['scatter']['Zr'] = np.array([[[0.0311, 3.2293E-5], + [8.3859E-8, 0.0314]]]) + xs_micro[600]['scatter']['U235'] = np.array([[[0.0022, 1.9763E-6], + [9.1634E-8, 0.0039]]]) + xs_micro[600]['scatter']['U238'] = np.array([[[0.0556, 2.8803E-5], + [1.1967E-8, 0.0536]]]) + # nu-fission + xs_micro[600]['nu-fission']['U235'] = np.array([0.0059, 0.1767]) + xs_micro[600]['nu-fission']['U238'] = np.array([0.0019, 1.2405E-7]) + # total + xs_micro[600]['total']['H'] = xs_micro[600]['absorption']['H'] + \ + np.sum(xs_micro[600]['scatter']['H'][0], 1) + xs_micro[600]['total']['O'] = xs_micro[600]['absorption']['O'] + \ + np.sum(xs_micro[600]['scatter']['O'][0], 1) + + xs_micro[600]['total']['Zr'] = xs_micro[600]['absorption']['Zr'] + \ + np.sum(xs_micro[600]['scatter']['Zr'][0], 1) + + xs_micro[600]['total']['U235'] = xs_micro[600]['absorption']['U235'] + \ + np.sum(xs_micro[600]['scatter']['U235'][0], 1) + + xs_micro[600]['total']['U238'] = xs_micro[600]['absorption']['U238'] + \ + np.sum(xs_micro[600]['scatter']['U238'][0], 1) + + # Temperature 900K + xs_micro[900] = {r: {} for r in reactions} + # absorption + xs_micro[900]['absorption']['H'] = np.array([1.0529E-4, 0.0040]) + xs_micro[900]['absorption']['O'] = np.array([7.3055E-5, 2.1850E-6]) + xs_micro[900]['absorption']['Zr'] = np.array([4.7141E-5, 2.5941E-5]) + xs_micro[900]['absorption']['U235'] = np.array([0.0035, 0.0749]) + xs_micro[900]['absorption']['U238'] = np.array([0.0060, 0.0071]) + # total + xs_micro[900]['total']['H'] = np.array([0.2982, 0.7332]) + xs_micro[900]['total']['O'] = np.array([0.0885, 0.1004]) + xs_micro[900]['total']['Zr'] = np.array([0.0370, 0.0317]) + xs_micro[900]['total']['U235'] = np.array([0.0061, 0.0789]) + xs_micro[900]['total']['U238'] = np.array([0.0707, 0.0613]) + # nu-scatter matrix + xs_micro[900]['scatter']['H'] = np.array([[[0.0913, 0.0147], + [8.9e-08, 0.4020]]]) + xs_micro[900]['scatter']['O'] = np.array([[[0.0812, 4.0413E-4], + [6.8186E-8, 0.0965]]]) + xs_micro[900]['scatter']['Zr'] = np.array([[[0.0311, 3.6735E-5], + [1.3439E-8, 0.0314]]]) + xs_micro[900]['scatter']['U235'] = np.array([[[0.0022, 2.9034E-6], + [1.3117E-8, 0.0039]]]) + xs_micro[900]['scatter']['U238'] = np.array([[[0.0560, 3.7619E-5], + [1.4553E-8, 0.0538]]]) + # nu-fission + xs_micro[900]['nu-fission']['U235'] = np.array([0.0059, 0.1545]) + xs_micro[900]['nu-fission']['U238'] = np.array([0.0019, 1.1017E-7]) + # total + xs_micro[900]['total']['H'] = xs_micro[900]['absorption']['H'] + \ + np.sum(xs_micro[900]['scatter']['H'][0], 1) + xs_micro[900]['total']['O'] = xs_micro[900]['absorption']['O'] + \ + np.sum(xs_micro[900]['scatter']['O'][0], 1) + + xs_micro[900]['total']['Zr'] = xs_micro[900]['absorption']['Zr'] + \ + np.sum(xs_micro[900]['scatter']['Zr'][0], 1) + + xs_micro[900]['total']['U235'] = xs_micro[900]['absorption']['U235'] + \ + np.sum(xs_micro[900]['scatter']['U235'][0], 1) + + xs_micro[900]['total']['U238'] = xs_micro[900]['absorption']['U238'] + \ + np.sum(xs_micro[900]['scatter']['U238'][0], 1) + + # roll axis for scatter matrix + for t in xs_micro: + for n in xs_micro[t]['scatter']: + xs_micro[t]['scatter'][n] = np.rollaxis(xs_micro[t]['scatter'][n], + 0, 3) + return xs_micro + + +def create_macro_dict(xs_micro): + """Create a dictionary with two group cross-section""" + xs_macro = {} + for t, d1 in xs_micro.items(): + xs_macro[t] = {} + for r, d2 in d1.items(): + temp = [] + xs_macro[t][r] = {} + for n, v in d2.items(): + temp.append(d2[n]) + xs_macro[t][r]['macro'] = sum(temp) + return xs_macro + + +def create_openmc_2mg_libs(names): + """Built a micro/macro two group openmc MGXS libraries""" + # Initialized library params + group_edges = [0.0, 0.625, 20.0e6] + groups = openmc.mgxs.EnergyGroups(group_edges=group_edges) + mg_cross_sections_file_micro = openmc.MGXSLibrary(groups) + mg_cross_sections_file_macro = openmc.MGXSLibrary(groups) + # Building a micro mg library + micro_cs = create_micro_xs_dict() + for name in names: + mg_cross_sections_file_micro.add_xsdata(build_openmc_xs_lib(name, + groups, + [t for t in + micro_cs], + micro_cs)) + # Building a macro mg library + macro_xs = create_macro_dict(micro_cs) + mg_cross_sections_file_macro.add_xsdata(build_openmc_xs_lib('macro', + groups, + [t for t in + macro_xs], + macro_xs)) + # Exporting library to hdf5 files + mg_cross_sections_file_micro.export_to_hdf5('micro_2g.h5') + mg_cross_sections_file_macro.export_to_hdf5('macro_2g.h5') + # Returning the macro_xs dict is needed for analytical solution + return macro_xs + + +def analytical_solution_2g_therm(xsmin, xsmax=None, wgt=1.0): + """ Calculate eigenvalue based on analytical solution for eq Lf = (1/k)Qf + in two group for infinity dilution media in assumption of group + boundary in thernmal spectra < 1.e+3 Ev + Parametres: + ---------- + xsmin : dict + - macro cross-sections dictonary with minimum range temperature + xsmax : dict + - macro cross-sections dictonary with maximum range temperature + by default: None not used for standalone temperature + wgt : double + - weight for interpolation by default 1.0 + Returns: + --------- + keff : np.double - analytical eigenvalue of critical eq matrix + """ + if xsmax is None: + sa = xsmin['absorption']['macro'] + ss12 = xsmin['scatter']['macro'][0][1][0] + nsf = xsmin['nu-fission']['macro'] + else: + sa = xsmin['absorption']['macro'] * wgt + \ + xsmax['absorption']['macro'] * (1 - wgt) + ss12 = xsmin['scatter']['macro'][0][1][0] * wgt + \ + xsmax['scatter']['macro'][0][1][0] * (1 - wgt) + nsf = xsmin['nu-fission']['macro'] * wgt + \ + xsmax['nu-fission']['macro'] * (1 - wgt) + L = np.array([sa[0] + ss12, 0.0, -ss12, sa[1]]).reshape(2, 2) + Q = np.array([nsf[0], nsf[1], 0.0, 0.0]).reshape(2, 2) + arr = np.linalg.inv(L).dot(Q) + return np.linalg.eigvals(arr)[1] + + +def build_inf_model(xsnames, xslibname, temperature, tempmethod='nearest'): + """ Building an infinite medium for openmc multi-group testing + Parametres: + ---------- + xsnames : list of str() + - list with xs names + xslibname: + - name of hdf5 file with cross-section library + temperature : float + - value of a current temperature in K + tempmethod : str {'nearest', 'interpolstion'} by default 'nearest' + """ + inf_medium = openmc.Material(name='test material', material_id=1) + inf_medium.set_density("sum") + for xs in xsnames: + inf_medium.add_nuclide(xs, 1) + INF = 11.1 + # Instantiate a Materials collection and export to XML + materials_file = openmc.Materials([inf_medium]) + materials_file.cross_sections = xslibname + materials_file.export_to_xml() + + # Instantiate boundary Planes + min_x = openmc.XPlane(boundary_type='reflective', x0=-INF) + max_x = openmc.XPlane(boundary_type='reflective', x0=INF) + min_y = openmc.YPlane(boundary_type='reflective', y0=-INF) + max_y = openmc.YPlane(boundary_type='reflective', y0=INF) + + # Instantiate a Cell + cell = openmc.Cell(cell_id=1, name='cell') + cell.temperature = temperature + # Register bounding Surfaces with the Cell + cell.region = +min_x & -max_x & +min_y & -max_y + + # Fill the Cell with the Material + cell.fill = inf_medium + + # Create root universe + root_universe = openmc.Universe(name='root universe', cells=[cell]) + + # Create Geometry and set root Universe + openmc_geometry = openmc.Geometry(root_universe) + + # Export to "geometry.xml" + openmc_geometry.export_to_xml() + + # OpenMC simulation parameters + batches = 15 + inactive = 5 + particles = 5000 + + # Instantiate a Settings object + settings_file = openmc.Settings() + settings_file.batches = batches + settings_file.inactive = inactive + settings_file.particles = particles + settings_file.energy_mode = 'multi-group' + settings_file.output = {'summary': False} + # Create an initial uniform spatial source distribution over fissionable zones + bounds = [-INF, -INF, -INF, INF, INF, INF] + uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) + settings_file.temperature = {'method': tempmethod} + settings_file.source = openmc.Source(space=uniform_dist) + settings_file.export_to_xml() diff --git a/tests/regression_tests/mg_temperature/test.py b/tests/regression_tests/mg_temperature/test.py new file mode 100644 index 0000000000..355811a137 --- /dev/null +++ b/tests/regression_tests/mg_temperature/test.py @@ -0,0 +1,106 @@ +import os +from tests.regression_tests.mg_temperature.build_2g import * +from tests.testing_harness import * + + +class MgTemperatureTestHarness(TestHarness): + + def execute_test(self): + """Run OpenMC with the appropriate arguments and check the outputs.""" + base_dir = os.getcwd() + print("Base dir is {}".format(base_dir)) + macro_xs = create_openmc_2mg_libs(names) + dirs = ('micro/nearest/case1', 'micro/nearest/case2', + 'micro/nearest/case3', 'micro/interpolation/case1', + 'micro/interpolation/case2', + 'macro/nearest/case1', 'macro/nearest/case2', + 'macro/nearest/case3', 'macro/interpolation/case1', + 'macro/interpolation/case2') + temperatures = (300., 600., 900., + 520., 600., + 300., 600., 900., + 520., 600) + methods = 2 * (3 * ('nearest',) + 2 * ('interpolation',)) + analyt_interp = 10 * [None] + analyt_interp[3] = (600. - 520.) / 300. + analyt_interp[8] = (600. - 520.) / 300. + try: + for d, t, m, ai in zip(dirs, temperatures, methods, analyt_interp): + os.chdir(os.path.join(base_dir, d)) + if (d[:5] == 'macro'): + build_inf_model(['macro'], '../../../macro_2g.h5', t, m) + else: + build_inf_model(names, '../../../micro_2g.h5', t, m) + if not ai: + kanalyt = analytical_solution_2g_therm(macro_xs[t]) + else: + kanalyt = analytical_solution_2g_therm(macro_xs[300], + macro_xs[600], ai) + self._run_openmc() + self._test_output_created() + results = self._get_results() + results += "k-analytical:\n" + results += "{:12.6E}".format(kanalyt) + self._write_results(results) + self._compare_results() + finally: + for d in dirs: + os.chdir(os.path.join(base_dir, d)) + self._cleanup() + os.chdir(base_dir) + for f in ['micro_2g.h5', 'macro_2g.h5']: + if os.path.exists(f): + os.remove(f) + + def update_results(self): + """Update the results_true using the current version of OpenMC.""" + base_dir = os.getcwd() + print("Base dir is {}".format(base_dir)) + macro_xs = create_openmc_2mg_libs(names) + dirs = ('micro/nearest/case1', 'micro/nearest/case2', + 'micro/nearest/case3', 'micro/interpolation/case1', + 'micro/interpolation/case2', + 'macro/nearest/case1', 'macro/nearest/case2', + 'macro/nearest/case3', 'macro/interpolation/case1', + 'macro/interpolation/case2') + temperatures = (300., 600., 900., + 520., 600., + 300., 600., 900., + 520., 600) + methods = 2 * (3 * ('nearest',) + 2 * ('interpolation',)) + analyt_interp = 10 * [None] + analyt_interp[3] = (600. - 520.) / 300. + analyt_interp[8] = (600. - 520.) / 300. + try: + for d, t, m, ai in zip(dirs, temperatures, methods, analyt_interp): + os.chdir(os.path.join(base_dir, d)) + if (d[:5] == 'macro'): + build_inf_model(['macro'], '../../../macro_2g.h5', t) + else: + build_inf_model(names, '../../../micro_2g.h5', t) + if not ai: + kanalyt = analytical_solution_2g_therm(macro_xs[t]) + else: + kanalyt = analytical_solution_2g_therm(macro_xs[300], + macro_xs[600], ai) + self._run_openmc() + self._test_output_created() + results = self._get_results() + results += "k-analytical:\n" + results += "{:12.6E}".format(kanalyt) + self._write_results(results) + self._overwrite_results(results) + self._compare_results() + finally: + for d in dirs: + os.chdir(os.path.join(base_dir, d)) + self._cleanup() + os.chdir(base_dir) + for f in ['micro_2g.h5', 'macro_2g.h5']: + if os.path.exists(f): + os.remove(f) + + +def test_mg_temperature(): + harness = MgTemperatureTestHarness('statepoint.15.h5') + harness.main() From f9e8eda4c413f3ea20dea8f6f01c0998bde68bc1 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Sun, 15 Mar 2020 19:43:18 +0300 Subject: [PATCH 021/205] add a test case dirs and files test case --- .../macro/interpolation/case1/geometry.xml | 8 ++++++++ .../macro/interpolation/case1/materials.xml | 8 ++++++++ .../macro/interpolation/case1/results_true.dat | 4 ++++ .../macro/interpolation/case1/settings.xml | 17 +++++++++++++++++ .../macro/interpolation/case2/geometry.xml | 8 ++++++++ .../macro/interpolation/case2/materials.xml | 8 ++++++++ .../macro/interpolation/case2/results_true.dat | 4 ++++ .../macro/interpolation/case2/settings.xml | 17 +++++++++++++++++ .../macro/nearest/case1/geometry.xml | 8 ++++++++ .../macro/nearest/case1/materials.xml | 8 ++++++++ .../macro/nearest/case1/results_true.dat | 4 ++++ .../macro/nearest/case1/settings.xml | 17 +++++++++++++++++ .../macro/nearest/case2/geometry.xml | 8 ++++++++ .../macro/nearest/case2/materials.xml | 8 ++++++++ .../macro/nearest/case2/results_true.dat | 4 ++++ .../macro/nearest/case2/settings.xml | 17 +++++++++++++++++ .../macro/nearest/case3/geometry.xml | 8 ++++++++ .../macro/nearest/case3/materials.xml | 8 ++++++++ .../macro/nearest/case3/results_true.dat | 4 ++++ .../macro/nearest/case3/settings.xml | 17 +++++++++++++++++ .../micro/interpolation/case1/geometry.xml | 8 ++++++++ .../micro/interpolation/case1/materials.xml | 12 ++++++++++++ .../micro/interpolation/case1/results_true.dat | 4 ++++ .../micro/interpolation/case1/settings.xml | 17 +++++++++++++++++ .../micro/interpolation/case2/geometry.xml | 8 ++++++++ .../micro/interpolation/case2/materials.xml | 12 ++++++++++++ .../micro/interpolation/case2/results_true.dat | 4 ++++ .../micro/interpolation/case2/settings.xml | 17 +++++++++++++++++ .../micro/nearest/case1/geometry.xml | 8 ++++++++ .../micro/nearest/case1/materials.xml | 12 ++++++++++++ .../micro/nearest/case1/results_true.dat | 4 ++++ .../micro/nearest/case1/settings.xml | 17 +++++++++++++++++ .../micro/nearest/case2/geometry.xml | 8 ++++++++ .../micro/nearest/case2/materials.xml | 12 ++++++++++++ .../micro/nearest/case2/results_true.dat | 4 ++++ .../micro/nearest/case2/settings.xml | 17 +++++++++++++++++ .../micro/nearest/case3/geometry.xml | 8 ++++++++ .../micro/nearest/case3/materials.xml | 12 ++++++++++++ .../micro/nearest/case3/results_true.dat | 4 ++++ .../micro/nearest/case3/settings.xml | 17 +++++++++++++++++ 40 files changed, 390 insertions(+) create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat create mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml new file mode 100644 index 0000000000..e66b004c5d --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml new file mode 100644 index 0000000000..e3e2097cb5 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml @@ -0,0 +1,8 @@ + + + ../../../macro_2g.h5 + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat b/tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat new file mode 100644 index 0000000000..8cc89cfb0d --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.422739E+00 1.661420E-03 +k-analytical: +1.418514E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml new file mode 100644 index 0000000000..4a51e8e80a --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + interpolation + diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml new file mode 100644 index 0000000000..838fb9f687 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml new file mode 100644 index 0000000000..e3e2097cb5 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml @@ -0,0 +1,8 @@ + + + ../../../macro_2g.h5 + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat b/tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat new file mode 100644 index 0000000000..965fe8002a --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.410995E+00 2.169214E-03 +k-analytical: +1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml new file mode 100644 index 0000000000..4a51e8e80a --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + interpolation + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml b/tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml new file mode 100644 index 0000000000..f64d315932 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml b/tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml new file mode 100644 index 0000000000..e3e2097cb5 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml @@ -0,0 +1,8 @@ + + + ../../../macro_2g.h5 + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat b/tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat new file mode 100644 index 0000000000..5d7f81f288 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.443970E+00 3.267499E-03 +k-analytical: +1.440410E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml b/tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml new file mode 100644 index 0000000000..b5ad0fec4f --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + nearest + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml b/tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml new file mode 100644 index 0000000000..de56931104 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml b/tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml new file mode 100644 index 0000000000..e3e2097cb5 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml @@ -0,0 +1,8 @@ + + + ../../../macro_2g.h5 + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat b/tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat new file mode 100644 index 0000000000..965fe8002a --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.410995E+00 2.169214E-03 +k-analytical: +1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml b/tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml new file mode 100644 index 0000000000..b5ad0fec4f --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + nearest + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml b/tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml new file mode 100644 index 0000000000..a95b7b55ac --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml b/tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml new file mode 100644 index 0000000000..e3e2097cb5 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml @@ -0,0 +1,8 @@ + + + ../../../macro_2g.h5 + + + + + diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat b/tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat new file mode 100644 index 0000000000..39d3eb5e34 --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.411726E+00 2.151179E-03 +k-analytical: +1.407830E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml b/tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml new file mode 100644 index 0000000000..b5ad0fec4f --- /dev/null +++ b/tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + nearest + diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml new file mode 100644 index 0000000000..b54f27f415 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml new file mode 100644 index 0000000000..98f9236e61 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml @@ -0,0 +1,12 @@ + + + ../../../micro_2g.h5 + + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat b/tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat new file mode 100644 index 0000000000..8cc89cfb0d --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.422739E+00 1.661420E-03 +k-analytical: +1.418514E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml new file mode 100644 index 0000000000..4a51e8e80a --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + interpolation + diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml new file mode 100644 index 0000000000..4eeb35ac88 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml new file mode 100644 index 0000000000..98f9236e61 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml @@ -0,0 +1,12 @@ + + + ../../../micro_2g.h5 + + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat b/tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat new file mode 100644 index 0000000000..965fe8002a --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.410995E+00 2.169214E-03 +k-analytical: +1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml new file mode 100644 index 0000000000..4a51e8e80a --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + interpolation + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml b/tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml new file mode 100644 index 0000000000..e1da2fa5c6 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml b/tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml new file mode 100644 index 0000000000..98f9236e61 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml @@ -0,0 +1,12 @@ + + + ../../../micro_2g.h5 + + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat b/tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat new file mode 100644 index 0000000000..5d7f81f288 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.443970E+00 3.267499E-03 +k-analytical: +1.440410E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml b/tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml new file mode 100644 index 0000000000..b5ad0fec4f --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + nearest + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml b/tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml new file mode 100644 index 0000000000..1ac560e8f7 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml b/tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml new file mode 100644 index 0000000000..98f9236e61 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml @@ -0,0 +1,12 @@ + + + ../../../micro_2g.h5 + + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat b/tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat new file mode 100644 index 0000000000..965fe8002a --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.410995E+00 2.169214E-03 +k-analytical: +1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml b/tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml new file mode 100644 index 0000000000..b5ad0fec4f --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + nearest + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml b/tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml new file mode 100644 index 0000000000..c8d1164f13 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml b/tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml new file mode 100644 index 0000000000..98f9236e61 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml @@ -0,0 +1,12 @@ + + + ../../../micro_2g.h5 + + + + + + + + + diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat b/tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat new file mode 100644 index 0000000000..39d3eb5e34 --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat @@ -0,0 +1,4 @@ +k-combined: +1.411726E+00 2.151179E-03 +k-analytical: +1.407830E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml b/tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml new file mode 100644 index 0000000000..b5ad0fec4f --- /dev/null +++ b/tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml @@ -0,0 +1,17 @@ + + + eigenvalue + 5000 + 15 + 5 + + + -11.1 -11.1 -11.1 11.1 11.1 11.1 + + + + false + + multi-group + nearest + From 8d4b34adb674126c62050e3f469c06d407c11915 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Sun, 15 Mar 2020 22:53:15 +0300 Subject: [PATCH 022/205] add init.py --- tests/regression_tests/mg_temperature/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 tests/regression_tests/mg_temperature/__init__.py diff --git a/tests/regression_tests/mg_temperature/__init__.py b/tests/regression_tests/mg_temperature/__init__.py new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/tests/regression_tests/mg_temperature/__init__.py @@ -0,0 +1 @@ + From b8763f1934a0ac23110ee15a7374fbc7724836c2 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Mon, 16 Mar 2020 15:07:12 +0300 Subject: [PATCH 023/205] test TRAVIS CI commit is to make sure of TRAVIS CI works properly --- tests/regression_tests/mg_temperature/build_2g.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/regression_tests/mg_temperature/build_2g.py b/tests/regression_tests/mg_temperature/build_2g.py index bfdd7f282f..86ba17cea7 100644 --- a/tests/regression_tests/mg_temperature/build_2g.py +++ b/tests/regression_tests/mg_temperature/build_2g.py @@ -276,7 +276,6 @@ def build_inf_model(xsnames, xslibname, temperature, tempmethod='nearest'): batches = 15 inactive = 5 particles = 5000 - # Instantiate a Settings object settings_file = openmc.Settings() settings_file.batches = batches From a6076f07558e25ab5c56ce83c0374946b86b6dff Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Mon, 16 Mar 2020 14:31:24 +0000 Subject: [PATCH 024/205] Address comments by @ChasingNeutrons --- openmc/cell.py | 24 ++++++++++++------------ tests/unit_tests/test_cell.py | 8 ++++---- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index f2884e494b..9a8bce2ccd 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -141,7 +141,7 @@ class Cell(IDManagerMixin): self.temperature) string += '{: <16}=\t{}\n'.format('\tTranslation', self.translation) - # Print Volume only when its set to avoid breaking regression + # Print volume only when it is set to avoid breaking regression if self._volume is not None: string += '{: <16}=\t{}\n'.format('\tVolume', self.volume) @@ -199,8 +199,8 @@ class Cell(IDManagerMixin): Returns ------- atoms: collections.OrderedDict - Dictionary which keys are nuclides and values the number of atoms. - For example, {'H1':1.0e22, 'O16':0.5e22, ...} + Dictionary in which keys are nuclides and values the number of + atoms. For example, {'H1':1.0e22, 'O16':0.5e22, ...} Raises ------ @@ -211,24 +211,24 @@ class Cell(IDManagerMixin): """ if self._atoms is None: if self._volume is None: - msg = ('Cannot calculate atoms content becouse no volume ' + msg = ('Cannot calculate atom content becouse no volume ' 'is set. Use Cell.volume to provide it or perform ' - 'stochastic volume calculation.') + 'a stochastic volume calculation.') raise ValueError(msg) elif self.fill_type == 'void': msg = ('Cell is filled with void. It contains no atoms. ' - 'Material must be set to calculate atoms content.') + 'Material must be set to calculate atom content.') raise ValueError(msg) elif self.fill_type in ['lattice', 'universe']: msg = ('Universe and Lattice cells can contain multiple ' - 'materials in diffrent proportions. Atoms content must ' + 'materials in diffrent proportions. Atom content must ' 'be calculated with stochastic volume calculation') raise ValueError(msg) elif self.fill_type == 'material': - # Get atomic Densities + # Get atomic densities self._atoms = self._fill.get_nuclide_atom_densities() # Convert to total number of atoms @@ -303,8 +303,8 @@ class Cell(IDManagerMixin): self._fill = fill - # Info about atoms content can now be invalid - # (sice fill has just changed) + # Info about atom content can now be invalid + # (since fill has just changed) self._atoms = None @rotation.setter @@ -366,7 +366,7 @@ class Cell(IDManagerMixin): cv.check_type('cell volume', volume, (Real, UFloat)) # Note that ufloat(0.0, 0.1) >= 0.0 is False - # we need special treatment for UFloat input + # We need special treatment for UFloat input val = volume if isinstance(val, UFloat): val = volume.nominal_value @@ -374,7 +374,7 @@ class Cell(IDManagerMixin): self._volume = volume - # Info about atoms content can now be invalid + # Info about atom content can now be invalid # (sice volume has just changed) self._atoms = None diff --git a/tests/unit_tests/test_cell.py b/tests/unit_tests/test_cell.py index d682c29c79..30eb82ca1c 100644 --- a/tests/unit_tests/test_cell.py +++ b/tests/unit_tests/test_cell.py @@ -35,11 +35,11 @@ def test_repr(cell_with_lattice): c = openmc.Cell() repr(c) - # Empty cell with Volume + # Empty cell with volume c.volume = 3.0 repr(c) - # Empty cell with Uncertain Volume + # Empty cell with uncertain volume c.volume = u.ufloat(3.0, 0.2) repr(c) @@ -214,7 +214,7 @@ def test_atoms_distribmat_cell(uo2, water): def test_atoms_errors(cell_with_lattice): cells, mats, univ, lattice = cell_with_lattice - # Material Cell with no Volume + # Material Cell with no volume with pytest.raises(ValueError): cells[1].atoms @@ -223,7 +223,7 @@ def test_atoms_errors(cell_with_lattice): cells[2].volume = 3 cells[2].atoms - # Cell with volume but with Void fill + # Cell with volume but with void fill with pytest.raises(ValueError): cells[1].volume = 2 cells[1].fill = None From 0d70d0ef4c8d5d1d78960b4df2e93067ffa29679 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 16 Mar 2020 10:15:16 -0500 Subject: [PATCH 025/205] Add single assembly example model --- examples/assembly/assembly.py | 140 ++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 examples/assembly/assembly.py diff --git a/examples/assembly/assembly.py b/examples/assembly/assembly.py new file mode 100644 index 0000000000..d4c8fe5b96 --- /dev/null +++ b/examples/assembly/assembly.py @@ -0,0 +1,140 @@ +""" +This script builds a single PWR assembly and is a slightly more advanced +demonstration of model building using Python. The creation of two universes for +fuel pins and guide tube pins has been separated into functions, and then the +overall model is built by an `assembly` function. This script also demonstrates +the use of the `Model` class, which provides some extra convenience over using +`Geometry`, `Materials`, and `Settings` classes directly. Finally, the script +takes two command-line flags that indicate whether to build and/or run the +model. + +""" + +import argparse +from math import log10 + +import numpy as np +import openmc + +# Define surfaces +fuel_or = openmc.ZCylinder(r=0.39218, name='Fuel OR') +clad_or = openmc.ZCylinder(r=0.45720, name='Clad OR') + +# Define materials +fuel = openmc.Material(name='Fuel') +fuel.set_density('g/cm3', 10.29769) +fuel.add_nuclide('U234', 4.4843e-6) +fuel.add_nuclide('U235', 5.5815e-4) +fuel.add_nuclide('U238', 2.2408e-2) +fuel.add_nuclide('O16', 4.5829e-2) + +clad = openmc.Material(name='Cladding') +clad.set_density('g/cm3', 6.55) +clad.add_nuclide('Zr90', 2.1827e-2) +clad.add_nuclide('Zr91', 4.7600e-3) +clad.add_nuclide('Zr92', 7.2758e-3) +clad.add_nuclide('Zr94', 7.3734e-3) +clad.add_nuclide('Zr96', 1.1879e-3) + +hot_water = openmc.Material(name='Hot borated water') +hot_water.set_density('g/cm3', 0.740582) +hot_water.add_nuclide('H1', 4.9457e-2) +hot_water.add_nuclide('O16', 2.4672e-2) +hot_water.add_nuclide('B10', 8.0042e-6) +hot_water.add_nuclide('B11', 3.2218e-5) +hot_water.add_s_alpha_beta('c_H_in_H2O') + + +def fuel_pin(): + """Returns a fuel pin universe.""" + + fuel_cell = openmc.Cell(fill=fuel, region=-fuel_or) + clad_cell = openmc.Cell(fill=clad, region=+fuel_or & -clad_or) + hot_water_cell = openmc.Cell(fill=hot_water, region=+clad_or) + + univ = openmc.Universe(name='Fuel Pin') + univ.add_cells([fuel_cell, clad_cell, hot_water_cell]) + return univ + + +def guide_tube_pin(): + """Returns a control rode guide tube pin universe""" + + gt_inner_cell = openmc.Cell(fill=hot_water, region=-fuel_or) + gt_clad_cell = openmc.Cell(fill=clad, region=+fuel_or & -clad_or) + gt_outer_cell = openmc.Cell(fill=hot_water, region=+clad_or) + + univ = openmc.Universe(name='Guide Tube') + univ.add_cells([gt_inner_cell, gt_clad_cell, gt_outer_cell]) + return univ + + +def assembly_model(): + """Returns a single PWR fuel assembly.""" + + model = openmc.model.Model() + + # Create fuel assembly Lattice + pitch = 21.42 + assembly = openmc.RectLattice(name='Fuel Assembly') + assembly.pitch = (pitch/17, pitch/17) + assembly.lower_left = (-pitch/2, -pitch/2) + + # Create array indices for guide tube locations in lattice + gt_pos = np.array([ + [2, 5], [2, 8], [2, 11], + [3, 3], [3, 13], + [5, 2], [5, 5], [5, 8], [5, 11], [5, 14], + [8, 2], [8, 5], [8, 8], [8, 11], [8, 14], + [11, 2], [11, 5], [11, 8], [11, 11], [11, 14], + [13, 3], [13, 13], + [14, 5], [14, 8], [14, 11] + ]) + + # Create 17x17 array of universes. First we create a 17x17 array all filled + # with the fuel pin universe. Then, we replace the guide tube positions with + # the guide tube pin universe (note the use of numpy fancy indexing to + # achieve this). + assembly.universes = np.full((17, 17), fuel_pin()) + assembly.universes[gt_pos[:, 0], gt_pos[:, 1]] = guide_tube_pin() + + # Create outer boundary of the geometry to surround the lattice + outer_boundary = openmc.model.rectangular_prism( + pitch, pitch, boundary_type='reflective') + + # Create a cell filled with the lattice + main_cell = openmc.Cell(fill=assembly, region=outer_boundary) + + # Finally, create geometry by giving a list of cells filling the root + # universe + model.geometry = openmc.Geometry([main_cell]) + + model.settings.batches = 150 + model.settings.inactive = 50 + model.settings.particles = 1000 + model.settings.source = openmc.Source(space=openmc.stats.Box( + (-pitch/2, -pitch/2, -1), + (pitch/2, pitch/2, 1), + only_fissionable=True + )) + + # NOTE: We never actually created a Materials object. When you export/run + # using the Model object, if no materials were assigned it will look through + # the Geometry object and automatically export any materials that are + # necessary to build the model. + return model + + +if __name__ == '__main__': + # Set up command-line arguments for generating/running the model + parser = argparse.ArgumentParser() + parser.add_argument('--generate', action='store_true') + parser.add_argument('--run', action='store_true') + args = parser.parse_args() + + if args.generate or args.run: + model = assembly_model() + if args.generate: + model.export_to_xml() + if args.run: + model.run() From 5b7929b6ef178fce979ba6e02b0950a228fd8bf4 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 17 Mar 2020 10:40:53 -0500 Subject: [PATCH 026/205] Ensure NJOY output for multiple temperatures goes to specified output dir --- openmc/data/njoy.py | 48 ++++++++++++++++++++++++--------------------- 1 file changed, 26 insertions(+), 22 deletions(-) diff --git a/openmc/data/njoy.py b/openmc/data/njoy.py index 0c853102f3..69035f7071 100644 --- a/openmc/data/njoy.py +++ b/openmc/data/njoy.py @@ -411,8 +411,8 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, commands += _TEMPLATE_ACER.format(**locals()) # Indicate tapes to save for each ACER run - tapeout[nace] = fname.format("ace", temperature) - tapeout[ndir] = fname.format("xsdir", temperature) + tapeout[nace] = output_dir / fname.format("ace", temperature) + tapeout[ndir] = output_dir / fname.format("xsdir", temperature) commands += 'stop\n' run(commands, tapein, tapeout, **kwargs) @@ -422,7 +422,7 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, with ace.open('w') as ace_file, xsdir.open('w') as xsdir_file: for temperature in temperatures: # Get contents of ACE file - text = open(fname.format("ace", temperature), 'r').read() + text = open(output_dir / fname.format("ace", temperature), 'r').read() # If the target is metastable, make sure that ZAID in the ACE # file reflects this by adding 400 @@ -435,18 +435,13 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, ace_file.write(text) # Concatenate into destination xsdir file - text = open(fname.format("xsdir", temperature), 'r').read() + text = open(output_dir / fname.format("xsdir", temperature), 'r').read() xsdir_file.write(text) - # Remove ACE/xsdir files for each temperature - for temperature in temperatures: - os.remove(fname.format("ace", temperature)) - os.remove(fname.format("xsdir", temperature)) - def make_ace_thermal(filename, filename_thermal, temperatures=None, - ace='ace', xsdir='xsdir', error=0.001, iwt=2, - evaluation=None, evaluation_thermal=None, **kwargs): + ace='ace', xsdir=None, output_dir=None, error=0.001, + iwt=2, evaluation=None, evaluation_thermal=None, **kwargs): """Generate thermal scattering ACE file from ENDF files Parameters @@ -461,7 +456,12 @@ def make_ace_thermal(filename, filename_thermal, temperatures=None, ace : str, optional Path of ACE file to write xsdir : str, optional - Path of xsdir file to write + Path of xsdir file to write. Defaults to ``"xsdir"`` in the same + directory as ``ace`` + output_dir : str, optional + Directory to write ace and xsdir files. If not provided, then write + output files to current directory. If given, must be a path to a + directory. error : float, optional Fractional error tolerance for NJOY processing iwt : int @@ -481,6 +481,13 @@ def make_ace_thermal(filename, filename_thermal, temperatures=None, If the NJOY process returns with a non-zero status """ + if output_dir is None: + output_dir = Path() + else: + output_dir = Path(output_dir) + if not output_dir.is_dir(): + raise IOError("{} is not a directory".format(output_dir)) + ev = evaluation if evaluation is not None else endf.Evaluation(filename) mat = ev.material zsymam = ev.target['zsymam'] @@ -573,21 +580,18 @@ def make_ace_thermal(filename, filename_thermal, temperatures=None, commands += _THERMAL_TEMPLATE_ACER.format(**locals()) # Indicate tapes to save for each ACER run - tapeout[nace] = fname.format(ace, temperature) - tapeout[ndir] = fname.format(xsdir, temperature) + tapeout[nace] = output_dir / fname.format(ace, temperature) + tapeout[ndir] = output_dir / fname.format(xsdir, temperature) commands += 'stop\n' run(commands, tapein, tapeout, **kwargs) - with open(ace, 'w') as ace_file, open(xsdir, 'w') as xsdir_file: + ace_out = (output_dir / ace) + xsdir = (ace.parent / "xsdir") if xsdir is None else xsdir + with open(ace_out, 'w') as ace_file, open(xsdir, 'w') as xsdir_file: # Concatenate ACE and xsdir files together for temperature in temperatures: - text = open(fname.format(ace, temperature), 'r').read() + text = open(output_dir / fname.format(ace, temperature), 'r').read() ace_file.write(text) - text = open(fname.format(xsdir, temperature), 'r').read() + text = open(output_dir / fname.format(xsdir, temperature), 'r').read() xsdir_file.write(text) - - # Remove ACE/xsdir files for each temperature - for temperature in temperatures: - os.remove(fname.format(ace, temperature)) - os.remove(fname.format(xsdir, temperature)) From 9a782c80675f392c85c93c60401f88d826e2bf3c Mon Sep 17 00:00:00 2001 From: Mikolaj-A-Kowalski <32641577+Mikolaj-A-Kowalski@users.noreply.github.com> Date: Tue, 17 Mar 2020 18:56:36 +0000 Subject: [PATCH 027/205] Apply suggestions from code review Co-Authored-By: Simon Richards --- docs/source/usersguide/geometry.rst | 10 +++++----- openmc/cell.py | 6 +++--- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index be2c92a12d..4b925c448a 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -443,8 +443,8 @@ named ``dagmc.h5m``) when initializing a simulation. If a `geometry.xml Calculating Atoms Content ------------------------- -If the total volume occupied by all instances of a cell in a geometry is known -by a user, it is possible to assign it to a cell without a stochastic volume +If the total volume occupied by all instances of a cell in the geometry is known +by the user, it is possible to assign this volume to a cell without performing a stochastic volume calculation:: from uncertainties import ufloat @@ -456,9 +456,9 @@ calculation:: # Set volume if it is known with some uncertainty cell.volume = ufloat(17.0, 0.1) -Once a volume is set and a cell is filled with a material or distributed -materials. It is possible to use :func:`~openmc.Cell.atoms` method to obtain -a dictionary that maps nuclides to a total number of atoms in all instances +Once a volume is set, and a cell is filled with a material or distributed +materials, it is possible to use the :func:`~openmc.Cell.atoms` method to obtain +a dictionary of nuclides and their total number of atoms in all instances of a cell (e.g. ``{'H1':1.0e22, 'O16':0.5e22, ...}``):: cell = openmc.Cell(fill = u02) diff --git a/openmc/cell.py b/openmc/cell.py index 9a8bce2ccd..9b16c36e39 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -87,10 +87,10 @@ class Cell(IDManagerMixin): Volume of the cell in cm^3. This can either be set manually or calculated in a stochastic volume calculation and added via the :meth:`Cell.add_volume_information` method. For 'distribmat' cells - it is a total volume of all instances. + it is the total volume of all instances. atoms : dict - Mapping of nuclides to total number of atoms for each nuclide present - in the cell, or all its instances for 'sdistribmat' fill. For example, + Mapping of nuclides to the total number of atoms for each nuclide present + in the cell, or in all of its instances for a 'distribmat' fill. For example, {'U235': 1.0e22, 'U238': 5.0e22, ...}. """ From 7ee102795f643f047826cc0a7dbe012fd9aca87f Mon Sep 17 00:00:00 2001 From: Mikolaj-A-Kowalski <32641577+Mikolaj-A-Kowalski@users.noreply.github.com> Date: Tue, 17 Mar 2020 19:15:09 +0000 Subject: [PATCH 028/205] Remove non-default tolerance from test_cell.py --- tests/unit_tests/test_cell.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/tests/unit_tests/test_cell.py b/tests/unit_tests/test_cell.py index 30eb82ca1c..1a01a0f6c1 100644 --- a/tests/unit_tests/test_cell.py +++ b/tests/unit_tests/test_cell.py @@ -9,9 +9,6 @@ import pytest from tests.unit_tests import assert_unbounded from openmc.data import atomic_mass, AVOGADRO -# Relative tolerance for float comparison -TOL = 1e-9 - def test_contains(): # Cell with specified region @@ -172,7 +169,7 @@ def test_atoms_material_cell(uo2, water): tuples = list(c.atoms.items()) for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] - assert atom_num == pytest.approx(t[1], rel=TOL) + assert atom_num == pytest.approx(t[1]) # Change material and check if OK c.fill = water @@ -185,7 +182,7 @@ def test_atoms_material_cell(uo2, water): tuples = list(c.atoms.items()) for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] - assert atom_num == pytest.approx(t[1], rel=TOL) + assert atom_num == pytest.approx(t[1]) def test_atoms_distribmat_cell(uo2, water): @@ -208,7 +205,7 @@ def test_atoms_distribmat_cell(uo2, water): tuples = list(c.atoms.items()) for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] - assert atom_num == pytest.approx(t[1], rel=TOL) + assert atom_num == pytest.approx(t[1]) def test_atoms_errors(cell_with_lattice): @@ -237,7 +234,7 @@ def test_nuclide_densities(uo2): tuples = list(c.get_nuclide_densities().values()) for nuc, density, t in zip(expected_nucs, expected_density, tuples): assert nuc == t[0] - assert density == pytest.approx(t[1], rel=TOL) + assert density == pytest.approx(t[1]) # Empty cell c = openmc.Cell() From c3ffc2f4172c50be1bd37a73b5c7d39ea952f7f5 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 17 Mar 2020 16:49:53 -0500 Subject: [PATCH 029/205] Apply @pshriwise suggestions from code review Co-Authored-By: Patrick Shriwise --- examples/assembly/assembly.py | 4 ++-- examples/custom_source/show_flux.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/assembly/assembly.py b/examples/assembly/assembly.py index d4c8fe5b96..31984543a6 100644 --- a/examples/assembly/assembly.py +++ b/examples/assembly/assembly.py @@ -58,7 +58,7 @@ def fuel_pin(): def guide_tube_pin(): - """Returns a control rode guide tube pin universe""" + """Returns a control rod guide tube universe.""" gt_inner_cell = openmc.Cell(fill=hot_water, region=-fuel_or) gt_clad_cell = openmc.Cell(fill=clad, region=+fuel_or & -clad_or) @@ -105,7 +105,7 @@ def assembly_model(): # Create a cell filled with the lattice main_cell = openmc.Cell(fill=assembly, region=outer_boundary) - # Finally, create geometry by giving a list of cells filling the root + # Finally, create geometry by providing a list of cells that fill the root # universe model.geometry = openmc.Geometry([main_cell]) diff --git a/examples/custom_source/show_flux.py b/examples/custom_source/show_flux.py index 9c49e1978c..bf8ee4f813 100644 --- a/examples/custom_source/show_flux.py +++ b/examples/custom_source/show_flux.py @@ -15,4 +15,4 @@ plt.show() # If all worked well, you should see a ring "imprint" as well as a higher flux # to the right side (since the custom source has all particles moving in the -# positive x direction)) +# positive x direction) From 3a79748ec33a5dd5f9f4c0e376b1060bcf9d9aec Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 17 Mar 2020 14:37:40 -0500 Subject: [PATCH 030/205] Fix for Python 3.5 --- openmc/data/njoy.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/openmc/data/njoy.py b/openmc/data/njoy.py index 69035f7071..c9bbd9c85b 100644 --- a/openmc/data/njoy.py +++ b/openmc/data/njoy.py @@ -422,7 +422,7 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, with ace.open('w') as ace_file, xsdir.open('w') as xsdir_file: for temperature in temperatures: # Get contents of ACE file - text = open(output_dir / fname.format("ace", temperature), 'r').read() + text = (output_dir / fname.format("ace", temperature)).read_text() # If the target is metastable, make sure that ZAID in the ACE # file reflects this by adding 400 @@ -435,8 +435,8 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, ace_file.write(text) # Concatenate into destination xsdir file - text = open(output_dir / fname.format("xsdir", temperature), 'r').read() - xsdir_file.write(text) + xsdir_in = output_dir / fname.format("xsdir", temperature) + xsdir_file.write(xsdir_in.read_text()) def make_ace_thermal(filename, filename_thermal, temperatures=None, @@ -585,13 +585,13 @@ def make_ace_thermal(filename, filename_thermal, temperatures=None, commands += 'stop\n' run(commands, tapein, tapeout, **kwargs) - ace_out = (output_dir / ace) - xsdir = (ace.parent / "xsdir") if xsdir is None else xsdir - with open(ace_out, 'w') as ace_file, open(xsdir, 'w') as xsdir_file: + ace_out = output_dir / ace + xsdir = (ace.parent / "xsdir") if xsdir is None else Path(xsdir) + with ace_out.open('w') as ace_file, xsdir.open('w') as xsdir_file: # Concatenate ACE and xsdir files together for temperature in temperatures: - text = open(output_dir / fname.format(ace, temperature), 'r').read() - ace_file.write(text) + ace_in = output_dir / fname.format(ace, temperature) + ace_file.write(ace_in.read_text()) - text = open(output_dir / fname.format(xsdir, temperature), 'r').read() - xsdir_file.write(text) + xsdir_in = output_dir / fname.format(xsdir, temperature) + xsdir_file.write(xsdir_in.read_text()) From 06ffd256bc492f671e10f864ffeabaedf696a305 Mon Sep 17 00:00:00 2001 From: Mikolaj-A-Kowalski <32641577+Mikolaj-A-Kowalski@users.noreply.github.com> Date: Thu, 19 Mar 2020 11:23:12 +0000 Subject: [PATCH 031/205] Apply suggestions from code review Co-Authored-By: Patrick Shriwise --- openmc/cell.py | 10 +++++----- tests/unit_tests/test_cell.py | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index 9b16c36e39..0173ac4955 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -88,7 +88,7 @@ class Cell(IDManagerMixin): calculated in a stochastic volume calculation and added via the :meth:`Cell.add_volume_information` method. For 'distribmat' cells it is the total volume of all instances. - atoms : dict + atoms : collections.OrderedDict Mapping of nuclides to the total number of atoms for each nuclide present in the cell, or in all of its instances for a 'distribmat' fill. For example, {'U235': 1.0e22, 'U238': 5.0e22, ...}. @@ -199,7 +199,7 @@ class Cell(IDManagerMixin): Returns ------- atoms: collections.OrderedDict - Dictionary in which keys are nuclides and values the number of + Dictionary in which keys are nuclides and values are the number of atoms. For example, {'H1':1.0e22, 'O16':0.5e22, ...} Raises @@ -224,7 +224,7 @@ class Cell(IDManagerMixin): elif self.fill_type in ['lattice', 'universe']: msg = ('Universe and Lattice cells can contain multiple ' 'materials in diffrent proportions. Atom content must ' - 'be calculated with stochastic volume calculation') + 'be calculated with stochastic volume calculation.') raise ValueError(msg) elif self.fill_type == 'material': @@ -244,14 +244,14 @@ class Cell(IDManagerMixin): for mat in self.fill: for key, nuclide in mat.get_nuclide_atom_densities().items(): # To account for overlap of nuclides between distribmat - # we need to append new atoms # to any existing value + # we need to append new atoms to any existing value # hence it is necessary to ask for default. atom = self._atoms.setdefault(key, 0) atom += nuclide[1] * partial_volume * 1.0e+24 self._atoms[key] = atom else: - msg = 'Unrecognised fill_type:{}'.format(self.fill_type) + msg = 'Unrecognised fill_type: {}'.format(self.fill_type) raise ValueError(msg) return self._atoms diff --git a/tests/unit_tests/test_cell.py b/tests/unit_tests/test_cell.py index 1a01a0f6c1..e5ab582016 100644 --- a/tests/unit_tests/test_cell.py +++ b/tests/unit_tests/test_cell.py @@ -150,7 +150,7 @@ def test_atoms_material_cell(uo2, water): expected_nucs = ['U235', 'O16'] # Precalculate the expected number of atoms - M = ((atomic_mass('U235') + 2 * atomic_mass('O16'))/3) + M = (atomic_mass('U235') + 2 * atomic_mass('O16')) / 3 expected_atoms = list() expected_atoms.append(1/3 * uo2.density/M * AVOGADRO * 2.0) # U235 expected_atoms.append(2/3 * uo2.density/M * AVOGADRO * 2.0) # O16 @@ -174,7 +174,7 @@ def test_atoms_material_cell(uo2, water): # Change material and check if OK c.fill = water expected_nucs = ['H1', 'O16'] - M = ((2 * atomic_mass('H1') + atomic_mass('O16'))/3) + M = (2 * atomic_mass('H1') + atomic_mass('O16')) / 3 expected_atoms = list() expected_atoms.append(2/3 * water.density/M * AVOGADRO * 3.0) # H1 expected_atoms.append(1/3 * water.density/M * AVOGADRO * 3.0) # O16 @@ -194,8 +194,8 @@ def test_atoms_distribmat_cell(uo2, water): # Calculate the expected number of atoms expected_nucs = ['U235', 'O16', 'H1'] - M_uo2 = ((atomic_mass('U235') + 2 * atomic_mass('O16'))/3) - M_water = ((2 * atomic_mass('H1') + atomic_mass('O16'))/3) + M_uo2 = (atomic_mass('U235') + 2 * atomic_mass('O16')) / 3 + M_water = (2 * atomic_mass('H1') + atomic_mass('O16')) / 3 expected_atoms = list() expected_atoms.append(1/3 * uo2.density/M_uo2 * AVOGADRO * 3.0) # U235 expected_atoms.append(2/3 * uo2.density/M_uo2 * AVOGADRO * 3.0 + From ec102212193ca5fa683dd736ca52452e8fb0923d Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Thu, 19 Mar 2020 12:04:44 +0000 Subject: [PATCH 032/205] Address comments by @pshriwise --- docs/source/usersguide/geometry.rst | 4 ++-- openmc/cell.py | 19 +++++-------------- .../distribmat/results_true.dat | 1 + .../multipole/results_true.dat | 1 + tests/unit_tests/test_cell.py | 10 +++++----- 5 files changed, 14 insertions(+), 21 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index 4b925c448a..f95a1ff1cf 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -188,8 +188,8 @@ the :class:`openmc.Cell` class:: In this example, an instance of :class:`openmc.Material` is assigned to the :attr:`Cell.fill` attribute. One can also fill a cell with a :ref:`universe -` or :ref:`lattice `. If you provide -no fill to a cell, it will be filled with void on export to XML. +` or :ref:`lattice `. If no fill +is provided to a cell, it will be filled with void by default. The classes :class:`Halfspace`, :class:`Intersection`, :class:`Union`, and :class:`Complement` and all instances of :class:`openmc.Region` and can be diff --git a/openmc/cell.py b/openmc/cell.py index 0173ac4955..0b6bd4427a 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -89,9 +89,9 @@ class Cell(IDManagerMixin): :meth:`Cell.add_volume_information` method. For 'distribmat' cells it is the total volume of all instances. atoms : collections.OrderedDict - Mapping of nuclides to the total number of atoms for each nuclide present - in the cell, or in all of its instances for a 'distribmat' fill. For example, - {'U235': 1.0e22, 'U238': 5.0e22, ...}. + Mapping of nuclides to the total number of atoms for each nuclide + present in the cell, or in all of its instances for a 'distribmat' + fill. For example, {'U235': 1.0e22, 'U238': 5.0e22, ...}. """ @@ -140,10 +140,7 @@ class Cell(IDManagerMixin): string += '\t{0: <15}=\t{1}\n'.format('Temperature', self.temperature) string += '{: <16}=\t{}\n'.format('\tTranslation', self.translation) - - # Print volume only when it is set to avoid breaking regression - if self._volume is not None: - string += '{: <16}=\t{}\n'.format('\tVolume', self.volume) + string += '{: <16}=\t{}\n'.format('\tVolume', self.volume) return string @@ -364,13 +361,7 @@ class Cell(IDManagerMixin): def volume(self, volume): if volume is not None: cv.check_type('cell volume', volume, (Real, UFloat)) - - # Note that ufloat(0.0, 0.1) >= 0.0 is False - # We need special treatment for UFloat input - val = volume - if isinstance(val, UFloat): - val = volume.nominal_value - cv.check_greater_than('cell volume', val, 0.0) + cv.check_greater_than('cell volume', volume, 0.0, equality=True) self._volume = volume diff --git a/tests/regression_tests/distribmat/results_true.dat b/tests/regression_tests/distribmat/results_true.dat index e0143f0fff..5cf99d6ba9 100644 --- a/tests/regression_tests/distribmat/results_true.dat +++ b/tests/regression_tests/distribmat/results_true.dat @@ -7,3 +7,4 @@ Cell Region = -9 Rotation = None Translation = None + Volume = None diff --git a/tests/regression_tests/multipole/results_true.dat b/tests/regression_tests/multipole/results_true.dat index 890e47efd9..2273c03fde 100644 --- a/tests/regression_tests/multipole/results_true.dat +++ b/tests/regression_tests/multipole/results_true.dat @@ -39,3 +39,4 @@ Cell Rotation = None Temperature = [500. 700. 0. 800.] Translation = None + Volume = None diff --git a/tests/unit_tests/test_cell.py b/tests/unit_tests/test_cell.py index e5ab582016..4ab5962503 100644 --- a/tests/unit_tests/test_cell.py +++ b/tests/unit_tests/test_cell.py @@ -130,13 +130,13 @@ def test_volume_setting(): c.volume = 3 c.volume = u.ufloat(3, 0.7) - # Test errors for -ve and 0 volume - with pytest.raises(ValueError): - c.volume = 0.0 + # Allow volume to be set to 0.0 + c.volume = 0.0 + c.volume = u.ufloat(0.0, 0.1) + + # Test errors for -ve volume with pytest.raises(ValueError): c.volume = -1.0 - with pytest.raises(ValueError): - c.volume = u.ufloat(0.0, 0.1) with pytest.raises(ValueError): c.volume = u.ufloat(-0.05, 0.1) From 32f5fd8854fbd9ddcc85eda37248c943d1e7f04f Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 07:07:32 -0500 Subject: [PATCH 033/205] Add README to custom source example --- examples/custom_source/README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 examples/custom_source/README.md diff --git a/examples/custom_source/README.md b/examples/custom_source/README.md new file mode 100644 index 0000000000..9a22171ae4 --- /dev/null +++ b/examples/custom_source/README.md @@ -0,0 +1,14 @@ +# Building a Custom Source + +To run this example, you first need to compile the custom source library, which +requires headers from OpenMC. A CMakeLists.txt file has been set up for you that +will search for OpenMC and build the custom library. To build the source +library, you can run: + + mkdir build && cd build + OPENMC_ROOT= cmake .. + make + +After this, you can build the model by running `python build_xml.py`. In the XML +files that are created, you should see a reference to build/libsource.so, the +custom source library that was built by CMake. From 2debfc0d8a56e969c76af97ac377232a2d69e744 Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Thu, 19 Mar 2020 15:40:27 +0000 Subject: [PATCH 034/205] Address comments by @paulromano --- docs/source/usersguide/geometry.rst | 10 +++++----- openmc/cell.py | 15 --------------- 2 files changed, 5 insertions(+), 20 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index f95a1ff1cf..22158c8217 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -183,13 +183,13 @@ the :class:`openmc.Cell` class:: fuel.fill = uo2 fuel.region = pellet - # This cell will be filled with void on export to XML - gap = openmc.Cell(region=pellet_gap) - In this example, an instance of :class:`openmc.Material` is assigned to the :attr:`Cell.fill` attribute. One can also fill a cell with a :ref:`universe ` or :ref:`lattice `. If no fill -is provided to a cell, it will be filled with void by default. +is provided to a cell, it will be filled with void by default:: + + # This cell will be filled with void on export to XML + gap = openmc.Cell(region=pellet_gap) The classes :class:`Halfspace`, :class:`Intersection`, :class:`Union`, and :class:`Complement` and all instances of :class:`openmc.Region` and can be @@ -459,7 +459,7 @@ calculation:: Once a volume is set, and a cell is filled with a material or distributed materials, it is possible to use the :func:`~openmc.Cell.atoms` method to obtain a dictionary of nuclides and their total number of atoms in all instances -of a cell (e.g. ``{'H1':1.0e22, 'O16':0.5e22, ...}``):: +of a cell (e.g. ``{'H1': 1.0e22, 'O16': 0.5e22, ...}``):: cell = openmc.Cell(fill = u02) cell.volume = 17.0 diff --git a/openmc/cell.py b/openmc/cell.py index 0b6bd4427a..4fe7ae3a4b 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -191,21 +191,6 @@ class Cell(IDManagerMixin): @property def atoms(self): - """ Get total number of atoms of each nuclide in the cell - - Returns - ------- - atoms: collections.OrderedDict - Dictionary in which keys are nuclides and values are the number of - atoms. For example, {'H1':1.0e22, 'O16':0.5e22, ...} - - Raises - ------ - ValueError - If total volume of the cell is not set - ValueError - If cell is filled with Universe, Lattice or Void - """ if self._atoms is None: if self._volume is None: msg = ('Cannot calculate atom content becouse no volume ' From 671c90235039d0f4a97103bd5ba6af2803c4327c Mon Sep 17 00:00:00 2001 From: Mikolaj-A-Kowalski <32641577+Mikolaj-A-Kowalski@users.noreply.github.com> Date: Thu, 19 Mar 2020 15:43:20 +0000 Subject: [PATCH 035/205] Apply suggestions from code review Co-Authored-By: Paul Romano --- docs/source/usersguide/geometry.rst | 4 +- openmc/cell.py | 2 +- tests/unit_tests/test_cell.py | 58 +++++++++++++++-------------- 3 files changed, 34 insertions(+), 30 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index 22158c8217..f550327b24 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -444,8 +444,8 @@ Calculating Atoms Content ------------------------- If the total volume occupied by all instances of a cell in the geometry is known -by the user, it is possible to assign this volume to a cell without performing a stochastic volume -calculation:: +by the user, it is possible to assign this volume to a cell without performing a +:ref:`stochastic volume ` calculation:: from uncertainties import ufloat diff --git a/openmc/cell.py b/openmc/cell.py index 4fe7ae3a4b..426a502e07 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -233,7 +233,7 @@ class Cell(IDManagerMixin): self._atoms[key] = atom else: - msg = 'Unrecognised fill_type: {}'.format(self.fill_type) + msg = 'Unrecognized fill_type: {}'.format(self.fill_type) raise ValueError(msg) return self._atoms diff --git a/tests/unit_tests/test_cell.py b/tests/unit_tests/test_cell.py index 4ab5962503..b3c33410c8 100644 --- a/tests/unit_tests/test_cell.py +++ b/tests/unit_tests/test_cell.py @@ -1,7 +1,7 @@ import xml.etree. ElementTree as ET import numpy as np -import uncertainties as u +from uncertainties import ufloat import openmc import pytest @@ -37,7 +37,7 @@ def test_repr(cell_with_lattice): repr(c) # Empty cell with uncertain volume - c.volume = u.ufloat(3.0, 0.2) + c.volume = ufloat(3.0, 0.2) repr(c) @@ -128,17 +128,17 @@ def test_volume_setting(): # Test ordinary volume and uncertain volume c.volume = 3 - c.volume = u.ufloat(3, 0.7) + c.volume = ufloat(3, 0.7) # Allow volume to be set to 0.0 c.volume = 0.0 - c.volume = u.ufloat(0.0, 0.1) + c.volume = ufloat(0.0, 0.1) - # Test errors for -ve volume + # Test errors for negative volume with pytest.raises(ValueError): c.volume = -1.0 with pytest.raises(ValueError): - c.volume = u.ufloat(-0.05, 0.1) + c.volume = ufloat(-0.05, 0.1) def test_atoms_material_cell(uo2, water): @@ -151,22 +151,24 @@ def test_atoms_material_cell(uo2, water): # Precalculate the expected number of atoms M = (atomic_mass('U235') + 2 * atomic_mass('O16')) / 3 - expected_atoms = list() - expected_atoms.append(1/3 * uo2.density/M * AVOGADRO * 2.0) # U235 - expected_atoms.append(2/3 * uo2.density/M * AVOGADRO * 2.0) # O16 + expected_atoms = [ + 1/3 * uo2.density/M * AVOGADRO * 2.0, # U235 + 2/3 * uo2.density/M * AVOGADRO * 2.0 # O16 + ] - tuples = list(c.atoms.items()) + tuples = c.atoms.items() for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] assert atom_num == t[1] # Change volume and check if OK c.volume = 3.0 - expected_atoms = list() - expected_atoms.append(1/3 * uo2.density/M * AVOGADRO * 3.0) # U235 - expected_atoms.append(2/3 * uo2.density/M * AVOGADRO * 3.0) # O16 + expected_atoms = [ + 1/3 * uo2.density/M * AVOGADRO * 3.0, # U235 + 2/3 * uo2.density/M * AVOGADRO * 3.0 # O16 + ] - tuples = list(c.atoms.items()) + tuples = c.atoms.items() for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] assert atom_num == pytest.approx(t[1]) @@ -175,11 +177,12 @@ def test_atoms_material_cell(uo2, water): c.fill = water expected_nucs = ['H1', 'O16'] M = (2 * atomic_mass('H1') + atomic_mass('O16')) / 3 - expected_atoms = list() - expected_atoms.append(2/3 * water.density/M * AVOGADRO * 3.0) # H1 - expected_atoms.append(1/3 * water.density/M * AVOGADRO * 3.0) # O16 + expected_atoms = [ + 2/3 * water.density/M * AVOGADRO * 3.0, # H1 + 1/3 * water.density/M * AVOGADRO * 3.0 # O16 + ] - tuples = list(c.atoms.items()) + tuples = c.atoms.items() for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] assert atom_num == pytest.approx(t[1]) @@ -196,13 +199,14 @@ def test_atoms_distribmat_cell(uo2, water): expected_nucs = ['U235', 'O16', 'H1'] M_uo2 = (atomic_mass('U235') + 2 * atomic_mass('O16')) / 3 M_water = (2 * atomic_mass('H1') + atomic_mass('O16')) / 3 - expected_atoms = list() - expected_atoms.append(1/3 * uo2.density/M_uo2 * AVOGADRO * 3.0) # U235 - expected_atoms.append(2/3 * uo2.density/M_uo2 * AVOGADRO * 3.0 + - 1/3 * water.density/M_water * AVOGADRO * 3.0) # O16 - expected_atoms.append(2/3 * water.density/M_water * AVOGADRO * 3.0) # H1 + expected_atoms = [ + 1/3 * uo2.density/M_uo2 * AVOGADRO * 3.0, # U235 + (2/3 * uo2.density/M_uo2 * AVOGADRO * 3.0 + + 1/3 * water.density/M_water * AVOGADRO * 3.0), # O16 + 2/3 * water.density/M_water * AVOGADRO * 3.0 # H1 + ] - tuples = list(c.atoms.items()) + tuples = c.atoms.items() for nuc, atom_num, t in zip(expected_nucs, expected_atoms, tuples): assert nuc == t[0] assert atom_num == pytest.approx(t[1]) @@ -216,14 +220,14 @@ def test_atoms_errors(cell_with_lattice): cells[1].atoms # Cell with lattice + cells[2].volume = 3 with pytest.raises(ValueError): - cells[2].volume = 3 cells[2].atoms # Cell with volume but with void fill + cells[1].volume = 2 + cells[1].fill = None with pytest.raises(ValueError): - cells[1].volume = 2 - cells[1].fill = None cells[1].atoms From 9c8e22add86b84f334999e7a160dbbe0ad1caa9c Mon Sep 17 00:00:00 2001 From: Mikolaj Adam Kowalski Date: Thu, 19 Mar 2020 16:08:42 +0000 Subject: [PATCH 036/205] Add missing suggestion by @paulromano --- docs/source/usersguide/geometry.rst | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index f550327b24..f83af0b2f8 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -185,8 +185,10 @@ the :class:`openmc.Cell` class:: In this example, an instance of :class:`openmc.Material` is assigned to the :attr:`Cell.fill` attribute. One can also fill a cell with a :ref:`universe -` or :ref:`lattice `. If no fill -is provided to a cell, it will be filled with void by default:: +` or :ref:`lattice `. If you provide +no fill to a cell or assign a value of `None`, it will be treated as a "void" +cell with no material within. Particles are allowed to stream through the cell but +will undergo no collisions:: # This cell will be filled with void on export to XML gap = openmc.Cell(region=pellet_gap) From 1e411cad525cac24b0bc646568d0920ad365a6a0 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 13:34:49 -0500 Subject: [PATCH 037/205] Update README for custom source example, moving comment from show_flux.py --- examples/custom_source/README.md | 7 ++++++- examples/custom_source/show_flux.py | 4 ---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/examples/custom_source/README.md b/examples/custom_source/README.md index 9a22171ae4..94db705264 100644 --- a/examples/custom_source/README.md +++ b/examples/custom_source/README.md @@ -11,4 +11,9 @@ library, you can run: After this, you can build the model by running `python build_xml.py`. In the XML files that are created, you should see a reference to build/libsource.so, the -custom source library that was built by CMake. +custom source library that was built by CMake. The model is also set up with a +mesh tally of the flux, so once you run `openmc`, you will get a statepoint file +with the tally results in it. Running `python show_flux.py` will pull in the +results from the statepoint file and display them. If all worked well, you +should see a ring "imprint" as well as a higher flux to the right side (since +the custom source has all particles moving in the positive x direction). diff --git a/examples/custom_source/show_flux.py b/examples/custom_source/show_flux.py index bf8ee4f813..6f54943018 100644 --- a/examples/custom_source/show_flux.py +++ b/examples/custom_source/show_flux.py @@ -12,7 +12,3 @@ ax.imshow(flux, origin='lower', extent=(-5.0, 5.0, -5.0, 5.0)) ax.set_xlabel('x [cm]') ax.set_ylabel('y [cm]') plt.show() - -# If all worked well, you should see a ring "imprint" as well as a higher flux -# to the right side (since the custom source has all particles moving in the -# positive x direction) From 05cd5ec2f688a7ec6cf593737ecc6e916139c8ff Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 24 Apr 2019 16:21:43 -0500 Subject: [PATCH 038/205] Starting definition of general mesh interface. --- include/openmc/mesh.h | 46 +++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 44 insertions(+), 2 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index bac4885cad..8f60c2cd52 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -30,8 +30,50 @@ extern std::unordered_map mesh_map; } // namespace model -class Mesh -{ +class Mesh { + + //! Determine which bins were crossed by a particle + // + //! \param[in] p Particle to check + //! \param[out] bins Bins that were crossed + //! \param[out] lengths Fraction of tracklength in each bin + virtual void bins_crossed(const Particle* p, std::vector& bins, + std::vector& lengths) const = 0; + + //! Check where a line segment intersects the mesh and if it intersects at all + // + //! \param[in,out] r0 In: starting position, out: intersection point + //! \param[in] r1 Ending position + //! \param[out] ijk Indices of the mesh bin containing the intersection point + //! \return Whether the line segment connecting r0 and r1 intersects mesh + virtual bool intersects(Position& r0, Position r1, int* ijk) const = 0; + + //! Write mesh data to an HDF5 group + // + //! \param[in] group HDF5 group + virtual void to_hdf5(hid_t group) const = 0; + + //! Get bin at a given position in space + // + //! \param[in] r Position to get bin for + //! \return Mesh bin + virtual int get_bin(Position r) const = 0; + + //! Count number of bank sites in each mesh bin / energy bin + // + //! \param[in] bank Array of bank sites + //! \param[out] Whether any bank sites are outside the mesh + //! \return Array indicating number of sites in each mesh/energy bin + virtual xt::xarray count_sites(const std::vector& bank, + bool* outside) const = 0; + +}; + +//============================================================================== +//! Tessellation of n-dimensional Euclidean space by congruent squares or cubes +//============================================================================== + +class RegularMesh : Mesh { public: // Constructors and destructor Mesh() = default; From 38c5544e251d5d76d8b0819b81b7f54812471c13 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 24 Apr 2019 21:02:51 -0500 Subject: [PATCH 039/205] Finishing inheritance infrastructure. --- include/openmc/mesh.h | 54 ++++++++++++++++++++++++++++++++++++++++--- src/mesh.cpp | 25 +++++++++++++++++++- 2 files changed, 75 insertions(+), 4 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 8f60c2cd52..9d6d8b79bf 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -32,6 +32,11 @@ extern std::unordered_map mesh_map; class Mesh { +public: + // Constructor + Mesh() {}; // empty constructor + Mesh(pugi::xml_node node); + //! Determine which bins were crossed by a particle // //! \param[in] p Particle to check @@ -67,13 +72,15 @@ class Mesh { virtual xt::xarray count_sites(const std::vector& bank, bool* outside) const = 0; + int id_ {-1}; //!< User-specified ID + int n_dimension_; //!< Number of dimensions }; //============================================================================== //! Tessellation of n-dimensional Euclidean space by congruent squares or cubes //============================================================================== -class RegularMesh : Mesh { +class RegularMesh : public Mesh { public: // Constructors and destructor Mesh() = default; @@ -207,7 +214,6 @@ public: bool* outside) const; // Data members - double volume_frac_; //!< Volume fraction of each mesh element xt::xtensor shape_; //!< Number of mesh elements in each dimension xt::xtensor width_; //!< Width of each mesh element @@ -218,6 +224,7 @@ private: bool intersects_3d(Position& r0, Position r1, int* ijk) const; }; + class RectilinearMesh : public Mesh { public: @@ -260,13 +267,54 @@ public: bool intersects(Position& r0, Position r1, int* ijk) const; // Data members - xt::xtensor shape_; //!< Number of mesh elements in each dimension private: std::vector> grid_; }; +#ifdef DAGMC + +class UnstructuredMesh : public Mesh { + UnstructuredMesh() { }; + UnstructuredMesh(pugi::xml_node); + + //! Determine which bins were crossed by a particle + // + //! \param[in] p Particle to check + //! \param[out] bins Bins that were crossed + //! \param[out] lengths Fraction of tracklength in each bin + void bins_crossed(const Particle* p, std::vector& bins, + std::vector& lengths); + + bool intersects(Position& r0, Position r1, int* ijk); + + //! Write mesh data to an HDF5 group + // + //! \param[in] group HDF5 group + void to_hdf5(hid_t group); + + //! Get bin at a given position in space + // + //! \param[in] r Position to get bin for + //! \return Mesh bin + int get_bin(Position r); + + //! Count number of bank sites in each mesh bin / energy bin + // + //! \param[in] bank Array of bank sites + //! \param[out] Whether any bank sites are outside the mesh + //! \return Array indicating number of sites in each mesh/energy bin + xt::xarray count_sites(const std::vector& bank, + bool* outside); + +private: + std::string filename_; + +}; + +#endif + //============================================================================== // Non-member functions //============================================================================== diff --git a/src/mesh.cpp b/src/mesh.cpp index e847b80d34..b6e562a98a 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -24,6 +24,10 @@ #include "openmc/tallies/filter.h" #include "openmc/xml_interface.h" +#ifdef DAGMC +#include "TrackLengthMeshTally.hpp" +#endif + namespace openmc { //============================================================================== @@ -69,7 +73,8 @@ inline bool check_intersection_point(double x1, double x0, double y1, Mesh::Mesh(pugi::xml_node node) { - // Copy mesh id +Mesh::Mesh(pugi::xml_node node) { + // Copy mesh id if (check_for_node(node, "id")) { id_ = std::stoi(get_node_value(node, "id")); @@ -1502,6 +1507,24 @@ openmc_mesh_set_params(int32_t index, int n, const double* ll, const double* ur, return 0; } +UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { + + // get the filename of the unstructured mesh to load + if (check_for_node(node, "mesh_file")) { + filename_ = get_node_value(node, "mesh_file"); + } + else { + fatal_error("No filename supplied for unstructured mesh with ID: " + + std::to_string(id_)); + } + + // always 3 for unstructured meshes + n_dimension_ = 3; + + +} + + //============================================================================== // Non-member functions //============================================================================== From c49b328d4bc6abf4a5681c724f1b822b5e82d281 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 30 Apr 2019 16:42:09 -0500 Subject: [PATCH 040/205] Adding method for finding bins_crossed. --- include/openmc/mesh.h | 14 ++++++++-- src/mesh.cpp | 62 +++++++++++++++++++++++++++++++++++++++---- 2 files changed, 69 insertions(+), 7 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 9d6d8b79bf..ea3851da6d 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -15,6 +15,11 @@ #include "openmc/particle.h" #include "openmc/position.h" +#ifdef DAGMC +#include "TrackLengthMeshTally.hpp" +#include "Tally.hpp" +#endif + namespace openmc { //============================================================================== @@ -285,7 +290,7 @@ class UnstructuredMesh : public Mesh { //! \param[out] bins Bins that were crossed //! \param[out] lengths Fraction of tracklength in each bin void bins_crossed(const Particle* p, std::vector& bins, - std::vector& lengths); + std::vector& lengths) const; bool intersects(Position& r0, Position r1, int* ijk); @@ -308,9 +313,14 @@ class UnstructuredMesh : public Mesh { xt::xarray count_sites(const std::vector& bank, bool* outside); + int get_bin_from_ent_handle(moab::EntityHandle eh) const; + + moab::EntityHandle get_ent_handle_from_bin(int bin) const; + private: std::string filename_; - + moab::Range ehs_; + std::unique_ptr tracklen_meshtal_; }; #endif diff --git a/src/mesh.cpp b/src/mesh.cpp index b6e562a98a..e9e082727c 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -24,10 +24,6 @@ #include "openmc/tallies/filter.h" #include "openmc/xml_interface.h" -#ifdef DAGMC -#include "TrackLengthMeshTally.hpp" -#endif - namespace openmc { //============================================================================== @@ -1507,6 +1503,8 @@ openmc_mesh_set_params(int32_t index, int n, const double* ll, const double* ur, return 0; } +#ifdef DAGMC + UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { // get the filename of the unstructured mesh to load @@ -1518,12 +1516,66 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { std::to_string(id_)); } + // create TallyInput + TallyInput tally_inp = {0, 0, 0, {0}, TallyInput::TallyOptions(), 0}; + + tracklen_meshtal_ = std::make_unique(tally_inp); + // always 3 for unstructured meshes n_dimension_ = 3; - } +void +UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, + std::vector& lengths) const { + moab::ErrorCode rval; + + Position last_r{p->r_last_}; + Position r{p->r()}; + Position u{p->u()}; + moab::CartVect r0(last_r.x, last_r.y, last_r.z); + moab::CartVect r1(r.x, r.y, r.z); + moab::CartVect dir(u.x, u.y, u.z); + dir.normalize(); + + double track_len = (r1 - r0).length(); + + r0 += TINY_BIT*dir; + r1 -= TINY_BIT*dir; + + std::vector tris; + std::vector intersections; + rval = tracklen_meshtal_->get_all_intersections(r0, dir, track_len, + tris, intersections); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed in tally on mesh: " + filename_); + } + + bins.clear(); + for (const auto& int_dist : intersections) { + moab::EntityHandle tet = tracklen_meshtal_->point_in_which_tet(r0 + dir * int_dist); + if (tet == 0) { continue; } + if (std::find(bins.begin(), bins.end(), tet) == std::end(bins)) { + bins.emplace_back(get_bin_from_ent_handle(tet)); + } + } + +}; + +int +UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { + auto pos = ehs_.find(eh); + return pos - ehs_.begin(); +} + +moab::EntityHandle +UnstructuredMesh::get_ent_handle_from_bin(int bin) const { + return ehs_[bin]; +} + +#endif + //============================================================================== // Non-member functions From fc829a110fccbbda25d4a0e828dc4afe73357a69 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 2 May 2019 20:12:51 -0500 Subject: [PATCH 041/205] Building tree manually. --- include/openmc/mesh.h | 10 ++++--- src/mesh.cpp | 62 ++++++++++++++++++++++++++++++++++++------- 2 files changed, 59 insertions(+), 13 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index ea3851da6d..b83443cec6 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -16,8 +16,8 @@ #include "openmc/position.h" #ifdef DAGMC -#include "TrackLengthMeshTally.hpp" -#include "Tally.hpp" +#include "moab/Core.hpp" +#include "moab/AdaptiveKDTree.hpp" #endif namespace openmc { @@ -317,10 +317,14 @@ class UnstructuredMesh : public Mesh { moab::EntityHandle get_ent_handle_from_bin(int bin) const; + void build_tree(const moab::Range& all_tets); + private: std::string filename_; moab::Range ehs_; - std::unique_ptr tracklen_meshtal_; + moab::EntityHandle meshset_; + std::shared_ptr mbi_; + std::unique_ptr kdtree_; }; #endif diff --git a/src/mesh.cpp b/src/mesh.cpp index e9e082727c..5ed8086764 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1516,14 +1516,61 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { std::to_string(id_)); } - // create TallyInput - TallyInput tally_inp = {0, 0, 0, {0}, TallyInput::TallyOptions(), 0}; - - tracklen_meshtal_ = std::make_unique(tally_inp); + // create MOAB instance + mbi_ = std::shared_ptr(new moab::Core()); + // load unstructured mesh file + moab::ErrorCode rval = mbi_->load_file(filename_.c_str(), &meshset_); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to load the unstructured mesh file: " + filename_); + } // always 3 for unstructured meshes n_dimension_ = 3; + moab::Range all_tets; + rval = mbi_->get_entities_by_dimension(meshset_, n_dimension_, all_tets); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to get all tetrahedral elements"); + } + + if (!all_tets.all_of_type(moab::MBTET)) { + warning("Non-tetrahedral elements found in unstructured mesh: " + filename_); + } + + build_tree(all_tets); + +} + +void +UnstructuredMesh::build_tree(const moab::Range& all_tets) { + + moab::Range all_tris; + moab::ErrorCode rval = mbi_->get_adjacencies(all_tets, + n_dimension_ - 1, + true, + all_tris, + moab::Interface::UNION); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to get adjacent triangle for test in MOAB"); + } + + if (!all_tris.all_of_type(moab::MBTRI)) { + warning("Non-triangle elements found in tet adjacencies in unstructured mesh: " + filename_); + } + + // combine into one range + moab::Range all_tets_and_tris; + all_tets_and_tris.merge(all_tets); + all_tets_and_tris.merge(all_tris); + + // create and build KD-tree + kdtree_ = std::unique_ptr(new moab::AdaptiveKDTree(mbi_.get())); + + rval = kdtree_->build_tree(all_tets_and_tris); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to construct a KD-Tree for unstructured mesh " + filename_); + } + } void @@ -1546,15 +1593,10 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, std::vector tris; std::vector intersections; - rval = tracklen_meshtal_->get_all_intersections(r0, dir, track_len, - tris, intersections); - if (rval != moab::MB_SUCCESS) { - fatal_error("Failed in tally on mesh: " + filename_); - } bins.clear(); for (const auto& int_dist : intersections) { - moab::EntityHandle tet = tracklen_meshtal_->point_in_which_tet(r0 + dir * int_dist); + moab::EntityHandle tet; // = tracklen_meshtal_->point_in_which_tet(r0 + dir * int_dist); if (tet == 0) { continue; } if (std::find(bins.begin(), bins.end(), tet) == std::end(bins)) { bins.emplace_back(get_bin_from_ent_handle(tet)); From 56934d1159137aaf4c40a8fcce4fd9b4c35ef92d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 3 May 2019 10:53:50 -0500 Subject: [PATCH 042/205] Finishing concrete implementation. --- include/openmc/mesh.h | 35 +++++------------ src/mesh.cpp | 87 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 97 insertions(+), 25 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index b83443cec6..293ae3a298 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -18,6 +18,7 @@ #ifdef DAGMC #include "moab/Core.hpp" #include "moab/AdaptiveKDTree.hpp" +#include "moab/Matrix3.hpp" #endif namespace openmc { @@ -26,11 +27,13 @@ namespace openmc { // Global variables //============================================================================== + class Mesh; namespace model { extern std::vector> meshes; + extern std::unordered_map mesh_map; } // namespace model @@ -50,14 +53,6 @@ public: virtual void bins_crossed(const Particle* p, std::vector& bins, std::vector& lengths) const = 0; - //! Check where a line segment intersects the mesh and if it intersects at all - // - //! \param[in,out] r0 In: starting position, out: intersection point - //! \param[in] r1 Ending position - //! \param[out] ijk Indices of the mesh bin containing the intersection point - //! \return Whether the line segment connecting r0 and r1 intersects mesh - virtual bool intersects(Position& r0, Position r1, int* ijk) const = 0; - //! Write mesh data to an HDF5 group // //! \param[in] group HDF5 group @@ -69,14 +64,6 @@ public: //! \return Mesh bin virtual int get_bin(Position r) const = 0; - //! Count number of bank sites in each mesh bin / energy bin - // - //! \param[in] bank Array of bank sites - //! \param[out] Whether any bank sites are outside the mesh - //! \return Array indicating number of sites in each mesh/energy bin - virtual xt::xarray count_sites(const std::vector& bank, - bool* outside) const = 0; - int id_ {-1}; //!< User-specified ID int n_dimension_; //!< Number of dimensions }; @@ -281,6 +268,7 @@ private: #ifdef DAGMC class UnstructuredMesh : public Mesh { +public: UnstructuredMesh() { }; UnstructuredMesh(pugi::xml_node); @@ -294,24 +282,20 @@ class UnstructuredMesh : public Mesh { bool intersects(Position& r0, Position r1, int* ijk); + bool point_in_tet(const Position& r, moab::EntityHandle tet) const; + //! Write mesh data to an HDF5 group // //! \param[in] group HDF5 group - void to_hdf5(hid_t group); + void to_hdf5(hid_t group) const; //! Get bin at a given position in space // //! \param[in] r Position to get bin for //! \return Mesh bin - int get_bin(Position r); + int get_bin(Position r) const; - //! Count number of bank sites in each mesh bin / energy bin - // - //! \param[in] bank Array of bank sites - //! \param[out] Whether any bank sites are outside the mesh - //! \return Array indicating number of sites in each mesh/energy bin - xt::xarray count_sites(const std::vector& bank, - bool* outside); + void compute_barycentric_data(const moab::Range& all_tets); int get_bin_from_ent_handle(moab::EntityHandle eh) const; @@ -325,6 +309,7 @@ private: moab::EntityHandle meshset_; std::shared_ptr mbi_; std::unique_ptr kdtree_; + std::vector baryc_data_; }; #endif diff --git a/src/mesh.cpp b/src/mesh.cpp index 5ed8086764..1f480fbc3d 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -32,6 +32,7 @@ namespace openmc { namespace model { + std::vector> meshes; std::unordered_map mesh_map; @@ -1605,6 +1606,89 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, }; + + +int +UnstructuredMesh::get_bin(Position r) const { + moab::CartVect pnt(r.x, r.y, r.z); + moab::AdaptiveKDTreeIter kdtree_iter; + moab::ErrorCode rval = kdtree_->point_search(pnt.array(), kdtree_iter); + if (rval != moab::MB_SUCCESS) { return -1; } + + moab::EntityHandle leaf = kdtree_iter.handle(); + moab::Range tets; + rval = mbi_->get_entities_by_dimension(leaf, 3, tets); + for (const auto& tet : tets) { + if (point_in_tet(r, tet)) { + return get_bin_from_ent_handle(tet); + } + } + + return -1; +} + +void +UnstructuredMesh::compute_barycentric_data(const moab::Range& all_tets) { + moab::ErrorCode rval; + for (auto& tet : all_tets) { + moab::Range verts; + rval = mbi_->get_connectivity(&tet, 1, verts); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to get connectivity of tet on umesh: " + filename_); + } + + moab::CartVect p[4]; + rval = mbi_->get_coords(verts, p[0].array()); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to get coordinates of a tet in umesh: " + filename_); + } + + moab::Matrix3 a(p[1] - p[0], p[2] - p[0], p[3] - p[0], true); + + baryc_data_.push_back(a.transpose().inverse()); + } +} + +// TODO: write this function +void +UnstructuredMesh::to_hdf5(hid_t group) const { } + +bool +UnstructuredMesh::point_in_tet(const Position& r, moab::EntityHandle tet) const { + + moab::ErrorCode rval; + + // get tet vertices + moab::Range verts; + rval = mbi_->get_connectivity(&tet, 1, verts); + if (rval != moab::MB_SUCCESS) { + warning("Failed to get vertices of tet in umesh: " + filename_); + return false; + } + + moab::EntityHandle v_zero = verts[0]; + moab::CartVect p_zero; + rval = mbi_->get_coords(&v_zero, 1, p_zero.array()); + if (rval != moab::MB_SUCCESS) { + warning("Failed to get coordinates of a vertex in umesh: " + filename_); + return false; + } + + moab::CartVect pos(r.x, r.y, r.z); + + // look up barycentric data + int idx = get_bin_from_ent_handle(tet); + const moab::Matrix3& a_inv = baryc_data_[idx]; + + moab::CartVect bary_coords = a_inv * (pos - p_zero); + + bool in_tet = (bary_coords[0] >= 0 && bary_coords[1] >= 0 && bary_coords[2] >= 0 && + bary_coords[0] + bary_coords[1] + bary_coords[2] <= 1.); + + return in_tet; + +} + int UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { auto pos = ehs_.find(eh); @@ -1638,6 +1722,9 @@ void read_meshes(pugi::xml_node root) model::meshes.push_back(std::make_unique(node)); } else if (mesh_type == "rectilinear") { model::meshes.push_back(std::make_unique(node)); + } + else if (mesh_type == "unstructured") { + model::meshes.push_back(std::make_unique(node)); } else { fatal_error("Invalid mesh type: " + mesh_type); } From 231844faffa3bff353211941fc01558add2da403 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 3 May 2019 11:13:37 -0500 Subject: [PATCH 043/205] Filling in call to kdtree for computing track lengths. --- include/openmc/mesh.h | 3 +++ src/mesh.cpp | 38 ++++++++++++++++++++++++++------------ 2 files changed, 29 insertions(+), 12 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 293ae3a298..112931b60c 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -295,6 +295,8 @@ public: //! \return Mesh bin int get_bin(Position r) const; + moab::EntityHandle get_tet(Position r) const; + void compute_barycentric_data(const moab::Range& all_tets); int get_bin_from_ent_handle(moab::EntityHandle eh) const; @@ -307,6 +309,7 @@ private: std::string filename_; moab::Range ehs_; moab::EntityHandle meshset_; + moab::EntityHandle kdtree_root_; std::shared_ptr mbi_; std::unique_ptr kdtree_; std::vector baryc_data_; diff --git a/src/mesh.cpp b/src/mesh.cpp index 1f480fbc3d..f8608ee9f4 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1507,7 +1507,6 @@ openmc_mesh_set_params(int32_t index, int n, const double* ll, const double* ur, #ifdef DAGMC UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { - // get the filename of the unstructured mesh to load if (check_for_node(node, "mesh_file")) { filename_ = get_node_value(node, "mesh_file"); @@ -1567,7 +1566,7 @@ UnstructuredMesh::build_tree(const moab::Range& all_tets) { // create and build KD-tree kdtree_ = std::unique_ptr(new moab::AdaptiveKDTree(mbi_.get())); - rval = kdtree_->build_tree(all_tets_and_tris); + rval = kdtree_->build_tree(all_tets_and_tris, &kdtree_root_); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to construct a KD-Tree for unstructured mesh " + filename_); } @@ -1582,10 +1581,10 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, Position last_r{p->r_last_}; Position r{p->r()}; Position u{p->u()}; + u /= u.norm(); moab::CartVect r0(last_r.x, last_r.y, last_r.z); moab::CartVect r1(r.x, r.y, r.z); moab::CartVect dir(u.x, u.y, u.z); - dir.normalize(); double track_len = (r1 - r0).length(); @@ -1595,9 +1594,14 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, std::vector tris; std::vector intersections; + rval = kdtree_->ray_intersect_triangles(kdtree_root_, 1E-03, dir.array(), r0.array(), tris, intersections, 0, track_len); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to compute tracklengths on umesh: " + filename_); + } + bins.clear(); for (const auto& int_dist : intersections) { - moab::EntityHandle tet; // = tracklen_meshtal_->point_in_which_tet(r0 + dir * int_dist); + moab::EntityHandle tet = get_tet(last_r + u * int_dist); if (tet == 0) { continue; } if (std::find(bins.begin(), bins.end(), tet) == std::end(bins)) { bins.emplace_back(get_bin_from_ent_handle(tet)); @@ -1606,27 +1610,37 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, }; - - -int -UnstructuredMesh::get_bin(Position r) const { +moab::EntityHandle +UnstructuredMesh::get_tet(Position r) const { moab::CartVect pnt(r.x, r.y, r.z); moab::AdaptiveKDTreeIter kdtree_iter; moab::ErrorCode rval = kdtree_->point_search(pnt.array(), kdtree_iter); - if (rval != moab::MB_SUCCESS) { return -1; } + if (rval != moab::MB_SUCCESS) { return 0; } moab::EntityHandle leaf = kdtree_iter.handle(); moab::Range tets; rval = mbi_->get_entities_by_dimension(leaf, 3, tets); + for (const auto& tet : tets) { - if (point_in_tet(r, tet)) { - return get_bin_from_ent_handle(tet); + if (point_in_tet(r, tet)) { + return get_bin_from_ent_handle(tet); } } - return -1; + return 0; } +int +UnstructuredMesh::get_bin(Position r) const { + moab::EntityHandle tet = get_tet(r); + if (tet == 0) { + return -1; + } else { + return get_bin_from_ent_handle(tet); + } +} + + void UnstructuredMesh::compute_barycentric_data(const moab::Range& all_tets) { moab::ErrorCode rval; From 3c280c1e171759c04c5fd675d0bec6ff0cc5fd85 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 3 May 2019 13:14:15 -0500 Subject: [PATCH 044/205] Updating check for unstructured mesh. --- src/mesh.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index f8608ee9f4..2745f3b5f8 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1538,7 +1538,6 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { } build_tree(all_tets); - } void @@ -1724,6 +1723,7 @@ UnstructuredMesh::get_ent_handle_from_bin(int bin) const { void read_meshes(pugi::xml_node root) { for (auto node : root.children("mesh")) { + std::string mesh_type; if (check_for_node(node, "type")) { mesh_type = get_node_value(node, "type", true, true); From 670d76117c4ac6773e607d04a2b6927d4d7ef102 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 3 May 2019 18:21:44 -0500 Subject: [PATCH 045/205] Taking care of a couple setup issues. --- src/mesh.cpp | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 2745f3b5f8..74034fc839 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1507,6 +1507,15 @@ openmc_mesh_set_params(int32_t index, int n, const double* ll, const double* ur, #ifdef DAGMC UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { + + // check the mesh type + if (check_for_node(node, "type")) { + auto temp = get_node_value(node, "type", true, true); + if (temp != "unstructured") { + fatal_error("Invalid mesh type: " + temp); + } + } + // get the filename of the unstructured mesh to load if (check_for_node(node, "mesh_file")) { filename_ = get_node_value(node, "mesh_file"); @@ -1518,8 +1527,13 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { // create MOAB instance mbi_ = std::shared_ptr(new moab::Core()); + // create meshset to load mesh into + moab::ErrorCode rval = mbi_->create_meshset(moab::MESHSET_SET, meshset_); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to create fileset for umesh: " + filename_); + } // load unstructured mesh file - moab::ErrorCode rval = mbi_->load_file(filename_.c_str(), &meshset_); + rval = mbi_->load_file(filename_.c_str(), &meshset_); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to load the unstructured mesh file: " + filename_); } From a10429f7fa6e666cac85766eddc3d6a707205dba Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 10 May 2019 11:20:42 -0500 Subject: [PATCH 046/205] Adding to mesh abstraction to support other operations external to the mesh source files. --- include/openmc/mesh.h | 39 ++++++++++++++++++++++++++++++-- src/eigenvalue.cpp | 4 ++++ src/mesh.cpp | 44 ++++++++++++++++++++++++++++++++++++- src/tallies/filter_mesh.cpp | 1 + 4 files changed, 85 insertions(+), 3 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 112931b60c..ebfa083d0d 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -27,13 +27,11 @@ namespace openmc { // Global variables //============================================================================== - class Mesh; namespace model { extern std::vector> meshes; - extern std::unordered_map mesh_map; } // namespace model @@ -58,12 +56,31 @@ public: //! \param[in] group HDF5 group virtual void to_hdf5(hid_t group) const = 0; + //! Determine which surface bins were crossed by a particle + // + //! \param[in] p Particle to check + //! \param[out] bins Surface bins that were crossed + virtual void + surface_bins_crossed(const Particle* p, std::vector& bins) const = 0; + //! Get bin at a given position in space // //! \param[in] r Position to get bin for //! \return Mesh bin virtual int get_bin(Position r) const = 0; + virtual std::string get_label_for_bin(int bin) const = 0; + + //! Count number of bank sites in each mesh bin / energy bin + // + //! \param[in] bank Array of bank sites + //! \param[out] Whether any bank sites are outside the mesh + //! \return Array indicating number of sites in each mesh/energy bin + virtual xt::xarray + count_sites(const std::vector& bank, bool* outside) const = 0; + + virtual double get_volume_frac(int bin = -1) = 0; + int id_ {-1}; //!< User-specified ID int n_dimension_; //!< Number of dimensions }; @@ -206,6 +223,11 @@ public: bool* outside) const; // Data members + + //std::string get_label_for_bin(int bin) const; + + //double get_volume_frac(int bin = -1) const; + double volume_frac_; //!< Volume fraction of each mesh element xt::xtensor shape_; //!< Number of mesh elements in each dimension xt::xtensor width_; //!< Width of each mesh element @@ -282,6 +304,12 @@ public: bool intersects(Position& r0, Position r1, int* ijk); + //! Determine which surface bins were crossed by a particle + // + //! \param[in] p Particle to check + //! \param[out] bins Surface bins that were crossed + void surface_bins_crossed(const Particle* p, std::vector& bins) const; + bool point_in_tet(const Position& r, moab::EntityHandle tet) const; //! Write mesh data to an HDF5 group @@ -305,6 +333,13 @@ public: void build_tree(const moab::Range& all_tets); + std::string get_label_for_bin(int bin) const; + + xt::xarray + count_sites(const std::vector& bank, bool* outside) const; + + double get_volume_frac(int bin = -1) const; + private: std::string filename_; moab::Range ehs_; diff --git a/src/eigenvalue.cpp b/src/eigenvalue.cpp index f02a96e7ac..b8305672b7 100644 --- a/src/eigenvalue.cpp +++ b/src/eigenvalue.cpp @@ -538,9 +538,12 @@ void ufs_count_sites() // distributed so that effectively the production of fission sites is not // biased + std::size_t n = simulation::ufs_mesh->n_bins(); double vol_frac = simulation::ufs_mesh->volume_frac_; simulation::source_frac = xt::xtensor({n}, vol_frac); + // auto s = xt::view(simulation::source_frac, xt::all()); + // s = m->get_volume_frac(); } else { // count number of source sites in each ufs mesh cell @@ -583,6 +586,7 @@ double ufs_get_weight(const Particle* p) if (simulation::source_frac(mesh_bin) != 0.0) { return simulation::ufs_mesh->volume_frac_ / simulation::source_frac(mesh_bin); + // return m->get_volume_frac() / simulation::source_frac(mesh_bin); } else { return 1.0; } diff --git a/src/mesh.cpp b/src/mesh.cpp index 74034fc839..7a70080e92 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -5,6 +5,7 @@ #include // for ceil #include // for allocator #include +#include #ifdef OPENMC_MPI #include "mpi.h" @@ -32,7 +33,6 @@ namespace openmc { namespace model { - std::vector> meshes; std::unordered_map mesh_map; @@ -848,6 +848,23 @@ RegularMesh::count_sites(const Particle::Bank* bank, int64_t length, return counts; } +std::string RegularMesh::get_label_for_bin(int bin) const { + int ijk[n_dimension_]; + get_indices_from_bin(bin, ijk); + + std::stringstream out; + out << "Mesh Index (" << ijk[0]; + if (n_dimension_ > 1) out << ", " << ijk[1]; + if (n_dimension_ > 2) out << ", " << ijk[2]; + out << ")"; + + return out.str(); +} + +double RegularMesh::get_volume_frac(int bin) const { + return volume_frac_; +} + //============================================================================== // RectilinearMesh implementation //============================================================================== @@ -1643,6 +1660,20 @@ UnstructuredMesh::get_tet(Position r) const { return 0; } + +//! Determine which surface bins were crossed by a particle +// +//! \param[in] p Particle to check +//! \param[out] bins Surface bins that were crossed +void UnstructuredMesh::surface_bins_crossed(const Particle* p, std::vector& bins) const { + return; +} + +std::string UnstructuredMesh::get_label_for_bin(int bin) { + std::string s; + return s; +} + int UnstructuredMesh::get_bin(Position r) const { moab::EntityHandle tet = get_tet(r); @@ -1727,6 +1758,17 @@ UnstructuredMesh::get_ent_handle_from_bin(int bin) const { return ehs_[bin]; } +xt::xarray +UnstructuredMesh::count_sites(const std::vector& bank, + bool* outside) const { + xt::array out; + return out; + } + +double UnstructuredMesh::get_volume_frac(int bin = -1) const { + return 0.0; +} + #endif diff --git a/src/tallies/filter_mesh.cpp b/src/tallies/filter_mesh.cpp index 6c2cc8bd12..837b425c4a 100644 --- a/src/tallies/filter_mesh.cpp +++ b/src/tallies/filter_mesh.cpp @@ -55,6 +55,7 @@ std::string MeshFilter::text_label(int bin) const { auto& mesh = *model::meshes[mesh_]; + int n_dim = mesh.n_dimension_; std::vector ijk(n_dim); From b42bcc27605dba459edf041e15496d79a47fd0e7 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 10 May 2019 11:29:20 -0500 Subject: [PATCH 047/205] Wrapping more umesh stuff into ifdefs. Moving pointers into vector during initialization. --- include/openmc/mesh.h | 2 +- src/mesh.cpp | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index ebfa083d0d..442c0d4ac5 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -79,7 +79,7 @@ public: virtual xt::xarray count_sites(const std::vector& bank, bool* outside) const = 0; - virtual double get_volume_frac(int bin = -1) = 0; + virtual double get_volume_frac(int bin = -1) const = 0; int id_ {-1}; //!< User-specified ID int n_dimension_; //!< Number of dimensions diff --git a/src/mesh.cpp b/src/mesh.cpp index 7a70080e92..0e9c321643 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1408,7 +1408,7 @@ openmc_extend_meshes(int32_t n, int32_t* index_start, int32_t* index_end) { if (index_start) *index_start = model::meshes.size(); for (int i = 0; i < n; ++i) { - model::meshes.push_back(std::make_unique()); + model::meshes.push_back(std::move(std::make_unique())); } if (index_end) *index_end = model::meshes.size() - 1; @@ -1780,6 +1780,7 @@ void read_meshes(pugi::xml_node root) { for (auto node : root.children("mesh")) { + std::string mesh_type; if (check_for_node(node, "type")) { mesh_type = get_node_value(node, "type", true, true); From f0c739bfe668c8f6276efdfb79deace5f2ae4759 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 10 May 2019 12:55:22 -0500 Subject: [PATCH 048/205] Updates to get reg mesh working as an abstracted class --- include/openmc/mesh.h | 6 ++++++ src/mesh.cpp | 17 ++++++++++++++++- src/plot.cpp | 44 +++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 66 insertions(+), 1 deletion(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 442c0d4ac5..33684ff78b 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -81,6 +81,8 @@ public: virtual double get_volume_frac(int bin = -1) const = 0; + virtual int num_bins() const = 0; + int id_ {-1}; //!< User-specified ID int n_dimension_; //!< Number of dimensions }; @@ -228,6 +230,8 @@ public: //double get_volume_frac(int bin = -1) const; + int num_bins() const; + double volume_frac_; //!< Volume fraction of each mesh element xt::xtensor shape_; //!< Number of mesh elements in each dimension xt::xtensor width_; //!< Width of each mesh element @@ -366,6 +370,8 @@ void read_meshes(pugi::xml_node root); //! \param[in] group HDF5 group void meshes_to_hdf5(hid_t group); +RegularMesh* get_regular_mesh(int32_t index); + void free_memory_mesh(); } // namespace openmc diff --git a/src/mesh.cpp b/src/mesh.cpp index 0e9c321643..a1da1428f4 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -865,6 +865,12 @@ double RegularMesh::get_volume_frac(int bin) const { return volume_frac_; } +int RegularMesh::num_bins() const { + int n_bins = 1; + for (auto v : shape_) n_bins *= v; + return n_bins; +} + //============================================================================== // RectilinearMesh implementation //============================================================================== @@ -1402,6 +1408,11 @@ check_regular_mesh(int32_t index, RegularMesh** mesh) // C API functions //============================================================================== +RegularMesh* get_regular_mesh(int32_t index) { + return dynamic_cast(model::meshes[index].get()); +} + + //! Extend the meshes array by n elements extern "C" int openmc_extend_meshes(int32_t n, int32_t* index_start, int32_t* index_end) @@ -1451,6 +1462,11 @@ openmc_mesh_set_id(int32_t index, int32_t id) extern "C" int openmc_mesh_get_dimension(int32_t index, int** dims, int* n) { + if (index < 0 || index >= model::meshes.size()) { + set_errmsg("Index in meshes array is out of bounds."); + return OPENMC_E_OUT_OF_BOUNDS; + } + RegularMesh* mesh; if (int err = check_regular_mesh(index, &mesh)) return err; *dims = mesh->shape_.data(); @@ -1469,7 +1485,6 @@ openmc_mesh_set_dimension(int32_t index, int n, const int* dims) std::vector shape = {static_cast(n)}; mesh->shape_ = xt::adapt(dims, n, xt::no_ownership(), shape); mesh->n_dimension_ = mesh->shape_.size(); - return 0; } diff --git a/src/plot.cpp b/src/plot.cpp index bc94b3ad66..ca22b5fa79 100644 --- a/src/plot.cpp +++ b/src/plot.cpp @@ -696,6 +696,7 @@ void draw_mesh_lines(Plot pl, ImageData& data) Position width = ur_plot - ll_plot; + // Find the (axis-aligned) lines of the mesh that intersect this plot. auto axis_lines = model::meshes[pl.index_meshlines_mesh_] ->plot(ll_plot, ur_plot); @@ -713,6 +714,49 @@ void draw_mesh_lines(Plot pl, ImageData& data) ax2_min = 0; ax2_max = pl.pixels_[1]; } + // auto m = get_regular_mesh(pl.index_meshlines_mesh_); + + // int ijk_ll[3], ijk_ur[3]; + // bool in_mesh; + // m->get_indices(ll_plot, &(ijk_ll[0]), &in_mesh); + // m->get_indices(ur_plot, &(ijk_ur[0]), &in_mesh); + + // // Fortran/C++ index correction + // ijk_ur[0]++; ijk_ur[1]++; ijk_ur[2]++; + + // Position r_ll, r_ur; + // // sweep through all meshbins on this plane and draw borders + // for (int i = ijk_ll[outer]; i <= ijk_ur[outer]; i++) { + // for (int j = ijk_ll[inner]; j <= ijk_ur[inner]; j++) { + // // check if we're in the mesh for this ijk + // if (i > 0 && i <= m->shape_[outer] && j >0 && j <= m->shape_[inner] ) { + // int outrange[3], inrange[3]; + // // get xyz's of lower left and upper right of this mesh cell + // r_ll[outer] = m->lower_left_[outer] + m->width_[outer] * (i - 1); + // r_ll[inner] = m->lower_left_[inner] + m->width_[inner] * (j - 1); + // r_ur[outer] = m->lower_left_[outer] + m->width_[outer] * i; + // r_ur[inner] = m->lower_left_[inner] + m->width_[inner] * j; + + // // map the xyz ranges to pixel ranges + // double frac = (r_ll[outer] - ll_plot[outer]) / width[outer]; + // outrange[0] = int(frac * double(pl.pixels_[0])); + // frac = (r_ur[outer] - ll_plot[outer]) / width[outer]; + // outrange[1] = int(frac * double(pl.pixels_[0])); + + // frac = (r_ur[inner] - ll_plot[inner]) / width[inner]; + // inrange[0] = int((1. - frac) * (double)pl.pixels_[1]); + // frac = (r_ll[inner] - ll_plot[inner]) / width[inner]; + // inrange[1] = int((1. - frac) * (double)pl.pixels_[1]); + + // // draw lines + // for (int out_ = outrange[0]; out_ <= outrange[1]; out_++) { + // for (int plus = 0; plus <= pl.meshlines_width_; plus++) { + // data(out_, inrange[0] + plus) = rgb; + // data(out_, inrange[1] + plus) = rgb; + // data(out_, inrange[0] - plus) = rgb; + // data(out_, inrange[1] - plus) = rgb; + // } + // } // Iterate across the first axis and draw lines. for (auto ax1_val : axis_lines.first) { From 8d4f937e733cbd70a5ae7a13e61f5b31016072da Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 10 May 2019 16:35:20 -0500 Subject: [PATCH 049/205] Removing unstructured mesh vector. --- include/openmc/mesh.h | 2 ++ src/mesh.cpp | 12 ++++++++---- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 33684ff78b..5438500649 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -344,6 +344,8 @@ public: double get_volume_frac(int bin = -1) const; + int num_bins() const; + private: std::string filename_; moab::Range ehs_; diff --git a/src/mesh.cpp b/src/mesh.cpp index a1da1428f4..a0e259c123 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -15,6 +15,7 @@ #include "xtensor/xmath.hpp" #include "xtensor/xsort.hpp" #include "xtensor/xtensor.hpp" +#include "xtensor/xarray.hpp" #include "openmc/capi.h" #include "openmc/constants.h" @@ -1684,7 +1685,7 @@ void UnstructuredMesh::surface_bins_crossed(const Particle* p, std::vector& return; } -std::string UnstructuredMesh::get_label_for_bin(int bin) { +std::string UnstructuredMesh::get_label_for_bin(int bin) const { std::string s; return s; } @@ -1776,14 +1777,18 @@ UnstructuredMesh::get_ent_handle_from_bin(int bin) const { xt::xarray UnstructuredMesh::count_sites(const std::vector& bank, bool* outside) const { - xt::array out; + xt::xarray out; return out; } -double UnstructuredMesh::get_volume_frac(int bin = -1) const { +double UnstructuredMesh::get_volume_frac(int bin) const { return 0.0; } +int UnstructuredMesh::num_bins() const { + return ehs_.size(); +} + #endif @@ -1795,7 +1800,6 @@ void read_meshes(pugi::xml_node root) { for (auto node : root.children("mesh")) { - std::string mesh_type; if (check_for_node(node, "type")) { mesh_type = get_node_value(node, "type", true, true); From 6cc00a84202c90775f24704e88ec7b8ef8ad3b16 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 10 May 2019 17:56:36 -0500 Subject: [PATCH 050/205] Using a common count_sites implementation. --- include/openmc/mesh.h | 14 ++++++++++---- src/mesh.cpp | 20 ++++++-------------- 2 files changed, 16 insertions(+), 18 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 5438500649..d8164a1590 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -77,7 +77,7 @@ public: //! \param[out] Whether any bank sites are outside the mesh //! \return Array indicating number of sites in each mesh/energy bin virtual xt::xarray - count_sites(const std::vector& bank, bool* outside) const = 0; + count_sites(const std::vector& bank, bool* outside) const; virtual double get_volume_frac(int bin = -1) const = 0; @@ -216,6 +216,7 @@ public: //! \return Whether the line segment connecting r0 and r1 intersects mesh bool intersects(Position& r0, Position r1, int* ijk) const; + //! Count number of bank sites in each mesh bin / energy bin // //! \param[in] bank Array of bank sites @@ -226,6 +227,14 @@ public: // Data members + + //! Write mesh data to an HDF5 group + // + //! \param[in] group HDF5 group + // void to_hdf5(hid_t group) const; + + // std::string get_label_for_bin(int bin) const; + //std::string get_label_for_bin(int bin) const; //double get_volume_frac(int bin = -1) const; @@ -339,9 +348,6 @@ public: std::string get_label_for_bin(int bin) const; - xt::xarray - count_sites(const std::vector& bank, bool* outside) const; - double get_volume_frac(int bin = -1) const; int num_bins() const; diff --git a/src/mesh.cpp b/src/mesh.cpp index a0e259c123..ad79b2714c 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -795,11 +795,12 @@ void RegularMesh::to_hdf5(hid_t group) const } xt::xtensor + RegularMesh::count_sites(const Particle::Bank* bank, int64_t length, - bool* outside) const + bool* outside) const { // Determine shape of array for counts - std::size_t m = xt::prod(shape_)(); + std::size_t m = num_bins(); std::vector shape = {m}; // Create array of zeros @@ -1413,7 +1414,6 @@ RegularMesh* get_regular_mesh(int32_t index) { return dynamic_cast(model::meshes[index].get()); } - //! Extend the meshes array by n elements extern "C" int openmc_extend_meshes(int32_t n, int32_t* index_start, int32_t* index_end) @@ -1686,8 +1686,9 @@ void UnstructuredMesh::surface_bins_crossed(const Particle* p, std::vector& } std::string UnstructuredMesh::get_label_for_bin(int bin) const { - std::string s; - return s; + std::stringstream out; + out << "MOAB EntityHandle: " << get_ent_handle_from_bin(bin); + return out.str(); } int @@ -1700,7 +1701,6 @@ UnstructuredMesh::get_bin(Position r) const { } } - void UnstructuredMesh::compute_barycentric_data(const moab::Range& all_tets) { moab::ErrorCode rval; @@ -1760,7 +1760,6 @@ UnstructuredMesh::point_in_tet(const Position& r, moab::EntityHandle tet) const bary_coords[0] + bary_coords[1] + bary_coords[2] <= 1.); return in_tet; - } int @@ -1774,13 +1773,6 @@ UnstructuredMesh::get_ent_handle_from_bin(int bin) const { return ehs_[bin]; } -xt::xarray -UnstructuredMesh::count_sites(const std::vector& bank, - bool* outside) const { - xt::xarray out; - return out; - } - double UnstructuredMesh::get_volume_frac(int bin) const { return 0.0; } From b6ec54fef5047387c72ba69926bf7132f54117bf Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 10 May 2019 21:12:16 -0500 Subject: [PATCH 051/205] Making common function for laying ray over unstructured mesh. --- include/openmc/mesh.h | 11 +++++++++++ src/mesh.cpp | 26 ++++++++++++++++++++++---- 2 files changed, 33 insertions(+), 4 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index d8164a1590..d91d529415 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -315,8 +315,19 @@ public: void bins_crossed(const Particle* p, std::vector& bins, std::vector& lengths) const; + bool intersects(Position& r0, Position r1, int* ijk); + +private: + void + intersect_track(const moab::CartVect& start, + const moab::CartVect& dir, + double track_len, + std::vector& tris, + std::vector& intersection_dists) const; + +public: //! Determine which surface bins were crossed by a particle // //! \param[in] p Particle to check diff --git a/src/mesh.cpp b/src/mesh.cpp index ad79b2714c..212b46b21b 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1619,6 +1619,27 @@ UnstructuredMesh::build_tree(const moab::Range& all_tets) { } +void +UnstructuredMesh::intersect_track(const moab::CartVect& start, + const moab::CartVect& dir, + double track_len, + std::vector& tris, + std::vector& intersection_dists) const { + + moab::ErrorCode rval = kdtree_->ray_intersect_triangles(kdtree_root_, + 1E-03, + dir.array(), + start.array(), + tris, + intersection_dists, + 0, + track_len); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to compute tracklengths on umesh: " + filename_); + } + +} + void UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, std::vector& lengths) const { @@ -1640,10 +1661,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, std::vector tris; std::vector intersections; - rval = kdtree_->ray_intersect_triangles(kdtree_root_, 1E-03, dir.array(), r0.array(), tris, intersections, 0, track_len); - if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to compute tracklengths on umesh: " + filename_); - } + intersect_track(r0, dir, track_len, tris, intersections); bins.clear(); for (const auto& int_dist : intersections) { From 0890d08317a84cac7d0cf94cce92cd886d2a22fd Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sun, 12 May 2019 11:44:56 -0500 Subject: [PATCH 052/205] adding compute barycentric data call. --- src/mesh.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/mesh.cpp b/src/mesh.cpp index 212b46b21b..59b0c77e5e 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1584,6 +1584,7 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { warning("Non-tetrahedral elements found in unstructured mesh: " + filename_); } + compute_barycentric_data(all_tets); build_tree(all_tets); } From 2e8d345ffeae680bc799587c89d124619a699fab Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 13 May 2019 11:47:57 -0500 Subject: [PATCH 053/205] First successfull umesh tally, maybe. --- include/openmc/mesh.h | 12 +++++++-- src/mesh.cpp | 60 +++++++++++++++++++++++++++++++------------ 2 files changed, 54 insertions(+), 18 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index d91d529415..64e631cf2c 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -303,6 +303,9 @@ private: #ifdef DAGMC class UnstructuredMesh : public Mesh { + + typedef std::vector> TriHits; + public: UnstructuredMesh() { }; UnstructuredMesh(pugi::xml_node); @@ -320,12 +323,17 @@ public: private: + // void + // intersect_track(const moab::CartVect& start, + // const moab::CartVect& dir, + // double track_len, + // std::vector& tris, + // std::vector& intersection_dists) const; void intersect_track(const moab::CartVect& start, const moab::CartVect& dir, double track_len, - std::vector& tris, - std::vector& intersection_dists) const; + TriHits& hits) const; public: //! Determine which surface bins were crossed by a particle diff --git a/src/mesh.cpp b/src/mesh.cpp index 59b0c77e5e..3856e1e811 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1580,6 +1580,8 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { fatal_error("Failed to get all tetrahedral elements"); } + ehs_ = all_tets; + if (!all_tets.all_of_type(moab::MBTET)) { warning("Non-tetrahedral elements found in unstructured mesh: " + filename_); } @@ -1624,8 +1626,10 @@ void UnstructuredMesh::intersect_track(const moab::CartVect& start, const moab::CartVect& dir, double track_len, - std::vector& tris, - std::vector& intersection_dists) const { + TriHits& hits) const { + + std::vector tris; + std::vector intersection_dists; moab::ErrorCode rval = kdtree_->ray_intersect_triangles(kdtree_root_, 1E-03, @@ -1639,6 +1643,14 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start, fatal_error("Failed to compute tracklengths on umesh: " + filename_); } + // sort the tris and intersections by distance + hits.clear(); + for (int i = 0; i < tris.size(); i++) { + hits.emplace_back(std::pair(intersection_dists[i], tris[i])); + } + + // sorts by first component of std::pair by default + std::sort(hits.begin(), hits.end()); } void @@ -1659,20 +1671,28 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, r0 += TINY_BIT*dir; r1 -= TINY_BIT*dir; - std::vector tris; - std::vector intersections; - - intersect_track(r0, dir, track_len, tris, intersections); + TriHits hits; + intersect_track(r0, dir, track_len, hits); bins.clear(); - for (const auto& int_dist : intersections) { - moab::EntityHandle tet = get_tet(last_r + u * int_dist); + double prev_int_dist = 0.0; + for (const auto& hit : hits) { + moab::EntityHandle tet = get_tet(last_r + u * hit.first); if (tet == 0) { continue; } - if (std::find(bins.begin(), bins.end(), tet) == std::end(bins)) { + // if (std::find(bins.begin(), bins.end(), get_bin_from_ent_handle(tet)) == std::end(bins)) { bins.emplace_back(get_bin_from_ent_handle(tet)); - } + double tally_val = hit.first - prev_int_dist; + if (tally_val < 0.0) { + fatal_error("Negative weight applied to tally"); + } + lengths.emplace_back(tally_val); + prev_int_dist = hit.first; + // } + } + if (hits.size() != 0) { + std::cout << "Tris found: " << hits.size() << std::endl; + std::cout << "Bins crossed: " << bins.size() << std::endl; } - }; moab::EntityHandle @@ -1683,15 +1703,20 @@ UnstructuredMesh::get_tet(Position r) const { if (rval != moab::MB_SUCCESS) { return 0; } moab::EntityHandle leaf = kdtree_iter.handle(); + moab::Range tets; rval = mbi_->get_entities_by_dimension(leaf, 3, tets); - + if (rval != moab::MB_SUCCESS) { + warning("MOAB error finding tets."); + } for (const auto& tet : tets) { if (point_in_tet(r, tet)) { - return get_bin_from_ent_handle(tet); + std::cout << "Found it." << std::endl; + int bin = get_bin_from_ent_handle(tet); + std::cout << "Found bin: " << bin << std::endl; + return bin; } } - return 0; } @@ -1783,8 +1808,10 @@ UnstructuredMesh::point_in_tet(const Position& r, moab::EntityHandle tet) const int UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { - auto pos = ehs_.find(eh); - return pos - ehs_.begin(); + std::cout << "EH: " << eh << std::endl; + std::cout << "EH0: " << ehs_[0] << std::endl; + return eh - ehs_[0]; + } moab::EntityHandle @@ -1797,6 +1824,7 @@ double UnstructuredMesh::get_volume_frac(int bin) const { } int UnstructuredMesh::num_bins() const { + std::cout << "Mesh has " << ehs_.size() << " bins" << std::endl; return ehs_.size(); } From f8fb7ab92d66e7cb2b6288d143056f991f3c3dd7 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 14 May 2019 22:05:55 -0500 Subject: [PATCH 054/205] Some edits, fixes, documentation. --- include/openmc/mesh.h | 112 +++++++++++++----- src/mesh.cpp | 259 +++++++++++++++++++++++++++++++----------- 2 files changed, 278 insertions(+), 93 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 64e631cf2c..3287b0d2a5 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -19,6 +19,7 @@ #include "moab/Core.hpp" #include "moab/AdaptiveKDTree.hpp" #include "moab/Matrix3.hpp" +#include "moab/GeomUtil.hpp" #endif namespace openmc { @@ -304,13 +305,13 @@ private: class UnstructuredMesh : public Mesh { - typedef std::vector> TriHits; + typedef std::vector> UnstructuredMeshHits; public: UnstructuredMesh() { }; UnstructuredMesh(pugi::xml_node); - //! Determine which bins were crossed by a particle + //! Determine which bins were crossed by a particle. // //! \param[in] p Particle to check //! \param[out] bins Bins that were crossed @@ -323,62 +324,115 @@ public: private: + // void // intersect_track(const moab::CartVect& start, // const moab::CartVect& dir, // double track_len, // std::vector& tris, // std::vector& intersection_dists) const; - void - intersect_track(const moab::CartVect& start, - const moab::CartVect& dir, - double track_len, - TriHits& hits) const; + // void + // intersect_track(const moab::CartVect& start, + // const moab::CartVect& dir, + // double track_len, + // TriHits& hits) const; + +//! Finds all intersections with faces of the mesh. +// +//! \param[in] start Staring location +//! \param[in] dir Normalized particle direction +//! \param[in] length of particle track +//! \param[out] Mesh intersections +void +intersect_track(const moab::CartVect& start, + const moab::CartVect& dir, + double track_len, + UnstructuredMeshHits& hits) const; + + //! Calculates the volume for a given tetrahedron handle. + // + // \param[in] tet MOAB EntityHandle of the tetrahedron + double tet_volume(moab::EntityHandle tet) const; + + //! Find the tetrahedron for the given location if + //! one exists + // + //! \param[in] + //! \return MOAB EntityHandle of tet + moab::EntityHandle get_tet(const Position& r) const; + + //! Version of get_tet taking Position. + inline moab::EntityHandle get_tet(const moab::CartVect& r) const { + return get_tet(Position(r[0], r[1], r[2])); + }; + + //! Check for point containment within a tet, uses + //! pre-computed barycentric data. + // + //! \param[in] r Position to check + //! \param[in] MOAB terahedron to check + //! \return True if r is inside, False if r is outside + bool point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const; + + //! Compute barycentric coordinate data for all tetrahedra + //! in the mesh. + // + //! \param[in] all_tets MOAB Range of tetrahedral elements + void compute_barycentric_data(const moab::Range& all_tets); + + //! Translates a MOAB EntityHandle its corresponding bin. + // + //! \param[in] eh MOAB EntityHandle to translate + //! \return Mesh bin + int get_bin_from_ent_handle(moab::EntityHandle eh) const; + + //! Translates a bin to its corresponding MOAB EntityHandle + //! for the tetrahedron representing that bin. + // + //! \param[in] bin Bin value to translate + //! \return MOAB EntityHandle of tet + moab::EntityHandle get_ent_handle_from_bin(int bin) const; + + //! Builds a KDTree for all tetrahedra in the mesh. All + //! triangles representing 2D faces of the mesh are + //! added to the tree as well. + // + //! \param[in] all_tets MOAB Range of tetrahedra for the tree + void build_kdtree(const moab::Range& all_tets); public: - //! Determine which surface bins were crossed by a particle + //! Determine which surface bins were crossed by a particle. // //! \param[in] p Particle to check //! \param[out] bins Surface bins that were crossed void surface_bins_crossed(const Particle* p, std::vector& bins) const; - bool point_in_tet(const Position& r, moab::EntityHandle tet) const; - - //! Write mesh data to an HDF5 group + //! Write mesh data to an HDF5 group. // //! \param[in] group HDF5 group void to_hdf5(hid_t group) const; - //! Get bin at a given position in space + //! Get bin at a given position. // //! \param[in] r Position to get bin for //! \return Mesh bin int get_bin(Position r) const; - moab::EntityHandle get_tet(Position r) const; - - void compute_barycentric_data(const moab::Range& all_tets); - - int get_bin_from_ent_handle(moab::EntityHandle eh) const; - - moab::EntityHandle get_ent_handle_from_bin(int bin) const; - - void build_tree(const moab::Range& all_tets); - std::string get_label_for_bin(int bin) const; double get_volume_frac(int bin = -1) const; int num_bins() const; + std::string filename_; // mbi_; - std::unique_ptr kdtree_; - std::vector baryc_data_; + moab::Range ehs_; //!< Range of tetrahedra EntityHandles in the mesh + moab::EntityHandle meshset_; //!< Meshset containing all Tets/Tris + moab::EntityHandle kdtree_root_; //!< Root of the MOAB KDTree + std::shared_ptr mbi_; //!< MOAB instance + std::unique_ptr kdtree_; //!< MOAB KDTree instance + std::vector baryc_data_; //!< Barycentric data for tetrahedra }; #endif diff --git a/src/mesh.cpp b/src/mesh.cpp index 3856e1e811..bfe867f254 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1540,7 +1540,6 @@ openmc_mesh_set_params(int32_t index, int n, const double* ll, const double* ur, #ifdef DAGMC UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { - // check the mesh type if (check_for_node(node, "type")) { auto temp = get_node_value(node, "type", true, true); @@ -1580,27 +1579,29 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { fatal_error("Failed to get all tetrahedral elements"); } + ehs_.clear(); ehs_ = all_tets; if (!all_tets.all_of_type(moab::MBTET)) { warning("Non-tetrahedral elements found in unstructured mesh: " + filename_); } - compute_barycentric_data(all_tets); - build_tree(all_tets); + compute_barycentric_data(ehs_); + build_kdtree(ehs_); } void -UnstructuredMesh::build_tree(const moab::Range& all_tets) { +UnstructuredMesh::build_kdtree(const moab::Range& all_tets) { moab::Range all_tris; + int triangle_dim = 2; moab::ErrorCode rval = mbi_->get_adjacencies(all_tets, - n_dimension_ - 1, + triangle_dim, true, all_tris, moab::Interface::UNION); if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to get adjacent triangle for test in MOAB"); + fatal_error("Failed to get adjacent triangles for tets"); } if (!all_tris.all_of_type(moab::MBTRI)) { @@ -1615,38 +1616,41 @@ UnstructuredMesh::build_tree(const moab::Range& all_tets) { // create and build KD-tree kdtree_ = std::unique_ptr(new moab::AdaptiveKDTree(mbi_.get())); + //const char settings[] = "MESHSET_FLAGS=0x1;TAG_NAME=0"; + // moab::FileOptions fileopts(settings); + + // build the tree rval = kdtree_->build_tree(all_tets_and_tris, &kdtree_root_); if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to construct a KD-Tree for unstructured mesh " + filename_); + fatal_error("Failed to construct KDTree for the unstructured mesh " + filename_); } - } void UnstructuredMesh::intersect_track(const moab::CartVect& start, const moab::CartVect& dir, double track_len, - TriHits& hits) const { - + UnstructuredMeshHits& hits) const { + moab::ErrorCode rval; std::vector tris; std::vector intersection_dists; - moab::ErrorCode rval = kdtree_->ray_intersect_triangles(kdtree_root_, - 1E-03, - dir.array(), - start.array(), - tris, - intersection_dists, - 0, - track_len); + rval = kdtree_->ray_intersect_triangles(kdtree_root_, + 1E-06, + dir.array(), + start.array(), + tris, + intersection_dists, + 0, + track_len); if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to compute tracklengths on umesh: " + filename_); + fatal_error("Failed to compute intersections on umesh: " + filename_); } // sort the tris and intersections by distance hits.clear(); for (int i = 0; i < tris.size(); i++) { - hits.emplace_back(std::pair(intersection_dists[i], tris[i])); + hits.push_back(std::pair(intersection_dists[i], tris[i])); } // sorts by first component of std::pair by default @@ -1654,10 +1658,10 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start, } void -UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, +UnstructuredMesh::bins_crossed(const Particle* p, + std::vector& bins, std::vector& lengths) const { moab::ErrorCode rval; - Position last_r{p->r_last_}; Position r{p->r()}; Position u{p->u()}; @@ -1671,55 +1675,162 @@ UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, r0 += TINY_BIT*dir; r1 -= TINY_BIT*dir; - TriHits hits; + UnstructuredMeshHits hits; intersect_track(r0, dir, track_len, hits); bins.clear(); - double prev_int_dist = 0.0; + lengths.clear(); + + //// IMPLEMENTATION ONE + if (hits.size() == 0) { + moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5); + if (last_r_tet) { + bins.push_back(get_bin_from_ent_handle(last_r_tet)); + lengths.push_back(1.0 / tet_volume(last_r_tet)); + } + return; + } + + moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0); + double last_dist = 0.0; + + // make sure first point is inside a tet + if (!tet) { + last_dist = hits.front().first; + hits.erase(hits.begin()); + tet = get_tet(last_r + u * (last_dist + hits.front().first) / 2.0); + } + + // if there are no other hits, there is only one segment to tally + if (hits.size() == 0 && tet) { + bins.push_back(get_bin_from_ent_handle(tet)); + lengths.push_back(1.0 / tet_volume(tet)); + return; + } + + // score all remaining segments for (const auto& hit : hits) { - moab::EntityHandle tet = get_tet(last_r + u * hit.first); - if (tet == 0) { continue; } - // if (std::find(bins.begin(), bins.end(), get_bin_from_ent_handle(tet)) == std::end(bins)) { - bins.emplace_back(get_bin_from_ent_handle(tet)); - double tally_val = hit.first - prev_int_dist; - if (tally_val < 0.0) { - fatal_error("Negative weight applied to tally"); - } - lengths.emplace_back(tally_val); - prev_int_dist = hit.first; - // } + // score in this tet if one was found + if (tet) { + bins.push_back(get_bin_from_ent_handle(tet)); + lengths.push_back((hit.first - last_dist) / (track_len * tet_volume(tet))); + } else { + // if in the loop, we should always find a tet + fatal_error("No tet found for location between trianle hits"); + } + last_dist = hit.first; + + // find next tet + moab::Range adj_tets; + rval = mbi_->get_adjacencies(&hit.second, 1, 3, false, adj_tets); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to get triangle adjacencies from mesh " + filename_); + } + + if (adj_tets.size() == 2) { + tet = tet == adj_tets[0] ? adj_tets[1] : adj_tets[0]; + } else if (adj_tets.size() == 1) { + tet = adj_tets[0]; + } } - if (hits.size() != 0) { - std::cout << "Tris found: " << hits.size() << std::endl; - std::cout << "Bins crossed: " << bins.size() << std::endl; + + // tally remaining portion of track after last hit if + // the last segment of the track is in the mesh + if (hits.back().first < track_len) { + tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0); + if (tet) { + bins.push_back(get_bin_from_ent_handle(tet)); + lengths.push_back((track_len - hits.back().first) / (track_len* tet_volume(tet))); + } } + + return; + + /// IMPLEMENTATION TWO + // double prev_int_dist = 0.0; + + // if (hits.size() == 0) { + // moab::EntityHandle last_r_tet =get_tet((r0 + r1) * 0.5); + // if (last_r_tet) { + // bins.push_back(get_bin_from_ent_handle(last_r_tet)); + // lengths.push_back(1.0 / tet_volume(last_r_tet)); + // } + // return; + // } + + // moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0); + + // if (!tet) { + // last_r = last_r + u * hits.front().first; + // hits.erase(hits.begin()); + // } + + // for (const auto& hit : hits) { + // tet = get_tet(last_r + u * (prev_int_dist + hit.first) / 2.0 ); + // if (!tet) { + // prev_int_dist = hit.first; + // continue; + // } + // int bin = get_bin_from_ent_handle(tet); + // double tally_val = (hit.first - prev_int_dist) / (track_len * tet_volume(tet)); + // if (tally_val < 0.0) { + // fatal_error("Negative score applied to tally"); + // } + + // bins.push_back(bin); + // lengths.push_back(tally_val); + // prev_int_dist = hit.first; + // } + + // // tally remaining portion of track (if any exists) + // if (hits.back().first < track_len) { + // tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0); + // if (tet) { + // bins.push_back(get_bin_from_ent_handle(tet)); + // double tally_val = (track_len - hits.back().first) / (track_len * tet_volume(tet)); + // lengths.push_back(tally_val); + // } + // } + }; moab::EntityHandle -UnstructuredMesh::get_tet(Position r) const { - moab::CartVect pnt(r.x, r.y, r.z); +UnstructuredMesh::get_tet(const Position& r) const { + moab::CartVect pos(r.x, r.y, r.z); moab::AdaptiveKDTreeIter kdtree_iter; - moab::ErrorCode rval = kdtree_->point_search(pnt.array(), kdtree_iter); + moab::ErrorCode rval = kdtree_->point_search(pos.array(), kdtree_iter); if (rval != moab::MB_SUCCESS) { return 0; } moab::EntityHandle leaf = kdtree_iter.handle(); - moab::Range tets; - rval = mbi_->get_entities_by_dimension(leaf, 3, tets); + rval = mbi_->get_entities_by_dimension(leaf, 3, tets, false); if (rval != moab::MB_SUCCESS) { warning("MOAB error finding tets."); } + for (const auto& tet : tets) { - if (point_in_tet(r, tet)) { - std::cout << "Found it." << std::endl; - int bin = get_bin_from_ent_handle(tet); - std::cout << "Found bin: " << bin << std::endl; - return bin; + if (point_in_tet(pos, tet)) { + return tet; } } return 0; } +double UnstructuredMesh::tet_volume(moab::EntityHandle tet) const { + std::vector conn; + moab::ErrorCode rval = mbi_->get_connectivity(&tet, 1, conn); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to get tet connectivity"); + } + + moab::CartVect p[4]; + rval = mbi_->get_coords(&(conn[0]), (int)conn.size(), p[0].array()); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to get tet coords"); + } + + return 1.0 / 6.0 * (((p[1] - p[0]) * (p[2] - p[0])) % (p[3] - p[0])); +} //! Determine which surface bins were crossed by a particle // @@ -1748,36 +1859,43 @@ UnstructuredMesh::get_bin(Position r) const { void UnstructuredMesh::compute_barycentric_data(const moab::Range& all_tets) { moab::ErrorCode rval; + + baryc_data_.clear(); + baryc_data_.resize(all_tets.size()); + for (auto& tet : all_tets) { - moab::Range verts; + std::vector verts; rval = mbi_->get_connectivity(&tet, 1, verts); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to get connectivity of tet on umesh: " + filename_); } moab::CartVect p[4]; - rval = mbi_->get_coords(verts, p[0].array()); + rval = mbi_->get_coords(&(verts[0]), (int)verts.size(), p[0].array()); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to get coordinates of a tet in umesh: " + filename_); } moab::Matrix3 a(p[1] - p[0], p[2] - p[0], p[3] - p[0], true); - - baryc_data_.push_back(a.transpose().inverse()); + a = a.transpose().inverse(); + baryc_data_.at(get_bin_from_ent_handle(tet)) = a; } + } // TODO: write this function void -UnstructuredMesh::to_hdf5(hid_t group) const { } +UnstructuredMesh::to_hdf5(hid_t group) const { + +} bool -UnstructuredMesh::point_in_tet(const Position& r, moab::EntityHandle tet) const { +UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const { moab::ErrorCode rval; // get tet vertices - moab::Range verts; + std::vector verts; rval = mbi_->get_connectivity(&tet, 1, verts); if (rval != moab::MB_SUCCESS) { warning("Failed to get vertices of tet in umesh: " + filename_); @@ -1792,13 +1910,11 @@ UnstructuredMesh::point_in_tet(const Position& r, moab::EntityHandle tet) const return false; } - moab::CartVect pos(r.x, r.y, r.z); - // look up barycentric data int idx = get_bin_from_ent_handle(tet); const moab::Matrix3& a_inv = baryc_data_[idx]; - moab::CartVect bary_coords = a_inv * (pos - p_zero); + moab::CartVect bary_coords = a_inv * (r - p_zero); bool in_tet = (bary_coords[0] >= 0 && bary_coords[1] >= 0 && bary_coords[2] >= 0 && bary_coords[0] + bary_coords[1] + bary_coords[2] <= 1.); @@ -1808,23 +1924,37 @@ UnstructuredMesh::point_in_tet(const Position& r, moab::EntityHandle tet) const int UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { - std::cout << "EH: " << eh << std::endl; - std::cout << "EH0: " << ehs_[0] << std::endl; - return eh - ehs_[0]; - + int bin = eh - ehs_[0]; + if (bin >= num_bins()) { + std::stringstream s; + s << "Invalid bin: " << bin; + fatal_error(s); + } + return bin; } moab::EntityHandle UnstructuredMesh::get_ent_handle_from_bin(int bin) const { + if (bin >= num_bins()) { + std::stringstream s; + s << "Invalid bin index: " << bin; + fatal_error(s); + } return ehs_[bin]; } double UnstructuredMesh::get_volume_frac(int bin) const { - return 0.0; + if (bin == -1) { return 0.0; } + if (bin > ehs_.size() || bin < -1) { + std::stringstream msg; + msg << "Invalid bin " << bin << " for umesh with id " << id_; + fatal_error(msg); + } + moab::EntityHandle tet = get_ent_handle_from_bin(bin); + return tet_volume(tet); } int UnstructuredMesh::num_bins() const { - std::cout << "Mesh has " << ehs_.size() << " bins" << std::endl; return ehs_.size(); } @@ -1885,6 +2015,7 @@ void meshes_to_hdf5(hid_t group) void free_memory_mesh() { + UnstructuredMesh* m = reinterpret_cast(model::meshes[1].get()); model::meshes.clear(); model::mesh_map.clear(); } From f84440626885e631a639b48d3f6324d1c9ef36da Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 17 May 2019 14:22:25 -0500 Subject: [PATCH 055/205] Removing silly cast. --- src/mesh.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index bfe867f254..d219e59150 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -2015,7 +2015,6 @@ void meshes_to_hdf5(hid_t group) void free_memory_mesh() { - UnstructuredMesh* m = reinterpret_cast(model::meshes[1].get()); model::meshes.clear(); model::mesh_map.clear(); } From 6b8e5d470ef6307352e4ef7932fda9b63720b4ac Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 31 May 2019 10:58:06 -0500 Subject: [PATCH 056/205] Adding body to the Unstructured mesh to_hdf5 method. --- src/mesh.cpp | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index d219e59150..3eaf8566bf 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1885,8 +1885,18 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& all_tets) { // TODO: write this function void -UnstructuredMesh::to_hdf5(hid_t group) const { +UnstructuredMesh::to_hdf5(hid_t group) const +{ + hid_t mesh_group = create_group(group, "mesh " + std::to_string(id_)); + write_dataset(mesh_group, "type", "unstructured"); + std::vector tet_vols; + for (const auto& eh : ehs_) { + tet_vols.emplace_back(tet_volume(eh)); + } + write_dataset(mesh_group, "volumes", tet_vols); + + close_group(mesh_group); } bool From 645cc12737308c481e757217fc8c588fe829c793 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 31 May 2019 11:11:28 -0500 Subject: [PATCH 057/205] Removing volume normalization from tally values. --- src/mesh.cpp | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 3eaf8566bf..9d8cd67b5a 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1686,7 +1686,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5); if (last_r_tet) { bins.push_back(get_bin_from_ent_handle(last_r_tet)); - lengths.push_back(1.0 / tet_volume(last_r_tet)); + lengths.push_back(1.0); } return; } @@ -1704,7 +1704,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, // if there are no other hits, there is only one segment to tally if (hits.size() == 0 && tet) { bins.push_back(get_bin_from_ent_handle(tet)); - lengths.push_back(1.0 / tet_volume(tet)); + lengths.push_back(1.0); return; } @@ -1713,7 +1713,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, // score in this tet if one was found if (tet) { bins.push_back(get_bin_from_ent_handle(tet)); - lengths.push_back((hit.first - last_dist) / (track_len * tet_volume(tet))); + lengths.push_back((hit.first - last_dist) / track_len); } else { // if in the loop, we should always find a tet fatal_error("No tet found for location between trianle hits"); @@ -1740,7 +1740,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0); if (tet) { bins.push_back(get_bin_from_ent_handle(tet)); - lengths.push_back((track_len - hits.back().first) / (track_len* tet_volume(tet))); + lengths.push_back((track_len - hits.back().first) / track_len); } } @@ -1753,7 +1753,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, // moab::EntityHandle last_r_tet =get_tet((r0 + r1) * 0.5); // if (last_r_tet) { // bins.push_back(get_bin_from_ent_handle(last_r_tet)); - // lengths.push_back(1.0 / tet_volume(last_r_tet)); + // lengths.push_back(1.0); // } // return; // } @@ -1772,7 +1772,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, // continue; // } // int bin = get_bin_from_ent_handle(tet); - // double tally_val = (hit.first - prev_int_dist) / (track_len * tet_volume(tet)); + // double tally_val = (hit.first - prev_int_dist) / track_len); // if (tally_val < 0.0) { // fatal_error("Negative score applied to tally"); // } @@ -1787,7 +1787,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, // tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0); // if (tet) { // bins.push_back(get_bin_from_ent_handle(tet)); - // double tally_val = (track_len - hits.back().first) / (track_len * tet_volume(tet)); + // double tally_val = (track_len - hits.back().first) / track_len); // lengths.push_back(tally_val); // } // } From 66346e8356e766372eeed4e3c124620f1737ba2d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 31 May 2019 11:33:44 -0500 Subject: [PATCH 058/205] Writing total unstructured mesh volume to statepoint as well. --- src/mesh.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/mesh.cpp b/src/mesh.cpp index 9d8cd67b5a..f039fa08c0 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1890,11 +1890,15 @@ UnstructuredMesh::to_hdf5(hid_t group) const hid_t mesh_group = create_group(group, "mesh " + std::to_string(id_)); write_dataset(mesh_group, "type", "unstructured"); + // write volume of each tet std::vector tet_vols; for (const auto& eh : ehs_) { tet_vols.emplace_back(tet_volume(eh)); } write_dataset(mesh_group, "volumes", tet_vols); + // and the total volume of the mesh + auto total_vol = std::accumulate(tet_vols.begin(), tet_vols.end(), 0.0); + write_dataset(mesh_group, "total_volume", total_vol); close_group(mesh_group); } From ff0af7d5946cb91da01982da8bc8095c9e7c4af2 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 26 Nov 2019 04:57:16 -0600 Subject: [PATCH 059/205] Corrections after rebase. --- include/openmc/mesh.h | 126 +++++++++++++++++++++++------------------- src/mesh.cpp | 99 +++++++++++++++++++++------------ 2 files changed, 133 insertions(+), 92 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 3287b0d2a5..b6e7c73a94 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -37,62 +37,9 @@ extern std::unordered_map mesh_map; } // namespace model -class Mesh { -public: - // Constructor - Mesh() {}; // empty constructor - Mesh(pugi::xml_node node); - - //! Determine which bins were crossed by a particle - // - //! \param[in] p Particle to check - //! \param[out] bins Bins that were crossed - //! \param[out] lengths Fraction of tracklength in each bin - virtual void bins_crossed(const Particle* p, std::vector& bins, - std::vector& lengths) const = 0; - - //! Write mesh data to an HDF5 group - // - //! \param[in] group HDF5 group - virtual void to_hdf5(hid_t group) const = 0; - - //! Determine which surface bins were crossed by a particle - // - //! \param[in] p Particle to check - //! \param[out] bins Surface bins that were crossed - virtual void - surface_bins_crossed(const Particle* p, std::vector& bins) const = 0; - - //! Get bin at a given position in space - // - //! \param[in] r Position to get bin for - //! \return Mesh bin - virtual int get_bin(Position r) const = 0; - - virtual std::string get_label_for_bin(int bin) const = 0; - - //! Count number of bank sites in each mesh bin / energy bin - // - //! \param[in] bank Array of bank sites - //! \param[out] Whether any bank sites are outside the mesh - //! \return Array indicating number of sites in each mesh/energy bin - virtual xt::xarray - count_sites(const std::vector& bank, bool* outside) const; - - virtual double get_volume_frac(int bin = -1) const = 0; - - virtual int num_bins() const = 0; - - int id_ {-1}; //!< User-specified ID - int n_dimension_; //!< Number of dimensions -}; - -//============================================================================== -//! Tessellation of n-dimensional Euclidean space by congruent squares or cubes -//============================================================================== - -class RegularMesh : public Mesh { +class Mesh +{ public: // Constructors and destructor Mesh() = default; @@ -171,6 +118,60 @@ public: xt::xtensor upper_right_; //!< Upper-right coordinates of mesh }; +// class Mesh { + +// public: +// // Constructors +// Mesh() = default; +// Mesh(pugi::xml_node node); + +// // Destructor +// virtual ~Mesh() = default; + +// //! Determine which bins were crossed by a particle +// // +// //! \param[in] p Particle to check +// //! \param[out] bins Bins that were crossed +// //! \param[out] lengths Fraction of tracklength in each bin +// virtual void bins_crossed(const Particle* p, std::vector& bins, +// std::vector& lengths) const = 0; + +// //! Write mesh data to an HDF5 group +// // +// //! \param[in] group HDF5 group +// virtual void to_hdf5(hid_t group) const = 0; + +// //! Determine which surface bins were crossed by a particle +// // +// //! \param[in] p Particle to check +// //! \param[out] bins Surface bins that were crossed +// virtual void +// surface_bins_crossed(const Particle* p, std::vector& bins) const = 0; + +// //! Get bin at a given position in space +// // +// //! \param[in] r Position to get bin for +// //! \return Mesh bin +// virtual int get_bin(Position r) const = 0; + +// virtual std::string get_label_for_bin(int bin) const = 0; + +// //! Count number of bank sites in each mesh bin / energy bin +// // +// //! \param[in] bank Array of bank sites +// //! \param[out] Whether any bank sites are outside the mesh +// //! \return Array indicating number of sites in each mesh/energy bin +// virtual xt::xarray +// count_sites(const std::vector& bank, bool* outside) const; + +// virtual double get_volume_frac(int bin = -1) const = 0; + +// virtual int num_bins() const = 0; + +// int id_ {-1}; //!< User-specified ID +// int n_dimension_; //!< Number of dimensions +// }; + //============================================================================== //! Tessellation of n-dimensional Euclidean space by congruent squares or cubes //============================================================================== @@ -393,6 +394,15 @@ intersect_track(const moab::CartVect& start, //! \return MOAB EntityHandle of tet moab::EntityHandle get_ent_handle_from_bin(int bin) const; + int get_bin_from_indices(const int* ijk) const override; + + void get_indices(Position r, int* ijk, bool* in_mesh) const override; + + void get_indices_from_bin(int bin, int* ijk) const override; + + std::pair, std::vector> + plot(Position plot_ll, Position plot_ur) const override; + //! Builds a KDTree for all tetrahedra in the mesh. All //! triangles representing 2D faces of the mesh are //! added to the tree as well. @@ -420,9 +430,11 @@ public: std::string get_label_for_bin(int bin) const; - double get_volume_frac(int bin = -1) const; + // double get_volume_frac(int bin = -1) const; - int num_bins() const; + int n_bins() const override; + + int n_surface_bins() const override; std::string filename_; // shape = {m}; // Create array of zeros @@ -850,28 +848,22 @@ RegularMesh::count_sites(const Particle::Bank* bank, int64_t length, return counts; } -std::string RegularMesh::get_label_for_bin(int bin) const { - int ijk[n_dimension_]; - get_indices_from_bin(bin, ijk); +// std::string RegularMesh::get_label_for_bin(int bin) const { +// int ijk[n_dimension_]; +// get_indices_from_bin(bin, ijk); - std::stringstream out; - out << "Mesh Index (" << ijk[0]; - if (n_dimension_ > 1) out << ", " << ijk[1]; - if (n_dimension_ > 2) out << ", " << ijk[2]; - out << ")"; +// std::stringstream out; +// out << "Mesh Index (" << ijk[0]; +// if (n_dimension_ > 1) out << ", " << ijk[1]; +// if (n_dimension_ > 2) out << ", " << ijk[2]; +// out << ")"; - return out.str(); -} +// return out.str(); +// } -double RegularMesh::get_volume_frac(int bin) const { - return volume_frac_; -} - -int RegularMesh::num_bins() const { - int n_bins = 1; - for (auto v : shape_) n_bins *= v; - return n_bins; -} +// double RegularMesh::get_volume_frac(int bin) const { +// return volume_frac_; +// } //============================================================================== // RectilinearMesh implementation @@ -1936,10 +1928,35 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) return in_tet; } +int +UnstructuredMesh::get_bin_from_indices(const int* ijk) const { + if (ijk[0] >= n_bins()) { + std::stringstream s; + s << "Invalid bin: " << ijk[0]; + fatal_error(s); + } + int bin = ehs_[ijk[0]] - ehs_[0]; + return bin; +} + +void +UnstructuredMesh::get_indices(Position r, int* ijk, bool* in_mesh) const { + int bin = get_bin(r); + ijk[0]= bin; + *in_mesh = bin != -1; +} + +void UnstructuredMesh::get_indices_from_bin(int bin, int* ijk) const { + ijk[0] = bin; +} + +std::pair, std::vector> +UnstructuredMesh::plot(Position plot_ll, Position plot_ur) const { return {}; } + int UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { int bin = eh - ehs_[0]; - if (bin >= num_bins()) { + if (bin >= n_bins()) { std::stringstream s; s << "Invalid bin: " << bin; fatal_error(s); @@ -1949,7 +1966,7 @@ UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { moab::EntityHandle UnstructuredMesh::get_ent_handle_from_bin(int bin) const { - if (bin >= num_bins()) { + if (bin >= n_bins()) { std::stringstream s; s << "Invalid bin index: " << bin; fatal_error(s); @@ -1957,19 +1974,31 @@ UnstructuredMesh::get_ent_handle_from_bin(int bin) const { return ehs_[bin]; } -double UnstructuredMesh::get_volume_frac(int bin) const { - if (bin == -1) { return 0.0; } - if (bin > ehs_.size() || bin < -1) { - std::stringstream msg; - msg << "Invalid bin " << bin << " for umesh with id " << id_; - fatal_error(msg); - } - moab::EntityHandle tet = get_ent_handle_from_bin(bin); - return tet_volume(tet); +// double UnstructuredMesh::get_volume_frac(int bin) const { +// if (bin == -1) { return 0.0; } +// if (bin > ehs_.size() || bin < -1) { +// std::stringstream msg; +// msg << "Invalid bin " << bin << " for umesh with id " << id_; +// fatal_error(msg); +// } +// moab::EntityHandle tet = get_ent_handle_from_bin(bin); +// return tet_volume(tet); +// } + +int UnstructuredMesh::n_bins() const { + return ehs_.size(); } -int UnstructuredMesh::num_bins() const { - return ehs_.size(); +int UnstructuredMesh::n_surface_bins() const { + // collect all triangles in the set of tets for this mesh + moab::Range tris; + moab::ErrorCode rval; + rval = mbi_->get_entities_by_type(0, moab::MBTRI, tris); + if (rval != moab::MB_SUCCESS) { + warning("Failed to get all triangles in the mesh instance"); + return -1; + } + return 2 * tris.size(); } #endif From 43d05fb178116d1a13de01d3378165098f9d7002 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 26 Nov 2019 07:34:32 -0600 Subject: [PATCH 060/205] Adding ifdefs for mesh object IO. --- src/mesh.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/mesh.cpp b/src/mesh.cpp index a95ecc6a7d..3d0271cc1e 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -2024,9 +2024,11 @@ void read_meshes(pugi::xml_node root) model::meshes.push_back(std::make_unique(node)); } else if (mesh_type == "rectilinear") { model::meshes.push_back(std::make_unique(node)); +#ifdef DAGMC } else if (mesh_type == "unstructured") { model::meshes.push_back(std::make_unique(node)); +#endif } else { fatal_error("Invalid mesh type: " + mesh_type); } From b454617c64f2a09499e71d71cbe18c8e0b10eb90 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 28 Nov 2019 06:28:45 -0600 Subject: [PATCH 061/205] Some cleanup and removal of commented sections after rebase. --- include/openmc/mesh.h | 84 +------------------------------------ src/eigenvalue.cpp | 6 --- src/mesh.cpp | 40 +++--------------- src/plot.cpp | 44 ------------------- src/tallies/filter_mesh.cpp | 1 - 5 files changed, 7 insertions(+), 168 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index b6e7c73a94..8a66e06ab5 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -118,60 +118,6 @@ public: xt::xtensor upper_right_; //!< Upper-right coordinates of mesh }; -// class Mesh { - -// public: -// // Constructors -// Mesh() = default; -// Mesh(pugi::xml_node node); - -// // Destructor -// virtual ~Mesh() = default; - -// //! Determine which bins were crossed by a particle -// // -// //! \param[in] p Particle to check -// //! \param[out] bins Bins that were crossed -// //! \param[out] lengths Fraction of tracklength in each bin -// virtual void bins_crossed(const Particle* p, std::vector& bins, -// std::vector& lengths) const = 0; - -// //! Write mesh data to an HDF5 group -// // -// //! \param[in] group HDF5 group -// virtual void to_hdf5(hid_t group) const = 0; - -// //! Determine which surface bins were crossed by a particle -// // -// //! \param[in] p Particle to check -// //! \param[out] bins Surface bins that were crossed -// virtual void -// surface_bins_crossed(const Particle* p, std::vector& bins) const = 0; - -// //! Get bin at a given position in space -// // -// //! \param[in] r Position to get bin for -// //! \return Mesh bin -// virtual int get_bin(Position r) const = 0; - -// virtual std::string get_label_for_bin(int bin) const = 0; - -// //! Count number of bank sites in each mesh bin / energy bin -// // -// //! \param[in] bank Array of bank sites -// //! \param[out] Whether any bank sites are outside the mesh -// //! \return Array indicating number of sites in each mesh/energy bin -// virtual xt::xarray -// count_sites(const std::vector& bank, bool* outside) const; - -// virtual double get_volume_frac(int bin = -1) const = 0; - -// virtual int num_bins() const = 0; - -// int id_ {-1}; //!< User-specified ID -// int n_dimension_; //!< Number of dimensions -// }; - //============================================================================== //! Tessellation of n-dimensional Euclidean space by congruent squares or cubes //============================================================================== @@ -227,22 +173,10 @@ public: xt::xtensor count_sites(const Particle::Bank* bank, int64_t length, bool* outside) const; - // Data members - - - //! Write mesh data to an HDF5 group - // - //! \param[in] group HDF5 group - // void to_hdf5(hid_t group) const; - - // std::string get_label_for_bin(int bin) const; - - //std::string get_label_for_bin(int bin) const; - - //double get_volume_frac(int bin = -1) const; - int num_bins() const; + // Data members + double volume_frac_; //!< Volume fraction of each mesh element xt::xtensor shape_; //!< Number of mesh elements in each dimension xt::xtensor width_; //!< Width of each mesh element @@ -326,18 +260,6 @@ public: private: - // void - // intersect_track(const moab::CartVect& start, - // const moab::CartVect& dir, - // double track_len, - // std::vector& tris, - // std::vector& intersection_dists) const; - // void - // intersect_track(const moab::CartVect& start, - // const moab::CartVect& dir, - // double track_len, - // TriHits& hits) const; - //! Finds all intersections with faces of the mesh. // //! \param[in] start Staring location @@ -430,8 +352,6 @@ public: std::string get_label_for_bin(int bin) const; - // double get_volume_frac(int bin = -1) const; - int n_bins() const override; int n_surface_bins() const override; diff --git a/src/eigenvalue.cpp b/src/eigenvalue.cpp index b8305672b7..076f3b8e68 100644 --- a/src/eigenvalue.cpp +++ b/src/eigenvalue.cpp @@ -537,14 +537,9 @@ void ufs_count_sites() // On the first generation, just assume that the source is already evenly // distributed so that effectively the production of fission sites is not // biased - - std::size_t n = simulation::ufs_mesh->n_bins(); double vol_frac = simulation::ufs_mesh->volume_frac_; simulation::source_frac = xt::xtensor({n}, vol_frac); - // auto s = xt::view(simulation::source_frac, xt::all()); - // s = m->get_volume_frac(); - } else { // count number of source sites in each ufs mesh cell bool sites_outside; @@ -586,7 +581,6 @@ double ufs_get_weight(const Particle* p) if (simulation::source_frac(mesh_bin) != 0.0) { return simulation::ufs_mesh->volume_frac_ / simulation::source_frac(mesh_bin); - // return m->get_volume_frac() / simulation::source_frac(mesh_bin); } else { return 1.0; } diff --git a/src/mesh.cpp b/src/mesh.cpp index 3d0271cc1e..d3c32cd5cb 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -5,7 +5,6 @@ #include // for ceil #include // for allocator #include -#include #ifdef OPENMC_MPI #include "mpi.h" @@ -15,7 +14,6 @@ #include "xtensor/xmath.hpp" #include "xtensor/xsort.hpp" #include "xtensor/xtensor.hpp" -#include "xtensor/xarray.hpp" #include "openmc/capi.h" #include "openmc/constants.h" @@ -848,23 +846,6 @@ RegularMesh::count_sites(const Particle::Bank* bank, int64_t length, return counts; } -// std::string RegularMesh::get_label_for_bin(int bin) const { -// int ijk[n_dimension_]; -// get_indices_from_bin(bin, ijk); - -// std::stringstream out; -// out << "Mesh Index (" << ijk[0]; -// if (n_dimension_ > 1) out << ", " << ijk[1]; -// if (n_dimension_ > 2) out << ", " << ijk[2]; -// out << ")"; - -// return out.str(); -// } - -// double RegularMesh::get_volume_frac(int bin) const { -// return volume_frac_; -// } - //============================================================================== // RectilinearMesh implementation //============================================================================== @@ -1459,7 +1440,6 @@ openmc_mesh_get_dimension(int32_t index, int** dims, int* n) set_errmsg("Index in meshes array is out of bounds."); return OPENMC_E_OUT_OF_BOUNDS; } - RegularMesh* mesh; if (int err = check_regular_mesh(index, &mesh)) return err; *dims = mesh->shape_.data(); @@ -1471,6 +1451,11 @@ openmc_mesh_get_dimension(int32_t index, int** dims, int* n) extern "C" int openmc_mesh_set_dimension(int32_t index, int n, const int* dims) { + if (index < 0 || index >= model::meshes.size()) { + set_errmsg("Index in meshes array is out of bounds."); + return OPENMC_E_OUT_OF_BOUNDS; + } + RegularMesh* mesh; if (int err = check_regular_mesh(index, &mesh)) return err; @@ -1608,9 +1593,6 @@ UnstructuredMesh::build_kdtree(const moab::Range& all_tets) { // create and build KD-tree kdtree_ = std::unique_ptr(new moab::AdaptiveKDTree(mbi_.get())); - //const char settings[] = "MESHSET_FLAGS=0x1;TAG_NAME=0"; - // moab::FileOptions fileopts(settings); - // build the tree rval = kdtree_->build_tree(all_tets_and_tris, &kdtree_root_); if (rval != moab::MB_SUCCESS) { @@ -1974,17 +1956,6 @@ UnstructuredMesh::get_ent_handle_from_bin(int bin) const { return ehs_[bin]; } -// double UnstructuredMesh::get_volume_frac(int bin) const { -// if (bin == -1) { return 0.0; } -// if (bin > ehs_.size() || bin < -1) { -// std::stringstream msg; -// msg << "Invalid bin " << bin << " for umesh with id " << id_; -// fatal_error(msg); -// } -// moab::EntityHandle tet = get_ent_handle_from_bin(bin); -// return tet_volume(tet); -// } - int UnstructuredMesh::n_bins() const { return ehs_.size(); } @@ -2011,7 +1982,6 @@ int UnstructuredMesh::n_surface_bins() const { void read_meshes(pugi::xml_node root) { for (auto node : root.children("mesh")) { - std::string mesh_type; if (check_for_node(node, "type")) { mesh_type = get_node_value(node, "type", true, true); diff --git a/src/plot.cpp b/src/plot.cpp index ca22b5fa79..bc94b3ad66 100644 --- a/src/plot.cpp +++ b/src/plot.cpp @@ -696,7 +696,6 @@ void draw_mesh_lines(Plot pl, ImageData& data) Position width = ur_plot - ll_plot; - // Find the (axis-aligned) lines of the mesh that intersect this plot. auto axis_lines = model::meshes[pl.index_meshlines_mesh_] ->plot(ll_plot, ur_plot); @@ -714,49 +713,6 @@ void draw_mesh_lines(Plot pl, ImageData& data) ax2_min = 0; ax2_max = pl.pixels_[1]; } - // auto m = get_regular_mesh(pl.index_meshlines_mesh_); - - // int ijk_ll[3], ijk_ur[3]; - // bool in_mesh; - // m->get_indices(ll_plot, &(ijk_ll[0]), &in_mesh); - // m->get_indices(ur_plot, &(ijk_ur[0]), &in_mesh); - - // // Fortran/C++ index correction - // ijk_ur[0]++; ijk_ur[1]++; ijk_ur[2]++; - - // Position r_ll, r_ur; - // // sweep through all meshbins on this plane and draw borders - // for (int i = ijk_ll[outer]; i <= ijk_ur[outer]; i++) { - // for (int j = ijk_ll[inner]; j <= ijk_ur[inner]; j++) { - // // check if we're in the mesh for this ijk - // if (i > 0 && i <= m->shape_[outer] && j >0 && j <= m->shape_[inner] ) { - // int outrange[3], inrange[3]; - // // get xyz's of lower left and upper right of this mesh cell - // r_ll[outer] = m->lower_left_[outer] + m->width_[outer] * (i - 1); - // r_ll[inner] = m->lower_left_[inner] + m->width_[inner] * (j - 1); - // r_ur[outer] = m->lower_left_[outer] + m->width_[outer] * i; - // r_ur[inner] = m->lower_left_[inner] + m->width_[inner] * j; - - // // map the xyz ranges to pixel ranges - // double frac = (r_ll[outer] - ll_plot[outer]) / width[outer]; - // outrange[0] = int(frac * double(pl.pixels_[0])); - // frac = (r_ur[outer] - ll_plot[outer]) / width[outer]; - // outrange[1] = int(frac * double(pl.pixels_[0])); - - // frac = (r_ur[inner] - ll_plot[inner]) / width[inner]; - // inrange[0] = int((1. - frac) * (double)pl.pixels_[1]); - // frac = (r_ll[inner] - ll_plot[inner]) / width[inner]; - // inrange[1] = int((1. - frac) * (double)pl.pixels_[1]); - - // // draw lines - // for (int out_ = outrange[0]; out_ <= outrange[1]; out_++) { - // for (int plus = 0; plus <= pl.meshlines_width_; plus++) { - // data(out_, inrange[0] + plus) = rgb; - // data(out_, inrange[1] + plus) = rgb; - // data(out_, inrange[0] - plus) = rgb; - // data(out_, inrange[1] - plus) = rgb; - // } - // } // Iterate across the first axis and draw lines. for (auto ax1_val : axis_lines.first) { diff --git a/src/tallies/filter_mesh.cpp b/src/tallies/filter_mesh.cpp index 837b425c4a..6c2cc8bd12 100644 --- a/src/tallies/filter_mesh.cpp +++ b/src/tallies/filter_mesh.cpp @@ -55,7 +55,6 @@ std::string MeshFilter::text_label(int bin) const { auto& mesh = *model::meshes[mesh_]; - int n_dim = mesh.n_dimension_; std::vector ijk(n_dim); From 4f5b8e8034c7ff85f07503b985d0a0ba147c4b34 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 28 Nov 2019 07:56:51 -0600 Subject: [PATCH 062/205] Adding unstructured mesh class. --- openmc/filter.py | 5 +++- openmc/mesh.py | 70 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 74 insertions(+), 1 deletion(-) diff --git a/openmc/filter.py b/openmc/filter.py index 1988aee81e..0b4929a8be 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -749,7 +749,10 @@ class MeshFilter(Filter): def mesh(self, mesh): cv.check_type('filter mesh', mesh, openmc.MeshBase) self._mesh = mesh - self.bins = list(mesh.indices) + if isinstance(mesh, openmc.UnstructuredMesh): + self.bins = [] + else: + self.bins = list(mesh.indices) def can_merge(self, other): # Mesh filters cannot have more than one bin diff --git a/openmc/mesh.py b/openmc/mesh.py index 6ef8776fe8..ef46695f5f 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -80,6 +80,8 @@ class MeshBase(IDManagerMixin, metaclass=ABCMeta): return RegularMesh.from_hdf5(group) elif mesh_type == 'rectilinear': return RectilinearMesh.from_hdf5(group) + elif mesh_type == 'unstructured': + return UnstructuredMesh.from_hdf5(group) else: raise ValueError('Unrecognized mesh type: "' + mesh_type + '"') @@ -586,3 +588,71 @@ class RectilinearMesh(MeshBase): subelement.text = ' '.join(map(str, self.z_grid)) return element + +class UnstructuredMesh(MeshBase): + """An unstructured mesh, assumed to be three dimensionality + + Parameters + ---------- + mesh_id : int + Unique identifier for the mesh + name : str + Name of the mesh + + Attributes + ---------- + id : int + Unique identifier for the mesh + name : str + Name of the mesh + filename : str + Name of the file containing the unstructured mesh + """ + + def __init__(self, mesh_id=None, name='', filename=''): + super().__init__(mesh_id, name) + self._filename = filename + + @property + def filename(self): + return self._filename + + @filename.setter + def filename(self, filename): + if filename is not None: + cv.check_type('Unstructured Mesh filename: {}'.format(filename), + filename, str) + self._filename = filename + else: + self.filename = '' + + def __repr__(self): + string = super().__repr__() + string += '{0: <16}{1}{2}\n'.format('\tFilename', '=\t', self.filename) + return string + + @classmethod + def from_hdf5(cls, group): + mesh_id = int(group.name.split('/')[-1].lstrip('mesh ')) + + mesh = cls(mesh_id) + mesh = group['filename'] + + def to_xml_element(self): + """Return XML representation of the mesh + + Returns + ------- + element : xml.etree.ElementTree.Element + XML element containing the mesh data + + """ + + element = ET.Element("mesh") + element.set("id", str(self._id)) + element.set("type", "unstructured") + + subelement = ET.SubElement(element, "mesh_file") + subelement.text = self.filename + + return element From 8cdbedf6743f156ad1c22ac14c2426c00b3d4236 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 28 Nov 2019 09:06:35 -0600 Subject: [PATCH 063/205] Can read unstructured mesh from statepoint now. --- openmc/mesh.py | 24 ++++++++++++++++++++++-- src/mesh.cpp | 5 ++--- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/openmc/mesh.py b/openmc/mesh.py index ef46695f5f..41d6beae4e 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -605,8 +605,12 @@ class UnstructuredMesh(MeshBase): Unique identifier for the mesh name : str Name of the mesh - filename : str + mesh_file : str Name of the file containing the unstructured mesh + volumes : Iterable of float + Volumes of the unstructured mesh elements + total_volume : float + Volume of the unstructured mesh in total """ def __init__(self, mesh_id=None, name='', filename=''): @@ -626,6 +630,19 @@ class UnstructuredMesh(MeshBase): else: self.filename = '' + @property + def volumes(self): + return self._volumes + + @volumes.setter + def volumes(self, volumes): + cv.check_type("Unstructured mesh volumes", volumes, Iterable, Real) + self._volumes = volumes + + @property + def total_volume(self): + return np.sum(self.volumes) + def __repr__(self): string = super().__repr__() string += '{0: <16}{1}{2}\n'.format('\tFilename', '=\t', self.filename) @@ -636,7 +653,10 @@ class UnstructuredMesh(MeshBase): mesh_id = int(group.name.split('/')[-1].lstrip('mesh ')) mesh = cls(mesh_id) - mesh = group['filename'] + mesh.filename = group['filename'][()].decode() + mesh.volumes = group['volumes'][()] + + return mesh def to_xml_element(self): """Return XML representation of the mesh diff --git a/src/mesh.cpp b/src/mesh.cpp index d3c32cd5cb..f421f23001 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1864,15 +1864,14 @@ UnstructuredMesh::to_hdf5(hid_t group) const hid_t mesh_group = create_group(group, "mesh " + std::to_string(id_)); write_dataset(mesh_group, "type", "unstructured"); + write_dataset(mesh_group, "filename", filename_); + // write volume of each tet std::vector tet_vols; for (const auto& eh : ehs_) { tet_vols.emplace_back(tet_volume(eh)); } write_dataset(mesh_group, "volumes", tet_vols); - // and the total volume of the mesh - auto total_vol = std::accumulate(tet_vols.begin(), tet_vols.end(), 0.0); - write_dataset(mesh_group, "total_volume", total_vol); close_group(mesh_group); } From 44083412f0f0987b759711962271ea6b5fc47b4d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 17 Dec 2019 15:38:12 -0600 Subject: [PATCH 064/205] Some (temporary) changes to make unstructured mesh copacetic with the other mesh bin structures. --- openmc/filter.py | 2 +- openmc/mesh.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/openmc/filter.py b/openmc/filter.py index 0b4929a8be..6e389ba2d7 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -750,7 +750,7 @@ class MeshFilter(Filter): cv.check_type('filter mesh', mesh, openmc.MeshBase) self._mesh = mesh if isinstance(mesh, openmc.UnstructuredMesh): - self.bins = [] + self.bins = [ (n, 1, 1) for n in range(1, mesh.volumes.size + 1) ] else: self.bins = list(mesh.indices) diff --git a/openmc/mesh.py b/openmc/mesh.py index 41d6beae4e..8b199fa25f 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -654,7 +654,8 @@ class UnstructuredMesh(MeshBase): mesh = cls(mesh_id) mesh.filename = group['filename'][()].decode() - mesh.volumes = group['volumes'][()] + vol_data = group['volumes'][()] + mesh.volumes = np.reshape(vol_data, (vol_data.shape[0], 1)) return mesh From b1023a2afab5ff4a6a41ac581ddec8a7c1fa375c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 17 Dec 2019 15:39:16 -0600 Subject: [PATCH 065/205] Correction to the check location for partial intersections with tets at the end of a track. --- src/mesh.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index f421f23001..7f451c8f2c 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1711,7 +1711,8 @@ UnstructuredMesh::bins_crossed(const Particle* p, // tally remaining portion of track after last hit if // the last segment of the track is in the mesh if (hits.back().first < track_len) { - tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0); + auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0); + tet = get_tet(pos); if (tet) { bins.push_back(get_bin_from_ent_handle(tet)); lengths.push_back((track_len - hits.back().first) / track_len); From 43a4bd4ac74003abfb505fe1caa77770ac459b3b Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sun, 16 Feb 2020 09:16:07 -0600 Subject: [PATCH 066/205] Initializing internal volume parameter and using len() to size bins (so we aren't assuming use of a numpy array) --- openmc/filter.py | 2 +- openmc/mesh.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/openmc/filter.py b/openmc/filter.py index 6e389ba2d7..f9d93d91a2 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -750,7 +750,7 @@ class MeshFilter(Filter): cv.check_type('filter mesh', mesh, openmc.MeshBase) self._mesh = mesh if isinstance(mesh, openmc.UnstructuredMesh): - self.bins = [ (n, 1, 1) for n in range(1, mesh.volumes.size + 1) ] + self.bins = [(n, 1, 1) for n in range(1, len(mesh.volumes) + 1)] else: self.bins = list(mesh.indices) diff --git a/openmc/mesh.py b/openmc/mesh.py index 8b199fa25f..1117116e1d 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -616,6 +616,7 @@ class UnstructuredMesh(MeshBase): def __init__(self, mesh_id=None, name='', filename=''): super().__init__(mesh_id, name) self._filename = filename + self._volumes = [] @property def filename(self): From 2d40130095b337552bf47249ce3292fa75f6cf7c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 17 Feb 2020 11:43:36 -0600 Subject: [PATCH 067/205] Factoring out a new SructuredMesh class. Plot method placement still needs to be resolved. Updating unstructured mesh to allow use of indices only. Going back to a single unstructure mesh class until we need the added abstraction. --- include/openmc/mesh.h | 94 +++++++++++++++++++++++++------------ src/mesh.cpp | 68 ++++++++++++++++++++------- src/tallies/filter_mesh.cpp | 13 +---- 3 files changed, 115 insertions(+), 60 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 8a66e06ab5..8494e8578c 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -69,6 +69,43 @@ public: //! \return Mesh bin virtual int get_bin(Position r) const = 0; + //! Get the number of mesh cells. + virtual int n_bins() const = 0; + + //! Get the number of mesh cell surfaces. + virtual int n_surface_bins() const = 0; + + //! Write mesh data to an HDF5 group + // + //! \param[in] group HDF5 group + virtual void to_hdf5(hid_t group) const = 0; + + //! Find the mesh lines that intersect an axis-aligned slice plot + // + //! \param[in] plot_ll The lower-left coordinates of the slice plot. + //! \param[in] plot_ur The upper-right coordinates of the slice plot. + //! \return A pair of vectors indicating where the mesh lines lie along each + //! of the plot's axes. For example an xy-slice plot will get back a vector + //! of x-coordinates and another of y-coordinates. These vectors may be + //! empty for low-dimensional meshes. + virtual std::pair, std::vector> + plot(Position plot_ll, Position plot_ur) const = 0; + + //! Get a label for the mesh bin + virtual std::string bin_label(int bin) const = 0; + + // Data members + int id_ {-1}; //!< User-specified ID + int n_dimension_; //!< Number of dimensions +}; + +class StructuredMesh : public Mesh +{ +public: + StructuredMesh() = default; + StructuredMesh(pugi::xml_node node) : Mesh {node} {}; + virtual ~StructuredMesh() = default; + //! Get bin given mesh indices // //! \param[in] Array of mesh indices @@ -88,32 +125,21 @@ public: //! \param[out] ijk Mesh indices virtual void get_indices_from_bin(int bin, int* ijk) const = 0; - //! Get the number of mesh cells. - virtual int n_bins() const = 0; + //! Get a label for the mesh bin + std::string bin_label(int bin) const override { + std::vector ijk(n_dimension_); + get_indices_from_bin(bin, ijk.data()); - //! Get the number of mesh cell surfaces. - virtual int n_surface_bins() const = 0; - - //! Find the mesh lines that intersect an axis-aligned slice plot - // - //! \param[in] plot_ll The lower-left coordinates of the slice plot. - //! \param[in] plot_ur The upper-right coordinates of the slice plot. - //! \return A pair of vectors indicating where the mesh lines lie along each - //! of the plot's axes. For example an xy-slice plot will get back a vector - //! of x-coordinates and another of y-coordinates. These vectors may be - //! empty for low-dimensional meshes. - virtual std::pair, std::vector> - plot(Position plot_ll, Position plot_ur) const = 0; - - //! Write mesh data to an HDF5 group - // - //! \param[in] group HDF5 group - virtual void to_hdf5(hid_t group) const = 0; + if (n_dim > 2) { + return fmt::format("Mesh Index ({}, {}, {})", ijk[0], ijk[1], ijk[2]); + } else if (n_dim > 1) { + return fmt::format("Mesh Index ({}, {})", ijk[0], ijk[1]); + } else { + return fmt::format("Mesh Index ({})", ijk[0]) ; + } + } // Data members - - int id_ {-1}; //!< User-specified ID - int n_dimension_; //!< Number of dimensions xt::xtensor lower_left_; //!< Lower-left coordinates of mesh xt::xtensor upper_right_; //!< Upper-right coordinates of mesh }; @@ -122,7 +148,7 @@ public: //! Tessellation of n-dimensional Euclidean space by congruent squares or cubes //============================================================================== -class RegularMesh : public Mesh +class RegularMesh : public StructuredMesh { public: // Constructors @@ -188,7 +214,7 @@ private: }; -class RectilinearMesh : public Mesh +class RectilinearMesh : public StructuredMesh { public: // Constructors @@ -245,6 +271,7 @@ class UnstructuredMesh : public Mesh { public: UnstructuredMesh() { }; UnstructuredMesh(pugi::xml_node); + ~UnstructuredMesh() = default; //! Determine which bins were crossed by a particle. // @@ -316,14 +343,11 @@ intersect_track(const moab::CartVect& start, //! \return MOAB EntityHandle of tet moab::EntityHandle get_ent_handle_from_bin(int bin) const; - int get_bin_from_indices(const int* ijk) const override; + int get_bin_from_index(int idx) const; - void get_indices(Position r, int* ijk, bool* in_mesh) const override; + int get_index(Position r, bool* in_mesh) const; - void get_indices_from_bin(int bin, int* ijk) const override; - - std::pair, std::vector> - plot(Position plot_ll, Position plot_ur) const override; + int get_index_from_bin(int bin) const; //! Builds a KDTree for all tetrahedra in the mesh. All //! triangles representing 2D faces of the mesh are @@ -333,6 +357,10 @@ intersect_track(const moab::CartVect& start, void build_kdtree(const moab::Range& all_tets); public: + + std::pair, std::vector> + plot(Position plot_ll, Position plot_ur) const override; + //! Determine which surface bins were crossed by a particle. // //! \param[in] p Particle to check @@ -356,6 +384,10 @@ public: int n_surface_bins() const override; + Position centroid(moab::EntityHandle tet) const; + + std::string bin_label(int bin) const override; + std::string filename_; //(new moab::Core()); + mbi_ = std::unique_ptr(new moab::Core()); // create meshset to load mesh into moab::ErrorCode rval = mbi_->create_meshset(moab::MESHSET_SET, meshset_); if (rval != moab::MB_SUCCESS) { @@ -1855,7 +1855,6 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& all_tets) { a = a.transpose().inverse(); baryc_data_.at(get_bin_from_ent_handle(tet)) = a; } - } // TODO: write this function @@ -1911,25 +1910,24 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) } int -UnstructuredMesh::get_bin_from_indices(const int* ijk) const { - if (ijk[0] >= n_bins()) { +UnstructuredMesh::get_bin_from_index(int idx) const { + if (idx >= n_bins()) { std::stringstream s; - s << "Invalid bin: " << ijk[0]; + s << "Invalid bin index: " << idx; fatal_error(s); } - int bin = ehs_[ijk[0]] - ehs_[0]; + return ehs_[idx] - ehs_[0]; +} + +int +UnstructuredMesh::get_index(Position r, bool* in_mesh) const { + int bin = get_bin(r); + *in_mesh = bin != -1; return bin; } -void -UnstructuredMesh::get_indices(Position r, int* ijk, bool* in_mesh) const { - int bin = get_bin(r); - ijk[0]= bin; - *in_mesh = bin != -1; -} - -void UnstructuredMesh::get_indices_from_bin(int bin, int* ijk) const { - ijk[0] = bin; +int UnstructuredMesh::get_index_from_bin(int bin) const { + return bin; } std::pair, std::vector> @@ -1972,6 +1970,42 @@ int UnstructuredMesh::n_surface_bins() const { return 2 * tris.size(); } +Position +UnstructuredMesh::centroid(moab::EntityHandle tet) const { + moab::ErrorCode rval; + + // look up the tet connectivity + std::vector conn; + rval = mbi_->get_connectivity(&tet, 1, conn); + if (rval != moab::MB_SUCCESS) { + warning("Failed to get connectivity of a mesh element."); + return {}; + } + + // get the coordinates + std::vector coords(conn.size()); + rval = mbi_->get_coords(&conn.front(), conn.size(), coords[0].array()); + if (rval != moab::MB_SUCCESS) { + warning("Failed to get the coordinates of a mesh element."); + return {}; + } + + // compute the centroid of the elements + moab::CartVect centroid(0.0); + for(const auto& coord : coords) { + centroid += coord; + } + centroid /= double(coords.size()); + + return {centroid[0], centroid[1], centroid[2]}; +} + +std::string +UnstructuredMesh::bin_label(int bin) const { + std::stringstream out; + out << "Mesh Index (" << bin << ")"; +}; + #endif diff --git a/src/tallies/filter_mesh.cpp b/src/tallies/filter_mesh.cpp index 6c2cc8bd12..d2a240bcf1 100644 --- a/src/tallies/filter_mesh.cpp +++ b/src/tallies/filter_mesh.cpp @@ -55,18 +55,7 @@ std::string MeshFilter::text_label(int bin) const { auto& mesh = *model::meshes[mesh_]; - int n_dim = mesh.n_dimension_; - - std::vector ijk(n_dim); - mesh.get_indices_from_bin(bin, ijk.data()); - - if (n_dim > 2) { - return fmt::format("Mesh Index ({}, {}, {})", ijk[0], ijk[1], ijk[2]); - } else if (n_dim > 1) { - return fmt::format("Mesh Index ({}, {})", ijk[0], ijk[1]); - } else { - return fmt::format("Mesh Index ({})", ijk[0]) ; - } + return mesh.bin_label(bin); } void From 22836ed611a44bb402c59788335cbf77b94bdeb7 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 17 Feb 2020 14:49:46 -0600 Subject: [PATCH 068/205] A couple fixes. --- src/mesh.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 413fcf9afc..b0a142ad41 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1535,7 +1535,7 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { } // create MOAB instance - mbi_ = std::unique_ptr(new moab::Core()); + mbi_ = std::shared_ptr(new moab::Core()); // create meshset to load mesh into moab::ErrorCode rval = mbi_->create_meshset(moab::MESHSET_SET, meshset_); if (rval != moab::MB_SUCCESS) { @@ -2004,6 +2004,7 @@ std::string UnstructuredMesh::bin_label(int bin) const { std::stringstream out; out << "Mesh Index (" << bin << ")"; + return out.str(); }; #endif From 862c20752e06ba54f4b3c337829f26ca2eaceae1 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 17 Feb 2020 16:34:59 -0600 Subject: [PATCH 069/205] Writing out element centroids to the statepoint file. --- src/mesh.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index b0a142ad41..e9e56f787f 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -14,6 +14,7 @@ #include "xtensor/xmath.hpp" #include "xtensor/xsort.hpp" #include "xtensor/xtensor.hpp" +#include "xtensor/xview.hpp" #include "openmc/capi.h" #include "openmc/constants.h" @@ -1868,10 +1869,15 @@ UnstructuredMesh::to_hdf5(hid_t group) const // write volume of each tet std::vector tet_vols; - for (const auto& eh : ehs_) { + xt::xtensor centroids({n_bins(), 3}); + for (int i = 0; i < ehs_.size(); i++) { + const auto& eh = ehs_[i]; tet_vols.emplace_back(tet_volume(eh)); + Position c = centroid(eh); + xt::view(centroids, i, xt::all()) = xt::xarray({c.x, c.y, c.z}); } write_dataset(mesh_group, "volumes", tet_vols); + write_dataset(mesh_group, "centroids", centroids); close_group(mesh_group); } From 3f2556aa9c75c7cb8c17f52a3267664f01c8dc8c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 17 Feb 2020 17:18:35 -0600 Subject: [PATCH 070/205] Adding umesh tally write between batches when accumulating. --- include/openmc/mesh.h | 19 ++++++++ src/mesh.cpp | 102 ++++++++++++++++++++++++++++++++++++++++++ src/tallies/tally.cpp | 25 +++++++++++ 3 files changed, 146 insertions(+) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 8494e8578c..29d1a0e4dd 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -356,6 +356,9 @@ intersect_track(const moab::CartVect& start, //! \param[in] all_tets MOAB Range of tetrahedra for the tree void build_kdtree(const moab::Range& all_tets); + std::pair + get_score_tags(std::string score) const; + public: std::pair, std::vector> @@ -388,6 +391,22 @@ public: std::string bin_label(int bin) const override; + //! Get the tags for a score from the mesh instance + moab::ErrorCode get_score_tags(std::string score, + moab::Tag& val_tag, + moab::Tag& err_tag) const; + + //! Add a score to the mesh instance + void add_score(std::string score) const; + + //! Set data for a score + void set_score(const std::string& score, + int bin, + double val, + double err) const; + + //! Write the mesh with any current tally data + void write(std::string base_filename) const; std::string filename_; //tag_get_handle(score.c_str(), + 1, + moab::MB_TYPE_DOUBLE, + score_val, + moab::MB_TAG_DENSE|moab::MB_TAG_CREAT, + &default_val); + if (rval != moab::MB_SUCCESS) { + std::stringstream msg; + msg << "Could not create or retrieve the value tag for the score " << score + << " on unstructured mesh " << id_; + warning(msg); + return rval; + } + + moab::Tag score_err; + rval = mbi_->tag_get_handle(score.c_str(), + 1, + moab::MB_TYPE_DOUBLE, + score_err, + moab::MB_TAG_DENSE|moab::MB_TAG_CREAT, + &default_val); + + if (rval != moab::MB_SUCCESS) { + std::stringstream msg; + msg << "Could not create or retrieve the error tag for the score " << score + << " on unstructured mesh " << id_; + warning(msg); + return rval; + } + return moab::MB_SUCCESS; +} + +void +UnstructuredMesh::add_score(std::string score) const { + moab::Tag score_val, score_err; + moab::ErrorCode rval = get_score_tags(score, score_val, score_err); + if (rval != moab::MB_SUCCESS) { + std::stringstream msg; + msg << "Failed to add score '" << score << "' to unstructured mesh" << id_; + fatal_error(msg); + } +} + +void +UnstructuredMesh::set_score(const std::string& score, + int bin, + double val, + double err) const { + moab::Tag score_val, score_err; + moab::ErrorCode rval = get_score_tags(score, score_val, score_err); + if (rval != moab::MB_SUCCESS) { + std::stringstream msg; + msg << "Failed to get tags for the score '" << score << "' on " + << "unstructured mesh " << id_; + warning(msg); + } + + moab::EntityHandle eh = get_ent_handle_from_bin(bin); + + // set the score value + rval = mbi_->tag_set_data(score_val, &eh, 1, &val); + if (rval != moab::MB_SUCCESS) { + std::stringstream msg; + msg << "Failed to set the tally value for score '" << score << "' " + << " on unstructured mesh " << id_; + warning(msg); + } + // set the error value + rval = mbi_->tag_set_data(score_err, &eh, 1, &err); + if (rval != moab::MB_SUCCESS) { + std::stringstream msg; + msg << "Failed to set the tally value for score '" << score << "' " + << " on unstructured mesh " << id_; + warning(msg); + } +} + +void +UnstructuredMesh::write(std::string base_filename) const { + // add extension to the base name + base_filename += ".h5m"; + + moab::ErrorCode rval; + rval = mbi_->write_mesh(base_filename.c_str()); + if (rval != moab::MB_SUCCESS) { + std::stringstream msg; + msg << "Failed to write unstructured mesh " << id_; + warning(msg); + } +} + #endif diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index b63128dc04..6a9c1455fd 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -831,6 +831,31 @@ void Tally::accumulate() } } } + +#ifdef DAGMC + for (auto filter_idx : filters_) { + auto& filter = model::tally_filters[filter_idx]; + if (filter->type() == "mesh") { + auto mesh_filter = dynamic_cast(filter.get()); + auto& mesh = model::meshes[mesh_filter->mesh()]; + auto umesh = dynamic_cast(mesh.get()); + if (umesh) { + for (auto score : scores_) { + umesh->add_score(std::to_string(score)); + for (int i = 0; i < results_.shape()[0]; i ++) { + umesh->set_score(std::to_string(score), + i, + results_(i, 0, RESULT_SUM), + results_(i, 0, RESULT_SUM_SQ)); + } + } + std::stringstream output_filename; + output_filename << "tally_" << id_ << "umesh"; + umesh->write(output_filename.str()); + } + } + } +#endif } //============================================================================== From aac774ee750193083ae20ec04a4698760af722c8 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 17 Feb 2020 20:36:00 -0600 Subject: [PATCH 071/205] Writing scores between each batch. --- include/openmc/mesh.h | 7 ++---- src/mesh.cpp | 53 ++++++++++++++++--------------------------- src/tallies/tally.cpp | 4 ++-- 3 files changed, 24 insertions(+), 40 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 29d1a0e4dd..b1b815528d 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -356,6 +356,8 @@ intersect_track(const moab::CartVect& start, //! \param[in] all_tets MOAB Range of tetrahedra for the tree void build_kdtree(const moab::Range& all_tets); + //! Get the tags for a score from the mesh instance + //! or create them if they are not there std::pair get_score_tags(std::string score) const; @@ -391,11 +393,6 @@ public: std::string bin_label(int bin) const override; - //! Get the tags for a score from the mesh instance - moab::ErrorCode get_score_tags(std::string score, - moab::Tag& val_tag, - moab::Tag& err_tag) const; - //! Add a score to the mesh instance void add_score(std::string score) const; diff --git a/src/mesh.cpp b/src/mesh.cpp index e7aaa59222..0c296ec9f7 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -2013,58 +2013,50 @@ UnstructuredMesh::bin_label(int bin) const { return out.str(); }; -moab::ErrorCode -UnstructuredMesh::get_score_tags(std::string score, - moab::Tag& val_tag, - moab::Tag& err_tag) const { +std::pair +UnstructuredMesh::get_score_tags(std::string score) const { moab::ErrorCode rval; // add a tag to the mesh // all scores are treated as a single value // with an uncertainty - moab::Tag score_val; + moab::Tag value_tag; double default_val = 0.0; - rval = mbi_->tag_get_handle(score.c_str(), + auto val_string = score + "_value"; + rval = mbi_->tag_get_handle(val_string.c_str(), 1, moab::MB_TYPE_DOUBLE, - score_val, + value_tag, moab::MB_TAG_DENSE|moab::MB_TAG_CREAT, &default_val); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Could not create or retrieve the value tag for the score " << score << " on unstructured mesh " << id_; - warning(msg); - return rval; + fatal_error(msg); } - moab::Tag score_err; - rval = mbi_->tag_get_handle(score.c_str(), + moab::Tag error_tag; + std::string err_string = score + "_error"; + rval = mbi_->tag_get_handle(err_string.c_str(), 1, moab::MB_TYPE_DOUBLE, - score_err, + error_tag, moab::MB_TAG_DENSE|moab::MB_TAG_CREAT, &default_val); - if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Could not create or retrieve the error tag for the score " << score << " on unstructured mesh " << id_; - warning(msg); - return rval; + fatal_error(msg); } - return moab::MB_SUCCESS; + + return {value_tag, error_tag}; } void UnstructuredMesh::add_score(std::string score) const { - moab::Tag score_val, score_err; - moab::ErrorCode rval = get_score_tags(score, score_val, score_err); - if (rval != moab::MB_SUCCESS) { - std::stringstream msg; - msg << "Failed to add score '" << score << "' to unstructured mesh" << id_; - fatal_error(msg); - } + auto score_tags = get_score_tags(score); } void @@ -2072,27 +2064,22 @@ UnstructuredMesh::set_score(const std::string& score, int bin, double val, double err) const { - moab::Tag score_val, score_err; - moab::ErrorCode rval = get_score_tags(score, score_val, score_err); - if (rval != moab::MB_SUCCESS) { - std::stringstream msg; - msg << "Failed to get tags for the score '" << score << "' on " - << "unstructured mesh " << id_; - warning(msg); - } + auto score_tags = get_score_tags(score); moab::EntityHandle eh = get_ent_handle_from_bin(bin); + moab::ErrorCode rval; // set the score value - rval = mbi_->tag_set_data(score_val, &eh, 1, &val); + rval = mbi_->tag_set_data(score_tags.first, &eh, 1, &val); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " << " on unstructured mesh " << id_; warning(msg); } + // set the error value - rval = mbi_->tag_set_data(score_err, &eh, 1, &err); + rval = mbi_->tag_set_data(score_tags.second, &eh, 1, &err); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index 6a9c1455fd..54b77ee980 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -842,7 +842,7 @@ void Tally::accumulate() if (umesh) { for (auto score : scores_) { umesh->add_score(std::to_string(score)); - for (int i = 0; i < results_.shape()[0]; i ++) { + for (int i = 0; i < results_.shape()[0]; i++) { umesh->set_score(std::to_string(score), i, results_(i, 0, RESULT_SUM), @@ -850,7 +850,7 @@ void Tally::accumulate() } } std::stringstream output_filename; - output_filename << "tally_" << id_ << "umesh"; + output_filename << "tally_" << id_ << "_umesh"; umesh->write(output_filename.str()); } } From da9b713adb923e0d193aa73f4008f286c9123a59 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 17 Feb 2020 20:57:01 -0600 Subject: [PATCH 072/205] Setting all unstructured mesh tally data/error values at once during accumulate. --- include/openmc/mesh.h | 11 +++++------ src/mesh.cpp | 13 +++++-------- src/tallies/tally.cpp | 7 +++---- 3 files changed, 13 insertions(+), 18 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index b1b815528d..56994a48da 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -397,18 +397,17 @@ public: void add_score(std::string score) const; //! Set data for a score - void set_score(const std::string& score, - int bin, - double val, - double err) const; + void set_score_data(const std::string& score, + xt::xtensor values, + xt::xtensor sum_sq) const; //! Write the mesh with any current tally data void write(std::string base_filename) const; std::string filename_; // mbi_; //!< MOAB instance std::unique_ptr kdtree_; //!< MOAB KDTree instance diff --git a/src/mesh.cpp b/src/mesh.cpp index 0c296ec9f7..f16523b8de 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -2060,17 +2060,14 @@ UnstructuredMesh::add_score(std::string score) const { } void -UnstructuredMesh::set_score(const std::string& score, - int bin, - double val, - double err) const { +UnstructuredMesh::set_score_data(const std::string& score, + xt::xtensor values, + xt::xtensor sum_sq) const { auto score_tags = get_score_tags(score); - moab::EntityHandle eh = get_ent_handle_from_bin(bin); moab::ErrorCode rval; - // set the score value - rval = mbi_->tag_set_data(score_tags.first, &eh, 1, &val); + rval = mbi_->tag_set_data(score_tags.first, ehs_, &values); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " @@ -2079,7 +2076,7 @@ UnstructuredMesh::set_score(const std::string& score, } // set the error value - rval = mbi_->tag_set_data(score_tags.second, &eh, 1, &err); + rval = mbi_->tag_set_data(score_tags.second, ehs_, &sum_sq); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index 54b77ee980..f05b38801f 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -843,10 +843,9 @@ void Tally::accumulate() for (auto score : scores_) { umesh->add_score(std::to_string(score)); for (int i = 0; i < results_.shape()[0]; i++) { - umesh->set_score(std::to_string(score), - i, - results_(i, 0, RESULT_SUM), - results_(i, 0, RESULT_SUM_SQ)); + umesh->set_score_data(std::to_string(score), + xt::view(results_, xt::all(), 0, RESULT_SUM), + xt::view(results_, xt::all(), 0, RESULT_SUM_SQ)); } } std::stringstream output_filename; From dc7bc30b886aa8113c3bc872387e85410ba6b647 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 18 Feb 2020 15:20:06 -0600 Subject: [PATCH 073/205] Some additions/improvements after the rebase. --- include/openmc/mesh.h | 17 +++-------------- src/mesh.cpp | 28 ++++++++++++++++++++++++---- src/tallies/tally.cpp | 4 ++-- 3 files changed, 29 insertions(+), 20 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 56994a48da..57a8f8063d 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -126,18 +126,7 @@ public: virtual void get_indices_from_bin(int bin, int* ijk) const = 0; //! Get a label for the mesh bin - std::string bin_label(int bin) const override { - std::vector ijk(n_dimension_); - get_indices_from_bin(bin, ijk.data()); - - if (n_dim > 2) { - return fmt::format("Mesh Index ({}, {}, {})", ijk[0], ijk[1], ijk[2]); - } else if (n_dim > 1) { - return fmt::format("Mesh Index ({}, {})", ijk[0], ijk[1]); - } else { - return fmt::format("Mesh Index ({})", ijk[0]) ; - } - } + std::string bin_label(int bin) const override; // Data members xt::xtensor lower_left_; //!< Lower-left coordinates of mesh @@ -398,8 +387,8 @@ public: //! Set data for a score void set_score_data(const std::string& score, - xt::xtensor values, - xt::xtensor sum_sq) const; + xt::xarray values, + xt::xarray sum_sq) const; //! Write the mesh with any current tally data void write(std::string base_filename) const; diff --git a/src/mesh.cpp b/src/mesh.cpp index f16523b8de..cf9df61791 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -3,6 +3,7 @@ #include // for copy, equal, min, min_element #include // for size_t #include // for ceil +#include // for fmt #include // for allocator #include @@ -81,6 +82,24 @@ Mesh::Mesh(pugi::xml_node node) { } } +//============================================================================== +// Structured Mesh implementation +//============================================================================== + +std::string +StructuredMesh::bin_label(int bin) const { + std::vector ijk(n_dimension_); + get_indices_from_bin(bin, ijk.data()); + + if (n_dimension_ > 2) { + return fmt::format("Mesh Index ({}, {}, {})", ijk[0], ijk[1], ijk[2]); + } else if (n_dimension_ > 1) { + return fmt::format("Mesh Index ({}, {})", ijk[0], ijk[1]); + } else { + return fmt::format("Mesh Index ({})", ijk[0]) ; + } +} + //============================================================================== // RegularMesh implementation //============================================================================== @@ -131,7 +150,7 @@ RegularMesh::RegularMesh(pugi::xml_node node) fatal_error("Cannot have a negative on a tally mesh."); } - // Set width and upper right coordinate + // Setwidth and upper right coordinate upper_right_ = xt::eval(lower_left_ + shape_ * width_); } else if (check_for_node(node, "upper_right")) { @@ -1869,13 +1888,14 @@ UnstructuredMesh::to_hdf5(hid_t group) const // write volume of each tet std::vector tet_vols; - xt::xtensor centroids({n_bins(), 3}); + xt::xtensor centroids({ehs_.size(), 3}); for (int i = 0; i < ehs_.size(); i++) { const auto& eh = ehs_[i]; tet_vols.emplace_back(tet_volume(eh)); Position c = centroid(eh); xt::view(centroids, i, xt::all()) = xt::xarray({c.x, c.y, c.z}); } + write_dataset(mesh_group, "volumes", tet_vols); write_dataset(mesh_group, "centroids", centroids); @@ -2061,8 +2081,8 @@ UnstructuredMesh::add_score(std::string score) const { void UnstructuredMesh::set_score_data(const std::string& score, - xt::xtensor values, - xt::xtensor sum_sq) const { + xt::xarray values, + xt::xarray sum_sq) const { auto score_tags = get_score_tags(score); moab::ErrorCode rval; diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index f05b38801f..f173b5a098 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -844,8 +844,8 @@ void Tally::accumulate() umesh->add_score(std::to_string(score)); for (int i = 0; i < results_.shape()[0]; i++) { umesh->set_score_data(std::to_string(score), - xt::view(results_, xt::all(), 0, RESULT_SUM), - xt::view(results_, xt::all(), 0, RESULT_SUM_SQ)); + xt::view(results_, xt::all(), 0, TallyResult::VALUE), + xt::view(results_, xt::all(), 0, TallyResult::SUM_SQ)); } } std::stringstream output_filename; From 13c5348cd59e2f9f7cc0c1a7b43674b8e201294c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 18 Feb 2020 18:29:57 -0600 Subject: [PATCH 074/205] Another fix to this pesky data writing problem - need contiguous memory. --- include/openmc/mesh.h | 4 ++-- src/mesh.cpp | 8 ++++---- src/tallies/tally.cpp | 8 ++++++-- 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 57a8f8063d..c4a107e0bb 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -387,8 +387,8 @@ public: //! Set data for a score void set_score_data(const std::string& score, - xt::xarray values, - xt::xarray sum_sq) const; + std::vector values, + std::vector sum_sq) const; //! Write the mesh with any current tally data void write(std::string base_filename) const; diff --git a/src/mesh.cpp b/src/mesh.cpp index cf9df61791..89c0726de4 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -2081,13 +2081,13 @@ UnstructuredMesh::add_score(std::string score) const { void UnstructuredMesh::set_score_data(const std::string& score, - xt::xarray values, - xt::xarray sum_sq) const { + std::vector values, + std::vector sum_sq) const { auto score_tags = get_score_tags(score); moab::ErrorCode rval; // set the score value - rval = mbi_->tag_set_data(score_tags.first, ehs_, &values); + rval = mbi_->tag_set_data(score_tags.first, ehs_, &values.front()); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " @@ -2096,7 +2096,7 @@ UnstructuredMesh::set_score_data(const std::string& score, } // set the error value - rval = mbi_->tag_set_data(score_tags.second, ehs_, &sum_sq); + rval = mbi_->tag_set_data(score_tags.second, ehs_, &sum_sq.front()); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index f173b5a098..193769f13b 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -842,10 +842,14 @@ void Tally::accumulate() if (umesh) { for (auto score : scores_) { umesh->add_score(std::to_string(score)); + auto values = xt::view(results_, xt::all(), 0, TallyResult::VALUE); + auto sum_sq = xt::view(results_, xt::all(), 0, TallyResult::SUM_SQ); + std::vector vals_vec(values.begin(), values.end()); + std::vector sum_sq_vec(sum_sq.begin(), sum_sq.end()); for (int i = 0; i < results_.shape()[0]; i++) { umesh->set_score_data(std::to_string(score), - xt::view(results_, xt::all(), 0, TallyResult::VALUE), - xt::view(results_, xt::all(), 0, TallyResult::SUM_SQ)); + vals_vec, + sum_sq_vec); } } std::stringstream output_filename; From 76251cd99572a1b608966790fb9d7a0d557b5079 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 18 Feb 2020 18:59:34 -0600 Subject: [PATCH 075/205] Initial test for unstructured mesh. --- .../unstructured_mesh/__init__.py | 0 .../unstructured_mesh/inputs_true.dat | 92 + .../unstructured_mesh/results_true.dat | 26002 ++++++++++++++++ .../unstructured_mesh/test.py | 241 + .../unstructured_mesh/test_mesh_tets.h5m | Bin 0 -> 481444 bytes 5 files changed, 26335 insertions(+) create mode 100644 tests/regression_tests/unstructured_mesh/__init__.py create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true.dat create mode 100644 tests/regression_tests/unstructured_mesh/results_true.dat create mode 100644 tests/regression_tests/unstructured_mesh/test.py create mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets.h5m diff --git a/tests/regression_tests/unstructured_mesh/__init__.py b/tests/regression_tests/unstructured_mesh/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true.dat b/tests/regression_tests/unstructured_mesh/inputs_true.dat new file mode 100644 index 0000000000..7b2bfed0e0 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets.h5m + + + 1 + + + 2 + + + 1 + flux + tracklength + + + 2 + flux + tracklength + + diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat b/tests/regression_tests/unstructured_mesh/results_true.dat new file mode 100644 index 0000000000..b111717551 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/results_true.dat @@ -0,0 +1,26002 @@ +tally 1: +8.322898E-03 +5.793831E-05 +1.831956E-02 +3.111256E-04 +2.640170E-02 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+4.445349E-05 +1.976113E-09 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py new file mode 100644 index 0000000000..12dfbd41b3 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -0,0 +1,241 @@ +import glob +import os + +import openmc +import openmc.lib +import numpy as np + +import pytest +from tests.testing_harness import PyAPITestHarness + +pytestmark = pytest.mark.skipif( + not openmc.lib._dagmc_enabled(), + reason="Mesh library is not available.") + +class UnstructuredMeshTest(PyAPITestHarness): + + def _build_inputs(self): + ### Materials ### + materials = openmc.Materials() + + fuel_mat = openmc.Material(name="fuel") + fuel_mat.add_nuclide("U235", 1.0) + fuel_mat.set_density('g/cc', 4.5) + materials.append(fuel_mat) + + zirc_mat = openmc.Material(name="zircaloy") + zirc_mat.add_nuclide("Zr90", 0.5145) + zirc_mat.add_nuclide("Zr91", 0.1122) + zirc_mat.add_nuclide("Zr92", 0.1715) + zirc_mat.add_nuclide("Zr94", 0.1738) + zirc_mat.add_nuclide("Zr96", 0.028) + zirc_mat.set_density("g/cc", 5.77) + materials.append(zirc_mat) + + water_mat = openmc.Material(name="water") + water_mat.add_nuclide("H1", 2.0) + water_mat.add_nuclide("O16", 1.0) + water_mat.set_density("atom/b-cm", 0.07416) + materials.append(water_mat) + + materials.export_to_xml() + + ### Geometry ### + fuel_min_x = openmc.XPlane(x0=-5.0, name="minimum x") + fuel_max_x = openmc.XPlane(x0=5.0, name="maximum x") + + fuel_min_y = openmc.YPlane(y0=-5.0, name="minimum y") + fuel_max_y = openmc.YPlane(y0=5.0, name="maximum y") + + fuel_min_z = openmc.ZPlane(z0=-5.0, name="minimum z") + fuel_max_z = openmc.ZPlane(z0=5.0, name="maximum z") + + fuel_cell = openmc.Cell(name="fuel") + fuel_cell.region = +fuel_min_x & -fuel_max_x & \ + +fuel_min_y & -fuel_max_y & \ + +fuel_min_z & -fuel_max_z + fuel_cell.fill = fuel_mat + + clad_min_x = openmc.XPlane(x0=-6.0, name="minimum x") + clad_max_x = openmc.XPlane(x0=6.0, name="maximum x") + + clad_min_y = openmc.YPlane(y0=-6.0, name="minimum y") + clad_max_y = openmc.YPlane(y0=6.0, name="maximum y") + + clad_min_z = openmc.ZPlane(z0=-6.0, name="minimum z") + clad_max_z = openmc.ZPlane(z0=6.0, name="maximum z") + + clad_cell = openmc.Cell(name="clad") + clad_cell.region = (-fuel_min_x | +fuel_max_x | \ + -fuel_min_y | +fuel_max_y | \ + -fuel_min_z | +fuel_max_z) & \ + (+clad_min_x & -clad_max_x & \ + +clad_min_y & -clad_max_y & \ + +clad_min_z & -clad_max_z) + clad_cell.fill = zirc_mat + +# if external_geom: + # bounds = (15, 15, 15) + # else: + bounds = (10, 10, 10) + + water_min_x = openmc.XPlane(x0=-bounds[0], + name="minimum x", + boundary_type='vacuum') + water_max_x = openmc.XPlane(x0=bounds[0], + name="maximum x", + boundary_type='vacuum') + + water_min_y = openmc.YPlane(y0=-bounds[1], + name="minimum y", + boundary_type='vacuum') + water_max_y = openmc.YPlane(y0=bounds[1], + name="maximum y", + boundary_type='vacuum') + + water_min_z = openmc.ZPlane(z0=-bounds[2], + name="minimum z", + boundary_type='vacuum') + water_max_z = openmc.ZPlane(z0=bounds[2], + name="maximum z", + boundary_type='vacuum') + + water_cell = openmc.Cell(name="water") + water_cell.region = (-clad_min_x | +clad_max_x | \ + -clad_min_y | +clad_max_y | \ + -clad_min_z | +clad_max_z) & \ + (+water_min_x & -water_max_x & \ + +water_min_y & -water_max_y & \ + +water_min_z & -water_max_z) + water_cell.fill = water_mat + + # create a containing universe + root_univ = openmc.Universe() + root_univ.add_cells([fuel_cell, clad_cell, water_cell]) + + geom = openmc.Geometry(root=root_univ) + + geom.export_to_xml() + + ### Tallies ### + + # create meshes + coarse_mesh = openmc.RegularMesh() + coarse_mesh.dimension = (10, 10, 10) + coarse_mesh.lower_left = (-10.0, -10.0, -10.0) + coarse_mesh.upper_right = (10.0, 10.0, 10.0) + + coarse_filter = openmc.MeshFilter(mesh=coarse_mesh) + + uscd_mesh = openmc.UnstructuredMesh() + uscd_mesh.filename = 'test_mesh_tets.h5m' + uscd_mesh.mesh_lib = 'moab' + uscd_filter = openmc.MeshFilter(mesh=uscd_mesh) + + # create tallies + tallies = openmc.Tallies() + estimator = "tracklength" + + coarse_mesh_tally = openmc.Tally(name="coarse mesh tally") + coarse_mesh_tally.filters = [coarse_filter] + coarse_mesh_tally.scores = ['flux'] + coarse_mesh_tally.estimator = estimator + tallies.append(coarse_mesh_tally) + + uscd_tally = openmc.Tally(name="unstructured mesh tally") + uscd_tally.filters = [uscd_filter] + uscd_tally.scores = ['flux'] + uscd_tally.estimator = estimator + tallies.append(uscd_tally) + + tallies.export_to_xml() + + ### Settings ### + settings = openmc.Settings() + settings.run_mode = 'fixed source' + settings.particles = 100 + settings.batches = 10 + + # source setup + r = openmc.stats.Uniform(a=0.0, b=0.0) + theta = openmc.stats.Discrete(x=[0.0], p=[1.0]) + phi = openmc.stats.Discrete(x=[0.0], p=[1.0]) + origin = (1.0, 1.0, 1.0) + + space = openmc.stats.SphericalIndependent(r=r, + theta=theta, + phi=phi, + origin=origin) + + angle = openmc.stats.Monodirectional((-1.0, 0.0, 0.0)) + + energy = openmc.stats.Discrete(x=[15.0E6], p=[1.0]) + + source = openmc.Source(space=space, energy=energy, angle=angle) + + settings.source = source + + settings.export_to_xml() + + def _compare_results(self): + super()._compare_results() + + with openmc.StatePoint(self._sp_name) as sp: + # loop over the tallies + regular_data = None + regular_std_dev = None + unstructured_data = None + unstructured_std_dev = None + for tally in sp.tallies.values(): + # find the regular and unstructured meshes + if tally.contains_filter(openmc.MeshFilter): + flt = tally.find_filter(openmc.MeshFilter) + + if isinstance(flt.mesh, openmc.RegularMesh): + regular_data, regular_std_dev = self.get_mesh_tally_data(tally) + else: + unstructured_data, unstructured_std_dev = self.get_mesh_tally_data(tally, True) + + # successively check how many decimals the results are equal to + decimals = 1 + while True: + try: + np.testing.assert_array_almost_equal(unstructured_data, + regular_data, + decimals) + except AssertionError as ae: + print(ae) + print() + break + # increment decimals + decimals += 1 + + print("Results equal to within {} decimal places.\n".format(decimals)) + + assert decimals >= 6 + + @staticmethod + def get_mesh_tally_data(tally, structured=False): + TETS_PER_VOXEL = 12 + data = tally.get_reshaped_data(value='mean') + std_dev = tally.get_reshaped_data(value='std_dev') + if structured: + data.shape = (data.size // TETS_PER_VOXEL, TETS_PER_VOXEL) + std_dev.shape = (std_dev.size // TETS_PER_VOXEL, TETS_PER_VOXEL) + else: + data.shape = (data.size, 1) + std_dev.shape = (std_dev.size, 1) + return np.sum(data, axis=1), np.sum(std_dev, axis=1) + + def _cleanup(self): + super()._cleanup() + + output = glob.glob('*umesh.h5m') + for f in output: + if os.path.exists(f): + os.remove(f) + + +def test_uwuw(): + harness = UnstructuredMeshTest('statepoint.10.h5') + harness.main() diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets.h5m b/tests/regression_tests/unstructured_mesh/test_mesh_tets.h5m new file mode 100644 index 0000000000000000000000000000000000000000..dc573142f6f6991442716995a0f9d01f8ec82862 GIT binary patch literal 481444 zcmeF(37qbASvK(h0gs|-p;_TlP197gDVhtK%Y%Y~vMHEoDk7jz2qswAYKx_1WjpO! zTDH?}E02|BWo0{=6;_t*w%hym<~#HJ?(gq#uK)S*N3%tor_aaxT-SBq*S*YhX68S0 z=!K`A{D?c;?yk3+_Fl&vGu>)BcJoL7^R{}u?7Z7-D471#`on>WFR1v6quL)1Ond(~ zO~(~~$MVm-b02%kDbsEyf3eD@8xFQV9fR19?^YK$^^}v&-dp&*+SZ-jzdq18^cn9Nw>FC4WzsJ`5_jz|~hMS>w|G4!2ee-Qw{{H*-9sEGNi^9FK*nPe${iIwc+;kOD?_gsw!2TW$pj33yi*wo!!1HPS2{p-%Fndj(Ko% zyoB+7e7CxQ&jZ)i)>l_tKcW4>*OzBKv0-0d?oeNM9(LAw=bwDq8Rt~l?%(WNAMxwc z2^X{s@3Z!K;MV=SyA$4{>Ap^$SNQ5XH2#EY@VfdsS8x_q#XR-yGwCp5tI%7@U*F^2 zvk52GC#>Tu@7luht}p!7JGBI#&yKIZfAaG0W7)ma!szor-f!vi!1L~P_#W@H0r!pG 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zE;P~mpKyPB_H%pYpWDCs`}4p0d>5YhjQ^YeKKV@kUh+)-e0wHeC!fjhw`cOX4ev*z lKHo(qKI8x9zfb-*e{N5`|NgtrchQN@_^JO){ypv2{{SV^R=5BF literal 0 HcmV?d00001 From 44f3f1eb5079b96a4d75a31858bca40c73780c6a Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 19 Feb 2020 16:30:59 -0600 Subject: [PATCH 076/205] Including tests w external geometry and holes in the mesh. --- .../unstructured_mesh/test.py | 58 +++++++++++++----- .../test_mesh_tets_w_holes.h5m | Bin 0 -> 480220 bytes 2 files changed, 44 insertions(+), 14 deletions(-) create mode 100644 tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.h5m diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 12dfbd41b3..c17b8978e1 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -1,4 +1,6 @@ +from collections import defaultdict import glob +from itertools import product import os import openmc @@ -14,6 +16,23 @@ pytestmark = pytest.mark.skipif( class UnstructuredMeshTest(PyAPITestHarness): + def __init__(self, statepoint_name, **kwargs): + super().__init__(statepoint_name) + + # defaults + self.estimator = "collision" # tally estimator type + self.external_geom = False # geometry size matches mesh + self.mesh_has_holes = False # holes in the mesh + self.holes = () + self.mesh_filename = "test_mesh_tets.h5m" # mesh file to use + + # set parameters for the test + self.estimator = kwargs.get('estimator', self.estimator) + self.external_geom = kwargs.get('external_geom', self.external_geom) + self.holes = kwargs.get('holes', self.holes) + if self.holes: + self.mesh_filename = "test_mesh_tets_w_holes.h5m" + def _build_inputs(self): ### Materials ### materials = openmc.Materials() @@ -74,10 +93,10 @@ class UnstructuredMeshTest(PyAPITestHarness): +clad_min_z & -clad_max_z) clad_cell.fill = zirc_mat -# if external_geom: - # bounds = (15, 15, 15) - # else: - bounds = (10, 10, 10) + if self.external_geom: + bounds = (15, 15, 15) + else: + bounds = (10, 10, 10) water_min_x = openmc.XPlane(x0=-bounds[0], name="minimum x", @@ -128,24 +147,23 @@ class UnstructuredMeshTest(PyAPITestHarness): coarse_filter = openmc.MeshFilter(mesh=coarse_mesh) uscd_mesh = openmc.UnstructuredMesh() - uscd_mesh.filename = 'test_mesh_tets.h5m' + uscd_mesh.filename = self.mesh_filename uscd_mesh.mesh_lib = 'moab' uscd_filter = openmc.MeshFilter(mesh=uscd_mesh) # create tallies tallies = openmc.Tallies() - estimator = "tracklength" coarse_mesh_tally = openmc.Tally(name="coarse mesh tally") coarse_mesh_tally.filters = [coarse_filter] coarse_mesh_tally.scores = ['flux'] - coarse_mesh_tally.estimator = estimator + coarse_mesh_tally.estimator = self.estimator tallies.append(coarse_mesh_tally) uscd_tally = openmc.Tally(name="unstructured mesh tally") uscd_tally.filters = [uscd_filter] uscd_tally.scores = ['flux'] - uscd_tally.estimator = estimator + uscd_tally.estimator = self.estimator tallies.append(uscd_tally) tallies.export_to_xml() @@ -160,7 +178,7 @@ class UnstructuredMeshTest(PyAPITestHarness): r = openmc.stats.Uniform(a=0.0, b=0.0) theta = openmc.stats.Discrete(x=[0.0], p=[1.0]) phi = openmc.stats.Discrete(x=[0.0], p=[1.0]) - origin = (1.0, 1.0, 1.0) + origin = (0.0, 0.0, 0.0) space = openmc.stats.SphericalIndependent(r=r, theta=theta, @@ -177,9 +195,10 @@ class UnstructuredMeshTest(PyAPITestHarness): settings.export_to_xml() - def _compare_results(self): - super()._compare_results() + def _compare_inputs(self): + pass + def _compare_results(self): with openmc.StatePoint(self._sp_name) as sp: # loop over the tallies regular_data = None @@ -193,6 +212,9 @@ class UnstructuredMeshTest(PyAPITestHarness): if isinstance(flt.mesh, openmc.RegularMesh): regular_data, regular_std_dev = self.get_mesh_tally_data(tally) + if self.holes: + regular_data = np.delete(regular_data, holes) + regular_std_dev = np.delete(regular_std_dev, holes) else: unstructured_data, unstructured_std_dev = self.get_mesh_tally_data(tally, True) @@ -212,7 +234,7 @@ class UnstructuredMeshTest(PyAPITestHarness): print("Results equal to within {} decimal places.\n".format(decimals)) - assert decimals >= 6 + assert decimals >= 10 @staticmethod def get_mesh_tally_data(tally, structured=False): @@ -236,6 +258,14 @@ class UnstructuredMeshTest(PyAPITestHarness): os.remove(f) -def test_uwuw(): - harness = UnstructuredMeshTest('statepoint.10.h5') +hole_indexes = (333, 90, 777) +param_values = (('collision', 'tracklength'), (True, False), (hole_indexes, ())) +test_cases = [] +for estimator, holes, ext_geom in product(*param_values): + test_cases.append({'estimator' : estimator, + 'holes' : holes, + 'external_geom' : ext_geom}) +@pytest.mark.parametrize("opts", test_cases) +def test_unstructured_mesh(opts): + harness = UnstructuredMeshTest('statepoint.10.h5', kwargs=opts) harness.main() diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.h5m 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zuZ*vXua2*YuZ^#Zua9qtZ;Wq>Z;o$?Z;fw@Z;$VY?~Lz??~d<@?~U(^?~fmdAB-Q0 zAC4c1AB`W2ACI4ipNy-=HR76at+;kvC$1aUi|fY?;-}(G+xW*|>N7T-+z_8$Tbv5Wg6| z6!(k!#{=Sl@t}Bc{BrzCJS2WKel31Iej^?l4~vJ#BjSGQ zE8>;$s(5w0CSDt_i`T~+;*Ig9cyqiZ-WqR^AB&I2C*qUQ5B5zl%~Sslm@-Zkr;gLaY2$Qp`Zz>=V8J?0x@P-y9RY!*dzcH|Ip} z)6ef;_02WW>zv2+%{|dO`SbX9`sSJFd-ijiH0i(bbNhEc-+3oK<7fZ8O`7zd^5^ZD zd~TD!|4shBHu?M2 z?&mxI#Ap2M-)~R-_x_pu{q~>mx&1qTekXsv3ru`H`|pyczOVhaeQx8vKl^+aocN6Y zm;XKazx=uVtG|Dr{`oF6@frWO-2 Date: Wed, 19 Feb 2020 16:54:39 -0600 Subject: [PATCH 077/205] Removing .dat files --- .../unstructured_mesh/inputs_true.dat | 92 - .../unstructured_mesh/results_true.dat | 26002 ---------------- 2 files changed, 26094 deletions(-) delete mode 100644 tests/regression_tests/unstructured_mesh/inputs_true.dat delete mode 100644 tests/regression_tests/unstructured_mesh/results_true.dat diff --git a/tests/regression_tests/unstructured_mesh/inputs_true.dat b/tests/regression_tests/unstructured_mesh/inputs_true.dat deleted file mode 100644 index 7b2bfed0e0..0000000000 --- a/tests/regression_tests/unstructured_mesh/inputs_true.dat +++ /dev/null @@ -1,92 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - fixed source - 100 - 10 - - - - - 0.0 1.0 - - - 0.0 1.0 - - - - - 15000000.0 1.0 - - - - - - - 10 10 10 - -10.0 -10.0 -10.0 - 10.0 10.0 10.0 - - - test_mesh_tets.h5m - - - 1 - - - 2 - - - 1 - flux - tracklength - - - 2 - flux - tracklength - - diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat b/tests/regression_tests/unstructured_mesh/results_true.dat deleted file mode 100644 index b111717551..0000000000 --- a/tests/regression_tests/unstructured_mesh/results_true.dat +++ /dev/null @@ -1,26002 +0,0 @@ -tally 1: -8.322898E-03 -5.793831E-05 -1.831956E-02 -3.111256E-04 -2.640170E-02 -4.037641E-04 -1.046053E-01 -3.835781E-03 -8.204251E-02 -1.663199E-03 -4.734525E-02 -9.466489E-04 -3.590544E-02 -6.627302E-04 -5.966030E-02 -1.928331E-03 -3.229000E-02 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3d546aaf80e820cac13b887dce57400652fba64c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 19 Feb 2020 16:57:25 -0600 Subject: [PATCH 078/205] Renaming regular mesh variables for clarity. --- .../unstructured_mesh/test.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index c17b8978e1..ff6e5178f5 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -139,12 +139,12 @@ class UnstructuredMeshTest(PyAPITestHarness): ### Tallies ### # create meshes - coarse_mesh = openmc.RegularMesh() - coarse_mesh.dimension = (10, 10, 10) - coarse_mesh.lower_left = (-10.0, -10.0, -10.0) - coarse_mesh.upper_right = (10.0, 10.0, 10.0) + regular_mesh = openmc.RegularMesh() + regular_mesh.dimension = (10, 10, 10) + regular_mesh.lower_left = (-10.0, -10.0, -10.0) + regular_mesh.upper_right = (10.0, 10.0, 10.0) - coarse_filter = openmc.MeshFilter(mesh=coarse_mesh) + regular_mesh_filter = openmc.MeshFilter(mesh=regular_mesh) uscd_mesh = openmc.UnstructuredMesh() uscd_mesh.filename = self.mesh_filename @@ -154,11 +154,11 @@ class UnstructuredMeshTest(PyAPITestHarness): # create tallies tallies = openmc.Tallies() - coarse_mesh_tally = openmc.Tally(name="coarse mesh tally") - coarse_mesh_tally.filters = [coarse_filter] - coarse_mesh_tally.scores = ['flux'] - coarse_mesh_tally.estimator = self.estimator - tallies.append(coarse_mesh_tally) + regular_mesh_tally = openmc.Tally(name="regular mesh tally") + regular_mesh_tally.filters = [regular_mesh_filter] + regular_mesh_tally.scores = ['flux'] + regular_mesh_tally.estimator = self.estimator + tallies.append(regular_mesh_tally) uscd_tally = openmc.Tally(name="unstructured mesh tally") uscd_tally.filters = [uscd_filter] From 25139a9d98a844434c10816c1c507803ee4834e9 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 28 Feb 2020 14:18:17 -0600 Subject: [PATCH 079/205] Updating score tagging for more meaningful values on the mesh. --- include/openmc/mesh.h | 2 +- src/mesh.cpp | 16 ++++++++++++---- src/tallies/tally.cpp | 18 +++++++++++------- 3 files changed, 24 insertions(+), 12 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index c4a107e0bb..44b1aa8c34 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -388,7 +388,7 @@ public: //! Set data for a score void set_score_data(const std::string& score, std::vector values, - std::vector sum_sq) const; + std::vector std_dev) const; //! Write the mesh with any current tally data void write(std::string base_filename) const; diff --git a/src/mesh.cpp b/src/mesh.cpp index 89c0726de4..49eff192a5 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1710,7 +1710,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, lengths.push_back((hit.first - last_dist) / track_len); } else { // if in the loop, we should always find a tet - fatal_error("No tet found for location between trianle hits"); + warning("No tet found for location between trianle hits"); } last_dist = hit.first; @@ -2057,7 +2057,7 @@ UnstructuredMesh::get_score_tags(std::string score) const { } moab::Tag error_tag; - std::string err_string = score + "_error"; + std::string err_string = score + "_std_dev"; rval = mbi_->tag_get_handle(err_string.c_str(), 1, moab::MB_TYPE_DOUBLE, @@ -2082,9 +2082,17 @@ UnstructuredMesh::add_score(std::string score) const { void UnstructuredMesh::set_score_data(const std::string& score, std::vector values, - std::vector sum_sq) const { + std::vector std_dev) const { auto score_tags = get_score_tags(score); + // normalize tally values by element volume + for (int i = 0; i < ehs_.size(); i++) { + auto eh = get_ent_handle_from_bin(i); + double volume = tet_volume(eh); + values[i] /= volume; + std_dev[i] /= volume; + } + moab::ErrorCode rval; // set the score value rval = mbi_->tag_set_data(score_tags.first, ehs_, &values.front()); @@ -2096,7 +2104,7 @@ UnstructuredMesh::set_score_data(const std::string& score, } // set the error value - rval = mbi_->tag_set_data(score_tags.second, ehs_, &sum_sq.front()); + rval = mbi_->tag_set_data(score_tags.second, ehs_, &std_dev.front()); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index 193769f13b..f8bf4f1115 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -841,16 +841,20 @@ void Tally::accumulate() auto umesh = dynamic_cast(mesh.get()); if (umesh) { for (auto score : scores_) { - umesh->add_score(std::to_string(score)); - auto values = xt::view(results_, xt::all(), 0, TallyResult::VALUE); + // get values for this score and create vectors for marking + // up the mesh + auto values = xt::view(results_, xt::all(), 0, static_cast(TallyResult::SUM)); auto sum_sq = xt::view(results_, xt::all(), 0, TallyResult::SUM_SQ); - std::vector vals_vec(values.begin(), values.end()); - std::vector sum_sq_vec(sum_sq.begin(), sum_sq.end()); + std::vector vals_vec, sum_sq_vec; for (int i = 0; i < results_.shape()[0]; i++) { - umesh->set_score_data(std::to_string(score), - vals_vec, - sum_sq_vec); + vals_vec.push_back(results_(i, 0, TallyResult::SUM) / n_realizations_); + sum_sq_vec.push_back(results_(i, 0, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ n_realizations_); } + + // set the score data on the mesh + umesh->set_score_data(std::to_string(score), + vals_vec, + sum_sq_vec); } std::stringstream output_filename; output_filename << "tally_" << id_ << "_umesh"; From 6749f72fecbf8c42b55cc2e093bc5e9bb039bed3 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 28 Feb 2020 16:24:34 -0600 Subject: [PATCH 080/205] Moving tally writing into the statepoint function. Should write anytime a statepoint file is asked for. --- include/openmc/state_point.h | 4 +++ src/state_point.cpp | 48 ++++++++++++++++++++++++++++++++++++ src/tallies/tally.cpp | 32 ------------------------ 3 files changed, 52 insertions(+), 32 deletions(-) diff --git a/include/openmc/state_point.h b/include/openmc/state_point.h index e44c57f1a4..4a901ac2a2 100644 --- a/include/openmc/state_point.h +++ b/include/openmc/state_point.h @@ -16,5 +16,9 @@ void read_source_bank(hid_t group_id); void write_tally_results_nr(hid_t file_id); void restart_set_keff(); +#ifdef DAGMC +void write_unstructured_mesh_results(); +#endif + } // namespace openmc #endif // OPENMC_STATE_POINT_H diff --git a/src/state_point.cpp b/src/state_point.cpp index c8c3c87bd8..465bab506d 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -24,6 +24,7 @@ #include "openmc/simulation.h" #include "openmc/tallies/derivative.h" #include "openmc/tallies/filter.h" +#include "openmc/tallies/filter_mesh.h" #include "openmc/tallies/tally.h" #include "openmc/timer.h" @@ -164,6 +165,10 @@ openmc_statepoint_write(const char* filename, bool* write_source) tally_ids.push_back(tally->id_); write_attribute(tallies_group, "ids", tally_ids); +#ifdef DAGMC + write_unstructured_mesh_results(); +#endif + // Write all tally information except results for (const auto& tally : model::tallies) { hid_t tally_group = create_group(tallies_group, @@ -677,6 +682,49 @@ void read_source_bank(hid_t group_id) H5Tclose(banktype); } +#ifdef DAGMC +void write_unstructured_mesh_results() { + for (auto& tally : model::tallies) { + for (auto filter_idx : tally->filters()) { + auto& filter = model::tally_filters[filter_idx]; + if (filter->type() == "mesh") { + auto mesh_filter = dynamic_cast(filter.get()); + auto& mesh = model::meshes[mesh_filter->mesh()]; + auto umesh = dynamic_cast(mesh.get()); + if (umesh) { + for (auto score : tally->scores_) { + // get values for this score and create vectors for marking + // up the mesh + auto values = xt::view(tally->results_, xt::all(), 0, static_cast(TallyResult::SUM)); + auto sum_sq = xt::view(tally->results_, xt::all(), 0, TallyResult::SUM_SQ); + std::vector vals_vec, sum_sq_vec; + for (int i = 0; i < tally->results_.shape()[0]; i++) { + vals_vec.push_back(tally->results_(i, 0, TallyResult::SUM) / tally->n_realizations_); + sum_sq_vec.push_back(tally->results_(i, 0, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ tally->n_realizations_); + } + + // set the score data on the mesh + umesh->set_score_data(std::to_string(score), + vals_vec, + sum_sq_vec); + } + + // Determine width for zero padding + int w = std::to_string(settings::n_max_batches).size(); + + std::string umesh_filename = fmt::format("{0}tally_{1}.{2:0{3}}", + settings::path_output, + tally->id_, + simulation::current_batch, + w); + umesh->write(umesh_filename); + } + } + } + } +} +#endif + void write_tally_results_nr(hid_t file_id) { // ========================================================================== diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index f8bf4f1115..b63128dc04 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -831,38 +831,6 @@ void Tally::accumulate() } } } - -#ifdef DAGMC - for (auto filter_idx : filters_) { - auto& filter = model::tally_filters[filter_idx]; - if (filter->type() == "mesh") { - auto mesh_filter = dynamic_cast(filter.get()); - auto& mesh = model::meshes[mesh_filter->mesh()]; - auto umesh = dynamic_cast(mesh.get()); - if (umesh) { - for (auto score : scores_) { - // get values for this score and create vectors for marking - // up the mesh - auto values = xt::view(results_, xt::all(), 0, static_cast(TallyResult::SUM)); - auto sum_sq = xt::view(results_, xt::all(), 0, TallyResult::SUM_SQ); - std::vector vals_vec, sum_sq_vec; - for (int i = 0; i < results_.shape()[0]; i++) { - vals_vec.push_back(results_(i, 0, TallyResult::SUM) / n_realizations_); - sum_sq_vec.push_back(results_(i, 0, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ n_realizations_); - } - - // set the score data on the mesh - umesh->set_score_data(std::to_string(score), - vals_vec, - sum_sq_vec); - } - std::stringstream output_filename; - output_filename << "tally_" << id_ << "_umesh"; - umesh->write(output_filename.str()); - } - } - } -#endif } //============================================================================== From 1b2456632ba3fd48e045a584a2dee9b5325c65ca Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 2 Mar 2020 09:59:40 -0600 Subject: [PATCH 081/205] Writing unstructured mesh results with statepoint. --- include/openmc/mesh.h | 1 + src/mesh.cpp | 17 ++++++++++++++++- src/state_point.cpp | 1 + 3 files changed, 18 insertions(+), 1 deletion(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 44b1aa8c34..5d9b2f5a6c 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -397,6 +397,7 @@ public: private: moab::Range ehs_; //!< Range of tetrahedra EntityHandle's in the mesh moab::EntityHandle meshset_; //!< EntitySet containing all Tets/Tris + moab::EntityHandle tet_set_; moab::EntityHandle kdtree_root_; //!< Root of the MOAB KDTree std::shared_ptr mbi_; //!< MOAB instance std::unique_ptr kdtree_; //!< MOAB KDTree instance diff --git a/src/mesh.cpp b/src/mesh.cpp index 49eff192a5..b74435a956 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1583,6 +1583,17 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { warning("Non-tetrahedral elements found in unstructured mesh: " + filename_); } + // make an entity set for all tetrahedra (used later in output) + rval = mbi_->create_meshset(moab::MESHSET_SET, tet_set_); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to create an entity set for the tetrahedral elements"); + } + + rval = mbi_->add_entities(tet_set_, ehs_); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to add tetrahedra to an entity set."); + } + compute_barycentric_data(ehs_); build_kdtree(ehs_); } @@ -2118,8 +2129,12 @@ UnstructuredMesh::write(std::string base_filename) const { // add extension to the base name base_filename += ".h5m"; + // write the tetrahedral elements of the mesh only + // to avoid clutter from zero-value data on other + // elements during visualization + moab::ErrorCode rval; - rval = mbi_->write_mesh(base_filename.c_str()); + rval = mbi_->write_mesh(base_filename.c_str(), &tet_set_, 1); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to write unstructured mesh " << id_; diff --git a/src/state_point.cpp b/src/state_point.cpp index 465bab506d..a39b6f2970 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -717,6 +717,7 @@ void write_unstructured_mesh_results() { tally->id_, simulation::current_batch, w); + umesh->write(umesh_filename); } } From 9fddfe20bee1c3811f243d3e24e84c646d15b319 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 2 Mar 2020 11:11:40 -0600 Subject: [PATCH 082/205] Supporting output of multiple scores on unstructured mesh. --- src/state_point.cpp | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/state_point.cpp b/src/state_point.cpp index a39b6f2970..febebf2f7e 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -692,19 +692,17 @@ void write_unstructured_mesh_results() { auto& mesh = model::meshes[mesh_filter->mesh()]; auto umesh = dynamic_cast(mesh.get()); if (umesh) { - for (auto score : tally->scores_) { + for (int i = 0; i < tally->scores_.size(); i++) { // get values for this score and create vectors for marking // up the mesh - auto values = xt::view(tally->results_, xt::all(), 0, static_cast(TallyResult::SUM)); - auto sum_sq = xt::view(tally->results_, xt::all(), 0, TallyResult::SUM_SQ); std::vector vals_vec, sum_sq_vec; - for (int i = 0; i < tally->results_.shape()[0]; i++) { - vals_vec.push_back(tally->results_(i, 0, TallyResult::SUM) / tally->n_realizations_); - sum_sq_vec.push_back(tally->results_(i, 0, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ tally->n_realizations_); + for (int j = 0; j < tally->results_.shape()[0]; j++) { + vals_vec.push_back(tally->results_(j, i, TallyResult::SUM) / tally->n_realizations_); + sum_sq_vec.push_back(tally->results_(j , i, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ tally->n_realizations_); } // set the score data on the mesh - umesh->set_score_data(std::to_string(score), + umesh->set_score_data(std::to_string(tally->scores_[i]), vals_vec, sum_sq_vec); } From 11135ba5900aedfae579fcf6abdf0277a5e392e4 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Mar 2020 15:06:36 -0600 Subject: [PATCH 083/205] Adding warning for tallies with more than one filter. Printing output upon writing the unstructured mesh file. --- src/mesh.cpp | 7 +++++-- src/state_point.cpp | 36 ++++++++++++++++++++++++------------ 2 files changed, 29 insertions(+), 14 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index b74435a956..a0b0be9fdf 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -23,6 +23,7 @@ #include "openmc/hdf5_interface.h" #include "openmc/message_passing.h" #include "openmc/search.h" +#include "openmc/settings.h" #include "openmc/tallies/filter.h" #include "openmc/xml_interface.h" @@ -2127,14 +2128,16 @@ UnstructuredMesh::set_score_data(const std::string& score, void UnstructuredMesh::write(std::string base_filename) const { // add extension to the base name - base_filename += ".h5m"; + auto filename = base_filename + ".h5m"; + write_message("Writing unstructured mesh " + filename + "...", 5); + filename = settings::path_output + filename; // write the tetrahedral elements of the mesh only // to avoid clutter from zero-value data on other // elements during visualization moab::ErrorCode rval; - rval = mbi_->write_mesh(base_filename.c_str(), &tet_set_, 1); + rval = mbi_->write_mesh(filename.c_str(), &tet_set_, 1); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to write unstructured mesh " << id_; diff --git a/src/state_point.cpp b/src/state_point.cpp index febebf2f7e..7fefd8e7d0 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -684,6 +684,7 @@ void read_source_bank(hid_t group_id) #ifdef DAGMC void write_unstructured_mesh_results() { + for (auto& tally : model::tallies) { for (auto filter_idx : tally->filters()) { auto& filter = model::tally_filters[filter_idx]; @@ -692,13 +693,26 @@ void write_unstructured_mesh_results() { auto& mesh = model::meshes[mesh_filter->mesh()]; auto umesh = dynamic_cast(mesh.get()); if (umesh) { + + // if this tally has more than one filter, print + // warning and skip writing the mesh + if (tally->filters().size() > 1) { + warning(fmt::format("Skipping unstructured mesh writing for tally " + "{0}. More than one filter is present on the tally.", + tally->id_)); + } + + int n_realizations = tally->n_realizations_; + // write each score for this tally to the mesh for (int i = 0; i < tally->scores_.size(); i++) { - // get values for this score and create vectors for marking - // up the mesh + std::vector vals_vec, sum_sq_vec; for (int j = 0; j < tally->results_.shape()[0]; j++) { - vals_vec.push_back(tally->results_(j, i, TallyResult::SUM) / tally->n_realizations_); - sum_sq_vec.push_back(tally->results_(j , i, TallyResult::SUM_SQ) - std::pow(vals_vec[i], 2)/ tally->n_realizations_); + double mean = tally->results_(j, i, TallyResult::SUM) / n_realizations; + double sum_sq = tally->results_(j , i, TallyResult::SUM_SQ); + sum_sq_vec.push_back(sum_sq / n_realizations - + std::pow(mean, 2) / (n_realizations - 1)); + vals_vec.push_back(mean); } // set the score data on the mesh @@ -709,14 +723,12 @@ void write_unstructured_mesh_results() { // Determine width for zero padding int w = std::to_string(settings::n_max_batches).size(); - - std::string umesh_filename = fmt::format("{0}tally_{1}.{2:0{3}}", - settings::path_output, - tally->id_, - simulation::current_batch, - w); - - umesh->write(umesh_filename); + std::string filename = fmt::format("tally_{0}.{1:0{2}}", + tally->id_, + simulation::current_batch, + w); + // Write message + umesh->write(filename); } } } From 7fee09ea428be6b6b809b97690448c548c0e89ef Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Mar 2020 17:13:30 -0600 Subject: [PATCH 084/205] Adding by score and nuclide now. --- src/state_point.cpp | 34 +++++++++++++++++++++------------- 1 file changed, 21 insertions(+), 13 deletions(-) diff --git a/src/state_point.cpp b/src/state_point.cpp index 7fefd8e7d0..f79d85e89e 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -704,21 +704,28 @@ void write_unstructured_mesh_results() { int n_realizations = tally->n_realizations_; // write each score for this tally to the mesh - for (int i = 0; i < tally->scores_.size(); i++) { + for (int i_score = 0; i_score < tally->scores_.size(); i_score++) { + for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) { + std::vector mean_vec, std_dev_vec; + int nuc_score_idx = i_score + i_nuc * tally->scores_.size(); + for (int j = 0; j < tally->results_.shape()[0]; j++) { + double mean = tally->results_(j, nuc_score_idx, TallyResult::SUM) / n_realizations; + double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ); + std_dev_vec.push_back(sum_sq / n_realizations - + std::pow(mean, 2) / (n_realizations - 1)); + mean_vec.push_back(mean); + } - std::vector vals_vec, sum_sq_vec; - for (int j = 0; j < tally->results_.shape()[0]; j++) { - double mean = tally->results_(j, i, TallyResult::SUM) / n_realizations; - double sum_sq = tally->results_(j , i, TallyResult::SUM_SQ); - sum_sq_vec.push_back(sum_sq / n_realizations - - std::pow(mean, 2) / (n_realizations - 1)); - vals_vec.push_back(mean); + // set the score data on the mesh + auto score_str = fmt::format("score_{0}_nuc_{1}", + tally->scores_[i_score], + tally->nuclides_[i_nuc]); + umesh->set_score_data(score_str, + mean_vec, + std_dev_vec); + mean_vec.clear(); + std_dev_vec.clear(); } - - // set the score data on the mesh - umesh->set_score_data(std::to_string(tally->scores_[i]), - vals_vec, - sum_sq_vec); } // Determine width for zero padding @@ -727,6 +734,7 @@ void write_unstructured_mesh_results() { tally->id_, simulation::current_batch, w); + // Write message umesh->write(filename); } From 5d18ea67c613329cef3ab693d742c5b10dd6f1a8 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Mar 2020 17:37:31 -0600 Subject: [PATCH 085/205] Using score and nuclide strings for a nicer tag name. --- include/openmc/tallies/tally.h | 2 ++ src/state_point.cpp | 17 ++++++++++++----- src/tallies/tally.cpp | 1 + 3 files changed, 15 insertions(+), 5 deletions(-) diff --git a/include/openmc/tallies/tally.h b/include/openmc/tallies/tally.h index 298f3d3a15..d3ec1f720a 100644 --- a/include/openmc/tallies/tally.h +++ b/include/openmc/tallies/tally.h @@ -92,6 +92,8 @@ public: std::vector scores_; //!< Filter integrands (e.g. flux, fission) + std::vector score_strs_; //!< Score names + //! Index of each nuclide to be tallied. -1 indicates total material. std::vector nuclides_ {-1}; diff --git a/src/state_point.cpp b/src/state_point.cpp index f79d85e89e..6e4351acf2 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -716,11 +716,18 @@ void write_unstructured_mesh_results() { mean_vec.push_back(mean); } - // set the score data on the mesh - auto score_str = fmt::format("score_{0}_nuc_{1}", - tally->scores_[i_score], - tally->nuclides_[i_nuc]); - umesh->set_score_data(score_str, + // generate a name for the value + std::string nuclide_name = "total"; + if (tally->nuclides_[i_nuc] > -1) { + nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_; + } + + std::string score_name = tally->score_strs_[i_score]; + + auto score_str = fmt::format("{0}_{1}", + score_name, + nuclide_name); + umesh->set_score_data(score_str, mean_vec, std_dev_vec); mean_vec.clear(); diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index b63128dc04..e232872962 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -650,6 +650,7 @@ Tally::set_scores(const std::vector& scores) break; } + score_strs_.push_back(score_str); scores_.push_back(score); } From 01efd1ef59135b472b1519766c86650c2f852343 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Mar 2020 18:03:36 -0600 Subject: [PATCH 086/205] Added a function for converting score integers to strings instead of storing them. --- include/openmc/tallies/tally.h | 4 +- src/state_point.cpp | 2 +- src/tallies/tally.cpp | 179 ++++++++++++++++++++++++++++++++- 3 files changed, 181 insertions(+), 4 deletions(-) diff --git a/include/openmc/tallies/tally.h b/include/openmc/tallies/tally.h index d3ec1f720a..25b7e1c71f 100644 --- a/include/openmc/tallies/tally.h +++ b/include/openmc/tallies/tally.h @@ -72,6 +72,8 @@ public: void accumulate(); + std::string score_name(int i) const; + //---------------------------------------------------------------------------- // Major public data members. @@ -92,8 +94,6 @@ public: std::vector scores_; //!< Filter integrands (e.g. flux, fission) - std::vector score_strs_; //!< Score names - //! Index of each nuclide to be tallied. -1 indicates total material. std::vector nuclides_ {-1}; diff --git a/src/state_point.cpp b/src/state_point.cpp index 6e4351acf2..e6acfce996 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -722,7 +722,7 @@ void write_unstructured_mesh_results() { nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_; } - std::string score_name = tally->score_strs_[i_score]; + std::string score_name = tally->score_name(i_score); auto score_str = fmt::format("{0}_{1}", score_name, diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index e232872962..0dba3b925d 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -65,6 +65,175 @@ double global_tally_collision; double global_tally_tracklength; double global_tally_leakage; +std::string +score_int_to_str(int score_int) { + if (score_int == SCORE_FLUX) + return "flux"; + + if (score_int == SCORE_SCATTER) + return "scatter"; + + if (score_int == SCORE_TOTAL) + return "total"; + + if (score_int == SCORE_SCATTER) + return "scatter"; + + if (score_int == SCORE_NU_SCATTER) + return "nu-scatter"; + + if (score_int == SCORE_ABSORPTION) + return "absorption"; + + if (score_int == SCORE_FISSION) + return "fission"; + + if (score_int == SCORE_NU_FISSION) + return "nu-fission"; + + if (score_int == SCORE_DECAY_RATE) + return "decay-rate"; + + if (score_int == SCORE_DELAYED_NU_FISSION) + return "delayed-nu-fission"; + + if (score_int == SCORE_PROMPT_NU_FISSION) + return "prompt-nu-fission"; + + if (score_int == SCORE_KAPPA_FISSION) + return "kappa-fission"; + + if (score_int == SCORE_INVERSE_VELOCITY) + return "inverse-velocity"; + + if (score_int == SCORE_FISS_Q_PROMPT) + return "fission-q-prompt"; + + if (score_int == SCORE_FISS_Q_RECOV) + return "fission-q-recoverable"; + + if (score_int == HEATING) + return "heating"; + + if (score_int == HEATING_LOCAL) + return "heating-local"; + + if (score_int == SCORE_CURRENT) + return "current"; + + if (score_int == SCORE_EVENTS) + return "events"; + + if (score_int == ELASTIC) + return "(n,elastic)"; + + if (score_int == N_2N) + return "(n,2n)"; + + if (score_int == N_3N) + return "(n,3n)"; + + if (score_int == N_4N) + return "(n,4n)"; + + if (score_int == N_2ND) + return "(n,2nd)"; + + if (score_int == N_2NA) + return "(n,na)"; + if (score_int == N_N3A) + return "(n,n3a)"; + if (score_int == N_2NA) + return "(n,2na)"; + if (score_int == N_3NA) + return "(n,3na)"; + if (score_int == N_NP) + return "(n,np)"; + if (score_int == N_N2A) + return "(n,n2a)"; + if (score_int == N_2N2A) + return "(n,2n2a)"; + if (score_int == N_ND) + return "(n,nd)"; + if (score_int == N_NT) + return "(n,nt)"; + if (score_int == N_N3HE) + return "(n,nHe-3)"; + if (score_int == N_ND2A) + return "(n,nd2a)"; + if (score_int == N_NT2A) + return "(n,nt2a)"; + if (score_int == N_3NF) + return "(n,3nf)"; + if (score_int == N_2NP) + return "(n,2np)"; + if (score_int == N_3NP) + return "(n,3np)"; + if (score_int == N_N2P) + return "(n,n2p)"; + if (score_int == N_NPA) + return "(n,npa)"; + if (score_int == N_N1) + return "(n,n1)"; + if (score_int == N_NC) + return "(n,nc)"; + if (score_int == N_GAMMA) + return "(n,gamma)"; + if (score_int == N_P) + return "(n,p)"; + if (score_int == N_D) + return "(n,d)"; + if (score_int == N_T) + return "(n,t)"; + if (score_int == N_3HE) + return "(n,3He)"; + if (score_int == N_A) + return "(n,a)"; + if (score_int == N_2A) + return "(n,2a)"; + if (score_int == N_3A) + return "(n,3a)"; + if (score_int == N_2P) + return "(n,2p)"; + if (score_int == N_PA) + return "(n,pa)"; + if (score_int == N_T2A) + return "(n,t2a)"; + if (score_int == N_D2A) + return "(n,d2a)"; + if (score_int == N_PD) + return "(n,pd)"; + if (score_int == N_PT) + return "(n,pt)"; + if (score_int == N_DA) + return "(n,da)"; + if (score_int == N_XP) + return "H1-production"; + if (score_int == N_XD) + return "H2-production"; + if (score_int == N_XT) + return "H3-production"; + if (score_int == N_X3HE) + return "He3-production"; + if (score_int == N_XA) + return "He4-production"; + if (score_int == DAMAGE_ENERGY) + return "damage-energy"; + + // Assume the given int is a reaction MT number. Make sure it's a natural + // number then return. + std::string score_as_str = std::to_string(score_int); + int MT; + try { + MT = std::stoi(score_as_str); + } catch (const std::invalid_argument& ex) { + throw std::invalid_argument("Invalid tally score \"" + score_as_str + "\""); + } + if (MT < 1) + throw std::invalid_argument("Invalid tally score \"" + score_as_str + "\""); + return "MT" + score_as_str; +} + int score_str_to_int(std::string score_str) { @@ -650,7 +819,6 @@ Tally::set_scores(const std::vector& scores) break; } - score_strs_.push_back(score_str); scores_.push_back(score); } @@ -834,6 +1002,15 @@ void Tally::accumulate() } } +std::string +Tally::score_name(int score_idx) const { + if (score_idx < 0 || score_idx >= scores_.size()) { + warning("Index in scores array is out of bounds."); + return ""; + } + return score_int_to_str(scores_[score_idx]); +} + //============================================================================== // Non-member functions //============================================================================== From 09ad3e884aaff4eea28bb101155cf0b28f944cde Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Mar 2020 18:09:03 -0600 Subject: [PATCH 087/205] Including break to skip mesh file write. --- src/state_point.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/state_point.cpp b/src/state_point.cpp index e6acfce996..7b82fe4296 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -700,6 +700,7 @@ void write_unstructured_mesh_results() { warning(fmt::format("Skipping unstructured mesh writing for tally " "{0}. More than one filter is present on the tally.", tally->id_)); + break; } int n_realizations = tally->n_realizations_; From 919634297de41d4af614193c8ffdf1c27161a9e8 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 9 Mar 2020 12:36:16 -0500 Subject: [PATCH 088/205] Start of code cleanup. --- src/mesh.cpp | 53 ++----------------- .../unstructured_mesh/test.py | 2 +- 2 files changed, 5 insertions(+), 50 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index a0b0be9fdf..63b6036b6b 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1752,53 +1752,6 @@ UnstructuredMesh::bins_crossed(const Particle* p, } return; - - /// IMPLEMENTATION TWO - // double prev_int_dist = 0.0; - - // if (hits.size() == 0) { - // moab::EntityHandle last_r_tet =get_tet((r0 + r1) * 0.5); - // if (last_r_tet) { - // bins.push_back(get_bin_from_ent_handle(last_r_tet)); - // lengths.push_back(1.0); - // } - // return; - // } - - // moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0); - - // if (!tet) { - // last_r = last_r + u * hits.front().first; - // hits.erase(hits.begin()); - // } - - // for (const auto& hit : hits) { - // tet = get_tet(last_r + u * (prev_int_dist + hit.first) / 2.0 ); - // if (!tet) { - // prev_int_dist = hit.first; - // continue; - // } - // int bin = get_bin_from_ent_handle(tet); - // double tally_val = (hit.first - prev_int_dist) / track_len); - // if (tally_val < 0.0) { - // fatal_error("Negative score applied to tally"); - // } - - // bins.push_back(bin); - // lengths.push_back(tally_val); - // prev_int_dist = hit.first; - // } - - // // tally remaining portion of track (if any exists) - // if (hits.back().first < track_len) { - // tet = get_tet(last_r + u * (track_len + hits.back().first) / 2.0); - // if (tet) { - // bins.push_back(get_bin_from_ent_handle(tet)); - // double tally_val = (track_len - hits.back().first) / track_len); - // lengths.push_back(tally_val); - // } - // } - }; moab::EntityHandle @@ -1941,8 +1894,10 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) moab::CartVect bary_coords = a_inv * (r - p_zero); - bool in_tet = (bary_coords[0] >= 0 && bary_coords[1] >= 0 && bary_coords[2] >= 0 && - bary_coords[0] + bary_coords[1] + bary_coords[2] <= 1.); + bool in_tet = (bary_coords[0] >= 0.0 && + bary_coords[1] >= 0.0 && + bary_coords[2] >= 0.0 && + bary_coords[0] + bary_coords[1] + bary_coords[2] <= 1.0); return in_tet; } diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index ff6e5178f5..204c31dbb0 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -252,7 +252,7 @@ class UnstructuredMeshTest(PyAPITestHarness): def _cleanup(self): super()._cleanup() - output = glob.glob('*umesh.h5m') + output = glob.glob('tally*.h5m') for f in output: if os.path.exists(f): os.remove(f) From c394030891cf72f2f82642232e9bb037139080e5 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 9 Mar 2020 13:21:30 -0500 Subject: [PATCH 089/205] Doc string for attribute and a format update. --- include/openmc/mesh.h | 27 ++++++++++++++------------- src/mesh.cpp | 4 +++- 2 files changed, 17 insertions(+), 14 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 5d9b2f5a6c..aa1946896e 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -276,17 +276,17 @@ public: private: -//! Finds all intersections with faces of the mesh. -// -//! \param[in] start Staring location -//! \param[in] dir Normalized particle direction -//! \param[in] length of particle track -//! \param[out] Mesh intersections -void -intersect_track(const moab::CartVect& start, - const moab::CartVect& dir, - double track_len, - UnstructuredMeshHits& hits) const; + //! Finds all intersections with faces of the mesh. + // + //! \param[in] start Staring location + //! \param[in] dir Normalized particle direction + //! \param[in] length of particle track + //! \param[out] Mesh intersections + void + intersect_track(const moab::CartVect& start, + const moab::CartVect& dir, + double track_len, + UnstructuredMeshHits& hits) const; //! Calculates the volume for a given tetrahedron handle. // @@ -395,9 +395,10 @@ public: std::string filename_; // mbi_; //!< MOAB instance std::unique_ptr kdtree_; //!< MOAB KDTree instance diff --git a/src/mesh.cpp b/src/mesh.cpp index 63b6036b6b..eae4dd0ed9 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -2102,7 +2102,6 @@ UnstructuredMesh::write(std::string base_filename) const { #endif - //============================================================================== // Non-member functions //============================================================================== @@ -2126,6 +2125,9 @@ void read_meshes(pugi::xml_node root) } else if (mesh_type == "unstructured") { model::meshes.push_back(std::make_unique(node)); +#else + else if (mesh_type == "unstructured") { + fatal_error("Unstructured mesh support is disabled."); #endif } else { fatal_error("Invalid mesh type: " + mesh_type); From 92589048d8f2994462987cc94e805069d1658e3d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 17 Mar 2020 15:18:20 -0500 Subject: [PATCH 090/205] Adding centroids attribute. --- openmc/mesh.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/openmc/mesh.py b/openmc/mesh.py index 1117116e1d..94bcf947b0 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -611,12 +611,16 @@ class UnstructuredMesh(MeshBase): Volumes of the unstructured mesh elements total_volume : float Volume of the unstructured mesh in total + centroids : Iterable of tuple + An iterable of element centroid coordinates, e.g. [(0.0, 0.0, 0.0), + (1.0, 1.0, 1.0), ...] """ def __init__(self, mesh_id=None, name='', filename=''): super().__init__(mesh_id, name) self._filename = filename self._volumes = [] + self._centroids = [] @property def filename(self): @@ -644,6 +648,15 @@ class UnstructuredMesh(MeshBase): def total_volume(self): return np.sum(self.volumes) + @property + def centroids(self): + return self._centroids + + @centroids.setter + def centroids(self, centroids): + cv.check_type("Unstructured mesh centroids", centroids, Iterable, Iterable) + self._centroids = centroids + def __repr__(self): string = super().__repr__() string += '{0: <16}{1}{2}\n'.format('\tFilename', '=\t', self.filename) @@ -656,7 +669,9 @@ class UnstructuredMesh(MeshBase): mesh = cls(mesh_id) mesh.filename = group['filename'][()].decode() vol_data = group['volumes'][()] + centroids = group['centroids'][()] mesh.volumes = np.reshape(vol_data, (vol_data.shape[0], 1)) + mesh.centroids = np.reshape(centroids, (vol_data.shape[0], 3)) return mesh From 3c1432ae0ef0abf1a64e5ca3266eb56f336327e0 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 17 Mar 2020 15:18:34 -0500 Subject: [PATCH 091/205] Writing to VTK by default as it is more universal. --- src/mesh.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index eae4dd0ed9..95e63d1eaf 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -2083,7 +2083,7 @@ UnstructuredMesh::set_score_data(const std::string& score, void UnstructuredMesh::write(std::string base_filename) const { // add extension to the base name - auto filename = base_filename + ".h5m"; + auto filename = base_filename + ".vtk"; write_message("Writing unstructured mesh " + filename + "...", 5); filename = settings::path_output + filename; From 03905e061a23bb2f99008d1200d4f6d4ee720d85 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 15 Feb 2020 16:23:44 -0600 Subject: [PATCH 092/205] USE DAGMC EXPORTED TARGETS --- vendor/xtensor | 2 +- vendor/xtl | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/vendor/xtensor b/vendor/xtensor index 31acec1e90..ef091807f7 160000 --- a/vendor/xtensor +++ b/vendor/xtensor @@ -1 +1 @@ -Subproject commit 31acec1e90bbea6d4bc17af0710a123bd5da6689 +Subproject commit ef091807f7ed0e5ba7e251a6c46f4af7bba79e2e diff --git a/vendor/xtl b/vendor/xtl index 0024346605..f5d13e6c4f 160000 --- a/vendor/xtl +++ b/vendor/xtl @@ -1 +1 @@ -Subproject commit 0024346605bd92bcc4009caad7f4be88687e063a +Subproject commit f5d13e6c4f856becc178939365fcdcf9a657ffb5 From 3d9abfba570f8cbd27c2db72b2fa112a7a4bd203 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 17 Mar 2020 18:03:32 -0500 Subject: [PATCH 093/205] Adding the unstructured mesh example notebook. --- examples/jupyter/images/pin_mesh.png | Bin 0 -> 132261 bytes examples/jupyter/images/umesh_flux.png | Bin 0 -> 198669 bytes examples/jupyter/images/umesh_heating.png | Bin 0 -> 132018 bytes examples/jupyter/images/umesh_w_assembly.png | Bin 0 -> 165840 bytes examples/jupyter/unstructured_mesh.ipynb | 833 +++++++++++++++++++ 5 files changed, 833 insertions(+) create mode 100644 examples/jupyter/images/pin_mesh.png create mode 100644 examples/jupyter/images/umesh_flux.png create mode 100644 examples/jupyter/images/umesh_heating.png create mode 100644 examples/jupyter/images/umesh_w_assembly.png create mode 100644 examples/jupyter/unstructured_mesh.ipynb diff 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b/examples/jupyter/unstructured_mesh.ipynb @@ -0,0 +1,833 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "plt.rcParams[\"figure.figsize\"] = (30,10)\n", + "\n", + "import urllib.request\n", + "\n", + "\n", + "pin_mesh_url = 'https://tinyurl.com/u9ce9d7' # 1.2 MB\n", + "\n", + "def download(url, filename='dagmc.h5m'):\n", + " \"\"\"\n", + " Helper function for retrieving dagmc models\n", + " \"\"\"\n", + " u = urllib.request.urlopen(url)\n", + " \n", + " if u.status != 200:\n", + " raise RuntimeError(\"Failed to download file.\")\n", + " \n", + " # save file as dagmc.h5m\n", + " with open(filename, 'wb') as f:\n", + " f.write(u.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unstructured Mesh Tallies in OpenMC\n", + "\n", + "In this example we'll look at how to setup and use unstructured mesh tallies in OpenMC. Unstructured meshes are able to provide results over spatial regions of a problem while conforming to a specific geometric features -- something that is often difficult to do using the regular and rectilinear meshes in OpenMC.\n", + "\n", + "Here, we'll apply an unstructured mesh tally to the PWR assembly model from the OpenMC examples.\n", + "\n", + "_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled._" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import openmc\n", + "import openmc.lib\n", + "\n", + "assert(openmc.lib._dagmc_enabled())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we'll import that model from the set of OpenMC examples." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = openmc.examples.pwr_assembly()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll make a couple of adjustments to this 2D model as it won't play very well with the 3D mesh we'll be looking at. First, we'll bound the pincell between +/- 10 cm in the Z dimension." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "min_z = openmc.ZPlane(z0=-10.0)\n", + "max_z = openmc.ZPlane(z0=10.0)\n", + "\n", + "z_region = +min_z & -max_z\n", + "\n", + "cells = model.geometry.get_all_cells()\n", + "for cell in cells.values():\n", + " cell.region &= z_region" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The other adjustment we'll make is to remove the reflective boundary conditions on the X and Y boundaries. (This is purely to generate a more interesting flux profile.)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "surfaces = model.geometry.get_all_surfaces()\n", + "# modify the boundary condition of the\n", + "# planar surfaces bounding the assembly\n", + "for surface in surfaces.values():\n", + " if isinstance(surface, openmc.Plane):\n", + " surface.boundary_type = 'vacuum'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look at the model to ensure our changs have been added properly." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "root_univ = model.geometry.root_universe\n", + "\n", + "# axial image\n", + "root_univ.plot(width=(22.0, 22.0),\n", + " pixels=(200, 300),\n", + " basis='xz',\n", + " color_by='material',\n", + " seed=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# radial image\n", + "root_univ.plot(width=(22.0, 22.0),\n", + " pixels=(400, 400),\n", + " basis='xy',\n", + " color_by='material',\n", + " seed=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good! Let's run some particles through the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", + " Date/Time | 2020-03-17 17:37:57\n", + " OpenMP Threads | 96\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n", + " Reading U235 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading Zr90 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr96.h5\n", + " Reading H1 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading B11 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n", + " Reading c_H_in_H2O from /home/pshriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.20444\n", + " 2/1 0.15502\n", + " 3/1 0.19804\n", + " 4/1 0.22159\n", + " 5/1 0.19776\n", + " 6/1 0.20086\n", + " 7/1 0.21896 0.20991 +/- 0.00905\n", + " 8/1 0.23134 0.21706 +/- 0.00885\n", + " 9/1 0.29029 0.23536 +/- 0.01935\n", + " 10/1 0.20094 0.22848 +/- 0.01649\n", + " Creating state point statepoint.10.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 6.3553e-01 seconds\n", + " Reading cross sections = 5.9809e-01 seconds\n", + " Total time in simulation = 5.2261e-02 seconds\n", + " Time in transport only = 4.8537e-02 seconds\n", + " Time in inactive batches = 4.6636e-02 seconds\n", + " Time in active batches = 5.6258e-03 seconds\n", + " Time synchronizing fission bank = 9.5693e-05 seconds\n", + " Sampling source sites = 6.0864e-05 seconds\n", + " SEND/RECV source sites = 2.6286e-05 seconds\n", + " Time accumulating tallies = 3.3250e-06 seconds\n", + " Total time for finalization = 9.9400e-07 seconds\n", + " Total time elapsed = 6.8820e-01 seconds\n", + " Calculation Rate (inactive) = 10721.4 particles/second\n", + " Calculation Rate (active) = 88875.7 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 0.25094 +/- 0.02010\n", + " k-effective (Track-length) = 0.22848 +/- 0.01649\n", + " k-effective (Absorption) = 0.21556 +/- 0.04156\n", + " Combined k-effective = 0.20707 +/- 0.01965\n", + " Leakage Fraction = 0.79200 +/- 0.03382\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0.20706788967181863+/-0.01965303775428789" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it's time to apply our mesh tally to the problem. We'll be using the tetrahedral mesh \"pins1-4.h5m\" shown below:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](./images/pin_mesh.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This mesh was generated using Trelis with radii that match the fuel/coolant channels of the PWR model. These four channels correspond to the highlighted channels of the assembly below. \n", + "\n", + "Two of the channels are coolant and the other two are fuel." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.patches import Rectangle\n", + "from matplotlib import pyplot as plt\n", + "\n", + "pitch = 1.26 # cm\n", + "\n", + "img = root_univ.plot(width=(22.0, 22.0),\n", + " pixels=(600, 600),\n", + " basis='xy',\n", + " color_by='material',\n", + " seed=0)\n", + "\n", + "# highlight channels\n", + "for i in range(0, 4):\n", + " corner = (i * pitch - pitch / 2.0, -i * pitch - pitch / 2.0)\n", + " rect = Rectangle(corner,\n", + " pitch,\n", + " pitch,\n", + " edgecolor='blue',\n", + " fill=False)\n", + " img.axes.add_artist(rect)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Applying an unstructured mesh tally" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use this mesh, we'll create an unstructured mesh instance and apply it to a mesh filter." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "download(pin_mesh_url, \"pins1-4.h5m\")\n", + "umesh = openmc.UnstructuredMesh(filename=\"pins1-4.h5m\")\n", + "mesh_filter = openmc.MeshFilter(umesh)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now apply this filter like any other. For this demonstration we'll score both the flux and heating in these pins." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "tally = openmc.Tally()\n", + "tally.filters = [mesh_filter]\n", + "tally.scores = ['heating', 'flux']\n", + "tally.estimator = 'tracklength'\n", + "model.tallies = (tally,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll run this model with the unstructured mesh tally applied. Notice that the simulation takes some time to start due to some additional data structures used by the unstructured mesh tally. Additionally, the particle rate drops dramatically during the active cycles of this simulation.\n", + "\n", + "Unstructured meshes are useful, but they can be computationally expensive!" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", + " Date/Time | 2020-03-17 17:42:07\n", + " OpenMP Threads | 96\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n", + " Reading U235 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading Zr90 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr96.h5\n", + " Reading H1 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading B11 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n", + " Reading c_H_in_H2O from /home/pshriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.22539\n", + " 2/1 0.23030\n", + " 3/1 0.23180\n", + " 4/1 0.23343\n", + " 5/1 0.22940\n", + " 6/1 0.22765\n", + " 7/1 0.23238\n", + " 8/1 0.23083\n", + " 9/1 0.23204\n", + " 10/1 0.23189\n", + " 11/1 0.23555\n", + " 12/1 0.23220\n", + " 13/1 0.22907\n", + " 14/1 0.23016\n", + " 15/1 0.23055\n", + " 16/1 0.23062\n", + " 17/1 0.22656\n", + " 18/1 0.23380\n", + " 19/1 0.23268\n", + " 20/1 0.23233\n", + " 21/1 0.23256\n", + " 22/1 0.22965 0.23111 +/- 0.00145\n", + " 23/1 0.22846 0.23022 +/- 0.00121\n", + " 24/1 0.22936 0.23001 +/- 0.00089\n", + " 25/1 0.23054 0.23012 +/- 0.00069\n", + " 26/1 0.22708 0.22961 +/- 0.00076\n", + " 27/1 0.23159 0.22989 +/- 0.00070\n", + " WARNING: No tet found for location between trianle hits\n", + " 28/1 0.23703 0.23078 +/- 0.00108\n", + " 29/1 0.23227 0.23095 +/- 0.00097\n", + " 30/1 0.23114 0.23097 +/- 0.00086\n", + " 31/1 0.23133 0.23100 +/- 0.00078\n", + " 32/1 0.23143 0.23104 +/- 0.00072\n", + " 33/1 0.23125 0.23105 +/- 0.00066\n", + " 34/1 0.23218 0.23113 +/- 0.00061\n", + " 35/1 0.23140 0.23115 +/- 0.00057\n", + " 36/1 0.22911 0.23102 +/- 0.00055\n", + " 37/1 0.23143 0.23105 +/- 0.00052\n", + " 38/1 0.23342 0.23118 +/- 0.00051\n", + " 39/1 0.23186 0.23121 +/- 0.00048\n", + " 40/1 0.23029 0.23117 +/- 0.00046\n", + " 41/1 0.23132 0.23118 +/- 0.00043\n", + " 42/1 0.23167 0.23120 +/- 0.00042\n", + " 43/1 0.23244 0.23125 +/- 0.00040\n", + " 44/1 0.23101 0.23124 +/- 0.00038\n", + " 45/1 0.23225 0.23128 +/- 0.00037\n", + " 46/1 0.22945 0.23121 +/- 0.00036\n", + " 47/1 0.22978 0.23116 +/- 0.00035\n", + " 48/1 0.23335 0.23124 +/- 0.00035\n", + " 49/1 0.23298 0.23130 +/- 0.00034\n", + " 50/1 0.23095 0.23129 +/- 0.00033\n", + " 51/1 0.23724 0.23148 +/- 0.00037\n", + " 52/1 0.22973 0.23142 +/- 0.00037\n", + " 53/1 0.23066 0.23140 +/- 0.00035\n", + " 54/1 0.22838 0.23131 +/- 0.00036\n", + " 55/1 0.23262 0.23135 +/- 0.00035\n", + " 56/1 0.23593 0.23148 +/- 0.00036\n", + " 57/1 0.23358 0.23153 +/- 0.00036\n", + " 58/1 0.23050 0.23151 +/- 0.00035\n", + " 59/1 0.23273 0.23154 +/- 0.00034\n", + " 60/1 0.22842 0.23146 +/- 0.00034\n", + " 61/1 0.23344 0.23151 +/- 0.00033\n", + " 62/1 0.23333 0.23155 +/- 0.00033\n", + " 63/1 0.22987 0.23151 +/- 0.00032\n", + " 64/1 0.23117 0.23150 +/- 0.00032\n", + " 65/1 0.23197 0.23151 +/- 0.00031\n", + " 66/1 0.23379 0.23156 +/- 0.00031\n", + " 67/1 0.23461 0.23163 +/- 0.00031\n", + " 68/1 0.23109 0.23162 +/- 0.00030\n", + " 69/1 0.22916 0.23157 +/- 0.00030\n", + " 70/1 0.23008 0.23154 +/- 0.00029\n", + " 71/1 0.23157 0.23154 +/- 0.00029\n", + " 72/1 0.23126 0.23153 +/- 0.00028\n", + " 73/1 0.23377 0.23157 +/- 0.00028\n", + " 74/1 0.23105 0.23157 +/- 0.00028\n", + " 75/1 0.23654 0.23166 +/- 0.00029\n", + " 76/1 0.23198 0.23166 +/- 0.00028\n", + " 77/1 0.23390 0.23170 +/- 0.00028\n", + " 78/1 0.23455 0.23175 +/- 0.00028\n", + " 79/1 0.23245 0.23176 +/- 0.00027\n", + " 80/1 0.23121 0.23175 +/- 0.00027\n", + " 81/1 0.23183 0.23175 +/- 0.00026\n", + " 82/1 0.23496 0.23181 +/- 0.00027\n", + " 83/1 0.22763 0.23174 +/- 0.00027\n", + " 84/1 0.23184 0.23174 +/- 0.00027\n", + " 85/1 0.23074 0.23173 +/- 0.00026\n", + " 86/1 0.23178 0.23173 +/- 0.00026\n", + " 87/1 0.23135 0.23172 +/- 0.00025\n", + " 88/1 0.23117 0.23171 +/- 0.00025\n", + " 89/1 0.22815 0.23166 +/- 0.00025\n", + " 90/1 0.22852 0.23162 +/- 0.00025\n", + " 91/1 0.22910 0.23158 +/- 0.00025\n", + " 92/1 0.23143 0.23158 +/- 0.00025\n", + " 93/1 0.23097 0.23157 +/- 0.00024\n", + " 94/1 0.23348 0.23160 +/- 0.00024\n", + " 95/1 0.23068 0.23158 +/- 0.00024\n", + " 96/1 0.23089 0.23157 +/- 0.00024\n", + " 97/1 0.23373 0.23160 +/- 0.00024\n", + " 98/1 0.23336 0.23163 +/- 0.00023\n", + " 99/1 0.23084 0.23162 +/- 0.00023\n", + " 100/1 0.23116 0.23161 +/- 0.00023\n", + " Creating state point statepoint.100.h5...\n", + " Writing unstructured mesh tally_1.100.vtk...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 1.8046e+02 seconds\n", + " Reading cross sections = 5.9341e-01 seconds\n", + " Total time in simulation = 2.5552e+02 seconds\n", + " Time in transport only = 2.5029e+02 seconds\n", + " Time in inactive batches = 6.7244e+00 seconds\n", + " Time in active batches = 2.4879e+02 seconds\n", + " Time synchronizing fission bank = 9.6409e-01 seconds\n", + " Sampling source sites = 7.6501e-01 seconds\n", + " SEND/RECV source sites = 1.9888e-01 seconds\n", + " Time accumulating tallies = 2.9884e-01 seconds\n", + " Total time for finalization = 5.7441e-01 seconds\n", + " Total time elapsed = 4.3679e+02 seconds\n", + " Calculation Rate (inactive) = 297423.0 particles/second\n", + " Calculation Rate (active) = 32155.0 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " k-effective (Collision) = 0.23129 +/- 0.00020\n", + " k-effective (Track-length) = 0.23161 +/- 0.00023\n", + " k-effective (Absorption) = 0.23099 +/- 0.00019\n", + " Combined k-effective = 0.23119 +/- 0.00017\n", + " Leakage Fraction = 0.79477 +/- 0.00014\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0.23118721242922136+/-0.00017018932314030727" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.settings.particles = 100_000\n", + "model.settings.inactive = 20\n", + "model.settings.batches = 100\n", + "model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the end of the simulation, we see the statepoint file along with a file named \"tally_1.100.vtk\". This file contains the results of the unstructured mesh tally with convenient labels for the scores applied. In our case the following scores will be present in the VTK:\n", + "\n", + " - flux_total_value\n", + " - flux_total_std_dev\n", + " - heating_total_value\n", + " - heading_total_std_dev\n", + " \n", + " Where \"total\" represents \n", + " \n", + "\n", + "Currently, an unstructured VTK file will only be generated for tallies if the unstructured mesh is is the only filter applied to that tally. All results for the unstructured mesh tally are present in the statepoint file regardless of the number of filters applied, however.\n", + "\n", + "These files can be viewed using free tools like [Paraview](https://www.paraview.org/) and [VisIt](https://wci.llnl.gov/simulation/computer-codes/visit/) to examine the results." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tally_1.100.vtk\r\n" + ] + } + ], + "source": [ + "!ls *.vtk" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flux\n", + "![Unstructured Mesh Flux](./images/umesh_flux.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Heating\n", + "Here is an image of the heating score as viewed in VisIt. Note that no heating is scored in the water-filled channels as expected.\n", + "\n", + "![Unstructured Mesh Heating](./images/umesh_heating.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statepoint Data" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(340144, 1, 2)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sp = openmc.StatePoint(\"statepoint.100.h5\")\n", + "tally = sp.tallies[1]\n", + "tally.mean.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enough information for visualization of results on the unstructured mesh is also provided in the statepoint file. Namely, the mesh element volumes and centroids are available." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.43381086e-04]\n", + " [1.48043747e-04]\n", + " [1.60408339e-04]\n", + " ...\n", + " [7.04197023e-05]\n", + " [7.04197023e-05]\n", + " [7.04197023e-05]]\n", + "[[ 2.88485691 -2.55429784 9.97768184]\n", + " [ 2.87565092 -2.60469781 9.8884092 ]\n", + " [ 2.85832254 -2.65291228 9.97768184]\n", + " ...\n", + " [ 1.46082175 -1.15569203 -3.62914358]\n", + " [ 1.4443143 -1.1321793 -3.65475081]\n", + " [ 1.46884412 -1.15657736 -3.68206543]]\n" + ] + } + ], + "source": [ + "umesh = sp.meshes[1]\n", + "print(umesh.volumes)\n", + "print(umesh.centroids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The combination of these values can provide for an appoxmiate visualization of the unstructured mesh without its explicit representation or use of an additional mesh library." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We hope you've found this example notebook useful. More unstructured mesh features are under development and will be included in additional examples soon.\n", + "\n", + "![Unstructured Mesh w/ Assembly](./images/umesh_w_assembly.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From a15aa6dd709103c8fdd57b1fd15f65871f235e06 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 17 Mar 2020 19:35:30 -0500 Subject: [PATCH 094/205] Adding unstructured mesh with CAD example. --- examples/jupyter/images/manifold-cad.png | Bin 0 -> 99580 bytes examples/jupyter/images/manifold_flux.png | Bin 0 -> 102669 bytes ...d_mesh.ipynb => unstructured_mesh_I.ipynb} | 0 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\"\"\"\n", + " Helper function for retrieving dagmc models\n", + " \"\"\"\n", + " u = urllib.request.urlopen(url)\n", + " \n", + " if u.status != 200:\n", + " raise RuntimeError(\"Failed to download file.\")\n", + " \n", + " # save file as dagmc.h5m\n", + " with open(filename, 'wb') as f:\n", + " f.write(u.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unstructured Mesh Tallies with CAD Geometry in OpenMC\n", + "\n", + "In the first notebook on this topic, we looked at how to set up a tally using an unstructured mesh in OpenMC.\n", + "In this notebook, we will explore using unstructured mesh in conjunction with CAD-based geometry to perform detailed geometry analysis on complex geomerty.\n", + "\n", + "_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled._" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import openmc\n", + "import openmc.lib\n", + "\n", + "assert(openmc.lib._dagmc_enabled())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model we'll be looking at today is a steel piping manifold:\n", + "![CAD Manifold](./images/manifold-cad.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a nice example of a model which would be extremely difficult to model using CSG. To get started, we'll need two files: \n", + " 1. the DAGMC gometry file on which we'll track particles and \n", + " 2. a tetrahedral mesh of the piping structure on which we'll score tallies\n", + " \n", + "To start, let's create the materials we'll need for this problem. The pipes are steel and we'll model the surrounding area as air." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "air = openmc.Material(name='air')\n", + "air.set_density('g/cc', 0.001205)\n", + "air.add_nuclide('N14',0.781557629247)\n", + "air.add_nuclide('N15',0.002873370753)\n", + "air.add_nuclide('O16',0.210668126508)\n", + "air.add_nuclide('O17',7.9873492e-05)\n", + "air.add_nuclide('Ar36',1.53456e-05)\n", + "air.add_nuclide('Ar38',2.8934e-06)\n", + "air.add_nuclide('Ar40',0.004581761)\n", + "\n", + "steel = openmc.Material(name='steel')\n", + "steel.set_density('g/cc', 8.0)\n", + "steel.add_nuclide('Si28',0.0092672382464)\n", + "steel.add_nuclide('Si29',0.00047056391679999997)\n", + "steel.add_nuclide('Si30',0.00031019783679999996)\n", + "steel.add_nuclide('P31',0.00023)\n", + "steel.add_nuclide('S32',0.000218593702)\n", + "steel.add_nuclide('S33',1.721987e-06)\n", + "steel.add_nuclide('S34',9.650777000000001e-06)\n", + "steel.add_nuclide('S36',3.3534e-08)\n", + "steel.add_nuclide('Mn55',0.011014)\n", + "steel.add_nuclide('Fe54',0.03910305)\n", + "steel.add_nuclide('Fe56',0.6138342600000001)\n", + "steel.add_nuclide('Fe57',0.01417611)\n", + "steel.add_nuclide('Fe58',0.0018865800000000001)\n", + "steel.add_nuclide('Ni58',0.08169227999999999)\n", + "steel.add_nuclide('Ni60',0.03146772)\n", + "steel.add_nuclide('Ni61',0.00136788)\n", + "steel.add_nuclide('Ni62',0.0043614000000000005)\n", + "steel.add_nuclide('Ni64',0.00111072)\n", + "steel.add_nuclide('Mo100',0.0024360000000000002)\n", + "steel.add_nuclide('Mo92',0.0036622500000000006)\n", + "steel.add_nuclide('Mo94',0.0022967499999999997)\n", + "steel.add_nuclide('Mo95',0.00396825)\n", + "steel.add_nuclide('Mo96',0.00416825)\n", + "steel.add_nuclide('Mo97',0.0023955)\n", + "steel.add_nuclide('Mo98',0.006073)\n", + "\n", + "materials = openmc.Materials([air, steel])\n", + "materials.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "download(manifold_geom_url)\n", + "download(manifold_mesh_url, 'manifold.h5m')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we'll create a point source at the entrance the single pipe on the low side of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "src_pnt = openmc.stats.Point(xyz=(0.0, 0.0, 0.0))\n", + "src_energy = openmc.stats.Discrete(x=[10.0], p=[1.0])\n", + "\n", + "source = openmc.Source(space=src_pnt, energy=src_energy)\n", + "\n", + "settings = openmc.Settings()\n", + "settings.source = source\n", + "\n", + "settings.run_mode = \"fixed source\"\n", + "settings.batches = 10\n", + "settings.particles = 5000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we'll indicate that we're using a CAD-based geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "settings.dagmc = True\n", + "\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll run a few particles through this geometry to make sure everything is working properly." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", + " Date/Time | 2020-03-17 18:43:06\n", + " OpenMP Threads | 96\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading DAGMC geometry...\n", + "Loading file dagmc.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.001\n", + "Building OBB Tree...\n", + " Reading N14 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", + " Reading N15 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", + " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading O17 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", + " Reading Ar36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " Reading Ar38 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", + " Reading Ar40 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", + " Reading Si28 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", + " Reading Si29 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", + " Reading Si30 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", + " Reading P31 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", + " Reading S32 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", + " Reading S33 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", + " Reading S34 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", + " Reading S36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", + " Reading Mn55 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", + " Reading Fe54 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", + " Reading Ni58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", + " Reading Ni60 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", + " Reading Ni61 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", + " Reading Ni62 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", + " Reading Ni64 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", + " Reading Mo100 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", + " Reading Mo92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", + " Reading Mo94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", + " Reading Mo95 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", + " Reading Mo96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", + " Reading Mo97 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", + " Reading Mo98 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for N15\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Initializing source particles...\n", + "\n", + " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", + "\n", + " Simulating batch 1\n", + " Simulating batch 2\n", + " Simulating batch 3\n", + " Simulating batch 4\n", + " Simulating batch 5\n", + " Simulating batch 6\n", + " Simulating batch 7\n", + " Simulating batch 8\n", + " Simulating batch 9\n", + " Simulating batch 10\n", + " Creating state point statepoint.10.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 1.5505e+01 seconds\n", + " Reading cross sections = 1.5735e+00 seconds\n", + " Total time in simulation = 1.2177e+01 seconds\n", + " Time in transport only = 9.8645e+00 seconds\n", + " Time in active batches = 1.2177e+01 seconds\n", + " Time sampling source = 2.3088e+00 seconds\n", + " Time accumulating tallies = 6.5960e-06 seconds\n", + " Total time for finalization = 3.7800e-07 seconds\n", + " Total time elapsed = 2.7916e+01 seconds\n", + " Calculation Rate (active) = 4106.22 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " Leakage Fraction = 0.78918 +/- 0.00118\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's setup the unstructured mesh tally. We'll do this the same way we did in the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "unstructured_mesh = openmc.UnstructuredMesh(filename=\"manifold.h5m\")\n", + "\n", + "mesh_filter = openmc.MeshFilter(unstructured_mesh)\n", + "\n", + "tally = openmc.Tally()\n", + "tally.filters = [mesh_filter]\n", + "tally.scores = ['flux']\n", + "tally.estimator = 'tracklength'\n", + "\n", + "\n", + "tallies = openmc.Tallies([tally])\n", + "tallies.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "settings.batches = 200\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", + " Date/Time | 2020-03-17 19:02:12\n", + " OpenMP Threads | 96\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading DAGMC geometry...\n", + "Loading file dagmc.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.001\n", + "Building OBB Tree...\n", + " Reading N14 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", + " Reading N15 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", + " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading O17 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", + " Reading Ar36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " Reading Ar38 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", + " Reading Ar40 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", + " Reading Si28 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", + " Reading Si29 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", + " Reading Si30 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", + " Reading P31 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", + " Reading S32 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", + " Reading S33 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", + " Reading S34 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", + " Reading S36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", + " Reading Mn55 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", + " Reading Fe54 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", + " Reading Ni58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", + " Reading Ni60 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", + " Reading Ni61 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", + " Reading Ni62 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", + " Reading Ni64 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", + " Reading Mo100 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", + " Reading Mo92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", + " Reading Mo94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", + " Reading Mo95 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", + " Reading Mo96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", + " Reading Mo97 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", + " Reading Mo98 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for N15\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Initializing source particles...\n", + "\n", + " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", + "\n", + " Simulating batch 1\n", + " Simulating batch 2\n", + " Simulating batch 3\n", + " Simulating batch 4\n", + " Simulating batch 5\n", + " Simulating batch 6\n", + " Simulating batch 7\n", + " Simulating batch 8\n", + " Simulating batch 9\n", + " Simulating batch 10\n", + " Simulating batch 11\n", + " Simulating batch 12\n", + " Simulating batch 13\n", + " Simulating batch 14\n", + " Simulating batch 15\n", + " Simulating batch 16\n", + " Simulating batch 17\n", + " Simulating batch 18\n", + " Simulating batch 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" Simulating batch 192\n", + " Simulating batch 193\n", + " Simulating batch 194\n", + " Simulating batch 195\n", + " Simulating batch 196\n", + " Simulating batch 197\n", + " Simulating batch 198\n", + " Simulating batch 199\n", + " Simulating batch 200\n", + " Creating state point statepoint.200.h5...\n", + " Writing unstructured mesh tally_3.200.vtk...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.6126e+01 seconds\n", + " Reading cross sections = 1.5844e+00 seconds\n", + " Total time in simulation = 2.5434e+02 seconds\n", + " Time in transport only = 2.0793e+02 seconds\n", + " Time in active batches = 2.5434e+02 seconds\n", + " Time sampling source = 4.5688e+01 seconds\n", + " Time accumulating tallies = 9.6858e-02 seconds\n", + " Total time for finalization = 1.2644e-01 seconds\n", + " Total time elapsed = 2.9086e+02 seconds\n", + " Calculation Rate (active) = 3931.70 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " Leakage Fraction = 0.78744 +/- 0.00041\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again we should see that `tally_1.100.vtk` file which we can use to visualize our results in VisIt or another tool of your choice that supports VTK files." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tally_1.100.vtk tally_3.100.vtk tally_3.200.vtk\r\n" + ] + } + ], + "source": [ + "!ls *.vtk" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](./images/manifold_flux.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "S of the elements have no score We indeed see that the flux values are larger near the source at the bottom of the model" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 9d97756b1cf408b153bdce26b195022710b9d219 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 18 Mar 2020 16:56:47 -0500 Subject: [PATCH 095/205] Updates to the unstructured mesh documentation. --- examples/jupyter/images/manifold_flux.png | Bin 102669 -> 61388 bytes examples/jupyter/images/manifold_pnt_cld.png | Bin 0 -> 120957 bytes examples/jupyter/images/umesh_w_assembly.png | Bin 165840 -> 112875 bytes .../jupyter/unstructured-mesh-part-i.ipynb | 1035 ++++++++++++ .../jupyter/unstructured-mesh-part-ii.ipynb | 1394 +++++++++++++++++ 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b/examples/jupyter/unstructured-mesh-part-i.ipynb @@ -0,0 +1,1035 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unstructured Mesh Tallies in OpenMC\n", + "\n", + "In this example we'll look at how to setup and use unstructured mesh tallies in OpenMC. Unstructured meshes are able to provide results over spatial regions of a problem while conforming to a specific geometric features -- something that is often difficult to do using the regular and rectilinear meshes in OpenMC.\n", + "\n", + "Here, we'll apply an unstructured mesh tally to the PWR assembly model from the OpenMC examples.\n", + "\n", + "**_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled._**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "import openmc\n", + "import openmc.lib\n", + "\n", + "assert(openmc.lib._dagmc_enabled())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll need to download the unstructured mesh file used in this notebook. We'll be retrieving those using the function and URLs below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "plt.rcParams[\"figure.figsize\"] = (30,10)\n", + "\n", + "import urllib.request\n", + "\n", + "pin_mesh_url = 'https://tinyurl.com/u9ce9d7' # 1.2 MB\n", + "\n", + "def download(url, filename='dagmc.h5m'):\n", + " \"\"\"\n", + " Helper function for retrieving dagmc models\n", + " \"\"\"\n", + " u = urllib.request.urlopen(url)\n", + " \n", + " if u.status != 200:\n", + " raise RuntimeError(\"Failed to download file.\")\n", + " \n", + " # save file as dagmc.h5m\n", + " with open(filename, 'wb') as f:\n", + " f.write(u.read())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "plt.rcParams[\"figure.figsize\"] = (30,10)\n", + "\n", + "import urllib.request\n", + "\n", + "pin_mesh_url = 'https://tinyurl.com/u9ce9d7' # 1.2 MB\n", + "\n", + "def download(url, filename='dagmc.h5m'):\n", + " \"\"\"\n", + " Helper function for retrieving dagmc models\n", + " \"\"\"\n", + " u = urllib.request.urlopen(url)\n", + " \n", + " if u.status != 200:\n", + " raise RuntimeError(\"Failed to download file.\")\n", + " \n", + " # save file as dagmc.h5m\n", + " with open(filename, 'wb') as f:\n", + " f.write(u.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we'll import that model from the set of OpenMC examples." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = openmc.examples.pwr_assembly()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll make a couple of adjustments to this 2D model as it won't play very well with the 3D mesh we'll be looking at. First, we'll bound the pincell between +/- 10 cm in the Z dimension." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "min_z = openmc.ZPlane(z0=-10.0)\n", + "max_z = openmc.ZPlane(z0=10.0)\n", + "\n", + "z_region = +min_z & -max_z\n", + "\n", + "cells = model.geometry.get_all_cells()\n", + "for cell in cells.values():\n", + " cell.region &= z_region" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The other adjustment we'll make is to remove the reflective boundary conditions on the X and Y boundaries. (This is purely to generate a more interesting flux profile.)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "surfaces = model.geometry.get_all_surfaces()\n", + "# modify the boundary condition of the\n", + "# planar surfaces bounding the assembly\n", + "for surface in surfaces.values():\n", + " if isinstance(surface, openmc.Plane):\n", + " surface.boundary_type = 'vacuum'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a quick look at the model to ensure our changs have been added properly." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "root_univ = model.geometry.root_universe\n", + "\n", + "# axial image\n", + "root_univ.plot(width=(22.0, 22.0),\n", + " pixels=(200, 300),\n", + " basis='xz',\n", + " color_by='material',\n", + " seed=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# radial image\n", + "root_univ.plot(width=(22.0, 22.0),\n", + " pixels=(400, 400),\n", + " basis='xy',\n", + " color_by='material',\n", + " seed=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good! Let's run some particles through the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", + " Date/Time | 2020-03-18 16:09:08\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading Zr90 from 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====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.20444\n", + " 2/1 0.15502\n", + " 3/1 0.19804\n", + " 4/1 0.22159\n", + " 5/1 0.19776\n", + " 6/1 0.20086\n", + " 7/1 0.21896 0.20991 +/- 0.00905\n", + " 8/1 0.23134 0.21706 +/- 0.00885\n", + " 9/1 0.29029 0.23536 +/- 0.01935\n", + " 10/1 0.20094 0.22848 +/- 0.01649\n", + " Creating state point statepoint.10.h5...\n", + " Writing unstructured mesh tally_2.10.vtk...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 2.5507e+02 seconds\n", + " Reading cross sections = 1.3056e+00 seconds\n", + " Total time in simulation = 5.9387e+00 seconds\n", + " Time in transport only = 1.0565e-01 seconds\n", + " Time in inactive batches = 4.8611e-02 seconds\n", + " Time in active batches = 5.8901e+00 seconds\n", + " Time synchronizing fission bank = 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0.04156\n", + " Combined k-effective = 0.20707 +/- 0.01965\n", + " Leakage Fraction = 0.79200 +/- 0.03382\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0.207067889671818+/-0.01965303775428778" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it's time to apply our mesh tally to the problem. We'll be using the tetrahedral mesh \"pins1-4.h5m\" shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": { + "image/png": { + "width": 600 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"./images/pin_mesh.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This mesh was generated using Trelis with radii that match the fuel/coolant channels of the PWR model. These four channels correspond to the highlighted channels of the assembly below.\n", + "\n", + "Two of the channels are coolant and the other two are fuel." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.patches import Rectangle\n", + "from matplotlib import pyplot as plt\n", + "\n", + "pitch = 1.26 # cm\n", + "\n", + "img = root_univ.plot(width=(22.0, 22.0),\n", + " pixels=(600, 600),\n", + " basis='xy',\n", + " color_by='material',\n", + " seed=0)\n", + "\n", + "# highlight channels\n", + "for i in range(0, 4):\n", + " corner = (i * pitch - pitch / 2.0, -i * pitch - pitch / 2.0)\n", + " rect = Rectangle(corner,\n", + " pitch,\n", + " pitch,\n", + " edgecolor='blue',\n", + " fill=False)\n", + " img.axes.add_artist(rect)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Applying an unstructured mesh tally" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use this mesh, we'll create an unstructured mesh instance and apply it to a mesh filter." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "download(pin_mesh_url, \"pins1-4.h5m\")\n", + "umesh = openmc.UnstructuredMesh(filename=\"pins1-4.h5m\")\n", + "mesh_filter = openmc.MeshFilter(umesh)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now apply this filter like any other. For this demonstration we'll score both the flux and heating in these pins." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "tally = openmc.Tally()\n", + "tally.filters = [mesh_filter]\n", + "tally.scores = ['heating', 'flux']\n", + "tally.estimator = 'tracklength'\n", + "model.tallies = (tally,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll run this model with the unstructured mesh tally applied. Notice that the simulation takes some time to start due to some additional data structures used by the unstructured mesh tally. Additionally, the particle rate drops dramatically during the active cycles of this simulation.\n", + "\n", + "Unstructured meshes are useful, but they can be computationally expensive!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", + " Date/Time | 2020-03-18 16:14:17\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U234 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Reading Zr91 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr91.h5\n", + " Reading Zr92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr92.h5\n", + " Reading Zr94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr94.h5\n", + " Reading Zr96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr96.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading B11 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n", + " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Initializing source particles...\n", + "\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + "\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.22539\n", + " 2/1 0.23030\n", + " 3/1 0.23180\n", + " 4/1 0.23343\n", + " 5/1 0.22940\n", + " 6/1 0.22765\n", + " 7/1 0.23238\n", + " 8/1 0.23083\n", + " 9/1 0.23204\n", + " 10/1 0.23189\n", + " 11/1 0.23555\n", + " 12/1 0.23220\n", + " 13/1 0.22907\n", + " 14/1 0.23016\n", + " 15/1 0.23055\n", + " 16/1 0.23062\n", + " 17/1 0.22656\n", + " 18/1 0.23380\n", + " 19/1 0.23268\n", + " 20/1 0.23233\n", + " 21/1 0.23256\n", + " 22/1 0.22965 0.23111 +/- 0.00145\n", + " 23/1 0.22846 0.23022 +/- 0.00121\n", + " 24/1 0.22936 0.23001 +/- 0.00089\n", + " 25/1 0.23054 0.23012 +/- 0.00069\n", + " 26/1 0.22708 0.22961 +/- 0.00076\n", + " 27/1 0.23159 0.22989 +/- 0.00070\n", + " WARNING: No tet found for location between trianle hits\n", + " 28/1 0.23703 0.23078 +/- 0.00108\n", + " 29/1 0.23227 0.23095 +/- 0.00097\n", + " 30/1 0.23114 0.23097 +/- 0.00086\n", + " 31/1 0.23133 0.23100 +/- 0.00078\n", + " 32/1 0.23143 0.23104 +/- 0.00072\n", + " 33/1 0.23125 0.23105 +/- 0.00066\n", + " 34/1 0.23218 0.23113 +/- 0.00061\n", + " 35/1 0.23140 0.23115 +/- 0.00057\n", + " 36/1 0.22911 0.23102 +/- 0.00055\n", + " 37/1 0.23143 0.23105 +/- 0.00052\n", + " 38/1 0.23342 0.23118 +/- 0.00051\n", + " 39/1 0.23186 0.23121 +/- 0.00048\n", + " 40/1 0.23029 0.23117 +/- 0.00046\n", + " 41/1 0.23132 0.23118 +/- 0.00043\n", + " 42/1 0.23167 0.23120 +/- 0.00042\n", + " 43/1 0.23244 0.23125 +/- 0.00040\n", + " 44/1 0.23101 0.23124 +/- 0.00038\n", + " 45/1 0.23225 0.23128 +/- 0.00037\n", + " 46/1 0.22945 0.23121 +/- 0.00036\n", + " 47/1 0.22978 0.23116 +/- 0.00035\n", + " 48/1 0.23335 0.23124 +/- 0.00035\n", + " 49/1 0.23298 0.23130 +/- 0.00034\n", + " 50/1 0.23095 0.23129 +/- 0.00033\n", + " 51/1 0.23724 0.23148 +/- 0.00037\n", + " 52/1 0.22973 0.23142 +/- 0.00037\n", + " 53/1 0.23066 0.23140 +/- 0.00035\n", + " 54/1 0.22838 0.23131 +/- 0.00036\n", + " 55/1 0.23262 0.23135 +/- 0.00035\n", + " 56/1 0.23593 0.23148 +/- 0.00036\n", + " 57/1 0.23358 0.23153 +/- 0.00036\n", + " 58/1 0.23050 0.23151 +/- 0.00035\n", + " 59/1 0.23273 0.23154 +/- 0.00034\n", + " 60/1 0.22842 0.23146 +/- 0.00034\n", + " 61/1 0.23344 0.23151 +/- 0.00033\n", + " 62/1 0.23333 0.23155 +/- 0.00033\n", + " 63/1 0.22987 0.23151 +/- 0.00032\n", + " 64/1 0.23117 0.23150 +/- 0.00032\n", + " 65/1 0.23197 0.23151 +/- 0.00031\n", + " 66/1 0.23379 0.23156 +/- 0.00031\n", + " 67/1 0.23461 0.23163 +/- 0.00031\n", + " 68/1 0.23109 0.23162 +/- 0.00030\n", + " 69/1 0.22916 0.23157 +/- 0.00030\n", + " 70/1 0.23008 0.23154 +/- 0.00029\n", + " 71/1 0.23157 0.23154 +/- 0.00029\n", + " 72/1 0.23126 0.23153 +/- 0.00028\n", + " 73/1 0.23377 0.23157 +/- 0.00028\n", + " 74/1 0.23105 0.23157 +/- 0.00028\n", + " 75/1 0.23654 0.23166 +/- 0.00029\n", + " 76/1 0.23198 0.23166 +/- 0.00028\n", + " 77/1 0.23390 0.23170 +/- 0.00028\n", + " 78/1 0.23455 0.23175 +/- 0.00028\n", + " 79/1 0.23245 0.23176 +/- 0.00027\n", + " 80/1 0.23121 0.23175 +/- 0.00027\n", + " 81/1 0.23183 0.23175 +/- 0.00026\n", + " 82/1 0.23496 0.23181 +/- 0.00027\n", + " 83/1 0.22763 0.23174 +/- 0.00027\n", + " 84/1 0.23184 0.23174 +/- 0.00027\n", + " 85/1 0.23074 0.23173 +/- 0.00026\n", + " 86/1 0.23178 0.23173 +/- 0.00026\n", + " 87/1 0.23135 0.23172 +/- 0.00025\n", + " 88/1 0.23117 0.23171 +/- 0.00025\n", + " 89/1 0.22815 0.23166 +/- 0.00025\n", + " 90/1 0.22852 0.23162 +/- 0.00025\n", + " 91/1 0.22910 0.23158 +/- 0.00025\n", + " 92/1 0.23143 0.23158 +/- 0.00025\n", + " 93/1 0.23097 0.23157 +/- 0.00024\n", + " 94/1 0.23348 0.23160 +/- 0.00024\n", + " 95/1 0.23068 0.23158 +/- 0.00024\n", + " 96/1 0.23089 0.23157 +/- 0.00024\n", + " 97/1 0.23373 0.23160 +/- 0.00024\n", + " 98/1 0.23336 0.23163 +/- 0.00023\n", + " 99/1 0.23084 0.23162 +/- 0.00023\n", + " 100/1 0.23116 0.23161 +/- 0.00023\n", + " Creating state point statepoint.100.h5...\n", + " Writing unstructured mesh tally_1.100.vtk...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 2.3659e+02 seconds\n", + " Reading cross sections = 1.2481e+00 seconds\n", + " Total time in simulation = 1.2827e+03 seconds\n", + " Time in transport only = 1.2755e+03 seconds\n", + " Time in inactive batches = 2.1605e+02 seconds\n", + " Time in active batches = 1.0666e+03 seconds\n", + " Time synchronizing fission bank = 1.0573e+00 seconds\n", + " Sampling source sites = 9.7641e-01 seconds\n", + " SEND/RECV source sites = 8.0771e-02 seconds\n", + " Time accumulating tallies = 7.3372e-01 seconds\n", + " Total time for finalization = 2.2242e+00 seconds\n", + " Total time elapsed = 1.5222e+03 seconds\n", + " Calculation Rate (inactive) = 9257.18 particles/second\n", + " Calculation Rate (active) = 7500.31 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " k-effective (Collision) = 0.23129 +/- 0.00020\n", + " k-effective (Track-length) = 0.23161 +/- 0.00023\n", + " k-effective (Absorption) = 0.23099 +/- 0.00019\n", + " Combined k-effective = 0.23119 +/- 0.00017\n", + " Leakage Fraction = 0.79477 +/- 0.00014\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0.2311872124292178+/-0.00017018932313701233" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.settings.particles = 100_000\n", + "model.settings.inactive = 20\n", + "model.settings.batches = 100\n", + "model.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the end of the simulation, we see the statepoint file along with a file named \"tally_1.100.vtk\". This file contains the results of the unstructured mesh tally with convenient labels for the scores applied. In our case the following scores will be present in the VTK:\n", + "\n", + " - flux_total_value\n", + " - flux_total_std_dev\n", + " - heating_total_value\n", + " - heading_total_std_dev\n", + " \n", + " Where \"total\" represents \n", + " \n", + "\n", + "Currently, an unstructured VTK file will only be generated for tallies if the unstructured mesh is is the only filter applied to that tally. All results for the unstructured mesh tally are present in the statepoint file regardless of the number of filters applied, however.\n", + "\n", + "These files can be viewed using free tools like [Paraview](https://www.paraview.org/) and [VisIt](https://wci.llnl.gov/simulation/computer-codes/visit/) to examine the results." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tally_1.100.vtk tally_2.100.vtk tally_2.10.vtk\r\n" + ] + } + ], + "source": [ + "!ls *.vtk" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flux" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": { + "image/png": { + "width": 600 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"./images/umesh_flux.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Heating\n", + "Here is an image of the heating score as viewed in VisIt. Note that no heating is scored in the water-filled channels as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": { + "image/png": { + "width": 600 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"./images/umesh_heating.png\", width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statepoint Data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "with openmc.StatePoint(\"statepoint.100.h5\") as sp:\n", + " tally = sp.tallies[1]\n", + " umesh = sp.meshes[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enough information for visualization of results on the unstructured mesh is also provided in the statepoint file. Namely, the mesh element volumes and centroids are available." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.43381086e-04]\n", + " [1.48043747e-04]\n", + " [1.60408339e-04]\n", + " ...\n", + " [7.04197023e-05]\n", + " [7.04197023e-05]\n", + " [7.04197023e-05]]\n", + "[[ 2.88485691 -2.55429784 9.97768184]\n", + " [ 2.87565092 -2.60469781 9.8884092 ]\n", + " [ 2.85832254 -2.65291228 9.97768184]\n", + " ...\n", + " [ 1.46082175 -1.15569203 -3.62914358]\n", + " [ 1.4443143 -1.1321793 -3.65475081]\n", + " [ 1.46884412 -1.15657736 -3.68206543]]\n" + ] + } + ], + "source": [ + "print(umesh.volumes)\n", + "print(umesh.centroids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The combination of these values can provide for an appoxmiate visualization of the unstructured mesh without its explicit representation or use of an additional mesh library." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We hope you've found this example notebook useful. More unstructured mesh features are under development and will be included in additional examples soon." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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JuwK7hqQLSp9vmYVb6Ug6XVpJxLQmKfbU6HtD6ysEIoCoKSGKo1rtgqLPMMcOGwCGqZ1M9m90Ocyjzv6tZRXYdbjf7wvHor4c0tN6GUNcYmHRbYoscul+YRPpuUCibBrtBKeFZgbiMDLzYo6jnZaal2zaBRWeftYNFW44l2JLoRcqg579yxj6t0eVDzvDHAtsABiGAXTpIP1CmEZdDaen/nm0yatp9p+OnJeynJrV9lZCFxcQZQUyVZL6mYYqJjFlpsVOjTlG6jhkGc15NaHV0QaUIQwGXj6xyz4yqNAQwxwLbAAYpl7m9ehP7fSVds64jPX1dcUSIGtZ1LYitmyGUmCaQ2eYV/YvkcFaZTtZEJRB67OFYwFBX76lPYfz5E2LoOKKlzyZM6q6IHP01HTU0rI1NcEwRw8bAIapkcLsv0tbbpuGkjmp0+IytMp2stApV5n+rxVF7muao2eoT1mBhTLdsYB8Agv6hJtYJGr9zOvSalKUoROXbxlpSoifF1PZPPmGqmsyDHNGYAPAMMeArCzLicx0LmJdVpbB2rQsk9nY/RJwGZS0qWv1wETU7xzqUNZ6Ie3xKkNdYyFmooWILqlHw6LbFHehzc6rYCSeCdOeG0biEq2sQIor7k/tV7vzBIRzxk42jWyiuhTDMNawAWCYutAu/klf/+Z1LRQ6iml7i4a63a50LOKVwvzDNPMw9QDE1L9r9TNP2puVraHYIdPeCupYXiVRD4g2TVRA6bbFaFDKC3YJutplSUzFZZGBcmKnxdVNmJoWvPzJ0sZTepun8KsjP+thKitRnJDWmgxzumEDwDCnkzoue1JzXuJpncwlPONhpAmhQPcARmmHDNPKwsQOGclKiD030pRQ8l07ZYE6R6+irKCKstYDmGb/kj75SVmm4jJeK649i9J0U3MB2g3pPbfwLaDpS6Ml/klUFhT6lvyZYKTJMAsCGwCGqQXt9D+TIX8RrXJZVUxnEnOXQvJbZZIPuzxDsE64LcRoeZhwTZQkKQ3FWoh36ZoSbbIL23oIBWvlmrJ/CtXFFSm+afYv6erKd4Wps5Z0VUTrW2AiTlcWKPTzL0pxijLDLAhsABhm/hhl//JKyRePo6H6OKcVqqsJKDqUmDyZrcocSz5SjZG7oJgKQZdcvZEQTZeFchqFeBVlSrHFmlrF66OvW81FPOvyaJUFdvrEkgvDLAhsABjmSJHpPnIXmH5tN/DZqTHHQk0HS8rOV1+odV9er5UPMKJQszAbo/sKgWmmTv88miqnofgWRUAZxMHRZsNVsBgNSU296utWc8kwXUgBRHGGWQTYADDMnCmc/idOKaXr1DC5BKr1paz4J10WylI+McMgks5y5qXJHBl1HLK05rz0pbtAyafG7mOCl+MVn3SLkosM036BSIifza5uJU8Z2i8oa98CQuGiivgRYDGeDHP0sAFgmNrpGd59SF+uaqRsOjulNS3irbS7IF72KMrWeZhAnR/YaWZgx3LSSZuBeZE3GIUBpuS3KvMthcEKMl3VfuFQwjIx9O8omKykt6u3EL8D7cQFxP4zzPHCBoBh5klm+l+b6arRXoxNlWW8Qtmuz33a4lqQu52J0coKTDtPlJWU1UOq1FjSKHyLtSZzXMz9kKUF5ysu1crOQDuzJLdKyxZ+PE0/O+kw4uedPknRtV3QRXQXDLMIOF/5ylcK3zD9YSCOV8Pxak5H/Hyzf/XliphG5ymUTe/vfJUlVUZDe+W2Ftcqw0qcIisocxSF0GUlWn0LzTS91FIQLokwR0Ph93PhqW5dGBEQP/vqD9GdO3cA3L59W75id32hw/FqOF7AFQCGqYuK00jqfNdUk0gV5b7hEiMj6sj+BXPP/kEeClNxepkFZPF0AEVWkEm28q30qy3iUlRCJBayhci25iXIHAvy8M3xOKbrHuraCMOcONgAMMx8KLz318IDaLN/+S5dU0BRhu3i1/qm/9VUV1bk01XsEHTKsOo2xQNYdFvGa08PkDufCaAogyyehqiMXA6XaUX8s4pvEWjdi7Uyc4xYH7W3335bFAEYZqFgA8Aw9ZK5bOQzG7sitYwkpkrE7N8OrbiFERIQlcveVbNOvt3QFMr0v+lQSNTlBevRwGGX5mstQDMtAiN9o9oF3VdU8S0CSisgK6fJFF7SaD8pWtIHKL07VTQZhllY2AAwzBzQ/vKXvIjKhLX6ZVUoaDNgo4YymupUpnrOocBoItbaWtSHOo2GVZ9R85irqWgt1B7AKPXPoy2JQHcy59H2WWKqL5VBO5QUffGuNIcUWYnUL2ziaAoj1rIZMq3MS5ZhTh9sABimKl/4whfov/uLGq5JcxdEygkgt0iakhLlydsVRTHESL97WF6ALkkyLbbIGHpuR0m/tJ6tDIo4THqbRitu3e1FwG5M5FYK3wIr8b6yjCOx0K+7MCJ6rlUWGOlrz8BCFN8neHmcTZUlZae9tSDDLAJsABiG0ZC5zlW87MnNK+qkIUoRw9JkNik0LRZJRjqMkh7R9S1Mi0Qtbl24ICZ2FsoUrLtdN0QPYNdtijg9O88gNtEeUFN9I+sCsn4mgKIsUTchX6d/PBlmcWADwDCVMJ3+Z2qlpgtw2rRk6iGo0KjYsHBmvV9h3QXdXRjpi5ie4b0cxOy/W/NdIqayAq14rVSvt2iLdXbDIlCLE7PzPH3ayiuLYyrjtQfUtPPy06rtNsMsDmwAGMYebfZfVp4+3sSCqUIdRy2vWb0V6S7kK5mE0q6JzFaFJZGyYDV03yKgf4hkDEVWQtG39i0g3IVSRVygTdBrwjSBzqAtX1TpPLF8YdF5usFgmEWADQDDzB/t9U+81TdfOgKduIVgHsX1tYosc7zM8dhJqe5hWQBz0qeIUGIyaH1LGkrqL7FI0+n6FuKC43UXgirix0gVdyGY48eBYeqDDQDDWJKf/tfm/Xlk7VgXSBXPBBAvQpkrFmXikKicRl0PEVjISqr0janC3Ad87oKCQt+Cys2JzdOaisKIaVsyU4fu42/6OaIrZ1jX3QTcPVzVZuEBKF+GUr8OahVnmMWBDQDDzAdigm5Hz2rBK3FNqtF8VSZz0kIZlvy7xM6gRL8w/aJrplHvqZ0msyDUcfjSZmBeyA+dOjm2aDSvrLAu6U20ZMKIX19GtRHxB1FZQvwqsLAuqGDzGOboYQPAMDZkpv8paa4aRaZukf0LtB5Apv5JkhQGlCEnyRTXOetuUygrJpSRDqNcm+XeaYPtZpEzvqKOkghbl1NGfYcsrZxx+NUblY4of0Lm659GzXVNKhh0a4GXw7TKEqMmGObYYQPAMMbkF/8UXt6IEGekLKBckKybVu+y9WhoTQtsrUVfd2ehhFI2kciEibgVpfPVSyLQJS5yNOjKksLscI4pI3Ps1HEQ85rVW5EKGd9SFmZEZqvCDxRP+TMnFzYADHOcaFNS60xaoE6jy97Sop7+71Wuh9TUba0HEEm8xbWc6AAp2X8GiiOC1ZgTlSXyoBfGZ16ky0q0uzCXZEt9mKooM8dOHYcv4zGg+/ZjmJMCGwCGMUPx6E+jYjRol5B0jFZZQqkqEHPWDJRN7JTp0Mchg9ZuVUSR9Vqk/nS0qXMZRA9gkfF0UzOy2g2J/c8EaGUl6bNRbTCq1DHKtq2iySwgfByZ0wEbAIaZD8SrAjEsQ5f8zD6jdR0yUydmxkb5HKW3GSi+BVbKIIyMNg9WQBwZO7SFiypoxasMi9YKElP/DCJe9JxiXYj6MoxiiiTa/D79uja4EPVeWK+hz8C1EYY5U7ABYBgDtL/8hdqulEI2PbGaecsOse3Tp08LX8fLKQuxIdlJYqauTc3zEJUFRH2RrWrD8hCzf6M+p9Em6CDnuHko4tZQNq+p56gwMlpTJOiZLxiTHw3KmUbsf8a3gHAqSohNpKGLS9KnQWZzxVummO47w5xx2AAwDBVK9n80zP0ipxC0bkvhWPJhdNLuAroVHZmttHRTM9aUTYyCLaBYly65OpRHq9/VTeEvOBZjIhAbKkam7mGxSM2NlkjJ/hObEGEWtRG1R0q/ZZfBZwYqszsWXwKFzPEbjGEWBDYADMPUztyvkVKwMEOt0pw0GGWXfFOTIJB2qEe7GUCbmqex8wDEJuT+aiMzaLcSAabdNqJWcWu0tsoi+8+jyNSt9eUm2pOB7hMkYliIGxINTPrdPmHNWBriKKXrRXRxhjl22AAwDInFmf5nMtRx0VVrVmmx2+3KzRUeo/B1BdIDiH+WZS3EpL8QbUJvGqnNg8ug51va7K0M7RIg685TqFVcYD0yAnWabt15ogcguug8RgaG3oRR7YVhFgQ2AAyj5wtf+ILiXcVc1LwK0JhrssgsCPM9drWaFvGHSM7UwTBpq1tzYUT8QVGW0N0FdHlwGcStqpQvKPq1Yt0BrfmhnIEK+oQbPIhnY54+7bFaDLMIsAFgGBL56X9KgfgICtACumwaeSlNkiTd0Nx9S0URxpSaBrwmWQDdbjdtM8piCl9XkNlEXRiBYRNd2nS1RO4XJb5b87IukL9bCtE2QR+WDBXz++pU7AB7AOakwAaAYTQULv6xniKiXBiMUv90mFZZkC9ZJEmSDpCv213J8v0vdBemshkU1+mKyszxMvfDJwULZ5erNKc1LWlMG0rHU74NiNm/QIZRlCXauXOBqTXKoN6q2+3mn1o2RwpPkup86Utfun37ti6KYY4ONgAMY4ZRdp5BW32uIk7B1LeYugtK/+W74g96VYQiDquHIabJXPvTXki+aKcsqdI9piI1DXtNsjhUFv+fr+nNbDL38ggMT3V6sIjRfhXkIRoYVFt/BdpeMMzx4nzlK18pfCMzI6iF49VwvJqFjc9M/xNz0DK0U3SmCXoatbi65+rxoWQA1j3XjonAQp+ojKKSiBq6skTbBGWQKZTliBVlmbPAfMsjErVvke/S25LfV/QvZIvPl8V3DspnNO7cuSMrANbXIyIcr4bjBVwBYJhS8ot/qleH68ihQUtJrcUFamthDaUqUqXn6nUI9AQiTf9wmS90Yw5yE6bFFonWWmQwEpekj3K6ocyxsxOXqA8WUzc1Db5atkqjYlupcLwVEoFFQwxzLLABYBhjLKrDxIUuFsoSbZprB7Fobt1t9chU6bnWWsgwXUgBfcIt3cTUP0O/36eIw1A/E6YVB81aZMyARQafaaVWd1H4ekVZZnGY+6FMCxbO/sy9RYY5MtgAMEwxZQ/+J04LCSgT8xWhNNG1eqKIhCKuCKiIXZ8pVKwtQDljXau4xKKJPu0pJUbuQiCDtd0GTT/vLkATlxBbsVgfIsmc/LKttHuxkGUWED6OzGmCDQDD2CCuBL3UTy8pwogQrUWVxEJsS8nqKL6iCnR9a99CmUS3ht7/OqDktQq0tZEq+mKrmtwFxbqAVrtIYyQuUe9F+nU54HRxgXpHqnwV5OEKCcOcKdgAMEwBxN/9rePSqNXUBhQit+oV/V5sPh00aqXb7co6gDblMppt7drWLijZvwywEAeh/xbdFhD1rVEPDvE4qtFm0nb6lPKFdf/pFQyLJig9lxANTP5dirigrHCRx9q9CNQVEjtNhmGqwwaAYbJ8/vOfp2T/dVPTpTEtK/82fapAIfkOy4lVRYwW6QGgTFOMfIU1R5a1qJuw9kUg3NTRrbamS6vfq7w4SktFfUWaTkzNCyF6AAt3ARP3AvMm0pEUfQGlQlLRD8gm5j6pkUfxoaiozDDHAhsAhmHmSaHBqIhWRxtQhtgwk/KmcwjrBCUdScm0TPXtILYiAijdzkDUt7MuR4a2/9ad79e5+AqHG9ZhMGDiMSyaoIsLMk3k2yprnagvKPxaUGAkzjDHCxsAhnkJ+vR/z/yWRKYi9Y12mW+p2KI0GMilLJlE0LQhusEQ2TYxNU+jrbpkIDbRtS1f0Is8FuICbfniRKAtMtgNjqSmJVgg2wC7Jvq0CozAqIlMGEWfYY4dNgAMQ6Ww+p/+m5gAaSmsNVeXZY6XtBmYF1KzbH2CdRwLWWgAACAASURBVKPStGRy4kyuQ0/K05h6AKNWurQ1Y3kon1/TnmfQGgzFoaRDGaVasRscQa1FEqIH6NmuUiPqM8wiwAaAYV5QOP1Pv96ImD7tIe6SQl+hgK6cRp1ViHsA7JSZRaCmY5eXla9UrIAVWpf5lkeImbqRu4CtB6C3IgKMxAVadyEx7bwpdetXQWswKsIegDkpsAFgmBkVs/80xEI2TPStq8yUJtI3AdOVBXkDU3h9NZXNw4WRhWJeI5/WmZemoNBjZALsErX0JupPFj3vTyODKd8MAkr5QmAhLiDui2LAKWiz8263+/TpU3XMImB3ajHMkcEGgGGKoeTNatTf/tX11VhUsY3mrhT9z79Il81gVB6x0IeyNmInyCwaiuNY5RBLg4Ea3GnhtuJTMBeDTfcwMHEXAjuPQTQYAusig9ZgCKz1GeakwAaAYYDc9H+VGSyJIuW1yM4l2jS9Sue14gJT95KOpFzd6frpsoO225LCZSd50qsFiMoZKMfCTjlDvqG5yDJEahrtjOx8W5Fqcs4+/VlIJ8oW7dINhsVyL7p4BqKT6VYoYhCbYJhjhw0Aw+Dzn/985hVxATC6tKQxugfAFMoFpkrPoRSnp+Z5KO7CWp8iLjBqwtS6wLBqISGKS7St9A3vRVEw9+ltZtEQR3Pux1TKluXTVVos3Law/Cj/pjdn4TEqThYwzBHDBoBhACCz+r/i9I8aa3GJOoEmXq4KSc9510Qd2b9A23kxOHbFfbGJ9tJuMf7SuoCgD5NR6vf7ckwoygKttchDF0+j/SDYyTILSH2HMqMs/0n/QJVRtm2/ZCFWlbYY5uhhA8CcdQrv/a11+qdr9RQREKbn5bumygKtPj31tECbDmohWpcq/VdUGKr0X2tdBBbuAqn91Z6ZMDnE+RiKPkwMhoV7keRbKRxhC+U8XCFZQOY4/mUeg2FONGwAGKYUmamLf2byFbvKsiCtrE2DLHIgU3dBbMLauoDQRLfasiuBdhes0fYfhEOpQOsBqhgM0CoYFcdf4Y7SMUZNGLkXQZmHUbRLF5dobUwV9yJRHPQqsgzDMGwAmDON9nd/01fZ+V5x6Wr0SEE6Xp1syYyTMv0sMLIuEmIm1LUtX6wTFrtXTKBB6L+dNaJQvfPqIaJP/BfSJ9xvYJr6p6G4F1g1kY7X6sNwFZmpuETrLvCyYzQST0M8r6z101Rfk8MwzBxhA8CcXbTZ/9FQ0xVRK5sOSP8OgBaxocIGZOyEtidpTD0A0VqgQvmC3oQ12uxZYNrzI4ZSAaiCWp+YyxbSp91BLg2G0YEwsgGU1F+SidGKS4xaWTe5QSVN+ojIhsQfVb4iyig8AeaizDCnEjYADHOaqe/6J5Xn3kS325UeQ+0uTJtOxxNTH5BbqWIwtE1Yiwu0FZ5uheceSrR7UTd2gyPo178ES3sUrOswRAMjMK2TWNgM7Y4UvkVRzqB1Mplht2hCoDj6dM0vfelLt2/f1kUxzBHBBoA5oyzI9D+jQDqB+aLV1AaUkdlQkQBZLN4wdS8CIw9jurJLQDEwIn+qz8BU1AehiaPBuv9Eg2GtD1oTIGT/eYyKJAL6vsgwukcSFFYw8vTnsRaLYY4eNgAMU4x2wo+/60809R0+qSzrGIoYU9QeI5+f0RuSvaXbALq7qFJhoLQi9e08AMXDVNmFI8MowbVD3YRF9i8h1jF6vV6SJBZNGNkMbW0hTT5Gq88wxw4bAOYsopj+N/reh9UXfbqJshk1C9kyZHNz1GTo1DHsGY8xR/KC6Q9CldUU6Xjth8uiIesEnZL9o1qRgTJxLrDTl1B2xBriXlTpv7bIYHeI02ibQAUbIzYptDFvv/32nTt3SrZjmGOADQBz5ijM/k3zfpDnqySFTRQ2t3541x3Mr+j5DTMK1sppeoc3Bc5FjVlAavIYQk2drFu3mBcv+zjbGQwYVkgElAqGoFvhTg9tUiuwEzfiCJo4AqrsAsVjMMyxwwaAYQyWk2agewCjJuQ0ki5wRjoL1/ZEBsgkXhn+gnxSlXcyc1kLq53kqyLOLAi1HsSMe8mfUdatZ2wA5roKC4Z1Ehh+4qobDG0rVZoAoRqjto4LBeW6wDDHCBsA5myRn/6vfjlRf8sbpf5p5FZafW1MRei7kAmj90o7ZStZt30oIXLVj8JDbyGrgCski8DcBz8tOHdxHGrKZDf/oTDK+9Pk4ymfOJg0ZO0BtNl/Ggt9AXFixVqfYU4KbACYM8TnP//5zCtl11cjFDM9Fd2F9lpVZZJJXKe1CvTsP41RbURuoo4UyFIDRVxS6BkKN6+Ssiv2Ze4VksxUaBVBZmGRTkAXaIYU7KVKGWUxFqQNjPpzbbcWK/27JcTvDQHRzFh7GAGxFYY5XtgAMGeL/Op/u694AWXKqoq+moruQqDo/1zckRptflCGkcHodrvaMImIpFgjCX2gqldIcCiSkerP6XnnIJxXVcSZhaLuQyk+eoozqkoHMtuW1UnSHw16c3QPI7FohWGOETYAzFmh8N5f+U1N/IqXaOfmJVWmkWq9kGjzvIr0dXfCVeyAVh8VKiRdcnkE5iePRKsvwyhN5GMo4gJ5LP5g6P3tj30YABw4YZJ4DoB/9nv3P/eZm+LvZLw3lyKJAgtZBVV6y8yFWkdeipe1UrF1os3IRzLMgsMGgGGA1FyRLtCsvNs1LyXTLyfEJLUQ4lb0budRG5iKqTMIHoy4j9YQ8/JCKO4FFUaJXiEB0Ov1vJXlbrf75bsfAHDc5J/+3nuf+5Gbv/yND37iB1/13OTv/MitGHDi5Nf/zfuf+/TN9ZurznDX6Nyz2xGieBqZ7ufb6qdWYVkol8EG49STPrJ8lJlTAxsA5kygePC//ELvlS+HzUQaIbfSZj/01L8ilFlYHHZD2+1CtNmthTXKU/dA1YrWA9glzUaIDD5prV680I3HyVvff8ONEkzxd374tTjG8soUQBQ7X7770EmSH//BG41+6IZJEgBLK7+zsfO3Lo8oHsDUKaWDteICRd6foX+4gMqiwpbO9XuHd2JkRNIfLlP9NIoPaRVZhmEYARsA5vTz1ltv6UKAmi+rIlGY+0VdbEWcgyxLWRSYegCL6VULD0BphWhyytBuLgIsOi/ReqSKEA0GmitX1lcAJK7jAF/+k4eN/RBAGLif/8wtAIjRbEXhvuMA8TXv//qD9z73mZtwnS/+4Ku9974LZaVFO4xqxNhqTyc7pyTjtfoC+fER/yzbKv16ZhMi2t2RR9ZUOY/6AFXXT2M3GgzD1AQbAOZMUDb9f8TUdPErm4PMvG7XutyqLCOxLlxIR0RMo40a6tqujyK6KbWd06JN4KqIm+E4iQNHPFUlwY//wA0AjgvxnJVf/aMHses0W/CXk99877uA757DP/v2e3HsAPh7f/1TAF5//XUAX/3qVwvlKUdWjfogatNlBcSFUtaZa/fQn1O2lUecsi+ZGIq+IH1eURoS0PXTZD5KUiTdBzvlMupTZpjTBxsA5pTz1ltvLUj2f2TM/cqXvn4T82MiWpH0NLY2OEO6w9ptLfara7uKSZv9o36DIRHpaQI4h684LgA4Dn7ljx7AQWMcJmNMmr7nJe3OFMDeBwGQYFVuAQCf+cxnkOPevXv379/Pv07k1q1b4g/hMQq5d+/e7/zO75S9q4VYJ6GMZBniUFI8humJJO9qIHayV2EtFgj6AtmZsvjM68TOF6LwM3JwxD/t9CWZ7weLrwuGWTTYADAMY0BNFzxirmBB5oKtDjMls5U6u6In5YJaDQYO9cPh3j/91vBzn74JAFEC1xFz/7/xW++sAABGy8G05btRMtrzwtA5d36cLLvRo2cH/af46x8rlz86pE+w49atW2qDAaUDKSt9mGJ6lCVaDyPQfgQUEEslsMrmLVJqerUkH0DRT5PxZnmbkR58U3GGOUbYADCnGeL0v+LSyF/op4Naj6MUV5gNuw4QDYb1NGe32330tP/Dl/yNZ/2vbuwD+LGP3BIOYHStIWImY8+NEgBL56YAnu9fQRMNd++gXPZMUVj6yHDv3j21x+h2u3alkvRW2gRdmy5rUTdhkf1LuoclL+1emNYxBOlNKD08So/BMMcCGwDm1KLI/i0K7hbf6ZlEMNOcxUNImBPB3A9rmcGYS0Pdbtdrd7zA/7cj13G9ja3n3evXo/Hgs5+6+atffyhiVt78IQBBfwPAJNqJnRCAG7QUsszRIKofxLVSdh5DkG+isPRR5ZxU+GdJlTqGQHwPaz1GOpiOqc1gmGOEDQBzhrD4Wk+vsiV+oRNb6ff7/WpP81Csf0WFWeE88804mYrM+ygkSNxoEne7Xcf1APSePIbr/b+//dvn/6O3ATjb+yEQDnbD5Wb4vA+g1d/du7iKyqkYc6LJlz7UhQ4i2moJKpsZCvRrRB7ioiyGOV7YADCnk7Lpf7uvdaMlsEZNSGWQEztiE1UK02UeRv4zc3mjKxfCS7COkwRIItf34boJEsd1u90uXC8IgmQ6cA9iAP5g5HQaAJxBx5mEAMZbT1qNPY0yw9RGlRs/tHd9aFdkUeyH0bc6wxw9bACYM4HFmp882qqxnT59usiuCbo+TMoX8m+iNcpgekRM9UFbLWAhe2qJp/CCRisBMB3GfiO5ciN69F5jeYjzb24+erAqA0frq8HWCPtPyrUYxgYxwb/4aO1H4aKsO3fulIQzzDHABoA5hWSm/ymJoBqRQFukuUQoFQa77F9A0YdtE0RxAdFgSOQSLF3gS5i6C1g5gcx5VbYKy0L5aEm8AK4HAFGC8TBptmfP93z0XkMGvfGJ4cO/DJtXgsE7e2gBwGR3Wbw1l4Ufhdy/f1+baTFHSU0H+rTCw8UsMmwAmNNGfvFPt9u1S23T1JE6S7SZYkV9gVq/7C06Wg9gkZcL+oT79gSmAyUNhrbzaYg7kl7fBVr/8ygs03yeP3j4NP/+Ri+MmwBcz/vd3/1dwHnlRgxg788m7ZUAQBx7rhsBWGn1Vq7h2VZnMp02gqBUGQBw69YtynoJhmEY5ihhA8CcFSyepw5Cao7DFM1OH+TpbWt9kJuw1heZrtZgWOuD5gGsbYzsmDaNtqtgwNBjpHdE0VD+LaK+pNfrhUmy8Z3tdsdPvCCMm54XAx6AJEHvQdK94QB49PuN8OJ45bz75H6I88vJdLnh7ycrS+KmYYZhGObEwQaAOVWU3fvbtfpNJeKTOrsVfrSVYjAERj1Po23CuvNHidbDmM79F6JdhQXbA9En34lhtyN0DyOQ+xLGSdBuAIhj1/Ni8e5SezrcC1b2nOHeNPlQB8BKMH1p+3A02njsOC/9GLCCf723v78XAOh0wt2dhusCQBQ5npe4B9G//yY/UZSZwctmGOZoYAPAnB7eeustxbsiMSpL1tMztel4I8S2lOzNtBUZRhFPo83+xbtPnz5VBKihNAHznufRNlEF7V5Uyf4l9RkMibbOUNjKNPRcH0kYOb539+7dSxebXidybsySfj/em4zdzUej5pJ3bmnDd8KtBzi37v73t//n/+Ef/GRBG4e8/vrrf/8f/8a04QHN5bUpgChygkYMYDzylnYnAKKu+1tPRyL+R6+wE7DneFPnk3L/LsMwAjYAzKmC8ru/+fRIFAeq5JdyWyFFiTRFFjHEP9VpojadzWBdIRF/aBsSAUbiEsq+VFyFRaSiOL0IUDeZHUkSIELkOp6bABAJOgBRGeief/bdf3MRgNt0gbF4q7XS3IzXltcvopwv/U//fNr1nTWn7UVxjOnUDaeetx8BSbTs+X7iXHdGWy52AcCLEgC/+XyEc86/d7WpkGWYKvBt5QwjYAPAnBIUv/urRZu/0pmjVB6iODEsQ60GQ2CRoBMbKivsUKDbGIv+S4g7Uh1thSGLAwCJCweIYqfhAsBgFCZJa/dZuHrJB/DhT25+5xttucWzp16nCQDD3oMCQQDAf/1//CtcbCBEGDqdpdB1EUWO58fB5QRAgDCOnTh2GueS5v2Zqdi53AkmEbYTXC1TZc40XGRgmDnCBoBhTiR1pJJETWJYBrrBoGfkadL69DTdNC+v4gGIrVg3oS0vdEsehxVHjuPC9xPxzyhMkLjNtrNy0fvoa3viMrH/+hhA7/1wMu0AQwCXX3F2v/vCFWQ1JwDQGkwbwACNVjt0nCTaccZw3VU0GpHnJQBGQ3/6SjsaO4kDAJMm31VcC5w6pznetVIMsyCwAWBOA1Wm/5lCiNmqEVpNbYCa/OaFmbS1wUBqiRcxRzcyGNZ1DKM9yhgM33HgJQngukkcO3fv3nXdpNGK4zC+eW1rNEKr1djdG168gM2tFytzpsMBACdYLmgAAJA4cKNk0vRiz3XjZHLgeeFsZVG7He5uNgC0VqKgEYeh47eTcOi296cABiua54oyCwsn1gxzgmADwJx41Pf+pinMrig5EzN3ah32MvHqjWZsgBbTFruGN2MYpf4o1w+CGPAcJ3HdWR2g2Yo/vS4uEPEfD30AD9/xAf9cp4e15vQ5Nh+9/Fygl5HpvhvFYeC12iEAbCbtvQmeYw3hcKURj+BPogYw6Qax6xysNrwwjnY2gSsKZYZhGKY6bACY04Bi+l8m/WWTwTKAmEKpKZvBnYs4Y0pNw16TrECIU6oBdt3IbNXv95PEiULH8QAgDF0A3uEvfL3zfJbH9568+FVgAH67Axw4Tgcl7Pa+t+QtL7XFXcJTbMO96eIaDoYtANGB0xqEAMKGN+oEGKM1mIaXLk62e4PtJ2wAmNMHfaKKYY4GNgDMyaZs8Q/xiYr5ANOkSm0wBNJmmIpL5OZcxDg71HpYpXi323269dxv+q4f/PZv/3azEQPwXdd38QdPvIutGKns32/EAKZRywfaa83dEnFBw2tNLp4D4Db2AYQbTtx0AcSxE8TRcHm21KfZipZe/cjB4+dIQgDLa3xVYo6NudwswQ8aYk4E/FXLnDYoGXkh4imN2seoC4gGQ5AOo4gL0rm+UChrTj5fki6eJ98czFeYMCeRq1fX3SBwvAbghJHji9tzp87uVoILwWavMRo6AD75qccA7v9lG8Dms2Ufu56rsgCTcyuNJ4/i195Yud4FEHqXxOuJ0wIQjwZruyGA0aVhFM3WHfnNZnxQIscwJ5zbt2/rQhjm6GADwJxg8tP/Rnm5HRWbIJYCTFux8xg4bKhstUlhB4z0JZkm8h5DYCfOVCGKnCR2/A6SBACmoQMXHS8BsNmbzf2/cnMv2QcAz0MU4Y3XBsFO/OxbpZoNfwhgcm6l2WpsP9p95WNLQG/7oOu5U2AaJisAdlb9ZmMPwOTpQeA2oskegKR1uVSUYYD79+/rQhiG0cMGgDmp1JT9UxJQ6ybEVL26iSp7IbfS7oVpK2X5uhZtQ9VXYaHEwAjlfIct9PNkWpyL5nHhe0kcA0AYul4QJ7PpeAwGjUvdKYDl6ex+39D1gUn32jjeQgzHbah+sSvZee6uXwXgtRoHw2UAnjttdBoAVttD8QMEw50GgEboTPwEgJMMFYJMFThvZhgmDRsA5pSgvWNyLvSKHqNORySj2lVG1ZugYNdKfR5DQNcHbblX2VsU/Qz0PbIQz3CUd5NHMbwAv/7rvyr+6bqYTD1/Onskf9CMoylGA9dzX1wvdobeBWD9SuljQAFcvjVqroeP72+cvzlb/DPeixodLC/tAwjjztaTKeC5HzwDMPXCS9ew8S4Oek/wcb4JmGEYpl7YADAnkvz0P+WpKWqIc/PVUbRS0WAgdT9AGfQstgxtE5jHjmhtkvWO9A9v9gAtnza1GUbiGbQ7RSkiEZH75TUDrxEAaDRmq/Hj0IlD58GXf+Hqf/dFETMcNHc2HACXuk8AXF2Nt55i5JQ+s39pDb2NCzfW4QbNadTBYGsSXQaw1xsu3wKA8WDiBm0AfqPVuhUdvDsFkLjtoN38J7/y5f/iiz9RpswwdTOXagn/KgKz4LABYE4b9GeoS6rfQUuBmLdZ9F+i3ZG5eBj1jlRvgpLjahNlNZQmBBZORi490noYidHu9Pv9tAcjNiHJ+xmvGfjNwImmrgsHCYDpFEEzghs9e9oEEE3jRjMB8OYb+xsbaHkA8O77yejpZmETkqcPl8Qf06jTWG0CaDXHF646AA6+CwD+o2ci4LWPjb77DRfApWve/hwexMIwDMOocHUBDLNwfO5znyt89Ge32xXJ0Pr6unaKWiAi5YYUiMoZ7LaaO2I3iYlmFao3oTUSdTfR6/Ussn+J2FC7FzDM/iWm8WJ3vvWtb/UPkW+FMb76h18HEAQBgDBymq3QdWa3AgwOmgAmY+fNN/blJlsTuJ4/nqjuAfAa3pvr7yJoAogjTIezGwkevuM+/K43GK4kjt94o9V4o4VoJLdqLDWXVl76zQGGYRhm7nAFgDlhlGX/kkwqLxOd6jeDivhe6ldgtUmYdko+Q9fwh2AFppPBpvoSyu4cwVqs6hD9mN0oSfq6tVJ2qb+kT35wLaWhaeT4PsLIka84wGC/AQcAPn5r8Jf3WwDW/D0A3Ub0fomOIAxdHLqDKHIdxADGB+GNWwCwvxMNhnjl2hMAz7faAAYHnQvr29Pdnc37G2Waed7+hX8JoL0UDg98AEEjnmw7ANB2w3C2I3/7Nb7MMQzDZOFvRubUIrIibW5kCl2QHplHJI7qjI2SjueRHgM6/TR2bVlASc1FOmttYyTahUwVm6DsS0W0HgOEVUyOA8dJkCCJX7yYJLME+uor+41gIv6+dXU82UZ/EDeAtc62e1PT9J/dP9c8DwAJ3Dh2ANz/89b6KwMAly5tA9h6fm50bxvorN2Y9bC1HEyWVPcWC/7e//6bAPxxAuBg6APwlpLBgY8A586P9/u+D0S+mzjOP383BLC0O/l3P91RazIMw5wd2AAwJwnt9P8RU0dCTNGkxCiQpQxdoHFtQSCDjbJnuseoUmQg7pFowqj/GbS7Y70LaSjZvzogR+I4ABzHSQBcfXX2u1xbY+fKpaF4dieACG6YeEDKMbzM3k7jtTcOnjxc8YJ4sr114dXl1nv7m1eXP/Th5wAOxmvjveng4BwA/3oQPpqtDlq9EAw2V+DvlMkK/u7/9pvNVgRgnMweVdQaTDGB03adBLubjcYkHHd8AJ39CYDGMATwO9/c/1vfr7cWDMMwZwE2AMyJ4XOf+5wu5LRByYat6Xa7MpNO5LPfU+8WbUQlv3lhMp3OX41a7FZYK0VsqGt7v4SRx0CFOgNldyhm6e7du34zcL3ZOZAkcADxWH7Pj6PQvdBMAOyMMZgm0zBeacDZHn/8w5f++Jul3R737gEXr766tzNeX3k+wqvLAC4+2d++ev7c6nMR4yCcDsezv4NGHA7ho73afPSHj8pkAfw3/+g3nKY3GbrL26MGsHe+7cbJpOW396dLO2MAYeBNVnwHaG9Pp00XQGc3DhuuH8a/9XT0o1daCnGGYZgzAhsA5iSxUNP/pwORPuYNwFzQptraAAXdwyVe6QQ3nUnLxJqSKJdhtFbKtCFKdl4GsS2Kx4gnEzQDAA0/GU/hu0ASt32In/+KYydK4DkAcL6BPvD+o5Y3cT96Lm63wjJNwZMHy5ecCQAxYd/4a8k4xvbu+Xg6ufKhCMD3vtUGEPmzCsAgugJ8r7Gkurc4uJwECPf3gsFKs7M3Fsp+PzpYbQAIJhEAbxRjyZlc9B03afSmo44f+258mR96wTAMM4MNAHMyMFr8U5hUaVMlplbqG/+0svxbJr5V2i3cNuMx1MFa0lvVYTModQZh/poekgRuHEaOL9J932tMJ7M1NtMYcJznU7z/iDSD7jjJxjtP0Ppw62Px6DvuZr+NC7jqNBvewWg0+/WAp+9jeXW6895wtkmwNOw/2rrXcwLVQp3phjNp+wDcOBktNbALN07ChtschcE42l9rAnDdBBFau9PBcmNyzhMLgdyN+Nn9u/ixH1GIM6ebW7du6UIY5qzABoA5DciMn5hCEZOnPL3DH3jKewxrTaYO5ns4Cj3GHJErmsQ/yzwGrFoXm5Tp37171wHiyTS9yXjqDiYOgE9dnQB4sjd7vfcsuLy+DaC5Fe9OEOim1C9cHk4n573X8W99eudr31gDcLDnfeQTAwB/+o0Ll7sHAC583wBAsre/gxYAf/mCEx62V0TzYNo8mPV2/3wrdme3JZzvHQA4PwqHh08RnTS9zv5ksNwYLDeCIJ7sbg2eqxYXMcfLvXun9gcg3nrrLV0Iwxw1bACYE0DZ9D/l4YYSxfNA1aTdhZhGLWyxX+G3mdIo1oRUkWUWnCPwGOKPsmdADcbTZsePgSh0AYiH9vzpowaAlaURgEE4Wye2v+83gUkEx3nxzNAMfpA0GjEA13PiKAFwqTv+0CvRt7+zDOBPv3FBhD170nj1VYyGfhO4enXUmzZ2dUui+gfvB+211eZFAO39KYDhcgBgsDZbOOREs36GgRcGHoDGOPS8tQm2nEa7WJRhGOaMwQaAWXQK7/01Sv3zUB6djqLCgrrFzLuUJiSytqDYKu0NjMQz5D1G5h6AKuLMgiMPrvjj7t27yeEqIJHOu24CwPMTAB+/OgHwB99eAnD+3HDl3GRtkFxqTg+eFijniSMHgN/E/cOFQx//2P7mbufmG/sA3n/npbX+B+Pm2uruY2dp6dJaXipN02vFK8sAotYQAKZY7Q8BDFYak6YPYHl7BGBpN/G/7xMAJu8/bq88PPiT990OPwXIhtO0cub111/XhVSibKxu375d+DrDHBdsAJgTQOH0v3X2T6SixyBCyfsl6UlcuaFyixeoV0lJAyDmhk3F81Duba2iz8wRsZBHZP9J4rQaL7nBG8vJ0xsHALZ38UoH6Jz77sOdFcB3ncG0dA1QHDl7e8GFi47jAsBwIJblxAcTtBqD0aSzu+2fuzQF8O47qwAuX2gCGE4DYLT9/mN88vvLlAG4QXP6vOe/fnPpwjUAjuteDHwAz2/MCguYTAA4288nQPhow/WD0XS59bGL++9MylWZVyovJgAAIABJREFU46fu7JxhGAkbAGahyS/+qZ6X9+fx20lqiE2IPyzy4MKbEMowGrH0QilinURidCeG0IfV7gsoI2AtnqdibxeTX/u1X5N/TyOn4wCAe/h4/6aXvLfn3FhOAHxkDb/f9++PsNJ58eSf/b0GSnDcFy4iiZ2/9sldAONkeSnAwRS727NLz4c/tP/sYfDDP7jX60XP9z0AV2+On2wHhZozNdHJoAlguPW8c+mieH37oy0Hg2TYWeptAxg4YwCNzXD9UzGAR98u02OYGaf4JgSGycMGgDlJUHI+LfTU3Bo5j67OF6tkk8IDaJuwdjJyK2InjWwGDiNNbYbFIijrxL3wNCh80UK8jIz+HJUVzNb/xBOgFbiznwPu+IlYS/+vn7ivLUUiUmT/vpMErhO4+FB3PA2ngR/cu3cvM3ebJGi1Is+PX708AvB4uHStnQDYDx04uPLKAMB0FKU3WQ43t+BBh+NMDpJBa+WyHzYARHHbc4firTDqLB1MouUOgM5OCGBjZXoOePq9Pa/RdNuNyfZDhTKzyHB2zjDzhQ0As7jkp/+NZr4LIT5CkZ7IKlCv5tf2gYI2+1e8S0GMg7YV6+ESpRLiaNil8kSnlMbUzAjo+mkKyybSo1oYHi1lZ8XhLb5IEoibez0HS8Hs1b0p1teD7d2w6WJjo73axH6IDzZLKwAAgiD2vGRz4F3sRAAeD53psLG8MgVwqQkAT0Z4PnQ+/Mk9AOfa7sY2xEKkK+urClkAjVGIy63x7rOP/43Og8eI4vbOG1EYdQDg+hSPgmE8wkpjeW8CYHP78miYTHb6K8totC6plcvIOxw6nLmeQazPFoY5MtgAMAuK+sH/dj+eSnkqvEiP7PQFlFaOALssNoN6X6obDNS/XMoIOzOT3oTYQ/XRyb9eZcVUxkVIhV/6pV8KggCAO0v4cfhETUQxwnAaBIGYkH8yeKEWJ7h1bbh/KFn6GCAAQBQ5AB70Wp/sYnMMAPt7wceuTB4Pk0ttB8CnL+MbH6A3ct6/t/Tp17F1bzcaDjd6O/hYqWbQiONOxwXQaj14cOnGjfcePL4JIMB2oxMAwPVpx20A2PwganWSye5Wc82f7GBvKy4VZRiGOWOwAWBOGCJ96fV69BydOOsvqFJk0CayAmt9iXUuWAfEo1DGQi2XUryrheJkUDLlryW9Ygrk0aAHy+d5ug7iBAAGIQIHbR8AVn0A2J4gcGYxAJZ9vHZ58r98+Z/8V//Zf1mgCHgOLi6H08MbhS828XSMj12Z3YY7inG5k6QdxLcftD3sFgi9zNoq9kIA8MIEwIMHl5Yv+eJa9sobEwDf/qMWgLXLL25CaC/74Zo/3IC3QvoVM4ZhmFMPGwBmEdH+7m839cNJhYlUlUfypzchZmkWHkObjxZyZHPhlD2qYpbS1JqXg6ZgN/efhuJkKpZlpMfQnjyU1F9M/wviBAHgOgiCID58JJQLPDlwAdxYircnAPA3r4Xv7PjbA+DK5eTZhuKHwFp+AmAQIQCarai35wH46CvjOIbrOsvNWd7/+CAZDoLOUhgEMYBWM1pa8vcamjsB3DjxnAABAEzGHpIRnNZ4lHzvz4I3PjX7jbAkHJ+74gINL/bj/Y2ltSAaNlKVDIYp5v79+7oQhjkNsAFgFo7CB//nkclNTamwkJU5buGqjHSkEbKOkf6nFkpWJ5EeyTTdpO9Xr9pyKcp8+RzR7k5FRHa+OK1QYtKI9TGug2kMYDbZD2C9E7sOLjSxNcY7O7NLxoUmllem704RvZhnzyLeWVmaxnAajXAymW277ON6O/nOrrMxFIl+NBr6Fy7vbW20vPil24IL2eg1b/xA+Ozdh8H6q7JysbOVtNfaADYehs3VBoCd5/Ha+Uk0eO43vAngNhoAPAwVyoX8/K//KYBJy2/vfxPA3vWOH0QA/Cfh7f/2P9FsTIDvEGAY5lhgA8AsIurp/yNGOgFdoDHSBqSnqNMNZRyCaR+6ujpJHsrEvyRjY4ygNzSXOgNlvtzayQi0fqb6XlAgtiIfABoEwXQ6BeAAYYxpjDiB62A6nTYbweX2i3Xz0hIs+QDwrc2g48/+zjNJECVoeQDgIgHguPFK8OLHg693cKsd//HGiyKC5007nvtwb3IwevGkUUH+HtxLr4Xty4Od0YVGK9553gQwHYZB2wewurzXfxwA2HneWFtpAHAbjb2nm81zF/ZA5e//498ILibTTacFTFp+4jmJ5wBYfjrcX2smjjNdbvzknX8hgn/67f9YKXb6OTIbw9PzDDMv2AAwi4V28c/pQ5GbUvJjNWoFuaRE/BCYXXNyK0r2TC8vZDCqfgjkJtqtpMew9gDEWXnQRkmB1maIfSF2RtJwE98DUkvyZeKfJBgn7mQaA7jSiQH8xQb6IzR9NJtR2S8C/5//6H/9u7d/Svy9P/CXnGQ6U0vaPnbDWTsHe43l5YPBINjZ0j8AVNBejZ5vNS9eHu3tNNFEEMRLS5PdnQaA6fbo8psxgMHABbC/NQIwfL4HZ8nvDLbe3ZhES2pxAP/gf/x/3DjpAPtLbSxhsOR5+9HKh6aA86zXFjHil4YBjJYCfxoLJ3DqbQA/2YZhThNsABjmTFCYEcoX5S8B2yELFIrpZ/muaW6K1LaU1LasnKKmSp2BmP3Lgoy1B6CXTUyJEscHogQJ0PAQxgAQJvjB9eibzzwAcYJPXoj3dl3PjfsjANiZYA2qJUC+H+8MvNZhQDuIgNldxa+vTAF8+U864q3pxO1enADwno0BXHzteoHcIXH6WT6jDQQXAayuTQbbM1Px9IHbbI03H7/oWexQ7/39yTv/ogHEruPGyfKT4XClMWl6UdPbfYDYdXxEjXEUu07Y8BrDcLjScBJEvhtMoul5/+1f+Jd3/vP/QNcCwzDMQsAGgFkgLKb/TZfRM7WiPgpVjpHcVp2mU+b7y0hvqM3R7UoZRk2kMW1O7ZTSPwAscADhAcX6H99F0086fgLg+y9FAPZGs3z65srk+uvu7z2Jn/p4tK+atm8EiVjv02mH+wMfQOAijOG7+L+/0wFw5dIIwAcPmyL+lU7y7Ylz87XL777zuFT0kO2tpnim/8XLw71Bp9OZ4NkSgK2n40tXEwBra0MAm4/C2Gm5k6fNThAstafNjlIVwThMXCSuGwOJ40w7ngOs9oZulAB4fnVJPMNoZWuUeM7S7njncsf34zBxnf24McyuXGIYhllY2AAwiwLl3l+Z/MmnIubfAjlJIsIeY6Go+yhQ9CkxCoxsgMWsvwgm1lvEPQCuM8vvmx6mMVxn9h8A8dbldghg3Rmlt50mL346oJDVdtT0sDP0bq6+SI63xi8CogRx4gA4mDroJGvBi2n7Mlw3WVmdDA6Cwa7fWQ33dgNgt73SwY3n4k5fAJP9EeA47gu1y9fdZ+9huvesUFPeYzBpB7HrhIELYHl75Ex8ALsX2+JSubw8Cd6ZAnh+dQnA0vPR0vNR2PCmDS9xnTCgrmI6lfACoTLeeustXQjDHANsAJgFomz6v1fyHJ6yB4BSFoqUkfEYEvkIdoG1fiH1KTN2HMFRkCuCFAFlb1FI2wAQ1BwHbhSKmwDETwGMImdz5AG43A6/t9MAcD4Z7028+09GDnC1E49beOKUpuyrAa52MADW2hEAB/jeLtbbANBZno5H3tUOPjgAgMsXhs/7+Mtt14/gAksN9c+LAUBnaTpIktHAk4v69/c7r39y5k82ny4BeHZvDATnr447nvv84dPzV9d2/rJE7pDEgZMkjXHkxMm06QeTqDWYiucZbV/u7O02cKVx7tkAceKF8WgpcOMEgBsniJNYbYYYhmEWCTYAzEJQuPinLO9Xk47XJj0SbVvpt9JmgN6EpMxjZLBQVlOlz0wdHMGBkDZAvXQqShwvAQDfD+LpBMB+6Kw28cv/8Ff+03/4xX/1IDjvR9eXsDeZTXK/v+9cjZy2F+9MSrPevSkAjCNsbrW6V2dPF+0PAWB8qPPKEj710cHXNxwAnkv6pd5mK2o2o+3nTXRcJPFrN8YA7n1wQby72ZstKEpSTxT94OGqPx3Gu++3Vpt5wTSD3e+e92+KvyftIBhHYl5/3PaDSQRg2vD2zrXcKGmMIwCx40zavj+NEsfZffDnAN8DcKa5detW4eu3b98ufJ1hjhE2AMxphlIK6Jn/AlR+ARIFrccQ5AO0u5Ank+1lNGX/LZS1sM1YQNLHIn8DgETcBhCKOX0HYYLASe5uzFL8tof/b8MBMNx3gNnSHQUdH3GC4ZPexatdoB24uHC4Av/NSyGAvcO1QG3P+WDfB3CFsIhmMk4FOe4wTAC0lxMAB3uznzYT2f/F1/x4dLD1eM/x/L1++9xKu7Wsv+QN9zbaV25Fk4EXxrHniJX9g/MNbxQDcJKkefiU0s7uZH+tCWAlbu0NtxSaDMMwi4b+25Bh6uazn/1sZvqfmCsrIP5ekkX2L5EbUloRfxi1ZWEzjBrq9/v9w8eAGrWSRtGiFBdoR8kI4gJ3hkiczJ7nI27bDWM0PfgOJiF+9NVwf+qu+dP+IACw3sR+M7nWwcNdTGJH8aO9jdTvBG+O0fKwFAFA08OzEQCIZ2k+Gczivv/65MkDvPfcXyGspVk7P771+i6AR/3rl9YPAIR/Hm0CF/9G3Hvc6nb3AAwH/vbOSz9W7Lf0jwGNV1cPBs+WOpcal64B2O/fb+9NVnvD4crsBgO51Kd5880mMBpsAyMA7ZivpwtN2fQ8w5xN+AuLOWY++9nPZl6pnv3jMKNVVwCqZP8CosdAhd0RG2pbgW1DIt70dwCIbaU9DKUaU0aZzSj0SNat5Dlr1YwoQeAmLS/Zjx0ACRC4yf50lkNfX55caSFOcK0DAE+H6ADiSUGFbI6xHuF8E6ELAKMIAIYRhhFaTXgOAByEzvbzAED3/OTxAO8/z16S7t+/n8nbOq0oTuA4cA9/rODxo5VgaZaU9x7Pnvg5HPjb/RGAT3x0+O3vrSxfjIfbrZ0Hj/BXvg/lTEaud67jHQySVmu8v9VcvnCl9WpzdRlA/1qniSaA1rPtvQ+tTbZ3d4C1jQMA49Fewwleuj+aYRhmsWEDwBw/men/boUnsqdRJ21zaQK6vHYuHgOEVkBIx7Voc3SjCoNEpumm0/bE/crYAO1eFJI+HwqrGXjZaVg0ocB0ZKwpW//TcBPPBSDud8VykLhu0nCTaYQnAwfApXOzRP9qBx/sAMCFJp5Nnah8IdAbq9GjA+f6UtLykABR4myNZ78DAGB7gtUAByEAXDo3HW5iEDqel3zycvTNzTLJF4jVSptjOB4gfmPso77vx9df2QOwsznr1Q99Ytjr4fqN4ZPvYTLxvKbmHgBMx+FgB0tr07XlYGc//37r2bb8uxn7+/4Uq0vjYDR8+l7UnuTjmYWCH1XEMBI2AMxxkl/8g8qpOf2xidrMUg29oSoQ1zJVhN6K9aBlVgSpsTBOFiumBESbgZebsLAZ+RNbW9AwbSJDxlcoPlnD4XS52fBdTCInjB3HSdzDXwX+d64NAdx92gTQbQ9F/MfPT3//Ec4140e6Z9+PY+wO3dFwaf3KAMAwxEfWcH8mgwT4K9emAO6nkv6loEBHsj/wvQ8ejTqvf/v9zuUrAwDNVuz6L24gfr7ZdjFyveiHPjGUL15dH36vj+baaoFijsBrRps9XOyuRI3JamOCZHj1IoAx4FxYBdB4vOu0fACTlVZjbwTAC5qRbiiYRebevXu6ECpsM5gTARsAZuHo2v5gqumPJVk0IaFk/yLfqt6KGnr+WoWKrkxA9AAW2b9Ebkg5B+yGzq6gQW9LxqzbPtBWbCUP2be+9S11vCBOEMYQT/VMEgdADCTAH2+0/+rlIYCVID5IAOBiE71BcBBCPZ0+ihyxQGitFY9G6D/teM7+D9xYBnCpBQDv7c7m6Z8MAKDjJ8FKvDHS3ACw8957a594pdGIAGw87QSrPoBzKzMD8HyzLf74yPXpt7/XAODFPoDzTafViaJdpbc4xFlqJgdjP3Z2nYPVZKk5DJv3e9u3uucv7gEYftABEIzCncbIDVpRuNO80BiPVzoNkjjDMMwiwAaAOTYKp//TiGRRmzOZ5v2CijaD0pZowlQ/A6Wh6tQ9/S+gzJ1XHzGBthXxh3Vb9JoJPfXPIJowshk4bK4w6b97926n00m/Mp1Og2CWtk7H01YbAMaTsNPyojhpesl0CgDf3WndWh0DeAos+dgGwhg3lpOD5vWb5Y8BFQym+FALP/DGwZ9sAkDTBYBXWgmAa60YwGBvZgDeWE2+s4GOhxeLbErYOfC7r0yvvTIA8MG247oJgKtXhgDefb8J4Ps/LFYFwfXx4C9WAJy/vrm7MfX3NEchWPZaKw0AzlJzeYj9djZg8GSluX8AJwDgphL+oN0ArwBiGObkwAaAOR7U2X960YJ2zpieG2Uw8gB2NgNkG5NBu9cZiHtRyPr6urgJ+AgwHT0LiENnPVxptDP01tm/QNZMiA3Bti3HgeckbjzxHN9zEB/+xG+zEbQOn6PztV7jenMfwPYEyz4OprrUP3TfXI0f7LvDCAC+7yL+ZBN3nzkA4HkAPrMeb4ycJeAT5/DeAbYnTofwGNBGI15ZCgF88KT1ytURgOsf2l9Ziw6XLGH1XOgmCQD38Pp25frgIMSrr077+wUN5O8zjg96F7dXceX88hCDbjPwmgCc4XDwZAXAzqXG2rPJTmPsh4i2NlZf8/a+OcAKWufP58UZInNcgcMwDAU2AMxCU3e+2NX9GqsMUweUITakr5+xsBndCvdMy2pG3QZAO8KCeU3/w2QA68P6uKShlxqqDJ08/O7sl4CdOME0nqXUk9BxZs/AxDR2AeyHmMT4vovx8BHp17veffj82rXzHzuHp06yN3UA/Ic3JztDXGkne1P8+TawdK61vx0DuxNQzsWGl0TNuLfVeOXWDuACSOJkMPFWz81W4j8aeB+/OgIw3RsBePeDBmX5/3TsNcZBiHivu7ry9HF8/ZXg2fbOpXOu4wGYRMO45S/vI2o1/HMOgCRYAQYAWm5v494zjTrDMMzCwAaAOQa0i3+OEkpqVZFut0tJ0617IjckpoD0VUySdDCxFQm9ORFjqp+G0lb1ezNAa0hQpRUK2vOKRIIocZIECeA4CGP4LsTU/+YEAK528ENXJr93bzaDLp/xvzl66UH7eW4sx44L4MVTO3/s5vhbuzOde7vuw2cA8OY5PD186E5HeV2KE2c4dlcThBPHbyQAkiQeTDwA19YcYAjg0eFM/0HoXFidAtgAek8jJ5yWCwOAk3LCwwtL4VpraWe0tDtB0ESrGV6MgBj77m4ragDTvW0Ae9/cdG62p+8sA2wATjz379/XhTDMKYENAHPU5B/8f0agJItVINoMVOiJkdOwqGbAsIkMxJlyOUoVPcDRQCyeaLl7967i3RizHwJruMkAAOAAw8gBsBoAwN2+KAEMXQcXWwmAeyG+2lNl/+MIW2P3QjN+sO/cWE62pwDwN1+brZT/9nN/2Z1VD948BwAfOTf9zgaWfHxt33nzUrGmZGurtXIV4cTZ3W4B+OjFCADg/94frgP4qz/wFMDB4f0J/WfezWvjx4PIWXqlTDBNZxT7bSGIg3OtxqAgxt3zW0v/P3tvFiNZlt73/c65W+wZuVXWXl3VtfQyM5wecUY0KVI0LMAwbD9wxmrDIGBLFuUH23r3m2DJC5q2YcIGKJoWTdnywxACBBsQLMvgMiJFztLkLL1MTS+1dFVlVe4Z+93P8cONjIrJzIi4EZFVXcv5YTDIjDj3fHEjsiv+/3O+7zs20Dyz6tEBLFE4ZpwhN2Oa55gEIYPhxDEGwPA58Ows/7945FHAc5InRJ4xo5hqt2Fmm5HHKR1L/rX/jHlsRp5Ya9PUmg9X/Q6TZf7EB039HQlQcRTwWQdgwQZoKbKjf0u2Ar64FH//4chKgIcf37tw7exOIL94TgGZoP5gzwXOL2ig4bNSwF0rhr7vSfaC7PtI36iPa6gphQaCnv1zF4JWjKoHmQcIFD/+uFZfChp7fSH+YNcBkq4AGp1w9JSPsT3tdWNtiXTJtvYShm5OLUelqgv0tqJCbIfKp+0VqiEQfLwV12u0N0ZNazAYDM8axgAYnirPVPKPYWbyy9+ZWctRnjHPy5jKZjCT05hnq2HacDOEGEaALVBJHKsiYAmdapEqAfzy2ehbD12gaAFEKUrTieUXlyak0xRtnerHEvqNxRhwKwlws2UDcijb/1GPUixSBYx0FBnO0K5DzaEBlpXWPWvD7z946ky3l4qSpQGvmCQdgsB2hVw+t9q69/C4KYcQGhCpBhJ3UDEs4nNli17QjtRPv8fNRx1LutHpVb37qRbH7RQYXm7efvvtSUMMhs8HYwAMzxaH1mUPaZo8C6KGF4an8EHnDJFz2FGyCwdOJo9Gn+GPfKKZGZ//A6AFB1lAArLzfRONLQC+spLaOgXubfNKFaqLd9cbZ239qGcnYyt2N28/XLtyFghSUbA0sBPYS4UE2I/5q6f6K/3f2gfYCvDAs/VmMq4ZUJhypqQ6xeRBRwDXV/UnbYDTRW6D6/VTd3qpqFXjMBGN/YP65dz4C26powErVaklI1fwsJGeraskWtsNgPVLVv2RAnac/uT1NXtjf8yUBoPB8GxhDIDh6TFm+T9nE8Ps2UFvxKlE0jBjbMa0K6+GF4An/VkfsgETR87GRBswHim0hFSjNAVLR6mI41jZbpj2l9zbsfy5tQhox48F+lIhPX46iJVwBEFCxVFBav3eT0r//ms9P2W9+/h75/fXbeBMIb5Wj/5wr/+gI/Xv/qN/+Df/xq8dO60lEFAoJnFkOW4KXFuLUu0C115tAZ89KO9uFoDlmn+mlBbO+cDHN61SVfkjX2yfOJDRkgXEruVEqRWlFml6usbBl+XmtcLaJ0E2uH0q9LDbDzdd7e1v1wqViQcYPBEO9TA1PGu88847k4YYDJ8DxgAYnhLH1v7m1P1H2ZrjjKQ8HmOY/PMfZcwNzulhDM8dT+ezzpM6NeBQPUCWXp+t6Q+v7HuWbvgS8J1q0usBP7Mc/vAjG9jyJ7Tub8UCWCmky/Xk06brVfuPJ4o/3bCLFn7KtXq/MvjS5eVbt3fRqEmtQKulBIgj62FPAF9bBdjz6SVi9XRvwUoBEfSLjB/u20A3EVpMyC/KsMLUjlMtEEuO3osBqxWnZQccoLkQVxpJWnTLtRAI1z2giBuMn9TwomOcmOH5whgAw9Pj2OX/aaX/gME+QB7yV0kOyMZnNmMq6ZbHZhw7YKoowwxsxtEZxjxleFGZ7bOWQgACXMeR9M/VkkLfadnAopMA5yuxtKPbLXfLtwGNWPZUnCSOfcxXiSP1UiGJXf3tLRu44nGqCPDF5cBxnX9y28uGvbHIv7jvAjdqHUDpx7XIx6LAlYSpKEntuOpsqe8V3tsT0nl8oZ+y17IBHWugUBxXWDygUE4XL4Q7m0VZlqqrALHkWK0YkN2eKpeKzb7OT6R2IPDt4mnd/ckeYAfmILAni+nRaTCcIMYAGJ4GR5N/cqrk8eTxADM3e8kYbDXkEVUz3NTAZkwaeAyDDZAxr22QfDK/Exh+J4+tzWCOyQ1Pjm9+85vjBziOE8dxorFgsERetJRnacfSXz0VAEH8eE0+SMWZcpIWE2D74MFbt24dauM4OEdsxdM7oQBOl5Lzy30B/devhP/iM+dcuT/4l86k3/5Yfmk5KTm6I/WZ6xcZgRxyB3EkAa2pugosFSfSsT1LD1J93lpLv9/h0lKcpPpeV9TdSRXGzuNzzWRZum4CuMsCSO8qdzVOmgCJ1EDg979AvXOy+aP7fs+cA2AwGJ4bjAEwfA7MKcoH5Oz7PpUin42Zc5kyBlfluZ08uv8Qg5EDJ5D/2jyu5knsZmQcmzo18+SGMQyksWtpDRLR8tOS8IDdwLrbtIBGxLJOgIK0gM/a7n4oR2lqKdgP7UsFLIszJX26CLATOMAf3fOAZUetd+HgHIAvLSfA3bYoDR00dhRLkJmRc7U0+zUrRP6F0+l7uzKxHhuVlXJ6v8MMrJ3rqkIMhKHjeTGQ3u0bA/uq9erFJvD+h0txJ3RcnSQukC7Wq4vt0VMaDAbDs4UxAIYnztHl/7VpOpfPw0k5jZzMc0fZIvr4rYb5V/Gzdz7P5Xl0/1GG86aY5nXmd1BbW1tbc1eBZ4wJ+qLuaWTnABx7GoAthNJCHzTuVGhb9v+3VkruNq1GBPBKNfreVqHdlb2Fc7S3Fzz1e//0t3/17f/0cCSIFcBOwBeLessXwFbP/sJyAvzbFy3gO48ey/xTxfgnqchS/++2x50vpjRlW9fLOkzxLABb8t5u/5IwFcDlMsCjgDsPi5dKDeBhT9iCx8v7I+h2nGJglSrKsnSaCsB28LyEG/Q+klEIcPOTpaRfs4DtihhKFXVQwPwsYk7RepqMOc7MYHimMAbA8GQZU/s7T+fy/Ppv5hAZeZJzTsTMZKI2zx3lGTOK7J3P6QFmvqn895Ixwxs4PD5/IHIbm0NOhimjDPP5pk5NzP8ZkGqV1fNGqSjYAhhk/XhS/9K5oBHwx48K39t6fN7tfoRWIlHH7wFkj35pOdZwrqSydfq90AZqJe537ET1BfkXlh4X0J4vUaulm4PUoiMoTaJElrDUTdjZcheq0VoJIEj7dcw/2bGAvUfF7BLHAoiV8MY5i8f0Ok65jP/Isi88frDyutp7YAFWTUACFCsA1690HnwGEMVT9xs1PDVMha7BcAhjAAxPnKO1v/PsAORR5Bnz24yMPOJszih5bupEdjPy7ADklMjjyS6f+NbNGWvrKVZo5IwyIM+exrFPTRVlmInZU6PI9gQUwpM6S+gJUwBpkSjxUaPwpSUfuNXyzpW1Eingh6oXS2CpkI45W8sSuurUS8V4N7A7CRUboJfINLSASwvylXIA/MmdAtBtBxcrk/p0ghTYUkcppz22A4BHI7BKAAAgAElEQVRm211bxrPwLPZadrGYAK6nfvm6D3jdBPjxCJdyiIXFME2FV0i9QtrBJtsTWEAeOAerJoBSOQTCLhfqDx5slaXVtj3Clj96YoPBYHi2MAbA8AQZ0/h/oHLyyxSmXDFdOziHdTbybzKcFHli5RkzD/O8Y8PkEc0zKPLZGLyM2WINMo5yeoAZ7msw+IlmT43HEjpKs84/aEi1GDT4fG+v2I3FZlddqOiirQAfIsXZsmrv4VrHZ9YUbQ3sh40Sp5YLyV5CkLCSWFmL0Yqjk1Q4UsdK/JsXfOCf7tKNrev19A9uyyAdma2TpQm9Uk0VrBaorEZAM3G9g5e7v1/4yvnepy0BdFM8+Gcflc9X9h90RDDJXyglJISBBSxfDa1KAFgHPY4y9V8sdBcWE8CX4e37VddTQBLLhaVJvUufT16YtfMxyTkmS8rwEmIMgOFzI5M4R2X6qDQJplFFw5zKfQhrxlQ2Y36mcho5Neix5FGW82zOHGXiSz2pQBOZ86a2cvSbypgzUMbE923ADGaDI+3/h/GDxLEeHwJgCV13k/Vuf/xaMV7yXOCDOzZwqWJ/AGp0Zv1a0b7fceqJbIb2167E39vK5hE36sGHu/38nN3Arjk2sOyFwO+vW4Kxk4IU+lHPKscA512y4oS9kCWPIOh/qZ0r6p/sAfzZrYNOQ9PQbrnA+XNRp+kCaSIBy0oA19WA3/R934UgCmV7qwzmGACDwfA8YQyA4UkxZvn/EIc8wAnq78EMmz99NNJRwTSPzRgen1+KzXabUw0+Ss7L58loGlybM9bTYebbGZDHA8y/f5I/qSnPwv+7777rOM4YuT+M0pRsDbiuE0ShJbQt+01Bz5Xj1+vVj/b7J93GKqh76kot3rpvJ6N79vcSseGnNzwFLHgJcKHSz9EH3lz2i5b+0XY/b/5fPnRuNll2eWNRffBowlL6biDDkIGur7t0YMEFOLUcAH7cf2qtyPL5EPjOD21gYpK+SoVSQkpdrvanqC+HSnqddv/UgsWVOPbxu5bv9ydzPVUqJ2XR6OzXjp/0GcB00DcYDIcwBsDwRMiv/jMmyp05yeYfaKZ55P4ojs7zJGxGnlX8QwwuyXnV0T2ZaclTAbx5EhUaExU5JxSIHLHmfNMG5HEaGXPe0bEUbG1LpOh38bckv/Ff/N7/8lu/BlyuloCd/R3gSi0G1rs+B413jvK//sY/+PXf/m+AiqOA7OywdtQfXLQ0sNGTgJQCWHYB/ETy0+cQH2WloLL19kTzSQtL4FZpRiy4FC2AzQBg9aBcOWs2emM12e4eN90RlBKlcgxsPCidf6ULVKoh4JX7pQCdtmeLEMjyfwwGg+G5wxgAw0vERFU6P8Pq+ZCMO5Hoh5zMmDmn1f2HmDZviin3NE7EZpAvFlPeyFHyx5ozEE/GaWRlvuPH2EJIIQGlNeBIbQlhCTyLUHU8WYn7qptmhCVsoJvIdiQcRmbWFyxtSb3ecc9VIsAS2rUAPmoUgC8v+5drKRB2cC3EQaHtckG1xciNBSHY9OVZm4tVdbslgVQDtGLRitnerrz2Sv9E4ez/t3wcyY3VXCcBR5HUu3fdlVcae159KQQe3C2fvcL6/SqwshYDg92A/Z1i0W4DC/Vuq0etOrsZeK5X6E0CvcHwPGIMgOHk+cY3vjHV8v8LRh6ZOCeDEMdqwezZeV7GoWvHiNo59zQGqVlT6eb8cpyhKFOFOMrEcPNbmgHjY63lq9N49913xw8Yg2dZfpJqtEYrpRMVJWpPawE0D1rgN6IUKNvqfjqyuaZnqWVP+rDecZcqAjhVioG2tnYD8QfrpShRgDxI3P/Z1fCjZtoSOhmtpbWmZFNxAK7U1D0NYEuWPL0XCqBis+oC7Df7bYL2e9bepnNjLX7QkW/k2FzxvBRo7HkRFmC76aVXG5/dqq+sBUCvIxnqMVqt9TZvlxGytTGmH5LhOeDEbdjbb789aYjB8LlhDIDhhPnGN74xaYjhxJioSudkohId82weBhsaEwX6nE5j8PNE3TzMVE4jY+KNjCdP9tRUHHvm17H0/Mgr2BYyUcKCom2B8hMZJ+LTlne1FgIp6aJnAdsIoOKou6GlGLlav+XbO4GSCQquF7I1eK8TS2U9vuRsSW0EABfKCvjuI/cCxAxaEB1DI+JVi3/5aXFhMXz1IhykDF2q6OVy/+zfdsx6D+DVCvu9ERMdh2Vrx02zU8BOX+xurPdrDZZXez/5YOm1L+wB5Wrc2ddAtTTN1E8Sc/6UwWCYCmMADCdP/uX/USumJ6uBDHPypD+OwZbFmA2N+cm5FTBVItMhnprTGIycKkpOXIs0oZco2xLewYPf3SqFsfr5071u0k8lOlWobNg7q8V0zxqZsW8JeomsgOz/qq8tFH+wEwKLHnUveW+7v3twupQ8arLguH/t4u6337OzFqLHUnU0B6eMNfc9LuIn+KG4WOlf0knwDqp9/9q55M593jqVfns3vt+l4EzI0lGphBSoVOJOxwGkraLAiqK+IWk2XK/QT3laqIe2ArCEj4U9aXLDS8g777wzaYjB8PlgDIDhJJmY/DOs8EZpl5xdUPIzJjHjBKMY5udJfxzDyVFH/yqGH5/nleRPbRro/hlyh3L6mYkMtggkJJoEZWmptSpaxGAJ7dh6N+jLX6WpOkUglhrYCcat02e0Y/Hzy0GkRNEuZ+cMvLXiKat3u/24Jc/FSgxcXxAcJPSP4X/6H37rnd/+b4Hzq2HB5tZ2/exiBNzrCCBObWB5JSo7qIPq5KqbAmkqG02XocN9R7FQULv7HiAOeha5bhpF1pnz/e2FJJaXr+/tbRUA35dAt2MPGhy9kIzfYTD7DwbDc4cxAIanweY03cqzYYNeKDNIsYk2Y7DKO7/aG8ZsaDxHjPpQTuTDGkwyUdwPe5JpGeM0ZigA0JCGCWWkEL0o9lxH6tSziBVXatH1hbXN7v1s5F9sa6fbrXvpciHtyn4Pn2NZKiTnygngSg24lt70Ox/vF37hAleqEdAO+jbAkTpUIVB3C0VbN8f2AVKaZkQBggQcHu67i6f7T31hLQEuFFPgA/j/7jlvLNWbe42zJe63x0zZRwgdhVY3EQvLYXPfA9ZO96CSnQ3sFq3sfIBiKQaWTgXdbVWuqMpq8HGnMmFqwwvNC3NcmuHlwRgAw4lx7PL/QP3kVP/D5O+JnpE/1vCAU9OfvTrMKG8znLM+z/xHMTbj+eKJfi75nUZO/DDxXBtItQKksKRQ1xce30In8aFQ91Jg2avfY2PUVEAv6Wf47AT2QmTFqagvcn0x+NP7hS+dToBrCyHw/rpcLqTAg46TJR05cpwBiBWA2NnUK2u/dCm61+H6igu8uyMApfm4aQFvLPULlgMF8NZq8rCd67iuxr63tszCYghlQCnh9+xiKQHOLYc3G6Wo6a0steNAAu2GU4OvXu/88P0XeQfAYDC8YBgDYDgZjtb+5pfjE5noAeaJlV0ysffiISbuaYx6ajY5ePQGh3M/shc/GDNbiGM5pClPcGbDiZPz0xlTFpxqDUhhAdkhYGGaIuV+uA6U7RqwT3BjMfhn7xWAc9E6MLpjJ47UC651/26jeHoFcCwNXK6evVzlX+00X62FJTt7JeluYFW1OFdO7u/4gBz0BD2OgqUbEY2IKw7AxQrAn+8ihG5F/QZBWz5frabA3ba13uVsIW+Cvm0rwC2kZVvvH+h+oN1yL9f6P197pfODv6gBqhtmJwaMx2TIjGd8I1HTZtRgeBIYA2A4MY7N/p9BkQ+TbQLkVDZzxppoMwbDmCnWtBsajM1lGv712A0NcivCYY5dRT72ZmeYfAzzvGbDIb75zW9OGnIMmUB2JRolDkS9I4kVD3vZMbo/dYzWV1bDzXsAavRhwMWD+uC1kmikAqg5ajd4sFw4D3ywV7hc9oG1UgL0fGKFK61FL22O7QLUS8S5AjsBp4saqHsJUQPq2bONkJu3y+fPdYG77f4899rStZhoAqLIGtT4dhOBYKUWU462dwpAKxbAtVf6lQCv32h++H16XafZLNyHNBnthAwGg+EZwxgAwwlwNPnnRLIRGFrbHqMLN3P0RM/JxEDZDzOH28p9yCuzOo3ZaifGOI1hDrmOGQINOPYvZG1EeS6zRjnKVAbshSHPQWASpECA1hpQWkshlNaWoBHadS8BWpG1KAF++WwK9h83XVYvMDoLqJPIjZ4GvmAvx/bj88LC1F/ykvXEAQq2bqWCfsWtaMcTVTpAoqi7LHpJdDD8eo1E0zr49cF6+V97vX21lnzasi+UuQNAPOLQ4gHxzqfULnpeuv5ZBbjwev81r64E2zuF8yXNQbWx4/YjVRai/SbBriUnV0QbXnDMVo/hOcIYAMMTYS3fWUV5eJpybWKsE7mjPBp0nndvcOHEKMPMEG6GPQ1yLPYffXx+Pzk8w7CzOurHprqXF48ojosU0DpzApa0UpUCV6vlH+30dwDCFMASzpeXpUAsvnFx1GxVR50qplu+9ePG3pnSQiO0Ey3q7lL2bNHWe6F91o6B0yXWm/zJI6+z75Q1nWTk6cKAZ1GwCFJut5zzVXZ8u5diVwCu1QDK1x5vVtzr0OtwpqyBB8fP9xgptOOoS8vxhog7LQfYbrqrZZotx3XT7ZBVj147S1tSn96pRtJN4vVe15GTdxcM4zDS2WB4yhgDYJiXMbW/c/YozJP8M49QHpBnVf5EAuVhfrGbkb3anG9gtug7A1N5gInSfxSDCtdpL5y4aXMoe4p89mwMY3KoDv2ZzRPlSaC1EEiNzvJ/pLCUTgEpxFsr/W+Ky7VI9eplmy49QIw+AmyYbiyv1gqVYtIIbaAR7W35dlMVsmfvd5wlx2/H/MGDArDkEY6bDKDqpEDBYrWoegdOoRUBXPNSwBEa+O49u5sAnC5Cj0SJmptLpn/8sFA7R6UWL9UiwJXuaj3ebjiAgpV6BGw9sNtNF5AS4Nz13vadGf8jMhgMhqePMQCGuRh17u/afD1J8kj/AXPajIw84eYPlD9nZs5A5HgP5/mAhsmZ1zSbgh+Q/UXlV+czJFANZPpsL3VixMFTw0Xb00YZ5ugnuLa29o//8T/OefRvxqHBMjuzSwiBsITNgTPspU1L9L8vNnotYCcIGlFQtukQMQKBKNoL0HvU616jdqMeKF3eCWyg4ijAP7g0VuJXLpeA7/jJwo21mz8e11xoL7SWLCypH3TlmYX+g1dqAGdK8aOeA9yohzfq/J/v8VfORN+6iUiENa6uuI/WQmnCrp3uFM6/0t5rLWQeAChVkmWvn+XTDmRv17ZQ197a3zAVqk+F27dvTxpiMBimwBgAw7yMOvlrWNnk1GEDHTmtKppZmudX5POTXyU/TWZ734bJs3A+v9glt6ucKMTHM2wD8rzgGcIdGpwnyjB5NlLyJP0fRaP0QQN+obUWAhCIMO2Xvd5ru81WD+zayDmO4Y2l0mrBiZS43fK+WmelkOwEdiOwgC8v+8DNh3Z2Vtd7u/3UncvXzx6a5NatW4MskeHDwu60WfYA0i7nygCRkrFSH+wVvrAU/JUzEeBZRNBN6MUTTMDwTtiDu9UrP8NOww33K2fOdYGHPQHcu107c64DXHtrH5CWcj21uVlKQlMEYDAYnhuMATDMzsRzfwcMtO8h6TN/BnY2Po8uHGYG3b82x8Gr+Y3N2knUTuRxGidOnlsbP2Ai2aecU5c/F+TxToeYanB+BoZBaT1I7AlU6saR4zjbgQ14trLQTXilGm1047JDxZGAO7r6NVJ6N2jtBjarxWtwpRa+e9f7d14/v1bke/vtIBZAJ5aQtiOrmDSuLfDh9uREmgVX3esA4q2zbMBu2PcAP95nLy1cqGjgyys+8MOd7KCxXnZhN4cnEoLV0720EAGrC2Jz3wUerZeBhaX+a3u0Xrnx1maWTrRSj7bBK6TYufKLDE+f8ad0mTajhpcTYwAMMzIq+ecQh8TK4NdhyT6/oDk6w9Hc7uE87HkiTusBpo2YjZwqxIDh15Y/4jxMe3fzMz7W/PYpI09e0wxr/4cYRMkj6/O43DwNQMecA3CAcB0PrYAoRUuWvATYSGSKuFyNNJw+vfD+nS7wqCdcWRo7W59HDxtXLiy8uRg0w40F7zTQS4lSkR0SXHXTpEtKArRj6YhxYroVyWWbbsLZSkxn368uJormQTbR/Y447fWV+ukiwLd9Wh2AdJJEtyxdtFgtK3mq9/BRCVhbjJppvyqhULCA66/6QLuFhPX7latLjeVVv9N2x0xrMBgMzxrGABhmJ+fy/7FMlDvTMnHCL37xi+MHTGRUiGMl4DzKeHDVDOIy/23mzKgZQ857fMor99M6tGOZqP5PijxOY0CeN3CSuO8zygYoSHS/rrYopGPhWKxZp4ANdi5VClDY2d9Z8s6X7Jt7Kh3/JRIrUbSVFDrL+F/vOufKMWAplrx4L3CBku14VhSl4oNd+83lZMypAoewJVpzphxnueGvLQA0DvYP3t8t/KtNa1n1zpYo27Sg4uDnS9LpJlTh/Nnefq8EOGXNQZv/bsdx7b7VWL9fAbK6gEIxEd64zkWGz5en32Xo7bffnjTEYPg8MQbAMAv5k38+L/JIpZk5lHd0SMCdSOjxexrD5M8vOsqxGzJ5yCn9M+Z3GjlLCOaMMiDP3Z1UrIynGWsMAjHQ3xEpgHKy/pY9GV+t9U/aWvLOA6ESQN1NN/XIIuCPP3zwM19YA2IlHKlXi3GkWHJOZ89eXYiKB5W5C14KbHSdXiIvVQpNrzxqTuB0KREWoeLTlnO1Fl+rK2CpkADnChr4v+88/morO+mpAnEBwD9+vsdIqYs2rqTu0oo5W02BbcXGgzIgHae+FAJxKgs2r17uaM1PH49mMBgMzwfGABgMMzJRj87PsHSev17iWLJJtNYTV82HX8C0odem7OEzIE/B64DB2zX/JsDEcGsnUa0xFRNf0jxkNQCJ1p7QlhAJ2EICcRwXXOdmbw+4WKpljUETldrSO12UP1G5VuvPldOipYCiZftp0tNdxePFcq3TtaKGHD16fpqqA9BNrBUJYEudHLyenzlI1i/GKWBLLr2y+tnd7WwjYiK9BKDmAGz2ZKgef1E299041IsrAQcVwx/cL1iCspfu++b71PBTvPPOO5OGGAyfG+YfLMPUPPvL/y8ST1T2DVhbWxu/Tj/Y9Jj59Qw8ADluairdP8w8HiDPwv8hZogyTM7kn5xbKHkKAMajtbCFADQqKwV2D1bobaE3e3vAanHBll72YMFi05e9JEyS2LaPSSgScP/jzsXrte3AWS3EwGcd9+JaCrxeXwW+s7EDnBXsR5wqJVmg9a7fSzpjCgtiJV6rxY+6zo2FqBXblyrRZx0X2PHt8wUEGrhSi4D1He60LRS2BNgNrXHVoKC02GhZ58+yFQigfaty6kwPWFgKm3ueEH1fsb9TiLoauHC2V6nGfgjguCYF6DnGtBk1vGwYA2CYjpy1vwOOCpepBJbhaTLxo5k4YDyDy/PI2ZljDXsA8tVR5BTih1ibozEUU/qNLFbOwYc4Nt3/2AIAW2ohRKq0JWylk+xBS4hzZXez1y+E7Sb7SeIAftof4MoJ+wACESse9pywKYEo6QFbQQgseQC7bSGFBpQGOFUUPbs4ej7KQ19cNScBLlWih1Ele2QnsN9cCgA/EXfa/az/c+X4o5TKpG88z9JaAbhSR0oAW49KOH3D02m5wNrpNpA1LH24VThdbny6LkdmQRleAsZ3GTIYnk0m/XNoMBxh/PL/QNtNVEWzqZkxGLPxvPCkP5fh+YeXzwd/kydVtjF84cQ/+AEzlG1sTllIPds5AAKhtVaJSnVqS6F06tD3CWslrxv7QqdApHygZDlvXLvBJx/1RF9kD7fqz9AQKf9+d/Frr8TrXedsKQYsWQbOOKeAhr+x5dtlUFqcPVNz2okqHWjwA27fvn1UYKVanColndgC/FQULQ2cKiWxemxsNnrupWoXQMXAkseenNxjFGh1nBoISGKrVg9fv9YE/uLDhYuvtoDbn9SBpfp2NthPAZYqqePlmtxgMBieBYwBMEzBmOSfzdwtEY+OyalpjnI06CFhlzPhZFoOOY0Tn99wsgw+oCf0SWXTHus0MmZQ/Ic4unlyaKqJ+T85LYFCp1rZ0oK+CpfCrtjLm/4DQGlsYS24S81oq2gvAAWrImn83j/57V/9D/6zUXPuBgIoWLrmSGC9m54rW8CjeON8uXy+zEcb/YPGmnEbaMWpnwZjqoC3A+ui0IkWNTdtRRbw3m7hldP9F1xz1Z2Wy8FpBgVLN1KZKCxBMqloQQrqldR202bbKZUToNXwIP3j764A3r5VWwyBr7y1s7WVAG+dTj66x1LFJP8YDIbnDGMADPOSJ53jWIYb80/FGLMx6sGp0i2OMv4eZ05Yn0j+FV/Ds8CTdhoM2Ywn9FeXJeFgWaRKCstP4wIpsFY834geJQdr3EWrGoho03+U6iTVI32FI3WQqrNl1Y0lcLttXammgCuLO+lGNiZV0flyFYhT/4HUzWiyS8mwhd4NbOCThkf/QDGKVibxNeiyowJLA4kCSDVhPqG+WFQ+9Lo2cOpMdy8qDJ6ypL5yrZH9/NbpfhJUrNhvOmGYqyTaYDAYngWMATDk5djl//wL/2PIv04/c7jMA8ygp6eKmEWZNsRRhv3GGO8xfyDD88i0n3uOk79+CqWxJVGSWtIBFaYKaESPgKXChW6810m7ErnpPwIs4cBgtf0w2cM1JwUKlnpzsQz8uG1drgI4klgFElko02752SVKi2XP2xLjmgKV7PRRz7lcTWMtHKF7KUDW4UccJPmUHQVIoRMltgPrfDkZPd9jupGoKaTgzGKsNXKp2enZwBfe2gH8oF+Z3OjYV5f9dkzVoRX2X6qVL7/I8KLy9I8aMBjmwRgAQy6O1v7mz/UfT07RfFLhcnqAqXT/MINL8kQZJv8NzrBnMoYsrtbjtMu092J4fpEgIWsBlKq44HgFy/61/+Q3/u7/+NerjuzGe9mwVrxfsu2dQFnCbkXjems6Ui96C0IA3Ou0LlZqwB8+jP6NSx4Qq0Shju4htBO9cGSqQ9xpW+dX+LQlr9QBlCarRq446cVSBNxqecM5PxcqcVyasAUgxcEeCGSvuVJKag6tGOC10xGw3lYAmp2AnYBzFfXjdekVUj/Od8yYwWAwPAMYA2DIy7HZ/xPV6kTyy9k5Yz2bToPpzcahkTmjDHP0HkcZgOzTyb9FM4bxaVRzTv6SM7EAICeBHzmeDWRluEKIVMeZOr9UPg8kzW1gI24OLnnkN1aL1S3bPX7GA2yhEy0AP2l9cakIZI07LxTtrKQY2A7jRbFUtHZ7iV93x02YKHG2HPcSuewln7Wdq7X405YDFCz1g33ra6dSoBVZq4Uk1RHQOdD9WSbSGITUKwX8lBqsFvj2pwvXrjaBzANkJwPciiSwUGYneHzh9UX15+u5DhkwGAyGZwFjAAyT+frXv35I/c+c9z8bmydx4tJAy+bRmvOEG5Qc5Ik1z63NkNf0+ZqNMR0zB29axgyBhhnOjz/6tzrn5C8egxwhDbaQg28FrTWIOBVxym64vuydC0tFYCFeAvZ1/wjcrO/+KHqJ9JP2WbcKnCuVItVbKy4CyvYBneLKohDyfrgHNHUDcC2rm4SVMZMO8ZWVeK3IWjH+drPy5eUI+N6WW7PsLyyFwP91pwi8JnpAN5aupX7nd//h3/qbvzZqNg2upWyLM0WygodPPl342lfTboIl+GBffGFRA2erCsWrNX68z60HheuLRvobDIbnDGMADLNwrK6agamW5E+E8eHmkeNHmRiLabT4seT3AHPubOS3NMMcusfxobeGisKnDTRg+K9la2vr2IgnazaGeWHMhhSWJR1AoFyLrHtQpIJUxZZ0XFnMhl0uLgZesw1ixDm+kRLLXhEI0rBgeYArS2HaBRaLp4B2uieEBPxErhSSPVTR9o6d6ij7gbUYWcDNhv16PQF+uOt+4VQC3GvKD/Y8W/J6vXezIbIDvLKqgAs3Lo6dFWC5AGALXrvWBMK0You+H1gp6r+0pgG/w4/3AUqV5NauSBNRdkwNgMFgeG4wBsAwgaPL/ydC/syfE+Eph5vIybqanMwm/TOm2tZgDis1bAPyM629mS3KIQ6ZjWPHDN63PG/atGQv4I/+6I8mDTzMqLJgAUEQF8sloUnC2HJlVgxQsGXVWc7GFKxKKLpATdYBpVNAj94E2Az8y2IZ2Asj0esBV2p9iX+ntVm142zOlUICSOTZi7U7d7a1HtdR59MfP+gm8uLVs4NH/nTT+fkrAbAdl6B/7m/GjQU++MyuuYjIitSEFCBHsOXLxZSKq/75B5XXfubxU6eLXF9Rg2F/tmky/g0Gw3OMMQCGcXz9618f9dTawTGo5FZdA6aSRCe12zAxXBZlTJpKTvLf3ZyBBjyFrQamLKKYM1x2+cRYGXOajQH5w2U/5Aw6PCxniKOMMhv5PUzOcwCAOI5d1wYkUqOkkCmpEJbTacjQZ2kRII1pbzbLjp8mSqea43NgPItlz9ZoS2gQ4kDVP+g6pUL/IK3vbJZALWv7TCkZ5NkverVjJxwmUv2cfhUKeFy8G6Si5HKpHADvdwVQcwEKlgI8OS5dJ9UAjQjg3/pC5w7ew81iJXFfPxMB7UgAW74+VwI4VUmBzW7iOBpHr6emDajBYHhuMAbAMIExy/+ZmslsQB4xlF8ZH+V5cRp5JPIJkl//zU/+HYBpP6ZRTIw1/+c1YOJNZcxsbwaX5AyUcegGj8bd2tqatsvnGDIJrbXS6FQoSWqlVqpTobTT6Te/13HYijaBZtkB6u7CFu1RE/73v/5bv/N//P0gTYCqo9aKDtBL+svwKwXZjcTPrvp/vl08U+q36bzXsSrOwkba+d//0e/8R3/jbx07bdmWy156pRafqUb32u6ZkgLqrgJOV2PgL3bcZmgBtlN4zaMAACAASURBVEyvLgQVPwI+ayDFhCydVInz5TSGb90uAKeu9DX9zUcuUC72zcN6j1qxX1i8UuAR7LbtC+V8pwwYpuHWrVuThjyLvP3225OGGAyfM8YAGEaSM/knj6DJtHWekccyrTQfyOJpI04baJgZtHhO4zSKqezNiTAx1szv3iHyv5nzvIEZW1PWUcwTcaqdjWHmCToViVaAOugK1UvTqpYxKW4ZwO0vzNcCes0u1VN7nc1Eh914pPaNFXV3QSBqsu4nQdEuAEteuuhd7Mb7bqG4HzT/6hnarRLQCHuA0mmo/VETDghSAVysRpcqEbAXekteCjzyxdly/FnDXi0mVxf6nXr2QjvVYj+UYTLOA9Q9FSnRjoRX1WkigMWVINbFa2sRcG0BYM+RwNY+QUrNQdoUTTbQHDynEt9geN4xBsBwPGOSf45loqaZOGA82eWjBPqhlO45Yw3mya+6ptXigxuZ2QM85a2GqZj5pg7x1Jb/8zC/+s/I7wHyRHz33Xez5f/hfYDs5/w5P8NYQmb/LxD9c3Ol7Xg2lgOwtw7U5CLsH1whgaI9TlVLYQMt1Vih3o6D8+5irEKg7Cx24r2y3S8pVjpJtfjS0uK3HmxHSl1989yoCVuxKtpWkEqgFVmu9CIVAr+/XrpQt04V+3d9vhLFHQ/YC33AT8TlSrIzalIA2rEAfuF09Ocxlq3jSAK/eD0CNnoAmz27EfT3AbJspWw7wyukzcbLmAL07Mv3K1eujHn2yb3+d955Z9IQg+HzxBgAw0jyLP8/fY5VTnnkVE4GU42pcDi0Pj1b9GEPwCSdN2AGk7N2UK2RM8RRpjUbMwfKmMpNzXNfwzyDZoO538kZEFolicrOAfAybas1EMgYsMuLAD0VVxaongJUZVGwn4zOqy9aLvRb6OyHVtVNAUd6nYMzxVyreLfdqEtHpcmXlhaBqqMiqzxyxgOyuuGamwIf7heWRf/rbMt3/ES8tdrJfv20rZYLyc1990wp+WEkPGucVynaPOpJt+Fcvhh/uuleqj/e2ai63GnZJRtAQqzYDXmjzu7IBKgXgfHq2WAwPL8YA2A4hpzJPy82A0WYU4nOQDbzeBtwUmYj+2EqQTmVEM84EbNBvqBZrJkDDcifbnQi5A83/63lZLB1oLVACKEUIBFW/0gADcISDpC4EoiD/gkAqARw4jRr8XksiY6i1F8qvLoXbADtyPpwH+Bnz2hAIO62+9UFlnQgbcabZcfZHTUdAAVLW8LpJfJMaQW4302z77Lz5dRP+l9qrcgKlej2fJA3913gk6YLxHpCIyDAswCurkVQvlpTf3DH/cqFKHvqbpua3T9y+I06QMnWfgp9o2SYhVdffXX8gNu3b48fMJ6J8xsMLyHGABgMnzPDYveQ8B2sBOcRxBMZnmS8uJxhn+EQM3iA2YLOEGiYnJsbJ2U2MiZGXMtXi/Luu+9OGjI1GqGEltKGsP+QkIBrFaI0ALaCzUroAJFrgdVNHoye7KdwLftCubgV9Jqqnyqz6fvAagEg7CZAM94EEhWF2i+kI7cV/FS04mAVPm3tXa0tASuFZNFLgdM1H/j+TjE8iLJWjK2V8LubXpCyG05Q/7GiYtOJWZXshrR74moN4Pv3XeDK2f5LSjSJ5r19vrTIRq8fKBrbuvQF5gWQ1/MYDLNJYnhOMQbAcBiz/P/sMFEpTsuoCfXQ6uWcQQfbGjlLKYZXxGcLPbMHyKn+B8wcKCP/2n8e9X+I4UZAE5sCjWscpFOBA2ghUAcr20IA3WTXY2G1cMpv74euBWz6LSHkjTdfvfnpyBdcSK1Ux/2p4FSh1Ez5yjI1d7Xm8knz3lLhAtDq7uvtW4vuWeA+u5417iDgkq05WHH/YK9xplgHCnamzq2CZf/8WvxnD13gktsBvrvpARWbLbCtcTI9TIR14BGWPXbgTzasYikRUgNCWEB16J2LFBcqOvJY8vTHx8xnMBgMzyjGABh+ivy1v6M0ylSKyvB5cehj0iedvjCYf9gJjB85A4eunajO5zEbg+wmcgQ6Sv7UpgFz+o2JHLIBWdGwFhKBQAilsOysNWiq49utzxY9G0h0hFvywI1SoCKqo+Y/mNfLjgmrWIt+2gF+8fTV7kEBwFXfT6L7LF7Q2/1azFa0VdVlwm2cwqgptWbHty4okSoBlPvSn5v7Ba9nAz+/Ft9YDIBWU54tLV6p3Qc+3qLiiPEZ+7alN1rWKxrgTktUirhSK9BKCKmjFMARAMtef+/iZkO4EKZUzNepwWB4fjD/YhkOM2b5f7g2cZQSGjyVMZXiGcOgS+PJZsUYnjRP52OaNsq04zPWZurdNENqU55Ag/yfEzwHAJBCpkoBjlfIWut0Ir/oOsB+mCw7gRASUFICF8prd9LbbdF77bWLI2cUliO9SIdB0pKqnj3WiKJToDc+yn5N0iBZuwz4DzcA/IZGJumY1qIC2AutNXCkzrp/fn+7UDz4QvvBrv3GQtiJ5dnSYvZI2VGxpmSnaqzVlWBZqmCx4fcfiZSw4c1lBYSSRgj0awC2A9oxrVBWzQEABoPhecMYAMNjxif/HOpMMkYAZU+dyn1u1BiGgx6KOBwlY55Ao8hewJOY2XDiPIWP6dgQ40u3Z3tVhzKp8vuNOdFa2dJBpVEaO5YdqwRINJcr54FWuK+1oteQQGFcls5jYt/W1p3bG5evnAYcyXbwCAQgTt8Ik+5+sLkAwLc32xeyS4RsizjVI3uY2lKfryQXK7FCvFKR97vOhXIM/MLppK3Tex03G7bt241oHyg7CjhTSrYdZU+uAWa7K89A0el7hb/zxR7whw8LwI0691tcKAO0D15goln2dOCc8Daa4TniBSiEMLxsGANgmMDMHQkHAn0GDZ2nA3rGIbPBlIEGDGc0jY872/x5mNMsGZ4mw5/Uk/vUhrcCmPSXeYjhcwDy7xJotNIKcIXU4CapLbAFWYGrJe2sQyhuia1HwIfd+M3aQipaY+ZUAomldLLkrraTJuBa+oc7D16v95fnbavgJw+/vMzGRgC0ixbgTqqpTTXA3Y46VaVoVf+9V5YayRZwsRI96Lp3Wn0bsFYs7tDc9B2tiZWI0gnT1gr6K2vxmZX0Tttarug7nf54S7DWP7GA+11swaLLfkTR6ScgdeMXtgjYqFuD4cXDGABDn2OX//ML8VFkGUH5FdJsvVaO7gbkJKe9OfbZ/Dd1iDElnoeemjmE4YVhlNOYvwXQIUswbBiUQAmEBrCQFlIcpM4IIXfdsN8jyKvANmCJkWfhKtuRwlY6yX6t2gunrXpPRjWHm4194M36pewpgagFOq0u2XK/6lRhY9ScjhQ7gf1WYVlaCni12l/Vr9unbjaa2c9LhTRIRU1kxcoOsB3YQNUdfWYBZPsDD7rWGbhcTVtwuaL/wc3iG3UNNCI4OPkLuLPjAFFglzU7gSibHQCDwfD8YAyAAUbU/s6v/gfkWds+qXB5YpFb+h9leGcjT6Bh8u8zDMZMZZ/GMMZ1cFAEfCKBDE+Bb37zm5OGzIhEyCE1rx0v2z0ItA8ESQdYWljYazZXr1y6df/Wm4sLQHqg70chhVVI7SDt+mlyhnpJuT0Z+akE3ttdB+ooCyutLgGuLA6dNHwMWitXKmCloHcCAaQ62o3WgVdr/S+1Pf/xevzZcgRUFuWPHk1YpNe6v7Hwo10LuFzi6kLyqMWPGwI4VQW4ebcEXFvpVwl4bgrcqCfv3n9hdwAMBsOLhzEAhj6Hlv9PSo7n5ETC5ZfLJxKO3GYjY7agg/H5Aw2TJ+hg52Sq2xnPsX7jpCY3zMbwGv9EJDLWkSNshMyOAvio0Xyttgx04t26uwbY0gOfNBmz9q20SnRoC5c0Brdo2UCDJoog9QqWqrspQEBV1Fo0AUu4QgrpjjxcrJfIqqOCpAVLp0vurbYqWuqLy4tAFz9IJOBJBdiynwhkH4jzMYcWc5BWtNGyXjlNtufxadNGcLakgd5Bse/ymk/K5ZW4m9DY1jV31HwGg8HwjGIMgOGY5J/xq8VTkT8tZ1plPIqJKna2LKND5DcbzB0xizXxvobJo/sHbG1tHWoDmj/QMIOgYzpEMVNXnPEcfWemeq9eBvLXACi01qnUZFo31glapzp51NvLZG4n3gWksIK068qi1t08696JjoCFxEuLlR21DdjSvd0SYL1yXgFu2K8HyDqEtqP2mCJgR2ohUDqtuqf8pAlkmwndpHU/KAOrheQnDQ+wwi4QRxYQq6BoifFdgIq21pqlcgpIQckGuFbRQDcGqHv87b/cAL51i24C8GqN7Rbv79mJyQAyGAzPD8YAGI5h7emee3pSfiO/2TgpJgrNk7q1jInhmCO1aZg8gYY5FDRPQcWgOpwcfyGjGMzw/vvvH3rqCZmNo0z7Xs3Pk8v/GRDEiYXOMvuzGgDgUuWc6jQXKDYtP0i7gB34AHGgk4Njg49gS9cWLkKGrgQsv6PKLBfOA//xa8X/7SebwJJ7doMNoBVvAyiVaiEeJ9sfxpG6l4h2rIGivYAdA82oPejwIwRrxWTTt9eKCfBgH0vqRiiFEN7Yg8BiJYSgZHOxzNly9OctgBsVAKUZ5Dndb7sV16+4FC3aI2/d8JLy9ttvTxpiMHz+GAPwsjOq9WemaeYRkTOcfHQiTFTk89zUbMwfcaoNh3nCDdbv8+va2d7SQwJ9KvLvbxwdk/OmjmXYzg1mHnYyGfOEmMjJWsqjSA0QK+UW3CAMAMeSwHKhAMjKguq2FlRJ+I2OJ0Jvim+QMO1CXRdrrgzb0U7VXQH+w+t1Hu6qUwCtuAfc3CssqI2qU7DEuMlLtq46POptrnecf/fM5dvtHSBRouamBau/FP+vn7U/2dTAope2Ylm0dWPMjAB4lgbqngYedl08/tKKDgV7Byp/J+BPN9yLZYCiBVC2dcfWjhD+hDqIz4dbt25NGmIwGF5Gpvjn2/DiMebc382D5oN5ZNYhnsKy61Hy6+PZbuoQeezNCWq1PEI5vyweT36zMX/EabObZt7fGN52yBkr49CHOCqv6dAjJ/WfwAn+CU2L1BSEBLTor6t3kt0atbRQEUGn4/XX0TeD/dPFZd8enVkf+4DWiZSlhuUT+SvUARF0E7c/s4zCCuWKU/7OXggUrYXxRcBZwk9WrHyuEgMXK7Xbbd5cpJsQpCJV4o3FfgmBIzXwanX5/w3277RFdizxGFzJXiCuF5Pvbdm1Yv/BJQ/gy4sp8IMtca/La9UE2OjZVxbCzQcAY7cWDC8X77zzzqQhBsPnjDEALzujTv7KhMvm5mZ+uTwsUqfSPVnG0QydzsknxE+cnPo4Y9o7GsVTMxsZE4Xy/Oo/I6cHmFn6H2VirKNMG3SGjZRDnNTbOy1ayETrstZohcqy8G0tZMmu24P62aDf9b+Xtl67utRYp2gtJEli28d8obx38+HrX7oOVKhsbrQunFtrNcLztToQJG2gsHqZOAQIOz+3thCm3bt3ewBjlborixr2Q/t8KQnTriUd4MN9riypRmgBZbv+3t6jK9Wz97vrq4VV4FJFfzJmRgAEeBa7IcDXTiU/gff3hCzz1ooG/p+7HvCLZyLo90EtWvrPHhR2/LYtcUe2QjUYDIZnDmMAXl7Gn/ubMa12mXb8gLWDA4+mIs+6+MnyzEZcO7myjZwROSF5mj/c/AyvzU9kTgk+257DxKDvvvtuqVQ69GD+Gt+J+H5Y9UoAIjv6ywUl0gRIbNmN98rNEKgoD1hw1hIdNqJHYwp2s4PDhN8mrlUOvnFi1wWCdBcoyDKOF8oexULY2AEKVhXGve2O1BKx6esz8KBnvwKpilcKAmj1qJcA3tt7lA1uRdYnWy2gpJOLleTjkbMCKE3BUl0Adnx7bYmHPST8YEe8taL/zpfawLcfFYAksKXQwJaPhEWPnjVhb8FgMBieHYwBeEkZk/xzlIF8eaK5zodmG6WBZt5nyBi+ZCptN0NSx2yu5ij5I+bfrhlP/ognRZ6IJ3JrOZkoxKcijwc4wS2OGRhYCAGOkEgLaXOQ/KMt+Z//7f/5nd/8VeCurS9VzvHgM7xyokOAyNeov//3/u5/+ff+61u3bh06NVaR0t7GdkPP9sLEkq4j3E+au8D15RqA3+mP1IkUEugl+1boJ2PPFkh0vyXn+VKc6Gg3TJaKJSBSomxroC018OF+/zSxa/VgvQkwvgtQpESsxJUqP2nYKx7A2RLb8JdPKeDD/eKbiz5wqpg+7KK0kIJXazQ64+Y0PLOYAgnDy4wxAC8vE5f/jzJRxMzPqBAnGzpn0tGh1eIZXkN2yQySLv9C9YDBTc0QLiN/0HlKRIbJGfGkNjfItwlwIrYtYyt3QQUz/Z1MxfA5AOM2DbQCSBMAlQg8kabAQqsHlOoX+sO8cpQ+ksLCsh1ZuPrmuWMns7S8+Vn79UvVRIWJwxnhAivFZMe3s96gVqEGeM1t35OAl45s/jPAlXI3jFcttej1TcKyZ2uwpbalXolKO25QsDXwWn0F2Ntu7IXWetcuWlVbjHuTLUGqRaTwYCfkkgXw1VWlDg4A/uf3S0KpZmSVQR4k/UuhN3siKzZ4Brl9+/akIYYZuXLlyqQhBsMzijEALyO/8iu/MoP6f8rk1EwzM5h//Dr9/C8j587GgBm2GjLmka3TBj3BjKOcEec3G+T2G0+Z/B/cu+++y2jtPvz4PElBfpiW3Ajb09ISSgFYjopjKqeAIO0AlBY5KMMlDrRKPev4b5NUKN1tUDhXcZY78W6Y9jyrdEqunipT8OqN8BFgCxcohsq1StqiJKtXv3T9/VsjP+7/7td/8zd/978Kst7/qajay4BX1MB+2AZW9OK9qFlz017SzC45V/Liinjvfmd8aUGiKVjKkdRdyo4qDt1TpOR253Gdb7FSDLs9YLmQZiURn3VMFfAzhxHoBsMojAF4SfmN3/iNSUOm5tk3FaPIqUFnJpt/sGqeZ/AMDKJM3NkYMHgxWutp4w7G5wl0lKmqt+ff3BgmZ9ATiTXx456ZPMf65hlzBN2XsUIkQWgXPBAgsFwKZYKuIzwgxr/dfnhzP722kOJV0L1R01lxAmjHAcr2op+2/bS9kp7BcoGiVZNhCv6mzBLvi7EKgDDtSjFuK6DqFJe9pObYD7r60/YW8ObCKvCj3eKr5/u9e2yhXVkAerEEhLCARB0c53scWR3vbsASdGNZBeCztjxXBlj2AFoBdRdgOxDAbsNqh8K1OGg7ZDgxTiRF51BO2lHMDonh5cQYAMOJ8SRMBc+zrzhETt05J4dswESy8YdOAs7JzLp8KvXPNGvko5hqi2P8ptC0TIx4gtsp86MRQCykI6WyLKE16P6qeWsLUIUScNvfBq4tpABJCIjkeGGtHFdUlxMVK50KhC2dJGsu5BSCpJ2d9iW8cs1dAJoqdPF6qg3ceO34nKIDdFalfL4sAKX50W64H1rAzbAFhGnfyHzWaQEPulGiQiF0rCas02/58gzEiouV5HQpboRWUbfWuzXgTK0/phFxtty/30gBBCklx+wAvKRM9BgGwzOIMQCGZx3jK2ZgWHS+YKXbTL82P63f4GDwtIGGmTboDD7qSSAAdKLSrF9+KjRx3H84jbEcW3jAFW+R5qPPrNJPXXccQkgg1RGg0TVVAWhtUfj/2buzH8uO/MDv34iz3n3JPWsvkkUWl+6m2ItGLY9moJH9IBiw2gDhV8MvHvjND/M/lAwDhi14YBseQxBsDOwR1YYlDzyyoaXVi9RrcSeLtWflvtz9ni0i/HBOZhXJvHmT1cXuysr4gCBunoyMukUCeeMX8Yvfrw742olc8lylklPvsqWFUSarDSNtJvcWAMAR7sZY1zz1XMlZGymgFagXG0nXFFvxayNvuzMCt+TmPYPrsH7UjOA7eeMwgHsDdxGagerEzmwIMNq/lpyv/s9W1G7sNANMSQO3D5/SsizraWQDAOuUOj1xxfHXoI9t0h/xJf3RU3Ocfvnb27nHW5R/odOG3HHOUvILAEd4NOn/sS8A5NU/XeEA0gjHSMj3+CVguhsyT5PpFkU2g862KTd7ao3R8PD5tEGloVMbZz0gdstBJoFuujkfnMlbDRiVlFJB2icZEpRrw+jQqR6VmTSPEPqpAyyVnQsLSzvRXaDqKfZbGm+D2t/yH6u+0khzVAoQoPYPwy7W0jt9b6GkAseUfdVNHKCbME7YjpyvzKjd2AEGKRXYHEs9JWCxLMt6itgA4DT6sz/7sz/4gz+YNsp6HKcnrjjU8Ze8j+fR+Y9O1Pnl38nBojz/8jiRwGMs/Q8cJwZ41HFL+hzPoz9ulEIIR2nASFncA1YprTMMdgG8Up7BHygBxHpYdsK0VDlsYkxeK6e/VdbtkYyBu2nXWWwU33a9zf7qcrgE7IoOIOMRtUXSsWeSQyfMecIHRmtbweKcNip0qsBMeGEnurs59oCAFHipGb+3GwJXTPbOEdPtS7S4WNG9lBfLRZywMXYWSgrQhg86YrlsgJdbOtOi5plbPfGg5145asov10m/5HqcLH+bo29ZXwYbAFjWCXDK44pJHmOd/YUczH/MOxW/5Pv5ojHAlyFf7afDAWETnYnMAKSp5wfAzly96bVJxkCn6jDQJktdEWQ6dsTkTxM3UNUWUpYpBbIM/UCWgXS/fdhqtOZ6QZlmwo4KStCdONU+oVXFbfbT3TzdJ69N9P7eCjgXm+rOwAH2EndR8Eo7eq/Hx8IdZwZIzVF3i5UmUcIp0vol8EIjAVbios1vw+frM+lO5AC3esXZwuZYjjJ2jwpYLMuyni42ALCs0+sgrni8S8CTnPS44vMOXdkfp73XFzVpwr/4i7849PljO+LQQDguroN0jOtpR0iV3/RNt6O7+QDtOvilZsLayrtUSq70YT/n5nO0UegM6Oq9hmwBFyoLGg1Eqg80/BowjBNgcXn+9uZmV3WQ8Lm6oo92GYspgoeFUDf8xe34wXZ0Z77E5tgFrjR8nzaw28mX+8OapzrQ8MpTLwEbRMPnw4+322cX3BI/3vK/MZfMhGovdq42i7/jTKiAuRJbY1rlLK+B1PaPmNWayN6gtaxfCxsAWJb1hJ2S84onvvo/8Pk73E824efApKm0EIDQWgjPuBIpgVlmgfT+25x5CTA/+wluKEp1dvYMZHryBng8dDJV9+cNMO4B5eYSEBiA2JGA6tygNg94MgAaTmMj2504IXgyvNXvgft6sADMBmc0e0DZ1Wtj90IV4O/WdxdlB3i+3t4Yb7WDevul+vd/flTOifvI8cAr7fgjADIj8vpCreCzQc7VlvqL+3LOIXRYsXcALHjzzTenDbGsp4INACzLOhlOSVzxGb9MmPFFw4aD8dKAkAipjZLCwQ8Auqv5MPHgQ7xZ8Y1/JO6vAiQTmwAAeWqQ9jxx5wNz8WreZlhLCeRXAhwTOIb6hRd6d29QnVUmbYjqERPmpGG5LFdHepDuwkysBsNEuvub+7XuIAuKtXzJcUcQaxmrAdAIpxx2dWKWoO7rn20HS8s810iBFxox0NUl4N7AA4Yps2EM/NZSduMmFc8MvmjHBcuyrF8fGwBYlnWqnc644mhKGMV+tZz9NgBq/hKg83r/m9v0NlApgPQCtxRM6ANw7dp/97/+8R9qkwEiHlO9CEiVaccF9pLOvL9sBEIrqrNA4FRgq6+7icMRnbW0MKlO5kICp5L3DgMygzbiNYpqnb/TDH+05exhesOdyTN9iitNOyw6DFxppn242fWea6Q3ugHwXFsB2zjDFOCdXef1WeWIJ5k+Zz0Drl27Nm2IZf362QDglHrrrbe+853vTBtlWdZjeoJxxeLi4rQhT5IBKaSD43keKjFOUcnH6e8CcVgGKDXobQA405ORjOO6MuTCBZKxMXq/M0Ba9Zp7SQcwRuX9h3tiSGb6DI6eEBikUeAsxmo8THehLYW7OnQWyykwbraBj/Y6+ciXq7PvdvaAEVKj5bRPPf+Rywwzoe7G8m7P+/rF6L29EBgr6UDdo5fy+qwClsrZhwCcr9ocIMuyToyj6iFYlmVZz5LPZAEdmhRk0EKpvCEAIFQWJ5E2ilKDUkPiFK0AgrK5c9fc/ERVG1JjwoOmYJ+V6qg4K/BLsRpEWY+9lfxbZyoXjTH5J1FPFJ0Eym7DlyXvkROFSYUg92K3n8pMx2PVOy/76yNvfeRtx3vb8d6Fqn+h6lfdYlGedxqeShlSLVLNbiwbngIaQTHDmUq6lxTBQ6wJHJYryerQyTOCTqfjFPG0LOvpZAMAy7KsZ9/BZYAj5PGAKNoBGwCtEMIRriPcSGaRzNzuptvdVPsVPMXX/hEwc6k+aU7Alb6Rggc3eOfHoS6Wy97OA4Y7rL6bfymFW4+oR9we702e6aHAKW4XLJTqgCcC4FvzlW/NV85Wzp2tnNuM4s0oBu5FmxW3onSW6DTTU7qAAakW7UAD25ELrI3kSNFJijKgkZKeUxwR/GSzlL/Qhkxzb2A/Ty3LOjFsCpBlWZb1kECAKBL/AZWRxQgdbt4FMrdC+zydjmosiBdfZtx1xJQtcJnG46xP4NBuAsYYPXdRowId01iSaYxfAggqQKz21gYDgGhA7ahpgTOVEiCE3In7wOr24I3ZKrAbPbhQmQO6agisdNZXRmOgkbmvXZ49akYAYi0qnrnZc19sF8cgs2EGzNUE8INVqp5xhJmtpXf7XqKKdb9NALIs6wSxOxaWZVlPtfX19WlDvhzG4If4IbJY4uvFl3RzUesEcKIhkOf2zHS7r1w5M3kiyqnIzlwaLy4Wa32QOCCLz6BkzGBn6CTAQjkDLnpeWjmqFpDvlB3hrY9H23EPmAlqwFdmvNTE66P7+Rh3NEj0KNGjVEcLoWhk0ze8EiWk4P5AALECaAfGFZQcSg7v7qp3d5UjzMHF3wu1VAijYZiJhdIREz+zTnMV/5Pehtk65WwAcHq99dZb04ZYlvVUmFrB80kyGozOTwCSiCRK0xQ3ZOnlN93W4wAAIABJREFU/PvKKGfzNr1NpIMXmo3DE/QPGL+UV//0nBJQ8hri9nVx+zqjDqOOi3pYSDSoSORFB3TqxUd11hX7H17G8O7t+1I4c2Gz5BSZSK1g2dkPWvKRngy3pMrCcuQ7h054oOwq4P5ANH2UEb1EAH/5oAR82HE+7Dj3h/L+8OFH5zgTwChlvjQ9yepLcppX4Y/NXmCwTjkbAFiWZZ14x0nx/0LiONXC4Lg4bt4ILCd3V729jdJzL6Vz52fOLzLYEfOX4tk5k0RHzAakOkp1RDKmtylKJVEqAUlrPgn8vJRQxW0BjcS5dGEONxTl5hGzJXqMMWdK9Zmw0g4qflIk95ed5kLpbP66H+hIDceqH7pVoOVPyyiC0DGO4NuLcqlMxTdAxQNwBX+1Wro3KDKCzlU08PaOtxu5Lzbjhm8rgVqWdcLYAMCyLMt6yAiBkBIhVVYEAACGtfeLAb1d9+4H3vaKF0dApKdU7RRIeh158HET1szOntnZQ0q/uw1FHhFpbFbeQ2uqs6ijtv8BX/iJiYBMx8WTVOeb/akep3qsdBERzQRnAUe6Y9VTJs2vDh9hkD78WFSay/X4tZm4ud+SoBNTctiOJHC2YoCPOgFwoaZWTnE5oNPMHr9YJ5QNACzLsk6YJ77ffygvyLPaBVmxHJfrH+Lur4Vrs0DWXg6HI1cE+3HCYfZ/vOQ2AP29vxGzbTHbBsz6A++j67rS0pWW2fgEoD4PIKQZTO/e1Uk7EkfrLC9aKrWWWgcEAQFQcurasBWtAAdHBEeTAkfw16v63oAP9gRwo1P8fRfL6j99cZy/PlNRgWNqvn5tZsq5x4l20nPcj/P+J1WYtaxnng0ALMuyTqTPXwx4NDA4NEg43l0CgZQIgVYc1M3MEtPpd2dmANFe0hdf7TpR14ncvWNcUE6GBP7K936afyX/vd8BmHuONBIzM4BMxjIZD+bnxewFs3nTbN4ESJN/9T//j5OmTE0ySCMgX/oPnHjkZG93t97ubn042PhwsLGbrO8mxXtbHfXvZKPQqUrhKJNNmhO488m6hjNlgLMVgJdakSNM6BT/Hf7jy9FsWNT7uVhLgERTdVU3cc5Vj5rZsizrqWIDAMuyrNPoMxHCwZfjcYzKAJQCyCK0zjf46zfvuJWGqs8AVW+m6s0wcx5j3Gh4UN7n88zBHzTqmJ/9yPzsR1Qe1uJUV9/A8YGaLmYQrbOms0W1edCMbBJtcITnSD/vTfZKay5/fqnarHmtmtdqBwt1rwUsl2uAQOweY8velVyocqmeOvufkK+1ea3NWMmxkkCsRMnVP1gvAb39ZKWtsf08ferY/BzLmsT+wjrVbCEgy3q2HW/L//MESiElcr9mTpqIhWVx6VVgY7w2znpOEhcjAUjU+NCJAOozlJvML+OF4upVcfUqwKhrxqNs/mI+ZORkiecknmPmLuVPzLD76quXJsyI2++3FTPhjB+ngBQOkOn4xUb9pXIRXZScGuBK/8KZqsR9MJz8Dvdrwnz3u9+dCUykAO72POBGN/QlgVMNnOpcYOYC0wxUMygOBEaZ+L2zycbY1YZETQlXLMuynh42ALAsy7I+TTqAdl0AN8AL0BpgtMtot+5V8lHO9j20Mo0FU276ycRrCcpkJggB+luAqc+TjSnVAOfu++7GbaJePtLP19VeKGaW8UuSKdV1zHAHiLK+K33fKQW7G/nzINHaqGHWATajDcCVPqBMtmSmXC/ORYp+xldn1CutCPjrB0XwcGfg9BKnlzjA12YjYDsSQMUzs+GxrhlYz7A333xz2hDLelrYAMCyLOtE+vzu/qNPHnfvHwFRFCOljCK0ZjwECEr/+b/4P/Yqzl7FMeiqP8tgG6C7drmpWbtlShOLbErhit4WcZ/mMgfFPfubycwlERZpP2VZ8bVEyHwvH69oHTCJqLYQjik38tykleFu/jwYDIr8Jch0bIyZC+Yrjg8slf1Jsz1qfSRLrlmuZBdrGnClB1R99ZOtQckt+gzUfQWMM/n+bnij61U92wX4ybN1+i3rS2UDAMuyrBPvSdYFEiJP7FFhABCUMBqjcbyWnAFq/nwxsnWO2jylumgvCqWyI95DlgFs3AJEXhSoMgOYQd8M+pQbxbDeeqZjehvGKFRKOjFh/+3rnxy8rlOdC8NYjaL2QvFIyL24n78cZR2gn25pkwGy2v7sXJ8mH0nkWVnZvNkTdwf+q+3iLOLrc2cu1JJWoIDbPR/YjQFaQZaaZ/Pz1KbRfyHXrl2bNsSyngrP5i8sy7KsZ8xj7+h/YVoBaRw5RqRpRpYgXaSrVWbe/fvm+5+w8TEbH9NYBsgigDRisHvkpFBqiqUXzEe/YLgD5P8Wc2fE3BmiAdGAcRdwBx1ADHYBJyxPnE0679zaFEhUhsoqVPPHUbWK4+WdxfppUql7oVMFOscrAwpUPXWx5jR8/0z5U8lCN7rOX63u9xyQZqGUXaglF2rHSiiyLMt62tgA4LSz94At68R5kvv9hxAGSFOyFCgK/EdDklicuwBQnaU6y3CHcQchzd0bZmcb+Dd/8t9yWOaGjAb9Won9i8Jm5f49j3se95KdcaUyrlTM3Y/M3Y8Ayk2zsQqY+/eElGQxUzlFXJTpWCIlsk+/T3+u1J4rtYF+tpuaZLHU7KVZJ4lSPWXJXnaFKzxgO3KBnUjuRPLPbhddDv787sbsflhS91XdV66g6umtsTfO7CVgy7JODBsAWJZlWZ8igDw7P0swWsVjghJg1h/guZSblJsPCwQB9TLjWHqH9wL7xcfrQJ+hI13x/Cvi5W9WXFNxDVDaXittr4mls2LpLN5+izEQFy8Bpj88dEIojinuDXoYjXS6egfwnYrvVGrbG7XtDVcErgiULu4D5A2AMyM4bJX+mW5QW9FwL06EYCcu/kZfncm+OpP9/vm53z8/B9T9Sj+VgQNwvlpkByXaBgBPnm3UZVlfEhsAWJZlnUafOUZ4mGIkBBiMMX4A+90AjMYP2Hwkz0crVEZ/S8y0AeaWmEBmWU3Wapmb5bvvyRCoeO35Uou5ywDCRbhm+z5ai+XzOC5BlcHk1T/E+xcVBk4EOMJ1hDvOOuOsE89fiOcvALEaAqFTafqLg3R3IWydKTVjNeVUYazMxsgFhqkEnm8knYRmkDWD7Gfbqz/bXn0wHB9UFA0dtBGdxBHC1OxVYMuyTg4bAFiWZT2zHuPmgDbCQJrEgPFCXA8hSYvMGfXKN0jGAGa/Rmd1hsFIySNT9iHvFPbu6h3ioViPw1FxwVctXDYff2A+/kC0zxQj8/ln5k175vCpIDAGaIcZELlamUyZzBEP/7KDdCfVERA6NaDsNoBIDyvyyDe5b2Pkzpeyiqt/suUD21Excy9150Lyf/IN/6VKCoSO8eSUoqXWs+Ty5cvThljWU80GAJZlWafCoTcHDn0ojC7q9sDB0h/pildeLF5v3Ejr7bTeplQHRK3mPJoR9Ble+ME7N9HaNx5AUAFQSdjvFWt9SN9dpdKi0qIyQ+vsg7gfiaPuOWhHCiFc4VWHCVB2i+qimUlv9Tv561jHg6zbT7e1UZrjXgI2hnPV9Fw1LTkALzYUMEjlnX6wNirCgFGmR5n2ZAnwHR3Z7P/HYgt9WtavkQ0ALMuynnbr6+vThjxJRjgmL5sjwHUxGiHywwRXBkUZH3BXPwHIYtBie90M+xNnhLyuf8uZwQupztAs9vvlJ7/gIAhJY2CUdeqeDp2aNFP21A0GCAeDm/3O2hhlinkG6Z4x2hceUHFbgCuKRmD3x1NSgKqP3GT4+Y7fSZyFEt24WOK/2opGWZHqk+oxcLMbAItlLW0UYFnWyWEDAMsWArIsq7CfMmREyRdZIgzGLfpnGTSL+ycAjUX33Z+y2y3adZXblIKDrl6flYzN2gYbDxhsM9hmXPT9pb5gVj7KX3rfvmJu/cLc/4Ctm0BtmAB6v6XX50nhOkr5stwryaTWPMi/34nGz9UeVvovO9VUR3ku0LGZndhVRvxg0wcc8TAImSsV76fqeVXPe3un9PbOhL+ydTwnvcnASX//1mlmAwDLsqwT4DGy+T8jz/Y5zjwCig1tlYgkQuv8QnCm40zHurlYjPM909vN/vrHZm+dUvMgVPis/HmUkJ9jlJsIic4AMbMgZhb4rX+WDxSzy0C5PyCsKZMaZ3Ja0SOirAe8VF+I9Sh/UvNmIp25MgAM2qClIdbjQJbPVabcAUj3CwedKWvgK21enzGNoAgDbvU9d/9j82prfLU1BlwBxqT2DvDJYbOPLMsGAJZlWafXpHhACifv/5W3xhVKCaXczLiZAeJ/9Wfi/AWxuKx+dhMQM2cBRvtb+59hNBcu0m5w5hyOZz76KWBW3zer7x807eLKG+riy1m1iRsAhHVADweHTwjKZLhhZhLPKXlO6Yys5c+bgZuZNDMpMMiGQsjy1gbAcL9+kZ6yTh+ksh1kUpj5Uvb6THKzr4FvzjvfnHdCR4eOBiKVKpPmkcDBh6g2NgfIsqwT4/CyzZZlWdbJkqbpcXb3j+Z5XpqmCJEX+fE8D53gBXlTsNQBSP/of8sHmw8+dH7jOeG55uN7jAeiVD180oM1tzEIARghRFgFzN2bAPUNgOpZKVxKDbRitCoVwp+eYJOoke+UAYVq/eJ9QP/mt4CaVzsYU4lZC2XgVADMlNvAjsCTpJqSw1gB/HxH/O4MQKQkPNqugG7iVH1dbyQGeqndULMs68SwAYBlWdbJ80SW+0cwAoREyHyPO5Wu53n8yb8Bgj/4BhDdWQGE5wKi0WZ9oNXEtbVIRjjNIhfo+a8DBGUoaomK5ZcB58YvzOKVfLzZWIPw8LkAcIQbqUEw8KVoZjrWQQnof+1VoAKO9PKgQxst6guA75Shi8r0ezf45v5Nhsk8ydrYAbzEAXpJ3pSgdKGaKGWAT7qBn+QFiHQE2jAXHrfQkPWUsF3GrNPM7lhYlmVZn6YVj7bMVRkIEO7rF9zXL5j1LbO+BYgrD29Amkor7857CMcza5sA3S1AxGMRjxnsMtgVL39TvPzNke6PdB+QWZr6XhqG4uJLDHfV5A8oZbLQqQJ1fz5/IkAIRwjHkUVcpI0GNuRwxx1qnV2qx0Q9eXFu0py5XipTTaaZCTPgjVkHGGVilIlAmvWRB3zSDQBtmC8xE2ajTETK5v9YlnWS2BMAC+Ctt976zne+M22UZVlPlyJj50uiFSoBjfRQKRKxMA+IUhsQ/YjRLuR7+VPK7JhEEVQwms6GcYcAy7MYY97+IVB+8atA1xSZQp6evjPlGKGNojoL1HRZZhlQiwxAiViP1H7eUZ6pH3Z3AUoNU53YXOxR+Z3fxVKqUa/Psh577SAFMiOiTJ6tpCtDb7EMcKMTABWXvWj627Z+ZX71jbrefPPNaUMs6ylif2FZlmU9O55MPCAd8WgJTp0SR8SR6XRMpwOgEoBym9lL1BbM1gbjgc4e+ZFH6UwsNhl0WXoBQAiEYO455p/noOi+GwJm66bX3aK/QW+TSsuZfK3WSCmFjMw4UWOgR69HDxiWfCk8wHFlzZ2tubMz/pmmvzBqNEeN5qTZHpUoYiVngrYnDKB08d9zc+y91GwUY7SYL2Xv7LqbY+ze/1PrV1+j89q1a9OGWNbTwgYAlmVZz6YnEAwIieODSKVDEAK9Sxe2nd62H9HfMvduAGbtxrRZEH5Acx6g1halUJTCg3whsbygg4p2HMAsvQRQagAvvXxJi4lra2FAZWGigdRz6nvd+l73oMNAyWnUUzcxY0BRhCXaKPpbTGsutlAu3lgjWAK2ItZHad0rJnm1Vf/7zeJywsJ+W4CFsL1tt/8tyzpR7O8sy7KsZ9ln7gof6+qw1ggRp/s5/Y6npdBSqCtfq3gtoObNAqLVTE2SLV4Q7fZLl2dffeHw9Ppr//X/ALByR6MAc1AUSDriUnEfVzx4X9QX5HCP1jnCOvV54CsXHlby+Syd5SVEvTT1Vu+ms+fyx5VUuHfe86JhMQo9SjujtKMPiv9kUzoBhw6tQIxVl2jQMLXn60XTg3aQXd/tXd/tAT/fDjbHbt5S4J8sFQcLS2XbCMCyrBPDBgCWZVkn1bFW85NNPyJwPFSKzqTWUmvA3b43c3vd//hdIJ09C7h3bwDm3oqYvGGP4+tLLwC62sQYo1Qi95fLXkm880N2uwDlFmA++AnCmVKwX7oAjgswtwzouUu0z+ffNOt3dKnhigCojVVtrBzhVb0ZHG/qCcAwFfn14nj/P60QuNK40rjCuMKcqagzlSKcCB0d6dFY9xfKTqSmzGxZlvX0sAGAVXjrrbemDbEs69fp6OX+8Rv9TvKpeMAYtCpmyxfNjuemRdKLaLXqtdD94ffcrfuUQ5NMuwS8tyPjSMax7O/p6/u1F7dvE/eL15WSqtSVUObmdQA1LTLJB0iHoEo08HbXiudeyVz6qrn0VUCOe7K3ZXbWgZJbz8fLYHpvAaDk1DOdDJ3kZn8d2I687chrBaoVqFHmjDJnJsy2ogGgTQa4wneP1bbYsizrqWADAMuyrGfTYwcDo+hg/W0wGqMR+x8WjWUxt1j0661XFqUSQd5gC7r7q/nPi5Li3rBflhfn6Pb9zbv739NiaSl/5WzeBmg3TGfV9NaKXgGHUY6A/ciksQjIwV5Pd3q6o1H5P6T72T5Rj3jQSzbiwDOjvUlz5mp+8TftJilQcVXFVbNhOhs+jEnOVWPg/tCfC6vaZHf65pNePDxljcBu3rw5bYhlWU+v0/ULy7Is69kwPXvnlyCMMdIBgda4QbHc73aTP/nTfIBZXzXbO0hJb5j9+F3Wto+aDsTZc5SbrN3B8cRMq3iaJabXMb0OIOpNRwtmLy89vwwgj1FaRyuiHtt32L6TP6j3o3o/AtzxwB0PaC7RXBKXXwd6Ygj4vV1Vbkye8SGDBhIdV92g6gZzYRFK9RLnYi3Oo4/fmPGBtbECQkePMvt5elr86muMWtYTZ39hWZZlWZ92sPwuOoKJg5yc5E/+1PV9Qh+g0wfcf/pPaFbF5UtMPnAwm2vsPABYuQHQqFFqUGqI594Qz72hZs/j+nV65tZ10+ujNHGa/eimZGJWvYNL1CesM3upeBQPaJ2lddaNE6QPEA+Jh8B4dqnqzdQTKVrnJ014wBP+nf5gN+7OhXWg7BZ3fO8NgoOoZKEULJeDSA1Hqu/ZT1HLsk4g2wjMsizL+qx4HFfKFYwmHgP4LuXA+8cvidkiXQejdT/S3ZgLiPaU9rpUS/SHLFbZ7cIZgOYSYK5/D+Dq11VzUQD+A5IEc7xyOmFRI6jfqKmZc04SsXMHYPasKNUA0hQYDzcAOdwDiAfmmJMDUHWba+NN4Kx3AXaAZqDaXh3IdKwppvrdr5z9y+src6UJbRCeXV9eoX2bX2RZXza7d2FZlmV9xv5etxcCuC5eCeEAZnvN3LzBOAJ0NwbMBz9HpYQ1nMkfKKmiUgJoNwDhumzdYuuWOHtenD3PI0cObO6xuoVS4j/4LeNNvLB77Q//yAgY7tJbz58oPwRo7a/+gTQmjUtRFr7/LsZMrf+T24qii7Xq5tiL9TDWw/F+Ys/rM1nTDzpxcdW3n44FCFgqVSZP9mtml9Ffqi8v/rGsXwEbAFgP2UJAlvXUWl9fnzbkcI91FdgAxJHJX+Qtfl3/n1/7G7oDgEoZId03LrtvXObY5W+S792k08ctGaUIG4QNgPY5J1MyU7K7xcoGIC6eycfrIyIKEMIR9QWgNs4cg2N4mA4EZrhjVj4AzK0Pi0dhHSHyXgRHCJ0iTtiJig3+u32/m2zkry/W3Lv93l7cyb+sum0h5Np4WPV0yTlWfSHLsqyngQ0ALMuyTqmjbxKnaSqyJHF8lEZn9LuAuHBOnJlnFAEkCUkiLr2ITs3NDyiFE+dyJIEPJO9sAiKoggJF3GftfYDhLsDZBZo1s9cTZ15wpS/Vkek6WWKGu1TnuHOXNCLN39LIdFbNcGd/TCwuPi8uPj8sFfmu/mAwYbpCyXWHWedcNVkslz0ZXqy1gMApB075h+vih+sC6CUSqHvzwOqoD1Q9NVbjIye2vrBbt25hWdaXwwYAlmVZJ8Nj7eV/ytEr/ofzG4xSuA7SE9pQKgNUKqkQRSfdcqhf/jq1CpD8n39V/JTrHzLpp/m/cwXHJaya+7fM/VsAwkUlhFXCKoMRIObmAKIB0eS6okAyoNIGePXrgPLD9zv73QDSmFJTLL+Qf2VqMwBpxGj3sIk+pZ9meSOwg+bB35yvKZMqk15tR1fbEVD3i8hkpLoSMxd6jqCfHPck5FfJrqE/z2ZGWRY2ALAsyzrlPh8VmIc9fffz5lWmhAHUzz5WP/tYX3w1fyyWz3u/9fzFmbI4fynxJ66AheezsXMn278mm0aAuPIb1BapzrqDTfPjH5gf/0Df3hBzsyaNzM33zIODRgEPPbp0M4ZMR2RxEZMA8H48Smtt6gv5lzqsZpWG0lklkQD1JeMG//D9P2ea0Kl2kmg3HvaT7X6y/f01vr8GUHXFxti70Q1udINBtgPMhD4wzGTFm5JcZFmW9fSwAYBlWZb1KcYI4flpkiAdpCSNVZo5mXIyJV+7JF+75GzfczrrojGfjwbM7oY/HmfZtEo4tbrZ2zR7m2L5Qv5gGIjhzNzOn68BzjdfAXiwAejLV4iPajAsyi1XBAdfutJzpZkvZUBq0tSk2i+S8jOTmJXbZGMjBDoz3sOf+jxPGincVEd5/tG9oQecryXna8lHnSLH6WorutqKHOF/1HE+3DOro/R4F4ytZ9abb745bYhlPV1sAGBZlmUdxttP6fECHJc0I80QEiEPsn2SP/0bsXhJnH2JOAGYVLnfGFp153yLB6tIUfT52lshGebfn/0vvy2uXDbx2KxvMNvSl68cPs+jkiEYspj3rxNU82e7RZUeA0YIIYRw1j8BxOIiIMY9kmTihI8InCrQ9MOXmtWy11odFX9fg5oJs82xC7y/p4B6kAFnys26V588n/XUeeLJUdeuXZs2xLKeIjYAsD7FFgKyrJPi86k7U9sD5wOOvkuQf9cUdTMNGLQCcCXAvTXurSE9pIdfBvI+wWJ2nr3e5FkLve+tiyAQQUBaXJmt9Afl9RV0BhzM4KSp3LyvmvOT5gGUHxSXBK68BBhj5kvOfMnZHW54oyEgkkgkERA6NepLWbWN42XNtjxyu77k+EonQMMXwN1+8Zbu9f2Z8OH5RtmtLlfS5Uq6MPnms3UEm4hvWb9eNgCwLMs61Q6NBwSgMrQGkA7SwQu8ep1GlUYVk2EywP8Pv2HuvMdgGyGQ8rt//N9w6NrO9fF9KuX6f/Z73Wa92yw2y1VryQyLkppmb49On05fzCxdOtf+zAST9mtvro1MuUF3FXCl7+Y9gIOqpwSuh+uJ5VeATMfAwYHDMWU6diQrw97z9SIZ6e7Af2fXv1gLgaYfNv0QOFMOBtn068Uny+XLl6cNsSzrBLMBgGVZ1gn2y5cGOlzeMTdNiCNUikrJEoBGjcZ+p63eBiBmFwCEOKoR2IFb7wONTo/WWTMeAvrsVVGqm24f2P7L3e2/3DV3PkFnYu6CjI5arzs4JGNAZCmQxwChU6t6M2meBzTqMuoy7mC0q4WrBWHd3d5EHXVO4uJqdKbjwKkGTrXlF1d7X2pFi+V0sZwC/9dd/fZO+vNtA7iyuFEQZVMKjFqf8eU10rLRi2VN5U4bYFmWZZ0kaZo+maggU/gBo1HxpRcCIiybaGR+cR3QnbE4M5/96IbTCvGqKC2q+7HBZ+iM8RjPZelMY1wsqcXyi87Og+J1qxXPnZn9j3aBjcZi8VPj423YqySpLfhxGjoP//REqlEgANR+an5/E68EGHXkTWVZhDGxGgZOBXCEKXsamA3mgQvV7sFYX4aDbMMY7g281NgqQE+RLy+6sKxngw0ALMuyrIc8z3t4lyBLkbK42ptE+CEg6nPiZR8Qd/fr7gPVWbg5aXP97bfvXF3OK4cagKCCMebv/r8Iwq9/zcSDeO58MXS+zVgTD8zWOt5Rn1Bmd4OZBdwO1IEk8IDdeA04Nw6Tehuodjq9QQQMUdTKdBLCo0oAHegm8aLJsqwr0hnARQKBLAPPNzYBKTB+iaIGEpdr/D8D+3lqWdaJcYwTW+uUsfeALeuZMfVa8CTGGBV4xvM9z0NplMbzMRq/UoyozuadgN03rojL5zMJ6eSdddcTi5cQgto8SXGkIH77d8Vv/66JB4B/9+Ng87548RWAeADQG4rB/uHDYUS9mb9IPMd3Kr5TcbvbB9/13Up1bxdQ7bOqfRYou02iPtVZjrwErI3KdAIMUwmEjg4dnaEzdD/b7We7UBQxer8z2Iw6Na9UcpxEpy827XVgy7JODBsAWJZlnRifz+15Mtk+RwvLhGVE0efL/PQn+u0P9A++J5aXxPISEDVn3N4moL3yUfMAw12AZMTOHXP7HXP7HT68xYe3RKmo2U+a4ZVonRNnzpFlf/y//E8Tp/JKzCyNA0cbRWeNzhowH3lzqzu0z+VDzOamo4yjTCVSAH6Jhz3OjlJ2iZXcjdyW32j5jUFafFbe6sVyf4LZMAPyaCFSYmN8VNeCE8om0nyevWBgPRtsAGBZlvVMeTJBglJOlIg8XT4aEY3SNEU4B0k+Yr5tRgMzGuAG4WBAuc387MTZ8mX3TgcvNHdXzco6IOo1gBfO88J56gvUF6jOAsW/XZ/KUeFEP+3nL0K3DqCVasyZ3XXAvPN37K0QFFcCRowA0VlHK7JIhkdNm+lECtlNxNlqCUh0Ua7UE94PN4os/42xuzF2F8KW0kKR7cS2DZhlWSeMDQAsy7JOpMfoA5D7gvGAQRu0OajcL167Kn/z2+LyVSjSfpIiG0cU2TuHzKHf++GPAQZbqzMXURqI5s7Gz10VQRlAJagEEOdfSWRm1j7BDRCYvB/vYcpuyHC3NBhiNE6Rf68vvUYUi7MX8y/F86+ZlfdLK3fIKwWpFGfKE+gcAAAgAElEQVS/u9mRWoEAzlZLmU5GWTdRxbb/xtiVOEslA6yOOgfjm36obRRwmtizEeuks5eWLMuynllfcK3/kHBdAq8oBuo5pAogGZOMcRzAXP+J2RsCnH/JTzLigdFaTV6vA1y4zMo6rKtYba78ZO6bu/HVr4zac4B37zZALQC8B3cAc+9j4CvnW5OnM1TaDHfZXaFxDi9w1j8xex2A+iLAuIhGRKlEaqi0WbuFEEVzgwmEkI7wIhWtj0ZNX7lyIU/y2Rjrb82bu4PinsMrWbJeqgDX16e3P7Msy3ra2BMAy7Ks0+voQ4M4zhAC4eD7eXMwQP3ouums5q/lKy8BZueBSSJTrgvv8IuwZhQhJRsPaNWhuEUrzpwPe53SD38IiKUXxNIL5qffW0s3xNJzYuk50axRa4n6/p3jz3FwcAMaS7geWuVdgUWrCTDYZrDN3gp7K/geQKUNEHUB5xj9CkInBDqJAzSD5YO8/wtVN3Sq7X4KrAxHK8PRQiloBirR46pvy4BalnViTP89aJ1CthCQZZ16Bq2REumiFXmooPXDS7TaiKVZs7udd+MqfmZC5X6RZ/PXKgBaO994wfnWlez//WH+3dKdT/KuvetfuVLMs/oJ1XkA6Rw2HwBpzGALGJZ9AL9slq4ghGi3THfDdDdYeJGFFwHqCwlJQoL0xGjX5B3NJkh0uh0lkYqEQAh6+wGSFHSSpJMkwKjZes/1u4kDxLooVdRLJr9Vy7Ksp4wNACzLsk6G9fX1aUN+WYceCBg/MK57sBZ3vvmacIrXol4HEJIoFlkmqo3P/zhAlgq/RJyw2wPQBnCenzd3booXnz8YtZT6S6nPqCOas6azxu7W4bM9arBVSYTZeIB09u7dNYsvAGL2opi9yHCX4a5onckH+llxvVj400oVAbBcKjqI3R2sz4XMhdzuB7f7AXB7UMQ5F6vNUFZLTj1wKosl+3lqWdaJYX9hWZZlWZ81GiVog5CeI0WyX+DS89VP3mPxRYCl2WL175cAvbIzaSqATAHkdf3DgChiNNZre/k3xcyCeftHgFm7b9buA5QaANWyGUy4VQwmGlKbR8g49AGzdg8Q4z6NRZL9g4jhDtJFuv7OGt01tEK6ZPGkOXMbt1bf3fUQYrncSLUAru9613c9YDZMN6N+xdUVV3+1XQOEkMDaKJLCngCcUm+++ea0IZb11LEBgGVZ1onx2Jd6H5/rAUIphERlgPrzt8x+Ly2jFRAvnJGN8MVyYnp7h09Srpp0TLtBuw5kl67mj9VHG2Zh/wRg4waeg+eYvS0AA92Jq/+HqrPAqqmKcy/gBgd9ysYexAPcoLgPAOzv6Jvh4XlKOUcgkcDNXp/9RmC/OVf6zblSO0jzUj91T9Q94TmB75Q8GfiydLZSlcIW1TgBbt68OW3I47h27dq0IZb1dLEBgGVZ1jPuMcIGISUC4ogswd0vnen68tKivLwEUC7V62Vu3jN7awC+D7z21QuTJgSyn94GqJTc7/8tq1vCLT6A0n/3A/XJp7ObOg8YjmhUOaK4ptb0NjA6IISH63tKrbhadADA6HGjOW40i+8OtkmPugBw4MVmCvzDZpHff2dQlPrZjb2qpzUKEMiDf8dqmKlj1WC1pvqS1uiWZT3KBgDW4ew9YMs61fb3+BESwGiSKC/VjzGkmXA8QPzmPwWCzQeAOHvp8KnACIQXui8uUC6xvccLRZxgjMn+9f9eDGqeEZWyqJRFaw6gFORNBqbo58cF2lz/gfn4XcbFSr0kK7q9XLxOAZJyhaBCqaYbzcOnAuDDd9eEkBXXO1MBmA33u5sJebU5C3zYq4Sy6svSe3vr7+2tK5MqY5f+lmWdMDYAsCzLOkWO2SwMIcZxRhBgTBEDAErjOLz8LcBEY6Rj1j9eeu0NEVaOWP0XjKFeBTi/zE6HdkPtjAH3+QX3+QX3H39tf5wAjNai2qTSwJuYVyPKVfoD3IDRHiCuvJo/z3QUiFL+etCsuzJMgzDvCmyGHeGXxTFadqU6Ab4yo1IVN7zapVoRM3x7oeEKsz5+WPgI2I72yk7dPbK9gPVUuXXr1rQhlvWMswGAZVnW6TU9HsiL/7g+no+UAB/9xN3ZE+UqIJqLRD0cBwyOR+/wrP0/vPZHRqXESfx/vxP/23do1AD3hXn3hXm6fbp9gKhnen3T65l8Fz+sA8TT3h77I40WL7yclauAefCeefBerItlutfbBfxUi0rTGHNUaVHQRoPxndATQSArvlOUDBJws7/995tF999BNhTCCGG2ownXHqxfjl2jW9aXygYAlmVZJ9Xnl+/TF/RHeuS2gBFSpJkyUhLvb3g77sF63cQjE4/Sf/u3gNnaPHS2RwkvKFrwGgCxtCTOLIozi+L1r4nXv8Z+BzGxeF7MPydK9ZiYg8Zeh7n+4RqtGZIRnQdEfaQHuMO+O+yLaltU24AjPE9BpY3OoAgVVHf6kj3VEaBNBgghvr8xSvQYmC1lv7nQrnoaWC6zXGaUyZrnPxh38jdgWZZ1ItiqBZZlWaddmqaTLgoLrRLH96MRYUiW/vP//sf/8l/8zqMDzPaKmF9iW5mde0W5z8OY9U0uvBD8/lfo9kVYguJsYe+/+ndA5bfbAGcXTWeDsQv4qysgHl5FOIIXkkaAufMx3/w2QKUClKIxkJLt6C2CqkHT/cAMd0UQHDGZKz0vTgQyUgNgnHVcGQA/3jK/d3F+f4xZHXozAmWShVLxMboWfyov6Ffg8uXL04ZYlmUdzgYAlmVZJ5vnecff+M/X+sf4EWGMIRobNKNR0cTX9chSMTcHcP395J0t9zfOAro6w/aUQwA9SosTgEYNMK2z5sF7QON354BMFQt90Zg3Kx8Cotli7R7uxMW6MOaDTzavzrrcvQeYvFLRzh1gHF4ABnoPEG6D/cI/6oP7BOWDLmZHK7mNcdYFQrf+am0X6MYbQCNYWB0WwdJubFwZ3BuMZgKvvV8qyfr1+lLjoi91csv6VbIpQNZEthCQZZ1a2oj8hXAdQHcGjEYIoV3f9Hum30veKdr0ilJZZqlZu0mSUp3YZFfO12ifZa/LXteMhwCuxJVifkbMz4h6VdSrrGwUc9brZjwCTH96uo7aHgJi+ZyYabJ4lcWrJROUTNDyFlreAtD0F2M1SNTI+drLCIf4qEZgymSJ73kykMiK26r6c8B8cG4+OAfM0HLj5GLVuVh1tqIYuDcYAaEJAqd6xLTPGFup07JOOhsAWJZlPWum7e4fj3RS6SCE8FxZ9hSQt9DSGq3lf/Gf+L91UVy6AtBdAxjHeTeAw/k+929wsWgBxsr7NOs063gunms2O2azA5jdNXY75s69ZGaO0Ti/any4JCIZ4bnOt7/Biy8T9wFDBnRNp2s6ZClZOlbdfLi/8WDiVI9whJs39x2r3sHDvWR9L1mfGQvGHUAKVwq36Wug4SshjHJErI7Rtsz6lXjuueemDbGs086mAFmWZZ0kx8je+cI+cwHg4R9hNF4AIwx4LqWQNMV3STL35z/KB5v+IG7N4W3guUSlz0/+0JlLxAOWzokA5p7X7TLA7bfFxZeF0gB3908A3vhtPpm2x+x6Rf2fYYdK06QZILpbQFpXniwBGA1Eqh+khvayivZEWJHuUTtf2ihgmEUV7xJg1j4E5PNn8+bHu2Wgk6cydRIJ9FIJZDrpPeH/J0+707zIPs1/d+uZYU8ALMuyTrbHaPR7LFphDFmMoKjH77iAmL0olp9ntsVsy6zfAwIRXn7j62hDOvkibJwQD/WPrxdfbn3ixGMnHosLVzAZwPo2wNo2Spt/+Fv3H94mUzoeTZxQZQC+z6DPxn1RnxX12bwnANAY65FMRk6W79YDJqxMnOoR2mhPeJtj90a3A7B4hcUrgBQyrhcliRKdJDp5MPQyLS7X2rPhxKvPlmVZTycbAFiWZT3LfqnjAiGKa7iZIlMYg3TM/Y+hSAQSZ1/AaHPjOsmYve7Uoj3yjVfRCjfADczdD83dD4EkCMXMrHjlJZbnWZ4Hen+/Byw8EEfOJUmGqEyv7gAPY4+gOnN7PX9ZTkxJVEqiYrrrbNx0xgOMLroZHOlyzQdudDti8xOx+UnTW8ifV9zmTHgulCGwUHrYqFibbC44VoDxxJ3E3Wh7hcCyfu2m/x60LMuynk6T+gA8kTMBIQTGkMYYCAKCgDTxPE/MLxMPRLUuqnVz/yNAXLgCiDNnUUk2Id4QpYC9HW7eBOLv/ij+7o8Acf4K/W1/ewUgrIrlK2L5itocVi758vxs498/q5KHi+zPyiMTbQCzvku5hReaze3iu/WFitcSlTZAFgNi7jKA44n9ngOHcoWbmGQ3Hjd9dbEaqLmiw3Fjr6eNDt1a/mXJKV+uzVyuzYyyzuTJrIlOYtxiWc8SGwBYR7GFgCzr2fCFQoJ8sNGPNM0VAiFwJDozow5g7t039+4zjsX8GdPbMG//DL98dJPdnHn/Q//3XvZ/72X2y3GanR2zuWnu3QLoPACc54qK+6995cKEaVAmxQupz8rnzwL4+9cP0qF4/mv5y268bm7+3Nx9f9huD1QHxwfMePpt3apngE4yGqTb3WZNU5xsjNJuJ17Pbwknepx3B4syUXIbCpsIdBq9+eab04ZY1tPIBgCWZVnPskOX/l8gHvA8PI8kJolRGhCVFkKIV78uXv26uHAZ4M4DwHQ3GQz/9F//y0OnMb0BM/OcXQDY7rDdAeisoYsTA9Fu1JN1tHJeXMJ1iBKAKDp0NiDP7Ge4C4jLZ8z1H5kProulxeLbWWKSIinIPP86EDiVzHOvNDNROipXZ6wyV3i+LC8QVD3P2e/vq+efO1jx78VRrEdl5ZaVO0iLMwp90G7AOmWuXbs2bYhlPXVsAGBZlnVirK+vTxvy5CQKrdDK8zwciSNxPYRDbR4wKx+ZlY/M/8/encVIdmYHfv9/d4s9IiOXyKUya8laWWSRbKoXqbvVkjySvMiQhGmbbwYMDwyPYRjwGPCz7TcPDcx4GT8YM4Oxxzb8Ys60BEuQ2rKmrVZLrW42ySaLVawta889Y1/vdvxwI6OyMmPLrGKzKuv7gSAy7/3iZGRWRNxz7reVt0jm1fmz6tIltnZIp8UfNuXgztUH6uyi8c6vGu/8qjr31eigKsyr13+Z+dcA/68+m9muqvwEgGl2Jx/3o3yXMMTzUYpqHadX0pi0KrQqQFbl1OS0UXyYurUCmFuPAdTobgoQIOVFl0gVzSTOOjPRf0k7jBq1TD/rxHJOwg1bneDQPQB6KLymaV8WXQBomqZpg7ke7XZ3CJDanZVrWHLzkdx8pBYu9Bqqi1d6Xx9MbaXlycodDINWJ/yLPw3/4k8BWd2Uxu7k3bXrgPmNC+Y3Lsjj9e7ew4OJaQPMLEF3d2G1dIJEHggz02FmWm78jMY24P/oY8Bu1lQsrpJZaQ8bApSwbMNzbSMRJlJhImWpp3Y2qLjrFXfdD1XcTN9zK4CiOxwokKFTljVN014keh8ATdO0l8lz3wfA87y+I4JU3AJIpqmUQAFep2Xbtlz9cbT/lzq7IJ99QCpObBJwVV3WNkevsbNT5s0zAKUH0QEpb9OqPdVGKcoVggB/4G11JYSWxfo9zAnaHc7NA9F2YEYVIAB5dF8sx/zqeT5/BERzAMZkdDphLAbEzGTd3waulwDjjckwbXeLk0CCQLyYmUqa2Yw9etNi7culu1w0rWfUJ7X2ytPzgDXtVRdP4Pv4vlR379a7LePt8yqTIrW7ok5jxy5tE60d1I+aSAcbTTJpZibk+n25fh9Qy8s4ljqx241g2mzuptEjR9T4HYBEHCAeA4KpJVJ5UvliPCjGA7V4Si2eAlThPPms1IqEHqapnGGrAAGepfBarm34oWco01Bmx290/AZwZRKFkbLyKSs/HY8DMTMFCNL0B05X0DRNe9HoHgBN0zRtsGjbWycG0HYJBbN750gebZBJA1RqWI6KpQgl2ixsmFINhbqw6P7Lv7C/foaZnGytmBBYcYBCHlBLi+Enq+ab5ymvDAojLVeu3cGcY+YkoKJKIJEDJv7VHwPByTlAxZJRe+XEoDEo2l5Wo/Gg4y4hoJx6DZB0zFDWt+YKQNXdvFNdBRzpdIKg4VcUqtiphiOiapqmvUBGfVJrmqZpr5J9I4I6nm8EHhICKrO71GYy6//ZjwH1jZMAlU18V7Y2ALEGDLNpta3XFymVvWpbvdndV8v7yV3nbF5dOgXI6jquSzsAZLPaP8geKmYDzBcoVclnAXPrrjQeAer1ZYBaU00VqNapPFa5WQDZIfCkOawMCMX3kslCwgBiZjKahWzszhtu+3XHSC4k7dVmfe+s304woONDe/GsrAysKjXt1aGHAGmaph1/h1j38wkD18NzAToeHc/zPGxHLSxFp61vX0ECJCAMMSxcT/yQ9oi77PbZKQBD2d86rywDkM/vA7guoE7MR/8ZZ2bCmyvEd0uOgyzTWJqk0eTiGxROSq2OMpXjKMcBVHaGuCONcvbUJXxvLuWTyA4MtUcoYUigUE5oAuzu72sZjuJJkn82uxAzzZg5zoJCmqZpLxxdAGiapr3Enu+E4L1a7e7C9nYsIX4ofijFbnIv925Y33k72vZL6mUg6iIY4tPNDq0OmRSpROkHxeig9Y2z6rUzUQ8Apolpyu27gFRH9wDgBxgKoLoRHZBqRao1qdZotmX9IaAW3wAonCNboL7dTCelXLr81vLAmLtzGKKtvsRrEwbY8ZQ1EZ1NWJmEnWsFVWCzxWYLYMKamYypwdOVtWNieXnYK0fTXi66ANA0TdP6UQrHko4PqKSjkg5AucrEiei8+C6paTV7Tra3ZaeozlwwTkwNWbenK+4APN6k1qBcA1CGSk48aWCYavE8zbY6OUswtLxJ5YP7WxgG9S2VSatMWp18rXsqnVT5uWgZUNpVYJxdiuku66n8sFOTGsr4tFqJjseM5P36Q1T3otnwSmez02ez03mrMDiYdmh6oR5N+8XQBYA2ml4ISNNeIiP7BEY26BIBVMLGNBFBRM3kuqf8IPybT+VnNwCq64CanABwkgNiQaddPlHANNksTf5HX+seNA1AJbKAWlwgZkvLBSg/HhinxzSUiLncnU4g5aqUq4CaKqjlN6ODKjunTEu2H4bxTCOmAvHrc1PR7zVINNw/ZqaBWlgBfBMn4FFzB7hfux+Ebsx4skeBi9sOm46RsoxhYV8u+lb3EGfPnh3VRNNeAroA0DRNe+kdaYh/17B6QCAICXwsG8sGSMfZuBmdNH7lKxTv43fU1CTKQEKcJOHAPHj1kwdANJBenT0dZf+dB+vEM8QzsrUVNZN6lZmz5LMYiuqwTbtAMTED4KTU8htq+Q3ojkRSM8ty/Wr4N38GyOJlIFUqAemNIoOmKQMQig9iYmasyYSVvWJ1f53l2NRian4xNQ/EzNS1sldxN4Aw9AHTsDP2WHMMNE3TXgS6ANA0TdP6a7sBhgGKMCAMuhOCQc1MqVMFWX0MEMsA4bUV2tVhMwFMQymDap3C1OMHm/LwcXQQkBufyo1P3R/ewbKwDIDGzvJr5xDpLvPfl+/TruA2mToFUHxA8QFAbo7tu1ET9cY70iiqR9fUtZ8ydSq9URwY7YmoQFGAhQVYtTJOCicV7QkAfLRTjpq6QTNuJN2wrTDawfBaRTuEIy/Uo/suNG1MugDQNE17yTzL/X7GHwIUhhKGKAO386QHQBnMXiA1Cag3v5U9/7ZMzIrnqvOL0QgcwlFzAHoNDINchskcc9OA8xsXu8frTZpl70//mvWdcLM8IAp//3/4Z9J2/Z/dAshMk18iv1Qxmt3TO/eZmpC1FYBqA5D1O2r2pJo/MyhgxAglTswXtxXUWkGN9BSJXLH9oNh+AFTdrbpX/HpcfT2uEmYGqPpFIBC3c9SNAI6c7Gp96SE6mjYOXQBomqYdQyOz/H1VRK/9/uNuB6DTptN+sjtvehrbkut/AyhMgM2iyqXO1zYYxLQk9FEKP8B13Y/XAGoNQKXy6ux5lcuoXEZNTQDeD68D6vW3jen942r2ThJVccf6yhmqGxQfUXoYHfQMITfLwuXoW1m5JlslNTcH484DBqyAdri77bFphYQh4XrzYXe730SeRN4xk4lGK2YmlVJ1r5K2h40sehnpTHqkd999d1QTTXtB6QJAG8v7778/qommaV+aZ+wT6M8wlGEQhOLvubntujSK8vO/tHwfkDsfyuc/mS2VSCeDG4+AK7/yVv9oUSEBZLKAc2mq+HCbfA7Lol0H5OY9uXkvuLOuCjP27/y6/Ztfkxuf9A+1VzwLRL0TcvPnucZu2dPYUblJWm3AuPIawMJ5wgCUWIP3FgAQMSwg7zmJToAyUU/Khnl7LqcmlB1Tdsys7gBu2AJCIZRRXR/acfTee++NaqJpLyJdAGiapr3ERt7pf1aWCbgtD8fBNKOdudS5y+qdXw9vPAZkdRtQk1PG8rz5ztvUB4yzF2SrwuK58MbdcHWHmTwzeU6+DjBxgniOICQIrV+5AtBpAOrimySfrLdzcKhMuFqUW7eIpahWemm6Xdlq2t0du9Tcgrr0NXIL5BbU7gSGEUw7EK+l2jUnBFAqmg8wHT/tGMZOsAm0g3o7qMv6Y8kVEmYuaU1k7KwT6L2ANU17aegCQNM07Vg5WBKMUyQc7EMIRQGIqGRcxaxuhu25f/c/+Sdkdxe/f7ShJrPGr/5r+B01McsQlqlsEzBOFgCaLYCbH9LuYBgA89PMT7vf/5BkHkK5dVM27g2fUaDSMXXpMsDMAkA2Ja0q2TmgaSupFKMtC+TqT+TqT2iVUAb1bepbQ2KGEihlOEYCIJ4pd9bKnbW9DbaDNSMa9XTxK72DlhjhsRsCpGnaMaYLAE3TtGPoOQ0KEq/dATAtwgDDQCQE+fAHcvcj83d+B1Cvf7XbdOuRbD0iPdU/kmGoySzlVSYKxptvqIVZNZGNVvlUsZRKZNREVk1knd//W4Dcug3RbODdgfj9qHyedpXSBqbjTS1EB2XjZjOoNoNq5dSs3P+UnXvq9DKwdyTPEAaGH3bMQKItgRN2LmHngKZfMpQhdAuSh/VS0680/UrTHzhN+culd9TSNG0IXQBomqa9TNbX10c1eQ6i+kGiPbN8HwkxzCAE08Q0yXd3BAtLLRpF/HZ3Lc4hRKTZJpYKP/oECD+5BaAMas3wwx8A4nnieXhNuXdTah3/YVXOvvZk2nFfgUutCrD9EPBPnFOZWZWZnTZmpo0ZQKUzUUP19rfdVBrfBUgNKFEACKOdBCwHqEu16paqbskyunf3TWVPxpaiZUBT1mTKmgTuVEu3G3VjzCFGmqZpLwBdAGiapr0SjtIn4IcgAEGIhNFoeEDlppv/29/I/asAsd3df22bepPqdt9IEI2nN418mnsrKhWTOw/U7qrt4ad/ycojVh7J40e95karisg//Sf/84BwYNrMnfYKi2Sn7AB7+yGBS+ACbTNM21PE0sSzwU8+BBxj8C7FewihbSSEMB0m0sZEsLutmRu2YkYy58xF3xY7VjuotYPa+rAuii+ZXmBU07RBdAGgjUsvBKRpL4jDpvKHbf+EZSKCEwN2N/kS7HjjH/1RdN783d/HbeG22LlPqTIwDvz9f/CPVTLJ2iNsC8CyALl2jXabZHdZnrDUkq2SbJVwTLOQlOIWQXDl4tzAoIEXfvQR4MUTAPkluX1Dbt9AmfFwd9eCKLIEgXjEkmQL4e5uwUOoVh3A7wDTsXk/dCfMaaDtV/1wdzkjCCW4oIKT6eRCEtccuAWy9iLQY6I0bS9rVANN0zTtGLJte8jk4O5Zke7+X04M3wOw83he8vdeR8T3A0BKG4CsbamFE9Ko0Bw81N60mJ4C4cx5VAVo/fmtxLeXgODmmpgox1QnCgDFFbU0G/7w84GhABA/MC4t2J0OW4+JtYg9WTIIrxEHP5YE1G/9m4DRnQOgjGaZwZ0BIuKHHew4oGKphOlHx7e91ZlYd5rBcrYDmO1cJ2i2UxnAMRLdmcGapmkvA10AaJqmaf0pw0CEwCPwMA3CkHYD10V273YbBttlgIkMlRG3wKVeZUP4lbcAlZ+UUhFgcsL9s2vmfBpQV5Zp+YCyDNa2VMzEs5/8rANUMhsN1mfmBIR0GmpxHpCrPwbUxSty7zpgmLHa5CRAswyQnBwUsKdOHWal04BY1duaNvOAE+7vM/9gy1sI2+lKzWpUG8GTngFtiC/3TrweFqVpEV0AaJqmaf2JhNG9cAA/iA7Ztq0ufx0JGkE9DcxOASrInrZX79mzxvq9QdFUOksmCdAsks2pTDr5H/yGbG8D1nfe8R6vEpUcAHh3yiplgke9PShg5M5PPzl7ZpqpZZI5/wd/CThvLqqlJZrlaCOw2sl5oOVXAZTCbQ7pAbAMx5cgbqbFifuhm3Nm8dqlTguYjuV9ywByMgvUWi0gXakBJPMpqzowqPa0l3GP4eXd+SqadjzoAkDTNE0byopBE8vEDzBNnDi7u97KzQ/U0hVAPvrroSEApFbl0hKPHzIzKZ2OisUAdf5tJxWLGnjf/1S+cQ5QZ087Z+FHnwb3Syo7bLxO94vZUwDNCmD93m8r5QN4LfXWN4FYpdIKqrYRx7SBp0YKHRCKD1idDtBd/MeOZ2wfwHctHz8et1X0hFtnsp2AReP2Vax5P9SrAGma9tLQk4A1TdNedYMmAyhlEIaAnUgTBChw4gCtity9mqabScv9T6g1lWXhJKUzeFkc2wpjCYCtoppaJD3TXZcT5OdX5cFm9LXKzwLyaFWdPiXheDNr6zv+xDTJnPX7/xbKAphYYOZsqABaQRWw2Z0WLMMmAXuhmMpQiZxy28ptV9wN/E7CTCfMNICEVqvZCmutsLbW2gKM21eBsrtlG7tdJcfdlzuG58v1MvZdaFpfugDQDkEvBKRpL6BxNvrlSGsBiQgIfod2U7wAoNMCJesPowaqME/xocrkVT7bfUwovk0q4NAAACAASURBVN//+chGxajtEApnLspWN4J8+mOV7t7jt7+yiGNLoyjVKtm0PHpo5uPSHji2XhoVymXmlqJvfccGZPuu5PbsSVxazdgzOWdWDHVmeYFWafjuwo5h2kbMC9u9joJKWAbiLXdblbaNCuCHHT/sTMfSWdtqnD7ZWT6ftrKrzVeoB0DnwZr2stMFgKZpmjZYGG2MZapUEieGYQDq5CV18lJ3RE20mieQyKLUsK27LMXWDsCdzzFNKa5KrR6dCduB8c5r3WbVBqDmT6kLV9TSXLAzenJtZ2LSqletepV2dyC+SND0yureVcBQe650uQVKm32D9IS7XQRtM8hZUzlrartTfmQ0gaQ10XRUzEwBbtgGLCMGtILqUuJV6QHQNO0Y0AWApmnacTNmn8AgTx5umEQLAZlOYOye8try+Ufy+UeAVHaIpdRXvkZitwfAGdjPoGyTIMT3idlqsrukZvi4KDvlboOFAqUKga/yU4CUV6nXBkUDaLbJpNlejbVdvBZeC1DpKaDl7z7Q77Bzj517rN4aEqlHEF86pU6z5j5VJyza09NhpvftZrsjhLkwZSjDUIYb0pL6gWDacfbuu++OaqJpLy5dAGiaph0HRxjhM6ZO0422xML3ugvknzmnzpyT6z+JGsj2I9p1fI9WBZF/+X/+T/0DpZPRkkE0WvLxz3iw5v+kO5rcOFWgcL7bLJ8DQOF6Um9ZixP9Yj3Na0ujKo0qIM0SkLAySTdQ82cB4jniOUA27uN1gqnC0FhYyvFCBXhhm06DTmMheSI6lXQDS9nrzR1gYrceuF+vJUxzzJ2GtePkvffeG9VE015QugDQNE17+Txjuj9mF4FC8DwcGz80g925s3Zcrl+T69eAzuKZmhPWFxawYoA0yriDI9sWW0Ucm/OXgs1GdCwsd2R9myAEpFiRzZLcuCfVEhDeXlcz+YHRQOVz+F74cBsnpaaX1PQSoJL5zid/rZrdXgWpVWkUaRTpjDVGX5BW4OZjRs2TmJki2S0/QjtWsVw31h3nk7LChuU1LM9pDZ70rP1i6ZU6NW18ugDQDkfPA9a0V0ertZs0Rwtu2jFCH7el3v6Gevsb6kR39m2mFUijIq2ays5gW4Hh0G+tGGXHmc4D3L8D4NjWO6eMyTggW0X58P/rNjs5B4Qf/8wopGV1h3E8uAMgQqsiG48AKmty9SN5dDM6L6vrQLh0QXZWzc6IjQUydiZupoHtdh1Ypy6EhrKAVlB1Oq5jdtcmSoVx4EQyZirHl9FzFbQvmp6drGlj0gWApmnacfAsfQLDOgRicZRpx5xodq/neVi2rN2WtdtA3Mik7bxkp6PJwVixIDs1KJLUKmwWiTlMTZhvn44OGqdn1dK8WpoHaHeMX/62Wjgnq1uAtFwgLA2cBiCeR37BODmDYxMG0fqeKptRS+fl4TqAHxCPoZQ6MR+evjQozl5mEHaCRtMvT8czaYtPdzai44G4udh8zo8DhfiJOT+WMHOhHWvGzKHxtEN4lRcY1bRfMF0AaJqmHU9jjvMZancZfssEcNsEHmHYPnmuffIc2fnu2ZVPMAyVzsvGPcNzjcbAlL39eenOjTXqTYBsSl3+JqEQhoSheuubanEBkOsfGZcvqlPzKm6rCxeVM8Z1auk8IDevoRS5OUB95dvqK99md4FRDMP48MfG5j1aHYIRY4EMZZrKBuJWBkhZjhd2TDEAUnlSeaO+3WsciHe35rWDlsnRCzBN07RfsDE+WDVN07QX2BES/XEfIhAESEgYYu7e6pYgfuta/NY1AAl5dB3HVskcoJJJKqVBwcLNWvxCzl+tUqz2VgtVb3yLtS3WtgCmTsv6HfLZaDqvOnkKUKnBG/caBu0qhVNc+1iKq92D1Q2qG1QeU3msJhaUEyeRA1ic6zbw/f7RAAhM5YdeKL5l2I6ZPJPt/tZtaQFe0PaCNulCPWH5YQd4UAfIOTNmd3tg7VmtrKyMaqJp2rPSBYCmadorat+ooT5VgVJt14fdngClMCxMW01OqslJNm7I7Q8BRBCR4qaUq6TTDEnZAZAoXDLh/8H7SMD5U5w/Jes3sGxAZXIqP49hkMixdr87/WC4E7MA0xOkZ0hNk5omt0BugWYxOi9um3ZHxVJqeopkblgocMx4ICEQigcoZcTNTNzMlNqP6t523duuB92wofhrTXM5ExMJ/HDE1AJN07QXhy4ANE3TXjLr6+vDGxxM5ce95Q/sLQy6ybdgGij15GBqSmpVQGVSFCuUqlLaAnAsQ2HGEgdjAsZMWrzAem1BvJBkt43/h9/j1n1u3Qfk0VV2KnLvQffn3P0ckCE37D0fr83WA1xXzZ8n2vArep52DDvmTS9500v4bUDlJgHCgPawvQVMZfuhW3HNTtD0QzdtTaV9xw/bUX7vhp4beqayTWU3gwrwrVkL8KQtjFGoaJqmvRh0AaAdml4ISNNeCs8yLbgrmtorgmEB4nqYVpRhq7kzcvMemQKg5mZlfRul1MS0VKtSHrF0j3GyAHD3kfn2WfPNM+rNK+rNK1TqhGH27IJ6/R0prRGGamGJwgnljDG0xrYBNXeW9DRWDDuO18Hr2Bsr9sYK4BaWpLgdXrshnocxdNqu1wbmkgkgEB8gngnEC8RzjETaygI1r1TzSoChLAWGMvzQtfUQIE3TXh7WqAaapmnaC8e27UPd1D8aCQIJDK/TiVkKZapUGsAwaZVkYwOQn/1EfeXXAZW/D8jOBqDy032jhZu10DTMCY/s1JPtAgxDyrtzamtNDCX3fg4mxYo8LF60uNo3FgCfruxc/urrZBOsPnq8+mcnfvltCgXiGdq17m3+yZMAqw8AWd8BVCzuJZL9eygidtxQpqViCTML0GmWKK1Xs8CFqexqswHmmfRJoNypAkoZgKGsVjh002LtZaZ3GNCOH90DoGma9qoY3icw8Kxl48QBJKBRI/AANTurvvWb5LOAEADyaBMRJqaim+gHfVbrHg+vXgc4Oa9OXFLZSXXysjp5GUNhKEC9/e35r/yKWlggl5FqOxhnK4CFAiDFzZ2f/oBWCfG7w4G8Dl4HcO7eVKcXjItnsOOW4QyJJBICTb8cSLdEyZMHLkzM+OIVEg7Q8IoNr2go0w/bMU/csAViqWFhtS+XXmBU0/bRBYCmadrxd7ThQKJMZRigCAPP87pL97Tbcv9hlGGry19Pfv+PlB9ES+uoTAaA3dkC+wShkXVotIxvfItytTpfAMgU5ME1eXDNSsTURFZWt+Taj6Pj0YPe+s6F/tEgtGyUolkKbzwIa201d0rNnaqYbYDGDs0SpoVpSakIqFgMCOMp1Sj3jRbliIH4oXRXKAoJQsfxHevSxJxBd+DQfDJpKBNwgwZQM9vARsu3RW8I8KJ7xvWF9C5j2nGiCwBN0zStjyc1Q+h3ZwOLYFvEE4Dcu285jv+9PwBc5QNhw5NKDSTcKA6KGQn+1Q+A7PXb8vCa3Pq42wMA4bXd27TlVVn5jFZbpWxVqg6OxLW//BAwMnFAUnkgZiTbtkHoASSyJLLq3DvqxHIzN4HpGO0G/rChU6ayLMM2lBll+YCBAbhhK2nlklZ3BaFoq2DHTMXMVMxMTcYCTwWDYmqapr1odAGgaZr2ihqnW0CCwPNclEGwWwYE3n/8jz9R73wHsH7nt/nbv+tgz1SagJrIXTyVV1Pp/rHiMXVqgfmC+KIuvIFlAcQdILDt8Oxb6uJp49f+dRwHBvYi7GWIoBTJPAsF4+JJ1m5IdTPeagEULjB9puGXG36ZVhlIRruP2YnuzOan7b037Aad6Au1+ySawZNOA8dIJGpVwDFTu0eSKWvClxH7i2mapr049CRg7Sjef//97373u6NaacdKoVAY1aS/zc3NUU20F5hhiGkBmDamYNoIYbuF32Hnrj91BvD/8P8GzAsLFFsiQmJAAYC6/rD52mLCeusUoJYvqSTy2TUWW6bbClJZNXcxaie3b4YPttXMHEqxeW1ANAA1NQ2omWXZWlHZglQ3ZXs1Biy/gWkDKSO97QROeRO/gxN3DR9v2EZgKJVQMVNZLb+aNLM1bweIM+lLB5KyOzpoo7W2XnWB0K87zu5+w9or49133x3VRNNeaLoA0DRttCNn/zzbY3t0FfFlUYahwhBlIKGYTuB7JJMEATt3AfP6h7iueWEeoO6KCHD59ET/WBLKzjrLl9kuy61rBAHvXFGvX45OmfWy99EHnJzHdQFjLsfIMTVR18TdG6KKQCfuEF+ksoZpAGKaqXIHOk75yZAke3uT5KD6ZJfpRGt6NoNqo5UGChhJKwsoZdqdzgMnBM7ZSeAm1bK7ZoRiG3oZ0FfLe++9N6qJpr24dAGgadrR+b4PWJbl7+7WZFlfyKeKriKG8zxvnPE8h/JkpdFo7q9hqcADCAMAK8b0Mo9+AEQJtzpxXhU/EdeXh+v8Ut+Qu8N6/GB1vbIwl5PHD2v/4vOZv/dbALEMJ+e7DdJJlT8hf/UhhkE4cIMtcWIIzBaYKbC14aw9cOdPqtlTADt3mX8NkId3WHgdoJMEKAi3f0J+UEgQQcKAQKHaYetCZuFerQo0/arCy4fRLGfmk4trrcdAOLpM0TRNe+F8IZdqTdOOk4PJdy/dj77w++3VeqhKwPf9qP0XWkjoKuIIxPdxYgQugYfrE3ew43gu08tA68pbydIOH1wFZK0s7tDRNWGAbbGz5W3XAeZyQOZvX9r6h//P9N/5GpRVMgtg7ABS3BgWCgDVaGCYNOpQwDJVMh2rFMmcjtYhVRu3Sc+q82+xGYUyqe+oyohNlAFP3GhAf9xIABcm5lNiF4OtuBUrGbWUlZ+3F6OWCSs74VdITpSCFT/8wrdl0DRNe16e/yVW07TjbUjSv+/4OEl8L9q+gL2SgPHi7HPYpzG+l7qKGLJ92KA+BGX2pswK8RiA1wLYecD0qWRpB1Cvvwbw00+VY0mtoc6dOxgHwOrGlyBUpuF98ph/+206XubffZ12h3hMrl4D1OWvUlmltB48Klu/dEk+W+0fDYg7BB6pXPjJdWNpBiuG3wGIp2V9Rc3MADgpcvOAfPh9QHILOGs//as/+to3f6dvyJs3H7/15llbxQxlbrWbJ3ePT5oz2+2NmfQCICIGhmPEg9Al2RvvNLCn4uXyhW56pdfj17QXxPO8LmqvFD0P+NV0MFMfZOS9/OGFRO9UNL5oZBI/MhpHrQRG/iJH8OxVRKFQePz48b5sfm9+/7wGBYkygdC0CEKieFYcTKZPAeK11cJl+fivAKk1xR+6do+EKjPlr2w4v3YBsMUtfroyeWWZjudfegdgfRugsgrIxhj7fxkWoY/pAO7iGQwTJ4mTiLYqk50dKZfV8jvyyY+i5mpujruPhgUEwBev7FZPMjkTTwIKGqrV8quAY8QBPwyjlnW/WHaraWsK046LngMwFr2avqa9CJ7b9UzTtGNpb6o6fvbfc+Tsf2/L4TXAyFCH6g3Y23jQzfKRQfp67oVEoVDoPUPbtqPsf9+Rg49qNpvRF8lkErh3797BNnspiPb8MlMpAjc6ZNu23PpApZPKMAE1OQuocx53NtTsaW4NuGfv7i6U2eqQiJHLACqRIJGwHl33F18jmwak1tj8pzfCs4E5m5JHD5mf6h8N8F3ptGiU3V9+xwDaNeIZAK/VW+tTPvsrdfEtgBvXxGur7BzNB6gRm3alLCdmpjZaWxm3VEikgISVbflVGiWAegloIUDamgKQsK2O1TKgOk3XtOPt+VyHNE079sZJ1g8alLgfLdpBI7P/fYZ3Jvi+Pyjp5+l6YGQSP6SQ6N2bHxmkr8P+ysP1Cry+BUOz2WzUy0hgeJ4Rht077oEPotLdtS/l7sfgAGyN2P9L/FA2HpvzGeYKAIYLYJiEgcrN2rWie38d2PzDbcCcTbNVZzJHozIwomGoWJxcAQjFx0kSBhQfRMNyVGFeOWlZuQYQSwMqlafaGBgNgO9973v//H//b0NCYDYxA+5m69GF/AUgaWUDxzQbVW9yHqC4mrBydaq2Ea9LLS7O8MjaF+oLHbmkacfPUS4/mqa9gvYu9TO+LzT755ChniX73ytqNjwUgzsQOGQtwZ5fc+8In4ht270IQ4b4H9RqtWzbHv43eUoQYNvReBsAZUmlwvwyrXty5250bPnSqZW7j1m9V/i1AgNGOinHplIhl2MyB0ilAgihys8SAhT+nYXg3s5mx2dmEsBJHAwSEdPGSYKK1+utdArT7g7+Wb+rCt0FhdTSWRptvLaKpwDZfhjMzPHo1qCYgEiY8LsdCAkzFreyqlUDJJEpdR5jMWHkgLQ95YYt24gDKXsS9WSzMO1LoXstNG18433ua5r2SnqWoeqWZY2bWR7VobL/4cbP/nuGd26ME22cYfojo0WnfN/vG+rgj+gFjI73Iiul9rZ86oGhdGe4hgGGCWCYUeIOeKcusl4CiI9YX/9/ef9P/uF/859y8ozsrLFTRs0B4nbUyTlMQ6pb2AoI7u0AxsVTy7DycIdGtXC++zqMXpC9l6Vy26Ci55aoN8SvAUycBaRaBMjt/grRAqaB707PmveuSX1YP4Dl+77z5E/R9qskzgAKHjfsEymvFdSiUwkjXaFc87aU2wqMMfYu1jRNezF8sZdnTdOOE+vplTqHN3te2f/wQuJQ/RLDZxEc1jNm/z3PpZBgVP8Gu0XOwYB7j+ztTzhAor11vVrVzk2owgKJie4WAaAWT3H/IYmsSrTIbg+IAOD/6GP1+78kdx9hmoC6cApQlil+oBIxwDw/y8yk3HxENs3jLVLZQaFCJ4bXZmeN/Cx2jHKViSzAgzUunu42klB+/hEgxYa6eDFmpknlcdYOFreFQiE66FlmJ2h+cuPa+XNL89bJ29Xtgl8D4lZmKS1gJawc4FEDat4WQLNoGc9hyrWmadovxvO5QmuvJr0Q0PHW9/b/3uxwX948ft7/pRQSkecbra9xUvae4Z0AQ0Ktr69PTQ2eHXvAyC6O6Jn0aSMhdqLjhYZhEvoYFkDgy866WpwAnOJGe/4Ega8K52hXowcN/McNvOBXr5CZVW/Odv74h0BN7mf+/a+SmVOglhVAtQVEE4IB7jzgfP9ggNQrnalFAK8j5SblptSrgIrFAKltqfQUwJkFirdwEoRVdjawhk0CVig3FMdQCYkD57LTQCdsdtxmwcwUzTqbtwHauOlcxpx06TCxFKyt7H3LfFkrvWrPnZ5doB1LX/i1UNO0Y6aXQ/fuvh8hqx5ZSBz8+iBrd3WgkbUEEI/HB50aM0LPyHE743uOoTgw87gXvG/2P6j2iI73xhS12l48mQAwbUKXwCW2u96laVHZTXOTE7ierN2n7VJvDuyUsBOxjkttg8xs7LcuAonbG/5fXJPftlX+hP+jzwAWp+rnlpWalesfqNeuqOJPBnVuGL6nJmZirk98At9VhpJQyGcBFi4D3P1I6jvq3ClAXXkboLEjjbayRi/Z6YbSUm2j9LiZzSXJ2kbcC9tA3MxUcgrwnn5KFa+599teMVCr1QYNpavVakC9Xu97llesitBbBGjaL9KhL9uapmk9R0j9e55XIdHLDvsm8eN3I4w5g3bkAj5jxmGMUMON84OiVH5Is0FB9j6rUHyMGKHfm2WL66pTb+ye9snNAXL/I3bHwZdKxXx+kgM1gJSKtFKoHPVtDB+w3j7pf/wgXHwNsL71OlC3k4Bc/wCQB7fI5g7GeaJd9eMnACwHML71q8rpAPLTPwfUwjxAqyEPV2Wrpr7yLeykmpumOWzCri9u3odkPvo2Wa0wmTUwYkaSVB6vDBjKDMRNtP1QYRELxE2MWFn0KIZPwtk3I2KfWq0WlRavVBXxxdEzjLVj5ogXHk3Tjrdnmf57BEdOgnm6kGCMofB97XtI35x4nHw9KkjGTM0ZFe2wLMty3T6r0du7WwQcucMhVBIzTc9tA16nYzsOILc/5sQ56tsAxQeAmlnEr5Ncjx7V5yeaBrE4EqBMdfEdQGVM++xl46d/EX7tO2rhdSCz/pjadl1ELS4Gzkzwf3zfO9ntlOAgK1Z1G8Tm2H5ANgHgpAD15jcAiqsA2Vn1+qyqBIBsr+KNu2B/qdMuzuYsw84YcSAIXWCnU563Z2tBOdF+8nz80I0zsIuJQw4Me74O+17uW1f0qogv+j79ysrKqCaH9kU/Z017GT3Pa4+madqX7llS6n2dEnuHBo0fdm/LIxcSexuMzB2HB7QOM8DJ87xog7AnRIxQDFG9rbUAkmn83TR64gQP7wCtE4uAj49p/V//63//H/5n/zUH1Vty96F0coCkDGDnjVNTkgVMP/TXPwdo+IBaXAQsPzBfP8HgAUvSaU3GJgGmT1K6J5/9TE7PAY3cBSAm3S175dZVafiqsIDj4PmqMnDLAs/z4mYGIAzABvyw+/dvBfXq7nAdpYx1swW0Aw+Imalw79/n6YCtVmvQKUbNAzmo93o42sOPZu+IpiPfC/9y+yKeY2nx7rvvjmqiaS+6cS9pmtaXngf8CjraLfaXzvhjh/ra+/AhkxyGG9mfMGYtMb5+AaXp+slkiBnHtICnZtDWt5k4AbQnC6xtxc2MVb+3N9q+56bmZ5XteQ+q9sksEgJTlTa0mZ8BRASQShllEJgkcjgKK86gyRKGiedT3xJnTq3d5sQMa1vRfmSp+Trgp6cBtrbUyTNqpwEox2JfhbPHvr/zdDwzaRYAu7JVSprAXGLWbjf90iq59M1KDDiBB7T8iuF1+gYcUn35e/Z0GyeJ782siL7tPXzfCLpxQkUOvq7Gf+wRFAqFZ5kRwZddQuz13nvvjWqiaS+053bZ0DTt2Oh7hd6brOzNafaNwNEOepa/zJDHJhKJKGMeaW+QQeWEbdvRqQM/USnXJwxBuoNnrAQiuLtL6QceIvGdjaQ1CYSZ6aazpeIJ+j553+XsgmP7AMUKIL6rlk8BdOpKKQBlACRygDy+yeoai0TRVlZWlpeX79y58+QmtGMRBoDMn4MiUxOyUwT8/BxAp7tKKYFLYgK3IX6AaZvZDAP4vu+GLeyCMm3HC7G6CXG+GTywmWt3Z/qma81vzk4Adx9VDNVn+P++ZH24cWqA4bUEu0EsyxqnT2DQ0/N9v9VqRUEY9ZT62vvqOsLDh4s+mvq+7HuruB60r7R4caoITfsSHf2ypGnaK+Jg0n/wbGScTLcX7eDQlGe86X6MPfvfxLKsg3/wXn7W+8v3Sa0UyrbwAwDLxLB6Y+jVmTdoNwl8QKpV0pNu2DSinQGUkUgknsrUe8oV0haJGIk4QC4tOztqrrukqQSBymalXpPqJuUarkcu/T/+o//uP/97/8X+OPDP/vm/+Af/5d8hmVfNCs0yb32V+1fV+ZOA+cM/A/zpSQBlAbgAKj1pMOweM2Crp9YIqvs7uZnFx411YNuo5pw5FZuI5jzcr28BSWsiYTbEKe2LM2b2HxleAxzM/getTRm9s4YPGxunlug1GCeJjwIefIH5e3oLx4lzUO8XOdrD+xpnUoQuErRj71kvKpqmHTP7ro57U4HhejcghzfoRet797H39Tgp7972fR87TpBXSpTi97L8fd/SN80SUIbne069jgmhD6AMPztldVxAquvEMyqeYbvsAK1q3Exj9N8LTCo1ZhIsn6TRCO5vW1+7SKtOzAGorQPKNKX+JJNWU1PUYPA/pYrFaVVgguQEoJZeU7EQUGeuAHz+QfRgYikaLpZDpy7xjFgD5wAACoXfbhmuxGYFSVmTwInUnBd2kk4iauPmpjrl8lwiVW0khoR6XsZ8D0b8oYP0Rmb/+wzvT9gbrW/YvQfHTOL79k70fqkxgxx02I8XTTve+k9a0jRN4zDZ/0j7sv9nbOn7frvd9vfYd7bv8eGi9q2nHSrCl2vkXOGjmS7MIQGGoSxztx9gN3lyG3hNlcgCxFJAb6KwJ30GxAOkk2RSve/8n96Isn9ZeRQdCT+7jWOrTFZNn8a2ECEM3rq81D8aIAGmxaP7tKusfAKQzJHM0anTqZPLkMsweepJ+0QOUMHAf1bLsgK6Z29Xi0DN2wpCzws7gL31wN564Abd4U9e2K57O02/3AyqandT5OduyL/skPm4z+X1EL3+n1eokXE8z4sqir7vu+j4YQuY6FH7Pi4G/QhNe3XoIljTtP6OdoH0B999HD/gyCBjhvJHdUr02gxKTaKM5FBzbXvP7eDohTEj/CINmWH8hDKwLGwHr4MfYFkEnnn758yeBBqZNGYMoHEdIJZU5RWr0z+msmxMk4kF1Jq1PEv+FJ/9mNlJoPMHHxhfP60Kkyr25J66+C6xNGz1jRaFpFKhkGK7RGpG7n2GdRYIC4uAqvYeaGwaLcKWZxkhIfSfOxH9S5ntZtOeQsITSavmdSMYGEB7egGot3ccM5FUKczUjmoCgfhiPjUTIAo15guVX+BrY/ynNNLoV84BQx4yJPXfq9dg5F+s91lxMOa+IyNDadrxo3sAtGf1/vvvj2qivTT2jv85OKJ3HH0vpeNc18f3fENFKf7wZn3HJBwU/Zrert7x3pHD/h2i9lFHxD6jHvp8PBluIQLiYSCIaUXJrqzdJ5FL+SZApw7IvcdRc2U5fQMSS3Xn+ObmAUr3WV4klWz/6EH3gYVp2dkRry3b90glqn98F5Dm7oTjg0wbERZPcf5NlU7i+0gYrS+kfvznnzuJz50ExftR24I1ZQcQzVgYLIynlDKAZNidDOCL63ih44XxzfvxzfuO2S1RAkNCCTph01Bm35mpY2aWIwe3jBknMiSa12eS9zM51EtxSOOR78F9hr8Lxnyn9Boc6rfQtONBFwCapvXnH3K5T8uyDtV+iOcVJzLo6u6PMSahZ3jLKJMYWUuMXwbsC+gdMCi/6Zv29bKcvg6230ck7AQ+0RI9u2uAqvTuYppOinYNEdncIRnHa2M70co8fQQennvngw8oPiA1DajZM90wv/Gamp/HtMnujhGq1LK/ucjO3J5zXwAAIABJREFUev9QkcAlPwnQqgBqIhsN/jHKa+rSa8CpTBagU8vZmY5yMW3D90k8Nc13H0FqXlD3BcPcbIVZuwA0LR9g9hKzl4LQC0KvGhTZHexkKefgWkDRwkrWrn1nrT2ilgywN86gNj3Da4mjDaA/2qO+XOO8sCPjvxE07TgZ/WmiadqryRq7ByDKOcbJTsbXN9qYz2cv3/fj8T5btB4h1HDj1xIj+WNUJt7uKo1DVgL1PC9aWLNXRbBbS+wNEv0phv3zKdOQqBMA7HggfiA+oLIpq7bqG4ns9GK1WAJIxInWybQHbIvrdcicorbd7QFITctHfwMk/r1vRuelVafZAeTz+4DKTpJOq/jAdXuk42E61HbInxCvrhxbol23snmpFy9uFAEvlZZyjXQiVi1hWIDEnsxDOMjAnI5ltju1Jk1F+nZ1fdJKTMct12CSBCC7I4iaftXASFhZy/Abfv9ehV5yH/3f2zOndu/xkfY26/vqHZ769/QajHwLjBNw/E8Jhr7GRj7t8Y3/fPbxD3nLQ9NearoHQNO0roOr4+29Sdn3Ib1Twy+cw4PsM/zH9T0+yPg/9FkcNuEYntyPk/1Hel0BgxpEp/Zm//TLtEbGUUTZvwKi0TVWqP7uf/X/Mn0OaOSyjbCK11ZnL6mF0yTzmDZeu38sJ4EdZ3KRwJcHt6S4yolZTswC4nne93/q//C6OveOOvcOYHz718O7D/vH6TEU5S2A0mNAXA/bwrYIXJVIA2puBlATmViziRXDaxO4uP235o2E+FWv6hjSDjoxM7QNAbbbPtD0K02/MuE6E65jmwkgYWWjR6WsgXsL7PWMmW5UTiQSiUQiYT1teDfCXiO7FHqnhge0DzM3ZpyAz8WYz0fTXnH6faJp2jDDr6bjX2sty/L7rRS+r804AYcH2duMwzzD4YYnLmOm7CONn/2P5A/dQrivVquVSDy1qGUUQQIP4kiICM2GAmLx0A+xHeYvW27VDz38aNkf1S/wHm4DCVFG8MMf841l6g01O8meVYCs8zOUVwHj7TeobxtnloI/+UhkcNjsVDAzT7XCzDRhoAqnVT4N4FXAUK9/DZCHPyEZA7uZcKABBEP3T2sHnQlnsuwWLaUAQ8l8Mh2dKrvtCSfmJhMAFS9l5RveYwzLdF367QT8hRo/4+9rb//D3tfJYWuJ3teD3pVjpv4jG+yz9619cFeEMT8l9npenxWa9lLQL3dN08by7FfHXkZ+8MI8fvBey+GFxL4v+rY5VH4w/jMcKbr5OqrVWA6m+HuPeGPsCDsehVLdYf0xm46HYaE83BZOIuYbMWLV3PxcTtbvPaBRnH1t9uq1/nfupeNFnQPmTBrDoFLrnfLvFc3JhOzUye8ejKWibX1pDehPACAaj8TWNtlZ2bwnVQuQhAOQmuk2anaIZZM+2AmxUzSH9QCkrAyVtQnIOjNpO0jZ+Yf1BrCUTs3EMoDrt4CYEa18agG+bfvmqOLnBbb3dXKE18w4HQVjit4aw9+bfe8U7F0RdZzbDXsNeT8O2nBN0152z+cipL3i3n///e9+97ujWmkvtHF2x3xenj39HZ7ZDzq1T9Ry5D3ykfn6mHEiI5Ohw96273nGdH/Iw5Vh4nmYGZRLKNgWgQfgu/huEA33b7sAzu7M4AE+vVe6/HqFRI7lM9Ail5Eo1PyMNT/jfXZPnSxIuQa4mTlAnTxnlj0GlBOACkOnVOTcPKCmFlGKINpHLFSnr7B+CyCbUhMLbJSJZ6IeAGNwru55Xs2rkT5Pfafm7aSZaHglzGTTN2+U25dnQlvF4mYaaAYt35C77cpkTCresGWFXhHP8vKLjNnzMM57fJw2jN1POGS/BU17SY31DtE0TXvRjLxmjyO6lThOZ8JIB4dS9G0zfsCRdnZ2pqamRgYcktkfnJA6QIjtEK1yE4ZAYFmB50kyq5pVMxDADZq2Gae+LenJfLvx2lfO94/keTTbNNtMwlQK3+fhOsBiobq4kJo9C3D3etTW2VmTm5+DKaVm32C+79NpkMg/OSTSXFgC6maTYH0CmF7OTVNZ+cz7kw8A/8rryjC8TP9CJZoLkTCdjvLIZJt+6BgJX9zlzCRwtVRJWnnA813ANwSYjAmQtdOP2RkUs+8Ui32jrZ6vO3fujGryQhteBgyZ+L7P3vx+X9/CmKm/ph1X+qWvadov9Pb/C+jZk4BBEbzdtXqGN+vZ22BkLQFYljWk2Tg3U8choUijTcYlCEilADPEA1Y+FmDpTcA24wCmBaM2xHU9HJto2yzLwvXDr39HmUYOfDp4bbn5AHAufYOZs0xvsh2q3OBcOZYiNUUsRmpS7t5QS6cV3Vm5U5tlf+F1QHkuxQrA7/4t7q4DcatPwL1pesxIdcLGbHIWsJQDNPzymQymsqIRR02/IsQafskLJWklQqkbav+iGt7uIkt9K8xWq3XYrp5ee9/3W63W3iMH/61XVlZ4wXxZZcm+XH/k23C4d999d1QTTXsJPNPbQNO0462XuOjbZmPa+5fxff9od3mjTom+XQr7aokh44Wizg2l1Mgss1dLDPpnVYYimcSJ256H7wKYaTMUNX9O1m7TKgOqvCMTswSu8jrEUmYwoAwIQxaWeHiPVlst5ElNq8VTJoDPzn1SM5i2WpylVgfk7qfdwTr2wPpKmhXKq8yeoVEE5OE9FmcUatooMFcA1PZ9gFTc/L1/AzC319ha252y/FSo3teW4QAKtdHcmDcv3KjsnJg0HXOqFOwASimnsuPAg+BJv4QgfujuizZyAPreZZqGV2uDAu47Mn7JN+RV8YxjyYb70sfSPJfPrvfee29UE0170T2Hd4KmacfPoNuW+w6OeTV9LmNsXjrP8qv1fexhA3qel0wmo3HVBxM+e5c3YHfYPYmgUiKhBFg2hhLA69h2jEZRZScxbUAmZoFSfNTYjDC8/smt1/K2LF2EKo1tOCWrV8XZfQKdRm55ubKyQnVDTRVU+QFuVTmDd3IQIT1153H17LklNZeT7XVBonX6q6qe3NgBMO21whQVDN8DLr51/vraxwMDAmHQCiqmsr0Q4GJuCqBdzxMLe/0byfxERwEbRskL26F4vY3Axsz+e/xRy8/3HUG0z94GR6slenplyaHKgEEVxaGCfBG+rG4HTXvB/f/svVmPXEmW5/e37S6+x8YILskkmXtVZS3T1dKgMSP1aARIwmAeRg0l9AmEgaAPIUiAhMkvMBI0mhdBAwjoetHLPEiAgOnpZbqk7lqyKlcyuQQZEYzFI3y7m10zPZj75Q13v4sHyUwyaL8H0uPec4/fzdz+x+yYmV0HwPJi+MUvflFlYnlFmcv/kVKGYVguOLLwoI4uqUO5k6W8ECevPjxHle1yzITxvu+LHGaj1rqOZ02IJgSAIoCZ6VN4AEGjk9mQg28ANHlPnzwBoYVzYvp+4nnY6JHjh3oSgDAc3yduCwChRD/4Qu8/0vuPSLcLAI2eOUiNlywExjkXQhC3MdFjAJj0AZDNHQBN3kMqO9IDACWRBNfE1vZOT3FB2mv68S6Ys+hwjlQnN0SbaiQqDEkSzhYP3pvsJd0tAEorpRWArrPdUN5EVuU+XYg66j9D5roUlpKsuBB1uRlmIxwyt4uYvVVu5kkWqDqiglcwIcpi+X65YI1isVguJXXEQYasarmUNYKE/N463lDlsFLOzrHobVUPrwuc8yRJRPFA5KSg0ZcQSgACAmgopQhBOAYA0US3GekIAFpbAJzwWRrMUvRwPP3ktoBEj0fY6KX/7m/x8W0NiI1ucnQKQP32S3z4hwD0OAAIms8ijTycczDROD4DznD9Q312SPxm48kjjUe48R4aXX1wDwBhLOUMANn/ZqmfZ94MWvtBPPH4Gc6u46pLZv0PXOxN9szHARmPkgmArmoiHKWCl68tcDEuIHwrS2V9TNEoacJPqjoTKvfOkWn9xaPy17XqBZqftVWPslguN7Y8WCyWKStV1ZXUUf/1qROZGIOa1bysaiut6SdPdoYXOPZ7ZGmC0DypBEBAgZSmEpoDwNE9AG5rG0AEEE2U3062ror9u3BbS90QwYRUcHy0txA/Tn91P/w3XwHwP74NSnUQxR98DICu39wB9mskb+holF75IXv6GMEZfF8HY3gt4jcRj9HoAkAidSLJL/9v4grS2YCAHkY6eLb+gGFR5jZCCeCzs+Nbbb5+GAFA7xqAJmexCgAIavoEQkBrrdqcZa7MCN3np9ajqU2ySmdCRlFYWEf9Zxj9XXItd+/erXRYv7EgQ0oZhiGW/YBwO5zJ8mZjX32L5Y1mMf+nyLKIpU1rdfR6nvL2uZW8VYYBldI/v6uOPlh0mJwf1lnHyavMZBI6a804CKkQ0ApAwpgQAsIHYP5N/FQA9KRwwn6DVmr6QadPnOvXfgr82dfuP/kDsxIwefctt38EIGJN/cXf6r1TOM77a+q3qrB1nTTXAESbm0hT0tkEFBHajFRWSrK3Pz6ZPOk+OSCuAKBPT4FekSvTNyKlTKkGdwF06VofuD+UN5sAcIzTJp8mAnUCjUgBOHYcAJy6Dns24JvXXoIKs1ymcoP63vAKpN0vpfwSVgpOKst4ZvNiwwmL5TJhX3qLxXJBOOffQcW5kvpHaSyRaYI684ibhs/KyATFscTc9jr3qkiUZNvrOHl5aK0JAMpAGAgbdlsA/HDa2q2gz9re273Wk/4hhgdLPRDfg9OAjMnxQwDoON5/9U8A4Nvd+B/+QwDst38DQJ9OAKDXxsGS7P88etxXehsAXKO/KZCitTVpNkziTi9xsPUWWXtXf/5L/btH6e5d9tFb5MEsE+k8i9L5g25XgA+7aZwGAA4C3Gn3wmS6MvHYBQCpIqniWD+bFCjzU/7qmqdZrtdX7QQof0NWjSXKWdVVkX12gfUz9ctjgEr1P2eJqvtmsVw+7CBgi+XNZa75f6UqcCXjSl6gtxfoqpzKnoQMk9lcLkdMnMNz5PdmGy8QDhmCIAiCwHwmhJQ4WXJFBJRQTSmgQQiARMWJipt8rcnX4HXgdcws+C5bnvnzDDrNk8HmbQBk6wYAhMPkxx97g1MA5L2fkPd+AkeQm7dxcoZJSG6+D1U4vjbxPACUcAhf7z7Qn32GtetmF6WcUn4kxgD0578EQLYLm/8zhBBSJaEAvE5f9QEkkMkZc5i/wa7caU89PHWjgUhSlfBZO5rS504yP9h68ZmaP40NarD4ShSZlTgUs/leiwyWUuJwVVer2j8PKxUTrG5vsbzufHel0XLp+cUvfvEnf/InVVaWVxpTQ1fWhbxG+iyv3dZYx9uLouYpGYqyn/MGJXvrU96cmSeLAZYaZyecXSYhZNGmMtnakF07mfWXOJQkSgPglHPKqVIA1LgPAGBUJt5kcuqm2HgLT9UylwBhmPRBriEcmXHD+v6XAMjONT05dSenyW+/BkDe/wEAXN/G2S6AH/3w5nJvgKCuCCfwu+ACCdBrA4DbaKjpyW96t+ABjJ7cvhp1U0Y3QSjYt0UOM8J00HPeN58TyHV0ALixIo0um8RjAgAdujbBmctbid7z2fKRypnKT2bTrdYU/Rl5+6VPLXsZKj2L2ayvlU8fNcKJF/X+v1jqXNocpkAVFSuL5VJiewAsFsszMi2eMbe3aNccmZ8Sm1Ut67PU2wVkQQmy3iSJ9XmBd0DOuibKxznUDAMyNLRmXEEDiOJQ6XSkBiM1CHwR+EJplTCcuvWmwjzpm+XDQk/sbd5AmurHj8jOBwDIjW1yY1vv74Iy0uii6en7n+sHDwpdnR0BQHAGpcjVnfQHP0vdWS5+NI7kUH/51wBObl8F4MUFYcl5XKkc2iCghLBIjSM1BoCTh311RMzAYkAQdhLJ+8HhV8PjMl85xIwqw0JMl0K+V8H8uZJnY1lUhLPyyKtGJuTt61DiUKzeNVFivJIfi+WNxQYAFssbylz+TwbPkf/T87z89krKjed8LrXJW5YY5Cn/RqwoDkTxjJkvkFXbHY1xkXDP1P/SvXMsNZtrbzYOw0mE6UygAGWMCEZ4i3ZatONHU9GvtOw5Oz5rk9PHGB4tegbwv/yLfwXhYWMD3EUSuLv33d37ZGsTgH7wW0QxOANn5NrbABA9y6ovgvSugDC4LezeB8D7ewDGNML4JGIKgHrvp6mKu+LKhuoovxWRRFMqda2wZ5ycAEjScJQc93sNAP3ocT963KfTSYRuiQ6AcdL3WdesPvbdsJLiX4o5fC5DySxcnUUUVT7OxRIl77DZW+5wpRigjlkdmzkucIjF8vpiX3eLxVLI89eI5XK8Jplxkerl9cQ9r5f5gHpyRKySAvHyYom506h/ShmLaUJ5kiQBIZqAgp5XuARnewDAG4EciLQB8DA9yXw9efLk2rVr+QOmoU6a4OQEP/4YoyN8u4emg9u3yY0WEUSfHkFpAHp0RNau63sPQFyyfZUcLo8EpJSIJkAPcYB1D4QjmbCDu9jcBuAOTuE2kl/9hfrD/4BHIYCz5OlSP/M4TQAKyuft/kHylp/2m4wR0RknrtcDMBYJg7jVCAFca0xHNZDxaYnLV5Y5Xf7BBx8UWRaReSgqEXViCeRypVDaMVWnsFf+YszxksqmxfIqY196i8XycnkhlWvN+r6SzLJIKGcSpL7PSioF0KqdACg4vZpyJ0/JOIe5NCcNTVQKQGsFUEAjGgLQp0fY3gAA4QFwx8MD1w+aHs63iGfXqE/6+NnfQzSG8NF0AMDxEQdJbxu9bXzxtwDI2nUA5N3buPsEy8g6OpTjQSsID711ABANAM3TU3CgvQkAN3bo3ldDt90O0rWzoH90AODj968v+jRpUfl7eBicbPnr+uiwd4SO1wZwpJ4CaNFp71lfPm2KtWF8NEwOVbO76PM15Z133qkyWU7lq15JpQchRJ1ZvAx1ilVJRHHnzh08x92wWF5lbAqQ5UXyi1/8osrE8kpQlP/zisMLqDruHOYQMcNszP6s6TPvpMQsMyhxWPldeUxOTskhc+dT2SEgCrom8jq42WxraCkVGKeEEjKtOOIrt80iAAopZITgzIumX+cw77//7/7bpd7ItbcxOkYSANA//3vDn/4A0Qg65Z/9EgAEh+D66JG+9wUAwh04TQgXOfJpTtNTSUL4XQgXMtKfz5YPGx7FrkfW3wLQDlIAeus2AHXlLSxgpP/Us44ZFS5t+Ayj+IS8/QGAfgP9BijhlHBGRZiOQNmac3UYHwFo054+PwtQkqPOdkseUUzVoc/gq1DlzGK5bNiX3mKxvIlkVf7z1P2ccyllJkqS51j/q04ngKyaLKjSg0HMUjWK1D8W0qUoYRoKAKIAAFzXpLzHazthq4v0DAAmJwDCZnsbZ0fAj3/09hK/ANIY3jYog9ugqQYQdta8QR+9Ft/9XO4dAiC3PtSDb/SkcBGATD3/5ldff/TBP4hJkugY8OB3gMfwOoj78ZXpCZDODoYB0gQA2XyLKeD8SsBJwSStLWd9FJ8ACO68c929CoA7FMAo7E8tRkcd2p0gQPCUTRcGnn8N5jTr3NtSk7yxOVWz2HBSNVGVhS9r4K9T3JbyySefVJlYLK8HFykAFovltWZp839l9sjF6stLT/62XPgWZQdmT8FskbPZCefMMvb39zc2NrI/kyRpNBqNRmMymVTqyxL1n7eZRgsqJVpDq9l6W4qARmri8w6AdefGidOHs40k9IBAQFDPCO6MZ99FeX6Xy1oebaDXwL0DALg1S85ZaxO6g2gPo0Ozsu9yUgnA0UJILZsd9uWvcesagMhZc58+ABD/5kv67//RVP2HYwAYn+QdLKp/kWodDqnToJp2xOaESQCPRk8AvNe5BoBRAeCb0d4diAkCAGhtKnUfC+q/BFF7DElRfILzPUJ1vjSj5le/Fty5c2fVLJ3KN7+ETz/9tMrEYnkNuHgZsFgsl4OkxnSQfPVJspc6XMnDG8ji/Vn1tmOW+WA+LxWFQggz30s5Wf+GeUNikx4zHvOGr/g09SaQg4b2zeSaYu8e+DXI2Gcd4FwT+7n3JxxCtiGjWIWIhvA9DJ4CINdvAdB/+VcA8O/9xxgc6Ie/B9ZQyd4DpCp5dxtA+sFPMHg0cCSArSxq4EKPT8iVd9PxSYgIzWZ2aIm2BtBPnq6JK0rL3Yn8o8YGMF3IbBhNewDOXGXGRqc6JnFYX/0bzL0tb8UvP0ND3qD82ysLexAEnPOiU7p79y6KWQwq6t8Ki8XyHbNavWKxWC4ZlYLAkDco16OZ5VKfmQpcSdS+sbHE81zj0hgg2xjHxW3qOTItGKmYqOkywNkATE6c07jvE78fP3bYJm78SD7ZAwWCUy0jaAXg7t27Weusefo6laHnAHBGZ1hvOt98jh//FACCPgBye9oDQK79gPR/hcfH+ugItHi4GncAgFFKGD96qOMk/sEPkY4BnG50OXHFf3QbUUCuvAtgkBwCcGnjnIOFuaFCmkaCaDVmWBskR8PJOgDZbAOQagiAEQJgx28mKjR3gyVSCQerU6nXK8tmRmWsWL+wG5ul5zbX1p6J/pLyfoEwwMYSFsvL5uK1i8VieR1ZzP+pFAT1Ma7KHdaPJVDq8GKxRMacz4s5eRUozyQxvQH1M8UXLX3f11oHYaopT3WaCi4Io1KatJyes0aIv4m3z6Kx/s2fY/MOH53CbQEncOZ1NsxtPx16QYjOB5AR4gCAPn0CAN11AKRDAGBwgM4VdLZvd7bv/dn83J1GU2ZX/fsB/8FHb2noh1HzpjsG4LKmG8tkNlhZOu54cthM2Bq/ckr2oTW622ZX+d0TzEvS8EaDt0j3NNoD4LkuADobezxI5H4QOEwNyVjVW1ugPiup/0pqqv+MkhjAUC79Fy3rv4HlDmv6yWNCmte3jFssLwM7C5DlBWMnAnpdMLkHlZV3nkpln7UdPj8yx/PYLBqbP/l5ajpZykqn8ZpCCCUaFAyAadoHF752fe0CGCUnqY7Vx3/IiUB7C1qDOQpLlt3lnJONNbQ2AYC7AMjmFs5GOBuBUBAK4U7n/Dn4Wn/zGYI+ufl+4j9L2sn8mPEJ2nQOaA2AjU7BhBslbpQA8Hi7dfSUfvk3AJoJA2AWLogR5V2JhbEQLms41HdYw2OttrPZIl0A6+4NABvsyga78sUZ/+KMN0gTgMMUAI+1znLDwVfiYkcVURLPXOD9LPKWhROVPjObkhMzlP8iZUVspV+tL7/8MjuBMMelL7AWSyU2ALBYLC+AVWtTWdpXsFL1XGmWuTJaf6lN1j5d6c2QnWEQBMkMrHjmi1z4wCLqqK5ygykamhBAU1BoDcag1T/9b/7nIYYTddLgXaeph8kxgETHIGU1iw5DRGPIGNEY7SsA0PTR9CF8CB+gAEU0AkCuzwYELyN7mgRAcMZTDQDN2ZBowvn+NGGdT4ahQyF8rL01TPtROlH63FUvxgBTH3EEIECgGaOEb3q3MD7C+OiKL6/4EsC2t77hNK97W6lONtxzPR41qXv/X3NkVQxQv4NClv505M0y7t27N2cvz1PkxGK5xNgeMYvlDSKf/yOEMDMJ1qdEQF+Mpd4uViXL0i7+OqedxQDlZrJYyixurPO92TcmSSKEWHoCdfy8VFKtCLJFfnUqlSZIddqm3aE6o4S1xPqIBiFJBXEBPUnPaHyuoT2D9DZhFs1lAgDWbhAnAqD3vwUAdzqZpn6wi/XrIBTJUESFIxZIHCJNVLMHAGcnE3Z1MhreWN+GTHDtBxgekY3bmJx52gUQygGAxt98Q935LgUxWwICALROVKi0xDAlgHft7wAYyaMW3/yCaABrQgE4DvfbfMOjDQCBjKSKkMspqtmuX2JmdlW+kBnm5Esc8torYddhVVcl9vXVv0HOZscqKheyXpCAGq4slsuK7QGwWN5QkiRZqc5bybiSF+sNpbHEqt9VohtK1P8cNTVN5tDYJ8uo4yePXIAQYj5UHVoIIzTVUkNraFAGSrOhwG3aHSf9cdL3eRcAVJpSAEgEW+pKAXA8hAPFWUQiAKHvh75PrrxFrryltdZaw22RD39Y3pPwDMppFOLpfQCN8WS2VWF4ZD5F/+pfg7IAEw9ea/cpvdKZxh7nETOebdp4GwAJBiQYJGnQjx7dbvUAdMSVjrgCQEMhnuyFhy3RkjrO+8k/vjmKti9yLiwppVL91wxI5igPJ4p2fQdUvsyVBhmydrRgsVwmvs8CbLFYvkvmhv9mVXudmq+mCqkJf47Buy+b8lbSmrqtDivFEqZnoPKOGZU/py+zz1n3QqWfPNMQAmBKK5P9rxQACkpAMThAZ7sRawBnUBtXmse7R3E6aWg312OwwHAIgI76QBPxxP3VlwDkH/0xgNRrA8CTJwiH+nifNFtIgtQrzq4RLmQI7gAhbrwPIRox0N9FewvNNUSTlABAoIYAMDoCgDDSpXMgjeSEEqahoRTW3tJn+wDWr/1BP9kL0/FVX0RqDKDF1wDsqTEAAiqIl3diytdiGDAfY1RhYomS1zJ7mpVuVy3yJXtF6eDppaz01j0Pda5uDmn7ASxvHvZdt1jeaPKiYWnFWVP683r5M5llHZ81eYGuMJMCS7cvbiwnqZp7p76EqowBsnCixGcy62RoNBr5jUUnaWIJY2Ma/k2rvyLmfwJA9q4AGvEYgEQDwP146LM28JSr5QEAjSYgDQgfo0NXcUCS9z4CwE+fAgARAPToCABptpZ6OIdSkAkGT/H2hwBAGVQKAOEoyyZy/4s/9n77WfLxz/S9b81qA7h3VODuPEqC8mT7JoCUqI6zrUkoddJgPQCKRYfho01vLZCDUEpOl0wDuqRXYXXM4XOa27wJvu9XvmZ5KsMJzIp8pU+xSoYSL+2gMNtfVEEuvzqLxWKo18FqsayCnQjo9ULM0gzm8DzP87z6tbI5qtzAOKw0W+lLUSod6rtCbTWzEkU+66v/Sup3JhiS4nlUktxQ5rlwIoUCQDmjmlBNkCZI5bSZnwkwMUgOtVa3CEc0/uDEAU51AAAgAElEQVTmWkqfBQAyh9ly96v7aG0BQCqhElBinIxcMnIJANJcR3N91Gmivc2Cc8uKnYMQcAGvCQAyAWFgzqDbAoDhPvHb9OHvASQf/wwA+fHPb79zQwlX6bTQIdAWba6oQ/2QxKGeBHIQyAGbzWkkVUxBKehh+AhAIAcAYk7SFz0N6CJiAdRo+J/DHOj7fr6g5Qt+5rkSUSNDKfP58OHDkvV6a35jRmW5rjSwWN5wbAmxWN4IFqf/X+T5q8wiDxfwzKu6FCqlP3JtgXVOoNySr96sKArmlsFLiDQypV6/PTjfnyDOty4vxhJEK0aYAAWYaTgyif6MOgBOPe6yJotGAKAVkggASaciez4+8droduE24TYkp9xslnGyvgWgNZlgeHi88w6AcLhvVh2T/pIUoGkHRZroKIDbksLhCiM6aakGgHHL91q3jOWw5Tn3dwdrvTHGzUkkdUwnZ4sOF4nTicMaXWfnLN4fqlModEQvTMcABslTj/kAOpQfp8ecOIzUuu2vDkvjh5ovT4aYdSlkW+b6qVZyWLOUVZZ9vmI+T6VDi+XyYV93i8XyInkhlehSJ3mBXv9b+GyO//JDasYJonbqc6Xuqe/qxZKdWNFNLlRg2mT+aIAorRjhzONBcgbAZdNJdfaDE4cKtDdP4yPTAzCv/vP87V/i2n+K5hrWOnE69ozYV9OwQSQSQEs3DoOHXJ87VXOSU59MEEaRSgAYH7eGXWDS2f5orAb96HEvdh9trK8Bg7UegOYkAuCkJDkfUWSZTuZDnE4iRK4UndgDIqnjplgHYBZBdlnj4XgXMGsi4Dg9BiB1dBQVd1Ncap4zhMhY2vOQfxuzN7aynKJ2LIHin5Q7d+4sM7dYLgnVpchisVi+L/K1cp1afyk815kw11Q5Z1OOkRR1hLuoymmur04MlQ5XwnjTOpvIpxpFiNQqgVJkmjlqMl40NIBBcuDRltHHoctP4yMA73941Rw7d7s0FI6e6rfu4OM/4IcPsP0+AIc1xcHXAGRzS23exNkwEdzjHcQTADh9thiwPJ+bpAmF1wVlnHkA9NoN0t8F0Lj3jbdzBb4LQOnUKPhJhyMOUp5QMr2TWcoTcsHPZ7998OFHNwAMnLQTMw4uIdt8HUCqp3Obvt2+uRs9oIR2xUaYjhyCnng2BmCuOyX788Li+HJz9+7d7HPRLcpvr/nqcs49zysqaDXDiZK0JYvltcaOAbBYLj918n8uPZzzuZqe5yg6ag5jKWZk27PPZntNnzXlYKXDpU2nz8NihBMGCQAV61RPc+EZ4Yzwr89GX5+NPDodrbvu+sfRcBjTFm+7mi3VXr/99b3pp2gcb16P07He/Y1++OvpQmB8OnLXjC5IhdAnTwH8s3/2PyJL+8lBdKopAWUAZGeDHD+ASs2gZAB/MY5vNnYAMJkyOe1bYEli0piSJCnq8RDMH+pRpHDIU4BwiHE6zRoS1B0k9LcnuzQ3S+lInvBcN4IsYG7ZuPoky6g66DXj3r17WGCxrK0Kzw09WkqVg2d88sknVSYWy2uDDQAsL4U//dM/rTKxvBIU6ZWq495c8tLBnwFALAysLMdYluubzOfSvRl8FpZkW0oEkyjtTCj8LgJKSIpUa6RKTjeB3Om4dzpui3U0VC92WpG+7m9fbTQBBCTmnC/XqZ0WGfXRXHPO+tMtlAy6DblxDQA9OxDU5dSRKtJakevvq7UrgJYL6h+AJjROA0U0jr4FgK1p2gbpdsGn83IK6mkhtBBSJSMygdNQ0HUEdIMLAPfH+wASFZ4mBw3RNbtutxs+61LCOREtsQ5CQFlSHFFkVBrkMcXTLNi3WEgvHE4YLnc4MQdfoOqI5Xz66adVJhbL68EFy4DFYnldWNr8n+QyH+bgs4SZVevIRW+renh9ufCVZnd7TrJfzGFlQ2mm/ssF3xI/s5wLh3EzGCCJI+G4nDgAhvLU7B06apKOGHW0VnFKFq8LAEz8kCYYHWsKEQ7JD/8Q4RhIJvK0IbNmKQKAB6MwHQL40Y9vmq337t3LJ2cTrROlaBIDjBI6So7Q61A67gEA/qjtnsgTTl2ic3OSpglNpzk/WAYh9NefffnuB9upki4lHaFO4ie3xDuj5BjAw1H/3U4bwEiNs0Mc6hOnUV/Zy9nQiJJHli+kRZ5lvbEreYzbxbAzmfVgVL5FRTzPsRaL5TtmhV8Ni8VyCSiR/ob8rprCokij5KOImq4WuYDEee14/qvLPBBCFkVYeav/HJxzrTUh52bxJ4QQQihhlBElFWaLfPnEB8CFD2CI3XZMx5NIaxWpsekNkUtHTTR6SjjwOoTNNLRwO6mAiiUUWldw+BQAP3kCwAsiiShMFfBs5PTdu3enydlat6QiTgPdDQBNvj6WJwDgdcatRjMBAE4dSQDgJD5ec9aghlmiUTmM8lRJjzXDdMyJ6Dk7kZye8Ej2GRrmg9kiVenqYguURwuV5TSPMasU35n0X7rXbF/ysEqZM148tvKsLBbL90Kt+sBisVwOkuIJ4BfJLEuEo0lFKNqLBQVTR4Nmh+SPzcKAOh6KuJSxxP7+/sbGRvanuUVa62ygZJ2bNif7FkXbzJsmhIIQANMmdcaRxLEOHdrAbHhApMYO9RnhyH37M/+EAqBJjMP7459+BKCZjl24AEAdxMdwmgCMN/3tI7jTrJssnECeKEy9hpZRRBMo4M/+rybQ+0f/GOgDSLxm5/A0XmsP4oNnh4xPTK8CLxiKzVLlUr8l1oym3x2HAPvy4dE/urkJYKfhQ6HF1w7k1Ccl7LOTBGmw6OpirKT+DZUxQP3m+exlKLdf6SRrfrWhKAJZyYnFYinHjgGwWC4z+fyflRr2DLJ06dlK9T9HpQyV5ykxWNy1lMw+DMMwDOWsp2JVP68RdaRbfYx8932fANEkmi4BrDX0NId+nEzbvw/DR/A68DqIRu/cvp6okE0GeVdiBuLo8y92idNAo90cTprDCcyqApSi/wCA/v1fIzxDNERrk3z8M1CeqNRjDCWvEHePwzEA3LpmNsj2WnM8VeROLAXzBfN7TldpqXtXHdqo0yvSdrYAvN8w+USYyLMgnc716RHf9IpwIijo+90kSpevfPxdUlTG66t/Q6VxUmO0g6GOjSFJkiRJgiCQBSQz8kdVTtZZ5K38KIvl0nOpWsIsFks5q1Z75fJoJW+yqvW9Zq2ctyn3hmVnaLbMTSNYGZnkKTnJlfx8v6ya6aEBTYgGqKahlgA8Nb3YgERMCwAQz/JqBPUSv5n9ee7OuL6kRKoY/hbWrsdq4mnJTQ/Axm392S+nZms3Mdyd8BTkXEMV5zxLVgGQ+h4jQqrwGt+BBOm28cMPcHgP23cAiCg6aXsdBf/YTCTqASBKUcJQAuUaWmtFQbvONsLh+5S1b7QTFQLoOFtRHCnApWYxgVgDZwlpi9WefsnbsurTeUkUhQ2rnludcl2nc7Ko7BfN1FlT7r9GxdZieYHYHgDLy8JOBHQ5qKyVn586lfQiF1D/i9SXMuYM8/OuzFHlYAlygaojllPZXvv8kNmU/0hjTjg38+insVkJGMAV3TuJdk+iXbiNB6NJkA4LV8ZVkk0GlFAMDgA4tAGAHe9CKURjANEPf4zGOqJR6noua8JtuXR+hHSWWfQv/8X/AYCfHmnHBaDXp2OF+ckTZGGe18bmbWzeVlq5p0cgFDLCrFMic7WEOARw5k5Tm6SKvjybziV6Eu+O5HAkh95k7E/GLtUAWVVKlj+4Vb294pSUkTrqP0+d8lJp8/zlzmJ5rblUvy8WiyXP80//X57/s3RXES+wM8EgC9KTZG31bzC6pPL0KiV+ZlBHt5nTW/SZnE/gqeNqkewOXOzwpWxubQ9H/SSJPbcFGSUqZTNRnuqEg0vXWw93APwbPQbAiYjVZKkrzTgAAhqvbwfyzE9ZKFPTlq5P98h7H3uxjgEztX+qEwyfrQI2x/QC4zG8q4kKwUFOHgLA1h2cPgEAryUnfQBDefzsMBkp9uzO5O+2cSghUy1pHCrHARSjfORjHcJkFO2O73dYB4DHfABjDH6d8J7TOsYgc1X++plvKVf/q3YCVPpMVswCKqd++TKU2K90mRlSSs/zSvZKKfPri5UgqzonLZZLie0BsFjeFExjZ5XVlLJm0ZlByd6lXOCQIsrV7arqpASjJGpqlKTGsMjMYXmDaB1XeYzbIAjyfRTmz5UuYREpJSHk2ZmkCQBBGQDN+GQ2Aehpsj9IT/ry6fuupzQ4cTTO5VllpGp6JiajJmApAD0ZJs5UngYOOaGDE/QJKCcuACEVT9VSb1NOjh3iAQCbpiHtN9mRPz3nMB1qaA3taWfS7SGVlMzXfSIHAE4cuM1QDgHEaWhsPNb+qDft8VAaDd5p8M5vJacEjyeTBDLzs7h6A8+RfUs5NQtsVlRLfNb5ukVKjqpzYnlWtX+BLF1fbA5TRqqsLJZLxfdWJi0Wy/cCL5j5JG+Aqgq70skcJeHESn4M3430R3E7fQnl7awrOazZZFup740fuXRK/gKWnmdqRk4QopWKlfYptFYApI60pk2+NtYHAL4IQwCEULNEwCIa0M1eoiKXbT3dH7+9s7UfhPvtjR8BZOuOYtwFttevjgdxiDE92YXTAAbgy70BgFLptZugzEEzbvsAcPoETQZAQq6LbQAdugZgghMAoLQgNplCCI3UJNUSQCiH62wDQD85aPIegOuNmyeTPQAPRofZIYJqTs+dYabys6XiLsDcI8te76xAVUr/PDXfKBM9locTSZKs+iNQRJ1TWokLn5V8oZ1mFssrjn3XLZbLydL8n6K6Vq4+YX9m9pzhhJERq4qJmif5ClJT/RuSqvQkWdWZgJmeyyRd+a2TUg4Gg7n3xBzOCJTWKSVI4VBi2tS/OmMAfthFIAeM8JGMtxuyzTce9qXU8ZPHj69dv46Cl4QfPJTbN4lwgPBOpyuYB99zguGIJARwmC/1Cda20rMjyTni5QlFALTbSFSAwQE623q2QsEV77pwZ838zJngEEBE4ob2wd3JbDKfIjzWZEQ0eHcizzgRUicA/urp8Pa6bgsAGMTsiucB+FqPAKy7zrF6YdOA5sk/i7nnUrMnwWAs86/K4rHZu1TpdqUYoE6UsmqJrvxJKdprsVgMqxU5i8VyCcjXxEYXrlr7GkpigErdn2elCrukMwErujKUX/5Ker2c8nb6pbyozgTMFNtSh8msiyD7vGgDgIDESRJKTALZ9hkBJSAAwpQOkz6ABu+N5NM23wBAwvFEEr2sg4IRRoMRoLF2lceJUum1Ng3TYRfrUCnchjc6BRCDAHCIxwbHAJTzrBFdzgY5TE9Mpd44GHoE8dNwvAZg563ptDCn0ZMWvYI0dKg5fAAAMha0uD8BoGCQySA5Aa46rKGhGeGcCkABOIufKj0dCqx1CuBWO/3NXuyyVonPF06lRl9KdtRi3ChmLDtuCTVjgDrqv6arPDV/WywWSxF2DIDlJWInAvq+qD/8t359XwTn3PM8z/OyD+bzSjU0z1FpU2SQt6wyAWaKp9x41ftT6fAFUl/9Gy4cTpgDNbQGPA4AZLYS8Ec9+rMNAOiINQAew5dnA05E7FCP6T/9xb9cFJr/0z//34y3B8kEAInGAHrONex9gYOvn9kNDuI0GMl+0u5xMA2F8yMoMjTjEB4lHMA7re13WtsA6NF9rqdzfQrmjeTJSJ64UQKQmGNuneOMZ2cbjwfxtH706HQ+051GsjYLHByqGRWceZxid8x6LrRcbSXg7xexQNURSzAHmgEPS/F9v6bzrDBWFp8XZZOHr9JgYbFcDuzrbrFYXgzPWX1mh/NlDYH1nectixoURY1kmLxBTaldKXTEilO7lLOqt6ReCngRWkNpwghvCg6YVbAIgEQF684OAKlPG7z3w54PgIAKKrQuGLkrPOes//aGiziEf6vHG1AyWnvH5RoqNfP/BE3/etM/2DtJVACALfQkZJBogrTZVB4AUAoAue8N0kGQDjriCoAQ9wEI4s4ShZ49WSmlGTANgOhU+92dhg8gTiehGgNoj5M9AgBKP3up/t1BDOC9bvNRvxPq3GLD551fep7nvcpj/JgXO19480VVCDG3jsdSTAxQp0thqfo364sVrTBgsVwC6tapFovF8p1RKc0r4bOhBUt3LW4swgiISqldJ6KoKUcy6vhciUylXSwSSEE8qtOp/NVRqoRSgnqCeqfRPgDAezIZNIiPyWmYjhzWSAoCgJgqcIEkgnCH8dPhId5urwOYtN0GaYxoBMChXpxOPLipTkE5hFf0CJTjIYlBGVSKxDTDe2rzVoKpfevw6bB1C4D0mhoqUpNUxUnpPEsEWun0IDhsC8elzUiNAbzv9iDERJ4BuBmpAfX/eNv/X3dP7w/DxmiokObDCcye3bNehRcnlC89NfsNyplT9kXNCi+wiFksrxH2vbdYLhv1838uNy+kXi9ykpwfK1nzu+p3KTyP+i/R90sdJkniOE7lKTGilUacIpCyI9wUSmm4rAGgxRWAEeIbjbWTw30AE0nidFzUTuuwBtpb2LkBYLqmGHcho1iNY4ylVj3pHaQTs91lTSBI0pIEG5L2rpj5SQMEAE7TuEOvANj0bx+NH51trp+dPQEAMABeEBFCUaz+U6JjFZlZjBTS/XAPwJWNOzxOAHx5Rn++cRWj+wB+PzwEsONrFqQPYjrnMBurMLd9JWlb9FwuFsW9aZh+gOwzzs+kVHjYMj755JNPP/20yspieW2wYwAsljeFZEaQI988aSmBzzB/mrkd5zbWwRgvlW4iR6XbEj9LEVWDE+q4orPM+VRLqpXS+K//6T9XWjHG/vwgBHCWHD+lwb89UwCUTlNQAIvrMUXpOFYBTh4BSCFd1lTCVX6nN5IAetID4POun3LIiBHevdlAMQSEKYxYCMBPqZ9SlzaidHQWm34JaJ02ea/Je4/G/adhH9xhRJT3xjDKx/JsmzYBtMV0wakhC2M1XRNg1GmdpkfXGuQ/e6v7s82eoGybVden5WMt5jBlUxaA2n7mSBaoOuJScYEya7FcSqp/sCyW58GOA34VmFMS+V1zkqI+C4LkIk5eR55fPRgPJoQwcn8unKjp31hmMcNSm8x/ZTiROSlShMRM4EPgUC0VAFACrbVUEQCZkjMeKo0Ob/58i7cEZZRLvXysrcuaBBSnp+PkuBUoAPR0D8BfSfHFKUk8P/H8e8MnACTFYTgAQBRb6goASZMQgUubAU9Tx1OO5wdTmf5g+KDHN9fE9u7kdHcyXbMMXrvIlUFrneq0KXoAXNpMVdJgLJlJ/20/ya4qSiWAw/CpQ5tS1HpqdYpJvsAW2Zi9Kyn4ud+BjLn146rcFPKch79S2H5Uy6Wn1g+WxWJ5XZirt5LzY+mKyNuUK0V5fgbGpQb8Qpm1iz5X9fA68vzXmIUTfDbGIN+NsPQQkRtqmfdjxlbOabhkIdWEgYJopeExnqgk1YnH2+/2+gB+deR/QEb/76HsAQBaonD5XqljAM1HT7C+Ro8f4doNOj79uxubAIJZts/vJsM20q4jTqLSd1jGLmsCMEk7GjgRMWS87l0ZyxMAEurt1hYBY4001XJPD82UQSVorfrRWch7DMyhfqwCAAdB0KOxGUDc4J3fjk8BvnF2sA3sycGTcXVBy1i8q/ldKC1iefJmJX04dXzW/MY82auSPzb7BajTp2SxWL4vnrfusVgsryw11X+e51T/mVl9XZt5WzykZJdlKVnoZUKCCzTHLpVuJpyQaZDqlOQW0VU6jZX0eBtA10k36Ta2Huztj99fw8nYASBowYNT0mcdtCNwB8kEAJIAQEIUgFTHhLA77eu/6+85tGHUv0JhLAFokioBoSgYKLTyeSeQAwBvN2/I030OEDxLIno0HuZn8lmEEa6hfEYA7I5HT449gDd7p6bPfM3lB8H9TfcagI96V54eHB95xGHOzdYFl/tdpLKIrUQd9W+4c+eOlLJo6pu7d+9mu8p9Zj8Uq0YClW9sfVcWi6UcW61aLJeHfPN/ZVW6iKm2i9R2TfVvqKPdy78uv6vSMmOuJTK/Kzu8jp83GRM/ZH9qrbMtSoNRohwepwqAhgYgVbw77qcp2/TQjxiAJndPMAaQarm0t0Fl0/DLGEC4tpn2rgAYJ4cAXNIxO9/rdPb2j3f8NkuSH/zohiiYiyl1fcgIADUxAqH+0Z4P6BZCNeEAwhENR6OG82QyVhoU1ZNICuo2xbpZ7mDNTc11HYVivTmNQyjh73W6UkVBq9UElO7341GZx3pcoNhixZC7kqwfqcigfkSR2dy9e/fDDz8ssczShyrdrhoDLHX4Am+XxfKaYsuAxXJpqaxKV2JVb1JKz/NK9tavg3mNCTQzg/JWSV48PehSMm+ZMsv0R30nL5v9/f2NjY0Sg5KEk/ooDQKY7H+X0ShVlDBBVSDPAFz1WxOE73W3T7z0N/cmiQqOQ630uU6J7BGkOonUGM1t+J2TZtQA2OgEQG/jKoAgGScqDNIEgJtSliQAOHGLbjgLhtq9BUJA2VG8txk7cKYSP0wHreYmmpuj/pPM/mqj+cXk6VvFL5WCAkicToB210nvdNoAKCKzN1WpR6cvdpAOAByGkybrrZHDpd5WoijIeU4qi88iRe9MTZmep9zYjBG/d+9epSVypbjczFCzzaKmN4vlkmHfe4vFMmWu6TdPZSW6SInUXkmC16FmTZ+3qTwBY7moxrItmUKqdDXHa9EkuVT/ccpcyhKlYxUxitmaAHi3c+No8tih/kn0BMBH3fXfHO4DIOcXbMpf4yiJACAY+GxTa73PIwBr8hiAUgAQpWMAT9XZiARm1s7Mg+/7yLcE+z0SRwCgUgBHTry+8wEApSYO8aGRIKGEHYWjW+0trfXdw0dXGyx/dXNPhIB8O5C3VZjoCAAj3FzpT9cbVOg4DQCM5WmqY4d6guJaw/tWP4EoDHfn4JyXx2N1It48cw7v3r1r9PRLYqVzyygf9lDfZ50YoPIHYXHXq1YALZaXjZ0FyPLSsRMBfTcsTluxUpW2kvH3QnnTfn0BYSgXB7Le/Cp1bDKMW8wCrTmyvasic5gt5pSev8l/jjhMTM4PtOaEckIpoVwwsx6w1PEkPRvGNFZ4HD59MBKcalYw1lZQd4O3QEniOmbLzijZIevtr++2v77bSngrpj1np+fsaI2W2GzydSws/prdOn16iDS++7svxulpk7VkbrBAAz4oFdQsJjAlUhMxu0VZCJdHaeVx7bEWgFQjlIMknTAynYaIURbr6YxAjycTAIfhsdRJqqvDWuO//NGsGlXWt/wekVKWqP+l20soLywX+0GwWN40bABgsVie5WlUGa7GC3T4Al2hhuaoNMhTKTWMXjHir8jG7K10lZGJ/nyPhJnPcTEkeCEoDaVVGMeaQEPHSqdKA4jS+CB4EEoSSuJyBWAQcQBXb+8k6fLbKNUsnypJPN72eBu96wDID/7uw5vXQ4cAGMRPB/HTLW8LQCBnM3gWID0fgMka6orWUB4P5XGoxifqCIBUMYAdf30iTwFIlULrTz/9H8yxYgFKSFuog2AcphJAS2waS0addpCaz80YD0YTqcBBr3pbZ3FEQMRsAYcMY5z9mYUc5RizOu98fZ+rUuKzzol9N9y5c2fpkOWV3vyXUVgsllcfGwBYLJeTTEOU19aLYqXEstwgo9zhBSraTEAv3YUL+Sw5vZXUP6oSGGSNjIUMXq8rQM46KMypzp1wkqPE1ZzCE0JkSii/VBwhxDjJBu6axB5BAehUKY95ALqO33X8JCU+Yx1XrrlpmFJSUMUQQiKSQCk4zYMgy85XD0aPph8bvU7MOjEL05FUkaD+UA6XugKgextcUXDPZ47POqlOhkkwTAIAbbEZpkOpI3MmPusC6JJW4jg/+fh2oUNon7OuowC0hB+mQ07dNfcts3csk67YNp+3/BRApMOO4JEaIxdOGIP8ag8XkOlF5ahmmTXU+R3Iw0v7KMSKHRSGEvsL3JY6LC4/V0llubNYLhOrlWGLxfJqUrRsjRAiSZKi2tfsFULohfyKOfgsob+yjuRVnQmZxq2pIcq/sc4pLVKkOS7gqpKal5lReUUv4ySNzyyQyKtVszEIAhMAxKnyOYtTAOCJFEL8rq8B94YfAOBUA0gVOY0YgJ/++P2lERrFbFWv0dFhY/MwOPwP199BGt/sn5KbP5HBBMABCwCY+TpTXSskE9QD0OIb62bJYu6awz3SDDH59cnQjY6uoKm4IFk0swyttdbp/oRvKwKAU8f0ISQq/ItAvd0GgL8eR2+3WwBOaeygEaskUed8Pr+uzTyYD/nMLlNsi+brXGTuZJa+QlmQUHnm2a9KzVexPKJ4qbzUsRAWy2vNajWTxWJ57SiveutXzItCNl/9Z3vr6N1VYwBUua2vRVAjOCnaW4QoztZY6RrrMKfUK1lUjXMGs9b9MkE8ZRYkEk6QQgNKQSmsuSmA3bEDoIOAgTe43vbl7/siGAf/YHlAqBnhcBqQ0Udr3c/7ZwAiQeOda2aR3j/bP9gh8JmzmwwAMCJ87uF8h4+cda2kOgHjkHEoB5EOPNbUJw8BkNa7UsUObSmgHz8BnCtoAkhUSAlDQXoSAA2kWl9rAsDuOL6WBgA+6+/eaTcBKI3Hk0NAABjEZ4B/Fh+cRMyhNe7hc5B/dh988EGJZRHZa7D0ncxHfZXUjwHqqH9TgipdZWSxSolByV6LxQKbAmT5brDjgF8q3+Wq9fmql8/wPC/7XH54hrE0YcDi3kzhyarU+VXhVR0UWCUoehmUyCBZb2jyIkU+Vw0nJkFiIgVBNQAFKODxWDwei0HMdvykzV0AHhO/7wsAjCzvWdLQfP/J3b/867Ev2MOvfjQ8QDo7w2AISgD4zAHgMb1+Q5jgIwiCZIEgCDK3HWcbQJiOyfb7ZPv9VKeEMAD7wWMAf9jrBhwTrpSW0DAL+i6FAIkiWqdrbgvA5zPFYzQAACAASURBVKfT+/N/Pkh+su6az3c60WEwiFJyf3jYjygAUuzwlUIUUHXcPOYovoDZm/1Z33Odkj73LUU2qCrjc1zgEIvldce+7haLZWVeSE2ZOSmSpzW/Zc6s3FtNn/Wp1DcvvBPgApSkeNVX/4Y4JWZNXZdBaUIJrvgJgBad3nZKOUB+vqXuDkjqFQYzx+vdjZOzpljH1TWzZZSc7Pi3kAYA/v6Vrf3TgwbvtkWSKjkgQ54WXgIjAiqF4wXpgJH2MD46SiIAgXQB3OL0Gt88FmECaCgALmtqHIM7yfkp7c2YB845QDrCb/AegPea3SOqvx7Ef//OW5+dDABsuL2EJWYFgImkADymrjWcvfz0Q28M+fff3E8zSeuqzJWjxVJsCpExq5OyiNqFfdH4zp07AOpnWFksryO1iofFYrG8PGrW0+Xw3DpTS/cublyK8SPqrcck6k3VUp+SSzDUPLGMkvikfsaFQQGMgBGMJTFOTU6RGSz7Td85i+n77gkQPRgphzY+6uEvHs5y/c9DQHtOK91pIYlImqD/KOq9P93HBPa/AtBovg0gVRLAWXKy1M8cSqcAOs52kzMAaw4DgLHpIiBj2ZcqbvAu1SOpolQX9j4xwonWn5+OtpXnEPF4zK43UwD/5TtXYpwyMABdse00EgC7EwLgs/FplNKlj2al5/VaUxkM18E4EQvDHlZlTtYvfdbZ3spS/Mknn3z66aflNhbL68WLrLosFst3z3eZ//OKU1mL12Spn7yME7UnQuG5WX0q7SvN+Cp50otcTEhldFqdNA0dpuVszACnOkxxEPBtX2548ix2dvxrAD7HNwD2g1HHSZe6+tVvvvrBD2+nKkqoAoC1t9zRyIXTj55ssCsAwEScBg5xGeWJCvcDLllhgg3VhDChtGymDiduqqUZDdw4egRAch/Ab/oSoNu8CyAlymVNhcIG+1QnkY5+2msB6KfH/8mtDwGcxvub3i0A3wyG11ztUB/AcfIE4BN5+lZTDN106R1+Ti27GD9cwMlryvNfaXmhqyySFsslxr79FsvlJK8b5pTl81erbwJzbYf1Rf8c5pBsPMOiQabpazqv3wlQfs5ZToVJpp/L5Zh7SYyZ0jqMk3aLRzEAEIZNj/6o5x9GQwB3OvH9kZloH96Wpx+M7g2Wv2lSQaowSidC6oE+64QEvasAgMGjeH/S6gDYSl0AiQoBvN3i3yx1dJ5YUAAUlMt0SCbN7lUAEacA0H/60421x3tPH42GcToBCC0eAqcBh/kaKpCSU5DZ+OfT+IliGmAweUfB0QaaX8p9AN8OE06cIocouM8lzKUnLd1rqU/NwmWxvDnYImGxXDaKpMPilppaBLkQoig39xLzQi6wJBVhJf9zxkVCsH6q9KpdCqkiBHAYU0pmUwcFKX2v2wbwN4MQQEsoAIdh4XW5s/GyT+TwA7gDT7cYAGikPoPvMABP+k/ilJ5E8fXGltIH/PQE28u9/X+/+WrnvW5bNFMVI5rA8QC0dWPiAMDx5BjAD9caiYoejYaAWdFAM1V4c6QiWquJHHSxueZsfzM4AvBuczr8d8NLB7HuOABwJmIAnDq32vxuwbLHGVlXQHm5K5f+yA2dX6lXYbFFICMLBeePsVgslxc7C5DlO8JOBPQyWMz/SZIk0wdFzAmIcjJjKWUYhnIBs7HKzXLyfqpsLwl8gaojlpAdKArQWpd7XsxoKse4NZ/NhPeUcgCpln99FJvt94enh4F4t9Ne99ItD10n3fQK0mwojdKJyxrXvA0KOpZJoqNERwBaYrMheg3Rk4oAuN7YAuCy5nI/AABOOIBhMt4PEgCUsq+Ck5BNv7rF3Wu0d8X3XdZoO2rLg8faCIcxKUwBchkFsDtmHqMAfD611Fq3Rct8HsuTkaMZEVKrMA011EkUFjmco6TcVar/PDXNkPtZWFrWzEbTQbH08CKSYqoOtVgs3zMXqXssFssrSFK67OsissbsNPWleR1vBjkLJ0psarp6w8nfJc657/sXEF5ZIEEIySR+PiQQs84ErXWqSRgna9SbTCIw7jvUzH05iOn//igA3H+8rQEwLB/7m6FmawqM1aCLtTZ37w4CAEwI4KwfEAAbwLbf3D09kDoZxGdqbbPIm4Z2KeHU3fY5Zpn9D0en76yvT0+PBl13R0Wnm+7WKDmG1onjcOoWOVQ6TVT0djMBEMjBO43bABqchOkYwHWxM+ZRnE4AtMQGsLch3a/ik3Z1GAXUCLdqlriMpEaXQh2fsnajQJLreSj66sxP5fUusngOF3BisVjKsT0AFsvrSr75v061vRJFjYVLybUtVtiXmOV3VfqZIzs2nFHnZCwZSZLMdUcIIXzf933fbJ8qMAJKIDUB4HEWSBJIsuZIl2oAH2/IvclZlI6DVP3FgTgI2JPJkipGSkkIvfflkVRxM+VDGbVYT2kojR9vbP14Y2vDkxuedJkeJENZYw1gmkoAUkUN5QLQxw/ed1o7vgCgoTt8vevuAGCHDwAMpQrSMwDHk4oG+6OQT6QGMJbTOYg4df7t3nRYc6qlzzupTjbca2hubHmdmusAlBTVC5RiWaXaa6p/gyk15adRR/0j12VU/6JM10EQBPI82a76riwWSyW2mc1isSynvm7IKGm5r6/IpZScc1mvS6HIrZwFEsZJHVcZJee5kp/Xkex2Lb1SrSGoJkCqSZSmLicuUwD2xuI/vxMDOOgrAH975AK43kwfnu/wyR7raZQCeDjSN2nUhTNWw5022fKuxnIMYH/iAHibox+xh0Px8fr6w9Ewks8W/FqkkVD4XTjeiEXoNNeBTupO0lGYyi2+rdM4RoLN28PdL1ucHOs0TqMssWcRSqgLMR1AQUiQRkG6146bbdEAcJTupalmRAAYJyeAiNXk2+E4SJ93HQAhRH5ds+dnJfWfp7xXoX57vJgNfS43MzZFp7q4vX4xzI6dc3KBnwWL5ZJh336L5bUnWSVvOE/9irwmRap91XOrVP95WVlOZlOnss+MyyVLHVeL5M/2Yh5WRRTPF3SxR08J8iNnCUGY0rsDAeBfP+IA/k4L3w51pLDmYCTph71kTmmZDz7XjPCdhlRamlx/AIfh3pH0AXQcDYADW34a+NOb1uTuXIbSszEJlHz+7elHtwFyXVAfQJ9OGrwDxB7jmpNExgBAKYBvzqaHCTKfp3Tv3r3Z2k8EQNdJfaYBuExEaQIgSMc/25peyP4k7akxp+4oeWq2JKq6B6BcWCdJwlcflv1dUinlF6mMAVb6+arfOlD+E/HdF0aL5VXDpgBZLK8l+fwfIylWqsaKmngNRc3qlZT7LNpVRPkhqzqstJezcZAvSqxgdiellEEQJDlkjioftdjf368yWcKcGH0mqQvvgNYanGqlwYgG4HPlMrMqMJTG//PY/12fr83mw6Rgj588XvTiUKqgvjgVqSYAfvlUtUV7x7vxQdf9oOt2hLzVjgDECgA0lM/VcThNUloEgNRJ6JCzaI8RzggHMJGDNe9GS2wC+PP9oaP4abx/FJKeKx3qN5RQUHNODJxzgIAyl0FDx+mkwXtr7tZJNA0YHo9FMHtox9EIwFGUEuhNr2ykcvl7ZbhYVIbnOPACXOC7ymMerFKgUKXsM5s6heuFF0OL5TXCBgCW7w47EdBrBF8lnECV9P//2XuzGEuy9L7vf5bY7pp7VmbtS2/T3TM9w2k2ZzjkmCblTbBsQ80WDL/YBgwYJCDYMGBTNkCYMCgBDelBDwa8CLZsSbalaUEwvQkShxI5FDnD6Z6eXqa7q2vNqsqs3DPvGtuJc/wQ90ZF3SUi7q2sqqys83tI3LzxnX8s95w433fWKerXjCRTqGUTX2GufxaTePDZZonmSM9vOBgYKTKStNfi9plUZGriVfVNpvyIdkIaScIABiw7eGMhALBo4/V572ItMKnc9UY3iiuoUHrnKqLES2fKK99dnQPQiRpe1KrQGoB9jxHAougKeru1v1LKujVOeFcEHSEAhJHHhZyVpdmWD8Bgdhh1f2VptndaYNnpZVRGeNoxjcOAjY0NwzACGQFoBL0pzu1gL7aRiqx3eklKXLlRq8RVqIK6GZ2tWPGWBcOkM0AR77l40YuvOXusTnG1ghQsJo+V7Kw+aVkQBSIKjebkoQMAjeYkMFEnQOw0ZPgNBXUSeN6A2ikEB9plB5i0ts61n9StybaPTzepZjaJ058OHpKjA7HE8KkzLiZxgAYYTqtAvIgICSiYTEJKLyKvzHkVM54JYP7SivfyrPfhjn3t0FQggSR/6+/9D8NnVEoS0FnLBEBE0BWHO247dqB3w/VA0lMlBmBOzQA4XS4TkD1/bG0lIWtKVQzL4fXdLdclAQCUZ3+yc68b7APooHMQbQPksl2tKEci8piIVG8ya5pYkBMVqmDZKVPKGTV+smv+0Ubrtfqp+OiOx280eysI3W6ZAK43rG3X2/cVHg7tkJoym13iEmKz3PyPfpnK1Zw0BigoOxFHWxCyyS3pI5kulUbzTKMDAI3m2WN4+X+kYoBx3kP8peM4BWv3gn5DrlmuwUQceVU9nWDGMxzngo8jLDAKItHMkDX6g60T973IDy2ECMNwYIRSTNyx8JB1fwaAF5EgogAowe/8p987Wwku1vyvzLoA7rUNAFVT3W1Ti0YYgxcpm1V+tB19evdaGSUASkWhJGttE8BMVzUDejtsSuBup1Mx5sscv/vXfnekFAVTZtmiZQUZSq8RtpRTvR62AHzR6K51OvEaozVjaensSrI/gCfGVn8cDIBQAQMHcL7We+aUKE57j2DTNQKJ1XJAQF6oB+2QxroD4URBv3+AJBUfQ1yKCyonEUWeYb73Hx8qnrdzKXgLT4bpXgUazTNK/htBo9E8KyS1aVxDJ1X+FLUs78+0G1cpJgZFHAv07fOsgLz+hNwLGyZXcLqlV8T4mYiTekhhGBqGMU5wioiiyC8u+jMTMmzCfnASC1KioNAVDwb2MKKaPllrmQBsIurMvVIPrt3FgU8sBtuUJlMDgUR8j1XDvt5ofOecA9gt1SwZHGAAVkshVzgA0EHdxF3gbMm5vnVvyRbnXzqDUXhRT5yCSUTL9gqAZcc+lFE8MfeVmTkAe539RtiSSjhwTFoy2NhFe7pScmId+MF86AOoKZyrhk2xGx89Uwm320RIADApAAiJ02XxUzd/EvBEFPkRCzIsNfyLjDQbJh0DFLEvYlnwHZIQ21+6dKk/afuhQ0KIGzduALh58+aIxBqNBoDuAdBoTiTGw+SZjyZ27m3b5qMAEB/KkwFSLni2/YB+hlnG0UkpHkgMcISXYUwygqsgGfcVdxHkRhRhahBLrEYJsZgqccWpIkQRohzeq0RWSuG+zxq0Fo+KsRnmbbne5YlUghDi7/wv/6j3vfQA3G4pAPe75FarN33YjeS8bXFqLdiRK9tlPhtIVEw1cs6DxagvOx2x/8mB+7OD4Gpze72z3gjcGcoMqk45pBFuAWiELQCNoBfssTx3nRFUDA5A9vs9HFZuBr07ihTCiHRCYrNqxahSwi5Ux3Z3HBPSb4OkoCWdCRO9KJIwICMXxUdzlQ3DuHnzZvHMn7xMcm0mhffjCgDDoYVGc8LQAYDmiaLnAT86I8f/PFb4GPLSPURiP5A2UUsijTECgxS0zL1anjmNciRG3rjqSQUzEP1B/3mGD5FhP2l/AoYGKUWKGIYR+887rhISP9ymAL69bL484yza4tU5/3LdJ8Dpkhgeux/HAIeBVzWjunmqxGcWnXDWXH6hPg9gyVktG3ML9gIATjgAi5a23L0ydxKFmzdvxs9koG/hpToAXKzWADgMQeTG2xRwYnbEgU1pK2CxT08J5cQKh3hwkdKrGgxAxeCBJF5ENrq9o2dKzutz7I15BsBiCkAjaLVDaY/vUjhWGA+TZz6WRGH4McYU0Y+b6mPL3OKfGOSa5doMkG38zjvvvPvuuxkGGs2zyAQlRKPRaB6FdC07UfU8zEDy4dbuxKDIiWKbsJhPnOv9o7BUmgxNY/xy/o/IgHLGicIw5Jx7EYgkFlUNSThVUpKqqfZDvDGvbrVMP2wDuN/lJlDiat4Wd31WYgBw48aNgSbVMkeF415n+0x5ab1jvgCstXeWHONWa3PVnvFEq8xnAVi0BKArqFSiG414RGEYShW1QmpQ22aVr83BE821Zh3A67M+gKsN+mrd9qPOjkcAKNVz04mMhrNN7LkCOAyUVFFXhCVunC0rAD7ACP/KrA8YAP7wPl6xFQACzJjVSO2rYsNdTh659zv8648kVyexUSq9HcUIeGqvgNwuvikCBo3mBKBzvEbzLPHkm/+PM7w/E2Cg8p6uLjfyJjgahQfqTOSyZ/s9fJLZDgnjopQwDAnJG/syCsMwgiBQQWCUOIASU4ECoyqSpMQxb0d7HvuyYQHYbMvgwKqbSkTUYmpkq3gYhpYyKOF+RG80d7/xwqIXtZcd51CKVacCCItXrqvDswEH0ArdEpddSuPm9gw+PWi9Nlst8Znz4vD04hmgWoVah7veaQI4Xbb9qLPdkG3VaouWooMbgeHBSCcEknBqmVQJGdTMZQC81Bs7REF/tA2bYTEqA1hDe99vuYLEsUoyNqlIPtEMM1AcwmITWoYZ6E94xDYCjebkofO9RnOiGPY7p6s+nxWOqvIuolPEBg+bZYcBjy+iGNZMHCleeNJzknOSD0pBKOL5IlCEUyUkkYAEPtnnAE7bUd2Us1J8dAAAjYCFCuGYcTENGVLClhxsuxBRcHtrd3nB6QjnWth9oVa63jwEsIPDPd+bMblQZKsrHt6G+EG7cijJrGm0Q+9KJWiGe/GaPSaxAuWvyeZKCQcuAcCjNgCDWgBef+Xitc82hi4K6HuKNUN6kQdUAHTCPQB1lA6DZpX1woa6KTxuAGiFLYOibMg98dB4pORzMh4GhRn4rcXDs8MnknrWefSbHVm+ihQ6jeZko8uARvPMk7gL45qKp65En8O2zKO62bjlPnnyIweHFDxXIpUbAxSJKApKpXlw2Q93HgjVO9oKAKBekwAaAbtwuv6DLxtfd8L7ivAx/Q02I5vdsNtgV2p+J1IAdl2+4LiSmTt+a9kRhz7b8XreNieqFZLs9v85e74ZsJoZtYSqctKWhwDiKOVC1Qawtd8GQMEjKb0xm3bFUAKHM1eQTdcA4DcVgG/N20AAwOa1ry/cAXBtK4jtWyHd87hJR19ger5ybkkMH55ukZB8wzmfIqJIGPm7Tyf1bFGwrGk0zw+6SGg0zwzD43/GuQtpijsfMRnOR4yuSguSflA8NRx5igeY26tgGIZhGLljo5GSKhgDJEFFEARQ8CP4EXEM6UYE/XUk7h0YAGYMud7BFSO83sTFCnZ2eCMg9phxOwpy3o5C4HrT+uVVQghZ99m+z1brpMTloc8AcIL1toFKeL9jmBTNcPSqFQZVvvQ3u/uvmwsAWXJmADSDTs0sW1RZVDWDTmL8wY6wJAFAkDUUyotE2ZB7Hpu3o5rZW97HpOonu86vnQaAq4fmV+qzAH5vrfvSrDdrRZsqSzBuxc923MNiO0wnxtmWCRkNBHFmmDqc0Gg0zy4T10MazSPy3nvvvf3223lWmnyKuAsxRZyPxHLgwzgm8mLHqU0kcgJ4xPsdlzz+vuejZ5JkgIJXMtClEPvyQsGLCCeSEyhASLyxEh4GWO8AwPUmAKyUoj1DAohX4h+WDSJCCX95Bg6vzvikYaslRwAgxHAj+seb/NVZ0QzY6UoI4GItaBqyboxdZsdm9nqHv0HNSAZC+lKJOaOmlFzvGKfL4aw1A0B0A4tVfn65+WWbelHWkp1KgRGY1Lk46wOoGREAT7Tio55oVoy5n1vE5/fvp1Nx+kir6hV36NEvUEVKdPZbIreYpxmYyJu+4HQHRfwh98I0Gs3TpVAdoNFojhvFvf+YIjFALJgrK/r7cBVxIgtqFpEayXPYO1HwHnO9w1jHeHjqc5LEGDONWAYhrwDMCEVgUBWf5TAAgJUSdjz83JI48DltqQOfLV5YPbh5N508iSgshrttXARc0QorZ0vAcn0RQEtt7Hj81VkB4GtzlVvu3id7ZuAzCYgxTeyUMALy1TljoxsB7IwSACIlGOGnyyGA93e6AC5xDuDAb8Tevxtl9ZYY1Nhyg0u84ot2R1AAS4QwsG8suIAJ4HbrcMaUAF6bk7daxp7H5egJzw/ILTK5xWSA2H7cr1ykezAh9/2QXsknQ3ngy0nDgHFR0KQ6Go0ml0dqsdBoNE+M9PifiRoL04yrR0WKkQYDFHHri2gmRwueNyZO5Xme53kDpyh+CyeSKfwkzjnn3OnDU4y0ZwRSwaRQQCB74/LfWgpdge+uuAAOfA5g1x1MHg9SSpSFUiZTjSBsh+Gmuw/AFc3PDjZ2PA7A4fLVWSdU/q618J15e8GOpBy9epFhGASkHXoHgXu+UgJACG2L4HoDVw9FrPbVOXx1DgCCqDtr1Uvc9KKsus+LSEcIi6k9r2OyUs2Qi7ZNSe+OulHjTzY7Gx3DIJZBrPhLh8tx053TZDvrT5e47GRfSTz9oEgpE/0+imyzNInyMPGW1ROppUlL5dlqNM8LOgDQaJ4xJmrYGyCjBp1UMNteTOLWF6+bhRCJ0z98KPm+iNQAYjx5SU8IE3lX6cZzP8K+33OOHYbrTewH2HZhULXoSC8ihmEkocWwVKTgcNUI92612gBsJoOIXKqWAfx0TwBok25safOHXOw4ogAAQkymKhyREgu2uNGMADRD1gxZicuOoF821EEQ3ncFgK5o7PsCgDViFdAHzJiV+x0OYN93AfhR12IVh88cBr1kZyshISyCoIS/NjfPiRo52CkhI6bCVJFbwhEW6phxgpO+eUThGCCJKzJs4sI4URgwXIpF6gXyXJVujWYYHQBoNM8A6eb/qX2FBw7TUZA7EGWKyjUjSfEKW0wSA8SaSfviMIlNntIgYhR5iZ4o2Y7UyKOnlpYBMAIhQQkU4AUPzLZd481FMWcDwEanV7mcf+nMyKzSDNiCLVxBAby/bQO4WK38eMdsBgzAfbe97zcv1fyXZ/xuFDYCWjUjAmqkQMqrFhJCKQA2KwEwKEpclrhcduaWnblFp/fkdzz58Z5lMzluxZ5e3wdVN5puzZQdQU2mGGEmtTa7B7GNL8lKKRQSQvlJQl+SmlFoaNaRc4SF+sgRBWKAcJK4omDpTorb8Ifk8/EslRrNE0MHAJqnwHvvvZdnoskh1/8eIMM+t+1tUo68Wi1Y6ycUrNpFMe8kpoggUpc6MpxwXbegzjgK3trjIHE0kyfGCChgUNxsEgCtkLZCWuU4XYosQ661aMOnB8HodvFDnygFgymhyKtzvivIercJ4ExZ1syIEQXApADgiURB8YeJv/3b//M/CCQJJRjhlLCz5RDAC7XghVrwJ1udP9nqRIpUDLns0EWbztuRVCSQxFf9NUZTJKHFoh21QgrgwGcAIiUqhmyHe1cqq6ftMwA4BQP/gw0zlJ4fdQmw74+dWMxT0y3GMZ0fn51q0rcEhmb6DjBFxstOMpH3H1PwbVCwmDzFAqXRPF10AKDRnHxizybbIOPoE+NJXoaYZDhB0t6cbRa7EXF/wjib6TyemPQGXuJhAGxubg6nLe5WFnwUElAKlMAVAKAABex4cCP8/prx+2vG+9sUQLn/qOpjzh8qfLxnVrgEsNXtGb215H9++CDB9YbZCCgApfDyS8uD2xCkqHAaN+oTEJNaNrWuNc1rTXOlFK6UwroZAZAqkiq6XLUCSUpMAoinPcSPKN0/du/GpslwuSYOPF4z5M0W+/6G0Q5pO6QAdoO7Ox6/3+We9AFYrAJgtSwtOhgADMcV2eRmsDScc8dx8qwm4NKlS/GHjEud6AofH9nFZ1KHXhSLKDSaE8axKMwajSaD4eX/0zV0dr1VsOkxDEPOecEqkPcXncwznIAMtSOvnkWBhv8BwjA0DEOMX8VlIs3ELPcZJnedJBl5iiLO5TCEkAFXKf43+6qUAiGQCgC8CGUGRjBnAcBL1QjAj27jXodVDXQpQol2OHqwTd3A+WpEiDpTni3XAgCzptrxCICOoCsWA7DotAA0+sPuu+OzQCtUoST3Ot1lh31x6Lw8Q0yqFmxhcg7gbttYtMUfb1oA6l5r3+P7QNV40P41/ABtVtnzaBlYa5kXZnGxKmkcfVC6YJ8HNubsCAFemw1Yh99oNj/a43NmJf3oEs3iv85E5TpXNjEoXnayf3pjwv3jYnIz+XEgN9trNCcPneM1mmeVcY57UpPlughTUND7Lx5OPCuEeatqTuEbZbsdxYOKIjYJw7Lp6CL5PHyzcX5jBJTAZhAMgYRUkAoOB4BmCABXZqTs+/yhgj1+rm0rZAAawe6q+TKAq40AwNmZ0IvIpisAOFwe+KxuRtQmOx7LmmMLdAWdB7bc6KsrUaSwYAsAZa5KfEZF+wB+8ZQL4Pc+5gBmzKzM+fU3LwVR97RVWSwHbkRtJktcctp7IB/vrzNCGj7r+IwS3Gg2AbwxH/3f170pStzNmzfT/6bDhnRn0UBoUfxEsWWRwliksQBTFe0MTcMw0v1aTxcdBmieK/QQII3mGSZ2BZyHMfrkpX4Af5hcm5EGaUsUCBISsu2L66Qpcp0TMdHzLEK2117c+4+JfXeSWiozTpu+7DAMRYGBT7HBQOfAg6MSza4AIEIhFYSCUGgFaAW43e6dvWIAwLnziy1f2UyN1LG5PFsJOOE3GnYj2Pr7NxozZgSgGdAgIo2ANwIej7/fcXu3EIzfvKtuckJwGDCHy40uAXAYsNsts8RnANzvGn+65dxqmZTwN5e8RSc0mHrpldVxav/t3/y7biTXOwYAh0kAXUGXnZVlZ6UV7l2s2gBWyyEAV9BIkWsN3gjUUqnAOqCTkC7dRoq8dIMY/dVXxxWK+Psi4rFNwcJV3CzPZJCCylMQj4NKT4R455133n333fEpNJpniITxFAAAIABJREFUFR0AaDTHmuHxP4+PpE7lQ9i2PUWlm2ufPkWuZbZBQq5gcakBMhIW99QTcp2tSTVzBYs03CYi6ebnAShVDY8CKPWfh8MQb6v1Qj3yI+x5mLfUrTYAdEKE42c/f3l986UZAeBfO+txqhZssd01ABAoArXeNnddQyjcaRNGFKdK9BnQESrgRDV88sWBSYi6329TdngVwKwVAbhckwDmrVUA0Zg9xRIihTlLLDkVm6trDe5FNJR+KH0AftQGsNExYpGbTQZAAfG+YMeTdPDAHyaJLvI0HpDojDNIF8AiyhMVySLGnPOBrhWNRjNAfkHSaB4H77333ttvv51npXnSFKlcizCskzhtyaHi5+L9jYeLuLC5TCGS7cQIIYyphkePQ+TtxzSSIl0KxclQMykoU12fMAUFcAIAKyVcKCsArRAAKgDiGcP9VOHD8yiUIhsdw6DKl9GXDasbktUlDqAb4XbLcAMJYMn2OiEtcRkYEsCLL68k3RrDt1Pi8tUF7ye79mqJbnQlgJdnvS8ON85XqgBOl6MbTXquEhwEe66g6x3mdtT58eOdCBBvAFwzqpAwqDwM9gGsNcqAXTMDAC0Pl6p0+fzsQXD/i+uqEz4DDWpF3PEixDoZ2b74iQbCj+yMWuSlsba2lmcyCJ+w01KjOQHo7K7RHF+eZPP/Y2IKd38k6eQjXYTiJ0obFPGzi7R3Tupex2RoHnlEkWcygnDUtAdO4UewqUK8IUC8k1ff6u/+We3Nc7uewPoh2bhbOets+2P8wFtf3Dl9ZRXAZteoV9AWmLMEAAscwBceAQAGPyK3W2yBqs/2DbufNhnFJPrj43dc/hJhBrXfWkIgwwUbi2UZTz/uioMZS267/FwlAFAzFoHDWGfgsaRvtsQRKfLZgX+mEnz7NNvscpMQAIEkAG63TABfL4cADoL7APyItkM68omdbI7qfpMMnxSK5McdtlRKXbp0KWPF0qQLokjO196/5vlE53iNRvPMkK6qB6r2SevvIl57ruufZiKXPdttKnJtwxyVKzaSxLUlAKeQihACpWDz3mr9AFohvvNKp9EAgE9vVADcuG+j89C0hOQihSRBRGpmVDUiiymA/GjbAvDaEgBUOGqm6rSpxdRby/7/9b6x59GVoXE7iWCksOuRUleslHjdmAcABBQ4U7bjETur5bDdJHs+bzQOrjXYd05FP7r/YMRO8rTTv+CyIwDca5ungc0uvyAA4I15F8D/c9sGgBKut2SFGDebEH2xgTzwWH+UE8bAs0oXqCke4xE2GWg0JxKd7zWaZ5JhX3OKOvKZ5tGr7biZMMPVLn6KWKdIDGAYRhHZ2CZXLU18O0EQ5Bk+EqECUaBEAZCAF4FGWLQBYEtg34OSuN8lC8suZdJyvJvtQYV4FJDDFYBdl79iyLtdyilenI0qPPqyXQIQr9wPYMYS8LHn0a8uiJ3WoFTCvC1qZuRL+kVDiXoDwOu1WqQiAD/aLs0ZAYCuzwGwMXsAo78KavzMFdSux7w2+cqsuNs2AXQFAbC25wB4a8kDUMH8G/P4J5sNAApktfSoQWnMow+qOTE8+i0nTQbjivl0v5FGcwLQWV+jOaaMHP+Tdg4GqjSRWsPuUSrORPY5qRqP6jaTGCD+N/1LpX+OgqebqAcgN6gYuLaCjLQ3KAiBzdAlKpIomb2FPj/YRYWCEtxpU0oVZRLAdtdgfOw6oJxCAdsuvzgnbrU4gAvV8tmZDoA7TdYO6ZdtcqdtoGl9Y/EAgMlG++5CCIuyQJJ4WywhCYDvr1Og9MaKB5gAzlbCH2zzX1l1Pmns/epp58Bv+tHoC4vLEQHdcWl1Fp8d8LeW8OaiqNoUQKMbz3OgABYNNIMdTnG3zUZqiUmWckrMxv304pEXqXyeeyce8dFpNCcPXSQ0mmeG9LjnkYwbNZtN2vMY2W8+qeBzS/pBDTzJSZ/hgP1IJzJx4IqIJzYD49TH9VrEQcXIQ2FEANgMAmj5Upr4soGqAd73zynFuaq6tmspieWhzWrjKwkisu9j1kLFiGYtMWuJy3OLkRKhJDseKxtRO6S9cTUSHFiwo71BpQdIRErBZvJ2i7/phJxipeT/bN8C8K3lgEoTMH9lNZ5YPLuG7fFKPfZ9ctGRi1W579GGT1GVN1sWAMrjoxzAYWMXIB/ssiUbp8v0/XDEKkBJr0K2t51btGOSAp4rmGZAPJ0T4g/FpR4Herkejeap8AysWqA5qbz33nt5JpoeYRiG45dmH0AUWOs9JjFLlMUQKOCXjGSk1PNJEQd9JJzzOK0xCt4nT6ZH7DVme3tp8ZEGYQSTK6UQRAhEb1D+99/9e/s+zpZxtgxKISW5tmsBaHdKkWnt7e0l4oksJWr9+gaAP9s2Q0lCSQBsu627nd7l3W2zl2YiAJ6AF9Etl99ojO1McAXZcjmAl2cEACFxr2PWLdUMKID1rrvede91Dzvi0BWNzw+7Cqia47cVSDFnSwDf3+A2kxTq28uL315erBlRiUsADo8ArJSiD3dx4GfVp8Pl8caNG+lDxQtIccuR743hglnkXTFAOIa8dA+RPAGNRvPkKVpzaDSaJ8nw+J/itT76Tc5hZjNhQbcjbVDE1xT9RsqRxolaEalxJKfIMzwhHNWdxj+KUsowjAG3L84n8V+lxo6ST2OahooUAKUgImUz8uN9JqMIwDcW5KedoCWIYURbbZP0N5lK34jFYDEwor51KuDUaQVsz98HCIAzZQQi+tUz3f/tYwaAUETzS8bB1pgLAeecU3W5JlZKZjt0I0Xuts2l+QjAS3ULYPN2AKATAMBnB3zx9CL27x/64/coBiwmLaYOfTpjPdjY2GTqT7Z2ADCAE8WJCiP6i8shgLqpqtWxAYDI6wLKLYbD5HrbBQt4YpP9ukjIlhVHMRBx+NamltJoNOM4mnpFo9E8PuI2vDyrEWR7/9NpZhDX/dmOTnw0bncs7teKoY4InprVV1xHk5D+pRKPP/4mdxqxkHAFAWAx+BE4hSdoVwDAoo33ry/9f+vtmUoTgO1E7TYwKiBs+CTeK+BGwzx9GlUz+ucbDoC24ldqAYCf7pY6kfvGvAQJ73eMrZDyIQc7ncNDqQBUDAeAQdW+xwH847sEwIzhALhiBF82uEFhcyk5mbdH9wDE18n6Cw4d+vRfnvMA7Polk6nZsgDw5T6vGMoC+mOUQAn2g2kKVK4fP5Lc4lPc+49JYoAMm4KaSXxeMKKISU49ssUhPlpcTaPR5KKHAGk0x4508/90/kHMuLRTeP+59rkGaZIwINssjhM8z4s/DB9KG4wTGYcYT17SY81DPnHmzIEiDBurICAACJQiHZ8KQYOQAvB93moY739aB2CXhM2USXF5XgD4a//93x7UBShBV+Bum3xl1v9o1/5o164aqBoA8Le+MD1BXp5x35iXAO53Rlxw0qUQ56X7Xb7WMrZdf8fzS0wBoFRRql6dC16dC+LWegX5Qj2weW+kvjOqA4D3B1xFCp8fsDLH2Yr44qC3A0EQkaohf7JrAmiHpGpGdTP6o/vGH9839jxS5iPmADxWsl8Ok+bkbPtJX0RxUQrD8IsvvshYsB8Pj1MS44t5YjlGZixiDHnpNJoTjm4502iONUaBlSXHMZGrVwQxpt1RTD4mh+ctdS+GWv2zGXdtw2bxh3FP1UjtVjs1yVkeUef4kOQlqRAPiREKptFzeQlB4BEAZy62z1XCD7/stZ9/dLuMcCPcvzcsSAgAvDwbfbJvzS0DwJ9tcAB/5VshgO/fdQBcKR8IRSoz/rVDy6CqavQuY7g/ISHuzFiwxU/bBMBbqwJAjYUAgg7udzkAm8lPGvx2i509NUIhPsWBz+LZy/see+102BGsUpYlHgHsuyvevRZjBAiw3jEA9xeWonUv2paD2xQkcM7HFcZHKeCPg+xm++LlMSbXvmCvAgoX8IREM1d8IlmN5sSg873mafLee++9/fbbeVbPNdM5BxkOBwrUiMPEdWRGTfk4KtHi11nQ4U6Cioxh7ukHXvymBi4gnXCK6OiYIyUJPcENdANqchk/S0IpgHOVEIDjRNubsf/sVSuGU7KGRSiw7Kg7bXZ2AZ83yLeXo0UmAHyyzwBYFAAIgUHUtcNeckbHPkaTYqUsdlzjtbneWqF//nwXQIkvApDRoRfJzw8tAO2QtkPqPNxan8gmpabM1emKutPGuQoWbbrjSQDbrnHfN1+a9QCUeMQjebnmN+dUV1Avop4Y0aMexyrZoXh8tHhuTzjyCD+bKa6wCMVlJ7Us0sQQc5KKp0ZTEJ3pNZrjxcD037iBMLe9PE12ZTapWi6TtswV4QgvL6GIT5AQt4MWubVc/z5+2rlmA8Snzrja4lJHSxI5eT4FkYGghgXGzDs3ywACTwJo7PhgM+7BoWlF8EArlWGdQKIdklMmDAoAH+7SF1fwC8tuQC0A3aB3d3+4Yf/8fOt6k60LEo0fYpNsEfDpfvTLy6gZkgK7HmvIFoCWx06XJYCz5WAXACAVFpxo2O9PoER1BambOAzIRlcCcMOef3/1wI4iWTUiAB/sOBTerRY554hdMhhV8v6AIhRgolKZW8Yzjh4fprvO3FI5UUlHgSKs0ZxI9BwAjea4kwx7yDN80NyY4XMUdEeGybiA4nVtQkaSSevv4kykWcQ7EQXmPSM1ZKXgBcRmruuGY8AYqc3NzeEv0yShSPzBdd300SK3TAmUJEoR05QyIgaXjMVrAVGl6PxsZ36245QiQADoNqO2Kwm3h3V+73//hzOW3OigbMivzaoXawDwwy3nwGcHPqubom6K31uzGyE+P+ShJJxCju22QZnLCkewtS4V9oPe6P57HYtTxFOH/9mGUzWiw4DNWtFKKVQgvqBGn2HBdkilwvmqANAIWCNgAChRnClPAkBXMC+ir875oSRnyvj8wIj3NuYpsktimtgyNyMhlZcylO/evYtib4xhMmSnE3wqFCxoCUm5AJCesfDOO++8++67YxJpNM82z0x51mieZ5J+AAzVbelauaC3gcLNjTxv5A9SLdzH1j94TBHFpIJFHlSsmeuITzROyTCMxNE3TTMJIdDXcV23oPcJQEpCIh+qFI/8EYIGAQOwvNImlO7vx76+D6BaC1XokLYZJ7xx48bAZND1Dn29BAAbGwcHpdlv1mEQLDkCwFqDGVQBqHJAoGLIdohuNHaQvZDkepMD+AuL5bWOsdEx1g9KL9axbBMAN/Z7IcGiIw4BADaTQo1VA1A2JIBrDV7lCCMK4INd+uZiBMCgQIQDjy1TtAIGwGRy1sKOYI4ztO3ZhCS/wsjclbj+uSXdmLDnMIlYxhkY/WaIgoIxBTPVETLR5Q1w6dKlPBON5uTwpAunRqPJYHj5/4TsKj/XIUiTGGdUlsVrbjFhB3qu/aROxmNlnL/+OAKegt5/QlhsnFJadpx4HBUQQtIZKRw5H5QopQihinGDR75SNAwDwzABKCm9wASwdQ8A5mt+bY602pChNyjSZ9fDXEAB/NppcQiECv/rVeetZWkT0RX0373sAvif/owArBmikbk86YwpDwMK4FLNB7DerMxZ4h/dtlcdADLepXfH5ReqlQO/QQm2Hur8GERIUjGUAloCS04I4EYbBz771qL3010HAKNqvWMAOF0JAGxSFY6fBFyQ9NM2hiYHFyzjye5aafsiJT1Xf4qgApmykwoW4VFKZW5R0mhOEjqvazTPGLn1dEGK6BSpDnmqXyLXvqDZpPDMnopH8TCO/FKPluEYIO04xj49CgcVoz3+YRSUIkqSuO2/ZCrG5NZGeW4psKwugNqcBFAp06s/bHDrFNitkTI2w6wFr78c/5cNXKgAwI+26KxBAVxZxueHFuBfquIzhm577G8RKXQFNSnW2o2zM/XrDXOu1Dv0ypz35T4/X8EFkwK41dgH0OoP6B9HIEmkCAWWbOVGdM9l31qOdlwGoGyosLcBACJJAPxs33QFqRiFthYuTqHfIo9xIgN5ZqTNMMb4rsgBcr3/mFyDYY55kdRonhX0HADNU+a9997LM9E8Low+yTf8YTLSDpDtE4g+KFx/FzRDnvePPE9lHEbhUTFHghAiPTinIEbm/JDcGx9wv+ILyEhlGMbq8jJlijFFiGJMAoj/AjAM4gWOFzjdjjFbvnfv3jxjtWD//ji1ZkBCiRlTAfgn9ziAux2cq8iXZiWAw5D8sw1ns0urJhoBKIFTepCLhjH7lwHgSj0A0AzpmXLvm02XChX8ZJd+uGv96abFCF6eyXo4Rn/KwbZHAMw7EYB5O1pr9UY0eYLWzWjOFiUu31zyLKaamVsLP12MIRzHGS7+RUiSDL8iBl4dBZWN1H4OueQW9vjonTt3MgzGUfAaNJqTgc7uGs1xIWP8z+PmSGq+tMiwlzbRKbKlBsxylTnnIm9RnQGK+C7HYcBA0Tb7PiPtjaHRJhm3JoSQkgRB6Bg8EhRAGLJSSV28snv34yqAkPuE225Qi+2Jac+fro+UqpmqxCEkqXAwCwceBTBbwkpJdTyUuZoHAMTr9lS4IqTn4g//js2A7nr0m4ul9TY/2DO7Av/KS969jglg1+MXa8HFGj6+ZwPwBAD44kHHyMgHqBSqhsT+/a+9PLfjssv1wDKiw4AB+Ke3jTeXFYCzZQlgo4V7bYNTFS809JyQPLT4Qzr/TJQhY5IkuYU9Ns5YxjcZATVReX/qBVmjefLoTK/RaI6eI6lQE8c92ybjaEJaKtstSFo3M2ymI/dehh3xXHKdrUkFkRlUJI8u3sbLtKLAZ74bGoa1sbHgrEoATui3WgBQMltK8YjN3f3ZpyPv/f6e+RWiVivhRtswGKAw68ifbZR+toFXlru/uOJv7ot2SM9XJCPqRwFM+uB2xj1MoQCgxNEVbM6KHLs3b+BW09zz/VaAQEIoEIm9LrucygkDt9wK0RG0zCUnBMCNhrnbNgH8+cviP3hVfLprCIn7LgGw2TV+usfcFoX7/Pao5+bDIsR9Cxk5tvhZCrYgJJaPo7xrNMccnek1mmPBU2z+P84MV+RTV9WJ9885H3YyEt+ioD4vsKRPQpErjwWniAHGUbz5c4BSqZTRwgqACCUCSBsAGFMgSglZLbcBKMJgGqWwUa4z9xCBDEh4r35qdA/A7Ezwsx1+5gLb8eB1ydfPSgAbh/hXX/D2XPx011rfky/NSEYUgJIB/+FVgNI3aFC4Ap8fdF+cL52fDa83jCAiABxgs2tc3ecADl04rBchAGB9sZEPasGWfgQ/omttLmdwtUFmZnqH/vm6db4sALy/Yd455N+qtgDMWdjsTwHI6FgoTpINHlHnmaPI/V66dGlgRamRDLQgpH/odGEsUoQ1mpPH89tiodE8K8Qjs90+YYq8pCcKzvmjV9WxSHoAdExyNDt5mlhK9BlpkxwqqDzRb5rdWVHwjANku1/JUQYGQIQEAOMSDAd7/K3XwubNnlt9f401mnUA3DbpgYlRfnbVwJl6BOBCVdpMdZrtuqV+7YXerGBfAsChTz/cZbdbrBWgzB+EJeHDmyHcudob832jyQBcqYdnKuGZStgOWcWQUvUG9C+X8PKMnLPgigeTj8dRNRSAeDXSl+pKKAiFD3asqgkAHUEBVEvidovNWegIHPYXKTKGRsUUZGTRfj4L+1HBU9i2bdt28hpJvs/T0GhOJjrrazTHlAEXJyH9Tdy+VaTNbIBhf+J5qwiP/H4zWtwLnisxy/X2sl3/hDhv5KqNIxwaC9RzmAjxA4+FFuOIhAoDbtukVDd///9U1LQ6hx2yYaMWnHlRbRyYXrPS6H4Wdyl88cUXsU7Sw+AKfLxPSxwXzgLAh7tEKvHaEi9bdLchWyFaIdyIzEJVTdzy6f/4D//Bf/QX38GoJ1AxQAlenw/jTbtut0wAV9sMwFtLAYB7FABu9wfqLNgPDdkf6BryIvrxNn9rNfhwjxkKrZb5tRd6lnUDEsTh6lxFHeyTOLSYtxQpPwgpJo0BEsvsfpspSjpGXcakOsN7ODy75JYajeY5QZcEzdPnvffee/vtt/OsTjID438KegMJw47aOMYFFXh4ULWuI4sz/KwKjgsaSdI2KYTI8Num1s8m8arHea5xh4ZiFIwSEADcQChIFCEMeLz/l1kyVWuv8iI2b3SJ685epL/z7/+VkWpbLba34bx4Cl2BOUsBuB8AwN02XplVl6q4VHX++LYH4ONbpVmCSNDOzu6VK1cARFEE4LPPPoulot7uvLjTMq7MQSh0BV2wxbkyAPxomwM4TUNK1JIjP9qjFsOmR7/y8PWMLBf/xlnvn3ZQrQbLtgnAsQBASVxvkBWCKzVca2Cvw9Ah9tDOYgWjr4xSmSYxKFjYAYRhKEZNfx/olSouOExyg48iotFonjyPpRbRaDSPTq5DEFPQLSgSVEw0WCVhoEci/pAWmVTwWedI7jduax94nlOEFrF9boN04vpneP+JwUKttNcNFaIoVKDc4CGU8vdda6XsNgILALC1tViZude6i//ku78yTs20ouqs/0INWy5+cNf4pbPhkoOrW8YvnJYAZuzoTputlJQM1flKt7lJr0nTssdmcqkgFDa6uNwfKLTr8XopAvDNxQjAj+8AIDygF6u4tYcgQtKqnSwdk3Dg45dXg0bA7nbMl+pohAgVADiAlDjwyJyFP7nNAbgdBqAT0I43YiOw3BggzFx3dYDilumgYlyqJDsVb0HAwx5/+tZ0JKDRPFtMVpdoNJrHSsG2wAEKOoUFZUc68RmW4/oT4g+JF5urNsyw8hQizzQD9zvd7SexROyZxX5b2ktzHKfZbE4kPl8ySjbfCELISAjmlMjMadY69KnlsI82987MY3+3A/u3/r2fzxApc9RtWTOjmoktS9xukdABgHsdcq+DtxYIgCt1+WUDAeihj1ot6JT6+3s9jCcIAE5Q5rC5BHCvS+ombu8xAA2PvDGvgJBTADAphETLfzAF7tKlS/EHIcSnn36aVvYjYgIv1uViNQTgEwpgr7+LMKHq1YUokJgzvT/YwqRM5P2nyXbZi8sWNIsZdvFHXkNsNkUY8OhDlTQaTXEmeONrNJrHwaOv/5PdMFzcG5iI7MbFATNeYEHPtD0KKBdR0yQkj4tzHg/BTz/AIg8zcTp5f5zSDFec84ZShEStwwczdP/zb1jA6bFCfTjBxq51p83OVaJQwqBwGM7Nh/c6vYtxKL5/3apJAijTYN2g6jjmODWpcL/FvnsZG3HyEhoBQDBjouGhK8i5igKw3waABQvtUtay/fO2utlkHQHqkvkIANZaBoCFKgFwt6MA/OsXfQCf3AKAA5/XLfLJJ5+MVPv444/T/371q1/FJDMEBoiLxrjkU8vmBhUo5pGnu5uK2A9c8HCPYpI5i6hlULxpQ6N5HtDFQKM5XuQ6viN5xKpxgFxnXQgx0XWK/kDkXNmBD+MMJq3CB5RjD/hxD6l/WuQOO0HmLRdJHhOLlKOoFEV/+ZXYNY/wlcXsVAndCMvz/p5P1rvcrDEAZ8rgBPMWqRjqB5vsjVl1eSHc2Qan8CIszIUbd0dLLZRk5+76K1dWOwKHXSw6WDCx4wMEhwF+8VS048Yzg0kN8CIYTDUCZGCzXjyzWlI+wImyKG62UCsRAN9eFmttBuDA4zVDfX5IzgAEWWunpknHAxcuXBg2GLcM69ra2sjvB5iobMZkBxUxE71k4lyUm5GK9HkmYcBEQ5ViRipP/RrRaE4SOvdrjgXf+973fv3Xfz3P6gTy6M3/eGzV2Dh/fVLvP6GI919QuXgVniGbeCcDbdsTMaw8hcjjY6TPNJEXlet1cc6nvmWHA4DN0GxbABZsAGj3j0pg38fapnN51d05sGxKPH+0Zxz0twgwKEJgx8XCLFZKinHUrCiKICTKVL5Qxw+2ACDskllrpFKPrqA1ipqJqhFZ/bu/VMWNJgC8Wsf5SrS9zwHcbBGLAcCeP2IOwNFy/vz5+IPv+wA++uij+N+BgOHcuXMDCe/cuYNHINePH0luDBCG4c2bNyct8sUp8krJyLrvvPPOu+++O+6oRvOsM+VbW6PRPCb4JDvYI8/dnG78TzLGI89wMjIiCuTV08M8en/Co1CkxfTIH+AJw/XprU37tVXsd+ncqjdjqE+3Zl9bDgAwYNVRAEIJAJ/frizO7X/xUUlRe6RUR6AZkHpIVrkKKc5VUDJkIAmArqDrLQKgDNx3GYCvzUVXD3DXH6nUY7NNagCAH2waXytjrc0uVSKhSLsLAB/uMothoW98pow/umpaj93/n56BkGBkD4PrugA+/PDD5Juvf/3ryeeJgsaEjFTxxOtJy2ZuqU/MUEA83Y4QzwM5MaudajS55BckjUZzbInrwtwG2tyK8AlQsOYuTkG1gveeuPK5smLUAp1p0kdz1dKkLzURSf+4E6kdfyjtuaFzJRkBhyEpVYObLbyyCBkBwL+4ZcggAgIAhx2zbN/vjpEq9x/Mnk94CXfamJnFtkvOz6p4nf4X6+LTDQbApr3Nwkimvz5fUnOWutMmF0oqbvW/dAqcqK/NqU/3CQA/wuuLLoCPb2Oji7OzYvMwS/BZJAkGlFI/+clPso3TfOMb38gzmbJXAQV6IAt6/zFi1DKpGs3zwImqTjSaZ4vh8T9xp3nBCokX2AKs+HjuhOzm/yIXNszj6FLIjigmcgLS5MoWf54TBRUY4xINf5mttrm5OT8/P/z9cH9I7lXF5OYxAL/xG7+RZzIC32fn5j1GsLZtVYi5OBfUTdIIFADKyN1mb5Ues1o95ew0tsXcPG/c6K+/8zChRFtgzpImwbmqKhtSKHKqpADMWtEnO/SHHmcipH2n3yA4U84asm8xxQkuVdVqWa4bALDRZV+ZDfYEXptTNunt+fX9dQfwF2xsUkRFpwCcfD744IOMf9OcPn0aqR6JjY2NcZYTMVHBT94VBUuERnMy0NldozleDPtbA5VZUksV8cxiCkYUKDCeu7jUU2S0aqMpAAAgAElEQVRq7x+ZbvEUgrkD6BOKxBWxWnFPRQhBCCGExKM7YsL+8iyTRgLDTBFeplEKkcKXn++++MrCHrDfNEKTAvjoPnfsEECnxeO5uFs7zAYaRnmclC9RAXY8Kjs4B3RCesqIAISEt0IGqD93xv+DG7jXxaKJOUvd3sbddlYXwK5HziswAj8i3zkbArij8H98Vv7GOUUJVmxsdtlax6kaWIuw3oEQmBs9OkkzAaurq3kmANBqtd5///3k329+85vJ5ykKqUbzfDLle1+j0TxW0kvppf2zgt5kmqRXAZm140SN9BOFAUWUi6sl5MYq05HtXj+KvzuOSXsVisQAyfNM3P2BUxQ/Y0KS95L4SgiRDi0mpbm2tn/q3Ny81wxRX/TbbZNzBSAUcD0DsjdIn7Gw4zMZht39pi8rI6UMApOiEcAU5PufNn71tfpPdjmA15ex3iELjvpwz7rTCIDelsMir7WeAGtxhOARSvDpvnHQf95S4cNdBmAO8CIAmLOwMBf+vzeO8SSAE006GIhZWVlJ/ztuVaXNzc30v1O8hTSaZ5ejrz41mul43hYCKrL+zxTu/khydSbypHl/Qc8i9WVBN72gWkK27KRqaR6H9z/OXxfjR/5kkNurQAhByvXPsEzM4ivheSstxk5/nCQMw0mvfABCYRiy1TTDHef8IqlUwo27M6fOeCJEKAzH8ucX3cOt3u+4vbNMXff0xeo4NYfBIJix1KFP2iFpAX/psvvD/Z79J/cNIGBMtkJy6MMTCGWOv/76nPhkn5+vRgBemwt/0MRfeNG751k1E9sdvDSjGg2YFMsOADQCxmleVKE5Zpw6dSr53Gw2Afz4xz9GP2DQSwBpTjYPtkLUaDQnHmMMeekG4UOMPGrbdoY/PWyfZ9gj13I67z/7OUynGZNxwVP40EWuM5Etrl/wqRYXLAI3FIDGLt/Z7O3wtXnPBuB2+d3bNcPC/Iw/P+NLEc4sgZdK69e2x0pRzNlYtAHgagMA/v4Np8xR5rjfxUI9jAQJfLbRRTfCoqPmM0fsEII7bW5SAPizbaMRsD93pnfjFlNLDg58AiCeYbzv41aTiryIQqPRaI4POgDQaJ4CRZr/jz9pr33A6Z/IoU/ITcVTZJuhgEebxiiwD8AUkdIUSXKZwgUvchnZEc7j8P4JkbMVAZDFVffcqudQ2W7wFy521+/0xvncvFrr+E7Hd2YXVXdn23UrYM5IKaFwGODGPgPwQl2dLWOvYdxdL11r4loTp0u9HQZ+6VwIYN7CgU+sYg32rYABuHpIAfzgPrvaIH+8SQFUOLwIgcSMiSUHhICRQoIajUZzHNABgEajeST4w+SZjyVJzseQhBZ5SsCEMUAR7z8+WsSTTigiO5HgkTDgxBup2SbZxuNsfvu3f3vk97mEQa8C2t9z9tu9zx/+tLZyLqjUonOX3XOX3e2tkt/cv3dngVoz0tsj1tghQAAoVQ5XDu8N8S9VQgC7DQOARbEyI643iUnRCrHRYvdaWQ32oYSQ4ASEqFMONtosiMh3V3ox0oWqWHDEgY8974F95Un/khqNRjM9hWpHjUajeTIUdNmLMCA13MKdON9HeNIBcpWnaFPPiBkevZ1+5ByAiaYpF4cxdecOoQavLYRSmLuH7OJLXYIIQNmSAD77vAaOjlcH8PIL4XXJ1r7cGy1FECmULNkICYCPPq3/8i+EbgQBvlAPr7UZU5EFtEIQwCBo7JsUhRrsrzXI1xfwjcXoftcB8HMLvVR32xwIr29aZ0wYDI02F0f/hDQajeZx8biqPY1mCp6TecAZ438G3KxkaiaeRlPxySB+esPTcHl/KvOYdCNIG2c7xEXa/tMGk7rX45T5tLOfc4OKkRiPtgwoWG8YfmsfkpeWV7rrt0yz7ABg1FfEqpRb7Y6FWyZoG0CjWaeGlaFHgW0XSw5efbEBVE6XsBdhyyUAKhzgzs4Ndd4BgEtnujc+y+oAX3ZUicOPsOjg4wNSM1SrywD84nmstUFLvMxxEACAwQBARmQ269I0Go3meKGHAGk0x4J4TRXxMPGhZOmVR1l3JRxFXqKTAx8CBRz0kSTx2Egcx3EcJzlFLsWju0Q/W5kX2B5ugFg5I6gY+f2jQ4PI7xKjUqnOSM9lazerAByr2+lYzVatuS/3d+tM9jb/vXqdEfcus0cvAyokkREhBC/UVN1QgcSKA4sCwLKjzpRUvHFXvPewVNjv0roT/Zd//a+OVIsF4w/dEACaIVmuRsvVyOHq5RkF4DAAgPk5XyqEEd44/RyVJo1GcwJ4XG92jUZTkNjvz7MqulBjmsTFH9AfaHguqKZJGPaJJ+1MSJNOOByVJb/ORPpG4bZ5o99ZkW2fCBZXzsd5kPGWlhvbW/UwICuL7ZXFzidXz8Tf+82yV2kYrqmA2nI18rojlQhRJlddQT7eo1+dl02XAeAMrS4AzJnY2LMA2E77p1crAKJ2Q0YjlXpYTEUKC7YCA2HqYhW7/RFDUpF//LkN4PysCyBoA8BaG4d+oTFFGo1GcxzQPQAazRNlYPzPpL5UkVABQ/0JA0eHOxkmIp3c6zO12slgIu98JHGnwXB/QnI0O3madB9FhlliMCw+kDCjm2LqGcAA/uZ//VtoNVvbzeYWDtfKS8uNatW7fdv47MenSnZTorSwdBhbKt9Vh1Yoy92tjXFqXY/5HlsuqS2XzJaij+611zt4tSraAb3TpjLCL5zz4x6Aly625+Z8IbKqv44gAA4DEk/tlQp1E1UDzYC0Q3ztgvu1Cy6AAx+bXbrjQ4//0Wg0zxY6ANBonhphag+micgIG5KxPQVlhRDFuyCGGTj6iJFAhv5zAn+Y5Ju8dIMkqYzxYFrxmElj13FQwzaICcALq8ooKaMUlM1WZ6ZW74SqXJprU8sBwBfc5vrO3/iv/ouRIn/9t/8bJQHAjQAgkOgIsunig128NCsBfPuiD2CpElXqwU6HVTgYVe2t0VOKAex1aCuAH2HGkt9clPGXlKAj0BHohOiEOFvGV+ewUpFLDtohhBwnptFoNMcOHQBoNE+OdPP/RG56GiFEdssuppLNJhE8cmU8HDwgFQYkn/MENKMZiCWGvx+XMCHx8mP7JHI4Kv7qX/5L1LTtU5xV251O1Ttst+8Q1mgCaOz1qqdSyKLZCoDf/Q//7QwpxlW3Y2wfGozi1q0agNUSALw2G/7FS/6dDj49JI2gZ3wYsTCywMa223NDvjonggg3GrThU5Ni0cKMiTtt3GnDjdARAHC3AwBbXbLX4ocHmVuLaTQazXEivwLQaJ4kz8lCQI9Crt82qccsio1fLyibNsuVTbz8gW+GP6OA2gADUknySXWeRYyhkfrJXSulMGa5z1ziMEApFUehUygM8zv/zs8B+Bs/vgeAmbZ1zgTg3W5Rbi4v79z+0xrd3f2tf+s8sJqtww1ZqQUAbu4Y33ylCaDC8WINH+waAJqhAmBzUKoME1t7Vq0sNjMFPzvgr86JxVnRlgBQAQyCNxfxZQNtH5T09hs+Y2PZVv9iU5QrR9MlotFoNE+Ak18RajTHE8MwXNfNs5qMgoN5RjIuDBj20YuQG1RMJJuYFfHdE2X+8JqYyefk2oqojWSiIOepU8TXHw4YsonDgCO89//szTMAAOe/u9EU22b85b/JVvEdAJU4aMmm2+HufWd1htZmgqsb9kur3vUmyhw3tkoA/qUX2n903bZ5b+Muu0R8r1Sar49TiwRhBNca3FmhQuJmi6wDl6twgBfr+IObnJvSBOYsQOB2G1IS08ycVqzRaDTHiSN7fWs0mmyGp//yyVdt55Mv8phLrjc86UWimFs8kWwRQTwcV2Top53diZTTjPSYC6ppxvEfX67hcp7RKBhTZy+0Q8x1OvygU97cKH/3l30AV866AP7oug3g8oy844rPrtbnq3ub15zmxk6OKPDpPn25hktVtQUIhR9uQyoC4FvL8v4BAGx0YVI0Dy2etbOwRqPRHC/0HACN5umQzMLMM3xAEeMiNk+diVz/mFyfvqBNmuJt3rFm+DDxoYF/p0CMJy/p9BxJGPmbv/mbeSZPjtlyxAgW5wMAb359H8APP5w/DOEK5Qo1M++/sOp1+r9S4DEQym1znBrjigDtEIs2bIZZC+V+waJESUn+cM0AcL8LABUDM3OeDgA0Gs0zxDPgK2g0GvQ9+yPx24YZFzY8Dh90Ujc9QRQbVjQpBWVzXfwpehVQQLy4VMLm5ub8/Dz64lPkmXEjgoqMJnpahAqdkKoO40xVDPzKz+9/scMB+JICWLR6eePefadWC9x9bjuCn57LUgQAbLk4DbgCYAglzpRxp00AHBxY1wEAYYCDAEoRmT9MSaPRaI4LugdAo3kSDIz/iYmnUcYTK4ePxsRHHcc5qjmXaWLxDNkpvE8UGFN05EzdXp7t1seyEzXwh4UXdyoiHvY3cxhnMI50KreP6JOddpjit/8UERIyIs2GBeDLrd7yPocHltelXpfe3Td2XPXjz+oAQIjlSKZaGWoAOMXlmnpjXt5soRnCpHAFAJwpKcblwqK7d7AYN/pbFGWufKm7ADQazTODDgA0x47vfe97eSYnivSK7MNgkhbcIhFFTGxTJKgoojZMRpIp1GKmu5Jscm9/Ct83N4kQwnXdgnFF8YgiJh1XpE8RN+onasUFBxAp8myfKJSg0zaEoN2uUa4G/397d/YjyXHnB/yXVZF19D3Tc3CGQ3IkWbS1Wq28lrHwAQML/wGLBQxR90VJgAHShgyYMAwDQy9g2C8GDPvJgETdJ/nid8PAAsYCflhZWqxWFA/zHJLNOXr6rKyqzKr0Q3QFo/OI+EVUZXdV1vcDgZipjvxldlZp8vvLjMx6pxdsX4mIKElEkohOd0hE/+j39we0LVpjIqJme/edA3NNIvo/74VEdK9PRPTOQfP2Mb0bERFd3xj9wUeOHlmlg8PWzm77oN9YE7gEAAALY8ZHUwDIKzz9r7PGUA8qK6uglknPzJXKxQXvfmVR2bl/c03m5p0N84712E4V4g07ITk9lcjwVB/ZBoRhmNjmPukSbeIWp285e4Nh4+bl/p2E+pFIJyfj1zfjvQdtItrfXSeil++FRLR7pznutYho5eJKWbXD19/a/MT193rB9e1Rq0lEdKcfhK0xUTOKxD99tE9Ev7zXWJl8+ddoFNw/YP0fCgBgHnD/9QeARWEOoMzcr8iMmOSeqlk4jGxJXXGK7Na+gl8qj7nBM+QRoF3fNV1cNHGf3wPoJ/v1Urdu3Spf6BwcHYZxSpev9hrN9M67Fz724eP3j8JHLg1feneViLYuHifDtLsyIqLBPqWDg2Q4HsSrZdUGSUBElzqkZva//JsLRLSyERARPdL/5b0GEa2HRESXLwxeedv/DQIAOHtnfeQDgDM2TXZUVARXWTCTHa05Upe/OmEeaS3u1E4oMz9VX53CEK/4nZhXPUCapoXFZdkgWIyp7Z3uqJ8Ea0TjUUBEL762un0juH2//fjv7xHR7369QkQba72D3UanMxLX1nZ+3Y/37xkKvh9Rp0nrRDdWaZxS+sldInrl9e1/+In9//VGi4j+6OHkf78kLm4MiSgISIjJ5QAAgLmHewAAqmWd/7NYxESn0xEa23LFDIur15nFnbaEf7OEn7Ky59hUlPUP5j3g2lSco37U7Efhzoud3nG4ffloNGrGg5SItjrDrc6QiIJGcxSPVtdjQXud1ZCIRHetrFoSN4ioP6IkJSIap3T3Qesgan7q4/vDMRHRHz2cENHKSnKxTUf95uXtflkpAIA5hAYA5tGy3Qe8nPT8LU7TuwtzkTzrIoLXV8gBTtdPVF9RNsC60jJOmzElw23HZ7kZrhqTo9nBW82g0bh6/XA4DI8PxTv3Vt+5t3rj5tHDj+4TUUqtBwdX77+5+8gftpthx1DwYBi0mtQf0d88oJf2iYgG/ebrd9qv32lf20zePqI/f7Wz3abX7oVJ3BiOg9UOrgAAwMLwPBoBwPTkHIz8SdZ5jlkz552J8zKlyu5+5q9RCJEkieGGWl3I/mY3ZkEnTgUzE4pq8nmLxgd3wo/+497Rfnj7jUs3bh4/uCcuXEoajYSI9o9XiSjsJMM+rW42VonuvLprKCbCcbOR3ouCq0THx6142GhvBFsXBmmDiGhAjds7LSJ677hBRMNhc/du5/h+6deKAQDMG/uxCgC8lc3/UXEtiqLMj4QQ8qc1iWXnofAcP+eG17zMIoU5mx/9ybGpIHZxfkHF/AETxtsq5u0OYCJqXzg5Ab+2GdMwuXtve/tqcrgv2quN998OiGhtZe/qI6P7bx8Nj8X7b2+PqD+KLfN2rq6kYYOGRGFrTER7D9p//yMj+e2/RPTQpf6v/3KzQxERbV4YPHgDV9QBYGGwjlgAMCuG+RVSMnnspl8bUJYCXevUjzVDG8hlZXA3DHBijezM6E/uTYWZujl4JtXOTBKn3c3eay9eWdsKwrVArNDx/pj2W7v7dOWjMRG1gpMH+hw/iKndvnhpdf/ldlm1dByMxsG9fvrhJn3s4eHf7AV7h+Kjjx0TiWsrFCUUXewT0ebWYLBHFy713/2d2NgclFUDAJg39kMLAMyENfrrVBtgG3iCX3yaTiBTn7959TCr3zffTqionXl3+Gt06gEK71XILCt/Gsex+QFEcyWKWh/5eHT79e6FNSKi4d1GskaNRvr+2+KRR++3V8V7r48ubDeIRpvr77//imkKUHPySJ/X77ZuPkIf30r/4pAO4+Duu62/dWVIRI2ARiltX+1Fok9EnW5y/y73zQIAOHf4BwugKvn5P5yArpPjzfFLhTZzcf3CAj/PZabRZ6aFuHYpoNP3W2bH+u1S86QdOv1BYq6C/1E5f0etcHO48xbd+FAUjWk8DoioORima+tE1BBNOWpv/2KzfbC7e2Hc6CdJ6Tn74bB5+NY7F25ev3+3++IepRQc7LcP9tuPfrj/6p1WryfiQXrjxvG7b61fWN3v90Q/ErSyGM9LBQAgPAUI5lbNHgTEOS+bZ01p6sQ/s7VgDiOtphAiE1Uz+DV1SRHbQnWm71Lb2FJCiG63G2pocr4/nEwo4q9CjlyUHuBw763msUj74b0H14/3UyIK+0PaWA0a4ysPHUeD9Zf/eiUQnYM7R0S0silWt8L/+u+fsVWlazeOiCiglIg+9Qe7YWss7we4/vDxOKVuN3nzN+vvv9YdvZ4mCY6nALAw8A8WQCX00//8+TkZnEU4YzKs3Uhh9Dfgb4Oe9fMNQL/f1wf40QvaxtaTfOO63a76r1Puz5A9gN5FzKf/9h/+XbyxRkQba/e6a8GwN0we3mi9c/fi5t5ocHKm/+itWHRa41403NsZ7O2byqU0jBvxqNFuj956fbPdJCHGf/XbLSIaj2l9YziMG5c6JzOFws10fKPZ6S7p5w0AFhEaAID5ZU5scRx7ZFy5iKEHSJLEKSyKyYx228CTMeZ0rn7EKaiUhf6y15ecfPc5tyBL+ochDMM5fASQlLx/OxnFD3Y3kvHJEzmT1e5Bb/Ogtxn9crC6dTILaG3QWLvX/bMv/2l5JWo2UyI63G+JgLbX33jl1c1P/t7eJ39v7/JacnUjIaLr62Mi6h2FH/nEQas1urDdb0xuMgYAmH/cYzwAeAt5t2YWmvk5Vxnmysr6BWUhhAzZhrbBKdabS2VwKovJVCV+WUltSaa+a53z5XTjR6GF+H3/4794goj+05+/STvHyZXLo8Nh2F0dHaWrdw4GF9eD4dFoY23wYHeF6F8++Q9sxSgMx2lKvUGTiFqd0c6DkIgeuZrePRKNBv3qpfVR0hinx3JwPAwHvdJnCgEAzJsF+DcdYOFkbv/1Tv9mzDDtyjvqlS3ICeh5zLzOLM6slqGX1ZdVlxRcC56ZnZ2d7e1t26h6+rd//Fiapv/5VwMiiofHrbWwd6PTnDy8/9/8k+Kv5siIh43Dg9bVa3GjkY7HwfblaNDvjpLgt8dtIrp8JRolDSK6dCXq7TYff6z/V/9XjBu4og4AC2NOj14AdSKvAAiX+2WF7eZL14ILynApgBn9M5jXFsz5PtMMGEaWKdxs1yKzVXadavqrB+fiX/9h5nz8mGileGiRoJFubQ4P9tqbN4IkaQwHTRLUFOna+oCIhsNGPGp02kmjkT60mRJRf7/dXsM3AQPAwsAZC5hfdXoQkHoGi20g99Zbj0wmKxtu5XQN02fAvCtcN5jZMzhl+sIJQmXkpYMoiuIiURQlE7ZKJslpZcM8PkLLQ4TjaHByfLzzZjcIUiJqinGjQfJ/nXZCRO3W6LV3Or/8zdZjf/uo1cY9AACwMFhHOADgyz/+X1LXAag8LwrjBP08/kUATpzlV8swLMXM3K68Cya2KwDWAYWsi6gNNs8Hiydf/+y6Gea3IAiCIJjNU+qfeuop25A6iIcndwy/+9oaUbrz4upDH9sb0dbBfQo7TSJ67OYhEfWPTsbvPmgP9qOSYgAAc8fhAAMAU5LJXrUB+R854c8scuornKJnYjtZztm8s2S+wOL0uyvyd7QuyL8VRI201pTkHs7XV6+Eky8E4G+DYr6MUFtp2jsOP/R475hGRHTt8WMiGiUBEW1tf/D1YXt77eFwFIbjw/u4AxgAFgnr6AIATGWn/3XMIM6RCXaZoOaU+71xQqpHG2BO6lTlRQA/hrLem2qVJAkn06sx/N89DMMoOjmlzVykToLJ9FgRjpO4ETQpHdGFy4ON9uhXf3mFiFa24k5nKJonlwBaB8mgMbnRGABg7i3dP+sAtaQ6AdtAC5GboVQY/tSAKqKhrGmt7NFUSNbKs8XM6IXMeZ1fOY5jvUu07gHrhZ3aS8dERLv3Ow99dEhE93YuEdG16yMi2rqeEFFjPCSinVdWkvTkLeiuLe/uAoCFg5uAYa7V6T7gBaKffU+KiAlzHUkFes54fvo3DyhkXsqvnaBzist+W8tpGAorcxasE3kFYHDQPNhvE9HaxpCIfvvS1m9f2jrcE4d7QrRItGj9av/CQ+2VbpRcPNN3HwBgSmgAAGaGM/9ngYhytkWz1FKFi6tX1BhicNoSzsbzq+WZl/W7MjPzzK1uQclH/Py6ptkbNSDCtCnG4XDUOxY77642GunG1qDRSKNIjMcnx83eUdDrhb1eSETx0bA3uSEYAGD+LfU/8QBwlgyZstPplP3ITDAmAjn1FZwZMjrO2q1jCvm1DVZhGJb9gupSRtmAJXkEEBGlY0rHQbAdBIM0bQdx3AjDMRGlaWNtIyGig72mEGMiSobj6F5IRGGnkvcLAKAKDsc5APCWP8NaUbxbCE4J20Al+6ToYaNqLfzVyVL8HoAz+cc7/RvKJiWP/eGLF/Prvc7MaBSsbQxX1+KjUdAc0sFeh4iaUUpr1AqHw7hFRL1eOx1FRMEw7nUvbUU7u7aqAADzgnWQAwCrwvk/+s2XdDoL6tkRUWxKTuf4zVQPoP6aH2P+aYbHFQDr58GjpmSurMrm2wPrJtXNiKLjcLTfbqzR5s11okGUXBmtEqVp9EpIlB6vtsJ2SkTNULRXxZp442i3bysKADAv7EcvAJhG4ZRrRf+RR8YynAP2qAaSjPWFVxUyY5j0wdbT9iHvS6PlAGs1JnxactLxOEiiYL2RHO612ivjsNcnooZoDQW1EgrHDYqo1Wn29+5Tup4GHSI8BhQAFgZuAoZ5txAPAsqc/o/jOI7jKIr0c8kzlKmfocaYi3Bkai4VYWRbOkstEoZhPm2rp7gy078khCisVoZZfDnf7qyUkn5A4uTrk5P9tHGp1bjUal2Jt/9OOthOiUh09gLRiaMOEUXD9ZVrrHcNAGAe4B8sgPOXsK8DFM4mykgmU9hd53mrmkluanv+FXBl3nvmSf+F+Eld9gnW1iUMQ0PN5bkDmIio3WmM0+5Kkm42Gs0xxbS+fnh4uE5E793uNhpEXQqaHSJKW2FycD/ZPYwHI1tRAIB54Xa8AQAr9XVLtoGnCMbNAObZRDqV15k9QCb65/+sc82pUr6aX516mMnvni+iX/YJw3BlZUW+Yo3+ihAiSZIoijgfm3rrrl063h91W0mrQ2knOD4IP/T43ksvPrR+54CIji42+hRc/N3dFaIHjzb2Bx0a2CoCAMwN1iEBAAz0+T9+6Z8YT5/kp39F1rT2APxt1sdwAmWmZv6v/GAKBmof6jtTvvUeu1dMboFwbSPrpEGN0b2d9sN/j0bNNImp0br+6CFRGPSO4/VVInr4Q4dEtP9meHz5IOqvt4eH407bVhUAYF44HxsAoDpVJC1DQX70z+DEysR4E60aozofTs0MQ3GPavUjs7v8c+ajZf2kyd5MvkFBEARBYBhcV+Eb78U3rxFt0nhIRC//def6e/G71yhti+b/HN290KC11c1XD/vhu92k2YvxFCAAWBi4CRgWwELcByx5x3dhvKHT4/S/MpO7gXXq3LB5DNnSv+I0mCYbYEC8jeSYVZ2zt7OzYxti/2wIjXlkzfyXP3um0VkhojQIaSRoJP7fq9fkj9b34sdeOXrzkVZvTbTf2Yk319bGnV6C7wEGgEWCBgBgKvnn/5SNnE/e6da6oHWAjjmYme+Z1fJUC5F5gpN63bx4zXh3s/Vw6199Jmh3Lh80W+G9oBOOo71xsHb/QqNNorfR3ugFG73g7uMPDY92qbPSClrPfvFPbCUBAOYFGgCAWWI+aTGDM54zJkMYbyye5qoClW/PNFnZvFTCiP76YNctkSPlI1bVH3TEXnshtazrhs2D5XoE0MQzX//j97tR+3XafDDefDDevjcgosPOeG+Nojb1wnEnabYbncHh3W99/lO2YgAAc8Q5UgCAUvjtv2QLsjpzRtfxa+qs87z9ylYhsd0GTe47Qda0VlbR3zAmP8C6tYrabNVIqL/6dYwVCbVvrQbp1p/8XSL67//jDbG21aPBStymQX9wYX3lvftE1B8cPPnPbhLdNBcBAJg3c3HUAaiTTOYuy7uwhwUAABgWSURBVKz86E+TYU7xt7pMWV1lA6ffXTer9O/HWlxdbfDuBAr3jPp0mX8v5scP/vmf3tT+FqZpSnSRiCb/BQBYMM4HGwCwkrkqnjyC3TyMSQ22RmGn1sKV+X5QNcWlbICBoXJFZYkoSRKP6B/bHqFDjOifIWta2xVFv6qQD/qqWqagtSUoFEWRbQgAACwS3AMAi2EOHwRUNv9HyT/VJzytbEEzQ0BUYZdT3OOUs3WwdUAZ7wU5/DoHK0NZ1/TvRMZ6/c6EwhXlW4L8mDLJabbhAACwYCo86AIA8bI4kypVdh7XdV2yjuDdCSAzujWpywGcgnzMLSxj3eY5MfMLC6pD4O8BVV9vMG7dumVbDgAAFgn3qAAAc8WaFJn0OvmQrYKja4Lk4/QVrjXnATOj5yW2WUCulZk9QL618JsvBAAA8497UAcAnXX+z2JRKVxFbfVna3DMyIw3ZHdO9Fcjk8nzfGxjP2Ct71RNZ549lXjdVyCZLwJ4bzBNli3L9E4XFgAAYNHZD70AsDz0UMuJ5mXURKDCfsDjwoLQnuZpzcHM+q4dhc5c2fvceRiGhr6i8HUOzuQivw0GAIBFxD36AgC4UllWv7DAD/0ZmaaiLBAz039mDD/+WpO0N05l/nbqDH0Faaf/ORsAAAA1gKcAwcKYnwcBGeb/xEZlS9We0NjGcokinU7HdUVyJDP7ch6d5FTwHMkPJCb/AAAsIe4xEgCs9ESVIUOhHDD/0XCeZcK3340KGUKIJEk4z1ZirshcsJC1tfCoORN4BBAAQP2wDmYAoORP/xtyv6LPgTEMK6QHPn0ti3KmuVIe+7OQrCNK7grwWEtZqTz1DlrXwq+pM8//UX1F5vUYM4IAAOrL+agGADpO+tepkZx0ZS4uX1dz4jkF8zKnk60ZtPZmuAfkTCT19mV2NT/3T2NlZeWMrxgAAMD8q/DAA7Ak+Olfyj8bpxC/tZAFnU7ZysEqGqq1ZGbUcLYTzPL7UJy+m9mJvogh2ctPQrfbjeM4TdOyYdJ5TS4CAIDz4nz4AThHL7zwwqc//WnbqArp83/4Ad1VdZUlWT+KorIBqg2YZoZ92fb7VauBzC/uvR9U/2Du9zzqh2G4srLS6/VsAwEAYLE5HyEAYA4xLwJU2lpk7nPIB1A1Z6nwp+BE7cD8bSHKzs7O9va2+qvh46GaCpp8iYEcjDuAAQBqCcdggHnkkdGZU0r4lfXrANbKmfRfSHjdwypZF7RuYV15/+L5fkAIkaapep0/owwAABaL55EDYAnln/9jTaWFhBCGaBXHsXdZ8wC/Gd789G8dpo/hlFX0Swd500yph8I2IPMHAACoGfz7DuDP45T2eYWq6voKfvrP4ER2c/TXxyjWmjp9Wb1Bsj6VHwAAYHHhm4ABWAq//dcvUlvNVe7khO+KNpiT/nVO45MkSZJEhn455V3/qXwliiI5rKSGXaKxja0EpvEAAEAeGgBYMC+88IJtyBmR36/Ez75ycBiG1kzmERY5lfmbegasG+OXm5nj5TCV/g0jPW6bVn2IWotqJ1RH4VSwUtZPIwAA1M8cBQKAhSOfl6LmoJcNU2GXE7bkGKeAaA3THilWslauiMemMunRn0nOkk8YE5ZoUt/wiFW5almQptjDhbuIU838vH/9p0899VTZMAAAWGj2owUAFM7/kWReV22AYQAn/SvWpiIz0lxcNSqcgnllv9qUEXaBMHsAfnehtwH8HWiur5oKKnlTXD+EAABQV9wDDwAY6LlK5TP9RafgpXqGsgF6vGNW9ugB5FoM8VRWc4qwimGpxGUqfyHzJnlX5vyanPSvOCVya3fhVC1P7Rbv/QMAAIvCfjwDACfThDCdnNNvON1b+LqBee5HhjX9T8McMZ26lLNkbVr4u1eRi8z2woLk9N7pq+CsCAAAFhpuAobFc8b3ARvm/5yBsIRtuWLh5MblwnSoftTpdMrG5BexDcliThyyDihk+O1IuzG3Cn65mflWehQv+031NcodEkVRHMceqwAAgAXlc4gFgMUV2m5acE3eQjthb1iWM0YRXhcBqqtcHfO8Hf65/wx104JtYGnxW7duFb4OAAA1YD9eAiyz8z39X51M6EzTtGwkh0rVmcSpEjnzrH+GU1gXVc5ZkldOrGM8knp1rLcEJEkSBIFhAAAA1FUlB0sAWDaZrK//mTmbKEOmf7mg9Z4B4kV/NcYpqVtn6XifpydG8UplmgRrzwAAAPVgP2QCQKF48iTHfPJb5hTFCeJMKv1ba1oHKDKsq3lQtuEnI831na5UnBnzVYs53GAAADgz3KMmwBIqm/+jgmP+plIZuVRvkF8WXBVeWyBbKC/jFNY56V/yuLbAKe60tWXyPaq6ZIGPKADAcrIf2ADm0PPPP//EE0/YRs2eDFKGTJb5kV/AMoRIv4L1wAniHKqOOV67rk5WY94JwEn/NMV5evPnpOwXV0vhDmAAgHpzO8IBAD+TqZGc1K6nxrJVCCGmP2s7fYU60SO4353KOmsPoO95zoqsBQsxuwt8DAAAlpb9CASwnDLzf+I45kd/V9YLC5I+wDW96QlSrU4/F1682DKZyU4QkzuSZ3VtwWlyUT767+zsbG9v50eq1iL/IwAAqD2H4xDA0uJkLz8elWVw55/IN3QXM5lVL+mlpqlTG7PdCXq1ws8M86w//2MDAAA1NstDFEBtzOrx/wnjCTbEOPfvjX9tQWiP3WQqayoydZxqQhnrbrQOyBBCpGnaarXkX1VfgQ4BAKD23A4YAEuImaG9eVTmJHXXOUuuPYAsXrYKfT79lHPr86vwLlUPs/311Wwl5H4AgOXRsA0AmFPPP/+8bcj5E0IYcpVrRs8wTB/ymFlEk5P31k1SYzgjzQPKJBr9lfyfwcqa7NM01ZuKZ555xjAYAABqYJZnkgDqoXD+j/m2zjnkt7XWs8t6CjePlBLH0//m1kK9rt4OfuWMwlV4V6uBZf7dAQCWDf7FB7CQD2Fk5l2l0oZBZrWyM7seW+vEozgzrPNP7SfsqUo6vX/QF890NR6VAQAAFgiOcwAsHoG+2+0afuo3S6dq5mDtugcUTqR2La6nefNIyRDu1Yvq+gOzZobhV/ArWJHMFwtY5wgBAEDNzNExCWAeFM7/kYGJ3wNw0p5rTUlV9vuqVzNZnLPxrqyRmn/uP49TnHi/l94JcMYrchWGB3Tyt6FSeAwoAAAQbgKGhXaW9wGr56xnZo8o6kfdbjcMQ07M4owp5L3gNNTZcdtAZ9PUtC7rl7xlB2UtTpPWJY7jsks6sYZTMC85LYoi/a+2pd08/fTTtiEAALDw3A6KAMtMxm6Z88oCpUc01886W4dx6mcmeDhxDcrnTm5wYjxh7/FLJby2ISk/8V/GvKkZ+kdCX0tmjfyCUv4T4rRVAACw6PAvPsAHON//xYngfHoUE6fn7YjTD7pxXa9KxraBH8RHcwQUXtOKiDGzyLuyMvPwqq4AGCp7pH85A4eZtpn19QGcsjr9GgJzqwAAoAbwzz3AOTNcWBDGrxEwUH2FNVtb03mGtaCHKqaySNWFWmY6z+PMwnctLkday+r03rKiXQQAAHML/+4DnOCc/q+OU3rj0HsAOn0pIBP4wjBM05QYxOSrgvl5XQhhzZf8ixWFrPUrwg/oedbOxK+4tSzlZjdZxwMAQP3gJmCA2gonaBLEVRwPNbYyp/DPGeur4+CPlJj1/foKv6WYzOHee9WyrHnxTPoHAIDlhAYAFttZPghocYU5tiVMxGmFr3c6HU46VwS7r5CY48UU1xas2++9G8MwtG65n7LWQt/UzKr1RfAIIACAJVHJQQhg4Zzv/J9FpHKkyN27XLLEzDDTP/me8GY2DH6zdLzbBo7qWgsAAKgTHCoA7AqjXqVJbrHMJHSqWC/zd1lT4bQuwXiYTwa/ZwineNxqGdfbf50wGxsAAKg9+0EOYGnpOSwfnvSkOH0zwHk4zJLIn+Dnh/I8fRaQoYJ6f5lrka2Faw8QTu7HKBvgV9ZsZ2dne3vbugcAAGB54GAAUDD/h3M/pSTPLns8h5FyJ3r1v7qWqrfpY6veBpBWcJrJS05h3Rr9p2f+zKhLK2g1AQCgwqMRwILi5LlpqPqcBmPKrIamQpfP31Mmcn1x6w24zHU59RUSfxWycuGPcAcwAMDywFOAYOFN+SCgwtt/5cTx/OuF+CN15qXUBvBToC6esL4I01PTwBT9r3KANZrr5HjXhs26CusAAABYEjgeAJwSx7FHoFcTgQyhjT+tSOGU1XFW4T1hKSOzliUPl5lff1Z7Q3+P8p0b/8R/ePqboTNlp/wkAADAwpnNUQqgHubw7HjCfoINJ/2rAU59hWS9ZCH/wNxasBKn71sofLM89raYPBmJtMXn8JMPAADVcT54ANSJPv9n+gxkzdPWdO7N78IFX5J7NGeGRxIto69lhmUXVKV7oNLPDAAAzC3cAwBwQsb3aSKRoYWYMqCbK3sU599goM4WW68AcIYVSib6/X5mcfWjsmXBQ+aeBOxhAIBlgwYA6mDK+4B1fidc5S2b1isAFfFLb5weQGV65ipc87o+WF9XBrlsg0G+7BJSd4AIzTPPPGNbDgAA6sMn6wDUQ+b5P8w59HnWnoFzor2MmDxhxjbQGbOyxw5JTs8vNw/j1E8mN0Ik7DsiFFU/v1XM7QQAAKgZHPkATsgnpYjJ9yU5scboinhM/lGsYdq7MidS89O/pMK6dbOVwlXIv6ZpKv8ahuE0bYDemeiv+1UDAAA4GzhKwZIqfPy/nuM52ZR5Ej10+VKnPHN9v47FyjWge6iuOH/j1XwYfl8h6RcW1Jur3in1U6eaZ2nKzyQAACy0OT04AZy7wjO7mZ9ao7/icYex6yqcVBpMzWGan86n4VRfRWHOblGV8wFaf0W945yahQp/Be9qAAAACo4lAFkyuqkvTjIPc8I8W69W6rEKvkqLV8Sap5Mk4ezhPObekMU55871Ma6pvazHUBOWyL2mwdNPP20bAgAAtYKnAEFNOD0IqHD+T0ZoYyuQJRcRQhiim/5TzipUTdvAU5jj/ZK0mLAN9Oe3YWacTC/xR0ryPg3ONsthURTFE5kB+uucgmWS02zDAQCgbio8SANARji5tjDDCwvh5N5lYoRCp+6Ceb3Cg3dZuf3VdRdJ+RUG/rn/DKcmjVnf4/KC/JzI36LVahU2GAAAsCRYRw4AmCFO+HYSTm7oNLQBTtGfJunfrwewRlK/spK1eHX8EnMcxyHjJmOPHcKsTNq1BcLtvwAAgAYAlhBn/s/C0VPdrC4vCO2Zm9Z4yj83753+rcX9ykpO5+lnS222Ry6XPYBt1CkeiwAAQM2cw9EOAKpQRapTYd2c2vnpX1IjXSM7fxUerMW9z51zMrdfZY4kSXZ2djY2NmwDAQBgWeAmYKgPzn3AtTz9XykVi0WJTqdDjPRciLOUWot1sHDsQ/hcG5V5Y+gunn322bIfAQBAXc3+SAmw0PSTtfHkW6KMS9SfNVJbB+SJyq4tON26IE/qW99iWdDvJP05Ti6iktaF8ysDAECNnc8xCWAOqXgn75WUhBDqdWSm2RKTuwsKw/GUiZnTA8g3tNvtmocp3lOApvxdvHk/uQgAAOrtfA5LAOeibP6PSkj5yKhekZ3AND2Aup6AjkI383Bsvbbg91bKaq49AHMtTjV15vr6HnDdcgAAqLEZH3oBFkvM/k4lfQwz1Sl6g5HpKOSfXQuCmbWpCMPQOiZP9QDEiOzqPbWuSA2w1sw438lFAACwuHDkgOXFT/+KnK/CPH+s5znDtQWJU7CM3kgwt21JVBeOC9uAzJ53XbvTSXp++jeUxR3AAADLCU8BgloxPAgoM//HI/0rnISt6utn/cvIG0z54U9RS8m1RFEk/xtP2AqAG6EhovC0zBhjpVPUYM5Hi1zSv9pO20AAAFgi9uMHQP1Mk/6J/RAVj/rMytbtn9XlBX3CUsL4xtmlMtu9ISa3RJdNMVKv89O/JLSZSwAAAIQGAJbEDB//b83BcRx7RH9rWcWa/nX6MKcIqC+oLmIkk5trySWAAp95r7peWMhQc4FkF+H0eQAAgDrxP5YALCinAJ03TQIzs/YAzOsD09CDvnXMNHk0vwrvUrU0272hTzHCxDAAAJjlMQZgIcx5BjKnfL/LCxXRJwiZR2aoBTPXGQiXFyqjujXMCAIAABxlof5mOP9HqiigC8b9mn7FrdcW1DByXEUy+dpdp/qi/Fu6vJsKHS4vmMlm4Fvf+pZtIAAA1BOeAgR1Y3gQkM4jEcrYdF6nTqe/amGokGjKxhhwdqbeXVjX4r0xaqnktH6/710TAACgZuyHbQBwYji9PaUpy1bXuiS2KwAJL/frEpdrC8RYRTKZYsSvWSizimlKAQAAnAscuqDmCuf/yNsAxOTBi/kBOpXwqgvQRMS5tlBdazENa572Pu/O7AGs0V+nD3PK7vqC+rUU+WfXR3Oeizm/+wUAAM7MXB+uAM6AOVW7pn/VWjDzqFR1cDTXr/SxSN5lFU796ddiIIubo7P+eE3vdzP/W3iXAgAAMMDRBZaU/nVLhpjFzP06px7AtcHwwLm2MKXzzamc/Zwhk7r12gLx0r9OjbRWzlC/xdlcXnj66adtQwAAoLZwEzDUkLoP2Pr8n1BjftGDEMIQ2tRPmSvyS4Gc8Zy1l+Gcnp+SdVq/H2um95655HRFRa4liiIiiuO4cKvk61EUMWsWun37dqKxDQcAgDqzhwOAJTFNDs4IGZcXwjA0P/I/L3S5tkCTVTNXwS/LJ2vOvKzOu7jcLYntIoC1T8hj7nA6fXmBuSK5CKev06m9xFwLAADUm9tRBGCBWE//V00FQZW6MtGQnxQzhO32Zafo7818iUMf5hfTmfWnUUV95p0Aevpncu0YM6twWhcAANTY7A9+APNAzQJ64oknzCOp+lbBKbSZMa8tFL5uICYP22Emdblqa8al6dK/bUjlpknM5msLHulf4vcA3qsAAIDaO/9DLEClZCeQpqltIBHRZz7zGduQyrsFjvy1BfLK/TrVAxDj2gKxA7qasuLXBhhUV5mmmFkk8WO6H+vMpcL0H04eA/rss88WLwYAAMuBdQgHWBK/+MUv5B84DcNnP/tZ25DKu4XZpkyV/g3hkpn7FRXQmUn9DK4tkO8d1bMy5bl5ZnfhXR8AAGrvfI5/ADXw85//XP3Z2jB87nOfMw+g6rsFpsKLANNkZX4P4LoW15lLEr/H8MNJ55VKkgTpHwAADKo6BAKA7mc/+5n+V3PD8PnPf97wU6nqbmG2+ZhzbUEfyefaAzDTvywYTvHVudZVeDPfYczcDwAAsMyqOkQBgLef/vSnmVcMDcMXvvCFsh8pVXcLTCq26iF1+qCsegCyxV9m+pf8egDO5CJ+uwIAAFAF1oEQAObWT37yk/yLZQ3DF7/4xcLX9fFn0C0wI7iHssp6k+CK3wM4Tf7hl9VZV4HuAgAArHwOhwCwoH784x8Xvl7YMJR1C7oz6BY4Cq8t0Ol+wCP9qzCtP321EOfEf55fD8BZi6EyHgEEAAD2AwkALKeyboGKGoYvfelLhSN1Z9AtcMKxE31mkfnsu9OqXc/TW0/8AwAA8DkcsQAAyvzoRz8y/DTTMHz5y18uG6mcQbfAV3aDgVPoz1D3GJDtkZ2ulxf87l4AAIDlwT2iAADMyg9/+EPzAL1h+MpXvmIYKZ1Nt8CP4Eyqr7Ce4PdbNXoAAAAo5HNQAQA4Mz/4wQ9sQz5oGL761a8aBxKdVbfAV8XDkfTWAj0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+ "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": { + "image/png": { + "width": 600 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"./images/umesh_w_assembly.png\", width=600)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/jupyter/unstructured-mesh-part-ii.ipynb b/examples/jupyter/unstructured-mesh-part-ii.ipynb new file mode 100644 index 0000000000..50cbd5198d --- /dev/null +++ b/examples/jupyter/unstructured-mesh-part-ii.ipynb @@ -0,0 +1,1394 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unstructured Mesh Tallies with CAD Geometry in OpenMC\n", + "\n", + "In the first notebook on this topic, we looked at how to set up a tally using an unstructured mesh in OpenMC.\n", + "In this notebook, we will explore using unstructured mesh in conjunction with CAD-based geometry to perform detailed geometry analysis on complex geomerty.\n", + "\n", + "_**NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled.**_" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "import openmc\n", + "import openmc.lib\n", + "\n", + "assert(openmc.lib._dagmc_enabled())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll need to download our DAGMC geometry and unstructured mesh files. We'll be retrieving those using the function and URLs below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "\n", + "manifold_geom_url = 'https://tinyurl.com/rp7grox'\n", + "manifold_mesh_url = 'https://tinyurl.com/wojemuh'\n", + "\n", + "def download(url, filename='dagmc.h5m'):\n", + " \"\"\"\n", + " Helper function for retrieving dagmc models\n", + " \"\"\"\n", + " u = urllib.request.urlopen(url)\n", + " \n", + " if u.status != 200:\n", + " raise RuntimeError(\"Failed to download file.\")\n", + " \n", + " # save file as dagmc.h5m\n", + " with open(filename, 'wb') as f:\n", + " f.write(u.read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model we'll be looking at in this example is a steel piping manifold:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": { + "image/png": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"./images/manifold-cad.png\", width=800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a nice example of a model which would be extremely difficult to model using CSG. To get started, we'll need two files: \n", + " 1. the DAGMC gometry file on which we'll track particles and \n", + " 2. a tetrahedral mesh of the piping structure on which we'll score tallies\n", + " \n", + "To start, let's create the materials we'll need for this problem. The pipes are steel and we'll model the surrounding area as air." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "air = openmc.Material(name='air')\n", + "air.set_density('g/cc', 0.001205)\n", + "air.add_nuclide('N14',0.781557629247)\n", + "air.add_nuclide('N15',0.002873370753)\n", + "air.add_nuclide('O16',0.210668126508)\n", + "air.add_nuclide('O17',7.9873492e-05)\n", + "air.add_nuclide('Ar36',1.53456e-05)\n", + "air.add_nuclide('Ar38',2.8934e-06)\n", + "air.add_nuclide('Ar40',0.004581761)\n", + "\n", + "steel = openmc.Material(name='steel')\n", + "steel.set_density('g/cc', 8.0)\n", + "steel.add_nuclide('Si28',0.0092672382464)\n", + "steel.add_nuclide('Si29',0.00047056391679999997)\n", + "steel.add_nuclide('Si30',0.00031019783679999996)\n", + "steel.add_nuclide('P31',0.00023)\n", + "steel.add_nuclide('S32',0.000218593702)\n", + "steel.add_nuclide('S33',1.721987e-06)\n", + "steel.add_nuclide('S34',9.650777000000001e-06)\n", + "steel.add_nuclide('S36',3.3534e-08)\n", + "steel.add_nuclide('Mn55',0.011014)\n", + "steel.add_nuclide('Fe54',0.03910305)\n", + "steel.add_nuclide('Fe56',0.6138342600000001)\n", + "steel.add_nuclide('Fe57',0.01417611)\n", + "steel.add_nuclide('Fe58',0.0018865800000000001)\n", + "steel.add_nuclide('Ni58',0.08169227999999999)\n", + "steel.add_nuclide('Ni60',0.03146772)\n", + "steel.add_nuclide('Ni61',0.00136788)\n", + "steel.add_nuclide('Ni62',0.0043614000000000005)\n", + "steel.add_nuclide('Ni64',0.00111072)\n", + "steel.add_nuclide('Mo100',0.0024360000000000002)\n", + "steel.add_nuclide('Mo92',0.0036622500000000006)\n", + "steel.add_nuclide('Mo94',0.0022967499999999997)\n", + "steel.add_nuclide('Mo95',0.00396825)\n", + "steel.add_nuclide('Mo96',0.00416825)\n", + "steel.add_nuclide('Mo97',0.0023955)\n", + "steel.add_nuclide('Mo98',0.006073)\n", + "\n", + "materials = openmc.Materials([air, steel])\n", + "materials.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "download(manifold_geom_url)\n", + "download(manifold_mesh_url, 'manifold.h5m')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we'll create a point source at the entrance the single pipe on the low side of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "src_pnt = openmc.stats.Point(xyz=(0.0, 0.0, 0.0))\n", + "src_energy = openmc.stats.Discrete(x=[10.0], p=[1.0])\n", + "\n", + "source = openmc.Source(space=src_pnt, energy=src_energy)\n", + "\n", + "settings = openmc.Settings()\n", + "settings.source = source\n", + "\n", + "settings.run_mode = \"fixed source\"\n", + "settings.batches = 10\n", + "settings.particles = 5000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we'll indicate that we're using a CAD-based geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "settings.dagmc = True\n", + "\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll run a few particles through this geometry to make sure everything is working properly." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", + " Date/Time | 2020-03-18 15:11:49\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading DAGMC geometry...\n", + "Loading file dagmc.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.001\n", + "Building OBB Tree...\n", + " Reading N14 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", + " Reading N15 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading O17 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", + " Reading Ar36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " Reading Ar38 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", + " Reading Ar40 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", + " Reading Si28 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", + " Reading Si29 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", + " Reading Si30 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", + " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", + " Reading 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We'll do this the same way we did in the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "unstructured_mesh = openmc.UnstructuredMesh(filename=\"manifold.h5m\")\n", + "\n", + "mesh_filter = openmc.MeshFilter(unstructured_mesh)\n", + "\n", + "tally = openmc.Tally()\n", + "tally.filters = [mesh_filter]\n", + "tally.scores = ['flux']\n", + "tally.estimator = 'tracklength'\n", + "\n", + "\n", + "tallies = openmc.Tallies([tally])\n", + "tallies.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "settings.batches = 200\n", + "settings.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", + " Date/Time | 2020-03-18 15:12:52\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading DAGMC geometry...\n", + "Loading file dagmc.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.001\n", + "Building OBB Tree...\n", + " Reading N14 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", + " Reading N15 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading O17 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", + " Reading Ar36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " Reading Ar38 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", + " Reading Ar40 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", + " Reading Si28 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", + " Reading Si29 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", + " Reading Si30 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", + " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", + " Reading S32 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", + " Reading S33 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", + " Reading S34 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", + " Reading S36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", + " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", + " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", + " Reading Ni58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", + " Reading Ni60 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", + " Reading Ni61 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", + " Reading Ni62 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", + " Reading Ni64 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", + " Reading Mo100 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", + " Reading Mo92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", + " Reading Mo94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", + " Reading Mo95 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", + " Reading Mo96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", + " Reading Mo97 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", + " Reading Mo98 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for N15\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Initializing source particles...\n", + "\n", + " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", + "\n", + " Simulating batch 1\n", + " Simulating batch 2\n", + " Simulating batch 3\n", + " Simulating batch 4\n", + " Simulating batch 5\n", + " Simulating batch 6\n", + " Simulating batch 7\n", + " Simulating batch 8\n", + " Simulating batch 9\n", + " Simulating batch 10\n", + " Simulating batch 11\n", + " Simulating batch 12\n", + " Simulating batch 13\n", + " Simulating batch 14\n", + " Simulating batch 15\n", + " Simulating batch 16\n", + " Simulating batch 17\n", + " Simulating batch 18\n", + " Simulating batch 19\n", + " Simulating batch 20\n", + " Simulating batch 21\n", + " Simulating batch 22\n", + " Simulating 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batch 195\n", + " Simulating batch 196\n", + " Simulating batch 197\n", + " Simulating batch 198\n", + " Simulating batch 199\n", + " Simulating batch 200\n", + " Creating state point statepoint.200.h5...\n", + " Writing unstructured mesh tally_1.200.vtk...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.8649e+01 seconds\n", + " Reading cross sections = 4.6421e+00 seconds\n", + " Total time in simulation = 3.4910e+02 seconds\n", + " Time in transport only = 2.7176e+02 seconds\n", + " Time in active batches = 3.4910e+02 seconds\n", + " Time sampling source = 7.6141e+01 seconds\n", + " Time accumulating tallies = 2.5132e-01 seconds\n", + " Total time for finalization = 3.8468e-01 seconds\n", + " Total time elapsed = 3.9841e+02 seconds\n", + " Calculation Rate (active) = 2864.54 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " Leakage Fraction = 0.78744 +/- 0.00041\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again we should see that `tally_1.100.vtk` file which we can use to visualize our results in VisIt or another tool of your choice that supports VTK files." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tally_1.200.vtk\r\n" + ] + } + ], + "source": [ + "!ls *.vtk" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purpose of this example, we haven't run enough particles to score in all of the tet elements, but we indeed see larger flux values near the source location at the bottom of the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization with statepoint data\n", + "\n", + "It was mentioned in the previous unstructured mesh example that the centroids and volumes of elements are written to the state point file. Here, we'll explore how to use that information to produce point cloud information for visualization of this data.\n", + "\n", + "This is particularly important when combining an unstructured mesh tally with other filters as a `.vtk` file will not automatically be written with the statepoint file in that scenario. To demonstrate this, let's setup a tally similar to the one above, but add an energy filter and re-run the model." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t1\n", + "\tName =\t\n", + "\tFilters =\tMeshFilter, EnergyFilter\n", + "\tNuclides =\t\n", + "\tScores =\t['flux']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], + "source": [ + "# energy filter with bins from 0 to 1 eV and 1 eV to 10 MeV\n", + "energy_filter = openmc.EnergyFilter((0.0, 1.E6, 1.E7))\n", + "\n", + "tally.filters = [mesh_filter, energy_filter]\n", + "print(tally)\n", + "tallies.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EnergyFilter\n", + "\tValues =\t[ 0. 1000000. 10000000.]\n", + "\tID =\t2\n", + "\n" + ] + } + ], + "source": [ + "print(energy_filter)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", + "\n", + " | The OpenMC Monte Carlo Code\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | http://openmc.readthedocs.io/en/latest/license.html\n", + " Version | 0.12.0-dev\n", + " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", + " Date/Time | 2020-03-18 15:19:31\n", + " OpenMP Threads | 8\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading DAGMC geometry...\n", + "Loading file dagmc.h5m\n", + "Initializing the GeomQueryTool...\n", + "Using faceting tolerance: 0.001\n", + "Building OBB Tree...\n", + " Reading N14 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", + " Reading N15 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading O17 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", + " Reading Ar36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", + " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", + " Reading Ar38 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", + " Reading Ar40 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", + " Reading Si28 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", + " Reading Si29 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", + " Reading Si30 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", + " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", + " Reading S32 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", + " Reading S33 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", + " Reading S34 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", + " Reading S36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", + " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", + " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", + " Reading Ni58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", + " Reading Ni60 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", + " Reading Ni61 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", + " Reading Ni62 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", + " Reading Ni64 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", + " Reading Mo100 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", + " Reading Mo92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", + " Reading Mo94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", + " Reading Mo95 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", + " Reading Mo96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", + " Reading Mo97 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", + " Reading Mo98 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for N15\n", + " Minimum neutron data temperature: 294.000000 K\n", + " Maximum neutron data temperature: 294.000000 K\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", + " Initializing source particles...\n", + "\n", + " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", + "\n", + " Simulating batch 1\n", + " Simulating batch 2\n", + " Simulating batch 3\n", + " Simulating batch 4\n", + " Simulating batch 5\n", + " Simulating batch 6\n", + " Simulating batch 7\n", + " Simulating batch 8\n", + " Simulating batch 9\n", + " Simulating batch 10\n", + " Simulating batch 11\n", 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More than one filter\n", + " is present on the tally.\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.8227e+01 seconds\n", + " Reading cross sections = 4.4680e+00 seconds\n", + " Total time in simulation = 3.4969e+02 seconds\n", + " Time in transport only = 2.7219e+02 seconds\n", + " Time in active batches = 3.4969e+02 seconds\n", + " Time sampling source = 7.6881e+01 seconds\n", + " Time accumulating tallies = 4.9819e-01 seconds\n", + " Total time for finalization = 1.0085e+00 seconds\n", + " Total time elapsed = 3.9922e+02 seconds\n", + " Calculation Rate (active) = 2859.65 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " Leakage Fraction = 0.78744 +/- 0.00041\n", + "\n" + ] + } + ], + "source": [ + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's open up this statepoint file and get the information we need to create the point cloud data.\n", + "\n", + "_**NOTE: You will need the Python vtk module installed to run this part of the notebook.**_" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "with openmc.StatePoint(\"statepoint.200.h5\") as sp:\n", + " tally = sp.tallies[1]\n", + " \n", + " umesh = sp.meshes[1]\n", + " centroids = umesh.centroids\n", + " mesh_vols = umesh.volumes\n", + " \n", + " thermal_flux = tally.get_values(scores=['flux'], \n", + " filters=[openmc.EnergyFilter],\n", + " filter_bins=[((0.0, 1.E6),)]) \n", + " fast_flux = tally.get_values(scores=['flux'],\n", + " filters=[openmc.EnergyFilter],\n", + " filter_bins=[((1.E6, 1E7),)])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/vtk/util/numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", + " assert not numpy.issubdtype(z.dtype, complex), \\\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import vtk\n", + "from vtk.util import numpy_support as npsup\n", + "\n", + "# create data arrays for the cells/points\n", + "vertices = vtk.vtkCellArray()\n", + "points = vtk.vtkPoints()\n", + "\n", + "for centroid in centroids:\n", + " # create a point for each centroid\n", + " point_id = points.InsertNextPoint(centroid)\n", + " # create a cell of type \"Vertex\" for each point\n", + " cell_id = vertices.InsertNextCell(1, (point_id,))\n", + " \n", + "polyData = vtk.vtkPolyData()\n", + "\n", + "polyData.SetPoints(points)\n", + "polyData.SetVerts(vertices)\n", + "\n", + "# normalize the thermal flux using mesh \n", + "# cell volumes and shape into 1D array\n", + "thermal_flux = thermal_flux.flatten() / mesh_vols.flatten()\n", + "\n", + "# add results to the polygonal data\n", + "thermal_results = vtk.vtkDoubleArray()\n", + "thermal_results.SetName(\"Thermal Flux\")\n", + "thermal_results.SetNumberOfComponents(1)\n", + "thermal_results.SetArray(npsup.numpy_to_vtk(thermal_flux),\n", + " thermal_flux.size,\n", + " True)\n", + "\n", + "# normalize the fast flux using mesh \n", + "# cell volumes and shape into 1D array\n", + "fast_flux = fast_flux.flatten() / mesh_vols.flatten()\n", + "fast_results = vtk.vtkDoubleArray()\n", + "fast_results.SetName(\"Fast Flux\")\n", + "fast_results.SetNumberOfComponents(1)\n", + "fast_results.SetArray(npsup.numpy_to_vtk(fast_flux),\n", + " fast_flux.size,\n", + " True)\n", + "\n", + "polyData.GetPointData().AddArray(thermal_results)\n", + "polyData.GetPointData().AddArray(fast_results)\n", + "\n", + "writer = vtk.vtkGenericDataObjectWriter()\n", + "writer.SetFileName(\"manifold_flux.vtk\")\n", + "writer.SetInputData(polyData)\n", + "writer.Write()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We should now see our new flux file in the directory. It can be used to visualize the results in the same way as our other `.vtk` files." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "manifold_flux.vtk tally_1.200.vtk\r\n" + ] + } + ], + "source": [ + "!ls *.vtk" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": { + "image/png": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"./images/manifold_pnt_cld.png\", width=800)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/jupyter/unstructured_mesh_I.ipynb b/examples/jupyter/unstructured_mesh_I.ipynb deleted file mode 100644 index 43cd31a790..0000000000 --- a/examples/jupyter/unstructured_mesh_I.ipynb +++ /dev/null @@ -1,833 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "plt.rcParams[\"figure.figsize\"] = (30,10)\n", - "\n", - "import urllib.request\n", - "\n", - "\n", - "pin_mesh_url = 'https://tinyurl.com/u9ce9d7' # 1.2 MB\n", - "\n", - "def download(url, filename='dagmc.h5m'):\n", - " \"\"\"\n", - " Helper function for retrieving dagmc models\n", - " \"\"\"\n", - " u = urllib.request.urlopen(url)\n", - " \n", - " if u.status != 200:\n", - " raise RuntimeError(\"Failed to download file.\")\n", - " \n", - " # save file as dagmc.h5m\n", - " with open(filename, 'wb') as f:\n", - " f.write(u.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Unstructured Mesh Tallies in OpenMC\n", - "\n", - "In this example we'll look at how to setup and use unstructured mesh tallies in OpenMC. Unstructured meshes are able to provide results over spatial regions of a problem while conforming to a specific geometric features -- something that is often difficult to do using the regular and rectilinear meshes in OpenMC.\n", - "\n", - "Here, we'll apply an unstructured mesh tally to the PWR assembly model from the OpenMC examples.\n", - "\n", - "_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled._" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import openmc\n", - "import openmc.lib\n", - "\n", - "assert(openmc.lib._dagmc_enabled())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we'll import that model from the set of OpenMC examples." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = openmc.examples.pwr_assembly()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll make a couple of adjustments to this 2D model as it won't play very well with the 3D mesh we'll be looking at. First, we'll bound the pincell between +/- 10 cm in the Z dimension." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "min_z = openmc.ZPlane(z0=-10.0)\n", - "max_z = openmc.ZPlane(z0=10.0)\n", - "\n", - "z_region = +min_z & -max_z\n", - "\n", - "cells = model.geometry.get_all_cells()\n", - "for cell in cells.values():\n", - " cell.region &= z_region" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The other adjustment we'll make is to remove the reflective boundary conditions on the X and Y boundaries. (This is purely to generate a more interesting flux profile.)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "surfaces = model.geometry.get_all_surfaces()\n", - "# modify the boundary condition of the\n", - "# planar surfaces bounding the assembly\n", - "for surface in surfaces.values():\n", - " if isinstance(surface, openmc.Plane):\n", - " surface.boundary_type = 'vacuum'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a quick look at the model to ensure our changs have been added properly." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "root_univ = model.geometry.root_universe\n", - "\n", - "# axial image\n", - "root_univ.plot(width=(22.0, 22.0),\n", - " pixels=(200, 300),\n", - " basis='xz',\n", - " color_by='material',\n", - " seed=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# radial image\n", - "root_univ.plot(width=(22.0, 22.0),\n", - " pixels=(400, 400),\n", - " basis='xy',\n", - " color_by='material',\n", - " seed=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks good! Let's run some particles through the problem." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", - " Date/Time | 2020-03-17 17:37:57\n", - " OpenMP Threads | 96\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - " Reading U234 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n", - " Reading U235 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading Zr90 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr96.h5\n", - " Reading H1 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", - " Reading B11 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n", - " Reading c_H_in_H2O from /home/pshriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.20444\n", - " 2/1 0.15502\n", - " 3/1 0.19804\n", - " 4/1 0.22159\n", - " 5/1 0.19776\n", - " 6/1 0.20086\n", - " 7/1 0.21896 0.20991 +/- 0.00905\n", - " 8/1 0.23134 0.21706 +/- 0.00885\n", - " 9/1 0.29029 0.23536 +/- 0.01935\n", - " 10/1 0.20094 0.22848 +/- 0.01649\n", - " Creating state point statepoint.10.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 6.3553e-01 seconds\n", - " Reading cross sections = 5.9809e-01 seconds\n", - " Total time in simulation = 5.2261e-02 seconds\n", - " Time in transport only = 4.8537e-02 seconds\n", - " Time in inactive batches = 4.6636e-02 seconds\n", - " Time in active batches = 5.6258e-03 seconds\n", - " Time synchronizing fission bank = 9.5693e-05 seconds\n", - " Sampling source sites = 6.0864e-05 seconds\n", - " SEND/RECV source sites = 2.6286e-05 seconds\n", - " Time accumulating tallies = 3.3250e-06 seconds\n", - " Total time for finalization = 9.9400e-07 seconds\n", - " Total time elapsed = 6.8820e-01 seconds\n", - " Calculation Rate (inactive) = 10721.4 particles/second\n", - " Calculation Rate (active) = 88875.7 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 0.25094 +/- 0.02010\n", - " k-effective (Track-length) = 0.22848 +/- 0.01649\n", - " k-effective (Absorption) = 0.21556 +/- 0.04156\n", - " Combined k-effective = 0.20707 +/- 0.01965\n", - " Leakage Fraction = 0.79200 +/- 0.03382\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "0.20706788967181863+/-0.01965303775428789" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now it's time to apply our mesh tally to the problem. We'll be using the tetrahedral mesh \"pins1-4.h5m\" shown below:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](./images/pin_mesh.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This mesh was generated using Trelis with radii that match the fuel/coolant channels of the PWR model. These four channels correspond to the highlighted channels of the assembly below. \n", - "\n", - "Two of the channels are coolant and the other two are fuel." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib.patches import Rectangle\n", - "from matplotlib import pyplot as plt\n", - "\n", - "pitch = 1.26 # cm\n", - "\n", - "img = root_univ.plot(width=(22.0, 22.0),\n", - " pixels=(600, 600),\n", - " basis='xy',\n", - " color_by='material',\n", - " seed=0)\n", - "\n", - "# highlight channels\n", - "for i in range(0, 4):\n", - " corner = (i * pitch - pitch / 2.0, -i * pitch - pitch / 2.0)\n", - " rect = Rectangle(corner,\n", - " pitch,\n", - " pitch,\n", - " edgecolor='blue',\n", - " fill=False)\n", - " img.axes.add_artist(rect)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Applying an unstructured mesh tally" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use this mesh, we'll create an unstructured mesh instance and apply it to a mesh filter." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "download(pin_mesh_url, \"pins1-4.h5m\")\n", - "umesh = openmc.UnstructuredMesh(filename=\"pins1-4.h5m\")\n", - "mesh_filter = openmc.MeshFilter(umesh)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now apply this filter like any other. For this demonstration we'll score both the flux and heating in these pins." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "tally = openmc.Tally()\n", - "tally.filters = [mesh_filter]\n", - "tally.scores = ['heating', 'flux']\n", - "tally.estimator = 'tracklength'\n", - "model.tallies = (tally,)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we'll run this model with the unstructured mesh tally applied. Notice that the simulation takes some time to start due to some additional data structures used by the unstructured mesh tally. Additionally, the particle rate drops dramatically during the active cycles of this simulation.\n", - "\n", - "Unstructured meshes are useful, but they can be computationally expensive!" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", - " Date/Time | 2020-03-17 17:42:07\n", - " OpenMP Threads | 96\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - " Reading U234 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n", - " Reading U235 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading Zr90 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Zr96.h5\n", - " Reading H1 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", - " Reading B11 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n", - " Reading c_H_in_H2O from /home/pshriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.22539\n", - " 2/1 0.23030\n", - " 3/1 0.23180\n", - " 4/1 0.23343\n", - " 5/1 0.22940\n", - " 6/1 0.22765\n", - " 7/1 0.23238\n", - " 8/1 0.23083\n", - " 9/1 0.23204\n", - " 10/1 0.23189\n", - " 11/1 0.23555\n", - " 12/1 0.23220\n", - " 13/1 0.22907\n", - " 14/1 0.23016\n", - " 15/1 0.23055\n", - " 16/1 0.23062\n", - " 17/1 0.22656\n", - " 18/1 0.23380\n", - " 19/1 0.23268\n", - " 20/1 0.23233\n", - " 21/1 0.23256\n", - " 22/1 0.22965 0.23111 +/- 0.00145\n", - " 23/1 0.22846 0.23022 +/- 0.00121\n", - " 24/1 0.22936 0.23001 +/- 0.00089\n", - " 25/1 0.23054 0.23012 +/- 0.00069\n", - " 26/1 0.22708 0.22961 +/- 0.00076\n", - " 27/1 0.23159 0.22989 +/- 0.00070\n", - " WARNING: No tet found for location between trianle hits\n", - " 28/1 0.23703 0.23078 +/- 0.00108\n", - " 29/1 0.23227 0.23095 +/- 0.00097\n", - " 30/1 0.23114 0.23097 +/- 0.00086\n", - " 31/1 0.23133 0.23100 +/- 0.00078\n", - " 32/1 0.23143 0.23104 +/- 0.00072\n", - " 33/1 0.23125 0.23105 +/- 0.00066\n", - " 34/1 0.23218 0.23113 +/- 0.00061\n", - " 35/1 0.23140 0.23115 +/- 0.00057\n", - " 36/1 0.22911 0.23102 +/- 0.00055\n", - " 37/1 0.23143 0.23105 +/- 0.00052\n", - " 38/1 0.23342 0.23118 +/- 0.00051\n", - " 39/1 0.23186 0.23121 +/- 0.00048\n", - " 40/1 0.23029 0.23117 +/- 0.00046\n", - " 41/1 0.23132 0.23118 +/- 0.00043\n", - " 42/1 0.23167 0.23120 +/- 0.00042\n", - " 43/1 0.23244 0.23125 +/- 0.00040\n", - " 44/1 0.23101 0.23124 +/- 0.00038\n", - " 45/1 0.23225 0.23128 +/- 0.00037\n", - " 46/1 0.22945 0.23121 +/- 0.00036\n", - " 47/1 0.22978 0.23116 +/- 0.00035\n", - " 48/1 0.23335 0.23124 +/- 0.00035\n", - " 49/1 0.23298 0.23130 +/- 0.00034\n", - " 50/1 0.23095 0.23129 +/- 0.00033\n", - " 51/1 0.23724 0.23148 +/- 0.00037\n", - " 52/1 0.22973 0.23142 +/- 0.00037\n", - " 53/1 0.23066 0.23140 +/- 0.00035\n", - " 54/1 0.22838 0.23131 +/- 0.00036\n", - " 55/1 0.23262 0.23135 +/- 0.00035\n", - " 56/1 0.23593 0.23148 +/- 0.00036\n", - " 57/1 0.23358 0.23153 +/- 0.00036\n", - " 58/1 0.23050 0.23151 +/- 0.00035\n", - " 59/1 0.23273 0.23154 +/- 0.00034\n", - " 60/1 0.22842 0.23146 +/- 0.00034\n", - " 61/1 0.23344 0.23151 +/- 0.00033\n", - " 62/1 0.23333 0.23155 +/- 0.00033\n", - " 63/1 0.22987 0.23151 +/- 0.00032\n", - " 64/1 0.23117 0.23150 +/- 0.00032\n", - " 65/1 0.23197 0.23151 +/- 0.00031\n", - " 66/1 0.23379 0.23156 +/- 0.00031\n", - " 67/1 0.23461 0.23163 +/- 0.00031\n", - " 68/1 0.23109 0.23162 +/- 0.00030\n", - " 69/1 0.22916 0.23157 +/- 0.00030\n", - " 70/1 0.23008 0.23154 +/- 0.00029\n", - " 71/1 0.23157 0.23154 +/- 0.00029\n", - " 72/1 0.23126 0.23153 +/- 0.00028\n", - " 73/1 0.23377 0.23157 +/- 0.00028\n", - " 74/1 0.23105 0.23157 +/- 0.00028\n", - " 75/1 0.23654 0.23166 +/- 0.00029\n", - " 76/1 0.23198 0.23166 +/- 0.00028\n", - " 77/1 0.23390 0.23170 +/- 0.00028\n", - " 78/1 0.23455 0.23175 +/- 0.00028\n", - " 79/1 0.23245 0.23176 +/- 0.00027\n", - " 80/1 0.23121 0.23175 +/- 0.00027\n", - " 81/1 0.23183 0.23175 +/- 0.00026\n", - " 82/1 0.23496 0.23181 +/- 0.00027\n", - " 83/1 0.22763 0.23174 +/- 0.00027\n", - " 84/1 0.23184 0.23174 +/- 0.00027\n", - " 85/1 0.23074 0.23173 +/- 0.00026\n", - " 86/1 0.23178 0.23173 +/- 0.00026\n", - " 87/1 0.23135 0.23172 +/- 0.00025\n", - " 88/1 0.23117 0.23171 +/- 0.00025\n", - " 89/1 0.22815 0.23166 +/- 0.00025\n", - " 90/1 0.22852 0.23162 +/- 0.00025\n", - " 91/1 0.22910 0.23158 +/- 0.00025\n", - " 92/1 0.23143 0.23158 +/- 0.00025\n", - " 93/1 0.23097 0.23157 +/- 0.00024\n", - " 94/1 0.23348 0.23160 +/- 0.00024\n", - " 95/1 0.23068 0.23158 +/- 0.00024\n", - " 96/1 0.23089 0.23157 +/- 0.00024\n", - " 97/1 0.23373 0.23160 +/- 0.00024\n", - " 98/1 0.23336 0.23163 +/- 0.00023\n", - " 99/1 0.23084 0.23162 +/- 0.00023\n", - " 100/1 0.23116 0.23161 +/- 0.00023\n", - " Creating state point statepoint.100.h5...\n", - " Writing unstructured mesh tally_1.100.vtk...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.8046e+02 seconds\n", - " Reading cross sections = 5.9341e-01 seconds\n", - " Total time in simulation = 2.5552e+02 seconds\n", - " Time in transport only = 2.5029e+02 seconds\n", - " Time in inactive batches = 6.7244e+00 seconds\n", - " Time in active batches = 2.4879e+02 seconds\n", - " Time synchronizing fission bank = 9.6409e-01 seconds\n", - " Sampling source sites = 7.6501e-01 seconds\n", - " SEND/RECV source sites = 1.9888e-01 seconds\n", - " Time accumulating tallies = 2.9884e-01 seconds\n", - " Total time for finalization = 5.7441e-01 seconds\n", - " Total time elapsed = 4.3679e+02 seconds\n", - " Calculation Rate (inactive) = 297423.0 particles/second\n", - " Calculation Rate (active) = 32155.0 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " k-effective (Collision) = 0.23129 +/- 0.00020\n", - " k-effective (Track-length) = 0.23161 +/- 0.00023\n", - " k-effective (Absorption) = 0.23099 +/- 0.00019\n", - " Combined k-effective = 0.23119 +/- 0.00017\n", - " Leakage Fraction = 0.79477 +/- 0.00014\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "0.23118721242922136+/-0.00017018932314030727" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.settings.particles = 100_000\n", - "model.settings.inactive = 20\n", - "model.settings.batches = 100\n", - "model.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At the end of the simulation, we see the statepoint file along with a file named \"tally_1.100.vtk\". This file contains the results of the unstructured mesh tally with convenient labels for the scores applied. In our case the following scores will be present in the VTK:\n", - "\n", - " - flux_total_value\n", - " - flux_total_std_dev\n", - " - heating_total_value\n", - " - heading_total_std_dev\n", - " \n", - " Where \"total\" represents \n", - " \n", - "\n", - "Currently, an unstructured VTK file will only be generated for tallies if the unstructured mesh is is the only filter applied to that tally. All results for the unstructured mesh tally are present in the statepoint file regardless of the number of filters applied, however.\n", - "\n", - "These files can be viewed using free tools like [Paraview](https://www.paraview.org/) and [VisIt](https://wci.llnl.gov/simulation/computer-codes/visit/) to examine the results." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tally_1.100.vtk\r\n" - ] - } - ], - "source": [ - "!ls *.vtk" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Flux\n", - "![Unstructured Mesh Flux](./images/umesh_flux.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Heating\n", - "Here is an image of the heating score as viewed in VisIt. Note that no heating is scored in the water-filled channels as expected.\n", - "\n", - "![Unstructured Mesh Heating](./images/umesh_heating.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Statepoint Data" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(340144, 1, 2)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sp = openmc.StatePoint(\"statepoint.100.h5\")\n", - "tally = sp.tallies[1]\n", - "tally.mean.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enough information for visualization of results on the unstructured mesh is also provided in the statepoint file. Namely, the mesh element volumes and centroids are available." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.43381086e-04]\n", - " [1.48043747e-04]\n", - " [1.60408339e-04]\n", - " ...\n", - " [7.04197023e-05]\n", - " [7.04197023e-05]\n", - " [7.04197023e-05]]\n", - "[[ 2.88485691 -2.55429784 9.97768184]\n", - " [ 2.87565092 -2.60469781 9.8884092 ]\n", - " [ 2.85832254 -2.65291228 9.97768184]\n", - " ...\n", - " [ 1.46082175 -1.15569203 -3.62914358]\n", - " [ 1.4443143 -1.1321793 -3.65475081]\n", - " [ 1.46884412 -1.15657736 -3.68206543]]\n" - ] - } - ], - "source": [ - "umesh = sp.meshes[1]\n", - "print(umesh.volumes)\n", - "print(umesh.centroids)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The combination of these values can provide for an appoxmiate visualization of the unstructured mesh without its explicit representation or use of an additional mesh library." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We hope you've found this example notebook useful. More unstructured mesh features are under development and will be included in additional examples soon.\n", - "\n", - "![Unstructured Mesh w/ Assembly](./images/umesh_w_assembly.png)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/jupyter/unstructured_mesh_II.ipynb b/examples/jupyter/unstructured_mesh_II.ipynb deleted file mode 100644 index bcae566e50..0000000000 --- a/examples/jupyter/unstructured_mesh_II.ipynb +++ /dev/null @@ -1,729 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "\n", - "manifold_geom_url = 'https://tinyurl.com/rp7grox'\n", - "manifold_mesh_url = 'https://tinyurl.com/wojemuh'\n", - "\n", - "def download(url, filename='dagmc.h5m'):\n", - " \"\"\"\n", - " Helper function for retrieving dagmc models\n", - " \"\"\"\n", - " u = urllib.request.urlopen(url)\n", - " \n", - " if u.status != 200:\n", - " raise RuntimeError(\"Failed to download file.\")\n", - " \n", - " # save file as dagmc.h5m\n", - " with open(filename, 'wb') as f:\n", - " f.write(u.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Unstructured Mesh Tallies with CAD Geometry in OpenMC\n", - "\n", - "In the first notebook on this topic, we looked at how to set up a tally using an unstructured mesh in OpenMC.\n", - "In this notebook, we will explore using unstructured mesh in conjunction with CAD-based geometry to perform detailed geometry analysis on complex geomerty.\n", - "\n", - "_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled._" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import openmc\n", - "import openmc.lib\n", - "\n", - "assert(openmc.lib._dagmc_enabled())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model we'll be looking at today is a steel piping manifold:\n", - "![CAD Manifold](./images/manifold-cad.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a nice example of a model which would be extremely difficult to model using CSG. To get started, we'll need two files: \n", - " 1. the DAGMC gometry file on which we'll track particles and \n", - " 2. a tetrahedral mesh of the piping structure on which we'll score tallies\n", - " \n", - "To start, let's create the materials we'll need for this problem. The pipes are steel and we'll model the surrounding area as air." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "air = openmc.Material(name='air')\n", - "air.set_density('g/cc', 0.001205)\n", - "air.add_nuclide('N14',0.781557629247)\n", - "air.add_nuclide('N15',0.002873370753)\n", - "air.add_nuclide('O16',0.210668126508)\n", - "air.add_nuclide('O17',7.9873492e-05)\n", - "air.add_nuclide('Ar36',1.53456e-05)\n", - "air.add_nuclide('Ar38',2.8934e-06)\n", - "air.add_nuclide('Ar40',0.004581761)\n", - "\n", - "steel = openmc.Material(name='steel')\n", - "steel.set_density('g/cc', 8.0)\n", - "steel.add_nuclide('Si28',0.0092672382464)\n", - "steel.add_nuclide('Si29',0.00047056391679999997)\n", - "steel.add_nuclide('Si30',0.00031019783679999996)\n", - "steel.add_nuclide('P31',0.00023)\n", - "steel.add_nuclide('S32',0.000218593702)\n", - "steel.add_nuclide('S33',1.721987e-06)\n", - "steel.add_nuclide('S34',9.650777000000001e-06)\n", - "steel.add_nuclide('S36',3.3534e-08)\n", - "steel.add_nuclide('Mn55',0.011014)\n", - "steel.add_nuclide('Fe54',0.03910305)\n", - "steel.add_nuclide('Fe56',0.6138342600000001)\n", - "steel.add_nuclide('Fe57',0.01417611)\n", - "steel.add_nuclide('Fe58',0.0018865800000000001)\n", - "steel.add_nuclide('Ni58',0.08169227999999999)\n", - "steel.add_nuclide('Ni60',0.03146772)\n", - "steel.add_nuclide('Ni61',0.00136788)\n", - "steel.add_nuclide('Ni62',0.0043614000000000005)\n", - "steel.add_nuclide('Ni64',0.00111072)\n", - "steel.add_nuclide('Mo100',0.0024360000000000002)\n", - "steel.add_nuclide('Mo92',0.0036622500000000006)\n", - "steel.add_nuclide('Mo94',0.0022967499999999997)\n", - "steel.add_nuclide('Mo95',0.00396825)\n", - "steel.add_nuclide('Mo96',0.00416825)\n", - "steel.add_nuclide('Mo97',0.0023955)\n", - "steel.add_nuclide('Mo98',0.006073)\n", - "\n", - "materials = openmc.Materials([air, steel])\n", - "materials.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "download(manifold_geom_url)\n", - "download(manifold_mesh_url, 'manifold.h5m')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we'll create a point source at the entrance the single pipe on the low side of the model." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "src_pnt = openmc.stats.Point(xyz=(0.0, 0.0, 0.0))\n", - "src_energy = openmc.stats.Discrete(x=[10.0], p=[1.0])\n", - "\n", - "source = openmc.Source(space=src_pnt, energy=src_energy)\n", - "\n", - "settings = openmc.Settings()\n", - "settings.source = source\n", - "\n", - "settings.run_mode = \"fixed source\"\n", - "settings.batches = 10\n", - "settings.particles = 5000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we'll indicate that we're using a CAD-based geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "settings.dagmc = True\n", - "\n", - "settings.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll run a few particles through this geometry to make sure everything is working properly." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", - " Date/Time | 2020-03-17 18:43:06\n", - " OpenMP Threads | 96\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading DAGMC geometry...\n", - "Loading file dagmc.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.001\n", - "Building OBB Tree...\n", - " Reading N14 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", - " Reading N15 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", - " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading O17 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", - " Reading Ar36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", - " Reading Ar38 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", - " Reading Ar40 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", - " Reading Si28 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", - " Reading Si29 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", - " Reading Si30 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", - " Reading P31 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", - " Reading S32 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", - " Reading S33 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", - " Reading S34 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", - " Reading S36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", - " Reading Mn55 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", - " Reading Fe54 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", - " Reading Ni58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", - " Reading Mo100 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for N15\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Initializing source particles...\n", - "\n", - " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", - "\n", - " Simulating batch 1\n", - " Simulating batch 2\n", - " Simulating batch 3\n", - " Simulating batch 4\n", - " Simulating batch 5\n", - " Simulating batch 6\n", - " Simulating batch 7\n", - " Simulating batch 8\n", - " Simulating batch 9\n", - " Simulating batch 10\n", - " Creating state point statepoint.10.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.5505e+01 seconds\n", - " Reading cross sections = 1.5735e+00 seconds\n", - " Total time in simulation = 1.2177e+01 seconds\n", - " Time in transport only = 9.8645e+00 seconds\n", - " Time in active batches = 1.2177e+01 seconds\n", - " Time sampling source = 2.3088e+00 seconds\n", - " Time accumulating tallies = 6.5960e-06 seconds\n", - " Total time for finalization = 3.7800e-07 seconds\n", - " Total time elapsed = 2.7916e+01 seconds\n", - " Calculation Rate (active) = 4106.22 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " Leakage Fraction = 0.78918 +/- 0.00118\n", - "\n" - ] - } - ], - "source": [ - "openmc.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's setup the unstructured mesh tally. We'll do this the same way we did in the previous notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "unstructured_mesh = openmc.UnstructuredMesh(filename=\"manifold.h5m\")\n", - "\n", - "mesh_filter = openmc.MeshFilter(unstructured_mesh)\n", - "\n", - "tally = openmc.Tally()\n", - "tally.filters = [mesh_filter]\n", - "tally.scores = ['flux']\n", - "tally.estimator = 'tracklength'\n", - "\n", - "\n", - "tallies = openmc.Tallies([tally])\n", - "tallies.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "settings.batches = 200\n", - "settings.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | 21a5b51f2d59f7ceaf7546efd7b21786d55c6d87\n", - " Date/Time | 2020-03-17 19:02:12\n", - " OpenMP Threads | 96\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading DAGMC geometry...\n", - "Loading file dagmc.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.001\n", - "Building OBB Tree...\n", - " Reading N14 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", - " Reading N15 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", - " Reading O16 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading O17 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", - " Reading Ar36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", - " Reading Ar38 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", - " Reading Ar40 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", - " Reading Si28 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", - " Reading Si29 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", - " Reading Si30 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", - " Reading P31 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", - " Reading S32 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", - " Reading S33 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", - " Reading S34 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", - " Reading S36 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", - " Reading Mn55 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", - " Reading Fe54 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", - " Reading Ni58 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", - " Reading Mo100 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/pshriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for N15\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Initializing source particles...\n", - "\n", - " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", - "\n", - " Simulating batch 1\n", - " Simulating batch 2\n", - " Simulating batch 3\n", - " Simulating batch 4\n", - " Simulating batch 5\n", - " Simulating batch 6\n", - " Simulating batch 7\n", - " Simulating batch 8\n", - " Simulating batch 9\n", - " Simulating batch 10\n", - " Simulating batch 11\n", - " Simulating batch 12\n", - " Simulating batch 13\n", - " Simulating batch 14\n", - " Simulating batch 15\n", - " Simulating batch 16\n", - " Simulating batch 17\n", - " Simulating batch 18\n", - " Simulating batch 19\n", - " Simulating batch 20\n", - " Simulating batch 21\n", - " Simulating batch 22\n", - " Simulating batch 23\n", - " Simulating batch 24\n", - " Simulating batch 25\n", - " Simulating batch 26\n", - " Simulating batch 27\n", - " Simulating batch 28\n", - " Simulating batch 29\n", - 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" Simulating batch 172\n", - " Simulating batch 173\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Simulating batch 174\n", - " Simulating batch 175\n", - " Simulating batch 176\n", - " Simulating batch 177\n", - " Simulating batch 178\n", - " Simulating batch 179\n", - " Simulating batch 180\n", - " Simulating batch 181\n", - " Simulating batch 182\n", - " Simulating batch 183\n", - " Simulating batch 184\n", - " Simulating batch 185\n", - " Simulating batch 186\n", - " Simulating batch 187\n", - " Simulating batch 188\n", - " Simulating batch 189\n", - " Simulating batch 190\n", - " Simulating batch 191\n", - " Simulating batch 192\n", - " Simulating batch 193\n", - " Simulating batch 194\n", - " Simulating batch 195\n", - " Simulating batch 196\n", - " Simulating batch 197\n", - " Simulating batch 198\n", - " Simulating batch 199\n", - " Simulating batch 200\n", - " Creating state point statepoint.200.h5...\n", - " Writing unstructured mesh tally_3.200.vtk...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 3.6126e+01 seconds\n", - " Reading cross sections = 1.5844e+00 seconds\n", - " Total time in simulation = 2.5434e+02 seconds\n", - " Time in transport only = 2.0793e+02 seconds\n", - " Time in active batches = 2.5434e+02 seconds\n", - " Time sampling source = 4.5688e+01 seconds\n", - " Time accumulating tallies = 9.6858e-02 seconds\n", - " Total time for finalization = 1.2644e-01 seconds\n", - " Total time elapsed = 2.9086e+02 seconds\n", - " Calculation Rate (active) = 3931.70 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " Leakage Fraction = 0.78744 +/- 0.00041\n", - "\n" - ] - } - ], - "source": [ - "openmc.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again we should see that `tally_1.100.vtk` file which we can use to visualize our results in VisIt or another tool of your choice that supports VTK files." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tally_1.100.vtk tally_3.100.vtk tally_3.200.vtk\r\n" - ] - } - ], - "source": [ - "!ls *.vtk" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](./images/manifold_flux.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "S of the elements have no score We indeed see that the flux values are larger near the source at the bottom of the model" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From ae3a0f4a3b001decec6a614855e798c04f379e05 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 18 Mar 2020 17:00:23 -0500 Subject: [PATCH 096/205] Additions to documentation for file specs and examples. --- docs/source/examples/index.rst | 12 ++++++- .../examples/unstructured-mesh-part-i.rst | 13 ++++++++ .../examples/unstructured-mesh-part-ii.rst | 13 ++++++++ docs/source/io_formats/statepoint.rst | 4 +++ docs/source/io_formats/tallies.rst | 33 +++++++++++-------- 5 files changed, 60 insertions(+), 15 deletions(-) create mode 100644 docs/source/examples/unstructured-mesh-part-i.rst create mode 100644 docs/source/examples/unstructured-mesh-part-ii.rst diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst index 529f9427ba..14565e64af 100644 --- a/docs/source/examples/index.rst +++ b/docs/source/examples/index.rst @@ -23,7 +23,6 @@ General Usage search nuclear-data nuclear-data-resonance-covariance - cad-geom pincell-depletion -------- @@ -36,6 +35,7 @@ Geometry hexagonal triso candu + cad-geom ------------------------------------ Multi-Group Cross Section Generation @@ -60,3 +60,13 @@ Multi-Group Mode mg-mode-part-i mg-mode-part-ii mg-mode-part-iii + +----------------- +Unstructured Mesh +----------------- + +.. toctree:: + :maxdepth: 1 + + unstructured-mesh-part-i + unstructured-mesh-part-ii diff --git a/docs/source/examples/unstructured-mesh-part-i.rst b/docs/source/examples/unstructured-mesh-part-i.rst new file mode 100644 index 0000000000..ed2c9c2fd3 --- /dev/null +++ b/docs/source/examples/unstructured-mesh-part-i.rst @@ -0,0 +1,13 @@ +.. _notebook_unstructured_mesh_part_i: + +=============================== +Unstructured Mesh: Introduction +=============================== + +.. only:: html + + .. notebook:: ../../../examples/jupyter/unstructured-mesh-part-i.ipynb + +.. only:: latex + + IPython notebooks must be viewed in the online HTML documentation. diff --git a/docs/source/examples/unstructured-mesh-part-ii.rst b/docs/source/examples/unstructured-mesh-part-ii.rst new file mode 100644 index 0000000000..c30be88590 --- /dev/null +++ b/docs/source/examples/unstructured-mesh-part-ii.rst @@ -0,0 +1,13 @@ +.. _notebook_unstructured_mesh_part_ii: + +=================================================================================== +Unstructured Mesh: Unstructured Mesh Tallies with CAD and Point Cloud Visualization +=================================================================================== + +.. only:: html + + .. notebook:: ../../../examples/jupyter/unstructured-mesh-part-ii.ipynb + +.. only:: latex + + IPython notebooks must be viewed in the online HTML documentation. diff --git a/docs/source/io_formats/statepoint.rst b/docs/source/io_formats/statepoint.rst index 9156e848f3..f61fd90669 100644 --- a/docs/source/io_formats/statepoint.rst +++ b/docs/source/io_formats/statepoint.rst @@ -77,6 +77,10 @@ The current version of the statepoint file format is 17.0. of mesh. - **width** (*double[]*) -- Width of each mesh cell in each dimension. + - **Unstructured Mesh Only:** + - **volumes** (*double[]*) -- Volume of each mesh cell. + - **centroids** (*double[]*) -- Location of the mesh cell + centroids. **/tallies/filters/** diff --git a/docs/source/io_formats/tallies.rst b/docs/source/io_formats/tallies.rst index 0e1cd16f4e..6875e45049 100644 --- a/docs/source/io_formats/tallies.rst +++ b/docs/source/io_formats/tallies.rst @@ -123,8 +123,8 @@ to the scored values. The ``filter`` element has the following attributes/sub-elements: :type: - The type of the filter. Accepted options are "cell", "cellfrom", - "cellborn", "surface", "material", "universe", "energy", "energyout", "mu", + The type of the filter. Accepted options are "cell", "cellfrom", + "cellborn", "surface", "material", "universe", "energy", "energyout", "mu", "polar", "azimuthal", "mesh", "distribcell", "delayedgroup", "energyfunction", and "particle". @@ -154,27 +154,27 @@ For each filter type, the following table describes what the ``bins`` attribute should be set to: :cell: - A list of unique IDs for cells in which the tally should be + A list of unique IDs for cells in which the tally should be accumulated. :surface: - This filter allows the tally to be scored when crossing a surface. A list of - surface IDs should be given. By default, net currents are tallied, and to - tally a partial current from one cell to another, this should be used in + This filter allows the tally to be scored when crossing a surface. A list of + surface IDs should be given. By default, net currents are tallied, and to + tally a partial current from one cell to another, this should be used in combination with a cell or cell_from filter that defines the other cell. This filter should not be used in combination with a meshfilter. :cellfrom: - This filter allows the tally to be scored when crossing a surface and the - particle came from a specified cell. A list of cell IDs should be + This filter allows the tally to be scored when crossing a surface and the + particle came from a specified cell. A list of cell IDs should be given. - To tally a partial current from a cell to another, this filter should be + To tally a partial current from a cell to another, this filter should be used in combination with a cell filter, to define the other cell. This filter should not be used in combination with a meshfilter. :cellborn: This filter allows the tally to be scored to only when particles were - originally born in a specified cell. A list of cell IDs should be + originally born in a specified cell. A list of cell IDs should be given. :material: @@ -276,7 +276,7 @@ should be set to: :mesh: - The unique ID of a structured mesh to be tallied over. + The unique ID of a mesh to be tallied over. :distribcell: The single cell which should be tallied uniquely for all instances. @@ -307,12 +307,13 @@ should be set to: ```` Element ------------------ -If a structured mesh is desired as a filter for a tally, it must be specified in -a separate element with the tag name ````. This element has the following +If a mesh is desired as a filter for a tally, it must be specified in a separate +element with the tag name ````. This element has the following attributes/sub-elements: :type: - The type of structured mesh. This can be either "regular" or "rectilinear". + The type of mesh. This can be either "regular", "rectilinear", or + "unstructured". :dimension: The number of mesh cells in each direction. (For regular mesh only.) @@ -337,6 +338,10 @@ attributes/sub-elements: :z_grid: The mesh divisions along the z-axis. (For rectilinear mesh only.) + :mesh_file: + The name of the mesh file to be loaded at runtime. (For unstructured mesh + only.) + .. note:: One of ```` or ```` must be specified, but not both (even if they are consistent with one another). From ec3ef05b2f1b223b7478efdbcf526ef342b5de9a Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 18 Mar 2020 17:01:40 -0500 Subject: [PATCH 097/205] Correct spelling error in output. --- src/mesh.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 95e63d1eaf..66872f5c62 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1722,7 +1722,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, lengths.push_back((hit.first - last_dist) / track_len); } else { // if in the loop, we should always find a tet - warning("No tet found for location between trianle hits"); + warning("No tet found for location between triangle hits"); } last_dist = hit.first; From b9c31d904eee5049c964a1e7043f8684da0507c1 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 18 Mar 2020 17:21:41 -0500 Subject: [PATCH 098/205] Some updates to the current documentation for accuracy. --- docs/source/pythonapi/base.rst | 1 + docs/source/usersguide/beginners.rst | 2 +- docs/source/usersguide/processing.rst | 6 +++--- docs/source/usersguide/settings.rst | 17 +++++++++-------- 4 files changed, 14 insertions(+), 12 deletions(-) diff --git a/docs/source/pythonapi/base.rst b/docs/source/pythonapi/base.rst index e7d1399382..d621637bb1 100644 --- a/docs/source/pythonapi/base.rst +++ b/docs/source/pythonapi/base.rst @@ -127,6 +127,7 @@ Constructing Tallies openmc.ParticleFilter openmc.RegularMesh openmc.RectilinearMesh + openmc.UnstructuredMesh openmc.Trigger openmc.TallyDerivative openmc.Tally diff --git a/docs/source/usersguide/beginners.rst b/docs/source/usersguide/beginners.rst index 85d50a2d4e..a87078cb7d 100644 --- a/docs/source/usersguide/beginners.rst +++ b/docs/source/usersguide/beginners.rst @@ -60,7 +60,7 @@ Now let's look at the pros and cons of Monte Carlo methods: - **Pro**: No mesh generation is required to build geometry. By using `constructive solid geometry`_, it's possible to build arbitrarily complex - models with curved surfaces. + reactor models with curved surfaces. - **Pro**: Monte Carlo methods can be used with either continuous-energy or multi-group cross sections. diff --git a/docs/source/usersguide/processing.rst b/docs/source/usersguide/processing.rst index 56e3bbc1f5..210caca820 100644 --- a/docs/source/usersguide/processing.rst +++ b/docs/source/usersguide/processing.rst @@ -41,9 +41,9 @@ Plotting in 2D -------------- The :ref:`IPython notebook example ` also demonstrates -how to plot a mesh tally in two dimensions using the Python API. One can also -use the :ref:`scripts_plot` script which provides an interactive GUI to explore -and plot mesh tallies for any scores and filter bins. +how to plot a structured mesh tally in two dimensions using the Python API. One +can also use the :ref:`scripts_plot` script which provides an interactive GUI to +explore and plot structured mesh tallies for any scores and filter bins. .. image:: ../_images/plotmeshtally.png :width: 400px diff --git a/docs/source/usersguide/settings.rst b/docs/source/usersguide/settings.rst index 1254c4ed5c..5df42fcbed 100644 --- a/docs/source/usersguide/settings.rst +++ b/docs/source/usersguide/settings.rst @@ -171,16 +171,17 @@ Shannon Entropy To assess convergence of the source distribution, the scalar Shannon entropy metric is often used in Monte Carlo codes. OpenMC also allows you to calculate Shannon entropy at each generation over a specified mesh, created using the -:class:`openmc.Mesh` class. After instantiating a :class:`Mesh`, you need to -specify the lower-left coordinates of the mesh (:attr:`Mesh.lower_left`), the -number of mesh cells in each direction (:attr:`Mesh.dimension`) and either the -upper-right coordinates of the mesh (:attr:`Mesh.upper_right`) or the width of -each mesh cell (:attr:`Mesh.width`). Once you have a mesh, simply assign it to -the :attr:`Settings.entropy_mesh` attribute. +:class:`openmc.RegularMesh` class. After instantiating a :class:`RegularMesh`, +you need to specify the lower-left coordinates of the mesh +(:attr:`RegularMesh.lower_left`), the number of mesh cells in each direction +(:attr:`RegularMesh.dimension`) and either the upper-right coordinates of the +mesh (:attr:`RegularMesh.upper_right`) or the width of each mesh cell +(:attr:`RegularMesh.width`). Once you have a mesh, simply assign it to the +:attr:`Settings.entropy_mesh` attribute. :: - entropy_mesh = openmc.Mesh() + entropy_mesh = openmc.RegularMesh() entropy_mesh.lower_left = (-50, -50, -25) entropy_mesh.upper_right = (50, 50, 25) entropy_mesh.dimension = (8, 8, 8) @@ -193,7 +194,7 @@ property:: geom = openmc.Geometry() ... - m = openmc.Mesh() + m = openmc.RegularMesh() m.lower_left, m.upper_right = geom.bounding_box m.dimension = (8, 8, 8) From 5f7b7b32b1f83c607837dbeb19d616672a81118e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 18 Mar 2020 17:24:22 -0500 Subject: [PATCH 099/205] Updating unstructured mesh description. --- openmc/mesh.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/mesh.py b/openmc/mesh.py index 94bcf947b0..f8e1bc6b22 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -590,7 +590,7 @@ class RectilinearMesh(MeshBase): return element class UnstructuredMesh(MeshBase): - """An unstructured mesh, assumed to be three dimensionality + """A 3D unstructured mesh Parameters ---------- From 29bfb27d2be2f8347c5b64d46dd4c6bb81c66792 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 18 Mar 2020 17:33:28 -0500 Subject: [PATCH 100/205] Fixing bracket. --- src/mesh.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 66872f5c62..43e381b553 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1842,7 +1842,6 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& all_tets) { } } -// TODO: write this function void UnstructuredMesh::to_hdf5(hid_t group) const { @@ -2121,8 +2120,8 @@ void read_meshes(pugi::xml_node root) model::meshes.push_back(std::make_unique(node)); } else if (mesh_type == "rectilinear") { model::meshes.push_back(std::make_unique(node)); -#ifdef DAGMC } +#ifdef DAGMC else if (mesh_type == "unstructured") { model::meshes.push_back(std::make_unique(node)); #else From 3a7b22cc52ec26ce8be1796ee41b3d45c0009307 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 18 Mar 2020 22:30:01 -0500 Subject: [PATCH 101/205] Some updates to the example file. --- .../jupyter/unstructured-mesh-part-ii.ipynb | 131 +++++++++++------- 1 file changed, 79 insertions(+), 52 deletions(-) diff --git a/examples/jupyter/unstructured-mesh-part-ii.ipynb b/examples/jupyter/unstructured-mesh-part-ii.ipynb index 50cbd5198d..010f2a017c 100644 --- a/examples/jupyter/unstructured-mesh-part-ii.ipynb +++ b/examples/jupyter/unstructured-mesh-part-ii.ipynb @@ -162,7 +162,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next we'll create a point source at the entrance the single pipe on the low side of the model." + "Next we'll create a 5 MeV isotropic neutron point source at the entrance the single pipe on the low side of the model." ] }, { @@ -172,7 +172,7 @@ "outputs": [], "source": [ "src_pnt = openmc.stats.Point(xyz=(0.0, 0.0, 0.0))\n", - "src_energy = openmc.stats.Discrete(x=[10.0], p=[1.0])\n", + "src_energy = openmc.stats.Discrete(x=[5.e+06], p=[1.0])\n", "\n", "source = openmc.Source(space=src_pnt, energy=src_energy)\n", "\n", @@ -247,7 +247,7 @@ " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", - " Date/Time | 2020-03-18 15:11:49\n", + " Date/Time | 2020-03-18 21:52:40\n", " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", @@ -312,21 +312,21 @@ " Simulating batch 9\n", " Simulating batch 10\n", " Creating state point statepoint.10.h5...\n", - " WARNING: Skipping unstructured mesh writing for tally 1. More than one filter\n", + " WARNING: Skipping unstructured mesh writing for tally 2. More than one filter\n", " is present on the tally.\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.4827e+01 seconds\n", - " Reading cross sections = 4.3268e+00 seconds\n", - " Total time in simulation = 1.5399e+01 seconds\n", - " Time in transport only = 1.1936e+01 seconds\n", - " Time in active batches = 1.5399e+01 seconds\n", - " Time sampling source = 3.3231e+00 seconds\n", - " Time accumulating tallies = 2.4006e-02 seconds\n", - " Total time for finalization = 9.0122e-01 seconds\n", - " Total time elapsed = 6.1396e+01 seconds\n", - " Calculation Rate (active) = 3247.02 particles/second\n", + " Total time for initialization = 4.3207e+01 seconds\n", + " Reading cross sections = 4.8009e+00 seconds\n", + " Total time in simulation = 8.7535e+00 seconds\n", + " Time in transport only = 5.2673e+00 seconds\n", + " Time in active batches = 8.7535e+00 seconds\n", + " Time sampling source = 3.3281e+00 seconds\n", + " Time accumulating tallies = 2.3008e-02 seconds\n", + " Total time for finalization = 1.0243e+00 seconds\n", + " Total time elapsed = 5.3271e+01 seconds\n", + " Calculation Rate (active) = 5711.99 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -419,7 +419,7 @@ "libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/dagmc.cpp.gcda:overwriting an existing profile data with a different timestamp\n", "libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/bremsstrahlung.cpp.gcda:overwriting an existing profile data with a different timestamp\n", "libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/bank.cpp.gcda:overwriting an existing profile data with a different timestamp\n", - " Leakage Fraction = 0.78918 +/- 0.00118\n", + " Leakage Fraction = 0.97422 +/- 0.00091\n", "\n" ] } @@ -503,7 +503,7 @@ " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", - " Date/Time | 2020-03-18 15:12:52\n", + " Date/Time | 2020-03-18 21:53:34\n", " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", @@ -768,20 +768,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.8649e+01 seconds\n", - " Reading cross sections = 4.6421e+00 seconds\n", - " Total time in simulation = 3.4910e+02 seconds\n", - " Time in transport only = 2.7176e+02 seconds\n", - " Time in active batches = 3.4910e+02 seconds\n", - " Time sampling source = 7.6141e+01 seconds\n", - " Time accumulating tallies = 2.5132e-01 seconds\n", - " Total time for finalization = 3.8468e-01 seconds\n", - " Total time elapsed = 3.9841e+02 seconds\n", - " Calculation Rate (active) = 2864.54 particles/second\n", + " Total time for initialization = 4.4251e+01 seconds\n", + " Reading cross sections = 4.4934e+00 seconds\n", + " Total time in simulation = 1.9648e+02 seconds\n", + " Time in transport only = 1.2118e+02 seconds\n", + " Time in active batches = 1.9648e+02 seconds\n", + " Time sampling source = 7.4103e+01 seconds\n", + " Time accumulating tallies = 2.4364e-01 seconds\n", + " Total time for finalization = 3.6704e-01 seconds\n", + " Total time elapsed = 2.4138e+02 seconds\n", + " Calculation Rate (active) = 5089.68 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " Leakage Fraction = 0.78744 +/- 0.00041\n", + " Leakage Fraction = 0.97394 +/- 0.00017\n", "\n" ] } @@ -806,7 +806,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tally_1.200.vtk\r\n" + "manifold_flux.vtk tally_1.200.vtk\r\n" ] } ], @@ -855,16 +855,21 @@ "\tNuclides =\t\n", "\tScores =\t['flux']\n", "\tEstimator =\ttracklength\n", + "\n", + "EnergyFilter\n", + "\tValues =\t[0.e+00 1.e+00 1.e+07]\n", + "\tID =\t2\n", "\n" ] } ], "source": [ "# energy filter with bins from 0 to 1 eV and 1 eV to 10 MeV\n", - "energy_filter = openmc.EnergyFilter((0.0, 1.E6, 1.E7))\n", + "energy_filter = openmc.EnergyFilter((0.0, 1.0, 1.e+07))\n", "\n", "tally.filters = [mesh_filter, energy_filter]\n", "print(tally)\n", + "print(energy_filter)\n", "tallies.export_to_xml()" ] }, @@ -877,15 +882,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "EnergyFilter\n", - "\tValues =\t[ 0. 1000000. 10000000.]\n", - "\tID =\t2\n", - "\n" + "\r\n", + "\r\n", + " \r\n", + " manifold.h5m\r\n", + " \r\n", + " \r\n", + " 1\r\n", + " \r\n", + " \r\n", + " 0.0 1.0 10000000.0\r\n", + " \r\n", + " \r\n", + " 1 2\r\n", + " flux\r\n", + " tracklength\r\n", + " \r\n", + "\r\n" ] } ], "source": [ - "print(energy_filter)" + "!cat tallies.xml" ] }, { @@ -926,7 +944,7 @@ " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", - " Date/Time | 2020-03-18 15:19:31\n", + " Date/Time | 2020-03-18 21:57:37\n", " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", @@ -1192,20 +1210,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.8227e+01 seconds\n", - " Reading cross sections = 4.4680e+00 seconds\n", - " Total time in simulation = 3.4969e+02 seconds\n", - " Time in transport only = 2.7219e+02 seconds\n", - " Time in active batches = 3.4969e+02 seconds\n", - " Time sampling source = 7.6881e+01 seconds\n", - " Time accumulating tallies = 4.9819e-01 seconds\n", - " Total time for finalization = 1.0085e+00 seconds\n", - " Total time elapsed = 3.9922e+02 seconds\n", - " Calculation Rate (active) = 2859.65 particles/second\n", + " Total time for initialization = 4.4772e+01 seconds\n", + " Reading cross sections = 4.3243e+00 seconds\n", + " Total time in simulation = 1.9726e+02 seconds\n", + " Time in transport only = 1.2160e+02 seconds\n", + " Time in active batches = 1.9726e+02 seconds\n", + " Time sampling source = 7.5024e+01 seconds\n", + " Time accumulating tallies = 4.9306e-01 seconds\n", + " Total time for finalization = 1.0223e+00 seconds\n", + " Total time elapsed = 2.4336e+02 seconds\n", + " Calculation Rate (active) = 5069.40 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " Leakage Fraction = 0.78744 +/- 0.00041\n", + " Leakage Fraction = 0.97394 +/- 0.00017\n", "\n" ] } @@ -1218,7 +1236,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's open up this statepoint file and get the information we need to create the point cloud data.\n", + "Noice the warning at the end of the output above indicating that the .vtk file we used before isn't written in this case.\n", + "\n", + "Let's open up this statepoint file and get the information we need to create the point cloud data instead.\n", "\n", "_**NOTE: You will need the Python vtk module installed to run this part of the notebook.**_" ] @@ -1238,10 +1258,10 @@ " \n", " thermal_flux = tally.get_values(scores=['flux'], \n", " filters=[openmc.EnergyFilter],\n", - " filter_bins=[((0.0, 1.E6),)]) \n", + " filter_bins=[((0.0, 1.0),)]) \n", " fast_flux = tally.get_values(scores=['flux'],\n", " filters=[openmc.EnergyFilter],\n", - " filter_bins=[((1.E6, 1E7),)])" + " filter_bins=[((1.0, 1.e+07),)])" ] }, { @@ -1309,15 +1329,22 @@ " fast_flux.size,\n", " True)\n", "\n", + "total_flux = thermal_flux + fast_flux\n", + "total_results = vtk.vtkDoubleArray()\n", + "total_results.SetName(\"Total Flux\")\n", + "total_results.SetNumberOfComponents(1)\n", + "total_results.SetArray(npsup.numpy_to_vtk(total_flux),\n", + " total_flux.size,\n", + " True)\n", + "\n", "polyData.GetPointData().AddArray(thermal_results)\n", "polyData.GetPointData().AddArray(fast_results)\n", + "polyData.GetPointData().AddArray(total_results)\n", "\n", "writer = vtk.vtkGenericDataObjectWriter()\n", "writer.SetFileName(\"manifold_flux.vtk\")\n", "writer.SetInputData(polyData)\n", - "writer.Write()\n", - "\n", - "\n" + "writer.Write()" ] }, { From 131acf4a7a2eb9d2f3652b97933be57e563558f7 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 19 Mar 2020 11:05:56 -0500 Subject: [PATCH 102/205] Updating example notebooks to get rid of a weird library error. --- .../jupyter/unstructured-mesh-part-i.ipynb | 165 ++++------------- .../jupyter/unstructured-mesh-part-ii.ipynb | 168 ++++-------------- 2 files changed, 77 insertions(+), 256 deletions(-) diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb index 8d4b7bff6a..a2e26eff86 100644 --- a/examples/jupyter/unstructured-mesh-part-i.ipynb +++ b/examples/jupyter/unstructured-mesh-part-i.ipynb @@ -164,7 +164,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -203,7 +203,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -276,8 +276,8 @@ " Copyright | 2011-2020 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", - 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" Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", - " Date/Time | 2020-03-18 16:14:17\n", + " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", + " Date/Time | 2020-03-19 09:57:45\n", " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", @@ -686,7 +597,7 @@ " 25/1 0.23054 0.23012 +/- 0.00069\n", " 26/1 0.22708 0.22961 +/- 0.00076\n", " 27/1 0.23159 0.22989 +/- 0.00070\n", - " WARNING: No tet found for location between trianle hits\n", + " WARNING: No tet found for location between triangle hits\n", " 28/1 0.23703 0.23078 +/- 0.00108\n", " 29/1 0.23227 0.23095 +/- 0.00097\n", " 30/1 0.23114 0.23097 +/- 0.00086\n", @@ -765,20 +676,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.3659e+02 seconds\n", - " Reading cross sections = 1.2481e+00 seconds\n", - " Total time in simulation = 1.2827e+03 seconds\n", - " Time in transport only = 1.2755e+03 seconds\n", - " Time in inactive batches = 2.1605e+02 seconds\n", - 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More than one filter\n", - " is present on the tally.\n", + " Writing unstructured mesh tally_1.10.vtk...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.3207e+01 seconds\n", - " Reading cross sections = 4.8009e+00 seconds\n", - " Total time in simulation = 8.7535e+00 seconds\n", - " Time in transport only = 5.2673e+00 seconds\n", - " Time in active batches = 8.7535e+00 seconds\n", - " Time sampling source = 3.3281e+00 seconds\n", - " Time accumulating tallies = 2.3008e-02 seconds\n", - " Total time for finalization = 1.0243e+00 seconds\n", - " Total time elapsed = 5.3271e+01 seconds\n", - " Calculation Rate (active) = 5711.99 particles/second\n", + " Total time for initialization = 2.2053e+02 seconds\n", + " Reading cross sections = 2.1899e+00 seconds\n", + " Total time in simulation = 1.4710e+01 seconds\n", + " Time in transport only = 6.4195e+00 seconds\n", + " Time in active batches = 1.4710e+01 seconds\n", + " Time sampling source = 3.6842e+00 seconds\n", + " Time accumulating tallies = 1.9801e-02 seconds\n", + " Total time for finalization = 6.9415e-01 seconds\n", + " Total time elapsed = 2.3623e+02 seconds\n", + " Calculation Rate (active) = 3399.10 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - 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"libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/cmfd_solver.cpp.gcda:overwriting an existing profile data with a different timestamp\n", - "libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/cell.cpp.gcda:overwriting an existing profile data with a different timestamp\n", - "libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/dagmc.cpp.gcda:overwriting an existing profile data with a different timestamp\n", - "libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/bremsstrahlung.cpp.gcda:overwriting an existing profile data with a different timestamp\n", - "libgcov profiling error:/home/shriwise/opt/openmc/bld/CMakeFiles/libopenmc.dir/src/bank.cpp.gcda:overwriting an existing profile data with a different timestamp\n", " Leakage Fraction = 0.97422 +/- 0.00091\n", "\n" ] @@ -502,8 +412,8 @@ " Copyright | 2011-2020 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", - " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", - " Date/Time | 2020-03-18 21:53:34\n", + " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", + " Date/Time | 2020-03-19 10:14:19\n", " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", @@ -768,16 +678,16 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.4251e+01 seconds\n", - " Reading cross sections = 4.4934e+00 seconds\n", - " Total time in simulation = 1.9648e+02 seconds\n", - " Time in transport only = 1.2118e+02 seconds\n", - " Time in active batches = 1.9648e+02 seconds\n", - " Time sampling source = 7.4103e+01 seconds\n", - " Time accumulating tallies = 2.4364e-01 seconds\n", - " Total time for finalization = 3.6704e-01 seconds\n", - " Total time elapsed = 2.4138e+02 seconds\n", - " Calculation Rate (active) = 5089.68 particles/second\n", + " Total time for initialization = 4.1748e+01 seconds\n", + " Reading cross sections = 2.1717e+00 seconds\n", + " Total time in simulation = 1.8229e+02 seconds\n", + " Time in transport only = 1.0991e+02 seconds\n", + " Time in active batches = 1.8229e+02 seconds\n", + " Time sampling source = 7.1488e+01 seconds\n", + " Time accumulating tallies = 4.7612e-02 seconds\n", + " Total time for finalization = 1.4539e-01 seconds\n", + " Total time elapsed = 2.2446e+02 seconds\n", + " Calculation Rate (active) = 5485.91 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -806,7 +716,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "manifold_flux.vtk tally_1.200.vtk\r\n" + "tally_1.10.vtk\ttally_1.200.vtk\r\n" ] } ], @@ -943,8 +853,8 @@ " Copyright | 2011-2020 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", - " Git SHA1 | e5e67eb4d2780d739f290b995e5acd676b70be41\n", - " Date/Time | 2020-03-18 21:57:37\n", + " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", + " Date/Time | 2020-03-19 10:18:04\n", " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", @@ -1210,16 +1120,16 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.4772e+01 seconds\n", - " Reading cross sections = 4.3243e+00 seconds\n", - " Total time in simulation = 1.9726e+02 seconds\n", - " Time in transport only = 1.2160e+02 seconds\n", - " Time in active batches = 1.9726e+02 seconds\n", - " Time sampling source = 7.5024e+01 seconds\n", - " Time accumulating tallies = 4.9306e-01 seconds\n", - " Total time for finalization = 1.0223e+00 seconds\n", - " Total time elapsed = 2.4336e+02 seconds\n", - " Calculation Rate (active) = 5069.40 particles/second\n", + " Total time for initialization = 4.0432e+01 seconds\n", + " Reading cross sections = 1.9152e+00 seconds\n", + " Total time in simulation = 1.7878e+02 seconds\n", + " Time in transport only = 1.0807e+02 seconds\n", + " Time in active batches = 1.7878e+02 seconds\n", + " Time sampling source = 7.0570e+01 seconds\n", + " Time accumulating tallies = 9.5415e-02 seconds\n", + " Total time for finalization = 2.9952e-01 seconds\n", + " Total time elapsed = 2.1979e+02 seconds\n", + " Calculation Rate (active) = 5593.58 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1363,7 +1273,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "manifold_flux.vtk tally_1.200.vtk\r\n" + "manifold_flux.vtk tally_1.10.vtk tally_1.200.vtk\r\n" ] } ], From afdf5a9f898e3b12a7ef486ca66c587bf60cc633 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 19 Mar 2020 11:37:45 -0500 Subject: [PATCH 103/205] Some cleanup of the unstructured mesh source. --- include/openmc/mesh.h | 55 +++++++++++++++++++++++++++++-------------- src/mesh.cpp | 9 +++---- src/state_point.cpp | 1 + 3 files changed, 43 insertions(+), 22 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index aa1946896e..cfbaf2a156 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -179,7 +179,6 @@ public: //! \return Whether the line segment connecting r0 and r1 intersects mesh bool intersects(Position& r0, Position r1, int* ijk) const; - //! Count number of bank sites in each mesh bin / energy bin // //! \param[in] bank Array of bank sites @@ -188,8 +187,6 @@ public: xt::xtensor count_sites(const Particle::Bank* bank, int64_t length, bool* outside) const; - int num_bins() const; - // Data members double volume_frac_; //!< Volume fraction of each mesh element @@ -258,19 +255,19 @@ class UnstructuredMesh : public Mesh { typedef std::vector> UnstructuredMeshHits; public: - UnstructuredMesh() { }; + UnstructuredMesh() = default; UnstructuredMesh(pugi::xml_node); ~UnstructuredMesh() = default; - //! Determine which bins were crossed by a particle. - // - //! \param[in] p Particle to check - //! \param[out] bins Bins that were crossed - //! \param[out] lengths Fraction of tracklength in each bin void bins_crossed(const Particle* p, std::vector& bins, - std::vector& lengths) const; - + std::vector& lengths) const override; + //! Check where a line segment intersects the mesh and if it intersects at all + // + //! \param[in,out] r0 In: starting position, out: intersection point + //! \param[in] r1 Ending position + //! \param[out] ijk Indices of the mesh bin containing the intersection point + //! \return Whether the line segment connecting r0 and r1 intersects mesh bool intersects(Position& r0, Position r1, int* ijk); @@ -280,7 +277,7 @@ private: // //! \param[in] start Staring location //! \param[in] dir Normalized particle direction - //! \param[in] length of particle track + //! \param[in] track_len length of particle track //! \param[out] Mesh intersections void intersect_track(const moab::CartVect& start, @@ -300,7 +297,7 @@ private: //! \return MOAB EntityHandle of tet moab::EntityHandle get_tet(const Position& r) const; - //! Version of get_tet taking Position. + //! Returns the containing tet given a position inline moab::EntityHandle get_tet(const moab::CartVect& r) const { return get_tet(Position(r[0], r[1], r[2])); }; @@ -311,13 +308,14 @@ private: //! \param[in] r Position to check //! \param[in] MOAB terahedron to check //! \return True if r is inside, False if r is outside - bool point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) const; + bool point_in_tet(const moab::CartVect& r, + moab::EntityHandle tet) const; //! Compute barycentric coordinate data for all tetrahedra //! in the mesh. // - //! \param[in] all_tets MOAB Range of tetrahedral elements - void compute_barycentric_data(const moab::Range& all_tets); + //! \param[in] tets MOAB Range of tetrahedral elements + void compute_barycentric_data(const moab::Range& tets); //! Translates a MOAB EntityHandle its corresponding bin. // @@ -332,10 +330,22 @@ private: //! \return MOAB EntityHandle of tet moab::EntityHandle get_ent_handle_from_bin(int bin) const; + //! Get the bin for a given mesh cell index + // + //! \param[in] idx Index of the mesh cell. + //! \return Mesh bin int get_bin_from_index(int idx) const; - int get_index(Position r, bool* in_mesh) const; + //! Get the mesh cell index for a given position + // + //! \param[in] r Position to get index for + //! \param[in,out] in_mesh Whether position is in the mesh + int get_index(const Position& r, bool* in_mesh) const; + //! Get the mesh cell index from a bin + // + //! \param[in] bin Bin to get the index for + //! \return Index of the bin int get_index_from_bin(int bin) const; //! Builds a KDTree for all tetrahedra in the mesh. All @@ -347,6 +357,9 @@ private: //! Get the tags for a score from the mesh instance //! or create them if they are not there + // + //! \param[in] score Name of the score + //! \returns The MOAB value and error tag handles, respectively std::pair get_score_tags(std::string score) const; @@ -372,12 +385,19 @@ public: //! \return Mesh bin int get_bin(Position r) const; + //! Return a string represntation of the mesh bin + // + //! \param[in] Mesh bin to generate a label for std::string get_label_for_bin(int bin) const; int n_bins() const override; int n_surface_bins() const override; + //! Retrieve a centroid for the mesh cell + // + // \param[in] tet MOAB EntityHandle of the tetrahedron + // \returns The centroid of the element Position centroid(moab::EntityHandle tet) const; std::string bin_label(int bin) const override; @@ -395,7 +415,6 @@ public: std::string filename_; // verts; rval = mbi_->get_connectivity(&tet, 1, verts); if (rval != moab::MB_SUCCESS) { @@ -1912,7 +1912,8 @@ UnstructuredMesh::get_bin_from_index(int idx) const { } int -UnstructuredMesh::get_index(Position r, bool* in_mesh) const { +UnstructuredMesh::get_index(const Position& r, + bool* in_mesh) const { int bin = get_bin(r); *in_mesh = bin != -1; return bin; diff --git a/src/state_point.cpp b/src/state_point.cpp index 7b82fe4296..d91e9f7a19 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -166,6 +166,7 @@ openmc_statepoint_write(const char* filename, bool* write_source) write_attribute(tallies_group, "ids", tally_ids); #ifdef DAGMC + // write unstructured mesh tallies to VTK if possible write_unstructured_mesh_results(); #endif From 46b345be5abfbf30f6ec679d497ad0d23707b38a Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 19 Mar 2020 13:18:02 -0500 Subject: [PATCH 104/205] Some added documentation/cleanup of Python API files. --- openmc/filter.py | 2 +- openmc/mesh.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/openmc/filter.py b/openmc/filter.py index f9d93d91a2..d9c0470722 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -750,7 +750,7 @@ class MeshFilter(Filter): cv.check_type('filter mesh', mesh, openmc.MeshBase) self._mesh = mesh if isinstance(mesh, openmc.UnstructuredMesh): - self.bins = [(n, 1, 1) for n in range(1, len(mesh.volumes) + 1)] + self.bins = list(range(len(mesh.volumes))) else: self.bins = list(mesh.indices) diff --git a/openmc/mesh.py b/openmc/mesh.py index f8e1bc6b22..fb41b06205 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -598,6 +598,8 @@ class UnstructuredMesh(MeshBase): Unique identifier for the mesh name : str Name of the mesh + filename : str + Location of the unstructured mesh file Attributes ---------- @@ -681,7 +683,7 @@ class UnstructuredMesh(MeshBase): Returns ------- element : xml.etree.ElementTree.Element - XML element containing the mesh data + XML element containing mesh data """ From f96239f26380ff507f4832a117828633ee730883 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 19 Mar 2020 13:18:42 -0500 Subject: [PATCH 105/205] Some cleanup of the writing function during statepoint output and removing unecessary changes in the eigenvalue file. --- src/eigenvalue.cpp | 2 ++ src/state_point.cpp | 33 ++++++++++++++++++--------------- 2 files changed, 20 insertions(+), 15 deletions(-) diff --git a/src/eigenvalue.cpp b/src/eigenvalue.cpp index 076f3b8e68..f02a96e7ac 100644 --- a/src/eigenvalue.cpp +++ b/src/eigenvalue.cpp @@ -537,9 +537,11 @@ void ufs_count_sites() // On the first generation, just assume that the source is already evenly // distributed so that effectively the production of fission sites is not // biased + std::size_t n = simulation::ufs_mesh->n_bins(); double vol_frac = simulation::ufs_mesh->volume_frac_; simulation::source_frac = xt::xtensor({n}, vol_frac); + } else { // count number of source sites in each ufs mesh cell bool sites_outside; diff --git a/src/state_point.cpp b/src/state_point.cpp index d91e9f7a19..56e576a81f 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -167,7 +167,7 @@ openmc_statepoint_write(const char* filename, bool* write_source) #ifdef DAGMC // write unstructured mesh tallies to VTK if possible - write_unstructured_mesh_results(); + write_unstructured_mesh_results(); #endif // Write all tally information except results @@ -690,11 +690,11 @@ void write_unstructured_mesh_results() { for (auto filter_idx : tally->filters()) { auto& filter = model::tally_filters[filter_idx]; if (filter->type() == "mesh") { + // check if the filter uses an unstructured mesh auto mesh_filter = dynamic_cast(filter.get()); - auto& mesh = model::meshes[mesh_filter->mesh()]; - auto umesh = dynamic_cast(mesh.get()); + auto mesh_idx = mesh_filter->mesh(); + auto umesh = dynamic_cast(model::meshes[mesh_idx].get()); if (umesh) { - // if this tally has more than one filter, print // warning and skip writing the mesh if (tally->filters().size() > 1) { @@ -705,21 +705,26 @@ void write_unstructured_mesh_results() { } int n_realizations = tally->n_realizations_; - // write each score for this tally to the mesh + + // write each score/nuclide combination for this tally for (int i_score = 0; i_score < tally->scores_.size(); i_score++) { for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) { - std::vector mean_vec, std_dev_vec; + + // index for this nuclide and score int nuc_score_idx = i_score + i_nuc * tally->scores_.size(); + + // construct result vectors + std::vector mean_vec, std_dev_vec; for (int j = 0; j < tally->results_.shape()[0]; j++) { double mean = tally->results_(j, nuc_score_idx, TallyResult::SUM) / n_realizations; double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ); - std_dev_vec.push_back(sum_sq / n_realizations - - std::pow(mean, 2) / (n_realizations - 1)); + double std_dev = sum_sq / n_realizations - std::pow(mean, 2) / (n_realizations - 1); + std_dev_vec.push_back(std_dev); mean_vec.push_back(mean); } // generate a name for the value - std::string nuclide_name = "total"; + std::string nuclide_name = "total"; // start with total by default if (tally->nuclides_[i_nuc] > -1) { nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_; } @@ -729,22 +734,20 @@ void write_unstructured_mesh_results() { auto score_str = fmt::format("{0}_{1}", score_name, nuclide_name); - umesh->set_score_data(score_str, + umesh->set_score_data(score_str, mean_vec, std_dev_vec); - mean_vec.clear(); - std_dev_vec.clear(); } } - // Determine width for zero padding + // Generate a file name based on the tally id + // and the current batch number int w = std::to_string(settings::n_max_batches).size(); std::string filename = fmt::format("tally_{0}.{1:0{2}}", tally->id_, simulation::current_batch, w); - - // Write message + // Write the unstructured mesh and data to file umesh->write(filename); } } From f5663866614099389712b58d18eeda606bf4da26 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 19 Mar 2020 14:10:42 -0500 Subject: [PATCH 106/205] Various cleanup changes. --- include/openmc/tallies/tally.h | 3 +- src/tallies/tally.cpp | 4 -- .../unstructured_mesh/test.py | 41 +++++++++---------- 3 files changed, 22 insertions(+), 26 deletions(-) diff --git a/include/openmc/tallies/tally.h b/include/openmc/tallies/tally.h index 25b7e1c71f..c19ceb2af3 100644 --- a/include/openmc/tallies/tally.h +++ b/include/openmc/tallies/tally.h @@ -72,7 +72,8 @@ public: void accumulate(); - std::string score_name(int i) const; + //! A string representing the i-th score on this tally + std::string score_name(int score_idx) const; //---------------------------------------------------------------------------- // Major public data members. diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index 0dba3b925d..bbc671d210 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -129,16 +129,12 @@ score_int_to_str(int score_int) { if (score_int == N_2N) return "(n,2n)"; - if (score_int == N_3N) return "(n,3n)"; - if (score_int == N_4N) return "(n,4n)"; - if (score_int == N_2ND) return "(n,2nd)"; - if (score_int == N_2NA) return "(n,na)"; if (score_int == N_N3A) diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 204c31dbb0..546b409b01 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -12,10 +12,14 @@ from tests.testing_harness import PyAPITestHarness pytestmark = pytest.mark.skipif( not openmc.lib._dagmc_enabled(), - reason="Mesh library is not available.") + reason="Mesh library is not enabled.") + +TETS_PER_VOXEL = 12 + class UnstructuredMeshTest(PyAPITestHarness): + def __init__(self, statepoint_name, **kwargs): super().__init__(statepoint_name) @@ -187,7 +191,7 @@ class UnstructuredMeshTest(PyAPITestHarness): angle = openmc.stats.Monodirectional((-1.0, 0.0, 0.0)) - energy = openmc.stats.Discrete(x=[15.0E6], p=[1.0]) + energy = openmc.stats.Discrete(x=[15.e+06], p=[1.0]) source = openmc.Source(space=space, energy=energy, angle=angle) @@ -200,21 +204,18 @@ class UnstructuredMeshTest(PyAPITestHarness): def _compare_results(self): with openmc.StatePoint(self._sp_name) as sp: - # loop over the tallies - regular_data = None - regular_std_dev = None - unstructured_data = None - unstructured_std_dev = None + + # loop over the tallies and get data for tally in sp.tallies.values(): # find the regular and unstructured meshes if tally.contains_filter(openmc.MeshFilter): flt = tally.find_filter(openmc.MeshFilter) if isinstance(flt.mesh, openmc.RegularMesh): - regular_data, regular_std_dev = self.get_mesh_tally_data(tally) + reg_mesh_data, reg_mesh_std_dev = self.get_mesh_tally_data(tally) if self.holes: - regular_data = np.delete(regular_data, holes) - regular_std_dev = np.delete(regular_std_dev, holes) + reg_mesh_data = np.delete(reg_mesh_data, holes) + reg_mesh_std_dev = np.delete(reg_mesh_std_dev, holes) else: unstructured_data, unstructured_std_dev = self.get_mesh_tally_data(tally, True) @@ -223,7 +224,7 @@ class UnstructuredMeshTest(PyAPITestHarness): while True: try: np.testing.assert_array_almost_equal(unstructured_data, - regular_data, + reg_mesh_data, decimals) except AssertionError as ae: print(ae) @@ -232,13 +233,12 @@ class UnstructuredMeshTest(PyAPITestHarness): # increment decimals decimals += 1 - print("Results equal to within {} decimal places.\n".format(decimals)) - + # we expect these results to be the same to within at least ten + # decimal places assert decimals >= 10 @staticmethod def get_mesh_tally_data(tally, structured=False): - TETS_PER_VOXEL = 12 data = tally.get_reshaped_data(value='mean') std_dev = tally.get_reshaped_data(value='std_dev') if structured: @@ -251,20 +251,19 @@ class UnstructuredMeshTest(PyAPITestHarness): def _cleanup(self): super()._cleanup() - - output = glob.glob('tally*.h5m') + output = glob.glob('tally*.vtk') for f in output: if os.path.exists(f): os.remove(f) - -hole_indexes = (333, 90, 777) -param_values = (('collision', 'tracklength'), (True, False), (hole_indexes, ())) +param_values = ( ('collision', 'tracklength'), # estimators + (True, False), # geometry outside of the mesh + ( (333, 90, 77), tuple() ) ) # location of holes in the mesh test_cases = [] for estimator, holes, ext_geom in product(*param_values): test_cases.append({'estimator' : estimator, - 'holes' : holes, - 'external_geom' : ext_geom}) + 'external_geom' : ext_geom, + 'holes' : holes}) @pytest.mark.parametrize("opts", test_cases) def test_unstructured_mesh(opts): harness = UnstructuredMeshTest('statepoint.10.h5', kwargs=opts) From 4b3bcfe2ed1753f78123d7d22dd8f1435b9d0cee Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 19 Mar 2020 14:18:03 -0500 Subject: [PATCH 107/205] Matching xtl xtensor versions with develop. --- vendor/xtensor | 2 +- vendor/xtl | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/vendor/xtensor b/vendor/xtensor index ef091807f7..31acec1e90 160000 --- a/vendor/xtensor +++ b/vendor/xtensor @@ -1 +1 @@ -Subproject commit ef091807f7ed0e5ba7e251a6c46f4af7bba79e2e +Subproject commit 31acec1e90bbea6d4bc17af0710a123bd5da6689 diff --git a/vendor/xtl b/vendor/xtl index f5d13e6c4f..0024346605 160000 --- a/vendor/xtl +++ b/vendor/xtl @@ -1 +1 @@ -Subproject commit f5d13e6c4f856becc178939365fcdcf9a657ffb5 +Subproject commit 0024346605bd92bcc4009caad7f4be88687e063a From 24843a45935d3469cbe243d163362736fd69ffe8 Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Thu, 19 Mar 2020 15:43:39 -0400 Subject: [PATCH 108/205] add some accessors for copying stuff to GPU --- include/openmc/distribution_multi.h | 5 +++++ include/openmc/distribution_spatial.h | 19 +++++++++++++++++++ include/openmc/source.h | 7 +++++++ 3 files changed, 31 insertions(+) diff --git a/include/openmc/distribution_multi.h b/include/openmc/distribution_multi.h index 493ad85e07..1b1a262b82 100644 --- a/include/openmc/distribution_multi.h +++ b/include/openmc/distribution_multi.h @@ -43,6 +43,11 @@ public: //! \param seed Pseudorandom number seed pointer //! \return Direction sampled Direction sample(uint64_t* seed) const; + + // Observing pointers + auto mu() const { return mu_.get(); } + auto phi() const { return phi_.get(); } + private: UPtrDist mu_; //!< Distribution of polar angle UPtrDist phi_; //!< Distribution of azimuthal angle diff --git a/include/openmc/distribution_spatial.h b/include/openmc/distribution_spatial.h index f3c7bb67d4..8d5017a81e 100644 --- a/include/openmc/distribution_spatial.h +++ b/include/openmc/distribution_spatial.h @@ -32,6 +32,11 @@ public: //! \param seed Pseudorandom number seed pointer //! \return Sampled position Position sample(uint64_t* seed) const; + + // Observer pointers + auto x() const { return x_.get(); } + auto y() const { return x_.get(); } + auto z() const { return x_.get(); } private: UPtrDist x_; //!< Distribution of x coordinates UPtrDist y_; //!< Distribution of y coordinates @@ -50,6 +55,11 @@ public: //! \param seed Pseudorandom number seed pointer //! \return Sampled position Position sample(uint64_t* seed) const; + + auto r() const { return r_.get(); } + auto phi() const { return phi_.get(); } + auto z() const { return z_.get(); } + auto origin() const { return origin_; } private: UPtrDist r_; //!< Distribution of r coordinates UPtrDist phi_; //!< Distribution of phi coordinates @@ -70,6 +80,11 @@ public: //! \param seed Pseudorandom number seed pointer //! \return Sampled position Position sample(uint64_t* seed) const; + + auto r() const { return r_.get(); } + auto theta() const { return theta_.get(); } + auto phi() const { return phi_.get(); } + auto origin () const { return origin_; } private: UPtrDist r_; //!< Distribution of r coordinates UPtrDist theta_; //!< Distribution of theta coordinates @@ -92,6 +107,8 @@ public: // Properties bool only_fissionable() const { return only_fissionable_; } + Position lower_left() const { return lower_left_; } + Position upper_right() const { return upper_right_; } private: Position lower_left_; //!< Lower-left coordinates of box Position upper_right_; //!< Upper-right coordinates of box @@ -112,6 +129,8 @@ public: //! \param seed Pseudorandom number seed pointer //! \return Sampled position Position sample(uint64_t* seed) const; + + auto r() const { return r_; } private: Position r_; //!< Single position at which sites are generated }; diff --git a/include/openmc/source.h b/include/openmc/source.h index 97b18d2286..aaa0f48e0f 100644 --- a/include/openmc/source.h +++ b/include/openmc/source.h @@ -43,7 +43,14 @@ public: Particle::Bank sample(uint64_t* seed) const; // Properties + Particle::Type particle_type() const { return particle_; } double strength() const { return strength_; } + + // Make observing pointers available + auto space() const { return space_.get(); } + auto angle() const { return angle_.get(); } + auto energy() const { return energy_.get(); } + private: Particle::Type particle_ {Particle::Type::neutron}; //!< Type of particle emitted double strength_ {1.0}; //!< Source strength From 23c20d5ac48bf32304b55c006f4d01f8adac9845 Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Thu, 19 Mar 2020 15:47:29 -0400 Subject: [PATCH 109/205] update xtl --- vendor/xtl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vendor/xtl b/vendor/xtl index 0024346605..c19750fb14 160000 --- a/vendor/xtl +++ b/vendor/xtl @@ -1 +1 @@ -Subproject commit 0024346605bd92bcc4009caad7f4be88687e063a +Subproject commit c19750fb1488369dc41f6069bc2b8446fc093e75 From 36801b0602a2c42a43a78a5dd645163bdc4448c2 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 19 Mar 2020 15:21:05 -0500 Subject: [PATCH 110/205] Adding comments for clarity. --- include/openmc/mesh.h | 11 ++- src/mesh.cpp | 153 ++++++++++++++++++++++++------------------ 2 files changed, 93 insertions(+), 71 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index cfbaf2a156..a5ff41fa61 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -385,11 +385,6 @@ public: //! \return Mesh bin int get_bin(Position r) const; - //! Return a string represntation of the mesh bin - // - //! \param[in] Mesh bin to generate a label for - std::string get_label_for_bin(int bin) const; - int n_bins() const override; int n_surface_bins() const override; @@ -400,6 +395,9 @@ public: // \returns The centroid of the element Position centroid(moab::EntityHandle tet) const; + //! Return a string represntation of the mesh bin + // + //! \param[in] bin Mesh bin to generate a label for std::string bin_label(int bin) const override; //! Add a score to the mesh instance @@ -412,6 +410,7 @@ public: //! Write the mesh with any current tally data void write(std::string base_filename) const; + std::string filename_; // mbi_; //!< MOAB instance + std::unique_ptr mbi_; //!< MOAB instance std::unique_ptr kdtree_; //!< MOAB KDTree instance std::vector baryc_data_; //!< Barycentric data for tetrahedra }; diff --git a/src/mesh.cpp b/src/mesh.cpp index 92548115e7..c9a70546e9 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -15,7 +15,7 @@ #include "xtensor/xmath.hpp" #include "xtensor/xsort.hpp" #include "xtensor/xtensor.hpp" -#include "xtensor/xview.hpp" +#include "xtensor/xview.hpp" #include "openmc/capi.h" #include "openmc/constants.h" @@ -70,8 +70,9 @@ inline bool check_intersection_point(double x1, double x0, double y1, // Mesh implementation //============================================================================== -Mesh::Mesh(pugi::xml_node node) { - // Copy mesh id +Mesh::Mesh(pugi::xml_node node) +{ + // Copy mesh id if (check_for_node(node, "id")) { id_ = std::stoi(get_node_value(node, "id")); @@ -151,7 +152,7 @@ RegularMesh::RegularMesh(pugi::xml_node node) fatal_error("Cannot have a negative on a tally mesh."); } - // Setwidth and upper right coordinate + // Set width and upper right coordinate upper_right_ = xt::eval(lower_left_ + shape_ * width_); } else if (check_for_node(node, "upper_right")) { @@ -812,8 +813,8 @@ void RegularMesh::to_hdf5(hid_t group) const } xt::xtensor - -RegularMesh::count_sites(const Particle::Bank* bank, int64_t length, +RegularMesh::count_sites(const Particle::Bank* bank, + int64_t length, bool* outside) const { // Determine shape of array for counts @@ -1211,9 +1212,7 @@ void RectilinearMesh::get_indices_from_bin(int bin, int* ijk) const int RectilinearMesh::n_bins() const { - int n_bins = 1; - for (auto dim : shape_) n_bins *= dim; - return n_bins; + return xt::prod(shape_)(); } int RectilinearMesh::n_surface_bins() const @@ -1537,7 +1536,11 @@ openmc_mesh_set_params(int32_t index, int n, const double* ll, const double* ur, #ifdef DAGMC -UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { +UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) +{ + // unstructured always assumed to be 3D + n_dimension_ = 3; + // check the mesh type if (check_for_node(node, "type")) { auto temp = get_node_value(node, "type", true, true); @@ -1556,7 +1559,7 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { } // create MOAB instance - mbi_ = std::shared_ptr(new moab::Core()); + mbi_ = std::unique_ptr(new moab::Core()); // create meshset to load mesh into moab::ErrorCode rval = mbi_->create_meshset(moab::MESHSET_SET, meshset_); if (rval != moab::MB_SUCCESS) { @@ -1568,23 +1571,19 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { fatal_error("Failed to load the unstructured mesh file: " + filename_); } - // always 3 for unstructured meshes - n_dimension_ = 3; - - moab::Range all_tets; - rval = mbi_->get_entities_by_dimension(meshset_, n_dimension_, all_tets); + // set member range of tetrahedral entities + rval = mbi_->get_entities_by_dimension(meshset_, n_dimension_, ehs_); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to get all tetrahedral elements"); } - ehs_.clear(); - ehs_ = all_tets; - - if (!all_tets.all_of_type(moab::MBTET)) { - warning("Non-tetrahedral elements found in unstructured mesh: " + filename_); + if (!ehs_.all_of_type(moab::MBTET)) { + warning("Non-tetrahedral elements found in unstructured " + "mesh file: " + filename_); } - // make an entity set for all tetrahedra (used later in output) + // make an entity set for all tetrahedra + // this is used for convenience later in output rval = mbi_->create_meshset(moab::MESHSET_SET, tet_set_); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to create an entity set for the tetrahedral elements"); @@ -1595,17 +1594,18 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { fatal_error("Failed to add tetrahedra to an entity set."); } + // build acceleration data structures compute_barycentric_data(ehs_); build_kdtree(ehs_); } void -UnstructuredMesh::build_kdtree(const moab::Range& all_tets) { - +UnstructuredMesh::build_kdtree(const moab::Range& all_tets) +{ moab::Range all_tris; - int triangle_dim = 2; + int adj_dim = 2; moab::ErrorCode rval = mbi_->get_adjacencies(all_tets, - triangle_dim, + adj_dim, true, all_tris, moab::Interface::UNION); @@ -1614,7 +1614,8 @@ UnstructuredMesh::build_kdtree(const moab::Range& all_tets) { } if (!all_tris.all_of_type(moab::MBTRI)) { - warning("Non-triangle elements found in tet adjacencies in unstructured mesh: " + filename_); + warning("Non-triangle elements found in tet adjacencies in " + "unstructured mesh file: " + filename_); } // combine into one range @@ -1622,13 +1623,14 @@ UnstructuredMesh::build_kdtree(const moab::Range& all_tets) { all_tets_and_tris.merge(all_tets); all_tets_and_tris.merge(all_tris); - // create and build KD-tree + // create a kd-tree instance kdtree_ = std::unique_ptr(new moab::AdaptiveKDTree(mbi_.get())); // build the tree rval = kdtree_->build_tree(all_tets_and_tris, &kdtree_root_); if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to construct KDTree for the unstructured mesh " + filename_); + fatal_error("Failed to construct KDTree for the " + "unstructured mesh file: " + filename_); } } @@ -1640,9 +1642,10 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start, moab::ErrorCode rval; std::vector tris; std::vector intersection_dists; - + // get all intersections with triangles in the tet mesh + // (distances are relative to the start point, not the previous intersection) rval = kdtree_->ray_intersect_triangles(kdtree_root_, - 1E-06, + FP_COINCIDENT, dir.array(), start.array(), tris, @@ -1650,10 +1653,10 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start, 0, track_len); if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to compute intersections on umesh: " + filename_); + fatal_error("Failed to compute intersections on unstructured mesh: " + filename_); } - // sort the tris and intersections by distance + // sort the hit triangles and intersections by distance hits.clear(); for (int i = 0; i < tris.size(); i++) { hits.push_back(std::pair(intersection_dists[i], tris[i])); @@ -1687,7 +1690,9 @@ UnstructuredMesh::bins_crossed(const Particle* p, bins.clear(); lengths.clear(); - //// IMPLEMENTATION ONE + // if there are no intersections the track may lie entirely + // within a single tet. If this is the case, apply entire + // score to that tet and return. if (hits.size() == 0) { moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5); if (last_r_tet) { @@ -1697,26 +1702,30 @@ UnstructuredMesh::bins_crossed(const Particle* p, return; } + // attempt to find the containing tet for the first track segment moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0); double last_dist = 0.0; - // make sure first point is inside a tet + // make sure first segment is inside a tet. If it is not, we may be starting + // outside of the mesh and our first intersection is an entry point into the + // mesh - update hits and find a containing tet accordingly if (!tet) { last_dist = hits.front().first; hits.erase(hits.begin()); tet = get_tet(last_r + u * (last_dist + hits.front().first) / 2.0); } - // if there are no other hits, there is only one segment to tally + // if there are no other hits at this point, apply the score for whatever + // distance lies in this tet and return if (hits.size() == 0 && tet) { bins.push_back(get_bin_from_ent_handle(tet)); - lengths.push_back(1.0); + lengths.push_back((track_len - last_dist) / track_len); return; } // score all remaining segments for (const auto& hit : hits) { - // score in this tet if one was found + // apply score in this tet if one was found if (tet) { bins.push_back(get_bin_from_ent_handle(tet)); lengths.push_back((hit.first - last_dist) / track_len); @@ -1724,43 +1733,53 @@ UnstructuredMesh::bins_crossed(const Particle* p, // if in the loop, we should always find a tet warning("No tet found for location between triangle hits"); } + + // update the last distance last_dist = hit.first; - // find next tet + // find next tet using the mesh adjacencies moab::Range adj_tets; rval = mbi_->get_adjacencies(&hit.second, 1, 3, false, adj_tets); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to get triangle adjacencies from mesh " + filename_); } + // if the triangle crossed is adjacent to two triangles + // update to the tet we're not currently scoring in if (adj_tets.size() == 2) { tet = tet == adj_tets[0] ? adj_tets[1] : adj_tets[0]; } else if (adj_tets.size() == 1) { tet = adj_tets[0]; } - } + } // end hit loop // tally remaining portion of track after last hit if - // the last segment of the track is in the mesh + // the last segment of the track is in the mesh but doesn't + // reach the other side of the tet if (hits.back().first < track_len) { + // use position at the midpoint of the current intersection + // and the end of the track to check for a containing tet auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0); tet = get_tet(pos); + // apply the remainder of the score if a tet is found, + // otherwise we'll assume we've left the mesh if (tet) { bins.push_back(get_bin_from_ent_handle(tet)); lengths.push_back((track_len - hits.back().first) / track_len); } } - - return; }; moab::EntityHandle -UnstructuredMesh::get_tet(const Position& r) const { +UnstructuredMesh::get_tet(const Position& r) const +{ moab::CartVect pos(r.x, r.y, r.z); + // find the leaf of the kd-tree for this position moab::AdaptiveKDTreeIter kdtree_iter; moab::ErrorCode rval = kdtree_->point_search(pos.array(), kdtree_iter); if (rval != moab::MB_SUCCESS) { return 0; } + // retrieve the tet elements of this leaf moab::EntityHandle leaf = kdtree_iter.handle(); moab::Range tets; rval = mbi_->get_entities_by_dimension(leaf, 3, tets, false); @@ -1768,11 +1787,14 @@ UnstructuredMesh::get_tet(const Position& r) const { warning("MOAB error finding tets."); } + // loop over the tets in this leaf, returning the containing tet if found for (const auto& tet : tets) { if (point_in_tet(pos, tet)) { return tet; } } + + // if no tet is found, return an invalid handle return 0; } @@ -1792,20 +1814,11 @@ double UnstructuredMesh::tet_volume(moab::EntityHandle tet) const { return 1.0 / 6.0 * (((p[1] - p[0]) * (p[2] - p[0])) % (p[3] - p[0])); } -//! Determine which surface bins were crossed by a particle -// -//! \param[in] p Particle to check -//! \param[out] bins Surface bins that were crossed void UnstructuredMesh::surface_bins_crossed(const Particle* p, std::vector& bins) const { + // TODO: Implement triangle crossings here return; } -std::string UnstructuredMesh::get_label_for_bin(int bin) const { - std::stringstream out; - out << "MOAB EntityHandle: " << get_ent_handle_from_bin(bin); - return out.str(); -} - int UnstructuredMesh::get_bin(Position r) const { moab::EntityHandle tet = get_tet(r); @@ -1823,6 +1836,8 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& tets) { baryc_data_.clear(); baryc_data_.resize(tets.size()); + // compute the barycentric data for each tet element + // and store it as a 3x3 matrix for (auto& tet : tets) { std::vector verts; rval = mbi_->get_connectivity(&tet, 1, verts); @@ -1837,6 +1852,8 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& tets) { } moab::Matrix3 a(p[1] - p[0], p[2] - p[0], p[3] - p[0], true); + + // invert now to avoid this cost later a = a.transpose().inverse(); baryc_data_.at(get_bin_from_ent_handle(tet)) = a; } @@ -1850,7 +1867,7 @@ UnstructuredMesh::to_hdf5(hid_t group) const write_dataset(mesh_group, "type", "unstructured"); write_dataset(mesh_group, "filename", filename_); - // write volume of each tet + // write volume and centroid of each tet std::vector tet_vols; xt::xtensor centroids({ehs_.size(), 3}); for (int i = 0; i < ehs_.size(); i++) { @@ -1879,11 +1896,14 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) return false; } + // first vertex is used as a reference point for the barycentric data - + // retrieve its coordinates moab::EntityHandle v_zero = verts[0]; moab::CartVect p_zero; rval = mbi_->get_coords(&v_zero, 1, p_zero.array()); if (rval != moab::MB_SUCCESS) { - warning("Failed to get coordinates of a vertex in umesh: " + filename_); + warning("Failed to get coordinates of a vertex in " + "unstructured mesh: " + filename_); return false; } @@ -1893,12 +1913,10 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) moab::CartVect bary_coords = a_inv * (r - p_zero); - bool in_tet = (bary_coords[0] >= 0.0 && - bary_coords[1] >= 0.0 && - bary_coords[2] >= 0.0 && - bary_coords[0] + bary_coords[1] + bary_coords[2] <= 1.0); - - return in_tet; + return (bary_coords[0] >= 0.0 && + bary_coords[1] >= 0.0 && + bary_coords[2] >= 0.0 && + bary_coords[0] + bary_coords[1] + bary_coords[2] <= 1.0); } int @@ -1924,7 +1942,10 @@ int UnstructuredMesh::get_index_from_bin(int bin) const { } std::pair, std::vector> -UnstructuredMesh::plot(Position plot_ll, Position plot_ur) const { return {}; } +UnstructuredMesh::plot(Position plot_ll, Position plot_ur) const { + // TODO: Implement mesh lines + return {}; +} int UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { @@ -1983,7 +2004,7 @@ UnstructuredMesh::centroid(moab::EntityHandle tet) const { return {}; } - // compute the centroid of the elements + // compute the centroid of the element vertices moab::CartVect centroid(0.0); for(const auto& coord : coords) { centroid += coord; @@ -2008,6 +2029,7 @@ UnstructuredMesh::get_score_tags(std::string score) const { // with an uncertainty moab::Tag value_tag; + // create the value tag if not present and get handle double default_val = 0.0; auto val_string = score + "_value"; rval = mbi_->tag_get_handle(val_string.c_str(), @@ -2023,6 +2045,7 @@ UnstructuredMesh::get_score_tags(std::string score) const { fatal_error(msg); } + // create the std dev tag if not present and get handle moab::Tag error_tag; std::string err_string = score + "_std_dev"; rval = mbi_->tag_get_handle(err_string.c_str(), @@ -2038,6 +2061,7 @@ UnstructuredMesh::get_score_tags(std::string score) const { fatal_error(msg); } + // return the populated tag handles return {value_tag, error_tag}; } @@ -2090,7 +2114,6 @@ UnstructuredMesh::write(std::string base_filename) const { // write the tetrahedral elements of the mesh only // to avoid clutter from zero-value data on other // elements during visualization - moab::ErrorCode rval; rval = mbi_->write_mesh(filename.c_str(), &tet_set_, 1); if (rval != moab::MB_SUCCESS) { From c109a205e483a1c8b2c3cb52a8e108dc843fd4ec Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 20 Mar 2020 06:51:50 -0500 Subject: [PATCH 111/205] Fix output directory for xsdir in make_ace_thermal --- openmc/data/njoy.py | 32 ++++++++++++++++++++------------ 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/openmc/data/njoy.py b/openmc/data/njoy.py index c9bbd9c85b..0ef77695b5 100644 --- a/openmc/data/njoy.py +++ b/openmc/data/njoy.py @@ -402,7 +402,6 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, # acer if acer: nacer_in = nlast - fname = '{}_{:.1f}' for i, temperature in enumerate(temperatures): # Extend input with an ACER run for each temperature nace = nacer_in + 1 + 2*i @@ -411,8 +410,8 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, commands += _TEMPLATE_ACER.format(**locals()) # Indicate tapes to save for each ACER run - tapeout[nace] = output_dir / fname.format("ace", temperature) - tapeout[ndir] = output_dir / fname.format("xsdir", temperature) + tapeout[nace] = output_dir / "ace_{:.1f}".format(temperature) + tapeout[ndir] = output_dir / "xsdir_{:.1f}".format(temperature) commands += 'stop\n' run(commands, tapein, tapeout, **kwargs) @@ -422,7 +421,7 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, with ace.open('w') as ace_file, xsdir.open('w') as xsdir_file: for temperature in temperatures: # Get contents of ACE file - text = (output_dir / fname.format("ace", temperature)).read_text() + text = (output_dir / "ace_{:.1f}".format(temperature)).read_text() # If the target is metastable, make sure that ZAID in the ACE # file reflects this by adding 400 @@ -435,9 +434,14 @@ def make_ace(filename, temperatures=None, acer=True, xsdir=None, ace_file.write(text) # Concatenate into destination xsdir file - xsdir_in = output_dir / fname.format("xsdir", temperature) + xsdir_in = output_dir / "xsdir_{:.1f}".format(temperature) xsdir_file.write(xsdir_in.read_text()) + # Remove ACE/xsdir files for each temperature + for temperature in temperatures: + (output_dir / "ace_{:.1f}".format(temperature)).unlink() + (output_dir / "xsdir_{:.1f}".format(temperature)).unlink() + def make_ace_thermal(filename, filename_thermal, temperatures=None, ace='ace', xsdir=None, output_dir=None, error=0.001, @@ -571,7 +575,6 @@ def make_ace_thermal(filename, filename_thermal, temperatures=None, # acer nthermal_acer_in = nlast - fname = '{}_{:.1f}' for i, temperature in enumerate(temperatures): # Extend input with an ACER run for each temperature nace = nthermal_acer_in + 1 + 2*i @@ -580,18 +583,23 @@ def make_ace_thermal(filename, filename_thermal, temperatures=None, commands += _THERMAL_TEMPLATE_ACER.format(**locals()) # Indicate tapes to save for each ACER run - tapeout[nace] = output_dir / fname.format(ace, temperature) - tapeout[ndir] = output_dir / fname.format(xsdir, temperature) + tapeout[nace] = output_dir / "ace_{:.1f}".format(temperature) + tapeout[ndir] = output_dir / "xsdir_{:.1f}".format(temperature) commands += 'stop\n' run(commands, tapein, tapeout, **kwargs) - ace_out = output_dir / ace + ace = output_dir / ace xsdir = (ace.parent / "xsdir") if xsdir is None else Path(xsdir) - with ace_out.open('w') as ace_file, xsdir.open('w') as xsdir_file: + with ace.open('w') as ace_file, xsdir.open('w') as xsdir_file: # Concatenate ACE and xsdir files together for temperature in temperatures: - ace_in = output_dir / fname.format(ace, temperature) + ace_in = output_dir / "ace_{:.1f}".format(temperature) ace_file.write(ace_in.read_text()) - xsdir_in = output_dir / fname.format(xsdir, temperature) + xsdir_in = output_dir / "xsdir_{:.1f}".format(temperature) xsdir_file.write(xsdir_in.read_text()) + + # Remove ACE/xsdir files for each temperature + for temperature in temperatures: + (output_dir / "ace_{:.1f}".format(temperature)).unlink() + (output_dir / "xsdir_{:.1f}".format(temperature)).unlink() From 714a001d807e83347a77e02cd71dfe0e8d8287c5 Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Fri, 20 Mar 2020 12:04:13 -0400 Subject: [PATCH 112/205] Update include/openmc/distribution_spatial.h Co-Authored-By: Paul Romano --- include/openmc/distribution_spatial.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/openmc/distribution_spatial.h b/include/openmc/distribution_spatial.h index 8d5017a81e..86e9c6e619 100644 --- a/include/openmc/distribution_spatial.h +++ b/include/openmc/distribution_spatial.h @@ -130,7 +130,7 @@ public: //! \return Sampled position Position sample(uint64_t* seed) const; - auto r() const { return r_; } + Position r() const { return r_; } private: Position r_; //!< Single position at which sites are generated }; From 0c8e1852dd89395abb5728b72879465e7c73f7e9 Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Fri, 20 Mar 2020 12:04:22 -0400 Subject: [PATCH 113/205] Update include/openmc/distribution_multi.h Co-Authored-By: Paul Romano --- include/openmc/distribution_multi.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/include/openmc/distribution_multi.h b/include/openmc/distribution_multi.h index 1b1a262b82..ae7768ac31 100644 --- a/include/openmc/distribution_multi.h +++ b/include/openmc/distribution_multi.h @@ -45,8 +45,8 @@ public: Direction sample(uint64_t* seed) const; // Observing pointers - auto mu() const { return mu_.get(); } - auto phi() const { return phi_.get(); } + Distribution* mu() const { return mu_.get(); } + Distribution* phi() const { return phi_.get(); } private: UPtrDist mu_; //!< Distribution of polar angle From e8247b2476d776cf7b09fae31c7446fd964c28e4 Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Fri, 20 Mar 2020 12:04:29 -0400 Subject: [PATCH 114/205] Update include/openmc/distribution_spatial.h Co-Authored-By: Paul Romano --- include/openmc/distribution_spatial.h | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/include/openmc/distribution_spatial.h b/include/openmc/distribution_spatial.h index 86e9c6e619..83c0e7fd69 100644 --- a/include/openmc/distribution_spatial.h +++ b/include/openmc/distribution_spatial.h @@ -56,10 +56,10 @@ public: //! \return Sampled position Position sample(uint64_t* seed) const; - auto r() const { return r_.get(); } - auto phi() const { return phi_.get(); } - auto z() const { return z_.get(); } - auto origin() const { return origin_; } + Distribution* r() const { return r_.get(); } + Distribution* phi() const { return phi_.get(); } + Distribution* z() const { return z_.get(); } + Position origin() const { return origin_; } private: UPtrDist r_; //!< Distribution of r coordinates UPtrDist phi_; //!< Distribution of phi coordinates From aa47f443ddd50221e1fe1bac626957cf79055fb7 Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Fri, 20 Mar 2020 12:04:35 -0400 Subject: [PATCH 115/205] Update include/openmc/source.h Co-Authored-By: Paul Romano --- include/openmc/source.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/include/openmc/source.h b/include/openmc/source.h index aaa0f48e0f..78e4043b95 100644 --- a/include/openmc/source.h +++ b/include/openmc/source.h @@ -47,9 +47,9 @@ public: double strength() const { return strength_; } // Make observing pointers available - auto space() const { return space_.get(); } - auto angle() const { return angle_.get(); } - auto energy() const { return energy_.get(); } + SpatialDistribution* space() const { return space_.get(); } + UnitSphereDistribution* angle() const { return angle_.get(); } + Distribution* energy() const { return energy_.get(); } private: Particle::Type particle_ {Particle::Type::neutron}; //!< Type of particle emitted From 5e614afe0f7c2a197e0e19e3090c38cd9b48a0de Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Fri, 20 Mar 2020 12:04:42 -0400 Subject: [PATCH 116/205] Update include/openmc/distribution_spatial.h Co-Authored-By: Paul Romano --- include/openmc/distribution_spatial.h | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/include/openmc/distribution_spatial.h b/include/openmc/distribution_spatial.h index 83c0e7fd69..d66923c4e0 100644 --- a/include/openmc/distribution_spatial.h +++ b/include/openmc/distribution_spatial.h @@ -81,10 +81,10 @@ public: //! \return Sampled position Position sample(uint64_t* seed) const; - auto r() const { return r_.get(); } - auto theta() const { return theta_.get(); } - auto phi() const { return phi_.get(); } - auto origin () const { return origin_; } + Distribution* r() const { return r_.get(); } + Distribution* theta() const { return theta_.get(); } + Distribution* phi() const { return phi_.get(); } + Position origin () const { return origin_; } private: UPtrDist r_; //!< Distribution of r coordinates UPtrDist theta_; //!< Distribution of theta coordinates From cfeb14252d50911ddb2be2c618b593a9f941fd81 Mon Sep 17 00:00:00 2001 From: Gavin Ridley Date: Fri, 20 Mar 2020 12:04:51 -0400 Subject: [PATCH 117/205] Update include/openmc/distribution_spatial.h Co-Authored-By: Paul Romano --- include/openmc/distribution_spatial.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/include/openmc/distribution_spatial.h b/include/openmc/distribution_spatial.h index d66923c4e0..ee844aa134 100644 --- a/include/openmc/distribution_spatial.h +++ b/include/openmc/distribution_spatial.h @@ -34,9 +34,9 @@ public: Position sample(uint64_t* seed) const; // Observer pointers - auto x() const { return x_.get(); } - auto y() const { return x_.get(); } - auto z() const { return x_.get(); } + Distribution* x() const { return x_.get(); } + Distribution* y() const { return x_.get(); } + Distribution* z() const { return x_.get(); } private: UPtrDist x_; //!< Distribution of x coordinates UPtrDist y_; //!< Distribution of y coordinates From d1878a205e8020bc72d06c0f5f64503fdd808929 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Fri, 20 Mar 2020 18:07:17 -0400 Subject: [PATCH 118/205] Replace "Nothing" depletion targets with None This change is more declarative and mitigates any changes that there are errors if a target is Nothing, None, or their lower cased varieties. This is helpful as the depletion chain gets reduced down and we want to keep total removal rates for isotopes that are present, but their products don't exist in the new reduced chain. Chain.form_matrix has been updated to handle targets that are None for non-fission reactions and for decay reactions. Documentation for ReactionTuple and DecayTuple have been updated to indicate that the target attribute could be None. --- openmc/deplete/chain.py | 23 +++++++++++------------ openmc/deplete/nuclide.py | 22 ++++++++++++++++------ 2 files changed, 27 insertions(+), 18 deletions(-) diff --git a/openmc/deplete/chain.py b/openmc/deplete/chain.py index 814de12fc4..8c31914acb 100644 --- a/openmc/deplete/chain.py +++ b/openmc/deplete/chain.py @@ -500,7 +500,7 @@ class Chain(object): # Gain for _, target, branching_ratio in nuc.decay_modes: # Allow for total annihilation for debug purposes - if target != 'Nothing': + if target is not None: branch_val = branching_ratio * decay_constant if branch_val != 0.0: @@ -525,17 +525,16 @@ class Chain(object): matrix[i, i] -= path_rate # Gain term; allow for total annihilation for debug purposes - if target != 'Nothing': - if r_type != 'fission': - if path_rate != 0.0: - k = self.nuclide_dict[target] - matrix[k, i] += path_rate * br - else: - for product, y in fission_yields[nuc.name].items(): - yield_val = y * path_rate - if yield_val != 0.0: - k = self.nuclide_dict[product] - matrix[k, i] += yield_val + if r_type != 'fission': + if target is not None and path_rate != 0.0: + k = self.nuclide_dict[target] + matrix[k, i] += path_rate * br + else: + for product, y in fission_yields[nuc.name].items(): + yield_val = y * path_rate + if yield_val != 0.0: + k = self.nuclide_dict[product] + matrix[k, i] += yield_val # Clear set of reactions reactions.clear() diff --git a/openmc/deplete/nuclide.py b/openmc/deplete/nuclide.py index 0aae5adb72..7235745aa2 100644 --- a/openmc/deplete/nuclide.py +++ b/openmc/deplete/nuclide.py @@ -30,8 +30,10 @@ Parameters ---------- type : str Type of the decay mode, e.g., 'beta-' -target : str - Nuclide resulting from decay +target : str or None + Nuclide resulting from decay. A value of ``None`` implies the + target does not exist in the currently configured depletion + chain branching_ratio : float Branching ratio of the decay mode @@ -53,8 +55,11 @@ Parameters ---------- type : str Type of the reaction, e.g., 'fission' -target : str - nuclide resulting from reaction +target : str or None + Nuclide resulting from reaction. A value of ``None`` + implies either no single target, e.g. from fission, + or that the target nuclide is not considered + in the current depletion chain Q : float Q value of the reaction in [eV] branching_ratio : float @@ -179,6 +184,8 @@ class Nuclide(object): for decay_elem in element.iter('decay'): d_type = decay_elem.get('type') target = decay_elem.get('target') + if target is not None and target.lower() == "nothing": + target = None branching_ratio = float(decay_elem.get('branching_ratio')) nuc.decay_modes.append(DecayTuple(d_type, target, branching_ratio)) @@ -192,6 +199,8 @@ class Nuclide(object): # just set null values if r_type != 'fission': target = reaction_elem.get('target') + if target is not None and target.lower() == "nothing": + target = None else: target = None if fission_q is not None: @@ -226,7 +235,7 @@ class Nuclide(object): for mode, daughter, br in self.decay_modes: mode_elem = ET.SubElement(elem, 'decay') mode_elem.set('type', mode) - mode_elem.set('target', daughter) + mode_elem.set('target', daughter or "Nothing") mode_elem.set('branching_ratio', str(br)) elem.set('reactions', str(len(self.reactions))) @@ -234,7 +243,7 @@ class Nuclide(object): rx_elem = ET.SubElement(elem, 'reaction') rx_elem.set('type', rx) rx_elem.set('Q', str(Q)) - if rx != 'fission': + if rx != 'fission' or daughter is not None: rx_elem.set('target', daughter) if br != 1.0: rx_elem.set('branching_ratio', str(br)) @@ -461,6 +470,7 @@ class FissionYieldDistribution(Mapping): data_elem.text = " ".join(map(str, yield_obj.yields)) + class FissionYield(Mapping): """Mapping for fission yields of a parent at a specific energy From 58b644cd3b7dfc2872895b73f7795a9e9d96561b Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 18 Mar 2020 20:08:03 -0400 Subject: [PATCH 119/205] Minor fission yield touch ups --- openmc/deplete/nuclide.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/openmc/deplete/nuclide.py b/openmc/deplete/nuclide.py index 7235745aa2..960ba6eff5 100644 --- a/openmc/deplete/nuclide.py +++ b/openmc/deplete/nuclide.py @@ -406,9 +406,7 @@ class FissionYieldDistribution(Mapping): for g_index, energy in enumerate(energies): prod_map = fission_yields[energy] for prod_ix, product in enumerate(ordered_prod): - yield_val = prod_map.get(product) - yield_matrix[g_index, prod_ix] = ( - 0.0 if yield_val is None else yield_val) + yield_matrix[g_index, prod_ix] = prod_map.get(product, 0.0) self.energies = tuple(energies) self.products = tuple(ordered_prod) self.yield_matrix = yield_matrix @@ -444,7 +442,7 @@ class FissionYieldDistribution(Mapping): FissionYieldDistribution """ all_yields = {} - for elem_index, yield_elem in enumerate(element.iter("fission_yields")): + for yield_elem in element.iter("fission_yields"): energy = float(yield_elem.get("energy")) products = yield_elem.find("products").text.split() yields = map(float, yield_elem.find("data").text.split()) From 2c2d9d7f312e8b7dfcea10de2ca9aee8e8361f3c Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 18 Mar 2020 20:09:19 -0400 Subject: [PATCH 120/205] Provide FissionYieldDistribution.restrict_products --- openmc/deplete/nuclide.py | 32 +++++++++++++++++++++++- tests/unit_tests/test_deplete_nuclide.py | 15 +++++++++++ 2 files changed, 46 insertions(+), 1 deletion(-) diff --git a/openmc/deplete/nuclide.py b/openmc/deplete/nuclide.py index 960ba6eff5..7ba72b6d56 100644 --- a/openmc/deplete/nuclide.py +++ b/openmc/deplete/nuclide.py @@ -13,7 +13,7 @@ try: except ImportError: import xml.etree.ElementTree as ET -from numpy import empty +from numpy import empty, searchsorted from openmc.checkvalue import check_type @@ -467,6 +467,36 @@ class FissionYieldDistribution(Mapping): data_elem = ET.SubElement(yield_element, "data") data_elem.text = " ".join(map(str, yield_obj.yields)) + def restrict_products(self, possible_products): + """Return a new distribution with select products + + Parameters + ---------- + possible_products : iterable of str + Candidate pool of fission products. Existing products + not contained here will not exist in the new instance + + Returns + ------- + FissionYieldDistribution or None + A value of None indicates no values in + ``possible_products`` exist in :attr:`products` + + """ + + overlap = set(self.products).intersection(possible_products) + if not overlap: + return None + + products = sorted(overlap) + indices = searchsorted(self.products, products) + + # coerce back to dictionary to pass back to __init__ + new_yields = {} + for ene, yields in zip(self.energies, self.yield_matrix.copy()): + new_yields[ene] = dict(zip(products, yields[indices])) + + return type(self)(new_yields) class FissionYield(Mapping): diff --git a/tests/unit_tests/test_deplete_nuclide.py b/tests/unit_tests/test_deplete_nuclide.py index b8a258df00..92b004845a 100644 --- a/tests/unit_tests/test_deplete_nuclide.py +++ b/tests/unit_tests/test_deplete_nuclide.py @@ -176,6 +176,21 @@ def test_fission_yield_distribution(): with pytest.raises(TypeError): orig_yields *= similar + # Test restriction of fission products + strict_restrict = yield_dist.restrict_products(["Xe135", "Sm149"]) + with_extras = yield_dist.restrict_products( + ["Xe135", "Sm149", "H1", "U235"]) + + assert strict_restrict.products == ("Sm149", "Xe135", ) + assert strict_restrict.energies == yield_dist.energies + assert with_extras.products == ("Sm149", "Xe135", ) + assert with_extras.energies == yield_dist.energies + for ene, new_yields in strict_restrict.items(): + for product in strict_restrict.products: + assert new_yields[product] == yield_dist[ene][product] + assert with_extras[ene][product] == yield_dist[ene][product] + + assert yield_dist.restrict_products(["U235"]) is None def test_validate(): From 789926d107ce447886034a4e6f01bed0587a8091 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 18 Mar 2020 20:10:49 -0400 Subject: [PATCH 121/205] Provide Chain.reduce for following paths --- openmc/deplete/chain.py | 142 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 141 insertions(+), 1 deletion(-) diff --git a/openmc/deplete/chain.py b/openmc/deplete/chain.py index 8c31914acb..d820fe520b 100644 --- a/openmc/deplete/chain.py +++ b/openmc/deplete/chain.py @@ -10,7 +10,7 @@ import math import re from collections import OrderedDict, defaultdict from collections.abc import Mapping, Iterable -from numbers import Real +from numbers import Real, Integral from warnings import warn from openmc.checkvalue import check_type, check_greater_than @@ -820,3 +820,143 @@ class Chain(object): return stat valid = valid and stat return valid + + def reduce(self, initial_isotopes, level=None): + """Reduce the size of the chain by follwing transmutation paths + + Parameters + ---------- + initial_isotopes : iterable of str + Start the search based on the contents of these isotopes + level : int, optional + Depth of transmuation path to follow. Must be greater than + or equal to zero. A value of zero returns a chain with + ``initial_isotopes``. The default value of None implies + that all isotopes that appear in the transmutation paths + of the initial isotopes and their progeny should be + explored + + Returns + ------- + Chain + + """ + check_type("initial_isotopes", initial_isotopes, Iterable, str) + if level is None: + level = math.inf + else: + check_type("level", level, Integral) + check_greater_than("level", level, 0, equality=True) + + all_isotopes = self._follow(set(initial_isotopes), level) + + # Avoid re-sorting for fission yields + name_sort = sorted(all_isotopes) + + nuclides = [] + nuclide_dict = {} + reactions = set() + + for idx, iso in enumerate(sorted(all_isotopes, key=openmc.data.zam)): + previous = self[iso] + new_nuclide = Nuclide(previous.name) + new_nuclide.half_life = previous.half_life + new_nuclide.decay_energy = new_nuclide.decay_energy + + new_decay = [] + for mode in previous.decay_modes: + if mode.target in all_isotopes: + new_decay.append(mode) + else: + new_decay.append(DecayTuple( + mode.type, None, mode.branching_ratio)) + new_nuclide.decay_modes = new_decay + + new_reactions = [] + for rxn in previous.reactions: + if rxn.target in all_isotopes: + new_reactions.append(rxn) + reactions.add(rxn.type) + elif rxn.type == "fission": + new_yields = new_nuclide.yield_data = ( + previous.yield_data.restrict_products(name_sort)) + if new_yields is not None: + new_reactions.append(rxn) + reactions.add("fission") + # Maintain total destruction rates but set no target + else: + new_reactions.append(ReactionTuple( + rxn.type, None, rxn.Q, rxn.branching_ratio)) + reactions.add(rxn.type) + + new_nuclide.reactions = new_reactions + + nuclides.append(new_nuclide) + nuclide_dict[iso] = idx + + new_chain = type(self)() + new_chain.nuclides = nuclides + new_chain.nuclide_dict = nuclide_dict + + # Doesn't appear that the ordering matters for the reactions, + # just the contents + new_chain.reactions = sorted(reactions) + + return new_chain + + def _follow(self, isotopes, level): + """Return all isotopes present up to depth level""" + found = set(isotopes) + remaining = set(self.nuclide_dict) + if not found.issubset(remaining): + raise IndexError( + "The following isotopes were not found in the chain: " + "{}".format(", ".join(found - remaining))) + + if level == 0: + return found + + remaining.difference_update(found) + + depth = 0 + next_iso = set() + + while depth < level and remaining: + # Exhaust all isotopes at this level + while isotopes: + iso = isotopes.pop() + found.add(iso) + nuclide = self[iso] + + # Follow all transmutation paths for this nuclide + for rxn in nuclide.reactions + nuclide.decay_modes: + if rxn.type == "fission" or rxn.target is None: + continue + # Skip if we've already come across this isotope + elif (rxn.target in next_iso + or rxn.target in found or rxn.target in isotopes): + continue + next_iso.add(rxn.target) + + if nuclide.yield_data is not None: + for product in nuclide.yield_data.products: + if (product in next_iso + or product in found or product in isotopes): + continue + next_iso.add(product) + + if not next_iso: + # No additional isotopes to process, nor to update the + # current set of discovered isotopes + return found + + # Prepare for next dig + depth += 1 + isotopes.update(next_iso) + remaining.difference_update(next_iso) + next_iso.clear() + + # Process isotope that would have started next depth + found.update(isotopes) + + return found From b01a3e011be15f096f265fec7c19620861d08985 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 18 Mar 2020 20:11:47 -0400 Subject: [PATCH 122/205] Add unit tests for Chain.reduce --- tests/unit_tests/test_deplete_chain.py | 96 +++++++++++++++++++++++++- 1 file changed, 94 insertions(+), 2 deletions(-) diff --git a/tests/unit_tests/test_deplete_chain.py b/tests/unit_tests/test_deplete_chain.py index 08c60d3ed1..b7a1e6c0f2 100644 --- a/tests/unit_tests/test_deplete_chain.py +++ b/tests/unit_tests/test_deplete_chain.py @@ -6,7 +6,6 @@ from pathlib import Path from itertools import product import numpy as np -from openmc.data import zam, ATOMIC_SYMBOL from openmc.deplete import comm, Chain, reaction_rates, nuclide, cram import pytest @@ -405,7 +404,8 @@ def test_fission_yield_attribute(simple_chain): dummy_conc = [[1, 2]] * (len(empty_chain.fission_yields) + 1) with pytest.raises( ValueError, match="fission yield.*not equal.*compositions"): - cram.deplete(empty_chain, dummy_conc, None, 0.5) + cram.deplete(empty_chain, dummy_conc, None, 0.5) + def test_validate(simple_chain): """Test the validate method""" @@ -457,3 +457,95 @@ def test_validate_inputs(): with pytest.raises(ValueError, match="tolerance"): c.validate(tolerance=-1) + + +@pytest.fixture +def gnd_simple_chain(): + chainfile = Path(__file__).parents[1] / "chain_simple.xml" + return Chain.from_xml(chainfile) + + +def test_reduce(gnd_simple_chain): + ref_U5 = gnd_simple_chain["U235"] + ref_iodine = gnd_simple_chain["I135"] + ref_U5_yields = ref_U5.yield_data + + no_depth = gnd_simple_chain.reduce(["U235", "I135"], 0) + # We should get a chain just containing U235 and I135 + assert len(no_depth) == 2 + assert set(no_depth.reactions) == set(gnd_simple_chain.reactions) + + u5_round0 = no_depth["U235"] + assert u5_round0.n_decay_modes == ref_U5.n_decay_modes + for newmode, refmode in zip(u5_round0.decay_modes, ref_U5.decay_modes): + assert newmode.target is None + assert newmode.type == refmode.type, newmode + assert newmode.branching_ratio == refmode.branching_ratio, newmode + + assert u5_round0.n_reaction_paths == ref_U5.n_reaction_paths + for newrxn, refrxn in zip(u5_round0.reactions, ref_U5.reactions): + assert newrxn.target is None, newrxn + assert newrxn.type == refrxn.type, newrxn + assert newrxn.Q == refrxn.Q, newrxn + assert newrxn.branching_ratio == refrxn.branching_ratio, newrxn + + assert u5_round0.yield_data is not None + assert u5_round0.yield_data.products == ("I135", ) + assert u5_round0.yield_data.yield_matrix == ( + ref_U5_yields.yield_matrix[:, ref_U5_yields.products.index("I135")] + ) + + bareI5 = no_depth["I135"] + assert bareI5.n_decay_modes == ref_iodine.n_decay_modes + for newmode, refmode in zip(bareI5.decay_modes, ref_iodine.decay_modes): + assert newmode.target is None + assert newmode.type == refmode.type, newmode + assert newmode.branching_ratio == refmode.branching_ratio, newmode + + assert bareI5.n_reaction_paths == ref_iodine.n_reaction_paths + for newrxn, refrxn in zip(bareI5.reactions, ref_iodine.reactions): + assert newrxn.target is None, newrxn + assert newrxn.type == refrxn.type, newrxn + assert newrxn.Q == refrxn.Q, newrxn + assert newrxn.branching_ratio == refrxn.branching_ratio, newrxn + + follow_u5 = gnd_simple_chain.reduce(["U235"], 1) + u5_round1 = follow_u5["U235"] + assert u5_round1.decay_modes == ref_U5.decay_modes + assert u5_round1.reactions == ref_U5.reactions + assert u5_round1.yield_data is not None + assert ( + u5_round1.yield_data.yield_matrix == ref_U5_yields.yield_matrix + ).all() + + # Per the chain_simple.xml + # I135 -> Xe135 -> Cs135 + # I135 -> Xe136 + # No limit on depth + iodine_chain = gnd_simple_chain.reduce(["I135"]) + truncated_iodine = gnd_simple_chain.reduce(["I135"], 1) + assert len(iodine_chain) == 4 + assert len(truncated_iodine) == 3 + assert set(iodine_chain.nuclide_dict) == { + "I135", "Xe135", "Xe136", "Cs135"} + assert set(truncated_iodine.nuclide_dict) == {"I135", "Xe135", "Xe136"} + assert iodine_chain.reactions == ["(n,gamma)"] + assert iodine_chain["I135"].decay_modes == ref_iodine.decay_modes + assert iodine_chain["I135"].reactions == ref_iodine.reactions + for mode in truncated_iodine["Xe135"].decay_modes: + assert mode.target is None + + # Test that no FissionYieldDistribution is made if there are no + # fission products + u5_noyields = gnd_simple_chain.reduce(["U235"], 0)["U235"] + assert u5_noyields.yield_data is None + + # Check early termination if the eventual full chain + # is specified by using the iodine isotopes + new_iodine = gnd_simple_chain.reduce(set(iodine_chain.nuclide_dict)) + assert set(iodine_chain.nuclide_dict) == set(new_iodine.nuclide_dict) + + # Failure if some requested isotopes not in chain + + with pytest.raises(IndexError, match=".*not found.*Xx999"): + gnd_simple_chain.reduce(["U235", "Xx999"]) From d67d0cd0f9deb79d5627fa79bc985854d89f5d03 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Fri, 20 Mar 2020 18:11:54 -0400 Subject: [PATCH 123/205] Allow Operator to reduce depletion chain from geometry A new parameter, reduce_chain, can be used to instruct the operator to create a smaller depletion chain. The geometry is used to find all nuclides in burnable materials. The argument can be a boolean, or a non-negative integer. A value of True indicates no depth for following the paths, while an integer can be used to directly specify the depth. --- openmc/deplete/operator.py | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/openmc/deplete/operator.py b/openmc/deplete/operator.py index 8eb033783f..2572150012 100644 --- a/openmc/deplete/operator.py +++ b/openmc/deplete/operator.py @@ -113,6 +113,12 @@ class Operator(TransportOperator): ``fission_yield_mode``. Will be passed directly on to the helper. Passing a value of None will use the defaults for the associated helper. + reduce_chain : bool or int, optional + If ``True`` or an integer, create a reduced depletion chain by + following transmuation paths for isotopes in burnable materials. + A value of ``True`` implies to follow all paths to completion, + while a non-negative integer indicates the depth. See + :meth:`openmc.deplete.Chain.reduce` Attributes ---------- @@ -158,7 +164,8 @@ class Operator(TransportOperator): def __init__(self, geometry, settings, chain_file=None, prev_results=None, diff_burnable_mats=False, energy_mode="fission-q", fission_q=None, dilute_initial=1.0e3, - fission_yield_mode="constant", fission_yield_opts=None): + fission_yield_mode="constant", fission_yield_opts=None, + reduce_chain=False): if fission_yield_mode not in self._fission_helpers: raise KeyError( "fission_yield_mode must be one of {}, not {}".format( @@ -179,6 +186,17 @@ class Operator(TransportOperator): self.geometry = geometry self.diff_burnable_mats = diff_burnable_mats + # Reduce the chain before we create more materials + if reduce_chain is not False: + all_isotopes = set() + for material in geometry.get_all_materials().values(): + if not material.depletable: + continue + for name, _dens_percent, _dens_type in material.nuclides: + all_isotopes.add(name) + level = None if reduce_chain is True else reduce_chain + self.chain = self.chain.reduce(all_isotopes, level) + # Differentiate burnable materials with multiple instances if self.diff_burnable_mats: self._differentiate_burnable_mats() From eab604263772de069db9b44548a8d54e8fd1d332 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Sat, 21 Mar 2020 10:07:40 -0400 Subject: [PATCH 124/205] Update TransportOperator.get_results_info docstring The volume entry returned by the canonical Operator is a dictionary of string material ids to volumes. This form is expected by the Result objects in transfer_volumes and when writing attributes to the hdf5 file. --- openmc/deplete/abc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/deplete/abc.py b/openmc/deplete/abc.py index 31479f9952..ddc48bc4de 100644 --- a/openmc/deplete/abc.py +++ b/openmc/deplete/abc.py @@ -190,7 +190,7 @@ class TransportOperator(ABC): Returns ------- - volume : list of float + volume : dict of str to float Volumes corresponding to materials in burn_list nuc_list : list of str A list of all nuclide names. Used for sorting the simulation. From ceaecef45a776d4bb95c8d96b3ed2ae3037ec114 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Sat, 21 Mar 2020 10:11:56 -0400 Subject: [PATCH 125/205] Update Result.volume docstring: dict of str -> float --- openmc/deplete/results.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/deplete/results.py b/openmc/deplete/results.py index b44739bc5f..0b6d92d7c9 100644 --- a/openmc/deplete/results.py +++ b/openmc/deplete/results.py @@ -36,7 +36,7 @@ class Results(object): Number of nuclides. rates : list of ReactionRates The reaction rates for each substep. - volume : OrderedDict of int to float + volume : OrderedDict of str to float Dictionary mapping mat id to volume. mat_to_ind : OrderedDict of str to int A dictionary mapping mat ID as string to index. From a86c3481d682743cb6d78735a56b6cffd46385c0 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Sat, 21 Mar 2020 10:15:32 -0400 Subject: [PATCH 126/205] ResultList.get_atoms supports atom densities, days Two new optional arguments to ResultsList.get_atoms are introduced that control the units on the return values. Time can be returned in second or in days if time_units is "s" or "d", respectively. Concentrations can be returned in atoms/cm^3 or atoms/b/cm if nuc_units is one of those values. The default is to return time in seconds and number of atoms, retaining back compatibility. --- openmc/deplete/results_list.py | 28 ++++++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/openmc/deplete/results_list.py b/openmc/deplete/results_list.py index 1b5b81ac4c..9e7cd9ffb9 100644 --- a/openmc/deplete/results_list.py +++ b/openmc/deplete/results_list.py @@ -2,7 +2,7 @@ import h5py import numpy as np from .results import Results, _VERSION_RESULTS -from openmc.checkvalue import check_filetype_version +from openmc.checkvalue import check_filetype_version, check_value __all__ = ["ResultsList"] @@ -40,7 +40,7 @@ class ResultsList(list): new.append(Results.from_hdf5(fh, i)) return new - def get_atoms(self, mat, nuc): + def get_atoms(self, mat, nuc, nuc_units="atoms", time_units="s"): """Get number of nuclides over time from a single material .. note:: @@ -57,15 +57,24 @@ class ResultsList(list): Material name to evaluate nuc : str Nuclide name to evaluate + nuc_units : {"atoms", "atoms/b/cm", "atoms/cm^3"}, optional + Units for the returned concentration. Default is ``"atoms"`` + time_units : {"s", "d"}, optional + Units for the returned time array. Default is ``"s"`` to + return the value in seconds Returns ------- time : numpy.ndarray - Array of times in [s] + Array of times in units of ``time_units`` concentration : numpy.ndarray - Total number of atoms for specified nuclide + Concentration of specified nuclide in units of ``nuc_units`` """ + check_value("time_units", time_units, {"s", "d"}) + check_value("nuc_units", nuc_units, + {"atoms", "atoms/b/cm", "atoms/cm^3"}) + time = np.empty_like(self, dtype=float) concentration = np.empty_like(self, dtype=float) @@ -74,6 +83,17 @@ class ResultsList(list): time[i] = result.time[0] concentration[i] = result[0, mat, nuc] + # Unit conversions + if time_units == "d": + time /= (60 * 60 * 24) + + if nuc_units != "atoms": + # Divide by volume to get density + concentration /= self[0].volume[mat] + if nuc_units == "atoms/b/cm": + # 1 barn = 1e-24 cm^2 + concentration *= 1e-24 + return time, concentration def get_reaction_rate(self, mat, nuc, rx): From 4677714866be47e2557cb602faaae1df1f568a8a Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Sat, 21 Mar 2020 10:18:06 -0400 Subject: [PATCH 127/205] Add tests for new ResultsList.get_atoms units --- tests/unit_tests/test_deplete_resultslist.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/tests/unit_tests/test_deplete_resultslist.py b/tests/unit_tests/test_deplete_resultslist.py index c3ceda5d56..f95bc31539 100644 --- a/tests/unit_tests/test_deplete_resultslist.py +++ b/tests/unit_tests/test_deplete_resultslist.py @@ -19,12 +19,24 @@ def test_get_atoms(res): """Tests evaluating single nuclide concentration.""" t, n = res.get_atoms("1", "Xe135") - t_ref = [0.0, 1296000.0, 2592000.0, 3888000.0] - n_ref = [6.67473282e+08, 3.76986925e+14, 3.68587383e+14, 3.91338675e+14] + t_ref = np.array([0.0, 1296000.0, 2592000.0, 3888000.0]) + n_ref = np.array( + [6.67473282e+08, 3.76986925e+14, 3.68587383e+14, 3.91338675e+14]) np.testing.assert_allclose(t, t_ref) np.testing.assert_allclose(n, n_ref) + # Check alternate units + volume = res[0].volume["1"] + + t_days, n_cm3 = res.get_atoms("1", "Xe135", nuc_units="atoms/cm^3", time_units="d") + + assert t_days == pytest.approx(t_ref / (60 * 60 * 24)) + assert n_cm3 == pytest.approx(n_ref / volume) + + _t, n_bcm = res.get_atoms("1", "Xe135", nuc_units="atoms/b/cm") + assert n_bcm == pytest.approx(n_cm3 * 1e-24) + def test_get_reaction_rate(res): """Tests evaluating reaction rate.""" From ce4439ef141c10e2ed500bc9f6bc62ef208a35ee Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Sun, 22 Mar 2020 19:24:38 +0300 Subject: [PATCH 128/205] made for review some fixes and increaseing a number of batches --- .../mg_temperature/build_2g.py | 84 ++++++++++--------- 1 file changed, 45 insertions(+), 39 deletions(-) diff --git a/tests/regression_tests/mg_temperature/build_2g.py b/tests/regression_tests/mg_temperature/build_2g.py index 86ba17cea7..820a3bf232 100644 --- a/tests/regression_tests/mg_temperature/build_2g.py +++ b/tests/regression_tests/mg_temperature/build_2g.py @@ -5,7 +5,7 @@ names = ['H', 'O', 'Zr', 'U235', 'U238'] def build_openmc_xs_lib(name, groups, temperatures, xsdict, micro=True): - """Build an Openm XSdata based on dictonary values""" + """Build an Openm XSdata based on dictionary values""" xsdata = openmc.XSdata(name, groups, temperatures=temperatures) xsdata.order = 0 for tt in temperatures: @@ -34,15 +34,15 @@ def create_micro_xs_dict(): xs_micro[300]['absorption']['U238'] = np.array([0.0056, 0.0094]) # nu-scatter matrix xs_micro[300]['scatter']['H'] = np.array([[[0.0910, 0.01469], - [2.1545E-8, 0.3316]]]) + [0.0, 0.3316]]]) xs_micro[300]['scatter']['O'] = np.array([[[0.0814, 3.3235E-4], - [1.4152E-8, 0.0960]]]) + [0.0, 0.0960]]]) xs_micro[300]['scatter']['Zr'] = np.array([[[0.0311, 2.6373E-5], - [6.1273E-8, 0.0315]]]) + [0.0, 0.0315]]]) xs_micro[300]['scatter']['U235'] = np.array([[[0.0311, 2.6373E-5], - [6.1273E-8, 0.0315]]]) + [0.0, 0.0315]]]) xs_micro[300]['scatter']['U238'] = np.array([[[0.0551, 2.2341E-5], - [8.7247E-8, 0.0526]]]) + [0.0, 0.0526]]]) # nu-fission xs_micro[300]['nu-fission']['U235'] = np.array([0.0059, 0.2160]) xs_micro[300]['nu-fission']['U238'] = np.array([0.0019, 1.4627E-7]) @@ -71,15 +71,15 @@ def create_micro_xs_dict(): xs_micro[600]['absorption']['U238'] = np.array([0.0058, 0.0079]) # nu-scatter matrix xs_micro[600]['scatter']['H'] = np.array([[[0.0910, 0.0138], - [8.9e-08, 0.3316]]]) + [0.0, 0.3316]]]) xs_micro[600]['scatter']['O'] = np.array([[[0.0814, 3.5367E-4], - [3.4404E-8, 0.0959]]]) + [0.0, 0.0959]]]) xs_micro[600]['scatter']['Zr'] = np.array([[[0.0311, 3.2293E-5], - [8.3859E-8, 0.0314]]]) + [0.0, 0.0314]]]) xs_micro[600]['scatter']['U235'] = np.array([[[0.0022, 1.9763E-6], [9.1634E-8, 0.0039]]]) xs_micro[600]['scatter']['U238'] = np.array([[[0.0556, 2.8803E-5], - [1.1967E-8, 0.0536]]]) + [0.0, 0.0536]]]) # nu-fission xs_micro[600]['nu-fission']['U235'] = np.array([0.0059, 0.1767]) xs_micro[600]['nu-fission']['U238'] = np.array([0.0019, 1.2405E-7]) @@ -114,15 +114,15 @@ def create_micro_xs_dict(): xs_micro[900]['total']['U238'] = np.array([0.0707, 0.0613]) # nu-scatter matrix xs_micro[900]['scatter']['H'] = np.array([[[0.0913, 0.0147], - [8.9e-08, 0.4020]]]) + [0.0, 0.4020]]]) xs_micro[900]['scatter']['O'] = np.array([[[0.0812, 4.0413E-4], - [6.8186E-8, 0.0965]]]) + [0.0, 0.0965]]]) xs_micro[900]['scatter']['Zr'] = np.array([[[0.0311, 3.6735E-5], - [1.3439E-8, 0.0314]]]) + [0.0, 0.0314]]]) xs_micro[900]['scatter']['U235'] = np.array([[[0.0022, 2.9034E-6], [1.3117E-8, 0.0039]]]) xs_micro[900]['scatter']['U238'] = np.array([[[0.0560, 3.7619E-5], - [1.4553E-8, 0.0538]]]) + [0.0, 0.0538]]]) # nu-fission xs_micro[900]['nu-fission']['U235'] = np.array([0.0059, 0.1545]) xs_micro[900]['nu-fission']['U238'] = np.array([0.0019, 1.1017E-7]) @@ -159,6 +159,9 @@ def create_macro_dict(xs_micro): xs_macro[t][r] = {} for n, v in d2.items(): temp.append(d2[n]) + # The name 'macro' is needed to store data at the same level + # of a xs_macro dictionary as for xs_micro and use it in + # function build_openmc_xs_lib xs_macro[t][r]['macro'] = sum(temp) return xs_macro @@ -194,20 +197,21 @@ def create_openmc_2mg_libs(names): def analytical_solution_2g_therm(xsmin, xsmax=None, wgt=1.0): """ Calculate eigenvalue based on analytical solution for eq Lf = (1/k)Qf - in two group for infinity dilution media in assumption of group - boundary in thernmal spectra < 1.e+3 Ev - Parametres: - ---------- - xsmin : dict - - macro cross-sections dictonary with minimum range temperature - xsmax : dict - - macro cross-sections dictonary with maximum range temperature - by default: None not used for standalone temperature - wgt : double - - weight for interpolation by default 1.0 - Returns: - --------- - keff : np.double - analytical eigenvalue of critical eq matrix + in two group for infinity dilution media in assumption of group + boundary in thermal spectra < 1.e+3 Ev + Parameters: + ---------- + xsmin : dict + macro cross-sections dictionary with minimum range temperature + xsmax : dict + macro cross-sections dictionary with maximum range temperature + by default: None not used for standalone temperature + wgt : float + weight for interpolation by default 1.0 + Returns: + ------- + keff : np.float64 + analytical eigenvalue of critical eq matrix """ if xsmax is None: sa = xsmin['absorption']['macro'] @@ -223,20 +227,21 @@ def analytical_solution_2g_therm(xsmin, xsmax=None, wgt=1.0): L = np.array([sa[0] + ss12, 0.0, -ss12, sa[1]]).reshape(2, 2) Q = np.array([nsf[0], nsf[1], 0.0, 0.0]).reshape(2, 2) arr = np.linalg.inv(L).dot(Q) - return np.linalg.eigvals(arr)[1] + return np.amax(np.linalg.eigvals(arr)) def build_inf_model(xsnames, xslibname, temperature, tempmethod='nearest'): """ Building an infinite medium for openmc multi-group testing - Parametres: - ---------- - xsnames : list of str() - - list with xs names - xslibname: - - name of hdf5 file with cross-section library - temperature : float - - value of a current temperature in K - tempmethod : str {'nearest', 'interpolstion'} by default 'nearest' + Parameters: + ---------- + xsnames : list of str() + list with xs names + xslibname: + name of hdf5 file with cross-section library + temperature : float + value of a current temperature in K + tempmethod : {'nearest', 'interpolation'} + by default 'nearest' """ inf_medium = openmc.Material(name='test material', material_id=1) inf_medium.set_density("sum") @@ -273,9 +278,10 @@ def build_inf_model(xsnames, xslibname, temperature, tempmethod='nearest'): openmc_geometry.export_to_xml() # OpenMC simulation parameters - batches = 15 + batches = 200 inactive = 5 particles = 5000 + # Instantiate a Settings object settings_file = openmc.Settings() settings_file.batches = batches From fc7af30f7684333842fa7d3bb1ccc13026e3fbc1 Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Sun, 22 Mar 2020 19:25:56 +0300 Subject: [PATCH 129/205] to fit to review same commit as before --- tests/regression_tests/mg_temperature/test.py | 96 +++++++++++-------- 1 file changed, 54 insertions(+), 42 deletions(-) diff --git a/tests/regression_tests/mg_temperature/test.py b/tests/regression_tests/mg_temperature/test.py index 355811a137..36395127c2 100644 --- a/tests/regression_tests/mg_temperature/test.py +++ b/tests/regression_tests/mg_temperature/test.py @@ -1,6 +1,7 @@ import os from tests.regression_tests.mg_temperature.build_2g import * from tests.testing_harness import * +import shutil class MgTemperatureTestHarness(TestHarness): @@ -8,14 +9,15 @@ class MgTemperatureTestHarness(TestHarness): def execute_test(self): """Run OpenMC with the appropriate arguments and check the outputs.""" base_dir = os.getcwd() + overall_results = [] print("Base dir is {}".format(base_dir)) macro_xs = create_openmc_2mg_libs(names) - dirs = ('micro/nearest/case1', 'micro/nearest/case2', - 'micro/nearest/case3', 'micro/interpolation/case1', - 'micro/interpolation/case2', - 'macro/nearest/case1', 'macro/nearest/case2', - 'macro/nearest/case3', 'macro/interpolation/case1', - 'macro/interpolation/case2') + types = ('micro', 'micro', + 'micro', 'micro', + 'micro', + 'macro', 'macro', + 'macro', 'macro', + 'macro') temperatures = (300., 600., 900., 520., 600., 300., 600., 900., @@ -25,12 +27,15 @@ class MgTemperatureTestHarness(TestHarness): analyt_interp[3] = (600. - 520.) / 300. analyt_interp[8] = (600. - 520.) / 300. try: - for d, t, m, ai in zip(dirs, temperatures, methods, analyt_interp): - os.chdir(os.path.join(base_dir, d)) - if (d[:5] == 'macro'): - build_inf_model(['macro'], '../../../macro_2g.h5', t, m) + if (os.path.isdir("./temp")): + shutil.rmtree("./temp") + os.mkdir("temp") + os.chdir(os.path.join(base_dir, "temp")) + for cs, t, m, ai in zip(types, temperatures, methods, analyt_interp): + if (cs == 'macro'): + build_inf_model(['macro'], '../macro_2g.h5', t, m) else: - build_inf_model(names, '../../../micro_2g.h5', t, m) + build_inf_model(names, '../micro_2g.h5', t, m) if not ai: kanalyt = analytical_solution_2g_therm(macro_xs[t]) else: @@ -38,16 +43,18 @@ class MgTemperatureTestHarness(TestHarness): macro_xs[600], ai) self._run_openmc() self._test_output_created() - results = self._get_results() - results += "k-analytical:\n" - results += "{:12.6E}".format(kanalyt) - self._write_results(results) - self._compare_results() - finally: - for d in dirs: - os.chdir(os.path.join(base_dir, d)) - self._cleanup() + string = "{}, method: {}, t: {}, {}kanalyt\n{:12.6E}\n" + results = string.format(cs, m, t, self._get_results(), kanalyt) + overall_results.append(results) os.chdir(base_dir) + self._write_results("".join(overall_results)) + self._compare_results() + finally: + os.chdir(base_dir) + shutil.copyfile("results_test.dat", "results_true.dat") + if (os.path.isdir("./temp")): + shutil.rmtree("./temp") + self._cleanup() for f in ['micro_2g.h5', 'macro_2g.h5']: if os.path.exists(f): os.remove(f) @@ -55,14 +62,15 @@ class MgTemperatureTestHarness(TestHarness): def update_results(self): """Update the results_true using the current version of OpenMC.""" base_dir = os.getcwd() + overall_results = [] print("Base dir is {}".format(base_dir)) macro_xs = create_openmc_2mg_libs(names) - dirs = ('micro/nearest/case1', 'micro/nearest/case2', - 'micro/nearest/case3', 'micro/interpolation/case1', - 'micro/interpolation/case2', - 'macro/nearest/case1', 'macro/nearest/case2', - 'macro/nearest/case3', 'macro/interpolation/case1', - 'macro/interpolation/case2') + types = ('micro', 'micro', + 'micro', 'micro', + 'micro', + 'macro', 'macro', + 'macro', 'macro', + 'macro') temperatures = (300., 600., 900., 520., 600., 300., 600., 900., @@ -72,12 +80,15 @@ class MgTemperatureTestHarness(TestHarness): analyt_interp[3] = (600. - 520.) / 300. analyt_interp[8] = (600. - 520.) / 300. try: - for d, t, m, ai in zip(dirs, temperatures, methods, analyt_interp): - os.chdir(os.path.join(base_dir, d)) - if (d[:5] == 'macro'): - build_inf_model(['macro'], '../../../macro_2g.h5', t) + if (os.path.isdir("./temp")): + shutil.rmtree("./temp") + os.mkdir("temp") + os.chdir(os.path.join(base_dir, "temp")) + for cs, t, m, ai in zip(types, temperatures, methods, analyt_interp): + if (cs == 'macro'): + build_inf_model(['macro'], '../macro_2g.h5', t, m) else: - build_inf_model(names, '../../../micro_2g.h5', t) + build_inf_model(names, '../micro_2g.h5', t, m) if not ai: kanalyt = analytical_solution_2g_therm(macro_xs[t]) else: @@ -85,22 +96,23 @@ class MgTemperatureTestHarness(TestHarness): macro_xs[600], ai) self._run_openmc() self._test_output_created() - results = self._get_results() - results += "k-analytical:\n" - results += "{:12.6E}".format(kanalyt) - self._write_results(results) - self._overwrite_results(results) - self._compare_results() - finally: - for d in dirs: - os.chdir(os.path.join(base_dir, d)) - self._cleanup() + string = "{}, method: {}, t: {}, {}kanalyt\n{:12.6E}\n" + results = string.format(cs, m, t, self._get_results(), kanalyt) + overall_results.append(results) os.chdir(base_dir) + self._write_results("".join(overall_results)) + self._compare_results() + finally: + os.chdir(base_dir) + shutil.copyfile("results_test.dat", "results_true.dat") + if (os.path.isdir("./temp")): + shutil.rmtree("./temp") + self._cleanup() for f in ['micro_2g.h5', 'macro_2g.h5']: if os.path.exists(f): os.remove(f) def test_mg_temperature(): - harness = MgTemperatureTestHarness('statepoint.15.h5') + harness = MgTemperatureTestHarness('statepoint.200.h5') harness.main() From ccf9ec1dbd57fba62d1c06d30f47a52834df582e Mon Sep 17 00:00:00 2001 From: dryuri92 Date: Sun, 22 Mar 2020 19:34:46 +0300 Subject: [PATCH 130/205] remove unused dir --- .../macro/interpolation/case1/geometry.xml | 8 ---- .../macro/interpolation/case1/materials.xml | 8 ---- .../interpolation/case1/results_true.dat | 4 -- .../macro/interpolation/case1/settings.xml | 17 -------- .../macro/interpolation/case2/geometry.xml | 8 ---- .../macro/interpolation/case2/materials.xml | 8 ---- .../interpolation/case2/results_true.dat | 4 -- .../macro/interpolation/case2/settings.xml | 17 -------- .../macro/nearest/case1/geometry.xml | 8 ---- .../macro/nearest/case1/materials.xml | 8 ---- .../macro/nearest/case1/results_true.dat | 4 -- .../macro/nearest/case1/settings.xml | 17 -------- .../macro/nearest/case2/geometry.xml | 8 ---- .../macro/nearest/case2/materials.xml | 8 ---- .../macro/nearest/case2/results_true.dat | 4 -- .../macro/nearest/case2/settings.xml | 17 -------- .../macro/nearest/case3/geometry.xml | 8 ---- .../macro/nearest/case3/materials.xml | 8 ---- .../macro/nearest/case3/results_true.dat | 4 -- .../macro/nearest/case3/settings.xml | 17 -------- .../micro/interpolation/case1/geometry.xml | 8 ---- .../micro/interpolation/case1/materials.xml | 12 ------ .../interpolation/case1/results_true.dat | 4 -- .../micro/interpolation/case1/settings.xml | 17 -------- .../micro/interpolation/case2/geometry.xml | 8 ---- .../micro/interpolation/case2/materials.xml | 12 ------ .../interpolation/case2/results_true.dat | 4 -- .../micro/interpolation/case2/settings.xml | 17 -------- .../micro/nearest/case1/geometry.xml | 8 ---- .../micro/nearest/case1/materials.xml | 12 ------ .../micro/nearest/case1/results_true.dat | 4 -- .../micro/nearest/case1/settings.xml | 17 -------- .../micro/nearest/case2/geometry.xml | 8 ---- .../micro/nearest/case2/materials.xml | 12 ------ .../micro/nearest/case2/results_true.dat | 4 -- .../micro/nearest/case2/settings.xml | 17 -------- .../micro/nearest/case3/geometry.xml | 8 ---- .../micro/nearest/case3/materials.xml | 12 ------ .../micro/nearest/case3/results_true.dat | 4 -- .../micro/nearest/case3/settings.xml | 17 -------- .../mg_temperature/results_true.dat | 40 +++++++++++++++++++ 41 files changed, 40 insertions(+), 390 deletions(-) delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat delete mode 100644 tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml create mode 100644 tests/regression_tests/mg_temperature/results_true.dat diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml deleted file mode 100644 index e66b004c5d..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case1/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml deleted file mode 100644 index e3e2097cb5..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case1/materials.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - ../../../macro_2g.h5 - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat b/tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat deleted file mode 100644 index 8cc89cfb0d..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case1/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.422739E+00 1.661420E-03 -k-analytical: -1.418514E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml deleted file mode 100644 index 4a51e8e80a..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case1/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - interpolation - diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml deleted file mode 100644 index 838fb9f687..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case2/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml deleted file mode 100644 index e3e2097cb5..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case2/materials.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - ../../../macro_2g.h5 - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat b/tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat deleted file mode 100644 index 965fe8002a..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case2/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.410995E+00 2.169214E-03 -k-analytical: -1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml b/tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml deleted file mode 100644 index 4a51e8e80a..0000000000 --- a/tests/regression_tests/mg_temperature/macro/interpolation/case2/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - interpolation - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml b/tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml deleted file mode 100644 index f64d315932..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case1/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml b/tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml deleted file mode 100644 index e3e2097cb5..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case1/materials.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - ../../../macro_2g.h5 - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat b/tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat deleted file mode 100644 index 5d7f81f288..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case1/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.443970E+00 3.267499E-03 -k-analytical: -1.440410E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml b/tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml deleted file mode 100644 index b5ad0fec4f..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case1/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - nearest - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml b/tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml deleted file mode 100644 index de56931104..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case2/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml b/tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml deleted file mode 100644 index e3e2097cb5..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case2/materials.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - ../../../macro_2g.h5 - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat b/tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat deleted file mode 100644 index 965fe8002a..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case2/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.410995E+00 2.169214E-03 -k-analytical: -1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml b/tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml deleted file mode 100644 index b5ad0fec4f..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case2/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - nearest - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml b/tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml deleted file mode 100644 index a95b7b55ac..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case3/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml b/tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml deleted file mode 100644 index e3e2097cb5..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case3/materials.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - ../../../macro_2g.h5 - - - - - diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat b/tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat deleted file mode 100644 index 39d3eb5e34..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case3/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.411726E+00 2.151179E-03 -k-analytical: -1.407830E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml b/tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml deleted file mode 100644 index b5ad0fec4f..0000000000 --- a/tests/regression_tests/mg_temperature/macro/nearest/case3/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - nearest - diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml deleted file mode 100644 index b54f27f415..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case1/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml deleted file mode 100644 index 98f9236e61..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case1/materials.xml +++ /dev/null @@ -1,12 +0,0 @@ - - - ../../../micro_2g.h5 - - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat b/tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat deleted file mode 100644 index 8cc89cfb0d..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case1/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.422739E+00 1.661420E-03 -k-analytical: -1.418514E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml deleted file mode 100644 index 4a51e8e80a..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case1/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - interpolation - diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml deleted file mode 100644 index 4eeb35ac88..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case2/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml deleted file mode 100644 index 98f9236e61..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case2/materials.xml +++ /dev/null @@ -1,12 +0,0 @@ - - - ../../../micro_2g.h5 - - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat b/tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat deleted file mode 100644 index 965fe8002a..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case2/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.410995E+00 2.169214E-03 -k-analytical: -1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml b/tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml deleted file mode 100644 index 4a51e8e80a..0000000000 --- a/tests/regression_tests/mg_temperature/micro/interpolation/case2/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - interpolation - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml b/tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml deleted file mode 100644 index e1da2fa5c6..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case1/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml b/tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml deleted file mode 100644 index 98f9236e61..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case1/materials.xml +++ /dev/null @@ -1,12 +0,0 @@ - - - ../../../micro_2g.h5 - - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat b/tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat deleted file mode 100644 index 5d7f81f288..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case1/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.443970E+00 3.267499E-03 -k-analytical: -1.440410E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml b/tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml deleted file mode 100644 index b5ad0fec4f..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case1/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - nearest - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml b/tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml deleted file mode 100644 index 1ac560e8f7..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case2/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml b/tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml deleted file mode 100644 index 98f9236e61..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case2/materials.xml +++ /dev/null @@ -1,12 +0,0 @@ - - - ../../../micro_2g.h5 - - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat b/tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat deleted file mode 100644 index 965fe8002a..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case2/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.410995E+00 2.169214E-03 -k-analytical: -1.410164E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml b/tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml deleted file mode 100644 index b5ad0fec4f..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case2/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - nearest - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml b/tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml deleted file mode 100644 index c8d1164f13..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case3/geometry.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml b/tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml deleted file mode 100644 index 98f9236e61..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case3/materials.xml +++ /dev/null @@ -1,12 +0,0 @@ - - - ../../../micro_2g.h5 - - - - - - - - - diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat b/tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat deleted file mode 100644 index 39d3eb5e34..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case3/results_true.dat +++ /dev/null @@ -1,4 +0,0 @@ -k-combined: -1.411726E+00 2.151179E-03 -k-analytical: -1.407830E+00 \ No newline at end of file diff --git a/tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml b/tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml deleted file mode 100644 index b5ad0fec4f..0000000000 --- a/tests/regression_tests/mg_temperature/micro/nearest/case3/settings.xml +++ /dev/null @@ -1,17 +0,0 @@ - - - eigenvalue - 5000 - 15 - 5 - - - -11.1 -11.1 -11.1 11.1 11.1 11.1 - - - - false - - multi-group - nearest - diff --git a/tests/regression_tests/mg_temperature/results_true.dat b/tests/regression_tests/mg_temperature/results_true.dat new file mode 100644 index 0000000000..e37bc533a3 --- /dev/null +++ b/tests/regression_tests/mg_temperature/results_true.dat @@ -0,0 +1,40 @@ +micro, method: nearest, t: 300.0, k-combined: +1.439920E+00 4.801994E-04 +kanalyt +1.440410E+00 +micro, method: nearest, t: 600.0, k-combined: +1.410482E+00 4.811844E-04 +kanalyt +1.410164E+00 +micro, method: nearest, t: 900.0, k-combined: +1.408198E+00 4.885882E-04 +kanalyt +1.407830E+00 +micro, method: interpolation, t: 520.0, k-combined: +1.418780E+00 5.222658E-04 +kanalyt +1.418514E+00 +micro, method: interpolation, t: 600.0, k-combined: +1.410482E+00 4.811844E-04 +kanalyt +1.410164E+00 +macro, method: nearest, t: 300.0, k-combined: +1.439920E+00 4.801994E-04 +kanalyt +1.440410E+00 +macro, method: nearest, t: 600.0, k-combined: +1.410482E+00 4.811844E-04 +kanalyt +1.410164E+00 +macro, method: nearest, t: 900.0, k-combined: +1.408198E+00 4.885882E-04 +kanalyt +1.407830E+00 +macro, method: interpolation, t: 520.0, k-combined: +1.418780E+00 5.222658E-04 +kanalyt +1.418514E+00 +macro, method: interpolation, t: 600, k-combined: +1.410482E+00 4.811844E-04 +kanalyt +1.410164E+00 From db0d5abbf40910a20e81da5a927d4bd01358f3ce Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Mon, 23 Mar 2020 14:14:55 +0300 Subject: [PATCH 131/205] indented to the level small fixes --- tests/regression_tests/mg_temperature/build_2g.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/regression_tests/mg_temperature/build_2g.py b/tests/regression_tests/mg_temperature/build_2g.py index 820a3bf232..1256ca0f7f 100644 --- a/tests/regression_tests/mg_temperature/build_2g.py +++ b/tests/regression_tests/mg_temperature/build_2g.py @@ -159,9 +159,9 @@ def create_macro_dict(xs_micro): xs_macro[t][r] = {} for n, v in d2.items(): temp.append(d2[n]) - # The name 'macro' is needed to store data at the same level - # of a xs_macro dictionary as for xs_micro and use it in - # function build_openmc_xs_lib + # The name 'macro' is needed to store data at the same level + # of a xs_macro dictionary as for xs_micro and use it in + # function build_openmc_xs_lib xs_macro[t][r]['macro'] = sum(temp) return xs_macro From 051ebfcff310385ee7c2978f8c5a0b50edb6c45a Mon Sep 17 00:00:00 2001 From: dryuri92 <39188804+dryuri92@users.noreply.github.com> Date: Mon, 23 Mar 2020 14:16:05 +0300 Subject: [PATCH 132/205] remove unused lines --- tests/regression_tests/mg_temperature/test.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/regression_tests/mg_temperature/test.py b/tests/regression_tests/mg_temperature/test.py index 36395127c2..6bcc55492a 100644 --- a/tests/regression_tests/mg_temperature/test.py +++ b/tests/regression_tests/mg_temperature/test.py @@ -10,7 +10,6 @@ class MgTemperatureTestHarness(TestHarness): """Run OpenMC with the appropriate arguments and check the outputs.""" base_dir = os.getcwd() overall_results = [] - print("Base dir is {}".format(base_dir)) macro_xs = create_openmc_2mg_libs(names) types = ('micro', 'micro', 'micro', 'micro', @@ -63,7 +62,6 @@ class MgTemperatureTestHarness(TestHarness): """Update the results_true using the current version of OpenMC.""" base_dir = os.getcwd() overall_results = [] - print("Base dir is {}".format(base_dir)) macro_xs = create_openmc_2mg_libs(names) types = ('micro', 'micro', 'micro', 'micro', From 07cfb0cfcc1dd022690e16e8e971606cec0c3374 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 15:10:13 -0500 Subject: [PATCH 133/205] Remove _utils.py (download function moved to data repository) --- openmc/_utils.py | 70 ---------------------------------- tests/unit_tests/test_utils.py | 26 ------------- 2 files changed, 96 deletions(-) delete mode 100644 openmc/_utils.py delete mode 100644 tests/unit_tests/test_utils.py diff --git a/openmc/_utils.py b/openmc/_utils.py deleted file mode 100644 index d00e4c58b7..0000000000 --- a/openmc/_utils.py +++ /dev/null @@ -1,70 +0,0 @@ -import hashlib -import os.path -from pathlib import Path -from urllib.parse import urlparse -from urllib.request import urlopen, Request - -_BLOCK_SIZE = 16384 - - -def download(url, checksum=None, as_browser=False, **kwargs): - """Download file from a URL - - Parameters - ---------- - url : str - URL from which to download - checksum : str or None - MD5 checksum to check against - as_browser : bool - Change User-Agent header to appear as a browser - kwargs : dict - Keyword arguments passed to :func:urllib.request.urlopen - - Returns - ------- - basename : str - Name of file written locally - - """ - if as_browser: - page = Request(url, headers={'User-Agent': 'Mozilla/5.0'}) - else: - page = url - - with urlopen(page, **kwargs) as response: - # Get file size from header - file_size = response.length - - # Check if file already downloaded - basename = Path(urlparse(url).path).name - if os.path.exists(basename): - if os.path.getsize(basename) == file_size: - print('Skipping {}, already downloaded'.format(basename)) - return basename - - # Copy file to disk in chunks - print('Downloading {}... '.format(basename), end='') - downloaded = 0 - with open(basename, 'wb') as fh: - while True: - chunk = response.read(_BLOCK_SIZE) - if not chunk: - break - fh.write(chunk) - downloaded += len(chunk) - status = '{:10} [{:3.2f}%]'.format( - downloaded, downloaded * 100. / file_size) - print(status + '\b'*len(status), end='', flush=True) - print('') - - if checksum is not None: - downloadsum = hashlib.md5(open(basename, 'rb').read()).hexdigest() - if downloadsum != checksum: - raise IOError("MD5 checksum for {} does not match. If this is " - "your first time receiving this message, please " - "re-run the script. Otherwise, please contact " - "OpenMC developers by emailing " - "openmc-users@googlegroups.com.".format(basename)) - - return basename diff --git a/tests/unit_tests/test_utils.py b/tests/unit_tests/test_utils.py deleted file mode 100644 index 9906f5f387..0000000000 --- a/tests/unit_tests/test_utils.py +++ /dev/null @@ -1,26 +0,0 @@ -import os -import filecmp - -from openmc import _utils -import pytest - -@pytest.fixture() -def download_photos(run_in_tmpdir): - """use _utils download() function to download the same picture three times, - twice to get unique names, & a third time to use the already downloaded - block of code""" - _utils.download("https://i.ibb.co/HhKFc8x/small.jpg") - _utils.download("https://tinyurl.com/y4t38ugb") - _utils.download("https://tinyurl.com/y4t38ugb", as_browser=True) - - -def test_checksum_error(run_in_tmpdir): - """use download() in such a way that will test the checksum error line""" - phrase = "MD5 checksum for y4t38ugb" - with pytest.raises(OSError, match=phrase): - _utils.download("https://tinyurl.com/y4t38ugb", as_browser=True, - checksum="not none") - - -def test_photos(download_photos): - assert filecmp.cmp("small.jpg", "y4t38ugb") From cf6b67c05d516185a0e3436e12d007213990b966 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 15:21:15 -0500 Subject: [PATCH 134/205] Remove explicit inheritance from object for Python classes --- openmc/_xml.py | 8 +------- openmc/arithmetic.py | 13 +++++++------ openmc/cmfd.py | 6 +++--- openmc/data/endf.py | 4 ++-- openmc/data/resonance.py | 8 ++++---- openmc/deplete/atom_number.py | 2 +- openmc/deplete/chain.py | 2 +- openmc/deplete/dummy_comm.py | 3 ++- openmc/deplete/nuclide.py | 2 +- openmc/deplete/results.py | 2 +- openmc/geometry.py | 6 +++--- openmc/lib/core.py | 4 ++-- openmc/lib/settings.py | 2 +- openmc/mgxs/groups.py | 2 +- openmc/mgxs/library.py | 2 +- openmc/mgxs_library.py | 4 ++-- openmc/mixin.py | 4 ++-- openmc/model/model.py | 2 +- openmc/particle_restart.py | 3 ++- openmc/polynomial.py | 14 +++++++------- openmc/settings.py | 22 +++++++++++----------- openmc/statepoint.py | 4 ++-- openmc/summary.py | 2 +- openmc/tallies.py | 30 ++++++++++-------------------- openmc/trigger.py | 2 +- openmc/volume.py | 2 +- tests/dummy_operator.py | 2 +- tests/testing_harness.py | 2 +- 28 files changed, 73 insertions(+), 86 deletions(-) diff --git a/openmc/_xml.py b/openmc/_xml.py index 7470b2c3b2..b49d192097 100644 --- a/openmc/_xml.py +++ b/openmc/_xml.py @@ -1,26 +1,20 @@ def clean_indentation(element, level=0, spaces_per_level=2): """ - copy and paste from http://effbot.org/zone/element-lib.htm#prettyprint + copy and paste from https://effbot.org/zone/element-lib.htm#prettyprint it basically walks your tree and adds spaces and newlines so the tree is printed in a nice way """ - i = "\n" + level*spaces_per_level*" " if len(element): - if not element.text or not element.text.strip(): element.text = i + spaces_per_level*" " - if not element.tail or not element.tail.strip(): element.tail = i - for sub_element in element: clean_indentation(sub_element, level+1, spaces_per_level) - if not sub_element.tail or not sub_element.tail.strip(): sub_element.tail = i - else: if level and (not element.tail or not element.tail.strip()): element.tail = i diff --git a/openmc/arithmetic.py b/openmc/arithmetic.py index 91e2f1ed44..c52dceb8a8 100644 --- a/openmc/arithmetic.py +++ b/openmc/arithmetic.py @@ -17,7 +17,7 @@ _TALLY_ARITHMETIC_OPS = ['+', '-', '*', '/', '^'] _TALLY_AGGREGATE_OPS = ['sum', 'avg'] -class CrossScore(object): +class CrossScore: """A special-purpose tally score used to encapsulate all combinations of two tally's scores as an outer product for tally arithmetic. @@ -101,7 +101,7 @@ class CrossScore(object): self._binary_op = binary_op -class CrossNuclide(object): +class CrossNuclide: """A special-purpose nuclide used to encapsulate all combinations of two tally's nuclides as an outer product for tally arithmetic. @@ -206,7 +206,7 @@ class CrossNuclide(object): self._binary_op = binary_op -class CrossFilter(object): +class CrossFilter: """A special-purpose filter used to encapsulate all combinations of two tally's filter bins as an outer product for tally arithmetic. @@ -418,7 +418,8 @@ class CrossFilter(object): return df -class AggregateScore(object): + +class AggregateScore: """A special-purpose tally score used to encapsulate an aggregate of a subset or all of tally's scores for tally aggregation. @@ -491,7 +492,7 @@ class AggregateScore(object): self._aggregate_op = aggregate_op -class AggregateNuclide(object): +class AggregateNuclide: """A special-purpose tally nuclide used to encapsulate an aggregate of a subset or all of tally's nuclides for tally aggregation. @@ -570,7 +571,7 @@ class AggregateNuclide(object): self._aggregate_op = aggregate_op -class AggregateFilter(object): +class AggregateFilter: """A special-purpose tally filter used to encapsulate an aggregate of a subset or all of a tally filter's bins for tally aggregation. diff --git a/openmc/cmfd.py b/openmc/cmfd.py index a599f36e18..6b3694774d 100644 --- a/openmc/cmfd.py +++ b/openmc/cmfd.py @@ -55,7 +55,7 @@ _CURRENTS = { } -class CMFDMesh(object): +class CMFDMesh: """A structured Cartesian mesh used for CMFD acceleration. Attributes @@ -189,7 +189,7 @@ class CMFDMesh(object): self._map = meshmap -class CMFDRun(object): +class CMFDRun: r"""Class for running CMFD acceleration through the C API. Attributes @@ -2047,7 +2047,7 @@ class CMFDRun(object): axis=5) # Compute current as aggregate of banked current_rate over tally window - self._current = np.where(is_accel[..., np.newaxis, np.newaxis], + self._current = np.where(is_accel[..., np.newaxis, np.newaxis], np.sum(self._current_rate, axis=5), 0.0) # Get p1 scatter rr from CMFD tally 3 diff --git a/openmc/data/endf.py b/openmc/data/endf.py index 92a558e217..937a3df23f 100644 --- a/openmc/data/endf.py +++ b/openmc/data/endf.py @@ -370,7 +370,7 @@ def get_evaluations(filename): return evaluations -class Evaluation(object): +class Evaluation: """ENDF material evaluation with multiple files/sections Parameters @@ -528,7 +528,7 @@ class Evaluation(object): self.target['isomeric_state']) -class Tabulated2D(object): +class Tabulated2D: """Metadata for a two-dimensional function. This is a dummy class that is not really used other than to store the diff --git a/openmc/data/resonance.py b/openmc/data/resonance.py index 628ad184b5..aa79aafa1f 100644 --- a/openmc/data/resonance.py +++ b/openmc/data/resonance.py @@ -17,7 +17,7 @@ except ImportError: import openmc.checkvalue as cv -class Resonances(object): +class Resonances: """Resolved and unresolved resonance data Parameters @@ -119,7 +119,7 @@ class Resonances(object): return cls(ranges) -class ResonanceRange(object): +class ResonanceRange: """Resolved resonance range Parameters @@ -867,7 +867,7 @@ class RMatrixLimited(ResonanceRange): return rml -class ParticlePair(object): +class ParticlePair: def __init__(self, first, second, q_value, penetrability, shift, mt): self.first = first @@ -878,7 +878,7 @@ class ParticlePair(object): self.mt = mt -class SpinGroup(object): +class SpinGroup: """Resonance spin group Attributes diff --git a/openmc/deplete/atom_number.py b/openmc/deplete/atom_number.py index b5357280c0..e4982f872e 100644 --- a/openmc/deplete/atom_number.py +++ b/openmc/deplete/atom_number.py @@ -7,7 +7,7 @@ from collections import OrderedDict import numpy as np -class AtomNumber(object): +class AtomNumber: """Stores local material compositions (atoms of each nuclide). Parameters diff --git a/openmc/deplete/chain.py b/openmc/deplete/chain.py index 814de12fc4..f018aaac7b 100644 --- a/openmc/deplete/chain.py +++ b/openmc/deplete/chain.py @@ -141,7 +141,7 @@ _SECONDARY_PARTICLES = { } -class Chain(object): +class Chain: """Full representation of a depletion chain. A depletion chain can be created by using the :meth:`from_endf` method which diff --git a/openmc/deplete/dummy_comm.py b/openmc/deplete/dummy_comm.py index 2648fdedce..7ac9be6c36 100644 --- a/openmc/deplete/dummy_comm.py +++ b/openmc/deplete/dummy_comm.py @@ -1,6 +1,7 @@ import sys -class DummyCommunicator(object): + +class DummyCommunicator: rank = 0 size = 1 diff --git a/openmc/deplete/nuclide.py b/openmc/deplete/nuclide.py index 0aae5adb72..52dd2bf96d 100644 --- a/openmc/deplete/nuclide.py +++ b/openmc/deplete/nuclide.py @@ -70,7 +70,7 @@ except AttributeError: pass -class Nuclide(object): +class Nuclide: """Decay modes, reactions, and fission yields for a single nuclide. Parameters diff --git a/openmc/deplete/results.py b/openmc/deplete/results.py index b44739bc5f..2f102913de 100644 --- a/openmc/deplete/results.py +++ b/openmc/deplete/results.py @@ -19,7 +19,7 @@ _VERSION_RESULTS = (1, 0) __all__ = ["Results"] -class Results(object): +class Results: """Output of a depletion run Attributes diff --git a/openmc/geometry.py b/openmc/geometry.py index 205fe8face..4b650f2e3e 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -11,7 +11,7 @@ import openmc._xml as xml from openmc.checkvalue import check_type -class Geometry(object): +class Geometry: """Geometry representing a collection of surfaces, cells, and universes. Parameters @@ -406,7 +406,7 @@ class Geometry(object): coeffs = tuple(surf._coefficients[k] for k in surf._coeff_keys) key = (surf._type,) + coeffs tally[key].append(surf) - return {replace.id: keep + return {replace.id: keep for keep, *redundant in tally.values() for replace in redundant} @@ -613,7 +613,7 @@ class Geometry(object): # Get redundant surfaces redundant_surfaces = self.get_redundant_surfaces() - # Iterate through all cells contained in the geometry + # Iterate through all cells contained in the geometry for cell in self.get_all_cells().values(): # Recursively remove redundant surfaces from regions cell.region.remove_redundant_surfaces(redundant_surfaces) diff --git a/openmc/lib/core.py b/openmc/lib/core.py index 6a2a7cdeaa..d0fd41e6fa 100644 --- a/openmc/lib/core.py +++ b/openmc/lib/core.py @@ -400,7 +400,7 @@ def run_in_memory(**kwargs): finalize() -class _DLLGlobal(object): +class _DLLGlobal: """Data descriptor that exposes global variables from libopenmc.""" def __init__(self, ctype, name): self.ctype = ctype @@ -413,7 +413,7 @@ class _DLLGlobal(object): self.ctype.in_dll(_dll, self.name).value = value -class _FortranObject(object): +class _FortranObject: def __repr__(self): return "{}[{}]".format(type(self).__name__, self._index) diff --git a/openmc/lib/settings.py b/openmc/lib/settings.py index 68d68c29e0..104bfdd937 100644 --- a/openmc/lib/settings.py +++ b/openmc/lib/settings.py @@ -15,7 +15,7 @@ _dll.openmc_set_seed.argtypes = [c_int64] _dll.openmc_get_seed.restype = c_int64 -class _Settings(object): +class _Settings: # Attributes that are accessed through a descriptor batches = _DLLGlobal(c_int32, 'n_batches') cmfd_run = _DLLGlobal(c_bool, 'cmfd_run') diff --git a/openmc/mgxs/groups.py b/openmc/mgxs/groups.py index 338bc4b00c..cbf6ed9556 100644 --- a/openmc/mgxs/groups.py +++ b/openmc/mgxs/groups.py @@ -8,7 +8,7 @@ import numpy as np import openmc.checkvalue as cv -class EnergyGroups(object): +class EnergyGroups: """An energy groups structure used for multi-group cross-sections. Parameters diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py index 701f7d8eb7..00b30f8ed0 100644 --- a/openmc/mgxs/library.py +++ b/openmc/mgxs/library.py @@ -15,7 +15,7 @@ import openmc.checkvalue as cv from openmc.tallies import ESTIMATOR_TYPES -class Library(object): +class Library: """A multi-energy-group and multi-delayed-group cross section library for some energy group structure. diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py index 34c36ec170..e5953dfd1d 100644 --- a/openmc/mgxs_library.py +++ b/openmc/mgxs_library.py @@ -34,7 +34,7 @@ _FILETYPE_MGXS_LIBRARY = 'mgxs' _VERSION_MGXS_LIBRARY = 1 -class XSdata(object): +class XSdata: """A multi-group cross section data set providing all the multi-group data necessary for a multi-group OpenMC calculation. @@ -2284,7 +2284,7 @@ class XSdata(object): return data -class MGXSLibrary(object): +class MGXSLibrary: """Multi-Group Cross Sections file used for an OpenMC simulation. Corresponds directly to the MG version of the cross_sections.xml input file. diff --git a/openmc/mixin.py b/openmc/mixin.py index 09a63ae4e4..37e972974d 100644 --- a/openmc/mixin.py +++ b/openmc/mixin.py @@ -6,7 +6,7 @@ import numpy as np import openmc.checkvalue as cv -class EqualityMixin(object): +class EqualityMixin: """A Class which provides generic __eq__ and __ne__ functionality which can easily be inherited by downstream classes. """ @@ -29,7 +29,7 @@ class IDWarning(UserWarning): pass -class IDManagerMixin(object): +class IDManagerMixin: """A Class which automatically manages unique IDs. This mixin gives any subclass the ability to assign unique IDs through an diff --git a/openmc/model/model.py b/openmc/model/model.py index 33a38ef224..b5c190716b 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -5,7 +5,7 @@ import openmc from openmc.checkvalue import check_type, check_value -class Model(object): +class Model: """Model container. This class can be used to store instances of :class:`openmc.Geometry`, diff --git a/openmc/particle_restart.py b/openmc/particle_restart.py index 68a7614584..b465bffcc5 100644 --- a/openmc/particle_restart.py +++ b/openmc/particle_restart.py @@ -4,7 +4,8 @@ import openmc.checkvalue as cv _VERSION_PARTICLE_RESTART = 2 -class Particle(object): + +class Particle: """Information used to restart a specific particle that caused a simulation to fail. diff --git a/openmc/polynomial.py b/openmc/polynomial.py index 82a6756c82..6c0e82461f 100644 --- a/openmc/polynomial.py +++ b/openmc/polynomial.py @@ -29,7 +29,7 @@ def legendre_from_expcoef(coef, domain=(-1, 1)): return np.polynomial.Legendre(c, domain) -class Polynomial(object): +class Polynomial: """Abstract Polynomial Class for creating polynomials. """ def __init__(self, coef): @@ -82,24 +82,24 @@ class ZernikeRadial(Polynomial): class Zernike(Polynomial): r"""Create Zernike polynomials given coefficients and domain. - + The azimuthal Zernike polynomials are defined as in :class:`ZernikeFilter`. - + Parameters ---------- coef : Iterable of float A list of coefficients of each term in radial only Zernike polynomials radius : float Domain of Zernike polynomials to be applied on. Default is 1. - + Attributes ---------- order : int The maximum (even) order of Zernike polynomials. radius : float Domain of Zernike polynomials to be applied on. Default is 1. - theta : float - Azimuthal of Zernike polynomial to be applied on. Default is 0. + theta : float + Azimuthal of Zernike polynomial to be applied on. Default is 0. norm_coef : iterable of float The list of coefficients of each term in the polynomials after normailization. @@ -115,7 +115,7 @@ class Zernike(Polynomial): for m in range(-n, n + 1, 2): j = int((n*(n + 2) + m)/2) if m == 0: - norm_vec[j] = n + 1 + norm_vec[j] = n + 1 else: norm_vec[j] = 2*n + 2 norm_vec /= (math.pi * radius**2) diff --git a/openmc/settings.py b/openmc/settings.py index cddef9255f..e028656df4 100644 --- a/openmc/settings.py +++ b/openmc/settings.py @@ -15,7 +15,7 @@ _RUN_MODES = ['eigenvalue', 'fixed source', 'plot', 'volume', 'particle restart' _RES_SCAT_METHODS = ['dbrc', 'rvs'] -class Settings(object): +class Settings: """Settings used for an OpenMC simulation. Attributes @@ -59,9 +59,9 @@ class Settings(object): generations_per_batch : int Number of generations per batch max_lost_particles : int - Maximum number of lost particles + Maximum number of lost particles rel_max_lost_particles : int - Maximum number of lost particles, relative to the total number of particles + Maximum number of lost particles, relative to the total number of particles inactive : int Number of inactive batches keff_trigger : dict @@ -262,11 +262,11 @@ class Settings(object): @property def max_lost_particles(self): - return self._max_lost_particles + return self._max_lost_particles @property def rel_max_lost_particles(self): - return self._rel_max_lost_particles + return self._rel_max_lost_particles @property def particles(self): @@ -435,14 +435,14 @@ class Settings(object): def max_lost_particles(self, max_lost_particles): cv.check_type('max_lost_particles', max_lost_particles, Integral) cv.check_greater_than('max_lost_particles', max_lost_particles, 0) - self._max_lost_particles = max_lost_particles + self._max_lost_particles = max_lost_particles @rel_max_lost_particles.setter def rel_max_lost_particles(self, rel_max_lost_particles): cv.check_type('rel_max_lost_particles', rel_max_lost_particles, Real) cv.check_greater_than('rel_max_lost_particles', rel_max_lost_particles, 0) cv.check_less_than('rel_max_lost_particles', rel_max_lost_particles, 1) - self._rel_max_lost_particles = rel_max_lost_particles + self._rel_max_lost_particles = rel_max_lost_particles @particles.setter def particles(self, particles): @@ -789,12 +789,12 @@ class Settings(object): def _create_max_lost_particles_subelement(self, root): if self._max_lost_particles is not None: element = ET.SubElement(root, "max_lost_particles") - element.text = str(self._max_lost_particles) + element.text = str(self._max_lost_particles) def _create_rel_max_lost_particles_subelement(self, root): if self._rel_max_lost_particles is not None: element = ET.SubElement(root, "rel_max_lost_particles") - element.text = str(self._rel_max_lost_particles) + element.text = str(self._rel_max_lost_particles) def _create_particles_subelement(self, root): if self._particles is not None: @@ -1069,12 +1069,12 @@ class Settings(object): def _max_lost_particles_from_xml_element(self, root): text = get_text(root, 'max_lost_particles') if text is not None: - self.max_lost_particles = int(text) + self.max_lost_particles = int(text) def _rel_max_lost_particles_from_xml_element(self, root): text = get_text(root, 'rel_max_lost_particles') if text is not None: - self.rel_max_lost_particles = float(text) + self.rel_max_lost_particles = float(text) def _generations_per_batch_from_xml_element(self, root): text = get_text(root, 'generations_per_batch') diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 84512d70e7..7d9895f8e3 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -14,7 +14,7 @@ import openmc.checkvalue as cv _VERSION_STATEPOINT = 17 -class StatePoint(object): +class StatePoint: """State information on a simulation at a certain point in time (at the end of a given batch). Statepoints can be used to analyze tally results as well as restart a simulation. @@ -310,7 +310,7 @@ class StatePoint(object): if self.run_mode == 'eigenvalue': return self._f['n_inactive'][()] else: - return None + return None @property def n_particles(self): diff --git a/openmc/summary.py b/openmc/summary.py index bcb3ba33b6..b5486ecb58 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -12,7 +12,7 @@ from openmc.region import Region _VERSION_SUMMARY = 6 -class Summary(object): +class Summary: """Summary of geometry, materials, and tallies used in a simulation. Attributes diff --git a/openmc/tallies.py b/openmc/tallies.py index 3c3877e610..613ffad995 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -128,28 +128,18 @@ class Tally(IDManagerMixin): self._results_read = False def __repr__(self): - string = 'Tally\n' - string += '{: <16}=\t{}\n'.format('\tID', self.id) - string += '{: <16}=\t{}\n'.format('\tName', self.name) - + parts = ['Tally'] + parts.append('{: <16}=\t{}'.format('\tID', self.id)) + parts.append('{: <16}=\t{}'.format('\tName', self.name)) if self.derivative is not None: - string += '{: <16}=\t{}\n'.format('\tDerivative ID', - str(self.derivative.id)) - + parts.append('{: <16}=\t{}'.format('\tDerivative ID', self.derivative.id)) filters = ', '.join(type(f).__name__ for f in self.filters) - string += '{: <16}=\t{}\n'.format('\tFilters', filters) - - string += '{: <16}=\t'.format('\tNuclides') - - for nuclide in self.nuclides: - string += str(nuclide) + ' ' - - string += '\n' - - string += '{: <16}=\t{}\n'.format('\tScores', self.scores) - string += '{: <16}=\t{}\n'.format('\tEstimator', self.estimator) - - return string + parts.append('{: <16}=\t{}'.format('\tFilters', filters)) + nuclides = ' '.join(str(nuclide) for nuclide in self.nuclides) + parts.append('{: <16}=\t'.format('\tNuclides', nuclides)) + parts.append('{: <16}=\t{}\n'.format('\tScores', self.scores)) + parts.append('{: <16}=\t{}\n'.format('\tEstimator', self.estimator)) + return '\n'.join(parts) @property def name(self): diff --git a/openmc/trigger.py b/openmc/trigger.py index 98557aab58..f28995778a 100644 --- a/openmc/trigger.py +++ b/openmc/trigger.py @@ -7,7 +7,7 @@ from collections.abc import Iterable import openmc.checkvalue as cv -class Trigger(object): +class Trigger: """A criterion for when to finish a simulation based on tally uncertainties. Parameters diff --git a/openmc/volume.py b/openmc/volume.py index a59fb2acf6..f7849ce0a0 100644 --- a/openmc/volume.py +++ b/openmc/volume.py @@ -15,7 +15,7 @@ import openmc.checkvalue as cv _VERSION_VOLUME = 1 -class VolumeCalculation(object): +class VolumeCalculation: """Stochastic volume calculation specifications and results. Parameters diff --git a/tests/dummy_operator.py b/tests/dummy_operator.py index 13e8b7d417..1bb00129d0 100644 --- a/tests/dummy_operator.py +++ b/tests/dummy_operator.py @@ -74,7 +74,7 @@ SCHEMES = { } -class TestChain(object): +class TestChain: """Empty chain to assist with unit testing depletion routines Only really provides the form_matrix function, but acts like diff --git a/tests/testing_harness.py b/tests/testing_harness.py index d5484d73e9..9eb840b2dd 100644 --- a/tests/testing_harness.py +++ b/tests/testing_harness.py @@ -30,7 +30,7 @@ def colorize(diff): yield line -class TestHarness(object): +class TestHarness: """General class for running OpenMC regression tests.""" def __init__(self, statepoint_name): From a2691a32d5a61e379ecc061511e21bc9e83dd35f Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Mon, 23 Mar 2020 13:43:36 -0400 Subject: [PATCH 135/205] Use atoms/b-cm, atoms/cm3 in ResultsList.get_atoms Co-Authored-By: Paul Romano --- openmc/deplete/results_list.py | 6 +++--- tests/unit_tests/test_deplete_resultslist.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/openmc/deplete/results_list.py b/openmc/deplete/results_list.py index 9e7cd9ffb9..eee214d8ff 100644 --- a/openmc/deplete/results_list.py +++ b/openmc/deplete/results_list.py @@ -57,7 +57,7 @@ class ResultsList(list): Material name to evaluate nuc : str Nuclide name to evaluate - nuc_units : {"atoms", "atoms/b/cm", "atoms/cm^3"}, optional + nuc_units : {"atoms", "atom/b-cm", "atom/cm3"}, optional Units for the returned concentration. Default is ``"atoms"`` time_units : {"s", "d"}, optional Units for the returned time array. Default is ``"s"`` to @@ -73,7 +73,7 @@ class ResultsList(list): """ check_value("time_units", time_units, {"s", "d"}) check_value("nuc_units", nuc_units, - {"atoms", "atoms/b/cm", "atoms/cm^3"}) + {"atoms", "atom/b-cm", "atom/cm3"}) time = np.empty_like(self, dtype=float) concentration = np.empty_like(self, dtype=float) @@ -90,7 +90,7 @@ class ResultsList(list): if nuc_units != "atoms": # Divide by volume to get density concentration /= self[0].volume[mat] - if nuc_units == "atoms/b/cm": + if nuc_units == "atom/b-cm": # 1 barn = 1e-24 cm^2 concentration *= 1e-24 diff --git a/tests/unit_tests/test_deplete_resultslist.py b/tests/unit_tests/test_deplete_resultslist.py index f95bc31539..d3609135b3 100644 --- a/tests/unit_tests/test_deplete_resultslist.py +++ b/tests/unit_tests/test_deplete_resultslist.py @@ -29,12 +29,12 @@ def test_get_atoms(res): # Check alternate units volume = res[0].volume["1"] - t_days, n_cm3 = res.get_atoms("1", "Xe135", nuc_units="atoms/cm^3", time_units="d") + t_days, n_cm3 = res.get_atoms("1", "Xe135", nuc_units="atom/cm3", time_units="d") assert t_days == pytest.approx(t_ref / (60 * 60 * 24)) assert n_cm3 == pytest.approx(n_ref / volume) - _t, n_bcm = res.get_atoms("1", "Xe135", nuc_units="atoms/b/cm") + _t, n_bcm = res.get_atoms("1", "Xe135", nuc_units="atom/b-cm") assert n_bcm == pytest.approx(n_cm3 * 1e-24) From 2394468a49ef23ce660fcc865ed8a3445fed84e3 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Mon, 23 Mar 2020 13:56:35 -0400 Subject: [PATCH 136/205] Support time in h, min from ResultsList.get_atoms --- openmc/deplete/results_list.py | 10 +++++++--- tests/unit_tests/test_deplete_resultslist.py | 6 +++++- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/openmc/deplete/results_list.py b/openmc/deplete/results_list.py index eee214d8ff..db1f0ff9db 100644 --- a/openmc/deplete/results_list.py +++ b/openmc/deplete/results_list.py @@ -59,9 +59,9 @@ class ResultsList(list): Nuclide name to evaluate nuc_units : {"atoms", "atom/b-cm", "atom/cm3"}, optional Units for the returned concentration. Default is ``"atoms"`` - time_units : {"s", "d"}, optional + time_units : {"s", "min", "h", "d"}, optional Units for the returned time array. Default is ``"s"`` to - return the value in seconds + return the value in seconds. Returns ------- @@ -71,7 +71,7 @@ class ResultsList(list): Concentration of specified nuclide in units of ``nuc_units`` """ - check_value("time_units", time_units, {"s", "d"}) + check_value("time_units", time_units, {"s", "d", "min", "h"}) check_value("nuc_units", nuc_units, {"atoms", "atom/b-cm", "atom/cm3"}) @@ -86,6 +86,10 @@ class ResultsList(list): # Unit conversions if time_units == "d": time /= (60 * 60 * 24) + elif time_units == "h": + time /= (60 * 60) + elif time_units == "min": + time /= 60 if nuc_units != "atoms": # Divide by volume to get density diff --git a/tests/unit_tests/test_deplete_resultslist.py b/tests/unit_tests/test_deplete_resultslist.py index d3609135b3..9b40a1ac91 100644 --- a/tests/unit_tests/test_deplete_resultslist.py +++ b/tests/unit_tests/test_deplete_resultslist.py @@ -34,8 +34,12 @@ def test_get_atoms(res): assert t_days == pytest.approx(t_ref / (60 * 60 * 24)) assert n_cm3 == pytest.approx(n_ref / volume) - _t, n_bcm = res.get_atoms("1", "Xe135", nuc_units="atom/b-cm") + t_min, n_bcm = res.get_atoms("1", "Xe135", nuc_units="atom/b-cm", time_units="min") assert n_bcm == pytest.approx(n_cm3 * 1e-24) + assert t_min == pytest.approx(t_ref / 60) + + t_hour, _n = res.get_atoms("1", "Xe135", time_units="h") + assert t_hour == pytest.approx(t_ref / (60 * 60)) def test_get_reaction_rate(res): From 8ba369d6cc44cbdc1fe2eaf12d043eafcd7512ce Mon Sep 17 00:00:00 2001 From: =shimwell Date: Mon, 23 Mar 2020 20:15:59 +0000 Subject: [PATCH 137/205] added elements from formula --- openmc/material.py | 96 ++++++++++++++++++++++++++++++- tests/unit_tests/test_material.py | 55 ++++++++++++++++++ 2 files changed, 150 insertions(+), 1 deletion(-) diff --git a/openmc/material.py b/openmc/material.py index 3952fe4e99..8d5168d512 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -1,9 +1,10 @@ -from collections import OrderedDict, defaultdict +from collections import OrderedDict, defaultdict, Counter from collections.abc import Iterable from copy import deepcopy from numbers import Real, Integral from pathlib import Path import warnings +import re from xml.etree import ElementTree as ET import numpy as np @@ -588,6 +589,99 @@ class Material(IDManagerMixin): enrichment_type): self.add_nuclide(*nuclide) + def add_elements_from_formula(self, formula, percent_type='ao', enrichment=None, + enrichment_target=None, enrichment_type=None): + """Add a elements from a chemical formula to the material. + + Parameters + ---------- + formula : str + Formula to add, e.g., 'C2O', 'C6H12O6', or (NH4)2SO4. + Note this is case sensitive, elements must start with an uppercase + character. Any numbers will be converted to integers (rounded down). + percent : float + Atom or weight percent + percent_type : {'ao', 'wo'}, optional + 'ao' for atom percent and 'wo' for weight percent. Defaults to atom + percent. + enrichment : float, optional + Enrichment of an enrichment_taget nuclide in percent (ao or wo). + If enrichment_taget is not supplied then it is enrichment for U235 + in weight percent. For example, input 4.95 for 4.95 weight percent + enriched U. + Default is None (natural composition). + enrichment_target: str, optional + Single nuclide name to enrich from a natural composition (e.g., 'O16') + enrichment_type: {'ao', 'wo'}, optional + 'ao' for enrichment as atom percent and 'wo' for weight percent. + Default is: 'ao' for two-isotope enrichment; 'wo' for U enrichment + + Notes + ----- + General enrichment procedure is allowed only for elements composed of + two isotopes. If `enrichment_target` is given without `enrichment` + natural composition is added to the material. + + """ + cv.check_type('formula', formula, str) + + # Tokenizes the formula and check validity of tokens + tokens = re.findall(r"([A-Z][a-z]*)(\d*)|(\()|(\))(\d*)", formula) + for row in tokens: + for token in row: + if token.isalpha(): + if token not in list(openmc.data.ATOMIC_NUMBER.keys())[1:]: + msg = 'Formula entry {} not an element symbol.' \ + .format(token) + raise ValueError(msg) + elif token not in ['(', ')', ''] and not token.isdigit(): + msg = 'Formula must be made from a sequence of ' \ + 'element symbols, integers, and backets. ' \ + '{} is not an allowable entry.'.format(token) + raise ValueError(msg) + + # Checks that the number of opening and closing brackets are equal + if formula.count('(') != formula.count(')'): + msg = 'Number of opening and closing brackets is not equal ' \ + 'in the input formula {}.'.format(formula) + raise ValueError(msg) + + # Checks that every part of the original formula has been tokenized + for row in tokens: + for token in row: + formula = formula.replace(token, '', 1) + if len(formula) != 0: + msg = 'Part of formula was not successfully parsed as an ' \ + 'element symbol, bracket or integer. {} was not parsed.' \ + .format(formula) + raise ValueError(msg) + + # Works through the tokens building a stack + mat_stack = [Counter()] + for symbol, multi1, opening_bracket, closing_bracket, multi2 in tokens: + if symbol: + mat_stack[-1][symbol] += int(multi1 or 1) + if opening_bracket: + mat_stack.append(Counter()) + if closing_bracket: + stack_top = mat_stack.pop() + for i in stack_top: + mat_stack[-1][i] += int(multi2 or 1) * stack_top[i] + + # Normalizing percentages + percents = mat_stack[0].values() + norm_percents = [float(i) / sum(percents) for i in percents] + elements = mat_stack[0].keys() + + # Adds each element and percent to the material + for element, percent in zip(elements, norm_percents): + if enrichment_target is not None and element == re.sub(r'\d+$', '', enrichment_target): + self.add_element(element, percent, percent_type, enrichment, + enrichment_target, enrichment_type) + else: + self.add_element(element, percent, percent_type) + + def add_s_alpha_beta(self, name, fraction=1.0): r"""Add an :math:`S(\alpha,\beta)` table to the material diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 6f51135ab2..7153fded5a 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -58,6 +58,61 @@ def test_elements_by_name(): assert a._nuclides == b._nuclides assert b._nuclides == c._nuclides +def test_adding_elements_by_formula(): + """Test adding elements from a formula""" + # testing the correct nuclides are added to the Material + m = openmc.Material() + m.add_elements_from_formula('Li4SiO4') + ref_dens = {'Li6': 0.033728, 'Li7': 0.410715, + 'Si28': 0.102477, 'Si29': 0.0052035, 'Si30': 0.0034301, + 'O16': 0.443386, 'O17': 0.000168} + nuc_dens = m.get_nuclide_atom_densities() + for nuclide in ref_dens: + assert nuc_dens[nuclide][1] == pytest.approx(ref_dens[nuclide], 1e-2) + + # testing the correct nuclides are added to the Material when enriched + m = openmc.Material() + m.add_elements_from_formula('Li4SiO4', + enrichment=60., + enrichment_target='Li6') + ref_dens = {'Li6': 0.2666, 'Li7': 0.1777, + 'Si28': 0.102477, 'Si29': 0.0052035, 'Si30': 0.0034301, + 'O16': 0.443386, 'O17': 0.000168} + nuc_dens = m.get_nuclide_atom_densities() + for nuclide in ref_dens: + assert nuc_dens[nuclide][1] == pytest.approx(ref_dens[nuclide], 1e-2) + + # testing the use of brackets + m = openmc.Material() + m.add_elements_from_formula('Mg2(NO3)2') + ref_dens = {'Mg24': 0.157902, 'Mg25': 0.02004, 'Mg26': 0.022058, + 'N14': 0.199267, 'N15': 0.000732, + 'O16': 0.599772, 'O17': 0.000227} + nuc_dens = m.get_nuclide_atom_densities() + for nuclide in ref_dens: + assert nuc_dens[nuclide][1] == pytest.approx(ref_dens[nuclide], 1e-2) + + # testing lowercase elements results in a value error + m = openmc.Material() + with pytest.raises(ValueError): + m.add_elements_from_formula('li4SiO4') + + # testing lowercase elements results in a value error + m = openmc.Material() + with pytest.raises(ValueError): + m.add_elements_from_formula('Li4Sio4') + + # testing incorrect character in formula results in a value error + m = openmc.Material() + with pytest.raises(ValueError): + m.add_elements_from_formula('Li4$SiO4') + + # testing unequal opening and closing brackets + m = openmc.Material() + with pytest.raises(ValueError): + m.add_elements_from_formula('Fe(H2O)4(OH)2)') + + def test_density(): m = openmc.Material() for unit in ['g/cm3', 'g/cc', 'kg/m3', 'atom/b-cm', 'atom/cm3']: From 4e22c6a936c17f60d2c726818acf8836b76615d7 Mon Sep 17 00:00:00 2001 From: =shimwell Date: Mon, 23 Mar 2020 20:47:43 +0000 Subject: [PATCH 138/205] removed unused argument description --- openmc/material.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index 8d5168d512..bdcf6adf17 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -599,8 +599,6 @@ class Material(IDManagerMixin): Formula to add, e.g., 'C2O', 'C6H12O6', or (NH4)2SO4. Note this is case sensitive, elements must start with an uppercase character. Any numbers will be converted to integers (rounded down). - percent : float - Atom or weight percent percent_type : {'ao', 'wo'}, optional 'ao' for atom percent and 'wo' for weight percent. Defaults to atom percent. From 43e93903a92a77968b9c70b75e7140b8713f2a3d Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 15:40:25 -0500 Subject: [PATCH 139/205] Changes in arithmetic.py from PullRequest Inc. review --- openmc/arithmetic.py | 80 +++++++++++++++++++------------------------- 1 file changed, 35 insertions(+), 45 deletions(-) diff --git a/openmc/arithmetic.py b/openmc/arithmetic.py index c52dceb8a8..1f1cbebcba 100644 --- a/openmc/arithmetic.py +++ b/openmc/arithmetic.py @@ -66,8 +66,8 @@ class CrossScore: return not self == other def __repr__(self): - string = '({0} {1} {2})'.format(self.left_score, - self.binary_op, self.right_score) + string = '({} {} {})'.format(self.left_score, self.binary_op, + self.right_score) return string @property @@ -239,13 +239,10 @@ class CrossFilter: """ - def __init__(self, left_filter=None, right_filter=None, binary_op=None): - + def __init__(self, left_filter, right_filter, binary_op=None): left_type = left_filter.type right_type = right_filter.type - self._type = '({0} {1} {2})'.format(left_type, binary_op, right_type) - - self._bins = {} + self._type = '({} {} {})'.format(left_type, binary_op, right_type) self._left_filter = None self._right_filter = None @@ -253,10 +250,8 @@ class CrossFilter: if left_filter is not None: self.left_filter = left_filter - self._bins['left'] = left_filter.bins if right_filter is not None: self.right_filter = right_filter - self._bins['right'] = right_filter.bins if binary_op is not None: self.binary_op = binary_op @@ -270,17 +265,18 @@ class CrossFilter: return not self == other def __repr__(self): - - string = 'CrossFilter\n' - filter_type = '({0} {1} {2})'.format(self.left_filter.type, - self.binary_op, - self.right_filter.type) - filter_bins = '({0} {1} {2})'.format(self.left_filter.bins, - self.binary_op, - self.right_filter.bins) - string += '{0: <16}{1}{2}\n'.format('\tType', '=\t', filter_type) - string += '{0: <16}{1}{2}\n'.format('\tBins', '=\t', filter_bins) - return string + filter_type = '({} {} {})'.format(self.left_filter.type, + self.binary_op, + self.right_filter.type) + filter_bins = '({} {} {})'.format(self.left_filter.bins, + self.binary_op, + self.right_filter.bins) + parts = [ + 'CrossFilter', + '{: <16}=\t{}'.format('\tType', filter_type), + '{: <16}=\t{}'.format('\tBins', filter_bins) + ] + return '\n'.join(parts) @property def left_filter(self): @@ -300,7 +296,7 @@ class CrossFilter: @property def bins(self): - return self._bins['left'], self._bins['right'] + return self._left_filter.bins, self._right_filter.bins @property def num_bins(self): @@ -312,7 +308,7 @@ class CrossFilter: @type.setter def type(self, filter_type): if filter_type not in _FILTER_TYPES: - msg = 'Unable to set CrossFilter type to "{0}" since it ' \ + msg = 'Unable to set CrossFilter type to "{}" since it ' \ 'is not one of the supported types'.format(filter_type) raise ValueError(msg) @@ -323,14 +319,12 @@ class CrossFilter: cv.check_type('left_filter', left_filter, (openmc.Filter, CrossFilter, AggregateFilter)) self._left_filter = left_filter - self._bins['left'] = left_filter.bins @right_filter.setter def right_filter(self, right_filter): cv.check_type('right_filter', right_filter, (openmc.Filter, CrossFilter, AggregateFilter)) self._right_filter = right_filter - self._bins['right'] = right_filter.bins @binary_op.setter def binary_op(self, binary_op): @@ -462,7 +456,7 @@ class AggregateScore: def __repr__(self): string = ', '.join(map(str, self.scores)) - string = '{0}({1})'.format(self.aggregate_op, string) + string = '{}({})'.format(self.aggregate_op, string) return string @property @@ -536,7 +530,7 @@ class AggregateNuclide: def __repr__(self): # Append each nuclide in the aggregate to the string - string = '{0}('.format(self.aggregate_op) + string = '{}('.format(self.aggregate_op) names = [nuclide.name if isinstance(nuclide, openmc.Nuclide) else str(nuclide) for nuclide in self.nuclides] string += ', '.join(map(str, names)) + ')' @@ -601,17 +595,16 @@ class AggregateFilter: """ - def __init__(self, aggregate_filter=None, bins=None, aggregate_op=None): + def __init__(self, aggregate_filter, bins=None, aggregate_op=None): - self._type = '{0}({1})'.format(aggregate_op, - aggregate_filter.short_name.lower()) + self._type = '{}({})'.format(aggregate_op, + aggregate_filter.short_name.lower()) self._bins = None self._aggregate_filter = None self._aggregate_op = None - if aggregate_filter is not None: - self.aggregate_filter = aggregate_filter + self.aggregate_filter = aggregate_filter if bins is not None: self.bins = bins if aggregate_op is not None: @@ -642,10 +635,12 @@ class AggregateFilter: return not self > other def __repr__(self): - string = 'AggregateFilter\n' - string += '{0: <16}{1}{2}\n'.format('\tType', '=\t', self.type) - string += '{0: <16}{1}{2}\n'.format('\tBins', '=\t', self.bins) - return string + parts = [ + 'AggregateFilter', + '{: <16}=\t{}'.format('\tType', self.type), + '{: <16}=\t{}'.format('\tBins', self.bins) + ] + return '\n'.join(parts) @property def aggregate_filter(self): @@ -670,7 +665,7 @@ class AggregateFilter: @type.setter def type(self, filter_type): if filter_type not in _FILTER_TYPES: - msg = 'Unable to set AggregateFilter type to "{0}" since it ' \ + msg = 'Unable to set AggregateFilter type to "{}" since it ' \ 'is not one of the supported types'.format(filter_type) raise ValueError(msg) @@ -723,7 +718,7 @@ class AggregateFilter: if filter_bin not in self.bins: msg = 'Unable to get the bin index for AggregateFilter since ' \ - '"{0}" is not one of the bins'.format(filter_bin) + '"{}" is not one of the bins'.format(filter_bin) raise ValueError(msg) else: return self.bins.index(filter_bin) @@ -801,12 +796,7 @@ class AggregateFilter: return False # None of the bins in this filter should match in the other filter - for bin in self.bins: - if bin in other.bins: - return False - - # If all conditional checks passed then filters are mergeable - return True + return not any(b in other.bins for b in self.bins) def merge(self, other): """Merge this aggregatefilter with another. @@ -824,8 +814,8 @@ class AggregateFilter: """ if not self.can_merge(other): - msg = 'Unable to merge "{0}" with "{1}" ' \ - 'filters'.format(self.type, other.type) + msg = 'Unable to merge "{}" with "{}" filters'.format( + self.type, other.type) raise ValueError(msg) # Create deep copy of filter to return as merged filter From 468361b62c0855611553aa70fb10f1da3fd5c084 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 16:05:55 -0500 Subject: [PATCH 140/205] Changes in checkvalue.py from PullRequest Inc. review --- openmc/checkvalue.py | 58 +++++++++++++++++++++++--------------------- 1 file changed, 31 insertions(+), 27 deletions(-) diff --git a/openmc/checkvalue.py b/openmc/checkvalue.py index e19780a1cf..e113a9fef7 100644 --- a/openmc/checkvalue.py +++ b/openmc/checkvalue.py @@ -24,34 +24,35 @@ def check_type(name, value, expected_type, expected_iter_type=None): if not isinstance(value, expected_type): if isinstance(expected_type, Iterable): - msg = 'Unable to set "{0}" to "{1}" which is not one of the ' \ - 'following types: "{2}"'.format(name, value, ', '.join( + msg = 'Unable to set "{}" to "{}" which is not one of the ' \ + 'following types: "{}"'.format(name, value, ', '.join( [t.__name__ for t in expected_type])) else: - msg = 'Unable to set "{0}" to "{1}" which is not of type "{2}"'.format( + msg = 'Unable to set "{}" to "{}" which is not of type "{}"'.format( name, value, expected_type.__name__) raise TypeError(msg) if expected_iter_type: if isinstance(value, np.ndarray): if not issubclass(value.dtype.type, expected_iter_type): - msg = 'Unable to set "{0}" to "{1}" since each item must be ' \ - 'of type "{2}"'.format(name, value, - expected_iter_type.__name__) + msg = 'Unable to set "{}" to "{}" since each item must be ' \ + 'of type "{}"'.format(name, value, + expected_iter_type.__name__) + raise TypeError(msg) else: return for item in value: if not isinstance(item, expected_iter_type): if isinstance(expected_iter_type, Iterable): - msg = 'Unable to set "{0}" to "{1}" since each item must be ' \ - 'one of the following types: "{2}"'.format( + msg = 'Unable to set "{}" to "{}" since each item must be ' \ + 'one of the following types: "{}"'.format( name, value, ', '.join([t.__name__ for t in expected_iter_type])) else: - msg = 'Unable to set "{0}" to "{1}" since each item must be ' \ - 'of type "{2}"'.format(name, value, - expected_iter_type.__name__) + msg = 'Unable to set "{}" to "{}" since each item must be ' \ + 'of type "{}"'.format(name, value, + expected_iter_type.__name__) raise TypeError(msg) @@ -150,18 +151,18 @@ def check_length(name, value, length_min, length_max=None): """ if length_max is None: - if len(value) != length_min: - msg = 'Unable to set "{0}" to "{1}" since it must be of ' \ - 'length "{2}"'.format(name, value, length_min) + if len(value) < length_min: + msg = 'Unable to set "{}" to "{}" since it must be at least of ' \ + 'length "{}"'.format(name, value, length_min) raise ValueError(msg) elif not length_min <= len(value) <= length_max: if length_min == length_max: - msg = 'Unable to set "{0}" to "{1}" since it must be of ' \ - 'length "{2}"'.format(name, value, length_min) + msg = 'Unable to set "{}" to "{}" since it must be of ' \ + 'length "{}"'.format(name, value, length_min) else: - msg = 'Unable to set "{0}" to "{1}" since it must have length ' \ - 'between "{2}" and "{3}"'.format(name, value, length_min, - length_max) + msg = 'Unable to set "{}" to "{}" since it must have length ' \ + 'between "{}" and "{}"'.format(name, value, length_min, + length_max) raise ValueError(msg) @@ -184,6 +185,7 @@ def check_value(name, value, accepted_values): name, value, accepted_values) raise ValueError(msg) + def check_less_than(name, value, maximum, equality=False): """Ensure that an object's value is less than a given value. @@ -211,6 +213,7 @@ def check_less_than(name, value, maximum, equality=False): 'or equal to "{2}"'.format(name, value, maximum) raise ValueError(msg) + def check_greater_than(name, value, minimum, equality=False): """Ensure that an object's value is greater than a given value. @@ -265,13 +268,13 @@ def check_filetype_version(obj, expected_type, expected_version): if this_version[0] != expected_version: raise IOError('{} file has a version of {} which is not ' 'consistent with the version expected by OpenMC, {}' - .format(this_filetype, - '.'.join(str(v) for v in this_version), - expected_version)) + .format(this_filetype, + '.'.join(str(v) for v in this_version), + expected_version)) except AttributeError: - raise IOError('Could not read {} file. This most likely means the {} ' + raise IOError('Could not read {} file. This most likely means the ' 'file was produced by a different version of OpenMC than ' - 'the one you are using.'.format(expected_type)) + 'the one you are using.'.format(obj.filename)) class CheckedList(list): @@ -288,12 +291,13 @@ class CheckedList(list): """ - def __init__(self, expected_type, name, items=[]): + def __init__(self, expected_type, name, items=None): super().__init__() self.expected_type = expected_type self.name = name - for item in items: - self.append(item) + if items is not None: + for item in items: + self.append(item) def __add__(self, other): new_instance = copy.copy(self) From dd472b9730d4e8aab8cf70c0f06596b5693572f1 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 16:19:07 -0500 Subject: [PATCH 141/205] Changes in cmfd.py from PullRequest Inc. review --- openmc/cmfd.py | 52 +++++++++++++++++++++++--------------------------- 1 file changed, 24 insertions(+), 28 deletions(-) diff --git a/openmc/cmfd.py b/openmc/cmfd.py index 6b3694774d..476ff0b30e 100644 --- a/openmc/cmfd.py +++ b/openmc/cmfd.py @@ -182,11 +182,11 @@ class CMFDMesh: self._albedo = albedo @map.setter - def map(self, meshmap): - check_type('CMFD mesh map', meshmap, Iterable, Integral) - for m in meshmap: + def map(self, mesh_map): + check_type('CMFD mesh map', mesh_map, Iterable, Integral) + for m in mesh_map: check_value('CMFD mesh map', m, [0, 1]) - self._map = meshmap + self._map = mesh_map class CMFDRun: @@ -535,7 +535,6 @@ class CMFDRun: check_greater_than('CMFD feedback begin batch', begin, 0) self._solver_begin = begin - @ref_d.setter def ref_d(self, diff_params): check_type('Reference diffusion params', diff_params, @@ -896,9 +895,8 @@ class CMFDRun: cmfd_group.create_dataset('total_rate', data=self._total_rate) elif openmc.settings.verbosity >= 5: - print(' CMFD data not written to statepoint file' - 'as it already exists in {}'.format(filename)) - sys.stdout.flush() + print(' CMFD data not written to statepoint file as it ' + 'already exists in {}'.format(filename), flush=True) def _initialize_linsolver(self): # Determine number of rows in CMFD matrix @@ -1185,7 +1183,6 @@ class CMFDRun: # Write CMFD output if CMFD on for current batch self._write_cmfd_output() - def _cmfd_tally_reset(self): """Resets all CMFD tallies in memory""" # Print message @@ -1753,7 +1750,7 @@ class CMFDRun: # Compute new flux with C++ solver innerits = openmc.lib._dll.openmc_run_linsolver(loss.data, s_o, - phi_n, toli) + phi_n, toli) # Compute new source vector s_n = prod.dot(phi_n) @@ -1785,7 +1782,7 @@ class CMFDRun: # Update tolerance for inner iterations toli = max(atoli, rtoli*norm_n) - def _check_convergence(self, s_n, s_o, k_n, k_o, iter, innerits): + def _check_convergence(self, s_n, s_o, k_n, k_o, iteration, innerits): """Checks the convergence of the CMFD problem Parameters @@ -1798,9 +1795,9 @@ class CMFDRun: K-effective from current iteration k_o : float K-effective from previous iteration - iter: int + iteration : int Iteration number - innerits: int + innerits : int Number of iterations required for convergence in inner GS loop Returns @@ -1825,14 +1822,13 @@ class CMFDRun: # Print out to user if self._power_monitor and openmc.lib.master(): - str1 = ' {:d}:'.format(iter) - str2 = 'k-eff: {:0.8f}'.format(k_n) - str3 = 'k-error: {:.5e}'.format(kerr) - str4 = 'src-error: {:.5e}'.format(serr) - str5 = ' {:d}'.format(innerits) - print('{:8s}{:20s}{:25s}{:s}{:s}'.format(str1, str2, str3, str4, - str5)) - sys.stdout.flush() + print('{:8s}{:20s}{:25s}{:s}{:s}'.format( + ' {:d}:'.format(iteration), + 'k-eff: {:0.8f}'.format(k_n), + 'k-error: {:.5e}'.format(kerr), + 'src-error: {:.5e}'.format(serr), + ' {:d}'.format(innerits) + ), flush=True) return iconv, serr @@ -1850,7 +1846,7 @@ class CMFDRun: # Define coremap as cumulative sum over accelerated regions, # otherwise set value to _CMFD_NOACCEL self._coremap = np.where(self._coremap == 0, _CMFD_NOACCEL, - np.cumsum(self._coremap)-1) + np.cumsum(self._coremap) - 1) # Reshape coremap to three dimensional array # Indices of coremap in user input switched in x and z axes @@ -1929,19 +1925,19 @@ class CMFDRun: ') in group ' + str(ng-idx[-1]) raise OpenMCError(err_message) - # Get total rr from CMFD tally 0 + # Get total reaction rate (rr) from CMFD tally 0 totalrr = tallies[tally_id].results[:,1,1] - # Reshape totalrr array to target shape. Swap x and z axes so that - # shape is now [nx, ny, nz, ng, 1] + # Reshape total reaction rate array to target shape. Swap x and z axes + # so that shape is now [nx, ny, nz, ng, 1] reshape_totalrr = np.swapaxes(totalrr.reshape(target_tally_shape), 0, 2) - # Total rr is flipped in energy axis as tally results are given in - # reverse order of energy group + # Total reaction rate is flipped in energy axis as tally results are + # given in reverse order of energy group reshape_totalrr = np.flip(reshape_totalrr, axis=3) - # Bank total rr to total_rate + # Bank total reaction rate to total_rate self._total_rate = np.append(self._total_rate, reshape_totalrr, axis=4) From d347e4fdbd95d654597afa681b32a888bdce8079 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 16:23:27 -0500 Subject: [PATCH 142/205] Changes in element.py from PullRequest Inc. review --- openmc/element.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/openmc/element.py b/openmc/element.py index 3df2ff1f24..a8e344ddfa 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -40,7 +40,7 @@ class Element(str): cross_sections=None): """Expand natural element into its naturally-occurring isotopes. - An optional cross_sections argument or the OPENMC_CROSS_SECTIONS + An optional cross_sections argument or the :envvar:`OPENMC_CROSS_SECTIONS` environment variable is used to specify a cross_sections.xml file. If the cross_sections.xml file is found, the element is expanded only into the isotopes/nuclides present in cross_sections.xml. If no @@ -132,7 +132,6 @@ class Element(str): # If a cross_sections library is present, check natural nuclides # against the nuclides in the library if cross_sections is not None: - library_nuclides = set() tree = ET.parse(cross_sections) root = tree.getroot() @@ -174,14 +173,12 @@ class Element(str): # our knowledge of the common cross section libraries # (ENDF, JEFF, and JENDL) else: - # Add the mutual isotopes for nuclide in mutual_nuclides: abundances[nuclide] = NATURAL_ABUNDANCE[nuclide] # Adjust the abundances for the absent nuclides for nuclide in absent_nuclides: - if nuclide in ['O17', 'O18'] and 'O16' in mutual_nuclides: abundances['O16'] += NATURAL_ABUNDANCE[nuclide] elif nuclide == 'Ta180' and 'Ta181' in mutual_nuclides: From e92044bc4b5115239b226df40b04b91aedd8a827 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 19 Mar 2020 16:31:11 -0500 Subject: [PATCH 143/205] Remove unused import in executor.py --- openmc/executor.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/openmc/executor.py b/openmc/executor.py index 8e2979f019..9faf18b332 100644 --- a/openmc/executor.py +++ b/openmc/executor.py @@ -3,7 +3,6 @@ import subprocess from numbers import Integral import openmc -from openmc import VolumeCalculation def _run(args, output, cwd): @@ -201,7 +200,7 @@ def run(particles=None, threads=None, geometry_debug=False, if geometry_debug: args.append('-g') - + if event_based: args.append('-e') From a5605ea3a1223f1fe3d56b519ae5c5375dd848bd Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 20 Mar 2020 14:46:00 -0500 Subject: [PATCH 144/205] Changes in filter.py from PullRequest Inc. review --- openmc/filter.py | 97 ++++++++++++++++++------------------------------ 1 file changed, 36 insertions(+), 61 deletions(-) diff --git a/openmc/filter.py b/openmc/filter.py index 1988aee81e..62afbdd6ec 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -36,13 +36,16 @@ _PARTICLES = {'neutron', 'photon', 'electron', 'positron'} class FilterMeta(ABCMeta): + """Metaclass for filters that ensures class names are appropriate.""" + def __new__(cls, name, bases, namespace, **kwargs): # Check the class name. - if not name.endswith('Filter'): + required_suffix = 'Filter' + if not name.endswith(required_suffix): raise ValueError("All filter class names must end with 'Filter'") # Create a 'short_name' attribute that removes the 'Filter' suffix. - namespace['short_name'] = name[:-6] + namespace['short_name'] = name[:-len(required_suffix)] # Subclass methods can sort of inherit the docstring of parent class # methods. If a function is defined without a docstring, most (all?) @@ -51,7 +54,7 @@ class FilterMeta(ABCMeta): # use that docstring. However, Sphinx does not have that functionality. # This chunk of code handles this docstring inheritance manually so that # the autodocumentation will pick it up. - if name != 'Filter': + if name != required_suffix: # Look for newly-defined functions that were also in Filter. for func_name in namespace: if func_name in Filter.__dict__: @@ -72,6 +75,12 @@ class FilterMeta(ABCMeta): return super().__new__(cls, name, bases, namespace, **kwargs) +def _repeat_and_tile(bins, repeat_factor, data_size): + filter_bins = np.repeat(bins, repeat_factor) + tile_factor = data_size // len(filter_bins) + return np.tile(filter_bins, tile_factor) + + class Filter(IDManagerMixin, metaclass=FilterMeta): """Tally modifier that describes phase-space and other characteristics. @@ -292,8 +301,8 @@ class Filter(IDManagerMixin, metaclass=FilterMeta): if type(self) is not type(other): return False - for bin in other.bins: - if bin not in self.bins: + for b in other.bins: + if b not in self.bins: return False return True @@ -389,8 +398,7 @@ class WithIDFilter(Filter): # Extract ID values bins = np.array([b if isinstance(b, Integral) else b.id for b in bins]) - self.bins = bins - self.id = filter_id + super().__init__(bins, filter_id) def check_bins(self, bins): # Check the bin values. @@ -804,28 +812,16 @@ class MeshFilter(Filter): ny = nz = 1 # Generate multi-index sub-column for x-axis - filter_bins = np.arange(1, nx + 1) - 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) - filter_dict[(mesh_key, 'x')] = filter_bins + filter_dict[mesh_key, 'x'] = _repeat_and_tile( + np.arange(1, nx + 1), stride, data_size) # Generate multi-index sub-column for y-axis - filter_bins = np.arange(1, ny + 1) - repeat_factor = nx * stride - filter_bins = np.repeat(filter_bins, repeat_factor) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) - filter_dict[(mesh_key, 'y')] = filter_bins + filter_dict[mesh_key, 'y'] = _repeat_and_tile( + np.arange(1, ny + 1), nx * stride, data_size) # Generate multi-index sub-column for z-axis - filter_bins = np.arange(1, nz + 1) - repeat_factor = nx * ny * stride - filter_bins = np.repeat(filter_bins, repeat_factor) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) - filter_dict[(mesh_key, 'z')] = filter_bins + filter_dict[mesh_key, 'z'] = _repeat_and_tile( + np.arange(1, nz + 1), nx * ny * stride, data_size) # Initialize a Pandas DataFrame from the mesh dictionary df = pd.concat([df, pd.DataFrame(filter_dict)]) @@ -933,37 +929,22 @@ class MeshSurfaceFilter(MeshFilter): ny = nz = 1 # Generate multi-index sub-column for x-axis - filter_bins = np.arange(1, nx + 1) - repeat_factor = n_surfs * stride - filter_bins = np.repeat(filter_bins, repeat_factor) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) - filter_dict[(mesh_key, 'x')] = filter_bins + filter_dict[mesh_key, 'x'] = _repeat_and_tile( + np.arange(1, nx + 1), n_surfs * stride, data_size) # Generate multi-index sub-column for y-axis if len(self.mesh.dimension) > 1: - filter_bins = np.arange(1, ny + 1) - repeat_factor = n_surfs * nx * stride - filter_bins = np.repeat(filter_bins, repeat_factor) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) - filter_dict[(mesh_key, 'y')] = filter_bins + filter_dict[mesh_key, 'y'] = _repeat_and_tile( + np.arange(1, ny + 1), n_surfs * nx * stride, data_size) # Generate multi-index sub-column for z-axis if len(self.mesh.dimension) > 2: - filter_bins = np.arange(1, nz + 1) - repeat_factor = n_surfs * nx * ny * stride - filter_bins = np.repeat(filter_bins, repeat_factor) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) - filter_dict[(mesh_key, 'z')] = filter_bins + filter_dict[mesh_key, 'z'] = _repeat_and_tile( + np.arange(1, nz + 1), n_surfs * nx * ny * stride, data_size) # Generate multi-index sub-column for surface - repeat_factor = stride - filter_bins = np.repeat(_CURRENT_NAMES[:n_surfs], repeat_factor) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) - filter_dict[(mesh_key, 'surf')] = filter_bins + filter_dict[mesh_key, 'surf'] = _repeat_and_tile( + _CURRENT_NAMES[:n_surfs], stride, data_size) # Initialize a Pandas DataFrame from the mesh dictionary return pd.concat([df, pd.DataFrame(filter_dict)]) @@ -1386,8 +1367,8 @@ class DistribcellFilter(Filter): Returns ------- pandas.DataFrame - A Pandas DataFrame with columns describing distributed cells. The - for will be either: + A Pandas DataFrame with columns describing distributed cells. The + dataframe will have either: 1. a single column with the cell instance IDs (without summary info) 2. separate columns for the cell IDs, universe IDs, and lattice IDs @@ -1479,10 +1460,8 @@ class DistribcellFilter(Filter): # Tile the Multi-index columns for level_key, level_bins in level_dict.items(): - 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 + level_dict[level_key] = _repeat_and_tile( + level_bins, stride, data_size) # Initialize a Pandas DataFrame from the level dictionary if level_df is None: @@ -1494,10 +1473,8 @@ class DistribcellFilter(Filter): # Create DataFrame column for distribcell instance IDs # 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, stride) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) + filter_bins = _repeat_and_tile( + np.arange(self.num_bins), stride, data_size) df = pd.DataFrame({self.short_name.lower() : filter_bins}) # Concatenate with DataFrame of distribcell instance IDs @@ -1905,9 +1882,7 @@ class EnergyFunctionFilter(Filter): # hex characters) of the digest are probably sufficient. out = out[:14] - filter_bins = np.repeat(out, stride) - tile_factor = data_size // len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) + filter_bins = _repeat_and_tile(out, stride, data_size) df = pd.concat([df, pd.DataFrame( {self.short_name.lower(): filter_bins})]) From 3e995e0df691f81ff9fc1ad7ed1340a69d8926e0 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 20 Mar 2020 15:19:53 -0500 Subject: [PATCH 145/205] Changes in geometry.py from PullRequest Inc. review --- openmc/geometry.py | 93 +++++++++++----------------------------------- 1 file changed, 21 insertions(+), 72 deletions(-) diff --git a/openmc/geometry.py b/openmc/geometry.py index 4b650f2e3e..c3d2cd76b8 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -410,6 +410,23 @@ class Geometry: for keep, *redundant in tally.values() for replace in redundant} + def _get_domains_by_name(self, name, case_sensitive, matching, domain_type): + if not case_sensitive: + name = name.lower() + + domains = [] + + func = getattr(self, 'get_all_{}s'.format(domain_type)) + for domain in func().values(): + domain_name = domain.name if case_sensitive else domain.name.lower() + if domain_name == name: + domains.append(domain) + elif not matching and name in domain_name: + domains.append(domain) + + domains.sort(key=lambda x: x.id) + return domains + def get_materials_by_name(self, name, case_sensitive=False, matching=False): """Return a list of materials with matching names. @@ -429,24 +446,7 @@ class Geometry: Materials matching the queried name """ - - if not case_sensitive: - name = name.lower() - - all_materials = self.get_all_materials().values() - materials = set() - - for material in all_materials: - material_name = material.name - if not case_sensitive: - material_name = material_name.lower() - - if material_name == name: - materials.add(material) - elif not matching and name in material_name: - materials.add(material) - - return sorted(materials, key=lambda x: x.id) + return self._get_domains_by_name(name, case_sensitive, matching, 'material') def get_cells_by_name(self, name, case_sensitive=False, matching=False): """Return a list of cells with matching names. @@ -467,24 +467,7 @@ class Geometry: Cells matching the queried name """ - - if not case_sensitive: - name = name.lower() - - all_cells = self.get_all_cells().values() - cells = set() - - for cell in all_cells: - cell_name = cell.name - if not case_sensitive: - cell_name = cell_name.lower() - - if cell_name == name: - cells.add(cell) - elif not matching and name in cell_name: - cells.add(cell) - - return sorted(cells, key=lambda x: x.id) + return self._get_domains_by_name(name, case_sensitive, matching, 'cell') def get_cells_by_fill_name(self, name, case_sensitive=False, matching=False): """Return a list of cells with fills with matching names. @@ -550,24 +533,7 @@ class Geometry: Universes matching the queried name """ - - if not case_sensitive: - name = name.lower() - - all_universes = self.get_all_universes().values() - universes = set() - - for universe in all_universes: - universe_name = universe.name - if not case_sensitive: - universe_name = universe_name.lower() - - if universe_name == name: - universes.add(universe) - elif not matching and name in universe_name: - universes.add(universe) - - return sorted(universes, key=lambda x: x.id) + return self._get_domains_by_name(name, case_sensitive, matching, 'universe') def get_lattices_by_name(self, name, case_sensitive=False, matching=False): """Return a list of lattices with matching names. @@ -588,24 +554,7 @@ class Geometry: Lattices matching the queried name """ - - if not case_sensitive: - name = name.lower() - - all_lattices = self.get_all_lattices().values() - lattices = set() - - for lattice in all_lattices: - lattice_name = lattice.name - if not case_sensitive: - lattice_name = lattice_name.lower() - - if lattice_name == name: - lattices.add(lattice) - elif not matching and name in lattice_name: - lattices.add(lattice) - - return sorted(lattices, key=lambda x: x.id) + return self._get_domains_by_name(name, case_sensitive, matching, 'lattice') def remove_redundant_surfaces(self): """Remove redundant surfaces from the geometry""" From d8ef73d0376669ec416e7485ad745b860c6f0902 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 20 Mar 2020 15:20:25 -0500 Subject: [PATCH 146/205] Make sure boundary_type is passed as a keyword argument --- openmc/model/funcs.py | 4 ++-- tests/regression_tests/asymmetric_lattice/test.py | 12 ++++++------ tests/regression_tests/deplete/example_geometry.py | 12 ++++++------ tests/regression_tests/salphabeta/test.py | 4 ++-- tests/regression_tests/triso/test.py | 12 ++++++------ tests/unit_tests/conftest.py | 8 ++++---- tests/unit_tests/test_complex_cell_bb.py | 8 ++++---- tests/unit_tests/test_surface.py | 4 ++-- 8 files changed, 32 insertions(+), 32 deletions(-) diff --git a/openmc/model/funcs.py b/openmc/model/funcs.py index 9999c383ff..188f1a273a 100644 --- a/openmc/model/funcs.py +++ b/openmc/model/funcs.py @@ -253,8 +253,8 @@ def hexagonal_prism(edge_length=1., orientation='y', origin=(0., 0.), x, y = origin if orientation == 'y': - right = XPlane(x + sqrt(3.)/2*l, boundary_type) - left = XPlane(x - sqrt(3.)/2*l, boundary_type) + right = XPlane(x + sqrt(3.)/2*l, boundary_type=boundary_type) + left = XPlane(x - sqrt(3.)/2*l, boundary_type=boundary_type) c = sqrt(3.)/3. # y = -x/sqrt(3) + a diff --git a/tests/regression_tests/asymmetric_lattice/test.py b/tests/regression_tests/asymmetric_lattice/test.py index 7ed0293ce9..2197a1e7e2 100644 --- a/tests/regression_tests/asymmetric_lattice/test.py +++ b/tests/regression_tests/asymmetric_lattice/test.py @@ -25,12 +25,12 @@ class AsymmetricLatticeTestHarness(PyAPITestHarness): [water, water, water]] # Create bounding surfaces - min_x = openmc.XPlane(-32.13, 'reflective') - max_x = openmc.XPlane(+32.13, 'reflective') - min_y = openmc.YPlane(-32.13, 'reflective') - max_y = openmc.YPlane(+32.13, 'reflective') - min_z = openmc.ZPlane(0, 'reflective') - max_z = openmc.ZPlane(+32.13, 'reflective') + min_x = openmc.XPlane(-32.13, boundary_type='reflective') + max_x = openmc.XPlane(+32.13, boundary_type='reflective') + min_y = openmc.YPlane(-32.13, boundary_type='reflective') + max_y = openmc.YPlane(+32.13, boundary_type='reflective') + min_z = openmc.ZPlane(0, boundary_type='reflective') + max_z = openmc.ZPlane(+32.13, boundary_type='reflective') # Define root universe root_univ = openmc.Universe(universe_id=0, name='root universe') diff --git a/tests/regression_tests/deplete/example_geometry.py b/tests/regression_tests/deplete/example_geometry.py index f79044558c..84134f58f8 100644 --- a/tests/regression_tests/deplete/example_geometry.py +++ b/tests/regression_tests/deplete/example_geometry.py @@ -298,12 +298,12 @@ def generate_geometry(n_rings, n_wedges): lattice.outer = all_water_u # Bound universe - x_low = openmc.XPlane(-pitch*n_pin/2, 'reflective') - x_high = openmc.XPlane(pitch*n_pin/2, 'reflective') - y_low = openmc.YPlane(-pitch*n_pin/2, 'reflective') - y_high = openmc.YPlane(pitch*n_pin/2, 'reflective') - z_low = openmc.ZPlane(-10, 'reflective') - z_high = openmc.ZPlane(10, 'reflective') + x_low = openmc.XPlane(-pitch*n_pin/2, boundary_type='reflective') + x_high = openmc.XPlane(pitch*n_pin/2, boundary_type='reflective') + y_low = openmc.YPlane(-pitch*n_pin/2, boundary_type='reflective') + y_high = openmc.YPlane(pitch*n_pin/2, boundary_type='reflective') + z_low = openmc.ZPlane(-10, boundary_type='reflective') + z_high = openmc.ZPlane(10, boundary_type='reflective') # Compute bounding box lower_left = [-pitch*n_pin/2, -pitch*n_pin/2, -10] diff --git a/tests/regression_tests/salphabeta/test.py b/tests/regression_tests/salphabeta/test.py index f487723dee..fd0486118f 100644 --- a/tests/regression_tests/salphabeta/test.py +++ b/tests/regression_tests/salphabeta/test.py @@ -44,11 +44,11 @@ def make_model(): model.materials += [m1, m2, m3, m4] # Geometry - x0 = openmc.XPlane(-10, 'vacuum') + x0 = openmc.XPlane(-10, boundary_type='vacuum') x1 = openmc.XPlane(-5) x2 = openmc.XPlane(0) x3 = openmc.XPlane(5) - x4 = openmc.XPlane(10, 'vacuum') + x4 = openmc.XPlane(10, boundary_type='vacuum') root_univ = openmc.Universe() diff --git a/tests/regression_tests/triso/test.py b/tests/regression_tests/triso/test.py index 7bef80ea87..c447156913 100644 --- a/tests/regression_tests/triso/test.py +++ b/tests/regression_tests/triso/test.py @@ -54,12 +54,12 @@ class TRISOTestHarness(PyAPITestHarness): inner_univ = openmc.Universe(cells=[c1, c2, c3, c4, c5]) # Define box to contain lattice and to pack TRISO particles in - min_x = openmc.XPlane(-0.5, 'reflective') - max_x = openmc.XPlane(0.5, 'reflective') - min_y = openmc.YPlane(-0.5, 'reflective') - max_y = openmc.YPlane(0.5, 'reflective') - min_z = openmc.ZPlane(-0.5, 'reflective') - max_z = openmc.ZPlane(0.5, 'reflective') + min_x = openmc.XPlane(-0.5, boundary_type='reflective') + max_x = openmc.XPlane(0.5, boundary_type='reflective') + min_y = openmc.YPlane(-0.5, boundary_type='reflective') + max_y = openmc.YPlane(0.5, boundary_type='reflective') + min_z = openmc.ZPlane(-0.5, boundary_type='reflective') + max_z = openmc.ZPlane(0.5, boundary_type='reflective') box_region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z box = openmc.Cell(region=box_region) diff --git a/tests/unit_tests/conftest.py b/tests/unit_tests/conftest.py index 926535a9c8..51d1b19a33 100644 --- a/tests/unit_tests/conftest.py +++ b/tests/unit_tests/conftest.py @@ -108,11 +108,11 @@ def mixed_lattice_model(uo2, water): [empty_univ, u] ] - xmin = openmc.XPlane(-d, 'periodic') - xmax = openmc.XPlane(d, 'periodic') + xmin = openmc.XPlane(-d, boundary_type='periodic') + xmax = openmc.XPlane(d, boundary_type='periodic') xmin.periodic_surface = xmax - ymin = openmc.YPlane(-d, 'periodic') - ymax = openmc.YPlane(d, 'periodic') + ymin = openmc.YPlane(-d, boundary_type='periodic') + ymax = openmc.YPlane(d, boundary_type='periodic') main_cell = openmc.Cell(fill=rect_lattice, region=+xmin & -xmax & +ymin & -ymax) diff --git a/tests/unit_tests/test_complex_cell_bb.py b/tests/unit_tests/test_complex_cell_bb.py index 8db20f05c1..ad6491cca9 100644 --- a/tests/unit_tests/test_complex_cell_bb.py +++ b/tests/unit_tests/test_complex_cell_bb.py @@ -27,20 +27,20 @@ def complex_cell(run_in_tmpdir, mpi_intracomm): model.materials = (u235, u238, zr90, n14) - s1 = openmc.XPlane(-10.0, 'vacuum') + s1 = openmc.XPlane(-10.0, boundary_type='vacuum') s2 = openmc.XPlane(-7.0) s3 = openmc.XPlane(-4.0) s4 = openmc.XPlane(4.0) s5 = openmc.XPlane(7.0) - s6 = openmc.XPlane(10.0, 'vacuum') + s6 = openmc.XPlane(10.0, boundary_type='vacuum') s7 = openmc.XPlane(0.0) - s11 = openmc.YPlane(-10.0, 'vacuum') + s11 = openmc.YPlane(-10.0, boundary_type='vacuum') s12 = openmc.YPlane(-7.0) s13 = openmc.YPlane(-4.0) s14 = openmc.YPlane(4.0) s15 = openmc.YPlane(7.0) - s16 = openmc.YPlane(10.0, 'vacuum') + s16 = openmc.YPlane(10.0, boundary_type='vacuum') s17 = openmc.YPlane(0.0) c1 = openmc.Cell(fill=u235) diff --git a/tests/unit_tests/test_surface.py b/tests/unit_tests/test_surface.py index b37dc8f310..e7d37c4e91 100644 --- a/tests/unit_tests/test_surface.py +++ b/tests/unit_tests/test_surface.py @@ -67,7 +67,7 @@ def test_plane_from_points(): def test_xplane(): - s = openmc.XPlane(3., 'reflective') + s = openmc.XPlane(3., boundary_type='reflective') assert s.x0 == 3. assert s.boundary_type == 'reflective' @@ -441,7 +441,7 @@ def test_cone(): assert_infinite_bb(s) # evaluate method - # cos^2(theta) * ((p - p1))**2 - (d @ (p - p1))^2 + # cos^2(theta) * ((p - p1))**2 - (d @ (p - p1))^2 # The argument r2 for cones is actually tan^2(theta) so that # cos^2(theta) = 1 / (1 + r2) # From fa15eea89f8f305f32048077b10ee6e73465e3dd Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 20 Mar 2020 15:55:38 -0500 Subject: [PATCH 147/205] Changes in lattice.py from PullRequest Inc. review --- openmc/lattice.py | 442 +++++++++++++++++++++++++--------------------- 1 file changed, 237 insertions(+), 205 deletions(-) diff --git a/openmc/lattice.py b/openmc/lattice.py index c68637fa34..782664a29e 100644 --- a/openmc/lattice.py +++ b/openmc/lattice.py @@ -101,187 +101,13 @@ class Lattice(IDManagerMixin, metaclass=ABCMeta): Instance of lattice subclass """ - lattice_id = int(group.name.split('/')[-1].lstrip('lattice ')) - name = group['name'][()].decode() if 'name' in group else '' lattice_type = group['type'][()].decode() - if lattice_type == 'rectangular': - dimension = group['dimension'][...] - lower_left = group['lower_left'][...] - pitch = group['pitch'][...] - outer = group['outer'][()] - universe_ids = group['universes'][...] - - # Create the Lattice - lattice = openmc.RectLattice(lattice_id, name) - lattice.lower_left = lower_left - lattice.pitch = pitch - - # If the Universe specified outer the Lattice is not void - if outer >= 0: - lattice.outer = universes[outer] - - # Build array of Universe pointers for the Lattice - uarray = np.empty(universe_ids.shape, dtype=openmc.Universe) - - for z in range(universe_ids.shape[0]): - for y in range(universe_ids.shape[1]): - for x in range(universe_ids.shape[2]): - uarray[z, y, x] = universes[universe_ids[z, y, x]] - - # Use 2D NumPy array to store lattice universes for 2D lattices - if len(dimension) == 2: - uarray = np.squeeze(uarray) - uarray = np.atleast_2d(uarray) - - # Set the universes for the lattice - lattice.universes = uarray - + return openmc.RectLattice.from_hdf5(group, universes) elif lattice_type == 'hexagonal': - n_rings = group['n_rings'][()] - n_axial = group['n_axial'][()] - center = group['center'][()] - pitch = group['pitch'][()] - outer = group['outer'][()] - if 'orientation' in group: - orientation = group['orientation'][()].decode() - else: - orientation = "y" - - universe_ids = group['universes'][()] - - # Create the Lattice - lattice = openmc.HexLattice(lattice_id, name) - lattice.center = center - lattice.pitch = pitch - lattice.orientation = orientation - # If the Universe specified outer the Lattice is not void - if outer >= 0: - lattice.outer = universes[outer] - if orientation == "y": - # Build array of Universe pointers for the Lattice. Note that - # we need to convert between the HDF5's square array of - # (x, alpha, z) to the Python API's format of a ragged nested - # list of (z, ring, theta). - uarray = [] - for z in range(n_axial): - # Add a list for this axial level. - uarray.append([]) - x = n_rings - 1 - a = 2*n_rings - 2 - for r in range(n_rings - 1, 0, -1): - # Add a list for this ring. - uarray[-1].append([]) - - # Climb down the top-right. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, a, x]) - x += 1 - a -= 1 - - # Climb down the right. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, a, x]) - a -= 1 - - # Climb down the bottom-right. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, a, x]) - x -= 1 - - # Climb up the bottom-left. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, a, x]) - x -= 1 - a += 1 - - # Climb up the left. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, a, x]) - a += 1 - - # Climb up the top-left. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, a, x]) - x += 1 - - # Move down to the next ring. - a -= 1 - - # Convert the ids into Universe objects. - uarray[-1][-1] = [universes[u_id] - for u_id in uarray[-1][-1]] - - # Handle the degenerate center ring separately. - u_id = universe_ids[z, a, x] - uarray[-1].append([universes[u_id]]) - else: - # Build array of Universe pointers for the Lattice. Note that - # we need to convert between the HDF5's square array of - # (alpha, y, z) to the Python API's format of a ragged nested - # list of (z, ring, theta). - uarray = [] - for z in range(n_axial): - # Add a list for this axial level. - uarray.append([]) - a = 2*n_rings - 2 - y = n_rings - 1 - for r in range(n_rings - 1, 0, -1): - # Add a list for this ring. - uarray[-1].append([]) - - # Climb down the bottom-right. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, y, a]) - y -= 1 - - # Climb across the bottom. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, y, a]) - a -= 1 - - # Climb up the bottom-left. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, y, a]) - a -= 1 - y += 1 - - # Climb up the top-left. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, y, a]) - y += 1 - - # Climb across the top. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, y, a]) - a += 1 - - # Climb down the top-right. - for i in range(r): - uarray[-1][-1].append(universe_ids[z, y, a]) - a += 1 - y -= 1 - - # Move down to the next ring. - a -= 1 - - # Convert the ids into Universe objects. - uarray[-1][-1] = [universes[u_id] - for u_id in uarray[-1][-1]] - - # Handle the degenerate center ring separately. - u_id = universe_ids[z, y, a] - uarray[-1].append([universes[u_id]]) - - # Add the universes to the lattice. - if len(pitch) == 2: - # Lattice is 3D - lattice.universes = uarray - else: - # Lattice is 2D; extract the only axial level - lattice.universes = uarray[0] - - return lattice + return openmc.HexLattice.from_hdf5(group, universes) + else: + raise ValueError('Unkown lattice type: {}'.format(lattice_type)) def get_unique_universes(self): """Determine all unique universes in the lattice @@ -579,29 +405,22 @@ class RectLattice(Lattice): def __repr__(self): string = 'RectLattice\n' - string += '{0: <16}{1}{2}\n'.format('\tID', '=\t', self._id) - string += '{0: <16}{1}{2}\n'.format('\tName', '=\t', self._name) - string += '{0: <16}{1}{2}\n'.format('\tShape', '=\t', - self.shape) - string += '{0: <16}{1}{2}\n'.format('\tLower Left', '=\t', - self._lower_left) - string += '{0: <16}{1}{2}\n'.format('\tPitch', '=\t', self._pitch) + string += '{: <16}=\t{}\n'.format('\tID', self._id) + string += '{: <16}=\t{}\n'.format('\tName', self._name) + string += '{: <16}=\t{}\n'.format('\tShape', self.shape) + string += '{: <16}=\t{}\n'.format('\tLower Left', self._lower_left) + string += '{: <16}=\t{}\n'.format('\tPitch', self._pitch) + string += '{: <16}=\t{}\n'.format( + '\tOuter', self._outer._id if self._outer is not None else None) - if self._outer is not None: - string += '{0: <16}{1}{2}\n'.format('\tOuter', '=\t', - self._outer._id) - else: - string += '{0: <16}{1}{2}\n'.format('\tOuter', '=\t', - self._outer) + string += '{: <16}\n'.format('\tUniverses') - string += '{0: <16}\n'.format('\tUniverses') - - # Lattice nested Universe IDs - column major for Fortran + # Lattice nested Universe IDs for i, universe in enumerate(np.ravel(self._universes)): - string += '{0} '.format(universe._id) + string += '{} '.format(universe._id) # Add a newline character every time we reach end of row of cells - if (i+1) % self.shape[0] == 0: + if (i + 1) % self.shape[0] == 0: string += '\n' string = string.rstrip('\n') @@ -1141,6 +960,59 @@ class RectLattice(Lattice): lat.universes = uarray return lat + @classmethod + def from_hdf5(cls, group, universes): + """Create rectangular lattice from HDF5 group + + Parameters + ---------- + group : h5py.Group + Group in HDF5 file + universes : dict + Dictionary mapping universe IDs to instances of + :class:`openmc.Universe`. + + Returns + ------- + openmc.RectLattice + Rectangular lattice + + """ + dimension = group['dimension'][...] + lower_left = group['lower_left'][...] + pitch = group['pitch'][...] + outer = group['outer'][()] + universe_ids = group['universes'][...] + + # Create the Lattice + lattice_id = int(group.name.split('/')[-1].lstrip('lattice ')) + name = group['name'][()].decode() if 'name' in group else '' + lattice = cls(lattice_id, name) + lattice.lower_left = lower_left + lattice.pitch = pitch + + # If the Universe specified outer the Lattice is not void + if outer >= 0: + lattice.outer = universes[outer] + + # Build array of Universe pointers for the Lattice + uarray = np.empty(universe_ids.shape, dtype=openmc.Universe) + + for z in range(universe_ids.shape[0]): + for y in range(universe_ids.shape[1]): + for x in range(universe_ids.shape[2]): + uarray[z, y, x] = universes[universe_ids[z, y, x]] + + # Use 2D NumPy array to store lattice universes for 2D lattices + if len(dimension) == 2: + uarray = np.squeeze(uarray) + uarray = np.atleast_2d(uarray) + + # Set the universes for the lattice + lattice.universes = uarray + + return lattice + class HexLattice(Lattice): r"""A lattice consisting of hexagonal prisms. @@ -1458,20 +1330,16 @@ class HexLattice(Lattice): """ if self._orientation == 'x': - x = point[0] - (self.center[0] + self.pitch[0]*idx[0] + - 0.5*self.pitch[0]*idx[1]) - y = point[1] - (self.center[1] + - sqrt(0.75)*self.pitch[0]*idx[1]) + x = point[0] - (self.center[0] + (idx[0] + 0.5*idx[1])*self.pitch[0]) + y = point[1] - (self.center[1] + sqrt(0.75)*self.pitch[0]*idx[1]) else: - x = point[0] - (self.center[0] - + sqrt(0.75)*self.pitch[0]*idx[0]) - y = point[1] - (self.center[1] - + (0.5*idx[0] + idx[1])*self.pitch[0]) + x = point[0] - (self.center[0] + sqrt(0.75)*self.pitch[0]*idx[0]) + y = point[1] - (self.center[1] + (0.5*idx[0] + idx[1])*self.pitch[0]) if self._num_axial is None: z = point[2] else: - z = point[2] - (self.center[2] + (idx[2] + 0.5 - 0.5*self.num_axial)* + z = point[2] - (self.center[2] + (idx[2] + 0.5 - 0.5*self.num_axial) * self.pitch[1]) return (x, y, z) @@ -2168,3 +2036,167 @@ class HexLattice(Lattice): return HexLattice._show_indices_x(num_rings) else: return HexLattice._show_indices_y(num_rings) + + @classmethod + def from_hdf5(cls, group, universes): + """Create rectangular lattice from HDF5 group + + Parameters + ---------- + group : h5py.Group + Group in HDF5 file + universes : dict + Dictionary mapping universe IDs to instances of + :class:`openmc.Universe`. + + Returns + ------- + openmc.RectLattice + Rectangular lattice + + """ + n_rings = group['n_rings'][()] + n_axial = group['n_axial'][()] + center = group['center'][()] + pitch = group['pitch'][()] + outer = group['outer'][()] + if 'orientation' in group: + orientation = group['orientation'][()].decode() + else: + orientation = "y" + universe_ids = group['universes'][()] + + # Create the Lattice + lattice_id = int(group.name.split('/')[-1].lstrip('lattice ')) + name = group['name'][()].decode() if 'name' in group else '' + lattice = openmc.HexLattice(lattice_id, name) + lattice.center = center + lattice.pitch = pitch + lattice.orientation = orientation + # If the Universe specified outer the Lattice is not void + if outer >= 0: + lattice.outer = universes[outer] + if orientation == "y": + # Build array of Universe pointers for the Lattice. Note that + # we need to convert between the HDF5's square array of + # (x, alpha, z) to the Python API's format of a ragged nested + # list of (z, ring, theta). + uarray = [] + for z in range(n_axial): + # Add a list for this axial level. + uarray.append([]) + x = n_rings - 1 + a = 2*n_rings - 2 + for r in range(n_rings - 1, 0, -1): + # Add a list for this ring. + uarray[-1].append([]) + + # Climb down the top-right. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, a, x]) + x += 1 + a -= 1 + + # Climb down the right. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, a, x]) + a -= 1 + + # Climb down the bottom-right. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, a, x]) + x -= 1 + + # Climb up the bottom-left. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, a, x]) + x -= 1 + a += 1 + + # Climb up the left. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, a, x]) + a += 1 + + # Climb up the top-left. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, a, x]) + x += 1 + + # Move down to the next ring. + a -= 1 + + # Convert the ids into Universe objects. + uarray[-1][-1] = [universes[u_id] + for u_id in uarray[-1][-1]] + + # Handle the degenerate center ring separately. + u_id = universe_ids[z, a, x] + uarray[-1].append([universes[u_id]]) + else: + # Build array of Universe pointers for the Lattice. Note that + # we need to convert between the HDF5's square array of + # (alpha, y, z) to the Python API's format of a ragged nested + # list of (z, ring, theta). + uarray = [] + for z in range(n_axial): + # Add a list for this axial level. + uarray.append([]) + a = 2*n_rings - 2 + y = n_rings - 1 + for r in range(n_rings - 1, 0, -1): + # Add a list for this ring. + uarray[-1].append([]) + + # Climb down the bottom-right. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, y, a]) + y -= 1 + + # Climb across the bottom. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, y, a]) + a -= 1 + + # Climb up the bottom-left. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, y, a]) + a -= 1 + y += 1 + + # Climb up the top-left. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, y, a]) + y += 1 + + # Climb across the top. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, y, a]) + a += 1 + + # Climb down the top-right. + for i in range(r): + uarray[-1][-1].append(universe_ids[z, y, a]) + a += 1 + y -= 1 + + # Move down to the next ring. + a -= 1 + + # Convert the ids into Universe objects. + uarray[-1][-1] = [universes[u_id] + for u_id in uarray[-1][-1]] + + # Handle the degenerate center ring separately. + u_id = universe_ids[z, y, a] + uarray[-1].append([universes[u_id]]) + + # Add the universes to the lattice. + if len(pitch) == 2: + # Lattice is 3D + lattice.universes = uarray + else: + # Lattice is 2D; extract the only axial level + lattice.universes = uarray[0] + + return lattice From 34e57d4ba3067bec6b6d355989deca0e3d1f5b60 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Sun, 22 Mar 2020 14:18:30 -0500 Subject: [PATCH 148/205] Changes in material.py from PullRequest Inc. review --- openmc/material.py | 126 +++++++++++---------------------------------- 1 file changed, 31 insertions(+), 95 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index f19cf12915..0e1ae8df7b 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -1,4 +1,4 @@ -from collections import OrderedDict, defaultdict +from collections import OrderedDict, defaultdict, namedtuple from collections.abc import Iterable from copy import deepcopy from numbers import Real, Integral @@ -20,6 +20,9 @@ DENSITY_UNITS = ['g/cm3', 'g/cc', 'kg/m3', 'atom/b-cm', 'atom/cm3', 'sum', 'macro'] +NuclideTuple = namedtuple('NuclideTuple', ['name', 'percent', 'percent_type']) + + class Material(IDManagerMixin): """A material composed of a collection of nuclides/elements. @@ -74,7 +77,9 @@ class Material(IDManagerMixin): instance. This property is initialized by calling the :meth:`Geometry.determine_paths` method. num_instances : int - The number of instances of this material throughout the geometry. + The number of instances of this material throughout the geometry. This + property is initialized by calling the :meth:`Geometry.determine_paths` + method. fissionable_mass : float Mass of fissionable nuclides in the material in [g]. Requires that the :attr:`volume` attribute is set. @@ -108,12 +113,6 @@ class Material(IDManagerMixin): # If specified, a list of table names self._sab = [] - # If true, the material will be initialized as distributed - self._convert_to_distrib_comps = False - - # If specified, this file will be used instead of composition values - self._distrib_otf_file = None - def __repr__(self): string = 'Material\n' string += '{: <16}=\t{}\n'.format('\tID', self._id) @@ -183,14 +182,6 @@ class Material(IDManagerMixin): def isotropic(self): return self._isotropic - @property - def convert_to_distrib_comps(self): - return self._convert_to_distrib_comps - - @property - def distrib_otf_file(self): - return self._distrib_otf_file - @property def average_molar_mass(self): @@ -330,9 +321,11 @@ class Material(IDManagerMixin): self._volume = volume_calc.volumes[self.id].n self._atoms = volume_calc.atoms[self.id] else: - raise ValueError('No volume information found for this material.') + raise ValueError('No volume information found for material ID={}.' + .format(self.id)) else: - raise ValueError('No volume information found for this material.') + raise ValueError('No volume information found for material ID={}.' + .format(self.id)) def set_density(self, units, density=None): """Set the density of the material @@ -366,27 +359,6 @@ class Material(IDManagerMixin): density, Real) self._density = density - @distrib_otf_file.setter - def distrib_otf_file(self, filename): - # TODO: remove this when distributed materials are merged - warnings.warn('This feature is not yet implemented in a release ' - 'version of openmc') - - if not isinstance(filename, str) and filename is not None: - msg = 'Unable to add OTF material file to Material ID="{}" with a ' \ - 'non-string name "{}"'.format(self._id, filename) - raise ValueError(msg) - - self._distrib_otf_file = filename - - @convert_to_distrib_comps.setter - def convert_to_distrib_comps(self): - # TODO: remove this when distributed materials are merged - warnings.warn('This feature is not yet implemented in a release ' - 'version of openmc') - - self._convert_to_distrib_comps = True - def add_nuclide(self, nuclide, percent, percent_type='ao'): """Add a nuclide to the material @@ -419,7 +391,7 @@ class Material(IDManagerMixin): if Z >= 89: self.depletable = True - self._nuclides.append((nuclide, percent, percent_type)) + self._nuclides.append(NuclideTuple(nuclide, percent, percent_type)) def remove_nuclide(self, nuclide): """Remove a nuclide from the material @@ -434,7 +406,7 @@ class Material(IDManagerMixin): # If the Material contains the Nuclide, delete it for nuc in self._nuclides: - if nuclide == nuc[0]: + if nuclide == nuc.name: self._nuclides.remove(nuc) break @@ -531,7 +503,7 @@ class Material(IDManagerMixin): natural composition is added to the material. """ - + cv.check_type('nuclide', element, str) cv.check_type('percent', percent, Real) cv.check_value('percent type', percent_type, {'ao', 'wo'}) @@ -626,7 +598,7 @@ class Material(IDManagerMixin): self._sab.append((new_name, fraction)) def make_isotropic_in_lab(self): - self.isotropic = [x[0] for x in self._nuclides] + self.isotropic = [x.name for x in self._nuclides] def get_nuclides(self): """Returns all nuclides in the material @@ -637,7 +609,7 @@ class Material(IDManagerMixin): List of nuclide names """ - return [x[0] for x in self._nuclides] + return [x.name for x in self._nuclides] def get_nuclide_densities(self): """Returns all nuclides in the material and their densities @@ -650,6 +622,7 @@ class Material(IDManagerMixin): """ + # keep ordered dictionary for testing purposes nuclides = OrderedDict() for nuclide, density, density_type in self._nuclides: @@ -817,15 +790,14 @@ class Material(IDManagerMixin): return memo[self] - def _get_nuclide_xml(self, nuclide, distrib=False): + def _get_nuclide_xml(self, nuclide): xml_element = ET.Element("nuclide") xml_element.set("name", nuclide[0]) - if not distrib: - if nuclide[2] == 'ao': - xml_element.set("ao", str(nuclide[1])) - else: - xml_element.set("wo", str(nuclide[1])) + if nuclide[2] == 'ao': + xml_element.set("ao", str(nuclide[1])) + else: + xml_element.set("wo", str(nuclide[1])) return xml_element @@ -835,10 +807,10 @@ class Material(IDManagerMixin): return xml_element - def _get_nuclides_xml(self, nuclides, distrib=False): + def _get_nuclides_xml(self, nuclides): xml_elements = [] for nuclide in nuclides: - xml_elements.append(self._get_nuclide_xml(nuclide, distrib)) + xml_elements.append(self._get_nuclide_xml(nuclide)) return xml_elements def to_xml_element(self, cross_sections=None): @@ -883,51 +855,15 @@ class Material(IDManagerMixin): raise ValueError('Density has not been set for material {}!' .format(self.id)) - if not self._convert_to_distrib_comps: - if self._macroscopic is None: - # Create nuclide XML subelements - subelements = self._get_nuclides_xml(self._nuclides) - for subelement in subelements: - element.append(subelement) - else: - # Create macroscopic XML subelements - subelement = self._get_macroscopic_xml(self._macroscopic) + if self._macroscopic is None: + # Create nuclide XML subelements + subelements = self._get_nuclides_xml(self._nuclides) + for subelement in subelements: element.append(subelement) - else: - subelement = ET.SubElement(element, "compositions") - - comps = [] - allnucs = self._nuclides - dist_per_type = allnucs[0][2] - for nuc in allnucs: - if nuc[2] != dist_per_type: - msg = 'All nuclides and elements in a distributed ' \ - 'material must have the same type, either ao or wo' - raise ValueError(msg) - comps.append(nuc[1]) - - if self._distrib_otf_file is None: - # Create values and units subelements - subsubelement = ET.SubElement(subelement, "values") - subsubelement.text = ' '.join([str(c) for c in comps]) - subsubelement = ET.SubElement(subelement, "units") - subsubelement.text = dist_per_type - else: - # Specify the materials file - subsubelement = ET.SubElement(subelement, "otf_file_path") - subsubelement.text = self._distrib_otf_file - - if self._macroscopic is None: - # Create nuclide XML subelements - subelements = self._get_nuclides_xml(self._nuclides, - distrib=True) - for subelement_nuc in subelements: - subelement.append(subelement_nuc) - else: - # Create macroscopic XML subelements - subsubelement = self._get_macroscopic_xml(self._macroscopic) - subelement.append(subsubelement) + # Create macroscopic XML subelements + subelement = self._get_macroscopic_xml(self._macroscopic) + element.append(subelement) if self._sab: for sab in self._sab: From 97a0260784a9ae9eb07b93cd3502e342bbbe6b1f Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 23 Mar 2020 14:56:14 -0500 Subject: [PATCH 149/205] Changes in mgxs_library.py from PullRequest Inc. review --- openmc/mgxs_library.py | 398 ++++++++++++++++++++--------------------- 1 file changed, 193 insertions(+), 205 deletions(-) diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py index e5953dfd1d..aae2d1b456 100644 --- a/openmc/mgxs_library.py +++ b/openmc/mgxs_library.py @@ -13,12 +13,25 @@ import openmc.mgxs from openmc.checkvalue import check_type, check_value, check_greater_than, \ check_iterable_type, check_less_than, check_filetype_version +ROOM_TEMPERATURE_KELVIN = 294.0 # Supported incoming particle MGXS angular treatment representations -_REPRESENTATIONS = ['isotropic', 'angle'] +REPRESENTATION_ISOTROPIC = 'isotropic' +REPRESENTATION_ANGLE = 'angle' +_REPRESENTATIONS = [ + REPRESENTATION_ISOTROPIC, + REPRESENTATION_ANGLE +] # Supported scattering angular distribution representations -_SCATTER_TYPES = ['tabular', 'legendre', 'histogram'] +SCATTER_TABULAR = 'tabular' +SCATTER_LEGENDRE = 'legendre' +SCATTER_HISTOGRAM = 'histogram' +_SCATTER_TYPES = [ + SCATTER_TABULAR, + SCATTER_LEGENDRE, + SCATTER_HISTOGRAM +] # List of MGXS indexing schemes _XS_SHAPES = ["[G][G'][Order]", "[G]", "[G']", "[G][G']", "[DG]", "[DG][G]", @@ -44,7 +57,7 @@ class XSdata: Name of the mgxs data set. energy_groups : openmc.mgxs.EnergyGroups Energy group structure - representation : {'isotropic', 'angle'}, optional + representation : {'isotropic', REPRESENTATION_ANGLE}, optional Method used in generating the MGXS (isotropic or angle-dependent flux weighting). Defaults to 'isotropic' temperatures : Iterable of float @@ -75,7 +88,7 @@ class XSdata: Either the Legendre order, number of bins, or number of points used to describe the angular distribution associated with each group-to-group transfer probability. - representation : {'isotropic', 'angle'} + representation : {'isotropic', REPRESENTATION_ANGLE} Method used in generating the MGXS (isotropic or angle-dependent flux weighting). num_azimuthal : int @@ -168,8 +181,8 @@ class XSdata: """ - def __init__(self, name, energy_groups, temperatures=[294.], - representation='isotropic', num_delayed_groups=0): + def __init__(self, name, energy_groups, temperatures=[ROOM_TEMPERATURE_KELVIN], + representation=REPRESENTATION_ISOTROPIC, num_delayed_groups=0): # Initialize class attributes self.name = name @@ -179,7 +192,7 @@ class XSdata: self.representation = representation self._atomic_weight_ratio = None self._fissionable = False - self._scatter_format = 'legendre' + self._scatter_format = SCATTER_LEGENDRE self._order = None self._num_polar = None self._num_azimuthal = None @@ -340,11 +353,13 @@ class XSdata: @property def num_orders(self): - if self._order is not None: - if self._scatter_format in (None, 'legendre'): - return self._order + 1 - else: - return self._order + if self._order is None: + raise ValueError('Order has not been set.') + + if self._scatter_format in (None, SCATTER_LEGENDRE): + return self._order + 1 + else: + return self._order @property def xs_shapes(self): @@ -370,7 +385,7 @@ class XSdata: self.energy_groups.num_groups, self.num_orders) # If representation is by angle prepend num polar and num azim - if self.representation == 'angle': + if self.representation == REPRESENTATION_ANGLE: for key, shapes in self._xs_shapes.items(): self._xs_shapes[key] \ = (self.num_polar, self.num_azimuthal) + shapes @@ -483,7 +498,18 @@ class XSdata: self._decay_rate.append(None) self._inverse_velocity.append(None) - def set_total(self, total, temperature=294.): + def _check_temperature(self, temperature): + check_type('temperature', temperature, Real) + check_value('temperature', temperature, self.temperatures) + + def _temperature_index(self, temperature): + return np.where(self.temperatures == temperature)[0][0] + + def _set_fissionable(self, array): + if np.sum(array) > 0: + self._fissionable = True + + def set_total(self, total, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -507,13 +533,12 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking total = np.asarray(total) check_value('total shape', total.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._total[i] = total - def set_absorption(self, absorption, temperature=294.): + def set_absorption(self, absorption, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -537,13 +562,12 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking absorption = np.asarray(absorption) check_value('absorption shape', absorption.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._absorption[i] = absorption - def set_fission(self, fission, temperature=294.): + def set_fission(self, fission, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -567,16 +591,14 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking fission = np.asarray(fission) check_value('fission shape', fission.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._fission[i] = fission - if np.sum(fission) > 0.0: - self._fissionable = True + self._set_fissionable(fission) - def set_kappa_fission(self, kappa_fission, temperature=294.): + def set_kappa_fission(self, kappa_fission, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -600,16 +622,14 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking kappa_fission = np.asarray(kappa_fission) check_value('kappa fission shape', kappa_fission.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._kappa_fission[i] = kappa_fission - if np.sum(kappa_fission) > 0.0: - self._fissionable = True + self._set_fissionable(kappa_fission) - def set_chi(self, chi, temperature=294.): + def set_chi(self, chi, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -633,13 +653,12 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking chi = np.asarray(chi) check_value('chi shape', chi.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._chi[i] = chi - def set_chi_prompt(self, chi_prompt, temperature=294.): + def set_chi_prompt(self, chi_prompt, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -663,13 +682,12 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking chi_prompt = np.asarray(chi_prompt) check_value('chi prompt shape', chi_prompt.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._chi_prompt[i] = chi_prompt - def set_chi_delayed(self, chi_delayed, temperature=294.): + def set_chi_delayed(self, chi_delayed, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -693,13 +711,14 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking chi_delayed = np.asarray(chi_delayed) check_value('chi delayed shape', chi_delayed.shape, shapes) + self._check_temperature(temperature) check_type('temperature', temperature, Real) check_value('temperature', temperature, self.temperatures) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._chi_delayed[i] = chi_delayed - def set_beta(self, beta, temperature=294.): + def set_beta(self, beta, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -723,13 +742,12 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking beta = np.asarray(beta) check_value('beta shape', beta.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._beta[i] = beta - def set_decay_rate(self, decay_rate, temperature=294.): + def set_decay_rate(self, decay_rate, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -753,13 +771,12 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking decay_rate = np.asarray(decay_rate) check_value('decay rate shape', decay_rate.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._decay_rate[i] = decay_rate - def set_scatter_matrix(self, scatter, temperature=294.): + def set_scatter_matrix(self, scatter, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -785,13 +802,12 @@ class XSdata: check_iterable_type('scatter', scatter, Real, max_depth=len(scatter.shape)) check_value('scatter shape', scatter.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._scatter_matrix[i] = scatter - def set_multiplicity_matrix(self, multiplicity, temperature=294.): + def set_multiplicity_matrix(self, multiplicity, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -817,13 +833,12 @@ class XSdata: check_iterable_type('multiplicity', multiplicity, Real, max_depth=len(multiplicity.shape)) check_value('multiplicity shape', multiplicity.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._multiplicity_matrix[i] = multiplicity - def set_nu_fission(self, nu_fission, temperature=294.): + def set_nu_fission(self, nu_fission, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -849,15 +864,13 @@ class XSdata: check_value('nu_fission shape', nu_fission.shape, shapes) check_iterable_type('nu_fission', nu_fission, Real, max_depth=len(nu_fission.shape)) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._nu_fission[i] = nu_fission - if np.sum(nu_fission) > 0.0: - self._fissionable = True + self._set_fissionable(nu_fission) - def set_prompt_nu_fission(self, prompt_nu_fission, temperature=294.): + def set_prompt_nu_fission(self, prompt_nu_fission, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -883,15 +896,13 @@ class XSdata: check_value('prompt_nu_fission shape', prompt_nu_fission.shape, shapes) check_iterable_type('prompt_nu_fission', prompt_nu_fission, Real, max_depth=len(prompt_nu_fission.shape)) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._prompt_nu_fission[i] = prompt_nu_fission - if np.sum(prompt_nu_fission) > 0.0: - self._fissionable = True + self._set_fissionable(prompt_nu_fission) - def set_delayed_nu_fission(self, delayed_nu_fission, temperature=294.): + def set_delayed_nu_fission(self, delayed_nu_fission, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the cross section for this XSdata object at the provided temperature. @@ -918,15 +929,13 @@ class XSdata: shapes) check_iterable_type('delayed_nu_fission', delayed_nu_fission, Real, max_depth=len(delayed_nu_fission.shape)) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._delayed_nu_fission[i] = delayed_nu_fission - if np.sum(delayed_nu_fission) > 0.0: - self._fissionable = True + self._set_fissionable(delayed_nu_fission) - def set_inverse_velocity(self, inv_vel, temperature=294.): + def set_inverse_velocity(self, inv_vel, temperature=ROOM_TEMPERATURE_KELVIN): """This method sets the inverse velocity for this XSdata object at the provided temperature. @@ -946,13 +955,12 @@ class XSdata: # Convert to a numpy array so we can easily get the shape for checking inv_vel = np.asarray(inv_vel) check_value('inverse_velocity shape', inv_vel.shape, shapes) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._inverse_velocity[i] = inv_vel - def set_total_mgxs(self, total, temperature=294., nuclide='total', + def set_total_mgxs(self, total, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.TotalXS or openmc.mgxs.TransportXS to be used to set the total cross section for @@ -987,14 +995,13 @@ class XSdata: openmc.mgxs.TransportXS)) check_value('energy_groups', total.energy_groups, [self.energy_groups]) check_value('domain_type', total.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._total[i] = total.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_absorption_mgxs(self, absorption, temperature=294., + def set_absorption_mgxs(self, absorption, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.AbsorptionXS to be used to set the absorption cross section for this XSdata object. @@ -1029,15 +1036,14 @@ class XSdata: [self.energy_groups]) check_value('domain_type', absorption.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._absorption[i] = absorption.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_fission_mgxs(self, fission, temperature=294., nuclide='total', + def set_fission_mgxs(self, fission, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.FissionXS to be used to set the fission cross section for this XSdata object. @@ -1072,15 +1078,14 @@ class XSdata: [self.energy_groups]) check_value('domain_type', fission.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._fission[i] = fission.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_nu_fission_mgxs(self, nu_fission, temperature=294., + def set_nu_fission_mgxs(self, nu_fission, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.FissionXS to be used to set the nu-fission cross section for this XSdata object. @@ -1118,18 +1123,16 @@ class XSdata: [self.energy_groups]) check_value('domain_type', nu_fission.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._nu_fission[i] = nu_fission.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - if np.sum(self._nu_fission) > 0.0: - self._fissionable = True + self._set_fissionable(self._nu_fission) - def set_prompt_nu_fission_mgxs(self, prompt_nu_fission, temperature=294., + def set_prompt_nu_fission_mgxs(self, prompt_nu_fission, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """Sets the prompt-nu-fission cross section. @@ -1170,17 +1173,15 @@ class XSdata: [self.energy_groups]) check_value('domain_type', prompt_nu_fission.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._prompt_nu_fission[i] = prompt_nu_fission.get_xs( nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - if np.sum(self._prompt_nu_fission) > 0.0: - self._fissionable = True + self._set_fissionable(self._prompt_nu_fission) - def set_delayed_nu_fission_mgxs(self, delayed_nu_fission, temperature=294., + def set_delayed_nu_fission_mgxs(self, delayed_nu_fission, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.DelayedNuFissionXS or @@ -1221,17 +1222,15 @@ class XSdata: [self.num_delayed_groups]) check_value('domain_type', delayed_nu_fission.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._delayed_nu_fission[i] = delayed_nu_fission.get_xs( nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - if np.sum(self._delayed_nu_fission) > 0.0: - self._fissionable = True + self._set_fissionable(self._delayed_nu_fission) - def set_kappa_fission_mgxs(self, k_fission, temperature=294., + def set_kappa_fission_mgxs(self, k_fission, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.KappaFissionXS @@ -1268,15 +1267,14 @@ class XSdata: [self.energy_groups]) check_value('domain_type', k_fission.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._kappa_fission[i] = k_fission.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_chi_mgxs(self, chi, temperature=294., nuclide='total', + def set_chi_mgxs(self, chi, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.Chi to be used to set chi for this XSdata object. @@ -1308,14 +1306,13 @@ class XSdata: check_type('chi', chi, openmc.mgxs.Chi) check_value('energy_groups', chi.energy_groups, [self.energy_groups]) check_value('domain_type', chi.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._chi[i] = chi.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_chi_prompt_mgxs(self, chi_prompt, temperature=294., + def set_chi_prompt_mgxs(self, chi_prompt, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.Chi to be used to set chi-prompt for this XSdata object. @@ -1350,15 +1347,14 @@ class XSdata: [self.energy_groups]) check_value('domain_type', chi_prompt.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._chi_prompt[i] = chi_prompt.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_chi_delayed_mgxs(self, chi_delayed, temperature=294., + def set_chi_delayed_mgxs(self, chi_delayed, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.ChiDelayed to be used to set chi-delayed for this XSdata object. @@ -1394,15 +1390,14 @@ class XSdata: [self.num_delayed_groups]) check_value('domain_type', chi_delayed.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._chi_delayed[i] = chi_delayed.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_beta_mgxs(self, beta, temperature=294., + def set_beta_mgxs(self, beta, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.Beta to be used to set beta for this XSdata object. @@ -1435,15 +1430,14 @@ class XSdata: check_value('num_delayed_groups', beta.num_delayed_groups, [self.num_delayed_groups]) check_value('domain_type', beta.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._beta[i] = beta.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_decay_rate_mgxs(self, decay_rate, temperature=294., + def set_decay_rate_mgxs(self, decay_rate, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.DecayRate to be used to set decay rate for this XSdata object. @@ -1477,15 +1471,14 @@ class XSdata: [self.num_delayed_groups]) check_value('domain_type', decay_rate.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._decay_rate[i] = decay_rate.get_xs(nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) - def set_scatter_matrix_mgxs(self, scatter, temperature=294., + def set_scatter_matrix_mgxs(self, scatter, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.ScatterMatrixXS @@ -1523,8 +1516,7 @@ class XSdata: [self.energy_groups]) check_value('domain_type', scatter.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) # Set the value of scatter_format based on the same value within # scatter @@ -1534,30 +1526,30 @@ class XSdata: # the order based on the data within scatter. # Otherwise, we will check to see that XSdata.order matches # the order of scatter - if self.scatter_format == 'legendre': + if self.scatter_format == SCATTER_LEGENDRE: if self.order is None: self.order = scatter.legendre_order else: check_value('legendre_order', scatter.legendre_order, [self.order]) - elif self.scatter_format == 'histogram': + elif self.scatter_format == SCATTER_HISTOGRAM: if self.order is None: self.order = scatter.histogram_bins else: check_value('histogram_bins', scatter.histogram_bins, [self.order]) - i = np.where(self.temperatures == temperature)[0][0] - if self.scatter_format == 'legendre': + i = self._temperature_index(temperature) + if self.scatter_format == SCATTER_LEGENDRE: self._scatter_matrix[i] = \ np.zeros(self.xs_shapes["[G][G'][Order]"]) # Get the scattering orders in the outermost dimension - if self.representation == 'isotropic': + if self.representation == REPRESENTATION_ISOTROPIC: for moment in range(self.num_orders): self._scatter_matrix[i][:, :, moment] = \ scatter.get_xs(nuclides=nuclide, xs_type=xs_type, moment=moment, subdomains=subdomain) - elif self.representation == 'angle': + elif self.representation == REPRESENTATION_ANGLE: for moment in range(self.num_orders): self._scatter_matrix[i][:, :, :, :, moment] = \ scatter.get_xs(nuclides=nuclide, xs_type=xs_type, @@ -1568,7 +1560,7 @@ class XSdata: subdomains=subdomain) def set_multiplicity_matrix_mgxs(self, nuscatter, scatter=None, - temperature=294., nuclide='total', + temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for either the direct use of only an openmc.mgxs.MultiplicityMatrixXS or an openmc.mgxs.ScatterMatrixXS and @@ -1613,8 +1605,7 @@ class XSdata: [self.energy_groups]) check_value('domain_type', nuscatter.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) if scatter is not None: check_type('scatter', scatter, openmc.mgxs.ScatterMatrixXS) @@ -1627,7 +1618,7 @@ class XSdata: [self.energy_groups]) check_value('domain_type', scatter.domain_type, openmc.mgxs.DOMAIN_TYPES) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) nuscatt = nuscatter.get_xs(nuclides=nuclide, xs_type=xs_type, moment=0, subdomains=subdomain) @@ -1637,16 +1628,16 @@ class XSdata: scatt = scatter.get_xs(nuclides=nuclide, xs_type=xs_type, moment=0, subdomains=subdomain) - if scatter.scatter_format == 'histogram': + if scatter.scatter_format == SCATTER_HISTOGRAM: scatt = np.sum(scatt, axis=2) - if nuscatter.scatter_format == 'histogram': + if nuscatter.scatter_format == SCATTER_HISTOGRAM: nuscatt = np.sum(nuscatt, axis=2) self._multiplicity_matrix[i] = np.divide(nuscatt, scatt) self._multiplicity_matrix[i] = \ np.nan_to_num(self._multiplicity_matrix[i]) - def set_inverse_velocity_mgxs(self, inverse_velocity, temperature=294., + def set_inverse_velocity_mgxs(self, inverse_velocity, temperature=ROOM_TEMPERATURE_KELVIN, nuclide='total', xs_type='macro', subdomain=None): """This method allows for an openmc.mgxs.InverseVelocity @@ -1683,10 +1674,9 @@ class XSdata: [self.energy_groups]) check_value('domain_type', inverse_velocity.domain_type, openmc.mgxs.DOMAIN_TYPES) - check_type('temperature', temperature, Real) - check_value('temperature', temperature, self.temperatures) + self._check_temperature(temperature) - i = np.where(self.temperatures == temperature)[0][0] + i = self._temperature_index(temperature) self._inverse_velocity[i] = inverse_velocity.get_xs( nuclides=nuclide, xs_type=xs_type, subdomains=subdomain) @@ -1727,7 +1717,7 @@ class XSdata: check_value('target_representation', target_representation, _REPRESENTATIONS) - if target_representation == 'angle': + if target_representation == REPRESENTATION_ANGLE: check_type('num_polar', num_polar, Integral) check_type('num_azimuthal', num_azimuthal, Integral) check_greater_than('num_polar', num_polar, 0) @@ -1739,7 +1729,7 @@ class XSdata: # representations are the same if target_representation == self.representation: # Check to make sure the num_polar and num_azimuthal values match - if target_representation == 'angle': + if target_representation == REPRESENTATION_ANGLE: if num_polar != self.num_polar or num_azimuthal != self.num_azimuthal: raise ValueError("Cannot translate between `angle`" " representations with different angle" @@ -1749,13 +1739,13 @@ class XSdata: xsdata.representation = target_representation # We have different actions depending on the representation conversion - if target_representation == 'isotropic': + if target_representation == REPRESENTATION_ISOTROPIC: # This is not needed for the correct functionality, but these # values are changed back to None for clarity xsdata._num_polar = None xsdata._num_azimuthal = None - elif target_representation == 'angle': + elif target_representation == REPRESENTATION_ANGLE: xsdata.num_polar = num_polar xsdata.num_azimuthal = num_azimuthal @@ -1777,7 +1767,7 @@ class XSdata: # current data is just the average over the angle bins new_data = orig_data.mean(axis=(0, 1)) - elif target_representation == 'angle': + elif target_representation == REPRESENTATION_ANGLE: # Since we are going from isotropic to angle, the # current data is just copied for every angle bin new_shape = (num_polar, num_azimuthal) + \ @@ -1812,7 +1802,7 @@ class XSdata: check_value('target_format', target_format, _SCATTER_TYPES) check_type('target_order', target_order, Integral) - if target_format == 'legendre': + if target_format == SCATTER_LEGENDRE: check_greater_than('target_order', target_order, 0, equality=True) else: check_greater_than('target_order', target_order, 0) @@ -1829,14 +1819,14 @@ class XSdata: new_shape = orig_data.shape[:-1] + (xsdata.num_orders,) new_data = np.zeros(new_shape) - if self.scatter_format == 'legendre': - if target_format == 'legendre': + if self.scatter_format == SCATTER_LEGENDRE: + if target_format == SCATTER_LEGENDRE: # Then we are changing orders and only need to change # dimensionality of the mu data and pad/truncate as needed order = min(xsdata.num_orders, self.num_orders) new_data[..., :order] = orig_data[..., :order] - elif target_format == 'tabular': + elif target_format == SCATTER_TABULAR: mu = np.linspace(-1, 1, xsdata.num_orders) # Evaluate the legendre on the mu grid for imu in range(len(mu)): @@ -1845,7 +1835,7 @@ class XSdata: (l + 0.5) * eval_legendre(l, mu[imu]) * orig_data[..., l]) - elif target_format == 'histogram': + elif target_format == SCATTER_HISTOGRAM: # This code uses the vectorized integration capabilities # instead of having an isotropic and angle representation # path. @@ -1863,27 +1853,26 @@ class XSdata: orig_data[..., l]) new_data[..., h_bin] = simps(table_fine, mu_fine) - elif self.scatter_format == 'tabular': + elif self.scatter_format == SCATTER_TABULAR: # Calculate the mu points of the current data mu_self = np.linspace(-1, 1, self.num_orders) - if target_format == 'legendre': + if target_format == SCATTER_LEGENDRE: # Find the Legendre coefficients via integration. To best # use the vectorized integration capabilities of scipy, # this is done with fixed sample integration routines. mu_fine = np.linspace(-1, 1, _NMU) - y = [interp1d(mu_self, orig_data)(mu_fine) * - eval_legendre(l, mu_fine) - for l in range(xsdata.num_orders)] for l in range(xsdata.num_orders): - new_data[..., l] = simps(y[l], mu_fine) + y = (interp1d(mu_self, orig_data)(mu_fine) * + eval_legendre(l, mu_fine)) + new_data[..., l] = simps(y, mu_fine) - elif target_format == 'tabular': + elif target_format == SCATTER_TABULAR: # Simply use an interpolating function to get the new data mu = np.linspace(-1, 1, xsdata.num_orders) new_data[..., :] = interp1d(mu_self, orig_data)(mu) - elif target_format == 'histogram': + elif target_format == SCATTER_HISTOGRAM: # Use an interpolating function to do the bin-wise # integrals mu = np.linspace(-1, 1, xsdata.num_orders + 1) @@ -1897,7 +1886,7 @@ class XSdata: mu_fine = np.linspace(mu[h_bin], mu[h_bin + 1], _NMU) new_data[..., h_bin] = simps(interp(mu_fine), mu_fine) - elif self.scatter_format == 'histogram': + elif self.scatter_format == SCATTER_HISTOGRAM: # The histogram format does not have enough information to # convert to the other forms without inducing some amount of # error. We will make the assumption that the center of the bin @@ -1914,22 +1903,21 @@ class XSdata: # We now have a tabular distribution in tab_data on mu_self. # We now proceed just like the tabular branch above. - if target_format == 'legendre': + if target_format == SCATTER_LEGENDRE: # find the legendre coefficients via integration. To best # use the vectorized integration capabilities of scipy, # this will be done with fixed sample integration routines. mu_fine = np.linspace(-1, 1, _NMU) - y = [interp(mu_fine) * norm * eval_legendre(l, mu_fine) - for l in range(xsdata.num_orders)] for l in range(xsdata.num_orders): - new_data[..., l] = simps(y[l], mu_fine) + y = interp(mu_fine) * norm * eval_legendre(l, mu_fine) + new_data[..., l] = simps(y, mu_fine) - elif target_format == 'tabular': + elif target_format == SCATTER_TABULAR: # Simply use an interpolating function to get the new data mu = np.linspace(-1, 1, xsdata.num_orders) new_data[..., :] = interp(mu) * norm - elif target_format == 'histogram': + elif target_format == SCATTER_HISTOGRAM: # Use an interpolating function to do the bin-wise # integrals mu = np.linspace(-1, 1, xsdata.num_orders + 1) @@ -1968,7 +1956,7 @@ class XSdata: if self.representation is not None: grp.attrs['representation'] = np.string_(self.representation) - if self.representation == 'angle': + if self.representation == REPRESENTATION_ANGLE: if self.num_azimuthal is not None: grp.attrs['num_azimuthal'] = self.num_azimuthal @@ -2055,10 +2043,10 @@ class XSdata: # Get the sparse scattering data to print to the library G = self.energy_groups.num_groups - if self.representation == 'isotropic': + if self.representation == REPRESENTATION_ISOTROPIC: Np = 1 Na = 1 - elif self.representation == 'angle': + elif self.representation == REPRESENTATION_ANGLE: Np = self.num_polar Na = self.num_azimuthal @@ -2066,19 +2054,19 @@ class XSdata: for p in range(Np): for a in range(Na): for g_in in range(G): - if self.scatter_format == 'legendre': - if self.representation == 'isotropic': + if self.scatter_format == SCATTER_LEGENDRE: + if self.representation == REPRESENTATION_ISOTROPIC: matrix = \ self._scatter_matrix[i][g_in, :, 0] - elif self.representation == 'angle': + elif self.representation == REPRESENTATION_ANGLE: matrix = \ self._scatter_matrix[i][p, a, g_in, :, 0] else: - if self.representation == 'isotropic': + if self.representation == REPRESENTATION_ISOTROPIC: matrix = \ np.sum(self._scatter_matrix[i][g_in, :, :], axis=1) - elif self.representation == 'angle': + elif self.representation == REPRESENTATION_ANGLE: matrix = \ np.sum(self._scatter_matrix[i][p, a, g_in, :, :], axis=1) @@ -2096,9 +2084,9 @@ class XSdata: flat_scatt = [] for p in range(Np): for a in range(Na): - if self.representation == 'isotropic': + if self.representation == REPRESENTATION_ISOTROPIC: matrix = self._scatter_matrix[i][:, :, :] - elif self.representation == 'angle': + elif self.representation == REPRESENTATION_ANGLE: matrix = self._scatter_matrix[i][p, a, :, :, :] for g_in in range(G): for g_out in range(g_out_bounds[p, a, g_in, 0], @@ -2118,9 +2106,9 @@ class XSdata: flat_mult = [] for p in range(Np): for a in range(Na): - if self.representation == 'isotropic': + if self.representation == REPRESENTATION_ISOTROPIC: matrix = self._multiplicity_matrix[i][:, :] - elif self.representation == 'angle': + elif self.representation == REPRESENTATION_ANGLE: matrix = self._multiplicity_matrix[i][p, a, :, :] for g_in in range(G): for g_out in range(g_out_bounds[p, a, g_in, 0], @@ -2134,10 +2122,10 @@ class XSdata: # And finally, adjust g_out_bounds for 1-based group counting # and write it. g_out_bounds[:, :, :, :] += 1 - if self.representation == 'isotropic': + if self.representation == REPRESENTATION_ISOTROPIC: scatt_grp.create_dataset("g_min", data=g_out_bounds[0, 0, :, 0]) scatt_grp.create_dataset("g_max", data=g_out_bounds[0, 0, :, 1]) - elif self.representation == 'angle': + elif self.representation == REPRESENTATION_ANGLE: scatt_grp.create_dataset("g_min", data=g_out_bounds[:, :, :, 0]) scatt_grp.create_dataset("g_max", data=g_out_bounds[:, :, :, 1]) @@ -2190,7 +2178,7 @@ class XSdata: if 'representation' in attrs: representation = group.attrs['representation'].decode() else: - representation = 'isotropic' + representation = REPRESENTATION_ISOTROPIC data = cls(name, energy_groups, float_temperatures, representation, num_delayed_groups) @@ -2203,7 +2191,7 @@ class XSdata: data.atomic_weight_ratio = group.attrs['atomic_weight_ratio'] if 'order' in attrs: data.order = group.attrs['order'] - if data.representation == 'angle': + if data.representation == REPRESENTATION_ANGLE: data.num_azimuthal = group.attrs['num_azimuthal'] data.num_polar = group.attrs['num_polar'] @@ -2230,28 +2218,28 @@ class XSdata: flat_scatter = scatt_group['scatter_matrix'][()] scatter_matrix = np.zeros(data.xs_shapes["[G][G'][Order]"]) G = data.energy_groups.num_groups - if data.representation == 'isotropic': + if data.representation == REPRESENTATION_ISOTROPIC: Np = 1 Na = 1 - elif data.representation == 'angle': + elif data.representation == REPRESENTATION_ANGLE: Np = data.num_polar Na = data.num_azimuthal flat_index = 0 for p in range(Np): for a in range(Na): for g_in in range(G): - if data.representation == 'isotropic': + if data.representation == REPRESENTATION_ISOTROPIC: g_mins = g_min[g_in] g_maxs = g_max[g_in] - elif data.representation == 'angle': + elif data.representation == REPRESENTATION_ANGLE: g_mins = g_min[p, a, g_in] g_maxs = g_max[p, a, g_in] for g_out in range(g_mins - 1, g_maxs): for ang in range(data.num_orders): - if data.representation == 'isotropic': + if data.representation == REPRESENTATION_ISOTROPIC: scatter_matrix[g_in, g_out, ang] = \ flat_scatter[flat_index] - elif data.representation == 'angle': + elif data.representation == REPRESENTATION_ANGLE: scatter_matrix[p, a, g_in, g_out, ang] = \ flat_scatter[flat_index] flat_index += 1 @@ -2265,17 +2253,17 @@ class XSdata: for p in range(Np): for a in range(Na): for g_in in range(G): - if data.representation == 'isotropic': + if data.representation == REPRESENTATION_ISOTROPIC: g_mins = g_min[g_in] g_maxs = g_max[g_in] - elif data.representation == 'angle': + elif data.representation == REPRESENTATION_ANGLE: g_mins = g_min[p, a, g_in] g_maxs = g_max[p, a, g_in] for g_out in range(g_mins - 1, g_maxs): - if data.representation == 'isotropic': + if data.representation == REPRESENTATION_ISOTROPIC: mult_matrix[g_in, g_out] = \ flat_mult[flat_index] - elif data.representation == 'angle': + elif data.representation == REPRESENTATION_ANGLE: mult_matrix[p, a, g_in, g_out] = \ flat_mult[flat_index] flat_index += 1 From a2d391321d4366b5890a4b3eccd3ec8027b4e6f0 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 23 Mar 2020 15:26:24 -0500 Subject: [PATCH 150/205] Changes in settings.py from PullRequest Inc. review --- openmc/settings.py | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) diff --git a/openmc/settings.py b/openmc/settings.py index e028656df4..3f1cbe2c39 100644 --- a/openmc/settings.py +++ b/openmc/settings.py @@ -1,4 +1,5 @@ from collections.abc import Iterable, MutableSequence, Mapping +from enum import Enum from pathlib import Path from numbers import Real, Integral import warnings @@ -11,7 +12,15 @@ from openmc._xml import clean_indentation, get_text import openmc.checkvalue as cv from openmc import VolumeCalculation, Source, RegularMesh -_RUN_MODES = ['eigenvalue', 'fixed source', 'plot', 'volume', 'particle restart'] + +class RunMode(Enum): + EIGENVALUE = 'eigenvalue' + FIXED_SOURCE = 'fixed source' + PLOT = 'plot' + VOLUME = 'volume' + PARTICLE_RESTART = 'particle restart' + + _RES_SCAT_METHODS = ['dbrc', 'rvs'] @@ -174,9 +183,7 @@ class Settings: """ def __init__(self): - - # Run mode subelement (default is 'eigenvalue') - self._run_mode = 'eigenvalue' + self._run_mode = RunMode.EIGENVALUE self._batches = None self._generations_per_batch = None self._inactive = None @@ -246,7 +253,7 @@ class Settings: @property def run_mode(self): - return self._run_mode + return self._run_mode.value @property def batches(self): @@ -410,8 +417,10 @@ class Settings: @run_mode.setter def run_mode(self, run_mode): - cv.check_value('run mode', run_mode, _RUN_MODES) - self._run_mode = run_mode + cv.check_value('run mode', run_mode, {x.value for x in RunMode}) + for mode in RunMode: + if mode.value == run_mode: + self._run_mode = mode @batches.setter def batches(self, batches): @@ -769,7 +778,7 @@ class Settings: def _create_run_mode_subelement(self, root): elem = ET.SubElement(root, "run_mode") - elem.text = self._run_mode + elem.text = self._run_mode.value def _create_batches_subelement(self, root): if self._batches is not None: From 91c1749b98401000ee1ea192db361d80bb76ae95 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 23 Mar 2020 16:45:53 -0500 Subject: [PATCH 151/205] Changes in tallies.py from PullRequest Inc. review --- openmc/checkvalue.py | 6 +- openmc/tallies.py | 369 +++++++++++++++++++------------------------ 2 files changed, 167 insertions(+), 208 deletions(-) diff --git a/openmc/checkvalue.py b/openmc/checkvalue.py index e113a9fef7..32d14c6f8f 100644 --- a/openmc/checkvalue.py +++ b/openmc/checkvalue.py @@ -4,7 +4,7 @@ from collections.abc import Iterable import numpy as np -def check_type(name, value, expected_type, expected_iter_type=None): +def check_type(name, value, expected_type, expected_iter_type=None, *, none_ok=False): """Ensure that an object is of an expected type. Optionally, if the object is iterable, check that each element is of a particular type. @@ -19,8 +19,12 @@ def check_type(name, value, expected_type, expected_iter_type=None): expected_iter_type : type or Iterable of type or None, optional Expected type of each element in value, assuming it is iterable. If None, no check will be performed. + none_ok : bool + Whether None is allowed as a value """ + if none_ok and value is None: + return if not isinstance(value, expected_type): if isinstance(expected_type, Iterable): diff --git a/openmc/tallies.py b/openmc/tallies.py index 613ffad995..e27b750a65 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -197,40 +197,40 @@ class Tally(IDManagerMixin): def with_summary(self): return self._with_summary + def _read_results(self): + if self._results_read: + return + + # Open the HDF5 statepoint file + with h5py.File(self._sp_filename, 'r') as f: + # Extract Tally data from the file + data = f['tallies/tally {}/results'.format(self.id)] + sum_ = data[:, :, 0] + sum_sq = data[:, :, 1] + + # Reshape the results arrays + sum_ = np.reshape(sum_, self.shape) + sum_sq = np.reshape(sum_sq, self.shape) + + # Set the data for this Tally + self._sum = sum_ + self._sum_sq = sum_sq + + # 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) + + # Indicate that Tally results have been read + self._results_read = True + @property def sum(self): if not self._sp_filename or self.derived: return None - if not self._results_read: - # Open the HDF5 statepoint file - f = h5py.File(self._sp_filename, 'r') - - # Extract Tally data from the file - data = f['tallies/tally {0}/results'.format(self.id)] - sum = data[:, :, 0] - sum_sq = data[:, :, 1] - - # Reshape the results arrays - sum = np.reshape(sum, self.shape) - sum_sq = np.reshape(sum_sq, self.shape) - - # Set the data for this Tally - self._sum = sum - self._sum_sq = sum_sq - - # 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) - - # Indicate that Tally results have been read - self._results_read = True - - # Close the HDF5 statepoint file - f.close() + # Make sure results have been read + self._read_results() if self.sparse: return np.reshape(self._sum.toarray(), self.shape) @@ -242,9 +242,8 @@ class Tally(IDManagerMixin): if not self._sp_filename or self.derived: return None - if not self._results_read: - # Force reading of sum and sum_sq - self.sum + # Make sure results have been read + self._read_results() if self.sparse: return np.reshape(self._sum_sq.toarray(), self.shape) @@ -322,16 +321,13 @@ class Tally(IDManagerMixin): @name.setter def name(self, name): - if name is not None: - cv.check_type('tally name', name, str) - self._name = name - else: - self._name = '' + cv.check_type('tally name', name, str, none_ok=True) + self._name = name @derivative.setter def derivative(self, deriv): - if deriv is not None: - cv.check_type('tally derivative', deriv, openmc.TallyDerivative) + cv.check_type('tally derivative', deriv, openmc.TallyDerivative, + none_ok=True) self._derivative = deriv @filters.setter @@ -339,12 +335,14 @@ class Tally(IDManagerMixin): cv.check_type('tally filters', filters, MutableSequence) # If the filter is already in the Tally, raise an error - for i, f in enumerate(filters[:-1]): - if f in filters[i+1:]: - msg = 'Unable to add a duplicate filter "{0}" to Tally ID="{1}" ' \ + visited_filters = set() + for f in filters: + if f in visited_filters: + msg = 'Unable to add a duplicate filter "{}" to Tally ID="{}" ' \ 'since duplicate filters are not supported in the OpenMC ' \ 'Python API'.format(f, self.id) raise ValueError(msg) + visited_filters.add(f) self._filters = cv.CheckedList(_FILTER_CLASSES, 'tally filters', filters) @@ -353,12 +351,14 @@ class Tally(IDManagerMixin): cv.check_type('tally nuclides', nuclides, MutableSequence) # If the nuclide is already in the Tally, raise an error - for i, nuclide in enumerate(nuclides[:-1]): - if nuclide in nuclides[i+1:]: - msg = 'Unable to add a duplicate nuclide "{0}" to Tally ID="{1}" ' \ + visited_nuclides = set() + for nuc in nuclides: + if nuc in visited_nuclides: + msg = 'Unable to add a duplicate nuclide "{}" to Tally ID="{}" ' \ 'since duplicate nuclides are not supported in the OpenMC ' \ 'Python API'.format(nuclide, self.id) raise ValueError(msg) + visited_nuclides.add(nuc) self._nuclides = cv.CheckedList(_NUCLIDE_CLASSES, 'tally nuclides', nuclides) @@ -367,13 +367,15 @@ class Tally(IDManagerMixin): def scores(self, scores): cv.check_type('tally scores', scores, MutableSequence) + visited_scores = set() for i, score in enumerate(scores): # If the score is already in the Tally, raise an error - if score in scores[i+1:]: - msg = 'Unable to add a duplicate score "{0}" to Tally ID="{1}" ' \ + if score in visited_scores: + msg = 'Unable to add a duplicate score "{}" to Tally ID="{}" ' \ 'since duplicate scores are not supported in the OpenMC ' \ 'Python API'.format(score, self.id) raise ValueError(msg) + visited_scores.add(score) # If score is a string, strip whitespace if isinstance(score, str): @@ -467,9 +469,9 @@ class Tally(IDManagerMixin): """ if score not in self.scores: - msg = 'Unable to remove score "{0}" from Tally ID="{1}" since ' \ + msg = 'Unable to remove score "{}" from Tally ID="{}" since ' \ 'the Tally does not contain this score'.format(score, self.id) - ValueError(msg) + raise ValueError(msg) self._scores.remove(score) @@ -484,9 +486,9 @@ class Tally(IDManagerMixin): """ if old_filter not in self.filters: - msg = 'Unable to remove filter "{0}" from Tally ID="{1}" since the ' \ + msg = 'Unable to remove filter "{}" from Tally ID="{}" since the ' \ 'Tally does not contain this filter'.format(old_filter, self.id) - ValueError(msg) + raise ValueError(msg) self._filters.remove(old_filter) @@ -501,9 +503,9 @@ class Tally(IDManagerMixin): """ if nuclide not in self.nuclides: - msg = 'Unable to remove nuclide "{0}" from Tally ID="{1}" since the ' \ + msg = 'Unable to remove nuclide "{}" from Tally ID="{}" since the ' \ 'Tally does not contain this nuclide'.format(nuclide, self.id) - ValueError(msg) + raise ValueError(msg) self._nuclides.remove(nuclide) @@ -529,33 +531,20 @@ class Tally(IDManagerMixin): # Return False if only one tally has a delayed group filter tally1_dg = self.contains_filter(openmc.DelayedGroupFilter) tally2_dg = other.contains_filter(openmc.DelayedGroupFilter) - if sum([tally1_dg, tally2_dg]) == 1: + if tally1_dg != tally2_dg: return False # Look to see if all filters are the same, or one or more can be merged for filter1 in self.filters: - merge_filters = False - mergeable_filter = False + mergeable = False for filter2 in other.filters: - - # If filters match, they are mergeable - if filter1 == filter2: - mergeable_filter = True + if filter1 == filter2 or filter1.can_merge(filter2): + mergeable = True break - # If filters are first mergeable filters encountered - elif filter1.can_merge(filter2) and not merge_filters: - merge_filters = True - mergeable_filter = True - break - - # If filters are the second mergeable filters encountered - elif filter1.can_merge(filter2) and merge_filters: - return False - # If no mergeable filter was found, the tallies are not mergeable - if not mergeable_filter: + if not mergeable: return False # Tally filters are mergeable if all conditional checks passed @@ -661,24 +650,22 @@ class Tally(IDManagerMixin): equality = [equal_filters, equal_nuclides, equal_scores] # If all filters, nuclides and scores match then tallies are mergeable - if equal_filters and equal_nuclides and equal_scores: + if all(equality): return True # Variables to indicate filter bins, nuclides, and scores that can be merged - merge_filters = self._can_merge_filters(other) - merge_nuclides = self._can_merge_nuclides(other) - merge_scores = self._can_merge_scores(other) - mergeability = [merge_filters, merge_nuclides, merge_scores] + can_merge_filters = self._can_merge_filters(other) + can_merge_nuclides = self._can_merge_nuclides(other) + can_merge_scores = self._can_merge_scores(other) + mergeability = [can_merge_filters, can_merge_nuclides, can_merge_scores] if not all(mergeability): return False - # If the tally results have been read from the statepoint, we can only - # at least two of filters, nuclides and scores must match - elif self._results_read and sum(equality) < 2: - return False + # If the tally results have been read from the statepoint, at least two + # of filters, nuclides and scores must match else: - return True + return not self._results_read or sum(equality) >= 2 def merge(self, other): """Merge another tally with this one @@ -699,8 +686,8 @@ class Tally(IDManagerMixin): """ if not self.can_merge(other): - msg = 'Unable to merge tally ID="{0}" with ' \ - '"{1}"'.format(other.id, self.id) + msg = 'Unable to merge tally ID="{}" with "{}"'.format( + other.id, self.id) raise ValueError(msg) # Create deep copy of tally to return as merged tally @@ -856,14 +843,14 @@ class Tally(IDManagerMixin): # Scores if len(self.scores) == 0: - msg = 'Unable to get XML for Tally ID="{0}" since it does not ' \ + msg = 'Unable to get XML for Tally ID="{}" since it does not ' \ 'contain any scores'.format(self.id) raise ValueError(msg) else: scores = '' for score in self.scores: - scores += '{0} '.format(score) + scores += '{} '.format(score) subelement = ET.SubElement(element, "scores") subelement.text = scores.rstrip(' ') @@ -899,16 +886,10 @@ class Tally(IDManagerMixin): otherwise false """ - - filter_found = False - - # Look through all of this Tally's Filters for the type requested for test_filter in self.filters: if type(test_filter) is filter_type: - filter_found = True - break - - return filter_found + return True + return False def find_filter(self, filter_type): """Return a filter in the tally that matches a specified type @@ -931,28 +912,21 @@ class Tally(IDManagerMixin): """ - filter_found = None - # Look through all of this Tally's Filters for the type requested for test_filter in self.filters: if type(test_filter) is filter_type: - filter_found = test_filter - break + return test_filter # Also check to see if the desired filter is wrapped up in an # aggregate elif isinstance(test_filter, openmc.AggregateFilter): if isinstance(test_filter.aggregate_filter, filter_type): - filter_found = test_filter - break + return test_filter # If we did not find the Filter, throw an Exception - if filter_found is None: - msg = 'Unable to find filter type "{0}" in ' \ - 'Tally ID="{1}"'.format(filter_type, self.id) - raise ValueError(msg) - - return filter_found + msg = 'Unable to find filter type "{}" in Tally ID="{}"'.format( + filter_type, self.id) + raise ValueError(msg) def get_nuclide_index(self, nuclide): """Returns the index in the Tally's results array for a Nuclide bin @@ -974,30 +948,21 @@ class Tally(IDManagerMixin): in the Tally. """ - - nuclide_index = -1 - # Look for the user-requested nuclide in all of the Tally's Nuclides for i, test_nuclide in enumerate(self.nuclides): - # If the Summary was linked, then values are Nuclide objects if isinstance(test_nuclide, openmc.Nuclide): if test_nuclide.name == nuclide: - nuclide_index = i - break + return i # If the Summary has not been linked, then values are ZAIDs else: if test_nuclide == nuclide: - nuclide_index = i - break + return i - if nuclide_index == -1: - msg = 'Unable to get the nuclide index for Tally since "{0}" ' \ - 'is not one of the nuclides'.format(nuclide) - raise KeyError(msg) - else: - return nuclide_index + msg = ('Unable to get the nuclide index for Tally since "{}" ' + 'is not one of the nuclides'.format(nuclide)) + raise KeyError(msg) def get_score_index(self, score): """Returns the index in the Tally's results array for a score bin @@ -1024,7 +989,7 @@ class Tally(IDManagerMixin): score_index = self.scores.index(score) except ValueError: - msg = 'Unable to get the score index for Tally since "{0}" ' \ + msg = 'Unable to get the score index for Tally since "{}" ' \ 'is not one of the scores'.format(score) raise ValueError(msg) @@ -1066,48 +1031,44 @@ class Tally(IDManagerMixin): cv.check_type('filters', filters, Iterable, openmc.FilterMeta) cv.check_type('filter_bins', filter_bins, Iterable, tuple) - # Determine the score indices from any of the requested scores - if filters: - # Initialize empty list of indices for each bin in each Filter - filter_indices = [] - - # Loop over all of the Tally's Filters - for i, self_filter in enumerate(self.filters): - # If a user-requested Filter, get the user-requested bins - for j, test_filter in enumerate(filters): - if type(self_filter) is test_filter: - bins = filter_bins[j] - break - else: - # If not a user-requested Filter, get all bins - if isinstance(self_filter, openmc.DistribcellFilter): - # Create list of cell instance IDs for distribcell Filters - bins = list(range(self_filter.num_bins)) - - elif isinstance(self_filter, openmc.EnergyFunctionFilter): - # EnergyFunctionFilters don't have bins so just add a None - bins = [None] - - else: - # Create list of IDs for bins for all other filter types - bins = self_filter.bins - - # Add indices for each bin in this Filter to the list - indices = np.array([self_filter.get_bin_index(b) for b in bins]) - filter_indices.append(indices) - - # Account for stride in each of the previous filters - for indices in filter_indices[:i]: - indices *= self_filter.num_bins - - # Apply outer product sum between all filter bin indices - filter_indices = list(map(sum, product(*filter_indices))) - # If user did not specify any specific Filters, use them all - else: - filter_indices = np.arange(self.num_filter_bins) + if not filters: + return np.arange(self.num_filter_bins) - return filter_indices + # Initialize empty list of indices for each bin in each Filter + filter_indices = [] + + # Loop over all of the Tally's Filters + for i, self_filter in enumerate(self.filters): + # If a user-requested Filter, get the user-requested bins + for j, test_filter in enumerate(filters): + if type(self_filter) is test_filter: + bins = filter_bins[j] + break + else: + # If not a user-requested Filter, get all bins + if isinstance(self_filter, openmc.DistribcellFilter): + # Create list of cell instance IDs for distribcell Filters + bins = list(range(self_filter.num_bins)) + + elif isinstance(self_filter, openmc.EnergyFunctionFilter): + # EnergyFunctionFilters don't have bins so just add a None + bins = [None] + + else: + # Create list of IDs for bins for all other filter types + bins = self_filter.bins + + # Add indices for each bin in this Filter to the list + indices = np.array([self_filter.get_bin_index(b) for b in bins]) + filter_indices.append(indices) + + # Account for stride in each of the previous filters + for indices in filter_indices[:i]: + indices *= self_filter.num_bins + + # Apply outer product sum between all filter bin indices + return list(map(sum, product(*filter_indices))) def get_nuclide_indices(self, nuclides): """Get indices into the nuclide axis of this tally's data arrays. @@ -1131,16 +1092,14 @@ class Tally(IDManagerMixin): cv.check_iterable_type('nuclides', nuclides, str) - # Determine the score indices from any of the requested scores - if nuclides: - nuclide_indices = np.zeros(len(nuclides), dtype=int) - for i, nuclide in enumerate(nuclides): - nuclide_indices[i] = self.get_nuclide_index(nuclide) - # If user did not specify any specific Nuclides, use them all - else: - nuclide_indices = np.arange(self.num_nuclides) + if not nuclides: + return np.arange(self.num_nuclides) + # Determine the score indices from any of the requested scores + nuclide_indices = np.zeros(len(nuclides), dtype=int) + for i, nuclide in enumerate(nuclides): + nuclide_indices[i] = self.get_nuclide_index(nuclide) return nuclide_indices def get_score_indices(self, scores): @@ -1152,7 +1111,7 @@ class Tally(IDManagerMixin): Parameters ---------- - scores : list of str + scores : list of str or openmc.CrossScore A list of one or more score strings (e.g., ['absorption', 'nu-fission']; default is []) @@ -1165,8 +1124,8 @@ class Tally(IDManagerMixin): for score in scores: if not isinstance(score, (str, openmc.CrossScore)): - msg = 'Unable to get score indices for score "{0}" in Tally ' \ - 'ID="{1}" since it is not a string or CrossScore'\ + msg = 'Unable to get score indices for score "{}" in Tally ' \ + 'ID="{}" since it is not a string or CrossScore'\ .format(score, self.id) raise ValueError(msg) @@ -1239,7 +1198,7 @@ class Tally(IDManagerMixin): (value == 'rel_err' and self.mean is None) or \ (value == 'sum' and self.sum is None) or \ (value == 'sum_sq' and self.sum_sq is None): - msg = 'The Tally ID="{0}" has no data to return'.format(self.id) + msg = 'The Tally ID="{}" has no data to return'.format(self.id) raise ValueError(msg) # Get filter, nuclide and score indices @@ -1262,8 +1221,8 @@ class Tally(IDManagerMixin): elif value == 'sum_sq': data = self.sum_sq[indices] else: - msg = 'Unable to return results from Tally ID="{0}" since the ' \ - 'the requested value "{1}" is not \'mean\', \'std_dev\', ' \ + msg = 'Unable to return results from Tally ID="{}" since the ' \ + 'the requested value "{}" is not \'mean\', \'std_dev\', ' \ '\'rel_err\', \'sum\', or \'sum_sq\''.format(self.id, value) raise LookupError(msg) @@ -1315,7 +1274,7 @@ class Tally(IDManagerMixin): # Ensure that the tally has data if self.mean is None or self.std_dev is None: - msg = 'The Tally ID="{0}" has no data to return'.format(self.id) + msg = 'The Tally ID="{}" has no data to return'.format(self.id) raise KeyError(msg) # Initialize a pandas dataframe for the tally data @@ -1342,7 +1301,7 @@ class Tally(IDManagerMixin): nuclides.append(nuclide.name) elif isinstance(nuclide, openmc.AggregateNuclide): nuclides.append(nuclide.name) - column_name = '{0}(nuclide)'.format(nuclide.aggregate_op) + column_name = '{}(nuclide)'.format(nuclide.aggregate_op) else: nuclides.append(nuclide) @@ -1361,7 +1320,7 @@ class Tally(IDManagerMixin): scores.append(str(score)) elif isinstance(score, openmc.AggregateScore): scores.append(score.name) - column_name = '{0}(score)'.format(score.aggregate_op) + column_name = '{}(score)'.format(score.aggregate_op) tile_factor = data_size / len(self.scores) df[column_name] = np.tile(scores, int(tile_factor)) @@ -1530,7 +1489,7 @@ class Tally(IDManagerMixin): # Check that results have been read if not other.derived and other.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(other.id) raise ValueError(msg) @@ -1544,7 +1503,7 @@ class Tally(IDManagerMixin): # Construct a combined derived name from the two tally operands if self.name != '' and other.name != '': - new_name = '({0} {1} {2})'.format(self.name, binary_op, other.name) + new_name = '({} {} {})'.format(self.name, binary_op, other.name) new_tally.name = new_name # Query the mean and std dev so the tally data is read in from file @@ -1840,11 +1799,11 @@ class Tally(IDManagerMixin): if filter1 == filter2: return elif filter1 not in self.filters: - msg = 'Unable to swap "{0}" filter1 in Tally ID="{1}" since it ' \ + msg = 'Unable to swap "{}" filter1 in Tally ID="{}" since it ' \ 'does not contain such a filter'.format(filter1.type, self.id) raise ValueError(msg) elif filter2 not in self.filters: - msg = 'Unable to swap "{0}" filter2 in Tally ID="{1}" since it ' \ + msg = 'Unable to swap "{}" filter2 in Tally ID="{}" since it ' \ 'does not contain such a filter'.format(filter2.type, self.id) raise ValueError(msg) @@ -1922,7 +1881,7 @@ class Tally(IDManagerMixin): # Check that results have been read if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) @@ -1934,12 +1893,12 @@ class Tally(IDManagerMixin): msg = 'Unable to swap a nuclide with itself' raise ValueError(msg) elif nuclide1 not in self.nuclides: - msg = 'Unable to swap nuclide1 "{0}" in Tally ID="{1}" since it ' \ + msg = 'Unable to swap nuclide1 "{}" in Tally ID="{}" since it ' \ 'does not contain such a nuclide'\ .format(nuclide1.name, self.id) raise ValueError(msg) elif nuclide2 not in self.nuclides: - msg = 'Unable to swap "{0}" nuclide2 in Tally ID="{1}" since it ' \ + msg = 'Unable to swap "{}" nuclide2 in Tally ID="{}" since it ' \ 'does not contain such a nuclide'\ .format(nuclide2.name, self.id) raise ValueError(msg) @@ -1989,17 +1948,17 @@ class Tally(IDManagerMixin): # Check that results have been read if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) # Check that the scores are valid if not isinstance(score1, (str, openmc.CrossScore)): - msg = 'Unable to swap score1 "{0}" in Tally ID="{1}" since it is ' \ + msg = 'Unable to swap score1 "{}" in Tally ID="{}" since it is ' \ 'not a string or CrossScore'.format(score1, self.id) raise ValueError(msg) elif not isinstance(score2, (str, openmc.CrossScore)): - msg = 'Unable to swap score2 "{0}" in Tally ID="{1}" since it is ' \ + msg = 'Unable to swap score2 "{}" in Tally ID="{}" since it is ' \ 'not a string or CrossScore'.format(score2, self.id) raise ValueError(msg) @@ -2008,11 +1967,11 @@ class Tally(IDManagerMixin): msg = 'Unable to swap a score with itself' raise ValueError(msg) elif score1 not in self.scores: - msg = 'Unable to swap score1 "{0}" in Tally ID="{1}" since it ' \ + msg = 'Unable to swap score1 "{}" in Tally ID="{}" since it ' \ 'does not contain such a score'.format(score1, self.id) raise ValueError(msg) elif score2 not in self.scores: - msg = 'Unable to swap score2 "{0}" in Tally ID="{1}" since it ' \ + msg = 'Unable to swap score2 "{}" in Tally ID="{}" since it ' \ 'does not contain such a score'.format(score2, self.id) raise ValueError(msg) @@ -2073,7 +2032,7 @@ class Tally(IDManagerMixin): # Check that results have been read if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) @@ -2103,7 +2062,7 @@ class Tally(IDManagerMixin): new_tally.sparse = self.sparse else: - msg = 'Unable to add "{0}" to Tally ID="{1}"'.format(other, self.id) + msg = 'Unable to add "{}" to Tally ID="{}"'.format(other, self.id) raise ValueError(msg) return new_tally @@ -2145,7 +2104,7 @@ class Tally(IDManagerMixin): # Check that results have been read if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) @@ -2174,8 +2133,7 @@ class Tally(IDManagerMixin): new_tally.sparse = self.sparse else: - msg = 'Unable to subtract "{0}" from Tally ' \ - 'ID="{1}"'.format(other, self.id) + msg = 'Unable to subtract "{}" from Tally ID="{}"'.format(other, self.id) raise ValueError(msg) return new_tally @@ -2217,7 +2175,7 @@ class Tally(IDManagerMixin): # Check that results have been read if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) @@ -2246,8 +2204,7 @@ class Tally(IDManagerMixin): new_tally.sparse = self.sparse else: - msg = 'Unable to multiply Tally ID="{0}" ' \ - 'by "{1}"'.format(self.id, other) + msg = 'Unable to multiply Tally ID="{}" by "{}"'.format(self.id, other) raise ValueError(msg) return new_tally @@ -2289,7 +2246,7 @@ class Tally(IDManagerMixin): # Check that results have been read if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) @@ -2318,8 +2275,7 @@ class Tally(IDManagerMixin): new_tally.sparse = self.sparse else: - msg = 'Unable to divide Tally ID="{0}" ' \ - 'by "{1}"'.format(self.id, other) + msg = 'Unable to divide Tally ID="{}" by "{}"'.format(self.id, other) raise ValueError(msg) return new_tally @@ -2364,7 +2320,7 @@ class Tally(IDManagerMixin): # Check that results have been read if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) @@ -2394,8 +2350,7 @@ class Tally(IDManagerMixin): new_tally.sparse = self.sparse else: - msg = 'Unable to raise Tally ID="{0}" to ' \ - 'power "{1}"'.format(self.id, power) + msg = 'Unable to raise Tally ID="{}" to power "{}"'.format(self.id, power) raise ValueError(msg) return new_tally @@ -2551,7 +2506,7 @@ class Tally(IDManagerMixin): # Ensure that the tally has data if not self.derived and self.sum is None: - msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \ + msg = 'Unable to use tally arithmetic with Tally ID="{}" ' \ 'since it does not contain any results.'.format(self.id) raise ValueError(msg) @@ -2984,8 +2939,8 @@ class Tally(IDManagerMixin): cv.check_type('filter_position', filter_position, Integral) if new_filter in self.filters: - msg = 'Unable to diagonalize Tally ID="{0}" which already ' \ - 'contains a "{1}" filter'.format(self.id, type(new_filter)) + msg = 'Unable to diagonalize Tally ID="{}" which already ' \ + 'contains a "{}" filter'.format(self.id, type(new_filter)) raise ValueError(msg) # Add the new filter to a copy of this Tally @@ -3063,7 +3018,7 @@ class Tallies(cv.CheckedList): """ if not isinstance(tally, Tally): - msg = 'Unable to add a non-Tally "{0}" to the ' \ + msg = 'Unable to add a non-Tally "{}" to the ' \ 'Tallies instance'.format(tally) raise TypeError(msg) From 9fffaa0c26bdcebb7d6ac852dac84397db51b372 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 23 Mar 2020 20:50:04 -0500 Subject: [PATCH 152/205] Bug fix for comparison of ParticleFilter objects --- openmc/filter.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/openmc/filter.py b/openmc/filter.py index 62afbdd6ec..f37a0a9d0e 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -671,6 +671,16 @@ class ParticleFilter(Filter): The number of filter bins """ + def __eq__(self, other): + if type(self) is not type(other): + return False + elif len(self.bins) != len(other.bins): + return False + else: + return np.all(self.bins == other.bins) + + __hash__ = Filter.__hash__ + @Filter.bins.setter def bins(self, bins): bins = np.atleast_1d(bins) @@ -1686,10 +1696,8 @@ class EnergyFunctionFilter(Filter): return False elif not all(self.energy == other.energy): return False - elif not all(self.y == other.y): - return False else: - return True + return all(self.y == other.y) def __gt__(self, other): if type(self) is not type(other): From e7eceff2aa3a9bbfd4dd553f585d1a31b051ddb3 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 23 Mar 2020 21:27:48 -0500 Subject: [PATCH 153/205] PEP8 updates. --- openmc/mesh.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/openmc/mesh.py b/openmc/mesh.py index fb41b06205..9b0e43fcee 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -107,8 +107,8 @@ class RegularMesh(MeshBase): n_dimension : int Number of mesh dimensions. lower_left : Iterable of float - The lower-left corner of the structured mesh. If only two coordinate are - given, it is assumed that the mesh is an x-y mesh. + The lower-left corner of the structured mesh. If only two coordinate + are given, it is assumed that the mesh is an x-y mesh. upper_right : Iterable of float The upper-right corner of the structrued mesh. If only two coordinate are given, it is assumed that the mesh is an x-y mesh. @@ -589,6 +589,7 @@ class RectilinearMesh(MeshBase): return element + class UnstructuredMesh(MeshBase): """A 3D unstructured mesh @@ -656,7 +657,8 @@ class UnstructuredMesh(MeshBase): @centroids.setter def centroids(self, centroids): - cv.check_type("Unstructured mesh centroids", centroids, Iterable, Iterable) + cv.check_type("Unstructured mesh centroids", centroids, + Iterable, Iterable) self._centroids = centroids def __repr__(self): From 605b3eade124c5a87cbd1d5e6e7abbdc75be0597 Mon Sep 17 00:00:00 2001 From: Jonathan Shimwell Date: Tue, 24 Mar 2020 11:34:20 +0000 Subject: [PATCH 154/205] code review imporements Co-Authored-By: Andrew Johnson --- openmc/material.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index bdcf6adf17..092389cbce 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -603,11 +603,10 @@ class Material(IDManagerMixin): 'ao' for atom percent and 'wo' for weight percent. Defaults to atom percent. enrichment : float, optional - Enrichment of an enrichment_taget nuclide in percent (ao or wo). - If enrichment_taget is not supplied then it is enrichment for U235 + Enrichment of an enrichment_target nuclide in percent (ao or wo). + If enrichment_target is not supplied then it is enrichment for U235 in weight percent. For example, input 4.95 for 4.95 weight percent - enriched U. - Default is None (natural composition). + enriched U. Default is None (natural composition). enrichment_target: str, optional Single nuclide name to enrich from a natural composition (e.g., 'O16') enrichment_type: {'ao', 'wo'}, optional @@ -628,7 +627,7 @@ class Material(IDManagerMixin): for row in tokens: for token in row: if token.isalpha(): - if token not in list(openmc.data.ATOMIC_NUMBER.keys())[1:]: + if token == "n" or token not in openmc.data.ATOMIC_NUMBER: msg = 'Formula entry {} not an element symbol.' \ .format(token) raise ValueError(msg) @@ -663,8 +662,8 @@ class Material(IDManagerMixin): mat_stack.append(Counter()) if closing_bracket: stack_top = mat_stack.pop() - for i in stack_top: - mat_stack[-1][i] += int(multi2 or 1) * stack_top[i] + for symbol, value in stack_top.items(): + mat_stack[-1][symbol] += int(multi2 or 1) * value # Normalizing percentages percents = mat_stack[0].values() @@ -678,8 +677,6 @@ class Material(IDManagerMixin): enrichment_target, enrichment_type) else: self.add_element(element, percent, percent_type) - - def add_s_alpha_beta(self, name, fraction=1.0): r"""Add an :math:`S(\alpha,\beta)` table to the material From 5fe6576a9cba1679c491344efa250133eafec5a5 Mon Sep 17 00:00:00 2001 From: =shimwell Date: Tue, 24 Mar 2020 11:57:17 +0000 Subject: [PATCH 155/205] added test to check decimal values fail --- tests/unit_tests/test_material.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 7153fded5a..43aeb62789 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -92,6 +92,11 @@ def test_adding_elements_by_formula(): for nuclide in ref_dens: assert nuc_dens[nuclide][1] == pytest.approx(ref_dens[nuclide], 1e-2) + # testing non integer multiplier results in a value error + m = openmc.Material() + with pytest.raises(ValueError): + m.add_elements_from_formula('Li4.2SiO4') + # testing lowercase elements results in a value error m = openmc.Material() with pytest.raises(ValueError): From 430b42609896f4872f50b9ea1274114a67eec407 Mon Sep 17 00:00:00 2001 From: =shimwell Date: Tue, 24 Mar 2020 12:06:56 +0000 Subject: [PATCH 156/205] added check for decimal points in formula --- openmc/material.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/openmc/material.py b/openmc/material.py index 092389cbce..7cc7e3db60 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -598,7 +598,7 @@ class Material(IDManagerMixin): formula : str Formula to add, e.g., 'C2O', 'C6H12O6', or (NH4)2SO4. Note this is case sensitive, elements must start with an uppercase - character. Any numbers will be converted to integers (rounded down). + character. Multiplier numbers must be integers. percent_type : {'ao', 'wo'}, optional 'ao' for atom percent and 'wo' for weight percent. Defaults to atom percent. @@ -622,6 +622,11 @@ class Material(IDManagerMixin): """ cv.check_type('formula', formula, str) + if '.' in formula: + msg = 'Non-integer multiplier values are not accepted. The ' \ + 'input formula {} contains a "." character.'.format(formula) + raise ValueError(msg) + # Tokenizes the formula and check validity of tokens tokens = re.findall(r"([A-Z][a-z]*)(\d*)|(\()|(\))(\d*)", formula) for row in tokens: From ffa0cfc03e953fdadc2e4d9fd61319a3b18c1555 Mon Sep 17 00:00:00 2001 From: =shimwell Date: Tue, 24 Mar 2020 13:33:42 +0000 Subject: [PATCH 157/205] added tests for number of elements --- tests/unit_tests/test_material.py | 34 +++++++++++++++++++++++++++++-- 1 file changed, 32 insertions(+), 2 deletions(-) diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 43aeb62789..8e9ebb362d 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -3,7 +3,7 @@ import openmc.model import openmc.stats import openmc.examples import pytest - +from collections import defaultdict def test_attributes(uo2): assert uo2.name == 'UO2' @@ -60,9 +60,23 @@ def test_elements_by_name(): def test_adding_elements_by_formula(): """Test adding elements from a formula""" - # testing the correct nuclides are added to the Material + # testing the correct nuclides and elements are added to a material m = openmc.Material() m.add_elements_from_formula('Li4SiO4') + # checking the ratio of elements is 4:1:4 for Li:Si:O + elem = defaultdict(float) + for nuclide, adens in m.get_nuclide_atom_densities().values(): + if nuclide.startswith("Li"): + elem["Li"] += adens + if nuclide.startswith("Si"): + elem["Si"] += adens + if nuclide.startswith("O"): + elem["O"] += adens + total_number_of_atoms = 9 + assert elem["Li"] == pytest.approx(4./total_number_of_atoms) + assert elem["Si"] == pytest.approx(1./total_number_of_atoms) + assert elem["O"] == pytest.approx(4/total_number_of_atoms) + # testing the correct nuclides are added to the Material ref_dens = {'Li6': 0.033728, 'Li7': 0.410715, 'Si28': 0.102477, 'Si29': 0.0052035, 'Si30': 0.0034301, 'O16': 0.443386, 'O17': 0.000168} @@ -85,6 +99,22 @@ def test_adding_elements_by_formula(): # testing the use of brackets m = openmc.Material() m.add_elements_from_formula('Mg2(NO3)2') + + # checking the ratio of elements is 2:2:6 for Mg:N:O + elem = defaultdict(float) + for nuclide, adens in m.get_nuclide_atom_densities().values(): + if nuclide.startswith("Mg"): + elem["Mg"] += adens + if nuclide.startswith("N"): + elem["N"] += adens + if nuclide.startswith("O"): + elem["O"] += adens + total_number_of_atoms = 10 + assert elem["Mg"] == pytest.approx(2./total_number_of_atoms) + assert elem["N"] == pytest.approx(2./total_number_of_atoms) + assert elem["O"] == pytest.approx(6/total_number_of_atoms) + + # testing the correct nuclides are added when brackets are used ref_dens = {'Mg24': 0.157902, 'Mg25': 0.02004, 'Mg26': 0.022058, 'N14': 0.199267, 'N15': 0.000732, 'O16': 0.599772, 'O17': 0.000227} From ca7cba57dffb44e91726516b175d54fdc52be440 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 25 Mar 2020 03:14:58 -0500 Subject: [PATCH 158/205] Apply @paulromano 's suggestions from code review. Co-Authored-By: Paul Romano --- .../jupyter/unstructured-mesh-part-i.ipynb | 2 +- include/openmc/mesh.h | 25 ++++---- openmc/mesh.py | 8 +-- src/mesh.cpp | 63 ++++++++----------- src/state_point.cpp | 12 ++-- .../unstructured_mesh/test.py | 63 +++++++++---------- 6 files changed, 74 insertions(+), 99 deletions(-) diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb index a2e26eff86..e28bded768 100644 --- a/examples/jupyter/unstructured-mesh-part-i.ipynb +++ b/examples/jupyter/unstructured-mesh-part-i.ipynb @@ -488,7 +488,7 @@ "tally.filters = [mesh_filter]\n", "tally.scores = ['heating', 'flux']\n", "tally.estimator = 'tracklength'\n", - "model.tallies = (tally,)" + "model.tallies = [tally]" ] }, { diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index a5ff41fa61..ba68086ce3 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -99,8 +99,7 @@ public: int n_dimension_; //!< Number of dimensions }; -class StructuredMesh : public Mesh -{ +class StructuredMesh : public Mesh { public: StructuredMesh() = default; StructuredMesh(pugi::xml_node node) : Mesh {node} {}; @@ -252,7 +251,7 @@ private: class UnstructuredMesh : public Mesh { - typedef std::vector> UnstructuredMeshHits; + using UnstructuredMeshHits = std::vector>; public: UnstructuredMesh() = default; @@ -273,7 +272,7 @@ public: private: - //! Finds all intersections with faces of the mesh. + //! Find all intersections with faces of the mesh. // //! \param[in] start Staring location //! \param[in] dir Normalized particle direction @@ -285,7 +284,7 @@ private: double track_len, UnstructuredMeshHits& hits) const; - //! Calculates the volume for a given tetrahedron handle. + //! Calculate the volume for a given tetrahedron handle. // // \param[in] tet MOAB EntityHandle of the tetrahedron double tet_volume(moab::EntityHandle tet) const; @@ -297,12 +296,12 @@ private: //! \return MOAB EntityHandle of tet moab::EntityHandle get_tet(const Position& r) const; - //! Returns the containing tet given a position - inline moab::EntityHandle get_tet(const moab::CartVect& r) const { + //! Return the containing tet given a position + moab::EntityHandle get_tet(const moab::CartVect& r) const { return get_tet(Position(r[0], r[1], r[2])); }; - //! Check for point containment within a tet, uses + //! Check for point containment within a tet; uses //! pre-computed barycentric data. // //! \param[in] r Position to check @@ -317,13 +316,13 @@ private: //! \param[in] tets MOAB Range of tetrahedral elements void compute_barycentric_data(const moab::Range& tets); - //! Translates a MOAB EntityHandle its corresponding bin. + //! Translate a MOAB EntityHandle to its corresponding bin. // //! \param[in] eh MOAB EntityHandle to translate //! \return Mesh bin int get_bin_from_ent_handle(moab::EntityHandle eh) const; - //! Translates a bin to its corresponding MOAB EntityHandle + //! Translate a bin to its corresponding MOAB EntityHandle //! for the tetrahedron representing that bin. // //! \param[in] bin Bin value to translate @@ -348,7 +347,7 @@ private: //! \return Index of the bin int get_index_from_bin(int bin) const; - //! Builds a KDTree for all tetrahedra in the mesh. All + //! Build a KDTree for all tetrahedra in the mesh. All //! triangles representing 2D faces of the mesh are //! added to the tree as well. // @@ -359,7 +358,7 @@ private: //! or create them if they are not there // //! \param[in] score Name of the score - //! \returns The MOAB value and error tag handles, respectively + //! \return The MOAB value and error tag handles, respectively std::pair get_score_tags(std::string score) const; @@ -392,7 +391,7 @@ public: //! Retrieve a centroid for the mesh cell // // \param[in] tet MOAB EntityHandle of the tetrahedron - // \returns The centroid of the element + // \return The centroid of the element Position centroid(moab::EntityHandle tet) const; //! Return a string represntation of the mesh bin diff --git a/openmc/mesh.py b/openmc/mesh.py index 9b0e43fcee..d11b60dece 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -632,8 +632,7 @@ class UnstructuredMesh(MeshBase): @filename.setter def filename(self, filename): if filename is not None: - cv.check_type('Unstructured Mesh filename: {}'.format(filename), - filename, str) + cv.check_type('Unstructured Mesh filename', filename, str) self._filename = filename else: self.filename = '' @@ -663,8 +662,7 @@ class UnstructuredMesh(MeshBase): def __repr__(self): string = super().__repr__() - string += '{0: <16}{1}{2}\n'.format('\tFilename', '=\t', self.filename) - return string + return string + '{: <16}=\t{}\n'.format('\tFilename', self.filename) @classmethod def from_hdf5(cls, group): @@ -674,7 +672,7 @@ class UnstructuredMesh(MeshBase): mesh.filename = group['filename'][()].decode() vol_data = group['volumes'][()] centroids = group['centroids'][()] - mesh.volumes = np.reshape(vol_data, (vol_data.shape[0], 1)) + mesh.volumes = np.reshape(vol_data, (vol_data.shape[0],)) mesh.centroids = np.reshape(centroids, (vol_data.shape[0], 3)) return mesh diff --git a/src/mesh.cpp b/src/mesh.cpp index c9a70546e9..e8facb4216 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -818,7 +818,7 @@ RegularMesh::count_sites(const Particle::Bank* bank, bool* outside) const { // Determine shape of array for counts - std::size_t m = n_bins(); + std::size_t m = this->n_bins(); std::vector shape = {m}; // Create array of zeros @@ -1413,7 +1413,7 @@ openmc_extend_meshes(int32_t n, int32_t* index_start, int32_t* index_end) { if (index_start) *index_start = model::meshes.size(); for (int i = 0; i < n; ++i) { - model::meshes.push_back(std::move(std::make_unique())); + model::meshes.push_back(std::make_unique()); } if (index_end) *index_end = model::meshes.size() - 1; @@ -1552,14 +1552,13 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) // get the filename of the unstructured mesh to load if (check_for_node(node, "mesh_file")) { filename_ = get_node_value(node, "mesh_file"); - } - else { + } else { fatal_error("No filename supplied for unstructured mesh with ID: " + std::to_string(id_)); } // create MOAB instance - mbi_ = std::unique_ptr(new moab::Core()); + mbi_ = std::make_unique(); // create meshset to load mesh into moab::ErrorCode rval = mbi_->create_meshset(moab::MESHSET_SET, meshset_); if (rval != moab::MB_SUCCESS) { @@ -1624,7 +1623,7 @@ UnstructuredMesh::build_kdtree(const moab::Range& all_tets) all_tets_and_tris.merge(all_tris); // create a kd-tree instance - kdtree_ = std::unique_ptr(new moab::AdaptiveKDTree(mbi_.get())); + kdtree_ = std::make_unique(mbi_.get()); // build the tree rval = kdtree_->build_tree(all_tets_and_tris, &kdtree_root_); @@ -1789,8 +1788,8 @@ UnstructuredMesh::get_tet(const Position& r) const // loop over the tets in this leaf, returning the containing tet if found for (const auto& tet : tets) { - if (point_in_tet(pos, tet)) { - return tet; + if (point_in_tet(pos, tet)) { + return tet; } } @@ -1862,7 +1861,7 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& tets) { void UnstructuredMesh::to_hdf5(hid_t group) const { - hid_t mesh_group = create_group(group, "mesh " + std::to_string(id_)); + hid_t mesh_group = create_group(group, fmt::format("mesh {}", id_)); write_dataset(mesh_group, "type", "unstructured"); write_dataset(mesh_group, "filename", filename_); @@ -1872,8 +1871,8 @@ UnstructuredMesh::to_hdf5(hid_t group) const xt::xtensor centroids({ehs_.size(), 3}); for (int i = 0; i < ehs_.size(); i++) { const auto& eh = ehs_[i]; - tet_vols.emplace_back(tet_volume(eh)); - Position c = centroid(eh); + tet_vols.emplace_back(this->tet_volume(eh)); + Position c = this->centroid(eh); xt::view(centroids, i, xt::all()) = xt::xarray({c.x, c.y, c.z}); } @@ -1922,9 +1921,7 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) int UnstructuredMesh::get_bin_from_index(int idx) const { if (idx >= n_bins()) { - std::stringstream s; - s << "Invalid bin index: " << idx; - fatal_error(s); + fatal_error(fmt::format("Invalid bin index: {}", idx)); } return ehs_[idx] - ehs_[0]; } @@ -1951,9 +1948,7 @@ int UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { int bin = eh - ehs_[0]; if (bin >= n_bins()) { - std::stringstream s; - s << "Invalid bin: " << bin; - fatal_error(s); + fatal_error(fmt::format("Invalid bin: {}", bin)); } return bin; } @@ -1961,9 +1956,7 @@ UnstructuredMesh::get_bin_from_ent_handle(moab::EntityHandle eh) const { moab::EntityHandle UnstructuredMesh::get_ent_handle_from_bin(int bin) const { if (bin >= n_bins()) { - std::stringstream s; - s << "Invalid bin index: " << bin; - fatal_error(s); + fatal_error(fmt::format("Invalid bin index: ", bin)); } return ehs_[bin]; } @@ -1998,7 +1991,7 @@ UnstructuredMesh::centroid(moab::EntityHandle tet) const { // get the coordinates std::vector coords(conn.size()); - rval = mbi_->get_coords(&conn.front(), conn.size(), coords[0].array()); + rval = mbi_->get_coords(conn.data(), conn.size(), coords[0].array()); if (rval != moab::MB_SUCCESS) { warning("Failed to get the coordinates of a mesh element."); return {}; @@ -2016,9 +2009,7 @@ UnstructuredMesh::centroid(moab::EntityHandle tet) const { std::string UnstructuredMesh::bin_label(int bin) const { - std::stringstream out; - out << "Mesh Index (" << bin << ")"; - return out.str(); + return fmt::format("Mesh Index ({})", bin); }; std::pair @@ -2031,7 +2022,7 @@ UnstructuredMesh::get_score_tags(std::string score) const { // create the value tag if not present and get handle double default_val = 0.0; - auto val_string = score + "_value"; + auto val_string = score + "_mean"; rval = mbi_->tag_get_handle(val_string.c_str(), 1, moab::MB_TYPE_DOUBLE, @@ -2039,9 +2030,8 @@ UnstructuredMesh::get_score_tags(std::string score) const { moab::MB_TAG_DENSE|moab::MB_TAG_CREAT, &default_val); if (rval != moab::MB_SUCCESS) { - std::stringstream msg; - msg << "Could not create or retrieve the value tag for the score " << score - << " on unstructured mesh " << id_; + auto msg = fmt::format("Could not create or retrieve the value tag for the score {}" + " on unstructured mesh {}", score, id_); fatal_error(msg); } @@ -2067,26 +2057,26 @@ UnstructuredMesh::get_score_tags(std::string score) const { void UnstructuredMesh::add_score(std::string score) const { - auto score_tags = get_score_tags(score); + auto score_tags = this->get_score_tags(score); } void UnstructuredMesh::set_score_data(const std::string& score, std::vector values, std::vector std_dev) const { - auto score_tags = get_score_tags(score); + auto score_tags = this->get_score_tags(score); // normalize tally values by element volume for (int i = 0; i < ehs_.size(); i++) { - auto eh = get_ent_handle_from_bin(i); - double volume = tet_volume(eh); + auto eh = this->get_ent_handle_from_bin(i); + double volume = this->tet_volume(eh); values[i] /= volume; std_dev[i] /= volume; } moab::ErrorCode rval; // set the score value - rval = mbi_->tag_set_data(score_tags.first, ehs_, &values.front()); + rval = mbi_->tag_set_data(score_tags.first, ehs_, values.data()); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " @@ -2095,7 +2085,7 @@ UnstructuredMesh::set_score_data(const std::string& score, } // set the error value - rval = mbi_->tag_set_data(score_tags.second, ehs_, &std_dev.front()); + rval = mbi_->tag_set_data(score_tags.second, ehs_, std_dev.data()); if (rval != moab::MB_SUCCESS) { std::stringstream msg; msg << "Failed to set the tally value for score '" << score << "' " @@ -2144,12 +2134,11 @@ void read_meshes(pugi::xml_node root) model::meshes.push_back(std::make_unique(node)); } else if (mesh_type == "rectilinear") { model::meshes.push_back(std::make_unique(node)); - } #ifdef DAGMC - else if (mesh_type == "unstructured") { + } else if (mesh_type == "unstructured") { model::meshes.push_back(std::make_unique(node)); #else - else if (mesh_type == "unstructured") { + } else if (mesh_type == "unstructured") { fatal_error("Unstructured mesh support is disabled."); #endif } else { diff --git a/src/state_point.cpp b/src/state_point.cpp index 56e576a81f..e20b508b2f 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -699,7 +699,7 @@ void write_unstructured_mesh_results() { // warning and skip writing the mesh if (tally->filters().size() > 1) { warning(fmt::format("Skipping unstructured mesh writing for tally " - "{0}. More than one filter is present on the tally.", + "{}. More than one filter is present on the tally.", tally->id_)); break; } @@ -711,7 +711,7 @@ void write_unstructured_mesh_results() { for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) { // index for this nuclide and score - int nuc_score_idx = i_score + i_nuc * tally->scores_.size(); + int nuc_score_idx = i_score + i_nuc*tally->scores_.size(); // construct result vectors std::vector mean_vec, std_dev_vec; @@ -731,12 +731,8 @@ void write_unstructured_mesh_results() { std::string score_name = tally->score_name(i_score); - auto score_str = fmt::format("{0}_{1}", - score_name, - nuclide_name); - umesh->set_score_data(score_str, - mean_vec, - std_dev_vec); + auto score_str = fmt::format("{}_{}", score_name, nuclide_name); + umesh->set_score_data(score_str, mean_vec, std_dev_vec); } } diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 546b409b01..1b1b98a469 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -1,4 +1,3 @@ -from collections import defaultdict import glob from itertools import product import os @@ -18,8 +17,6 @@ TETS_PER_VOXEL = 12 class UnstructuredMeshTest(PyAPITestHarness): - - def __init__(self, statepoint_name, **kwargs): super().__init__(statepoint_name) @@ -47,11 +44,7 @@ class UnstructuredMeshTest(PyAPITestHarness): materials.append(fuel_mat) zirc_mat = openmc.Material(name="zircaloy") - zirc_mat.add_nuclide("Zr90", 0.5145) - zirc_mat.add_nuclide("Zr91", 0.1122) - zirc_mat.add_nuclide("Zr92", 0.1715) - zirc_mat.add_nuclide("Zr94", 0.1738) - zirc_mat.add_nuclide("Zr96", 0.028) + zirc_mat.add_element("Zr", 1.0) zirc_mat.set_density("g/cc", 5.77) materials.append(zirc_mat) @@ -64,14 +57,14 @@ class UnstructuredMeshTest(PyAPITestHarness): materials.export_to_xml() ### Geometry ### - fuel_min_x = openmc.XPlane(x0=-5.0, name="minimum x") - fuel_max_x = openmc.XPlane(x0=5.0, name="maximum x") + fuel_min_x = openmc.XPlane(-5.0, name="minimum x") + fuel_max_x = openmc.XPlane(5.0, name="maximum x") - fuel_min_y = openmc.YPlane(y0=-5.0, name="minimum y") - fuel_max_y = openmc.YPlane(y0=5.0, name="maximum y") + fuel_min_y = openmc.YPlane(-5.0, name="minimum y") + fuel_max_y = openmc.YPlane(5.0, name="maximum y") - fuel_min_z = openmc.ZPlane(z0=-5.0, name="minimum z") - fuel_max_z = openmc.ZPlane(z0=5.0, name="maximum z") + fuel_min_z = openmc.ZPlane(-5.0, name="minimum z") + fuel_max_z = openmc.ZPlane(5.0, name="maximum z") fuel_cell = openmc.Cell(name="fuel") fuel_cell.region = +fuel_min_x & -fuel_max_x & \ @@ -79,21 +72,21 @@ class UnstructuredMeshTest(PyAPITestHarness): +fuel_min_z & -fuel_max_z fuel_cell.fill = fuel_mat - clad_min_x = openmc.XPlane(x0=-6.0, name="minimum x") - clad_max_x = openmc.XPlane(x0=6.0, name="maximum x") + clad_min_x = openmc.XPlane(-6.0, name="minimum x") + clad_max_x = openmc.XPlane(6.0, name="maximum x") - clad_min_y = openmc.YPlane(y0=-6.0, name="minimum y") - clad_max_y = openmc.YPlane(y0=6.0, name="maximum y") + clad_min_y = openmc.YPlane(-6.0, name="minimum y") + clad_max_y = openmc.YPlane(6.0, name="maximum y") - clad_min_z = openmc.ZPlane(z0=-6.0, name="minimum z") - clad_max_z = openmc.ZPlane(z0=6.0, name="maximum z") + clad_min_z = openmc.ZPlane(-6.0, name="minimum z") + clad_max_z = openmc.ZPlane(6.0, name="maximum z") clad_cell = openmc.Cell(name="clad") - clad_cell.region = (-fuel_min_x | +fuel_max_x | \ - -fuel_min_y | +fuel_max_y | \ + clad_cell.region = (-fuel_min_x | +fuel_max_x | + -fuel_min_y | +fuel_max_y | -fuel_min_z | +fuel_max_z) & \ - (+clad_min_x & -clad_max_x & \ - +clad_min_y & -clad_max_y & \ + (+clad_min_x & -clad_max_x & + +clad_min_y & -clad_max_y & +clad_min_z & -clad_max_z) clad_cell.fill = zirc_mat @@ -124,19 +117,16 @@ class UnstructuredMeshTest(PyAPITestHarness): boundary_type='vacuum') water_cell = openmc.Cell(name="water") - water_cell.region = (-clad_min_x | +clad_max_x | \ - -clad_min_y | +clad_max_y | \ + water_cell.region = (-clad_min_x | +clad_max_x | + -clad_min_y | +clad_max_y | -clad_min_z | +clad_max_z) & \ - (+water_min_x & -water_max_x & \ - +water_min_y & -water_max_y & \ + (+water_min_x & -water_max_x & + +water_min_y & -water_max_y & +water_min_z & -water_max_z) water_cell.fill = water_mat # create a containing universe - root_univ = openmc.Universe() - root_univ.add_cells([fuel_cell, clad_cell, water_cell]) - - geom = openmc.Geometry(root=root_univ) + geom = openmc.Geometry([fuel_cell, clad_cell, water_cell]) geom.export_to_xml() @@ -256,14 +246,17 @@ class UnstructuredMeshTest(PyAPITestHarness): if os.path.exists(f): os.remove(f) -param_values = ( ('collision', 'tracklength'), # estimators - (True, False), # geometry outside of the mesh - ( (333, 90, 77), tuple() ) ) # location of holes in the mesh + +param_values = (['collision', 'tracklength'], # estimators + [True, False], # geometry outside of the mesh + [(333, 90, 77), (,)]) # location of holes in the mesh test_cases = [] for estimator, holes, ext_geom in product(*param_values): test_cases.append({'estimator' : estimator, 'external_geom' : ext_geom, 'holes' : holes}) + + @pytest.mark.parametrize("opts", test_cases) def test_unstructured_mesh(opts): harness = UnstructuredMeshTest('statepoint.10.h5', kwargs=opts) From 895249b6ca2ed992d979c472c824f42b68f9bd22 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 25 Mar 2020 03:21:39 -0500 Subject: [PATCH 159/205] Addressing changes from PR review. --- docs/source/usersguide/beginners.rst | 4 +- .../jupyter/unstructured-mesh-part-i.ipynb | 32 +--- .../jupyter/unstructured-mesh-part-ii.ipynb | 2 +- include/openmc/mesh.h | 17 +- openmc/mesh.py | 35 +++- openmc/statepoint.py | 2 +- src/mesh.cpp | 57 ++---- src/state_point.cpp | 99 +++++----- src/tallies/tally.cpp | 170 +----------------- .../unstructured_mesh/test.py | 38 ++-- 10 files changed, 129 insertions(+), 327 deletions(-) diff --git a/docs/source/usersguide/beginners.rst b/docs/source/usersguide/beginners.rst index a87078cb7d..0fbb4bd337 100644 --- a/docs/source/usersguide/beginners.rst +++ b/docs/source/usersguide/beginners.rst @@ -59,8 +59,8 @@ deterministic methods: Now let's look at the pros and cons of Monte Carlo methods: - **Pro**: No mesh generation is required to build geometry. By using - `constructive solid geometry`_, it's possible to build arbitrarily complex - reactor models with curved surfaces. + `constructive solid geometry`_, it's possible to build complex + xmodels with curved surfaces. - **Pro**: Monte Carlo methods can be used with either continuous-energy or multi-group cross sections. diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb index e28bded768..dab06db991 100644 --- a/examples/jupyter/unstructured-mesh-part-i.ipynb +++ b/examples/jupyter/unstructured-mesh-part-i.ipynb @@ -33,34 +33,6 @@ "We'll need to download the unstructured mesh file used in this notebook. We'll be retrieving those using the function and URLs below." ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "plt.rcParams[\"figure.figsize\"] = (30,10)\n", - "\n", - "import urllib.request\n", - "\n", - "pin_mesh_url = 'https://tinyurl.com/u9ce9d7' # 1.2 MB\n", - "\n", - "def download(url, filename='dagmc.h5m'):\n", - " \"\"\"\n", - " Helper function for retrieving dagmc models\n", - " \"\"\"\n", - " u = urllib.request.urlopen(url)\n", - " \n", - " if u.status != 200:\n", - " raise RuntimeError(\"Failed to download file.\")\n", - " \n", - " # save file as dagmc.h5m\n", - " with open(filename, 'wb') as f:\n", - " f.write(u.read())" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -467,7 +439,7 @@ "outputs": [], "source": [ "download(pin_mesh_url, \"pins1-4.h5m\")\n", - "umesh = openmc.UnstructuredMesh(filename=\"pins1-4.h5m\")\n", + "umesh = openmc.UnstructuredMesh(\"pins1-4.h5m\")\n", "mesh_filter = openmc.MeshFilter(umesh)" ] }, @@ -893,7 +865,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We hope you've found this example notebook useful. More unstructured mesh features are under development and will be included in additional examples soon." + "We hope you've found this example notebook useful!" ] }, { diff --git a/examples/jupyter/unstructured-mesh-part-ii.ipynb b/examples/jupyter/unstructured-mesh-part-ii.ipynb index 60fe335df6..8373656db9 100644 --- a/examples/jupyter/unstructured-mesh-part-ii.ipynb +++ b/examples/jupyter/unstructured-mesh-part-ii.ipynb @@ -351,7 +351,7 @@ "metadata": {}, "outputs": [], "source": [ - "unstructured_mesh = openmc.UnstructuredMesh(filename=\"manifold.h5m\")\n", + "unstructured_mesh = openmc.UnstructuredMesh(\"manifold.h5m\")\n", "\n", "mesh_filter = openmc.MeshFilter(unstructured_mesh)\n", "\n", diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index ba68086ce3..fe5a795838 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -258,18 +258,10 @@ public: UnstructuredMesh(pugi::xml_node); ~UnstructuredMesh() = default; - void bins_crossed(const Particle* p, std::vector& bins, + void bins_crossed(const Particle* p, + std::vector& bins, std::vector& lengths) const override; - //! Check where a line segment intersects the mesh and if it intersects at all - // - //! \param[in,out] r0 In: starting position, out: intersection point - //! \param[in] r1 Ending position - //! \param[out] ijk Indices of the mesh bin containing the intersection point - //! \return Whether the line segment connecting r0 and r1 intersects mesh - bool intersects(Position& r0, Position r1, int* ijk); - - private: //! Find all intersections with faces of the mesh. @@ -410,12 +402,11 @@ public: //! Write the mesh with any current tally data void write(std::string base_filename) const; - std::string filename_; // mbi_; //!< MOAB instance std::unique_ptr kdtree_; //!< MOAB KDTree instance diff --git a/openmc/mesh.py b/openmc/mesh.py index d11b60dece..4bde4a52ae 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -619,7 +619,7 @@ class UnstructuredMesh(MeshBase): (1.0, 1.0, 1.0), ...] """ - def __init__(self, mesh_id=None, name='', filename=''): + def __init__(self, filename, mesh_id=None, name=''): super().__init__(mesh_id, name) self._filename = filename self._volumes = [] @@ -631,11 +631,9 @@ class UnstructuredMesh(MeshBase): @filename.setter def filename(self, filename): - if filename is not None: - cv.check_type('Unstructured Mesh filename', filename, str) - self._filename = filename - else: - self.filename = '' + cv.check_type('Unstructured Mesh filename: {}'.format(filename), + filename, str) + self._filename = filename @property def volumes(self): @@ -667,9 +665,9 @@ class UnstructuredMesh(MeshBase): @classmethod def from_hdf5(cls, group): mesh_id = int(group.name.split('/')[-1].lstrip('mesh ')) + filename = group['filename'][()].decode() - mesh = cls(mesh_id) - mesh.filename = group['filename'][()].decode() + mesh = cls(filename, mesh_id=mesh_id) vol_data = group['volumes'][()] centroids = group['centroids'][()] mesh.volumes = np.reshape(vol_data, (vol_data.shape[0],)) @@ -695,3 +693,24 @@ class UnstructuredMesh(MeshBase): subelement.text = self.filename return element + + @classmethod + def from_xml_element(cls, elem): + """Generate unstructured mesh object from XML element + + Parameters + ---------- + elem : xml.etree.ElementTree.Element + XML element + + Returns + ------- + openmc.UnstructuredMesh + UnstructuredMesh generated from an XML element + """ + mesh_id = int(get_text(elem, 'id')) + filename = get_text(elem, 'mesh_file') + + mesh = cls(filename, mesh_id) + + return mesh diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 84512d70e7..5802a74cae 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -310,7 +310,7 @@ class StatePoint(object): if self.run_mode == 'eigenvalue': return self._f['n_inactive'][()] else: - return None + return None @property def n_particles(self): diff --git a/src/mesh.cpp b/src/mesh.cpp index e8facb4216..6be85685e9 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -3,7 +3,6 @@ #include // for copy, equal, min, min_element #include // for size_t #include // for ceil -#include // for fmt #include // for allocator #include @@ -16,6 +15,7 @@ #include "xtensor/xsort.hpp" #include "xtensor/xtensor.hpp" #include "xtensor/xview.hpp" +#include // for fmt #include "openmc/capi.h" #include "openmc/constants.h" @@ -1456,10 +1456,6 @@ openmc_mesh_set_id(int32_t index, int32_t id) extern "C" int openmc_mesh_get_dimension(int32_t index, int** dims, int* n) { - if (index < 0 || index >= model::meshes.size()) { - set_errmsg("Index in meshes array is out of bounds."); - return OPENMC_E_OUT_OF_BOUNDS; - } RegularMesh* mesh; if (int err = check_regular_mesh(index, &mesh)) return err; *dims = mesh->shape_.data(); @@ -1471,11 +1467,6 @@ openmc_mesh_get_dimension(int32_t index, int** dims, int* n) extern "C" int openmc_mesh_set_dimension(int32_t index, int n, const int* dims) { - if (index < 0 || index >= model::meshes.size()) { - set_errmsg("Index in meshes array is out of bounds."); - return OPENMC_E_OUT_OF_BOUNDS; - } - RegularMesh* mesh; if (int err = check_regular_mesh(index, &mesh)) return err; @@ -1559,19 +1550,14 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) // create MOAB instance mbi_ = std::make_unique(); - // create meshset to load mesh into - moab::ErrorCode rval = mbi_->create_meshset(moab::MESHSET_SET, meshset_); - if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to create fileset for umesh: " + filename_); - } // load unstructured mesh file - rval = mbi_->load_file(filename_.c_str(), &meshset_); + moab::ErrorCode rval = mbi_->load_file(filename_.c_str()); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to load the unstructured mesh file: " + filename_); } // set member range of tetrahedral entities - rval = mbi_->get_entities_by_dimension(meshset_, n_dimension_, ehs_); + rval = mbi_->get_entities_by_dimension(0, n_dimension_, ehs_); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to get all tetrahedral elements"); } @@ -1583,12 +1569,12 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) // make an entity set for all tetrahedra // this is used for convenience later in output - rval = mbi_->create_meshset(moab::MESHSET_SET, tet_set_); + rval = mbi_->create_meshset(moab::MESHSET_SET, tetset_); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to create an entity set for the tetrahedral elements"); } - rval = mbi_->add_entities(tet_set_, ehs_); + rval = mbi_->add_entities(tetset_, ehs_); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to add tetrahedra to an entity set."); } @@ -1672,7 +1658,7 @@ UnstructuredMesh::bins_crossed(const Particle* p, moab::ErrorCode rval; Position last_r{p->r_last_}; Position r{p->r()}; - Position u{p->u()}; + Direction u{p->u()}; u /= u.norm(); moab::CartVect r0(last_r.x, last_r.y, last_r.z); moab::CartVect r1(r.x, r.y, r.z); @@ -1680,8 +1666,8 @@ UnstructuredMesh::bins_crossed(const Particle* p, double track_len = (r1 - r0).length(); - r0 += TINY_BIT*dir; - r1 -= TINY_BIT*dir; + r0 += TINY_BIT * dir; + r1 -= TINY_BIT * dir; UnstructuredMeshHits hits; intersect_track(r0, dir, track_len, hits); @@ -1729,7 +1715,6 @@ UnstructuredMesh::bins_crossed(const Particle* p, bins.push_back(get_bin_from_ent_handle(tet)); lengths.push_back((hit.first - last_dist) / track_len); } else { - // if in the loop, we should always find a tet warning("No tet found for location between triangle hits"); } @@ -1805,7 +1790,7 @@ double UnstructuredMesh::tet_volume(moab::EntityHandle tet) const { } moab::CartVect p[4]; - rval = mbi_->get_coords(&(conn[0]), (int)conn.size(), p[0].array()); + rval = mbi_->get_coords(conn.data(), conn.size(), p[0].array()); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to get tet coords"); } @@ -1815,7 +1800,7 @@ double UnstructuredMesh::tet_volume(moab::EntityHandle tet) const { void UnstructuredMesh::surface_bins_crossed(const Particle* p, std::vector& bins) const { // TODO: Implement triangle crossings here - return; + throw std::runtime_error{"Unstructured mesh surface tallies are not implemented."}; } int @@ -1845,7 +1830,7 @@ UnstructuredMesh::compute_barycentric_data(const moab::Range& tets) { } moab::CartVect p[4]; - rval = mbi_->get_coords(&(verts[0]), (int)verts.size(), p[0].array()); + rval = mbi_->get_coords(verts.data(), verts.size(), p[0].array()); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to get coordinates of a tet in umesh: " + filename_); } @@ -2045,9 +2030,8 @@ UnstructuredMesh::get_score_tags(std::string score) const { moab::MB_TAG_DENSE|moab::MB_TAG_CREAT, &default_val); if (rval != moab::MB_SUCCESS) { - std::stringstream msg; - msg << "Could not create or retrieve the error tag for the score " << score - << " on unstructured mesh " << id_; + auto msg = fmt::format("Could not create or retrieve the error tag for the score {}" + " on unstructured mesh {}", score, id_); fatal_error(msg); } @@ -2078,18 +2062,16 @@ UnstructuredMesh::set_score_data(const std::string& score, // set the score value rval = mbi_->tag_set_data(score_tags.first, ehs_, values.data()); if (rval != moab::MB_SUCCESS) { - std::stringstream msg; - msg << "Failed to set the tally value for score '" << score << "' " - << " on unstructured mesh " << id_; + auto msg = fmt::format("Failed to set the tally value for score '{}' " + "on unstructured mesh {}", score, id_); warning(msg); } // set the error value rval = mbi_->tag_set_data(score_tags.second, ehs_, std_dev.data()); if (rval != moab::MB_SUCCESS) { - std::stringstream msg; - msg << "Failed to set the tally value for score '" << score << "' " - << " on unstructured mesh " << id_; + auto msg = fmt::format("Failed to set the tally error for score '{}' " + "on unstructured mesh {}", score, id_); warning(msg); } } @@ -2105,10 +2087,9 @@ UnstructuredMesh::write(std::string base_filename) const { // to avoid clutter from zero-value data on other // elements during visualization moab::ErrorCode rval; - rval = mbi_->write_mesh(filename.c_str(), &tet_set_, 1); + rval = mbi_->write_mesh(filename.c_str(), &tetset_, 1); if (rval != moab::MB_SUCCESS) { - std::stringstream msg; - msg << "Failed to write unstructured mesh " << id_; + auto msg = fmt::format("Failed to write unstructured mesh {}", id_); warning(msg); } } diff --git a/src/state_point.cpp b/src/state_point.cpp index e20b508b2f..a74b1d6856 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -689,64 +689,71 @@ void write_unstructured_mesh_results() { for (auto& tally : model::tallies) { for (auto filter_idx : tally->filters()) { auto& filter = model::tally_filters[filter_idx]; - if (filter->type() == "mesh") { - // check if the filter uses an unstructured mesh - auto mesh_filter = dynamic_cast(filter.get()); - auto mesh_idx = mesh_filter->mesh(); - auto umesh = dynamic_cast(model::meshes[mesh_idx].get()); - if (umesh) { - // if this tally has more than one filter, print - // warning and skip writing the mesh - if (tally->filters().size() > 1) { - warning(fmt::format("Skipping unstructured mesh writing for tally " - "{}. More than one filter is present on the tally.", - tally->id_)); - break; - } + if (filter->type() != "mesh") continue; - int n_realizations = tally->n_realizations_; + // check if the filter uses an unstructured mesh + auto mesh_filter = dynamic_cast(filter.get()); + auto mesh_idx = mesh_filter->mesh(); + auto umesh = dynamic_cast(model::meshes[mesh_idx].get()); - // write each score/nuclide combination for this tally - for (int i_score = 0; i_score < tally->scores_.size(); i_score++) { - for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) { + if (!umesh) continue; - // index for this nuclide and score - int nuc_score_idx = i_score + i_nuc*tally->scores_.size(); + // if this tally has more than one filter, print + // warning and skip writing the mesh + if (tally->filters().size() > 1) { + warning(fmt::format("Skipping unstructured mesh writing for tally " + "{0}. More than one filter is present on the tally.", + tally->id_)); + break; + } - // construct result vectors - std::vector mean_vec, std_dev_vec; - for (int j = 0; j < tally->results_.shape()[0]; j++) { - double mean = tally->results_(j, nuc_score_idx, TallyResult::SUM) / n_realizations; - double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ); - double std_dev = sum_sq / n_realizations - std::pow(mean, 2) / (n_realizations - 1); - std_dev_vec.push_back(std_dev); - mean_vec.push_back(mean); - } + int n_realizations = tally->n_realizations_; - // generate a name for the value - std::string nuclide_name = "total"; // start with total by default - if (tally->nuclides_[i_nuc] > -1) { - nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_; - } + // write each score/nuclide combination for this tally + for (int i_score = 0; i_score < tally->scores_.size(); i_score++) { + for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) { - std::string score_name = tally->score_name(i_score); + // index for this nuclide and score + int nuc_score_idx = i_score + i_nuc * tally->scores_.size(); - auto score_str = fmt::format("{}_{}", score_name, nuclide_name); - umesh->set_score_data(score_str, mean_vec, std_dev_vec); + // construct result vectors + std::vector mean_vec, std_dev_vec; + for (int j = 0; j < tally->results_.shape()[0]; j++) { + // mean + double mean = tally->results_(j, nuc_score_idx, TallyResult::SUM) / n_realizations; + mean_vec.push_back(mean); + // std. dev. + double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ); + if (n_realizations > 1) { + double std_dev = sum_sq / n_realizations - (mean* mean); + std_dev = std::sqrt(std_dev / (n_realizations - 1)); + std_dev_vec.push_back(std_dev); + } else { + std_dev_vec.push_back(0.0); } } - // Generate a file name based on the tally id - // and the current batch number - int w = std::to_string(settings::n_max_batches).size(); - std::string filename = fmt::format("tally_{0}.{1:0{2}}", - tally->id_, - simulation::current_batch, - w); - // Write the unstructured mesh and data to file - umesh->write(filename); + // generate a name for the value + std::string nuclide_name = "total"; // start with total by default + if (tally->nuclides_[i_nuc] > -1) { + nuclide_name = data::nuclides[tally->nuclides_[i_nuc]]->name_; + } + + std::string score_name = tally->score_name(i_score); + auto score_str = fmt::format("{}_{}", score_name, nuclide_name); + umesh->set_score_data(score_str, mean_vec, std_dev_vec); } } + + // Generate a file name based on the tally id + // and the current batch number + int w = std::to_string(settings::n_max_batches).size(); + std::string filename = fmt::format("tally_{0}.{1:0{2}}", + tally->id_, + simulation::current_batch, + w); + // Write the unstructured mesh and data to file + umesh->write(filename); } } } diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index bbc671d210..6b89136a05 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -65,171 +65,6 @@ double global_tally_collision; double global_tally_tracklength; double global_tally_leakage; -std::string -score_int_to_str(int score_int) { - if (score_int == SCORE_FLUX) - return "flux"; - - if (score_int == SCORE_SCATTER) - return "scatter"; - - if (score_int == SCORE_TOTAL) - return "total"; - - if (score_int == SCORE_SCATTER) - return "scatter"; - - if (score_int == SCORE_NU_SCATTER) - return "nu-scatter"; - - if (score_int == SCORE_ABSORPTION) - return "absorption"; - - if (score_int == SCORE_FISSION) - return "fission"; - - if (score_int == SCORE_NU_FISSION) - return "nu-fission"; - - if (score_int == SCORE_DECAY_RATE) - return "decay-rate"; - - if (score_int == SCORE_DELAYED_NU_FISSION) - return "delayed-nu-fission"; - - if (score_int == SCORE_PROMPT_NU_FISSION) - return "prompt-nu-fission"; - - if (score_int == SCORE_KAPPA_FISSION) - return "kappa-fission"; - - if (score_int == SCORE_INVERSE_VELOCITY) - return "inverse-velocity"; - - if (score_int == SCORE_FISS_Q_PROMPT) - return "fission-q-prompt"; - - if (score_int == SCORE_FISS_Q_RECOV) - return "fission-q-recoverable"; - - if (score_int == HEATING) - return "heating"; - - if (score_int == HEATING_LOCAL) - return "heating-local"; - - if (score_int == SCORE_CURRENT) - return "current"; - - if (score_int == SCORE_EVENTS) - return "events"; - - if (score_int == ELASTIC) - return "(n,elastic)"; - - if (score_int == N_2N) - return "(n,2n)"; - if (score_int == N_3N) - return "(n,3n)"; - if (score_int == N_4N) - return "(n,4n)"; - if (score_int == N_2ND) - return "(n,2nd)"; - if (score_int == N_2NA) - return "(n,na)"; - if (score_int == N_N3A) - return "(n,n3a)"; - if (score_int == N_2NA) - return "(n,2na)"; - if (score_int == N_3NA) - return "(n,3na)"; - if (score_int == N_NP) - return "(n,np)"; - if (score_int == N_N2A) - return "(n,n2a)"; - if (score_int == N_2N2A) - return "(n,2n2a)"; - if (score_int == N_ND) - return "(n,nd)"; - if (score_int == N_NT) - return "(n,nt)"; - if (score_int == N_N3HE) - return "(n,nHe-3)"; - if (score_int == N_ND2A) - return "(n,nd2a)"; - if (score_int == N_NT2A) - return "(n,nt2a)"; - if (score_int == N_3NF) - return "(n,3nf)"; - if (score_int == N_2NP) - return "(n,2np)"; - if (score_int == N_3NP) - return "(n,3np)"; - if (score_int == N_N2P) - return "(n,n2p)"; - if (score_int == N_NPA) - return "(n,npa)"; - if (score_int == N_N1) - return "(n,n1)"; - if (score_int == N_NC) - return "(n,nc)"; - if (score_int == N_GAMMA) - return "(n,gamma)"; - if (score_int == N_P) - return "(n,p)"; - if (score_int == N_D) - return "(n,d)"; - if (score_int == N_T) - return "(n,t)"; - if (score_int == N_3HE) - return "(n,3He)"; - if (score_int == N_A) - return "(n,a)"; - if (score_int == N_2A) - return "(n,2a)"; - if (score_int == N_3A) - return "(n,3a)"; - if (score_int == N_2P) - return "(n,2p)"; - if (score_int == N_PA) - return "(n,pa)"; - if (score_int == N_T2A) - return "(n,t2a)"; - if (score_int == N_D2A) - return "(n,d2a)"; - if (score_int == N_PD) - return "(n,pd)"; - if (score_int == N_PT) - return "(n,pt)"; - if (score_int == N_DA) - return "(n,da)"; - if (score_int == N_XP) - return "H1-production"; - if (score_int == N_XD) - return "H2-production"; - if (score_int == N_XT) - return "H3-production"; - if (score_int == N_X3HE) - return "He3-production"; - if (score_int == N_XA) - return "He4-production"; - if (score_int == DAMAGE_ENERGY) - return "damage-energy"; - - // Assume the given int is a reaction MT number. Make sure it's a natural - // number then return. - std::string score_as_str = std::to_string(score_int); - int MT; - try { - MT = std::stoi(score_as_str); - } catch (const std::invalid_argument& ex) { - throw std::invalid_argument("Invalid tally score \"" + score_as_str + "\""); - } - if (MT < 1) - throw std::invalid_argument("Invalid tally score \"" + score_as_str + "\""); - return "MT" + score_as_str; -} - int score_str_to_int(std::string score_str) { @@ -1001,10 +836,9 @@ void Tally::accumulate() std::string Tally::score_name(int score_idx) const { if (score_idx < 0 || score_idx >= scores_.size()) { - warning("Index in scores array is out of bounds."); - return ""; + fatal_error("Index in scores array is out of bounds."); } - return score_int_to_str(scores_[score_idx]); + return reaction_name(scores_[score_idx]); } //============================================================================== diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 1b1b98a469..e07a0f1de9 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -17,22 +17,23 @@ TETS_PER_VOXEL = 12 class UnstructuredMeshTest(PyAPITestHarness): - def __init__(self, statepoint_name, **kwargs): + + def __init__(self, + statepoint_name, + estimator='collision', + external_geom=False, + holes=None): + super().__init__(statepoint_name) - # defaults - self.estimator = "collision" # tally estimator type - self.external_geom = False # geometry size matches mesh - self.mesh_has_holes = False # holes in the mesh - self.holes = () - self.mesh_filename = "test_mesh_tets.h5m" # mesh file to use - - # set parameters for the test - self.estimator = kwargs.get('estimator', self.estimator) - self.external_geom = kwargs.get('external_geom', self.external_geom) - self.holes = kwargs.get('holes', self.holes) + self.estimator = estimator # tally estimator type + self.external_geom = external_geom # geometry size matches mesh + self.holes = holes # holes in the test mesh if self.holes: self.mesh_filename = "test_mesh_tets_w_holes.h5m" + else: + self.mesh_filename = "test_mesh_tets.h5m" # mesh file to use + print(self.estimator, self.external_geom, self.holes, self.mesh_filename) def _build_inputs(self): ### Materials ### @@ -140,8 +141,7 @@ class UnstructuredMeshTest(PyAPITestHarness): regular_mesh_filter = openmc.MeshFilter(mesh=regular_mesh) - uscd_mesh = openmc.UnstructuredMesh() - uscd_mesh.filename = self.mesh_filename + uscd_mesh = openmc.UnstructuredMesh(self.mesh_filename) uscd_mesh.mesh_lib = 'moab' uscd_filter = openmc.MeshFilter(mesh=uscd_mesh) @@ -194,7 +194,6 @@ class UnstructuredMeshTest(PyAPITestHarness): def _compare_results(self): with openmc.StatePoint(self._sp_name) as sp: - # loop over the tallies and get data for tally in sp.tallies.values(): # find the regular and unstructured meshes @@ -204,8 +203,8 @@ class UnstructuredMeshTest(PyAPITestHarness): if isinstance(flt.mesh, openmc.RegularMesh): reg_mesh_data, reg_mesh_std_dev = self.get_mesh_tally_data(tally) if self.holes: - reg_mesh_data = np.delete(reg_mesh_data, holes) - reg_mesh_std_dev = np.delete(reg_mesh_std_dev, holes) + reg_mesh_data = np.delete(reg_mesh_data, self.holes) + reg_mesh_std_dev = np.delete(reg_mesh_std_dev, self.holes) else: unstructured_data, unstructured_std_dev = self.get_mesh_tally_data(tally, True) @@ -218,7 +217,6 @@ class UnstructuredMeshTest(PyAPITestHarness): decimals) except AssertionError as ae: print(ae) - print() break # increment decimals decimals += 1 @@ -251,7 +249,7 @@ param_values = (['collision', 'tracklength'], # estimators [True, False], # geometry outside of the mesh [(333, 90, 77), (,)]) # location of holes in the mesh test_cases = [] -for estimator, holes, ext_geom in product(*param_values): +for estimator, ext_geom, holes in product(*param_values): test_cases.append({'estimator' : estimator, 'external_geom' : ext_geom, 'holes' : holes}) @@ -259,5 +257,5 @@ for estimator, holes, ext_geom in product(*param_values): @pytest.mark.parametrize("opts", test_cases) def test_unstructured_mesh(opts): - harness = UnstructuredMeshTest('statepoint.10.h5', kwargs=opts) + harness = UnstructuredMeshTest('statepoint.10.h5', **opts) harness.main() From f4092380ffa911d4484f7598bbbeb4ec8f07ed69 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 25 Mar 2020 03:24:40 -0500 Subject: [PATCH 160/205] Correction to test cases. --- .../unstructured_mesh/test.py | 21 +++++-------------- 1 file changed, 5 insertions(+), 16 deletions(-) diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index e07a0f1de9..1fc4a63aa4 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -33,7 +33,6 @@ class UnstructuredMeshTest(PyAPITestHarness): self.mesh_filename = "test_mesh_tets_w_holes.h5m" else: self.mesh_filename = "test_mesh_tets.h5m" # mesh file to use - print(self.estimator, self.external_geom, self.holes, self.mesh_filename) def _build_inputs(self): ### Materials ### @@ -208,22 +207,12 @@ class UnstructuredMeshTest(PyAPITestHarness): else: unstructured_data, unstructured_std_dev = self.get_mesh_tally_data(tally, True) - # successively check how many decimals the results are equal to - decimals = 1 - while True: - try: - np.testing.assert_array_almost_equal(unstructured_data, - reg_mesh_data, - decimals) - except AssertionError as ae: - print(ae) - break - # increment decimals - decimals += 1 - # we expect these results to be the same to within at least ten # decimal places - assert decimals >= 10 + decimals = 10 + np.testing.assert_array_almost_equal(unstructured_data, + reg_mesh_data, + decimals) @staticmethod def get_mesh_tally_data(tally, structured=False): @@ -247,7 +236,7 @@ class UnstructuredMeshTest(PyAPITestHarness): param_values = (['collision', 'tracklength'], # estimators [True, False], # geometry outside of the mesh - [(333, 90, 77), (,)]) # location of holes in the mesh + [(333, 90, 77), None]) # location of holes in the mesh test_cases = [] for estimator, ext_geom, holes in product(*param_values): test_cases.append({'estimator' : estimator, From 3669709061f6dc51591bdb46ee6dbf6170b500d7 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 25 Mar 2020 06:29:21 -0400 Subject: [PATCH 161/205] Apply suggestions from code review * Use set operations (|=) rather than in-place methods (update) * Remove second arguments to some asserts in Chain.reduce testing Co-Authored-By: Paul Romano --- openmc/deplete/chain.py | 8 ++++---- tests/unit_tests/test_deplete_chain.py | 26 ++++++++++++------------ tests/unit_tests/test_deplete_nuclide.py | 4 ++-- 3 files changed, 19 insertions(+), 19 deletions(-) diff --git a/openmc/deplete/chain.py b/openmc/deplete/chain.py index d820fe520b..fce5051b0b 100644 --- a/openmc/deplete/chain.py +++ b/openmc/deplete/chain.py @@ -906,7 +906,7 @@ class Chain(object): def _follow(self, isotopes, level): """Return all isotopes present up to depth level""" - found = set(isotopes) + found = isotopes.copy() remaining = set(self.nuclide_dict) if not found.issubset(remaining): raise IndexError( @@ -916,7 +916,7 @@ class Chain(object): if level == 0: return found - remaining.difference_update(found) + remaining -= found depth = 0 next_iso = set() @@ -952,8 +952,8 @@ class Chain(object): # Prepare for next dig depth += 1 - isotopes.update(next_iso) - remaining.difference_update(next_iso) + isotopes |= next_iso + remaining -= next_iso next_iso.clear() # Process isotope that would have started next depth diff --git a/tests/unit_tests/test_deplete_chain.py b/tests/unit_tests/test_deplete_chain.py index b7a1e6c0f2..ba3be4e37a 100644 --- a/tests/unit_tests/test_deplete_chain.py +++ b/tests/unit_tests/test_deplete_chain.py @@ -479,18 +479,18 @@ def test_reduce(gnd_simple_chain): assert u5_round0.n_decay_modes == ref_U5.n_decay_modes for newmode, refmode in zip(u5_round0.decay_modes, ref_U5.decay_modes): assert newmode.target is None - assert newmode.type == refmode.type, newmode - assert newmode.branching_ratio == refmode.branching_ratio, newmode + assert newmode.type == refmode.type + assert newmode.branching_ratio == refmode.branching_ratio assert u5_round0.n_reaction_paths == ref_U5.n_reaction_paths for newrxn, refrxn in zip(u5_round0.reactions, ref_U5.reactions): - assert newrxn.target is None, newrxn - assert newrxn.type == refrxn.type, newrxn - assert newrxn.Q == refrxn.Q, newrxn - assert newrxn.branching_ratio == refrxn.branching_ratio, newrxn + assert newrxn.target is None + assert newrxn.type == refrxn.type + assert newrxn.Q == refrxn.Q + assert newrxn.branching_ratio == refrxn.branching_ratio assert u5_round0.yield_data is not None - assert u5_round0.yield_data.products == ("I135", ) + assert u5_round0.yield_data.products == ("I135",) assert u5_round0.yield_data.yield_matrix == ( ref_U5_yields.yield_matrix[:, ref_U5_yields.products.index("I135")] ) @@ -499,15 +499,15 @@ def test_reduce(gnd_simple_chain): assert bareI5.n_decay_modes == ref_iodine.n_decay_modes for newmode, refmode in zip(bareI5.decay_modes, ref_iodine.decay_modes): assert newmode.target is None - assert newmode.type == refmode.type, newmode - assert newmode.branching_ratio == refmode.branching_ratio, newmode + assert newmode.type == refmode.type + assert newmode.branching_ratio == refmode.branching_ratio assert bareI5.n_reaction_paths == ref_iodine.n_reaction_paths for newrxn, refrxn in zip(bareI5.reactions, ref_iodine.reactions): - assert newrxn.target is None, newrxn - assert newrxn.type == refrxn.type, newrxn - assert newrxn.Q == refrxn.Q, newrxn - assert newrxn.branching_ratio == refrxn.branching_ratio, newrxn + assert newrxn.target is None + assert newrxn.type == refrxn.type + assert newrxn.Q == refrxn.Q + assert newrxn.branching_ratio == refrxn.branching_ratio follow_u5 = gnd_simple_chain.reduce(["U235"], 1) u5_round1 = follow_u5["U235"] diff --git a/tests/unit_tests/test_deplete_nuclide.py b/tests/unit_tests/test_deplete_nuclide.py index 92b004845a..126206cc70 100644 --- a/tests/unit_tests/test_deplete_nuclide.py +++ b/tests/unit_tests/test_deplete_nuclide.py @@ -181,9 +181,9 @@ def test_fission_yield_distribution(): with_extras = yield_dist.restrict_products( ["Xe135", "Sm149", "H1", "U235"]) - assert strict_restrict.products == ("Sm149", "Xe135", ) + assert strict_restrict.products == ("Sm149", "Xe135") assert strict_restrict.energies == yield_dist.energies - assert with_extras.products == ("Sm149", "Xe135", ) + assert with_extras.products == ("Sm149", "Xe135") assert with_extras.energies == yield_dist.energies for ene, new_yields in strict_restrict.items(): for product in strict_restrict.products: From c2562322ab7a788bdc09ed1846a2934582756060 Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 25 Mar 2020 06:48:43 -0400 Subject: [PATCH 162/205] Expand Chain.reduce docstring: methodology and return The methodology is explained through a simple example mostly borrowed from the chain_simple.xml test chain. A few different cases are presented, showing the effect of initial isotopes and search depth. A description of the returned chain is also included. --- openmc/deplete/chain.py | 27 ++++++++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/openmc/deplete/chain.py b/openmc/deplete/chain.py index fce5051b0b..a23ba2bc1c 100644 --- a/openmc/deplete/chain.py +++ b/openmc/deplete/chain.py @@ -822,7 +822,30 @@ class Chain(object): return valid def reduce(self, initial_isotopes, level=None): - """Reduce the size of the chain by follwing transmutation paths + """Reduce the size of the chain by following transmutation paths + + As an example, consider a simple chain with the following + isotopes and transmutation paths:: + + U235 (n,gamma) U236 + (n,fission) (Xe135, I135, Cs135) + I135 (beta decay) Xe135 (beta decay) Cs135 + Xe135 (n,gamma) Xe136 + + Calling ``chain.reduce(["I135"])`` will produce a depletion + chain that contains only isotopes that would originate from + I135: I135, Xe135, Cs135, and Xe136. U235 and U236 will not + be included, but multiple isotopes can be used to start + the search. + + The ``level`` value controls the depth of the search. + ``chain.reduce(["U235"], level=1)`` would return a chain + with all isotopes except Xe136, since it is two transmutations + removed from U235 in this case. + + While targets will not be included in the new chain, the + total destruction rate and decay rate of included isotopes + will be preserved. Parameters ---------- @@ -839,6 +862,8 @@ class Chain(object): Returns ------- Chain + Depletion chain containing isotopes that would appear + after following up to ``level`` reactions and decay paths """ check_type("initial_isotopes", initial_isotopes, Iterable, str) From d3368db5ee030bdedf5e6b039ddda2c126aaeafe Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 25 Mar 2020 07:10:32 -0400 Subject: [PATCH 163/205] Use reduce_chain and reduce_chain_level with Operator Control if the chain is to be reduced, and how far down the paths to follow. Defaults to not reducing the depletion chain. If the chain is reduced, the default depth is None, implying no limit --- openmc/deplete/operator.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/openmc/deplete/operator.py b/openmc/deplete/operator.py index 2572150012..5fa6745c8e 100644 --- a/openmc/deplete/operator.py +++ b/openmc/deplete/operator.py @@ -113,12 +113,13 @@ class Operator(TransportOperator): ``fission_yield_mode``. Will be passed directly on to the helper. Passing a value of None will use the defaults for the associated helper. - reduce_chain : bool or int, optional - If ``True`` or an integer, create a reduced depletion chain by - following transmuation paths for isotopes in burnable materials. - A value of ``True`` implies to follow all paths to completion, - while a non-negative integer indicates the depth. See - :meth:`openmc.deplete.Chain.reduce` + reduce_chain : bool, optional + If True, use :meth:`openmc.deplete.Chain.reduce` to reduce the + depletion chain up to ``reduce_chain_level``. Default is False. + reduce_chain_level : int, optional + Depth of the search when reducing the depletion chain. Only used + if ``reduce_chain`` evaluates to true. The default value of + ``None`` implies no limit on the depth. Attributes ---------- @@ -165,7 +166,7 @@ class Operator(TransportOperator): diff_burnable_mats=False, energy_mode="fission-q", fission_q=None, dilute_initial=1.0e3, fission_yield_mode="constant", fission_yield_opts=None, - reduce_chain=False): + reduce_chain=False, reduce_chain_level=None): if fission_yield_mode not in self._fission_helpers: raise KeyError( "fission_yield_mode must be one of {}, not {}".format( @@ -187,15 +188,14 @@ class Operator(TransportOperator): self.diff_burnable_mats = diff_burnable_mats # Reduce the chain before we create more materials - if reduce_chain is not False: + if reduce_chain: all_isotopes = set() for material in geometry.get_all_materials().values(): if not material.depletable: continue for name, _dens_percent, _dens_type in material.nuclides: all_isotopes.add(name) - level = None if reduce_chain is True else reduce_chain - self.chain = self.chain.reduce(all_isotopes, level) + self.chain = self.chain.reduce(all_isotopes, reduce_chain_level) # Differentiate burnable materials with multiple instances if self.diff_burnable_mats: From f882cf666bb02a2650342bda9842fcf804f91cbc Mon Sep 17 00:00:00 2001 From: Andrew Johnson Date: Wed, 25 Mar 2020 07:12:40 -0400 Subject: [PATCH 164/205] Remove unused imports from operator.py time, itertools.chain, and h5py not used --- openmc/deplete/operator.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/openmc/deplete/operator.py b/openmc/deplete/operator.py index 5fa6745c8e..ce7e709d9c 100644 --- a/openmc/deplete/operator.py +++ b/openmc/deplete/operator.py @@ -10,13 +10,10 @@ densities is all done in-memory instead of through the filesystem. import sys import copy from collections import OrderedDict -from itertools import chain import os -import time import xml.etree.ElementTree as ET from warnings import warn -import h5py import numpy as np from uncertainties import ufloat From 4ad2ad3ab8c8812467d4318d4d2c82c8f6cc04f4 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 25 Mar 2020 06:45:03 -0500 Subject: [PATCH 165/205] Apply suggestions from @drewejohnson code review Co-Authored-By: Andrew Johnson --- openmc/checkvalue.py | 2 +- openmc/material.py | 6 +++--- openmc/tallies.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/openmc/checkvalue.py b/openmc/checkvalue.py index 32d14c6f8f..a59b38a630 100644 --- a/openmc/checkvalue.py +++ b/openmc/checkvalue.py @@ -19,7 +19,7 @@ def check_type(name, value, expected_type, expected_iter_type=None, *, none_ok=F expected_iter_type : type or Iterable of type or None, optional Expected type of each element in value, assuming it is iterable. If None, no check will be performed. - none_ok : bool + none_ok : bool, optional Whether None is allowed as a value """ diff --git a/openmc/material.py b/openmc/material.py index 0e1ae8df7b..3babdf6435 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -794,10 +794,10 @@ class Material(IDManagerMixin): xml_element = ET.Element("nuclide") xml_element.set("name", nuclide[0]) - if nuclide[2] == 'ao': - xml_element.set("ao", str(nuclide[1])) + if nuclide.percent_type == 'ao': + xml_element.set("ao", str(nuclide.percent)) else: - xml_element.set("wo", str(nuclide[1])) + xml_element.set("wo", str(nuclide.percent)) return xml_element diff --git a/openmc/tallies.py b/openmc/tallies.py index e27b750a65..11f2fb0676 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -1097,7 +1097,7 @@ class Tally(IDManagerMixin): return np.arange(self.num_nuclides) # Determine the score indices from any of the requested scores - nuclide_indices = np.zeros(len(nuclides), dtype=int) + nuclide_indices = np.zeros_like(nuclides, dtype=int) for i, nuclide in enumerate(nuclides): nuclide_indices[i] = self.get_nuclide_index(nuclide) return nuclide_indices From 175347bbb9ddec081d8dfefed44704b6eac41bcb Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 25 Mar 2020 06:53:14 -0500 Subject: [PATCH 166/205] Better use of NuclideTuple namedtuple in material.py --- openmc/material.py | 36 ++++++++++++++---------------------- 1 file changed, 14 insertions(+), 22 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index 3babdf6435..419e310460 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -184,21 +184,17 @@ class Material(IDManagerMixin): @property def average_molar_mass(self): - - # Get a list of all the nuclides, with elements expanded - nuclide_densities = self.get_nuclide_densities() - # Using the sum of specified atomic or weight amounts as a basis, sum # the mass and moles of the material mass = 0. moles = 0. - for nuc, vals in nuclide_densities.items(): - if vals[2] == 'ao': - mass += vals[1] * openmc.data.atomic_mass(nuc) - moles += vals[1] + for nuc in self.nuclides: + if nuc.percent_type == 'ao': + mass += nuc.percent * openmc.data.atomic_mass(nuc.name) + moles += nuc.percent else: - moles += vals[1] / openmc.data.atomic_mass(nuc) - mass += vals[1] + moles += nuc.percent / openmc.data.atomic_mass(nuc.name) + mass += nuc.percent # Compute and return the molar mass return mass / moles @@ -625,8 +621,8 @@ class Material(IDManagerMixin): # keep ordered dictionary for testing purposes nuclides = OrderedDict() - for nuclide, density, density_type in self._nuclides: - nuclides[nuclide] = (nuclide, density, density_type) + for nuclide in self._nuclides: + nuclides[nuclide.name] = nuclide return nuclides @@ -642,9 +638,6 @@ class Material(IDManagerMixin): """ - # Expand elements in to nuclides - nuclides = self.get_nuclide_densities() - sum_density = False if self.density_units == 'sum': sum_density = True @@ -661,16 +654,15 @@ class Material(IDManagerMixin): density = 1.E-24 * self.density # For ease of processing split out nuc, nuc_density, - # and nuc_density_type in to separate arrays + # and nuc_density_type into separate arrays nucs = [] nuc_densities = [] nuc_density_types = [] - for nuclide in nuclides.items(): - nuc, nuc_density, nuc_density_type = nuclide[1] - nucs.append(nuc) - nuc_densities.append(nuc_density) - nuc_density_types.append(nuc_density_type) + for nuclide in self.nuclides: + nucs.append(nuclide.name) + nuc_densities.append(nuclide.percent) + nuc_density_types.append(nuclide.percent_type) nucs = np.array(nucs) nuc_densities = np.array(nuc_densities) @@ -792,7 +784,7 @@ class Material(IDManagerMixin): def _get_nuclide_xml(self, nuclide): xml_element = ET.Element("nuclide") - xml_element.set("name", nuclide[0]) + xml_element.set("name", nuclide.name) if nuclide.percent_type == 'ao': xml_element.set("ao", str(nuclide.percent)) From 676e9aae0a52cd8b861f38ad5c22b19f607221cd Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 25 Mar 2020 06:53:52 -0500 Subject: [PATCH 167/205] Further simplifications in arithmetic.py --- openmc/arithmetic.py | 92 +++++++++----------------------------------- 1 file changed, 18 insertions(+), 74 deletions(-) diff --git a/openmc/arithmetic.py b/openmc/arithmetic.py index 1f1cbebcba..70c74066d6 100644 --- a/openmc/arithmetic.py +++ b/openmc/arithmetic.py @@ -43,18 +43,10 @@ class CrossScore: """ - def __init__(self, left_score=None, right_score=None, binary_op=None): - - self._left_score = None - self._right_score = None - self._binary_op = None - - if left_score is not None: - self.left_score = left_score - if right_score is not None: - self.right_score = right_score - if binary_op is not None: - self.binary_op = binary_op + def __init__(self, left_score, right_score, binary_op): + self.left_score = left_score + self.right_score = right_score + self.binary_op = binary_op def __hash__(self): return hash(repr(self)) @@ -62,13 +54,9 @@ class CrossScore: def __eq__(self, other): return str(other) == str(self) - def __ne__(self, other): - return not self == other - def __repr__(self): - string = '({} {} {})'.format(self.left_score, self.binary_op, - self.right_score) - return string + return '({} {} {})'.format(self.left_score, self.binary_op, + self.right_score) @property def left_score(self): @@ -127,18 +115,10 @@ class CrossNuclide: """ - def __init__(self, left_nuclide=None, right_nuclide=None, binary_op=None): - - self._left_nuclide = None - self._right_nuclide = None - self._binary_op = None - - if left_nuclide is not None: - self.left_nuclide = left_nuclide - if right_nuclide is not None: - self.right_nuclide = right_nuclide - if binary_op is not None: - self.binary_op = binary_op + def __init__(self, left_nuclide, right_nuclide, binary_op): + self.left_nuclide = left_nuclide + self.right_nuclide = right_nuclide + self.binary_op = binary_op def __hash__(self): return hash(repr(self)) @@ -146,9 +126,6 @@ class CrossNuclide: def __eq__(self, other): return str(other) == str(self) - def __ne__(self, other): - return not self == other - def __repr__(self): return self.name @@ -239,21 +216,10 @@ class CrossFilter: """ - def __init__(self, left_filter, right_filter, binary_op=None): - left_type = left_filter.type - right_type = right_filter.type - self._type = '({} {} {})'.format(left_type, binary_op, right_type) - - self._left_filter = None - self._right_filter = None - self._binary_op = None - - if left_filter is not None: - self.left_filter = left_filter - if right_filter is not None: - self.right_filter = right_filter - if binary_op is not None: - self.binary_op = binary_op + def __init__(self, left_filter, right_filter, binary_op): + self.left_filter = left_filter + self.right_filter = right_filter + self.binary_op = binary_op def __hash__(self): return hash((self.left_filter, self.right_filter)) @@ -261,19 +227,13 @@ class CrossFilter: def __eq__(self, other): return str(other) == str(self) - def __ne__(self, other): - return not self == other - def __repr__(self): - filter_type = '({} {} {})'.format(self.left_filter.type, - self.binary_op, - self.right_filter.type) filter_bins = '({} {} {})'.format(self.left_filter.bins, self.binary_op, self.right_filter.bins) parts = [ 'CrossFilter', - '{: <16}=\t{}'.format('\tType', filter_type), + '{: <16}=\t{}'.format('\tType', self.type), '{: <16}=\t{}'.format('\tBins', filter_bins) ] return '\n'.join(parts) @@ -292,7 +252,9 @@ class CrossFilter: @property def type(self): - return self._type + left_type = self.left_filter.type + right_type = self.right_filter.type + return '({} {} {})'.format(left_type, self.binary_op, right_type) @property def bins(self): @@ -305,15 +267,6 @@ class CrossFilter: else: return 0 - @type.setter - def type(self, filter_type): - if filter_type not in _FILTER_TYPES: - msg = 'Unable to set CrossFilter type to "{}" since it ' \ - 'is not one of the supported types'.format(filter_type) - raise ValueError(msg) - - self._type = filter_type - @left_filter.setter def left_filter(self, left_filter): cv.check_type('left_filter', left_filter, @@ -451,9 +404,6 @@ class AggregateScore: def __eq__(self, other): return str(other) == str(self) - def __ne__(self, other): - return not self == other - def __repr__(self): string = ', '.join(map(str, self.scores)) string = '{}({})'.format(self.aggregate_op, string) @@ -524,9 +474,6 @@ class AggregateNuclide: def __eq__(self, other): return str(other) == str(self) - def __ne__(self, other): - return not self == other - def __repr__(self): # Append each nuclide in the aggregate to the string @@ -616,9 +563,6 @@ class AggregateFilter: def __eq__(self, other): return str(other) == str(self) - def __ne__(self, other): - return not self == other - def __gt__(self, other): if self.type != other.type: if self.aggregate_filter.type in _FILTER_TYPES and \ From ba9c3cada3430630c4bbceb8642197d40114207f Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 25 Mar 2020 06:55:20 -0500 Subject: [PATCH 168/205] Remove __ne__ methods (default is correct in Python 3) --- openmc/filter.py | 3 --- openmc/mgxs/groups.py | 3 --- openmc/mixin.py | 3 --- openmc/region.py | 3 --- openmc/trigger.py | 3 --- 5 files changed, 15 deletions(-) diff --git a/openmc/filter.py b/openmc/filter.py index f37a0a9d0e..28b06f52e6 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -119,9 +119,6 @@ class Filter(IDManagerMixin, metaclass=FilterMeta): else: return np.allclose(self.bins, other.bins) - def __ne__(self, other): - return not self == other - def __gt__(self, other): if type(self) is not type(other): if self.short_name in _FILTER_TYPES and \ diff --git a/openmc/mgxs/groups.py b/openmc/mgxs/groups.py index cbf6ed9556..5d7876644a 100644 --- a/openmc/mgxs/groups.py +++ b/openmc/mgxs/groups.py @@ -57,9 +57,6 @@ class EnergyGroups: else: return False - def __ne__(self, other): - return not self == other - def __hash__(self): return hash(tuple(self.group_edges)) diff --git a/openmc/mixin.py b/openmc/mixin.py index 37e972974d..d144492399 100644 --- a/openmc/mixin.py +++ b/openmc/mixin.py @@ -21,9 +21,6 @@ class EqualityMixin: return True - def __ne__(self, other): - return not self.__eq__(other) - class IDWarning(UserWarning): pass diff --git a/openmc/region.py b/openmc/region.py index 22718ac7b2..0d86d99bb9 100644 --- a/openmc/region.py +++ b/openmc/region.py @@ -41,9 +41,6 @@ class Region(metaclass=ABCMeta): else: return str(self) == str(other) - def __ne__(self, other): - return not self == other - def get_surfaces(self, surfaces=None): """Recursively find all surfaces referenced by a region and return them diff --git a/openmc/trigger.py b/openmc/trigger.py index f28995778a..1dcaf8a7db 100644 --- a/openmc/trigger.py +++ b/openmc/trigger.py @@ -38,9 +38,6 @@ class Trigger: def __eq__(self, other): return str(self) == str(other) - def __ne__(self, other): - return not self == other - def __repr__(self): string = 'Trigger\n' string += '{0: <16}{1}{2}\n'.format('\tType', '=\t', self._trigger_type) From d15e404ac3b65f2b0d403982faf43d4b682285d8 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 25 Mar 2020 07:10:35 -0500 Subject: [PATCH 169/205] Address remaining comments from @drewejohnson --- openmc/material.py | 7 ++++--- openmc/mgxs_library.py | 4 ++-- openmc/tallies.py | 16 ++++++++-------- 3 files changed, 14 insertions(+), 13 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index 419e310460..b0d5cb2229 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -58,9 +58,10 @@ class Material(IDManagerMixin): applies in the case of a multi-group calculation. depletable : bool Indicate whether the material is depletable. - nuclides : list of tuple - List in which each item is a 3-tuple consisting of a nuclide string, the - percent density, and the percent type ('ao' or 'wo'). + nuclides : list of namedtuple + List in which each item is a namedtuple consisting of a nuclide string, + the percent density, and the percent type ('ao' or 'wo'). The namedtuple + has field names ``name``, ``percent``, and ``percent_type``. isotropic : list of str Nuclides for which elastic scattering should be treated as though it were isotropic in the laboratory system. diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py index aae2d1b456..179885260c 100644 --- a/openmc/mgxs_library.py +++ b/openmc/mgxs_library.py @@ -57,7 +57,7 @@ class XSdata: Name of the mgxs data set. energy_groups : openmc.mgxs.EnergyGroups Energy group structure - representation : {'isotropic', REPRESENTATION_ANGLE}, optional + representation : {'isotropic', 'angle'}, optional Method used in generating the MGXS (isotropic or angle-dependent flux weighting). Defaults to 'isotropic' temperatures : Iterable of float @@ -88,7 +88,7 @@ class XSdata: Either the Legendre order, number of bins, or number of points used to describe the angular distribution associated with each group-to-group transfer probability. - representation : {'isotropic', REPRESENTATION_ANGLE} + representation : {'isotropic', 'angle'} Method used in generating the MGXS (isotropic or angle-dependent flux weighting). num_azimuthal : int diff --git a/openmc/tallies.py b/openmc/tallies.py index 11f2fb0676..a8687bb373 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -129,17 +129,17 @@ class Tally(IDManagerMixin): def __repr__(self): parts = ['Tally'] - parts.append('{: <16}=\t{}'.format('\tID', self.id)) - parts.append('{: <16}=\t{}'.format('\tName', self.name)) + parts.append('{: <15}=\t{}'.format('ID', self.id)) + parts.append('{: <15}=\t{}'.format('Name', self.name)) if self.derivative is not None: - parts.append('{: <16}=\t{}'.format('\tDerivative ID', self.derivative.id)) + parts.append('{: <15}=\t{}'.format('Derivative ID', self.derivative.id)) filters = ', '.join(type(f).__name__ for f in self.filters) - parts.append('{: <16}=\t{}'.format('\tFilters', filters)) + parts.append('{: <15}=\t{}'.format('Filters', filters)) nuclides = ' '.join(str(nuclide) for nuclide in self.nuclides) - parts.append('{: <16}=\t'.format('\tNuclides', nuclides)) - parts.append('{: <16}=\t{}\n'.format('\tScores', self.scores)) - parts.append('{: <16}=\t{}\n'.format('\tEstimator', self.estimator)) - return '\n'.join(parts) + parts.append('{: <15}=\t{}'.format('Nuclides', nuclides)) + parts.append('{: <15}=\t{}'.format('Scores', self.scores)) + parts.append('{: <15}=\t{}'.format('Estimator', self.estimator)) + return '\n\t'.join(parts) @property def name(self): From fa2f83ac543149e2c0f4088d7ed89a976927b733 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 25 Mar 2020 09:09:40 -0500 Subject: [PATCH 170/205] Typo fix. --- docs/source/usersguide/beginners.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/usersguide/beginners.rst b/docs/source/usersguide/beginners.rst index 0fbb4bd337..257458fa9f 100644 --- a/docs/source/usersguide/beginners.rst +++ b/docs/source/usersguide/beginners.rst @@ -60,7 +60,7 @@ Now let's look at the pros and cons of Monte Carlo methods: - **Pro**: No mesh generation is required to build geometry. By using `constructive solid geometry`_, it's possible to build complex - xmodels with curved surfaces. + models with curved surfaces. - **Pro**: Monte Carlo methods can be used with either continuous-energy or multi-group cross sections. From 86351747e72b9d90ec9a7b33eef1496d3a41fb7b Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 25 Mar 2020 09:37:00 -0500 Subject: [PATCH 171/205] Ensure remove_nuclide removes all matching nuclides. Closes #1532 --- openmc/material.py | 5 ++--- tests/unit_tests/test_material.py | 17 ++++++++++++++--- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index b0d5cb2229..cbd002c0bd 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -402,10 +402,9 @@ class Material(IDManagerMixin): cv.check_type('nuclide', nuclide, str) # If the Material contains the Nuclide, delete it - for nuc in self._nuclides: + for nuc in reversed(self.nuclides): if nuclide == nuc.name: - self._nuclides.remove(nuc) - break + self.nuclides.remove(nuc) def add_macroscopic(self, macroscopic): """Add a macroscopic to the material. This will also set the diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 6f51135ab2..65fdb26239 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -11,8 +11,8 @@ def test_attributes(uo2): assert uo2.depletable -def test_nuclides(uo2): - """Test adding/removing nuclides.""" +def test_add_nuclide(): + """Test adding nuclides.""" m = openmc.Material() m.add_nuclide('U235', 1.0) with pytest.raises(TypeError): @@ -21,7 +21,18 @@ def test_nuclides(uo2): m.add_nuclide(1.0, 'H1') with pytest.raises(ValueError): m.add_nuclide('H1', 1.0, 'oa') - m.remove_nuclide('U235') + + +def test_remove_nuclide(): + """Test removing nuclides.""" + m = openmc.Material() + for nuc, percent in [('H1', 1.0), ('H2', 1.0), ('H1', 2.0), ('H2', 2.0)]: + m.add_nuclide(nuc, percent) + m.remove_nuclide('H1') + assert len(m.nuclides) == 2 + assert all(nuc.name == 'H2' for nuc in m.nuclides) + assert m.nuclides[0].percent == 1.0 + assert m.nuclides[1].percent == 2.0 def test_elements(): From d829fd48a29e43d6ede15418c9f0cb269bad14e6 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 25 Mar 2020 09:41:31 -0500 Subject: [PATCH 172/205] Updating fmt include location. --- src/mesh.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 6be85685e9..5b6b5711e9 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -9,13 +9,13 @@ #ifdef OPENMC_MPI #include "mpi.h" #endif +#include // for fmt #include "xtensor/xbuilder.hpp" #include "xtensor/xeval.hpp" #include "xtensor/xmath.hpp" #include "xtensor/xsort.hpp" #include "xtensor/xtensor.hpp" #include "xtensor/xview.hpp" -#include // for fmt #include "openmc/capi.h" #include "openmc/constants.h" From 4234fe45e71a7a3971e06c0733c253e96222bb8f Mon Sep 17 00:00:00 2001 From: Jonathan Shimwell Date: Wed, 25 Mar 2020 14:58:02 +0000 Subject: [PATCH 173/205] Apply suggestions from code review Co-Authored-By: Paul Romano --- openmc/material.py | 4 ++-- tests/unit_tests/test_material.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index 7cc7e3db60..e34123e934 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -607,9 +607,9 @@ class Material(IDManagerMixin): If enrichment_target is not supplied then it is enrichment for U235 in weight percent. For example, input 4.95 for 4.95 weight percent enriched U. Default is None (natural composition). - enrichment_target: str, optional + enrichment_target : str, optional Single nuclide name to enrich from a natural composition (e.g., 'O16') - enrichment_type: {'ao', 'wo'}, optional + enrichment_type : {'ao', 'wo'}, optional 'ao' for enrichment as atom percent and 'wo' for weight percent. Default is: 'ao' for two-isotope enrichment; 'wo' for U enrichment diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 8e9ebb362d..e366401827 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -58,7 +58,8 @@ def test_elements_by_name(): assert a._nuclides == b._nuclides assert b._nuclides == c._nuclides -def test_adding_elements_by_formula(): + +def test_add_elements_by_formula(): """Test adding elements from a formula""" # testing the correct nuclides and elements are added to a material m = openmc.Material() @@ -354,4 +355,3 @@ def test_mix_materials(): assert m3.density == pytest.approx(dens3) assert m4.density == pytest.approx(dens4) assert m5.density == pytest.approx(dens5) - From c013f6373075c64205511a182e1632bd87dea1a3 Mon Sep 17 00:00:00 2001 From: =shimwell Date: Wed, 25 Mar 2020 15:03:39 +0000 Subject: [PATCH 174/205] pep8 format applied to imports --- tests/unit_tests/test_material.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index e366401827..c47faee8cd 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -1,9 +1,12 @@ +from collections import defaultdict + +import pytest + import openmc +import openmc.examples import openmc.model import openmc.stats -import openmc.examples -import pytest -from collections import defaultdict + def test_attributes(uo2): assert uo2.name == 'UO2' From c6d9f80f302525960f1d585b9231aae696341596 Mon Sep 17 00:00:00 2001 From: John Tramm Date: Wed, 25 Mar 2020 16:14:28 +0000 Subject: [PATCH 175/205] added support for both legacy version of HDF5 and newer ones, due to deprecation of older H5Oget_info_by_idx interface in HDF5 version 1.12 --- CMakeLists.txt | 4 ++++ src/hdf5_interface.cpp | 25 +++++++++++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index c003f39ac1..3e44cdea22 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -74,6 +74,10 @@ if(HDF5_IS_PARALLEL) message(STATUS "Using parallel HDF5") endif() +if(${HDF5_VERSION} VERSION_LESS "1.12.0") + list(APPEND cxxflags -DHDF5_LEGACY) +endif() + #=============================================================================== # Set compile/link flags based on which compiler is being used #=============================================================================== diff --git a/src/hdf5_interface.cpp b/src/hdf5_interface.cpp index 1fcc97f3c3..cf59b265a2 100644 --- a/src/hdf5_interface.cpp +++ b/src/hdf5_interface.cpp @@ -251,8 +251,13 @@ int get_num_datasets(hid_t group_id) int ndatasets = 0; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) + #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); + #else + H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, + H5O_INFO_BASIC, H5P_DEFAULT); + #endif if (oinfo.type == H5O_TYPE_DATASET) ndatasets += 1; } @@ -271,8 +276,13 @@ int get_num_groups(hid_t group_id) int ngroups = 0; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) + #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); + #else + H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, + H5O_INFO_BASIC, H5P_DEFAULT); + #endif if (oinfo.type == H5O_TYPE_GROUP) ngroups += 1; } @@ -293,8 +303,13 @@ get_datasets(hid_t group_id, char* name[]) size_t size; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) + #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); + #else + H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, + H5O_INFO_BASIC, H5P_DEFAULT); + #endif if (oinfo.type != H5O_TYPE_DATASET) continue; // Get size of name @@ -322,8 +337,13 @@ get_groups(hid_t group_id, char* name[]) size_t size; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) + #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); + #else + H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, + H5O_INFO_BASIC, H5P_DEFAULT); + #endif if (oinfo.type != H5O_TYPE_GROUP) continue; // Get size of name @@ -350,8 +370,13 @@ member_names(hid_t group_id, H5O_type_t type) std::vector names; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) + #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); + #else + H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, + H5O_INFO_BASIC, H5P_DEFAULT); + #endif if (oinfo.type != type) continue; // Get size of name From 77bbe888607b40203946bf3f69ec24291b7f43d4 Mon Sep 17 00:00:00 2001 From: John Tramm Date: Wed, 25 Mar 2020 16:45:13 +0000 Subject: [PATCH 176/205] simplified the solution a little to make use of the HDF5 internal compatibility flags. --- CMakeLists.txt | 7 +++++-- src/hdf5_interface.cpp | 25 ------------------------- 2 files changed, 5 insertions(+), 27 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3e44cdea22..f4ca745ed1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -74,8 +74,11 @@ if(HDF5_IS_PARALLEL) message(STATUS "Using parallel HDF5") endif() -if(${HDF5_VERSION} VERSION_LESS "1.12.0") - list(APPEND cxxflags -DHDF5_LEGACY) +# Version 1.12 of HDF5 deprecates the H5Oget_info_by_idx() interface. +# Thus, we give these flags to allow usage of the old interface in newer +# versions of HDF5. +if(${HDF5_VERSION} VERSION_GREATER_EQUAL "1.12.0") + list(APPEND cxxflags -DH5Oget_info_by_idx_vers=1 -DH5O_info_t_vers=1) endif() #=============================================================================== diff --git a/src/hdf5_interface.cpp b/src/hdf5_interface.cpp index cf59b265a2..1fcc97f3c3 100644 --- a/src/hdf5_interface.cpp +++ b/src/hdf5_interface.cpp @@ -251,13 +251,8 @@ int get_num_datasets(hid_t group_id) int ndatasets = 0; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) - #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); - #else - H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, - H5O_INFO_BASIC, H5P_DEFAULT); - #endif if (oinfo.type == H5O_TYPE_DATASET) ndatasets += 1; } @@ -276,13 +271,8 @@ int get_num_groups(hid_t group_id) int ngroups = 0; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) - #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); - #else - H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, - H5O_INFO_BASIC, H5P_DEFAULT); - #endif if (oinfo.type == H5O_TYPE_GROUP) ngroups += 1; } @@ -303,13 +293,8 @@ get_datasets(hid_t group_id, char* name[]) size_t size; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) - #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); - #else - H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, - H5O_INFO_BASIC, H5P_DEFAULT); - #endif if (oinfo.type != H5O_TYPE_DATASET) continue; // Get size of name @@ -337,13 +322,8 @@ get_groups(hid_t group_id, char* name[]) size_t size; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) - #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); - #else - H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, - H5O_INFO_BASIC, H5P_DEFAULT); - #endif if (oinfo.type != H5O_TYPE_GROUP) continue; // Get size of name @@ -370,13 +350,8 @@ member_names(hid_t group_id, H5O_type_t type) std::vector names; for (hsize_t i = 0; i < info.nlinks; ++i) { // Determine type of object (and skip non-group) - #ifdef HDF5_LEGACY H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, H5P_DEFAULT); - #else - H5Oget_info_by_idx(group_id, ".", H5_INDEX_NAME, H5_ITER_INC, i, &oinfo, - H5O_INFO_BASIC, H5P_DEFAULT); - #endif if (oinfo.type != type) continue; // Get size of name From 16a6f00c172fbb67cfa7b99f14ea33df4434c576 Mon Sep 17 00:00:00 2001 From: John Tramm Date: Wed, 25 Mar 2020 14:17:39 -0500 Subject: [PATCH 177/205] fix CMake command issue spotted by @paulromano Co-Authored-By: Paul Romano --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f4ca745ed1..ac05e56b23 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -77,7 +77,7 @@ endif() # Version 1.12 of HDF5 deprecates the H5Oget_info_by_idx() interface. # Thus, we give these flags to allow usage of the old interface in newer # versions of HDF5. -if(${HDF5_VERSION} VERSION_GREATER_EQUAL "1.12.0") +if(NOT (${HDF5_VERSION} VERSION_LESS 1.12.0)) list(APPEND cxxflags -DH5Oget_info_by_idx_vers=1 -DH5O_info_t_vers=1) endif() From 2bf9e7157454e57bd47dd610026bd30750b2b21f Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 25 Mar 2020 15:29:39 -0500 Subject: [PATCH 178/205] Updating unstructured mesh test file. --- .../test_mesh_tets_w_holes.h5m | Bin 480220 -> 480220 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.h5m b/tests/regression_tests/unstructured_mesh/test_mesh_tets_w_holes.h5m index 845dd88207fd6d077b4a8ea0c701019837d13e1f..971de0ad3cc1cf1c08166a7670b749422a8fb181 100644 GIT binary patch delta 66843 zcmYhEb(|bk)3)D9u)~97u@3H#;6Z~2&)|awcbHx*g9dj<;{n2ryJYd8!GmY;K?4N0 z>BTZ=@b9YXK74uqNNshUzV51>+~N7XsXdu=>->{$o&TP1=j(g-1;<_d!+f7DxYF_~ zuCn}!EA;ul;{RqSu^=?a?eep-11^qj)vf#zvf3ncU>-6@& 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zuZ*vXua2*YuZ^#Zua9qtZ;Wq>Z;o$?Z;fw@Z;$VY?~Lz??~d<@?~U(^?~fmdAB-Q0 zAC4c1AB`W2ACI4ipNy-=HR76at+;kvC$1aUi|fY?;-}(G+xW*|>N7T-+z_8$Tbv5Wg6| z6!(k!#{=Sl@t}Bc{BrzCJS2WKel31Iej^?l4~vJ#BjSGQ zE8>;$s(5w0CSDt_i`T~+;*Ig9cyqiZ-WqR^AB&I2C*qUQ5B5zl%~Sslm@-Zkr;gLaY2$Qp`Zz>=V8J?0x@P-y9RY!*dzcH|Ip} z)6ef;_02WW>zv2+%{|dO`SbX9`sSJFd-ijiH0i(bbNhEc-+3oK<7fZ8O`7zd^5^ZD zd~TD!|4shBHu?M2 z?&mxI#Ap2M-)~R-_x_pu{q~>mx&1qTekXsv3ru`H`|pyczOVhaeQx8vKl^+aocN6Y zm;XKazx=uVtG|Dr{`oF6@frWO-2 Date: Thu, 26 Mar 2020 10:22:56 +0000 Subject: [PATCH 179/205] added get_elements function and tests --- openmc/material.py | 12 +++++++++++ tests/unit_tests/test_material.py | 33 +++++++++++++++++++++++++++++++ 2 files changed, 45 insertions(+) diff --git a/openmc/material.py b/openmc/material.py index 8826aa30a3..66502a65e1 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -722,6 +722,18 @@ class Material(IDManagerMixin): def make_isotropic_in_lab(self): self.isotropic = [x[0] for x in self._nuclides] + def get_elements(self): + """Returns all elements in the material + + Returns + ------- + elements : list of str + List of element names + + """ + + return list({re.split(r'(\d+)', i)[0] for i in self.get_nuclides()}) + def get_nuclides(self): """Returns all nuclides in the material diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index c47faee8cd..546ee47885 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -223,6 +223,39 @@ def test_isotropic(): assert m2.isotropic == ['H1'] +def test_get_elements(): + # test that zero elements exist on creation + m = openmc.Material() + assert len(m.get_elements()) == 0 + + # test addition of a single element + m.add_element('Li', 0.2) + assert len(m.get_elements()) == 1 + assert 'Li' in m.get_elements() + + # test that adding the same element + m.add_element('Li', 0.3) + assert len(m.get_elements()) == 1 + assert 'Li' in m.get_elements() + + # test adding another element + m.add_element('Si', 0.3) + assert len(m.get_elements()) == 2 + assert 'Si' in m.get_elements() + + # test adding a third element + m.add_element('O', 0.4) + assert len(m.get_elements()) == 3 + + # test removal of nuclides + m.remove_nuclide('O16') + m.remove_nuclide('O17') + assert 'O' not in m.get_elements() + assert 'Si' in m.get_elements() + assert 'Li' in m.get_elements() + assert len(m.get_elements()) == 2 + + def test_get_nuclide_densities(uo2): nucs = uo2.get_nuclide_densities() for nuc, density, density_type in nucs.values(): From 6ab702400f50409c9dee5f7ae3139052569b9b93 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 26 Mar 2020 07:07:00 -0500 Subject: [PATCH 180/205] Make sure output_dir gets set for ThermalScattering.from_njoy --- openmc/data/thermal.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/openmc/data/thermal.py b/openmc/data/thermal.py index 067c0a7034..5f939e59e8 100644 --- a/openmc/data/thermal.py +++ b/openmc/data/thermal.py @@ -766,8 +766,8 @@ class ThermalScattering(EqualityMixin): """ with tempfile.TemporaryDirectory() as tmpdir: # Run NJOY to create an ACE library - kwargs.setdefault('ace', os.path.join(tmpdir, 'ace')) - kwargs.setdefault('xsdir', os.path.join(tmpdir, 'xsdir')) + kwargs.setdefault('output_dir', tmpdir) + kwargs.setdefault('ace', os.path.join(kwargs['output_dir'], 'ace')) kwargs['evaluation'] = evaluation kwargs['evaluation_thermal'] = evaluation_thermal make_ace_thermal(filename, filename_thermal, temperatures, **kwargs) From 1c3373ce61df05e2337808a2fd771fc4c1a6c212 Mon Sep 17 00:00:00 2001 From: billingsley-john <56687624+billingsley-john@users.noreply.github.com> Date: Thu, 26 Mar 2020 12:59:12 +0000 Subject: [PATCH 181/205] Sorted returned element list and updated tests Returned element list is now sorted alphabetically. Tests updated to check list elements instead of list length. Co-Authored-By: Jonathan Shimwell --- openmc/material.py | 2 +- tests/unit_tests/test_material.py | 12 ++++-------- 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/openmc/material.py b/openmc/material.py index 66502a65e1..16448f0ea0 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -732,7 +732,7 @@ class Material(IDManagerMixin): """ - return list({re.split(r'(\d+)', i)[0] for i in self.get_nuclides()}) + return sorted({re.split(r'(\d+)', i)[0] for i in self.get_nuclides()}) def get_nuclides(self): """Returns all nuclides in the material diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 546ee47885..9b5300fd2f 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -230,30 +230,26 @@ def test_get_elements(): # test addition of a single element m.add_element('Li', 0.2) - assert len(m.get_elements()) == 1 assert 'Li' in m.get_elements() # test that adding the same element m.add_element('Li', 0.3) - assert len(m.get_elements()) == 1 + assert m.get_elements() == ["Li"] assert 'Li' in m.get_elements() # test adding another element m.add_element('Si', 0.3) - assert len(m.get_elements()) == 2 - assert 'Si' in m.get_elements() + assert m.get_elements() == ["Li", "Si"] # test adding a third element m.add_element('O', 0.4) assert len(m.get_elements()) == 3 - + assert m.get_elements() == ["Li", "O", "Si"] # test removal of nuclides m.remove_nuclide('O16') m.remove_nuclide('O17') assert 'O' not in m.get_elements() - assert 'Si' in m.get_elements() - assert 'Li' in m.get_elements() - assert len(m.get_elements()) == 2 + assert m.get_elements() == ["Si", "Li"] def test_get_nuclide_densities(uo2): From a48f679b4e14a5297cc15e30e3862be425ee9270 Mon Sep 17 00:00:00 2001 From: billingsley-john <56687624+billingsley-john@users.noreply.github.com> Date: Thu, 26 Mar 2020 14:04:53 +0000 Subject: [PATCH 182/205] removed checks for length and specific entries Co-Authored-By: Andrew Johnson --- tests/unit_tests/test_material.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 9b5300fd2f..9b06fae972 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -235,7 +235,6 @@ def test_get_elements(): # test that adding the same element m.add_element('Li', 0.3) assert m.get_elements() == ["Li"] - assert 'Li' in m.get_elements() # test adding another element m.add_element('Si', 0.3) @@ -243,12 +242,10 @@ def test_get_elements(): # test adding a third element m.add_element('O', 0.4) - assert len(m.get_elements()) == 3 assert m.get_elements() == ["Li", "O", "Si"] # test removal of nuclides m.remove_nuclide('O16') m.remove_nuclide('O17') - assert 'O' not in m.get_elements() assert m.get_elements() == ["Si", "Li"] From 08849c3fada7b55d9f5257ebecf35f74b1d20c8b Mon Sep 17 00:00:00 2001 From: billingsley-john <56687624+billingsley-john@users.noreply.github.com> Date: Thu, 26 Mar 2020 14:33:44 +0000 Subject: [PATCH 183/205] removed further check for specific entry Co-Authored-By: Andrew Johnson --- tests/unit_tests/test_material.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 9b06fae972..e6d8102799 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -230,7 +230,7 @@ def test_get_elements(): # test addition of a single element m.add_element('Li', 0.2) - assert 'Li' in m.get_elements() + assert m.get_elements() == ["Li"] # test that adding the same element m.add_element('Li', 0.3) @@ -246,7 +246,7 @@ def test_get_elements(): # test removal of nuclides m.remove_nuclide('O16') m.remove_nuclide('O17') - assert m.get_elements() == ["Si", "Li"] + assert m.get_elements() == ["Li", "Si"] def test_get_nuclide_densities(uo2): From 765104d19e3d6c6baa70961598c69b3f16a7921e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 26 Mar 2020 11:06:40 -0500 Subject: [PATCH 184/205] Adding a midpoint option for crossing bins in unstructured mesh. --- src/mesh.cpp | 174 ++++++++++++++++++++++++++++++++------------------- 1 file changed, 110 insertions(+), 64 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 5b6b5711e9..7d4d321e34 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1666,8 +1666,8 @@ UnstructuredMesh::bins_crossed(const Particle* p, double track_len = (r1 - r0).length(); - r0 += TINY_BIT * dir; - r1 -= TINY_BIT * dir; + r0 -= TINY_BIT * dir; + r1 += TINY_BIT * dir; UnstructuredMeshHits hits; intersect_track(r0, dir, track_len, hits); @@ -1675,83 +1675,129 @@ UnstructuredMesh::bins_crossed(const Particle* p, bins.clear(); lengths.clear(); - // if there are no intersections the track may lie entirely - // within a single tet. If this is the case, apply entire - // score to that tet and return. if (hits.size() == 0) { - moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5); - if (last_r_tet) { - bins.push_back(get_bin_from_ent_handle(last_r_tet)); + Position midpoint = last_r + u * (track_len * 0.5); + int bin = this->get_bin(midpoint); + if (bin != -1) { + bins.push_back(bin); lengths.push_back(1.0); } return; } - // attempt to find the containing tet for the first track segment - moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0); + // for each segment in the set of tracks, try to look up a tet + // at the midpoint of the segment + Position current = last_r; double last_dist = 0.0; - - // make sure first segment is inside a tet. If it is not, we may be starting - // outside of the mesh and our first intersection is an entry point into the - // mesh - update hits and find a containing tet accordingly - if (!tet) { - last_dist = hits.front().first; - hits.erase(hits.begin()); - tet = get_tet(last_r + u * (last_dist + hits.front().first) / 2.0); - } - - // if there are no other hits at this point, apply the score for whatever - // distance lies in this tet and return - if (hits.size() == 0 && tet) { - bins.push_back(get_bin_from_ent_handle(tet)); - lengths.push_back((track_len - last_dist) / track_len); - return; - } - - // score all remaining segments for (const auto& hit : hits) { - // apply score in this tet if one was found - if (tet) { - bins.push_back(get_bin_from_ent_handle(tet)); - lengths.push_back((hit.first - last_dist) / track_len); - } else { - warning("No tet found for location between triangle hits"); - } - - // update the last distance + // get the segment length + double segment_length = hit.first - last_dist; last_dist = hit.first; + // determine the start point for this segment + current = last_r + u * hit.first; + // find the midpoint of this segment + Position midpoint = current + u * (segment_length * 0.5); + // try to find a tet for this position + int bin = this->get_bin(midpoint); - // find next tet using the mesh adjacencies - moab::Range adj_tets; - rval = mbi_->get_adjacencies(&hit.second, 1, 3, false, adj_tets); - if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to get triangle adjacencies from mesh " + filename_); - } + if (bin == -1) { continue; } - // if the triangle crossed is adjacent to two triangles - // update to the tet we're not currently scoring in - if (adj_tets.size() == 2) { - tet = tet == adj_tets[0] ? adj_tets[1] : adj_tets[0]; - } else if (adj_tets.size() == 1) { - tet = adj_tets[0]; - } - } // end hit loop + bins.push_back(bin); + lengths.push_back(segment_length / track_len); + } - // tally remaining portion of track after last hit if - // the last segment of the track is in the mesh but doesn't - // reach the other side of the tet + // check end of the track to see if that section lies in a tet if (hits.back().first < track_len) { - // use position at the midpoint of the current intersection - // and the end of the track to check for a containing tet - auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0); - tet = get_tet(pos); - // apply the remainder of the score if a tet is found, - // otherwise we'll assume we've left the mesh - if (tet) { - bins.push_back(get_bin_from_ent_handle(tet)); - lengths.push_back((track_len - hits.back().first) / track_len); + Position segment_start = last_r + u * hits.back().first; + double segment_length = track_len - hits.back().first; + Position midpoint = segment_start + u * (segment_length * 0.5); + int bin = this->get_bin(midpoint); + if (bin != -1) { + bins.push_back(bin); + lengths.push_back(segment_length / track_len); } } + + return; + + // // if there are no intersections the track may lie entirely + // // within a single tet. If this is the case, apply entire + // // score to that tet and return. + // if (hits.size() == 0) { + // moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5); + // if (last_r_tet) { + // bins.push_back(get_bin_from_ent_handle(last_r_tet)); + // lengths.push_back(1.0); + // } + // return; + // } + + // // attempt to find the containing tet for the first track segment + // moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0); + // double last_dist = 0.0; + + // // make sure first segment is inside a tet. If it is not, we may be starting + // // outside of the mesh and our first intersection is an entry point into the + // // mesh - update hits and find a containing tet accordingly + // if (!tet) { + // last_dist = hits.front().first; + // hits.erase(hits.begin()); + // tet = get_tet(last_r + u * (last_dist + hits.front().first) / 2.0); + // } + + // // if there are no other hits at this point, apply the score for whatever + // // distance lies in this tet and return + // if (hits.size() == 0 && tet) { + // bins.push_back(get_bin_from_ent_handle(tet)); + // lengths.push_back(1.0); + // lengths.push_back((track_len - last_dist) / track_len); + // return; + // } + + // // score all remaining segments + // for (const auto& hit : hits) { + // // apply score in this tet if one was found + // if (tet) { + // bins.push_back(get_bin_from_ent_handle(tet)); + // lengths.push_back((hit.first - last_dist) / track_len); + // } else { + // warning("No tet found for location between triangle hits"); + // } + + // // update the last distance + // last_dist = hit.first; + + // // find next tet using the mesh adjacencies + // moab::Range adj_tets; + // rval = mbi_->get_adjacencies(&hit.second, 1, 3, false, adj_tets); + // if (rval != moab::MB_SUCCESS) { + // fatal_error("Failed to get triangle adjacencies from mesh " + filename_); + // } + + // // if the triangle crossed is adjacent to two triangles + // // update to the tet we're not currently scoring in + // if (adj_tets.size() == 2) { + // tet = tet == adj_tets[0] ? adj_tets[1] : adj_tets[0]; + // } else if (adj_tets.size() == 1) { + // tet = adj_tets[0]; + // } + // } // end hit loop + + // // tally remaining portion of track after last hit if + // // the last segment of the track is in the mesh but doesn't + // // reach the other side of the tet + // if (hits.back().first < track_len) { + // // use position at the midpoint of the current intersection + // // and the end of the track to check for a containing tet + // auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0); + // tet = get_tet(pos); + // // apply the remainder of the score if a tet is found, + // // otherwise we'll assume we've left the mesh + // if (tet) { + // bins.push_back(get_bin_from_ent_handle(tet)); + // lengths.push_back((track_len - hits.back().first) / track_len); + // } + // } }; moab::EntityHandle From 7e386ff7ffeb5e932e120d1d511f40c50de55943 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 26 Mar 2020 13:56:23 -0500 Subject: [PATCH 185/205] Updating to new midpoint method. --- include/openmc/mesh.h | 4 +- src/mesh.cpp | 125 ++++-------------- .../unstructured_mesh/test.py | 2 +- 3 files changed, 28 insertions(+), 103 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index fe5a795838..6454563cc7 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -251,8 +251,6 @@ private: class UnstructuredMesh : public Mesh { - using UnstructuredMeshHits = std::vector>; - public: UnstructuredMesh() = default; UnstructuredMesh(pugi::xml_node); @@ -274,7 +272,7 @@ private: intersect_track(const moab::CartVect& start, const moab::CartVect& dir, double track_len, - UnstructuredMeshHits& hits) const; + std::vector& hits) const; //! Calculate the volume for a given tetrahedron handle. // diff --git a/src/mesh.cpp b/src/mesh.cpp index 7d4d321e34..5b4665cb73 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1623,10 +1623,11 @@ void UnstructuredMesh::intersect_track(const moab::CartVect& start, const moab::CartVect& dir, double track_len, - UnstructuredMeshHits& hits) const { + std::vector& hits) const { + hits.clear(); + moab::ErrorCode rval; std::vector tris; - std::vector intersection_dists; // get all intersections with triangles in the tet mesh // (distances are relative to the start point, not the previous intersection) rval = kdtree_->ray_intersect_triangles(kdtree_root_, @@ -1634,18 +1635,15 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start, dir.array(), start.array(), tris, - intersection_dists, + hits, 0, track_len); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to compute intersections on unstructured mesh: " + filename_); } - // sort the hit triangles and intersections by distance - hits.clear(); - for (int i = 0; i < tris.size(); i++) { - hits.push_back(std::pair(intersection_dists[i], tris[i])); - } + // remove duplicate intersection distances + std::unique(hits.begin(), hits.end()); // sorts by first component of std::pair by default std::sort(hits.begin(), hits.end()); @@ -1655,6 +1653,7 @@ void UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, std::vector& lengths) const { + moab::ErrorCode rval; Position last_r{p->r_last_}; Position r{p->r()}; @@ -1669,12 +1668,15 @@ UnstructuredMesh::bins_crossed(const Particle* p, r0 -= TINY_BIT * dir; r1 += TINY_BIT * dir; - UnstructuredMeshHits hits; + std::vector hits; intersect_track(r0, dir, track_len, hits); bins.clear(); lengths.clear(); + // if there are no intersections the track may lie entirely + // within a single tet. If this is the case, apply entire + // score to that tet and return. if (hits.size() == 0) { Position midpoint = last_r + u * (track_len * 0.5); int bin = this->get_bin(midpoint); @@ -1691,25 +1693,31 @@ UnstructuredMesh::bins_crossed(const Particle* p, double last_dist = 0.0; for (const auto& hit : hits) { // get the segment length - double segment_length = hit.first - last_dist; - last_dist = hit.first; - // determine the start point for this segment - current = last_r + u * hit.first; + double segment_length = hit - last_dist; + last_dist = hit; // find the midpoint of this segment Position midpoint = current + u * (segment_length * 0.5); // try to find a tet for this position int bin = this->get_bin(midpoint); - if (bin == -1) { continue; } + // determine the start point for this segment + current = last_r + u * hit; + + if (bin == -1) { + continue; + } bins.push_back(bin); lengths.push_back(segment_length / track_len); + } - // check end of the track to see if that section lies in a tet - if (hits.back().first < track_len) { - Position segment_start = last_r + u * hits.back().first; - double segment_length = track_len - hits.back().first; + // tally remaining portion of track after last hit if + // the last segment of the track is in the mesh but doesn't + // reach the other side of the tet + if (hits.back() < track_len) { + Position segment_start = last_r + u * hits.back(); + double segment_length = track_len - hits.back(); Position midpoint = segment_start + u * (segment_length * 0.5); int bin = this->get_bin(midpoint); if (bin != -1) { @@ -1717,87 +1725,6 @@ UnstructuredMesh::bins_crossed(const Particle* p, lengths.push_back(segment_length / track_len); } } - - return; - - // // if there are no intersections the track may lie entirely - // // within a single tet. If this is the case, apply entire - // // score to that tet and return. - // if (hits.size() == 0) { - // moab::EntityHandle last_r_tet = get_tet(last_r + u * track_len * 0.5); - // if (last_r_tet) { - // bins.push_back(get_bin_from_ent_handle(last_r_tet)); - // lengths.push_back(1.0); - // } - // return; - // } - - // // attempt to find the containing tet for the first track segment - // moab::EntityHandle tet = get_tet(last_r + u * hits.front().first / 2.0); - // double last_dist = 0.0; - - // // make sure first segment is inside a tet. If it is not, we may be starting - // // outside of the mesh and our first intersection is an entry point into the - // // mesh - update hits and find a containing tet accordingly - // if (!tet) { - // last_dist = hits.front().first; - // hits.erase(hits.begin()); - // tet = get_tet(last_r + u * (last_dist + hits.front().first) / 2.0); - // } - - // // if there are no other hits at this point, apply the score for whatever - // // distance lies in this tet and return - // if (hits.size() == 0 && tet) { - // bins.push_back(get_bin_from_ent_handle(tet)); - // lengths.push_back(1.0); - // lengths.push_back((track_len - last_dist) / track_len); - // return; - // } - - // // score all remaining segments - // for (const auto& hit : hits) { - // // apply score in this tet if one was found - // if (tet) { - // bins.push_back(get_bin_from_ent_handle(tet)); - // lengths.push_back((hit.first - last_dist) / track_len); - // } else { - // warning("No tet found for location between triangle hits"); - // } - - // // update the last distance - // last_dist = hit.first; - - // // find next tet using the mesh adjacencies - // moab::Range adj_tets; - // rval = mbi_->get_adjacencies(&hit.second, 1, 3, false, adj_tets); - // if (rval != moab::MB_SUCCESS) { - // fatal_error("Failed to get triangle adjacencies from mesh " + filename_); - // } - - // // if the triangle crossed is adjacent to two triangles - // // update to the tet we're not currently scoring in - // if (adj_tets.size() == 2) { - // tet = tet == adj_tets[0] ? adj_tets[1] : adj_tets[0]; - // } else if (adj_tets.size() == 1) { - // tet = adj_tets[0]; - // } - // } // end hit loop - - // // tally remaining portion of track after last hit if - // // the last segment of the track is in the mesh but doesn't - // // reach the other side of the tet - // if (hits.back().first < track_len) { - // // use position at the midpoint of the current intersection - // // and the end of the track to check for a containing tet - // auto pos = (last_r + u * hits.back().first) + u * ((track_len - hits.back().first) / 2.0); - // tet = get_tet(pos); - // // apply the remainder of the score if a tet is found, - // // otherwise we'll assume we've left the mesh - // if (tet) { - // bins.push_back(get_bin_from_ent_handle(tet)); - // lengths.push_back((track_len - hits.back().first) / track_len); - // } - // } }; moab::EntityHandle diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 1fc4a63aa4..82ca6488ee 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -209,7 +209,7 @@ class UnstructuredMeshTest(PyAPITestHarness): # we expect these results to be the same to within at least ten # decimal places - decimals = 10 + decimals = 8 np.testing.assert_array_almost_equal(unstructured_data, reg_mesh_data, decimals) From c54d3abf1c047e312c16ede0fe9eb978e9a25f10 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 26 Mar 2020 14:01:57 -0500 Subject: [PATCH 186/205] Adjust expected precision based on estimator. --- tests/regression_tests/unstructured_mesh/test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 82ca6488ee..65216ccb88 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -209,7 +209,7 @@ class UnstructuredMeshTest(PyAPITestHarness): # we expect these results to be the same to within at least ten # decimal places - decimals = 8 + decimals = 10 if self.estimator == 'collision' else 8 np.testing.assert_array_almost_equal(unstructured_data, reg_mesh_data, decimals) From 7bb93041d323f749c0f7178631a15ebdc8e3db94 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 26 Mar 2020 15:59:09 -0500 Subject: [PATCH 187/205] Updating type check for centroids. --- openmc/mesh.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/mesh.py b/openmc/mesh.py index 4bde4a52ae..dc48e1ca0f 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -655,7 +655,7 @@ class UnstructuredMesh(MeshBase): @centroids.setter def centroids(self, centroids): cv.check_type("Unstructured mesh centroids", centroids, - Iterable, Iterable) + Iterable, Real) self._centroids = centroids def __repr__(self): From 72e85dd4fa70c258de95d3463e83f45480afe3e1 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 26 Mar 2020 17:02:15 -0500 Subject: [PATCH 188/205] Updating PyAPITestHarness to allow inputs_true file to be specified. --- .../unstructured_mesh/inputs_true0.dat | 92 + .../unstructured_mesh/inputs_true1.dat | 92 + .../unstructured_mesh/inputs_true2.dat | 92 + .../unstructured_mesh/inputs_true3.dat | 92 + .../unstructured_mesh/inputs_true4.dat | 92 + .../unstructured_mesh/inputs_true5.dat | 92 + .../unstructured_mesh/inputs_true6.dat | 92 + .../unstructured_mesh/inputs_true7.dat | 92 + .../unstructured_mesh/results_true.dat | 26002 ++++++++++++++++ .../unstructured_mesh/test.py | 12 +- tests/testing_harness.py | 11 +- 11 files changed, 26750 insertions(+), 11 deletions(-) create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true0.dat create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true1.dat create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true2.dat create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true3.dat create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true4.dat create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true5.dat create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true6.dat create mode 100644 tests/regression_tests/unstructured_mesh/inputs_true7.dat create mode 100644 tests/regression_tests/unstructured_mesh/results_true.dat diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat new file mode 100644 index 0000000000..37cecc8c02 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets_w_holes.h5m + + + 1 + + + 2 + + + 1 + flux + collision + + + 2 + flux + collision + + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat new file mode 100644 index 0000000000..e378d6a98c --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets.h5m + + + 3 + + + 4 + + + 3 + flux + collision + + + 4 + flux + collision + + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat new file mode 100644 index 0000000000..620fef6fde --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets_w_holes.h5m + + + 5 + + + 6 + + + 5 + flux + collision + + + 6 + flux + collision + + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat new file mode 100644 index 0000000000..863d2bdd85 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets.h5m + + + 7 + + + 8 + + + 7 + flux + collision + + + 8 + flux + collision + + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true4.dat b/tests/regression_tests/unstructured_mesh/inputs_true4.dat new file mode 100644 index 0000000000..c1a60ea708 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true4.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets_w_holes.h5m + + + 9 + + + 10 + + + 9 + flux + tracklength + + + 10 + flux + tracklength + + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true5.dat b/tests/regression_tests/unstructured_mesh/inputs_true5.dat new file mode 100644 index 0000000000..030878dfd3 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true5.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets.h5m + + + 11 + + + 12 + + + 11 + flux + tracklength + + + 12 + flux + tracklength + + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true6.dat b/tests/regression_tests/unstructured_mesh/inputs_true6.dat new file mode 100644 index 0000000000..d033657534 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true6.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets_w_holes.h5m + + + 13 + + + 14 + + + 13 + flux + tracklength + + + 14 + flux + tracklength + + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true7.dat b/tests/regression_tests/unstructured_mesh/inputs_true7.dat new file mode 100644 index 0000000000..726455b0f5 --- /dev/null +++ b/tests/regression_tests/unstructured_mesh/inputs_true7.dat @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + 10 10 10 + -10.0 -10.0 -10.0 + 10.0 10.0 10.0 + + + test_mesh_tets.h5m + + + 15 + + + 16 + + + 15 + flux + tracklength + + + 16 + flux + tracklength + + diff --git a/tests/regression_tests/unstructured_mesh/results_true.dat 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+1.781986E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +6.756013E-03 +4.564371E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +3.677964E-03 +1.352742E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +2.555184E-03 +6.528967E-06 +0.000000E+00 +0.000000E+00 diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 65216ccb88..85359d6edd 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -20,11 +20,12 @@ class UnstructuredMeshTest(PyAPITestHarness): def __init__(self, statepoint_name, + inputs_true, estimator='collision', external_geom=False, holes=None): - super().__init__(statepoint_name) + super().__init__(statepoint_name, inputs_true=inputs_true) self.estimator = estimator # tally estimator type self.external_geom = external_geom # geometry size matches mesh @@ -188,9 +189,6 @@ class UnstructuredMeshTest(PyAPITestHarness): settings.export_to_xml() - def _compare_inputs(self): - pass - def _compare_results(self): with openmc.StatePoint(self._sp_name) as sp: # loop over the tallies and get data @@ -238,10 +236,12 @@ param_values = (['collision', 'tracklength'], # estimators [True, False], # geometry outside of the mesh [(333, 90, 77), None]) # location of holes in the mesh test_cases = [] -for estimator, ext_geom, holes in product(*param_values): +for i, (estimator, ext_geom, holes) in enumerate(product(*param_values)): test_cases.append({'estimator' : estimator, 'external_geom' : ext_geom, - 'holes' : holes}) + 'holes' : holes, + 'inputs_true' : 'inputs_true{}.dat'.format(i)}) +inputs = ['inputs_true{}.dat'.format(i) for i, opts in enumerate(test_cases)] @pytest.mark.parametrize("opts", test_cases) diff --git a/tests/testing_harness.py b/tests/testing_harness.py index d5484d73e9..16b56c205d 100644 --- a/tests/testing_harness.py +++ b/tests/testing_harness.py @@ -276,7 +276,7 @@ class ParticleRestartTestHarness(TestHarness): class PyAPITestHarness(TestHarness): - def __init__(self, statepoint_name, model=None): + def __init__(self, statepoint_name, model=None, inputs_true=None): super().__init__(statepoint_name) if model is None: self._model = pwr_core() @@ -284,6 +284,7 @@ class PyAPITestHarness(TestHarness): self._model = model self._model.plots = [] + self.inputs_true = "inputs_true.dat" if not inputs_true else inputs_true def main(self): """Accept commandline arguments and either run or update tests.""" @@ -342,15 +343,15 @@ class PyAPITestHarness(TestHarness): def _overwrite_inputs(self): """Overwrite inputs_true.dat with inputs_test.dat""" - shutil.copyfile('inputs_test.dat', 'inputs_true.dat') + shutil.copyfile('inputs_test.dat', self.inputs_true) def _compare_inputs(self): """Make sure the current inputs agree with the _true standard.""" - compare = filecmp.cmp('inputs_test.dat', 'inputs_true.dat') + compare = filecmp.cmp('inputs_test.dat', self.inputs_true) if not compare: - expected = open('inputs_true.dat', 'r').readlines() + expected = open(self.inputs_true, 'r').readlines() actual = open('inputs_test.dat', 'r').readlines() - diff = unified_diff(expected, actual, 'inputs_true.dat', + diff = unified_diff(expected, actual, self.inputs_true, 'inputs_test.dat') print('Input differences:') print(''.join(colorize(diff))) From 954154fa4828662da65b7db0ce13aa5ee1b35c5f Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Fri, 7 Feb 2020 16:24:10 +0000 Subject: [PATCH 189/205] Changed PyAPI model and executor to read correct last statepoint after runs with trigger, including new test case --- openmc/executor.py | 20 ++- openmc/model/model.py | 21 +-- .../trigger_statepoint_restart/__init__.py | 0 .../inputs_true.dat | 34 +++++ .../results_true.dat | 5 + .../trigger_statepoint_restart/test.py | 120 ++++++++++++++++++ 6 files changed, 189 insertions(+), 11 deletions(-) create mode 100644 tests/regression_tests/trigger_statepoint_restart/__init__.py create mode 100644 tests/regression_tests/trigger_statepoint_restart/inputs_true.dat create mode 100644 tests/regression_tests/trigger_statepoint_restart/results_true.dat create mode 100644 tests/regression_tests/trigger_statepoint_restart/test.py diff --git a/openmc/executor.py b/openmc/executor.py index 9faf18b332..76d100d32a 100644 --- a/openmc/executor.py +++ b/openmc/executor.py @@ -1,6 +1,7 @@ from collections.abc import Iterable -import subprocess from numbers import Integral +import re +import subprocess import openmc @@ -10,6 +11,10 @@ def _run(args, output, cwd): p = subprocess.Popen(args, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True) + # Compile a regex object to identify statepoint writes + sp_rxprog = re.compile(r'Creating state point (.+)\.\.\.') + last_statepoint = None + # Capture and re-print OpenMC output in real-time lines = [] while True: @@ -18,6 +23,11 @@ def _run(args, output, cwd): if not line and p.poll() is not None: break + # If a statepoint was written, capture its filename + sp_match = sp_rxprog.search(line) + if sp_match: + last_statepoint = sp_match.group(1) + lines.append(line) if output: # If user requested output, print to screen @@ -27,6 +37,7 @@ def _run(args, output, cwd): if p.returncode != 0: raise subprocess.CalledProcessError(p.returncode, ' '.join(args), ''.join(lines)) + return last_statepoint def plot_geometry(output=True, openmc_exec='openmc', cwd='.'): @@ -184,6 +195,11 @@ def run(particles=None, threads=None, geometry_debug=False, event_based : bool, optional Turns on event-based parallelism, instead of default history-based + Returns + ------- + str + Name of the last written statepoint file, or None + Raises ------ subprocess.CalledProcessError @@ -213,4 +229,4 @@ def run(particles=None, threads=None, geometry_debug=False, if mpi_args is not None: args = mpi_args + args - _run(args, output, cwd) + return _run(args, output, cwd) diff --git a/openmc/model/model.py b/openmc/model/model.py index b5c190716b..08ba300dd1 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -41,6 +41,8 @@ class Model: Tallies information plots : openmc.Plots Plot information + statepoint : openmc.Statepoint + The last statepoint filename written when the model is run """ @@ -51,6 +53,7 @@ class Model: self.settings = openmc.Settings() self.tallies = openmc.Tallies() self.plots = openmc.Plots() + self.statepoint = None if geometry is not None: self.geometry = geometry @@ -197,7 +200,8 @@ class Model: self.plots.export_to_xml(d) def run(self, **kwargs): - """Creates the XML files, runs OpenMC, and returns k-effective + """Creates the XML files, runs OpenMC, and returns k-effective. + The last statepoint filename is available in model.statepoint Parameters ---------- @@ -210,14 +214,13 @@ class Model: Combined estimator of k-effective from the statepoint """ + self.export_to_xml() - openmc.run(**kwargs) + self.statepoint = openmc.run(**kwargs) - n = self.settings.batches - if self.settings.statepoint is not None: - if 'batches' in self.settings.statepoint: - n = self.settings.statepoint['batches'][-1] - - with openmc.StatePoint('statepoint.{}.h5'.format(n)) as sp: - return sp.k_combined + keff = None + if self.statepoint: + with openmc.StatePoint(self.statepoint) as sp: + keff = sp.k_combined + return keff diff --git a/tests/regression_tests/trigger_statepoint_restart/__init__.py b/tests/regression_tests/trigger_statepoint_restart/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat b/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat new file mode 100644 index 0000000000..decc87b0c5 --- /dev/null +++ b/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + eigenvalue + 200 + 10 + 5 + + std_dev + 0.002 + + + true + 1000 + 1 + + + + + + flux + + diff --git a/tests/regression_tests/trigger_statepoint_restart/results_true.dat b/tests/regression_tests/trigger_statepoint_restart/results_true.dat new file mode 100644 index 0000000000..84363f8696 --- /dev/null +++ b/tests/regression_tests/trigger_statepoint_restart/results_true.dat @@ -0,0 +1,5 @@ +k-combined: +2.909598E-01 6.161987E-03 +tally 1: +3.852896E+01 +2.976246E+02 diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py new file mode 100644 index 0000000000..66416297b1 --- /dev/null +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -0,0 +1,120 @@ +import glob +import os + +import openmc +import pytest + +from tests.testing_harness import PyAPITestHarness +from tests.regression_tests import config + + +@pytest.fixture +def model(): + + # Materials + materials = openmc.Materials() + mat = openmc.Material() + mat.set_density('g/cm3', 4.5) + mat.add_nuclide('U235', 1.0) + materials.append(mat) + + # Geometry + sph = openmc.Sphere(r=10.0, boundary_type='vacuum') + cell = openmc.Cell(fill=mat, region=-sph) + geometry = openmc.Geometry([cell]) + + # Settings + settings = openmc.Settings() + settings.run_mode = 'eigenvalue' + settings.batches = 10 + settings.inactive = 5 + settings.particles = 200 + settings.keff_trigger = {'type': 'std_dev', 'threshold': 0.002} + settings.trigger_max_batches = 1000 + settings.trigger_batch_interval = 1 + settings.trigger_active = True + + # Tallies + tallies = openmc.Tallies() + t = openmc.Tally() + t.scores = ['flux'] + tallies.append(t) + + # Plots (none) + plots = openmc.Plots() + + # Put it all together + model = openmc.model.Model(materials=materials, + geometry=geometry, + settings=settings, + tallies=tallies, + plots=plots) + return model + +class TriggerStatepointRestartTestHarness(PyAPITestHarness): + def __init__(self, statepoint, model=None): + super().__init__(statepoint, model) + self._restart_sp = None + self._final_sp = None + + def _test_output_created(self): + """Make sure statepoint files have been created.""" + spfiles = glob.glob(self._sp_name) + assert len(spfiles) == 2, \ + 'Two statepoint files should have been created' + if self._final_sp: + # Second restart run + assert spfiles[1] == self._final_sp, \ + 'Final statepoint names were different' + else: + # First non-restart run + self._restart_sp = spfiles[0] + self._final_sp = spfiles[1] + + def execute_test(self): + """ + Perform initial and restart runs using the model.run method, + Check all inputs and outputs which should be the same as those + generated using the normal PyAPITestHarness update methods. + """ + try: + args = {'openmc_exec': config['exe'], 'event_based': config['event']} + if config['mpi']: + args['mpi_args'] = [config['mpiexec'], '-n', config['mpi_np']] + # First non-restart run + k_combined_1 = self._model.run(**args) + sp_batchno_1 = 0 + with openmc.StatePoint(self._model.statepoint) as sp: + sp_batchno_1 = sp.current_batch + assert sp_batchno_1 > 10 + self._write_inputs(self._get_inputs()) + self._compare_inputs() + self._test_output_created() + self._write_results(self._get_results()) + self._compare_results() + # Second restart run + restart_spfile = glob.glob(os.path.join(os.getcwd(), self._restart_sp)) + assert len(restart_spfile) == 1 + args['restart_file'] = restart_spfile[0] + k_combined_2 = self._model.run(**args) + sp_batchno_2 = 0 + with openmc.StatePoint(self._model.statepoint) as sp: + sp_batchno_2 = sp.current_batch + assert sp_batchno_2 > 10 + self._write_inputs(self._get_inputs()) + self._compare_inputs() + self._test_output_created() + self._write_results(self._get_results()) + self._compare_results() + assert str(k_combined_1) == str(k_combined_2), \ + 'Different final k_combined after restart' + assert sp_batchno_1 == sp_batchno_2, \ + 'Different final batch number after restart' + finally: + self._cleanup() + +def test_trigger_statepoint_restart(model): + # Assuming we converge within 1000 batches, the statepoint filename + # should include the batch number padded by at least one '0'. + harness = TriggerStatepointRestartTestHarness('statepoint.0*.h5', model) + harness.main() From f98c34b7c9bc84cf827c7e403552d137356d25f0 Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Fri, 7 Feb 2020 16:36:53 +0000 Subject: [PATCH 190/205] fix comment description --- openmc/model/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index 08ba300dd1..e59c679b52 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -41,7 +41,7 @@ class Model: Tallies information plots : openmc.Plots Plot information - statepoint : openmc.Statepoint + statepoint : str The last statepoint filename written when the model is run """ From 4c96fc11d0e69601468afd7fb5f9ba1d0d188657 Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Mon, 17 Feb 2020 16:13:11 +0000 Subject: [PATCH 191/205] ensure results are loaded from the last statepoint for testing --- .../results_true.dat | 6 ++--- .../trigger_statepoint_restart/test.py | 24 +++++++++++-------- 2 files changed, 17 insertions(+), 13 deletions(-) diff --git a/tests/regression_tests/trigger_statepoint_restart/results_true.dat b/tests/regression_tests/trigger_statepoint_restart/results_true.dat index 84363f8696..dc5ed98310 100644 --- a/tests/regression_tests/trigger_statepoint_restart/results_true.dat +++ b/tests/regression_tests/trigger_statepoint_restart/results_true.dat @@ -1,5 +1,5 @@ k-combined: -2.909598E-01 6.161987E-03 +3.040704E-01 1.976825E-03 tally 1: -3.852896E+01 -2.976246E+02 +3.389935E+02 +2.743454E+03 diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py index 66416297b1..961a576a55 100644 --- a/tests/regression_tests/trigger_statepoint_restart/test.py +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -56,20 +56,24 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): super().__init__(statepoint, model) self._restart_sp = None self._final_sp = None + # store the statepoint filename pattern separately to sp_name so we can reuse it + self._sp_pattern = self._sp_name def _test_output_created(self): """Make sure statepoint files have been created.""" - spfiles = glob.glob(self._sp_name) + spfiles = glob.glob(self._sp_pattern) assert len(spfiles) == 2, \ 'Two statepoint files should have been created' - if self._final_sp: - # Second restart run - assert spfiles[1] == self._final_sp, \ - 'Final statepoint names were different' - else: + if not self._final_sp: # First non-restart run self._restart_sp = spfiles[0] self._final_sp = spfiles[1] + else: + # Second restart run + assert spfiles[1] == self._final_sp, \ + 'Final statepoint names were different' + # Use the final_sp as the sp_name for the 'standard' results tests + self._sp_name = self._final_sp def execute_test(self): """ @@ -101,15 +105,15 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): with openmc.StatePoint(self._model.statepoint) as sp: sp_batchno_2 = sp.current_batch assert sp_batchno_2 > 10 + assert sp_batchno_1 == sp_batchno_2, \ + 'Different final batch number after restart' + assert str(k_combined_1) == str(k_combined_2), \ + 'Different final k_combined after restart' self._write_inputs(self._get_inputs()) self._compare_inputs() self._test_output_created() self._write_results(self._get_results()) self._compare_results() - assert str(k_combined_1) == str(k_combined_2), \ - 'Different final k_combined after restart' - assert sp_batchno_1 == sp_batchno_2, \ - 'Different final batch number after restart' finally: self._cleanup() From 1312a1cc637b82c69757a282849e20edf57ca5eb Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Tue, 3 Mar 2020 13:37:24 +0000 Subject: [PATCH 192/205] Obtain last statepoint from a glob of output dir. Reverted regex filter in executor as this is no longer needed --- openmc/executor.py | 17 +----------- openmc/model/model.py | 27 ++++++++++++++++--- .../inputs_true.dat | 1 + .../trigger_statepoint_restart/test.py | 3 +++ 4 files changed, 28 insertions(+), 20 deletions(-) diff --git a/openmc/executor.py b/openmc/executor.py index 76d100d32a..ebd1a450bc 100644 --- a/openmc/executor.py +++ b/openmc/executor.py @@ -11,10 +11,6 @@ def _run(args, output, cwd): p = subprocess.Popen(args, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True) - # Compile a regex object to identify statepoint writes - sp_rxprog = re.compile(r'Creating state point (.+)\.\.\.') - last_statepoint = None - # Capture and re-print OpenMC output in real-time lines = [] while True: @@ -23,11 +19,6 @@ def _run(args, output, cwd): if not line and p.poll() is not None: break - # If a statepoint was written, capture its filename - sp_match = sp_rxprog.search(line) - if sp_match: - last_statepoint = sp_match.group(1) - lines.append(line) if output: # If user requested output, print to screen @@ -37,7 +28,6 @@ def _run(args, output, cwd): if p.returncode != 0: raise subprocess.CalledProcessError(p.returncode, ' '.join(args), ''.join(lines)) - return last_statepoint def plot_geometry(output=True, openmc_exec='openmc', cwd='.'): @@ -195,11 +185,6 @@ def run(particles=None, threads=None, geometry_debug=False, event_based : bool, optional Turns on event-based parallelism, instead of default history-based - Returns - ------- - str - Name of the last written statepoint file, or None - Raises ------ subprocess.CalledProcessError @@ -229,4 +214,4 @@ def run(particles=None, threads=None, geometry_debug=False, if mpi_args is not None: args = mpi_args + args - return _run(args, output, cwd) + _run(args, output, cwd) diff --git a/openmc/model/model.py b/openmc/model/model.py index e59c679b52..2089721032 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -1,5 +1,6 @@ from collections.abc import Iterable from pathlib import Path +import time import openmc from openmc.checkvalue import check_type, check_value @@ -175,7 +176,7 @@ class Model: will be created. """ - # Create directory if + # Create directory if required d = Path(directory) if not d.is_dir(): d.mkdir(parents=True) @@ -201,7 +202,7 @@ class Model: def run(self, **kwargs): """Creates the XML files, runs OpenMC, and returns k-effective. - The last statepoint filename is available in model.statepoint + The last statepoint Path is available in model.statepoint Parameters ---------- @@ -211,14 +212,32 @@ class Model: Returns ------- uncertainties.UFloat - Combined estimator of k-effective from the statepoint + Combined estimator of k-effective from the last statepoint + (None if no statepoint was written) """ self.export_to_xml() - self.statepoint = openmc.run(**kwargs) + # Setting tstart here ensures we don't pick up any old statepoint + # files that might preexist in the output directory + tstart = time.time() + self.statepoint = None + openmc.run(**kwargs) + + # Get output directory and last statepoint written by this run + if self.settings.output and 'path' in self.settings.output: + output_dir = Path(self.settings.output['path']) + else: + output_dir = Path.cwd() + for sp in output_dir.glob('statepoint.*.h5'): + mtime = sp.stat().st_mtime + if mtime >= tstart: # >= allows for poor clock resolution + tstart = mtime + self.statepoint = sp + + # Open the last statepoint to get the final k-effective keff = None if self.statepoint: with openmc.StatePoint(self.statepoint) as sp: diff --git a/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat b/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat index decc87b0c5..26890f9f86 100644 --- a/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat +++ b/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat @@ -25,6 +25,7 @@ 1000 1 + 1 diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py index 961a576a55..44b809454b 100644 --- a/tests/regression_tests/trigger_statepoint_restart/test.py +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -33,6 +33,7 @@ def model(): settings.trigger_max_batches = 1000 settings.trigger_batch_interval = 1 settings.trigger_active = True + settings.verbosity = 1 # to test that this works even with no output # Tallies tallies = openmc.Tallies() @@ -88,6 +89,7 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): # First non-restart run k_combined_1 = self._model.run(**args) sp_batchno_1 = 0 + assert self._model.statepoint with openmc.StatePoint(self._model.statepoint) as sp: sp_batchno_1 = sp.current_batch assert sp_batchno_1 > 10 @@ -102,6 +104,7 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): args['restart_file'] = restart_spfile[0] k_combined_2 = self._model.run(**args) sp_batchno_2 = 0 + assert self._model.statepoint with openmc.StatePoint(self._model.statepoint) as sp: sp_batchno_2 = sp.current_batch assert sp_batchno_2 > 10 From 5a9fe17323047048b6f9609b51d9cbda06aa4022 Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Wed, 4 Mar 2020 09:17:44 +0000 Subject: [PATCH 193/205] remove unneeded import --- openmc/executor.py | 1 - 1 file changed, 1 deletion(-) diff --git a/openmc/executor.py b/openmc/executor.py index ebd1a450bc..55e16a711b 100644 --- a/openmc/executor.py +++ b/openmc/executor.py @@ -1,6 +1,5 @@ from collections.abc import Iterable from numbers import Integral -import re import subprocess import openmc From e98ddf9d8a54b9cbb0b3ba50a00ba8c6b9343be0 Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Wed, 4 Mar 2020 14:43:10 +0000 Subject: [PATCH 194/205] Model.run now returns last statepoint path rather than k_eff. Updated tests and search. --- openmc/model/model.py | 20 ++++++------------- openmc/search.py | 4 +++- openmc/statepoint.py | 5 +++-- .../trigger_statepoint_restart/test.py | 18 ++++++++++------- tests/unit_tests/test_filters.py | 4 ++-- 5 files changed, 25 insertions(+), 26 deletions(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index 2089721032..cb9907331f 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -42,8 +42,6 @@ class Model: Tallies information plots : openmc.Plots Plot information - statepoint : str - The last statepoint filename written when the model is run """ @@ -211,8 +209,8 @@ class Model: Returns ------- - uncertainties.UFloat - Combined estimator of k-effective from the last statepoint + Path + the name of the last statepoint written by this run (None if no statepoint was written) """ @@ -222,11 +220,11 @@ class Model: # Setting tstart here ensures we don't pick up any old statepoint # files that might preexist in the output directory tstart = time.time() - self.statepoint = None + last_statepoint = None openmc.run(**kwargs) - # Get output directory and last statepoint written by this run + # Get output directory and return the last statepoint written by this run if self.settings.output and 'path' in self.settings.output: output_dir = Path(self.settings.output['path']) else: @@ -235,11 +233,5 @@ class Model: mtime = sp.stat().st_mtime if mtime >= tstart: # >= allows for poor clock resolution tstart = mtime - self.statepoint = sp - - # Open the last statepoint to get the final k-effective - keff = None - if self.statepoint: - with openmc.StatePoint(self.statepoint) as sp: - keff = sp.k_combined - return keff + last_statepoint = sp + return last_statepoint diff --git a/openmc/search.py b/openmc/search.py index 6be8a50ea5..ee5dd1f077 100644 --- a/openmc/search.py +++ b/openmc/search.py @@ -51,7 +51,9 @@ def _search_keff(guess, target, model_builder, model_args, print_iterations, model = model_builder(guess, **model_args) # Run the model and obtain keff - keff = model.run(output=print_output) + sp_filepath = model.run(output=print_output) + with openmc.StatePoint(sp_filepath) as sp: + keff = sp.k_combined # Record the history guesses.append(guess) diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 7d9895f8e3..dcb8b7924c 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -21,7 +21,7 @@ class StatePoint: Parameters ---------- - filename : str + filepath : str or Path Path to file to load autolink : bool, optional Whether to automatically link in metadata from a summary.h5 file and @@ -115,7 +115,8 @@ class StatePoint: """ - def __init__(self, filename, autolink=True): + def __init__(self, filepath, autolink=True): + filename = str(filepath) # in case it's a Path self._f = h5py.File(filename, 'r') self._meshes = {} self._filters = {} diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py index 44b809454b..5f37383abd 100644 --- a/tests/regression_tests/trigger_statepoint_restart/test.py +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -86,31 +86,35 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): args = {'openmc_exec': config['exe'], 'event_based': config['event']} if config['mpi']: args['mpi_args'] = [config['mpiexec'], '-n', config['mpi_np']] + # First non-restart run - k_combined_1 = self._model.run(**args) + spfile = self._model.run(**args) sp_batchno_1 = 0 - assert self._model.statepoint - with openmc.StatePoint(self._model.statepoint) as sp: + assert sp_file + with openmc.StatePoint(spfile) as sp: sp_batchno_1 = sp.current_batch + k_combined_1 = sp.k_combined assert sp_batchno_1 > 10 self._write_inputs(self._get_inputs()) self._compare_inputs() self._test_output_created() self._write_results(self._get_results()) self._compare_results() + # Second restart run restart_spfile = glob.glob(os.path.join(os.getcwd(), self._restart_sp)) assert len(restart_spfile) == 1 args['restart_file'] = restart_spfile[0] - k_combined_2 = self._model.run(**args) + spfile = self._model.run(**args) sp_batchno_2 = 0 - assert self._model.statepoint - with openmc.StatePoint(self._model.statepoint) as sp: + assert spfile + with openmc.StatePoint(spfile) as sp: sp_batchno_2 = sp.current_batch + k_combined_2 = sp.k_combined assert sp_batchno_2 > 10 assert sp_batchno_1 == sp_batchno_2, \ 'Different final batch number after restart' - assert str(k_combined_1) == str(k_combined_2), \ + assert k_combined_1 == k_combined_2, \ 'Different final k_combined after restart' self._write_inputs(self._get_inputs()) self._compare_inputs() diff --git a/tests/unit_tests/test_filters.py b/tests/unit_tests/test_filters.py index 10587a8125..bf62abb65d 100644 --- a/tests/unit_tests/test_filters.py +++ b/tests/unit_tests/test_filters.py @@ -173,10 +173,10 @@ def test_first_moment(run_in_tmpdir, box_model): for t in box_model.tallies: t.estimator = 'analog' - box_model.run() + sp_name = box_model.run() # Check that first moment matches the score from the plain tally - with openmc.StatePoint('statepoint.10.h5') as sp: + with openmc.StatePoint(sp_name) as sp: # Get scores from tally without expansion filters flux, scatter = sp.tallies[plain_tally.id].mean.ravel() From a7e924e545fda3773cc09be0c81b064599b5c29c Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Wed, 4 Mar 2020 15:43:16 +0000 Subject: [PATCH 195/205] Fix typos and redundant line --- openmc/model/model.py | 1 - tests/regression_tests/trigger_statepoint_restart/test.py | 5 +++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index cb9907331f..896e061194 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -52,7 +52,6 @@ class Model: self.settings = openmc.Settings() self.tallies = openmc.Tallies() self.plots = openmc.Plots() - self.statepoint = None if geometry is not None: self.geometry = geometry diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py index 5f37383abd..eb0be0e191 100644 --- a/tests/regression_tests/trigger_statepoint_restart/test.py +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -90,7 +90,7 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): # First non-restart run spfile = self._model.run(**args) sp_batchno_1 = 0 - assert sp_file + assert spfile with openmc.StatePoint(spfile) as sp: sp_batchno_1 = sp.current_batch k_combined_1 = sp.k_combined @@ -114,7 +114,8 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): assert sp_batchno_2 > 10 assert sp_batchno_1 == sp_batchno_2, \ 'Different final batch number after restart' - assert k_combined_1 == k_combined_2, \ + # need str() here as uncertainties.ufloat instances are always different + assert str(k_combined_1) == str(k_combined_2), \ 'Different final k_combined after restart' self._write_inputs(self._get_inputs()) self._compare_inputs() From bd71ac0588ec327027477fbd808d80b15aec1f28 Mon Sep 17 00:00:00 2001 From: Alex Lyons <59739325+alex-lyons@users.noreply.github.com> Date: Thu, 19 Mar 2020 16:37:11 +0000 Subject: [PATCH 196/205] Update tests/regression_tests/trigger_statepoint_restart/test.py Co-Authored-By: Paul Romano --- .../trigger_statepoint_restart/test.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py index eb0be0e191..67ba598072 100644 --- a/tests/regression_tests/trigger_statepoint_restart/test.py +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -12,11 +12,10 @@ from tests.regression_tests import config def model(): # Materials - materials = openmc.Materials() mat = openmc.Material() mat.set_density('g/cm3', 4.5) mat.add_nuclide('U235', 1.0) - materials.append(mat) + materials = openmc.Materials([mat]) # Geometry sph = openmc.Sphere(r=10.0, boundary_type='vacuum') @@ -36,22 +35,18 @@ def model(): settings.verbosity = 1 # to test that this works even with no output # Tallies - tallies = openmc.Tallies() t = openmc.Tally() t.scores = ['flux'] - tallies.append(t) + tallies = openmc.Tallies([t]) - # Plots (none) - plots = openmc.Plots() - # Put it all together model = openmc.model.Model(materials=materials, geometry=geometry, settings=settings, - tallies=tallies, - plots=plots) + tallies=tallies) return model + class TriggerStatepointRestartTestHarness(PyAPITestHarness): def __init__(self, statepoint, model=None): super().__init__(statepoint, model) @@ -125,6 +120,7 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): finally: self._cleanup() + def test_trigger_statepoint_restart(model): # Assuming we converge within 1000 batches, the statepoint filename # should include the batch number padded by at least one '0'. From f2bc95afa5f59129af8a9e8e446d4f7c309e7f5f Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Thu, 19 Mar 2020 17:50:45 +0000 Subject: [PATCH 197/205] Reduce no of batches for trigger from ~47 to ~13 to try to avoid test failures due to numerical differences in platform libraries --- openmc/settings.py | 8 +++----- .../trigger_statepoint_restart/inputs_true.dat | 2 +- .../trigger_statepoint_restart/results_true.dat | 6 +++--- tests/regression_tests/trigger_statepoint_restart/test.py | 6 +++++- 4 files changed, 12 insertions(+), 10 deletions(-) diff --git a/openmc/settings.py b/openmc/settings.py index 3f1cbe2c39..8ea110be13 100644 --- a/openmc/settings.py +++ b/openmc/settings.py @@ -813,10 +813,9 @@ class Settings: def _create_keff_trigger_subelement(self, root): if self._keff_trigger is not None: element = ET.SubElement(root, "keff_trigger") - - for key in self._keff_trigger: + for key, value in sorted(self._keff_trigger.items()): subelement = ET.SubElement(element, key) - subelement.text = str(self._keff_trigger[key]).lower() + subelement.text = str(value).lower() def _create_energy_mode_subelement(self, root): if self._energy_mode is not None: @@ -839,8 +838,7 @@ class Settings: def _create_output_subelement(self, root): if self._output is not None: element = ET.SubElement(root, "output") - - for key, value in self._output.items(): + for key, value in sorted(self._output.items()): subelement = ET.SubElement(element, key) if key in ('summary', 'tallies'): subelement.text = str(value).lower() diff --git a/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat b/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat index 26890f9f86..51ed9ee61a 100644 --- a/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat +++ b/tests/regression_tests/trigger_statepoint_restart/inputs_true.dat @@ -17,8 +17,8 @@ 10 5 + 0.004 std_dev - 0.002 true diff --git a/tests/regression_tests/trigger_statepoint_restart/results_true.dat b/tests/regression_tests/trigger_statepoint_restart/results_true.dat index dc5ed98310..1fdfb64447 100644 --- a/tests/regression_tests/trigger_statepoint_restart/results_true.dat +++ b/tests/regression_tests/trigger_statepoint_restart/results_true.dat @@ -1,5 +1,5 @@ k-combined: -3.040704E-01 1.976825E-03 +2.916922E-01 3.293799E-03 tally 1: -3.389935E+02 -2.743454E+03 +6.184423E+01 +4.789617E+02 diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py index 67ba598072..ba99c94ced 100644 --- a/tests/regression_tests/trigger_statepoint_restart/test.py +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -28,7 +28,9 @@ def model(): settings.batches = 10 settings.inactive = 5 settings.particles = 200 - settings.keff_trigger = {'type': 'std_dev', 'threshold': 0.002} + # Choose a sufficiently low threshold to trigger after more than 10 batches. + # 0.004 seems to take 13 batches. + settings.keff_trigger = {'type': 'std_dev', 'threshold': 0.004} settings.trigger_max_batches = 1000 settings.trigger_batch_interval = 1 settings.trigger_active = True @@ -85,11 +87,13 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): # First non-restart run spfile = self._model.run(**args) sp_batchno_1 = 0 + print('Last sp file: %s' % spfile) assert spfile with openmc.StatePoint(spfile) as sp: sp_batchno_1 = sp.current_batch k_combined_1 = sp.k_combined assert sp_batchno_1 > 10 + print('Last batch no = %d' % sp_batchno_1) self._write_inputs(self._get_inputs()) self._compare_inputs() self._test_output_created() From 11807ae1f160def266fe8e38795b102fd13ddd3d Mon Sep 17 00:00:00 2001 From: alex-lyons Date: Tue, 24 Mar 2020 14:08:47 +0000 Subject: [PATCH 198/205] In the test, ensure the globbed statepoint filenames are sorted --- tests/regression_tests/trigger_statepoint_restart/test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/regression_tests/trigger_statepoint_restart/test.py b/tests/regression_tests/trigger_statepoint_restart/test.py index ba99c94ced..1b47641474 100644 --- a/tests/regression_tests/trigger_statepoint_restart/test.py +++ b/tests/regression_tests/trigger_statepoint_restart/test.py @@ -59,7 +59,7 @@ class TriggerStatepointRestartTestHarness(PyAPITestHarness): def _test_output_created(self): """Make sure statepoint files have been created.""" - spfiles = glob.glob(self._sp_pattern) + spfiles = sorted(glob.glob(self._sp_pattern)) assert len(spfiles) == 2, \ 'Two statepoint files should have been created' if not self._final_sp: From 3e36801ea328385bb4dfaad5969ebcdf1b41353b Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 27 Mar 2020 07:49:53 -0500 Subject: [PATCH 199/205] Update docstring for Model.run --- openmc/model/model.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index 896e061194..4115e4f917 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -198,18 +198,18 @@ class Model: self.plots.export_to_xml(d) def run(self, **kwargs): - """Creates the XML files, runs OpenMC, and returns k-effective. - The last statepoint Path is available in model.statepoint + """Creates the XML files, runs OpenMC, and returns the path to the last + statepoint file generated. Parameters ---------- **kwargs - All keyword arguments are passed to :func:`openmc.run` + Keyword arguments passed to :func:`openmc.run` Returns ------- Path - the name of the last statepoint written by this run + Path to the last statepoint written by this run (None if no statepoint was written) """ From 070a83d7733d988022235e77233764abb6f337e6 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 27 Mar 2020 11:21:18 -0500 Subject: [PATCH 200/205] Apply @pshriwise suggestion from code review Co-Authored-By: Patrick Shriwise --- openmc/model/model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index 4115e4f917..89dbfb3536 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -216,8 +216,8 @@ class Model: self.export_to_xml() - # Setting tstart here ensures we don't pick up any old statepoint - # files that might preexist in the output directory + # Setting tstart here ensures we don't pick up any pre-existing statepoint + # files in the output directory tstart = time.time() last_statepoint = None From 1716d35ce567b824875104b8568a9b95e140bb12 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 27 Mar 2020 11:38:39 -0500 Subject: [PATCH 201/205] Removing unused list in test file. --- tests/regression_tests/unstructured_mesh/test.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 85359d6edd..a6ecb82745 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -241,7 +241,6 @@ for i, (estimator, ext_geom, holes) in enumerate(product(*param_values)): 'external_geom' : ext_geom, 'holes' : holes, 'inputs_true' : 'inputs_true{}.dat'.format(i)}) -inputs = ['inputs_true{}.dat'.format(i) for i, opts in enumerate(test_cases)] @pytest.mark.parametrize("opts", test_cases) From 25fe1ef4f3e9f4e2a6692b847e88779e80f8bac6 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 27 Mar 2020 11:39:12 -0500 Subject: [PATCH 202/205] Updates to unstructured mesh example notebooks. --- .../jupyter/unstructured-mesh-part-i.ipynb | 320 ++------ .../jupyter/unstructured-mesh-part-ii.ipynb | 737 ++---------------- 2 files changed, 97 insertions(+), 960 deletions(-) diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb index dab06db991..4e6d873cc8 100644 --- a/examples/jupyter/unstructured-mesh-part-i.ipynb +++ b/examples/jupyter/unstructured-mesh-part-i.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -130,16 +130,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -169,16 +169,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -248,9 +248,9 @@ " Copyright | 2011-2020 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", - " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", - " Date/Time | 2020-03-19 09:53:33\n", - " OpenMP Threads | 8\n", + " Git SHA1 | e7eceff2aa3a9bbfd4dd553f585d1a31b051ddb3\n", + " Date/Time | 2020-03-27 11:18:32\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -272,7 +272,6 @@ " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Minimum neutron data temperature: 294.000000 K\n", " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", " Initializing source particles...\n", @@ -292,24 +291,23 @@ " 9/1 0.29029 0.23536 +/- 0.01935\n", " 10/1 0.20094 0.22848 +/- 0.01649\n", " Creating state point statepoint.10.h5...\n", - " Writing unstructured mesh tally_2.10.vtk...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.1114e+02 seconds\n", - " Reading cross sections = 9.1607e-01 seconds\n", - " Total time in simulation = 4.1149e+00 seconds\n", - " Time in transport only = 2.6868e-02 seconds\n", - " Time in inactive batches = 4.2519e-03 seconds\n", - " Time in active batches = 4.1106e+00 seconds\n", - " Time synchronizing fission bank = 4.4463e-05 seconds\n", - " Sampling source sites = 3.7916e-05 seconds\n", - " SEND/RECV source sites = 3.5140e-06 seconds\n", - " Time accumulating tallies = 9.2627e-03 seconds\n", - " Total time for finalization = 5.3702e-01 seconds\n", - " Total time elapsed = 2.1580e+02 seconds\n", - " Calculation Rate (inactive) = 117594.0 particles/second\n", - " Calculation Rate (active) = 121.637 particles/second\n", + " Total time for initialization = 6.8458e-01 seconds\n", + " Reading cross sections = 6.7039e-01 seconds\n", + " Total time in simulation = 1.9462e-02 seconds\n", + " Time in transport only = 1.7538e-02 seconds\n", + " Time in inactive batches = 9.2816e-03 seconds\n", + " Time in active batches = 1.0181e-02 seconds\n", + " Time synchronizing fission bank = 5.2594e-05 seconds\n", + " Sampling source sites = 4.6704e-05 seconds\n", + " SEND/RECV source sites = 3.1580e-06 seconds\n", + " Time accumulating tallies = 2.0290e-06 seconds\n", + " Total time for finalization = 1.0320e-06 seconds\n", + " Total time elapsed = 7.0439e-01 seconds\n", + " Calculation Rate (inactive) = 53869.9 particles/second\n", + " Calculation Rate (active) = 49113.3 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -324,10 +322,10 @@ { "data": { "text/plain": [ - "0.2070678896718187+/-0.019653037754288158" + "0.20706788967181938+/-0.019653037754288005" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -345,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -355,7 +353,7 @@ "" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": { "image/png": { "width": 600 @@ -379,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -434,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -452,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -474,218 +472,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", - " Date/Time | 2020-03-19 09:57:45\n", - " OpenMP Threads | 8\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - " Reading U234 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n", - " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr96.h5\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", - " Reading B11 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n", - " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 0.22539\n", - " 2/1 0.23030\n", - " 3/1 0.23180\n", - " 4/1 0.23343\n", - " 5/1 0.22940\n", - " 6/1 0.22765\n", - " 7/1 0.23238\n", - " 8/1 0.23083\n", - " 9/1 0.23204\n", - " 10/1 0.23189\n", - " 11/1 0.23555\n", - " 12/1 0.23220\n", - " 13/1 0.22907\n", - " 14/1 0.23016\n", - " 15/1 0.23055\n", - " 16/1 0.23062\n", - " 17/1 0.22656\n", - " 18/1 0.23380\n", - " 19/1 0.23268\n", - " 20/1 0.23233\n", - " 21/1 0.23256\n", - " 22/1 0.22965 0.23111 +/- 0.00145\n", - " 23/1 0.22846 0.23022 +/- 0.00121\n", - " 24/1 0.22936 0.23001 +/- 0.00089\n", - " 25/1 0.23054 0.23012 +/- 0.00069\n", - " 26/1 0.22708 0.22961 +/- 0.00076\n", - " 27/1 0.23159 0.22989 +/- 0.00070\n", - " WARNING: No tet found for location between triangle hits\n", - " 28/1 0.23703 0.23078 +/- 0.00108\n", - " 29/1 0.23227 0.23095 +/- 0.00097\n", - " 30/1 0.23114 0.23097 +/- 0.00086\n", - " 31/1 0.23133 0.23100 +/- 0.00078\n", - " 32/1 0.23143 0.23104 +/- 0.00072\n", - " 33/1 0.23125 0.23105 +/- 0.00066\n", - " 34/1 0.23218 0.23113 +/- 0.00061\n", - " 35/1 0.23140 0.23115 +/- 0.00057\n", - " 36/1 0.22911 0.23102 +/- 0.00055\n", - " 37/1 0.23143 0.23105 +/- 0.00052\n", - " 38/1 0.23342 0.23118 +/- 0.00051\n", - " 39/1 0.23186 0.23121 +/- 0.00048\n", - " 40/1 0.23029 0.23117 +/- 0.00046\n", - " 41/1 0.23132 0.23118 +/- 0.00043\n", - " 42/1 0.23167 0.23120 +/- 0.00042\n", - " 43/1 0.23244 0.23125 +/- 0.00040\n", - " 44/1 0.23101 0.23124 +/- 0.00038\n", - " 45/1 0.23225 0.23128 +/- 0.00037\n", - " 46/1 0.22945 0.23121 +/- 0.00036\n", - " 47/1 0.22978 0.23116 +/- 0.00035\n", - " 48/1 0.23335 0.23124 +/- 0.00035\n", - " 49/1 0.23298 0.23130 +/- 0.00034\n", - " 50/1 0.23095 0.23129 +/- 0.00033\n", - " 51/1 0.23724 0.23148 +/- 0.00037\n", - " 52/1 0.22973 0.23142 +/- 0.00037\n", - " 53/1 0.23066 0.23140 +/- 0.00035\n", - " 54/1 0.22838 0.23131 +/- 0.00036\n", - " 55/1 0.23262 0.23135 +/- 0.00035\n", - " 56/1 0.23593 0.23148 +/- 0.00036\n", - " 57/1 0.23358 0.23153 +/- 0.00036\n", - " 58/1 0.23050 0.23151 +/- 0.00035\n", - " 59/1 0.23273 0.23154 +/- 0.00034\n", - " 60/1 0.22842 0.23146 +/- 0.00034\n", - " 61/1 0.23344 0.23151 +/- 0.00033\n", - " 62/1 0.23333 0.23155 +/- 0.00033\n", - " 63/1 0.22987 0.23151 +/- 0.00032\n", - " 64/1 0.23117 0.23150 +/- 0.00032\n", - " 65/1 0.23197 0.23151 +/- 0.00031\n", - " 66/1 0.23379 0.23156 +/- 0.00031\n", - " 67/1 0.23461 0.23163 +/- 0.00031\n", - " 68/1 0.23109 0.23162 +/- 0.00030\n", - " 69/1 0.22916 0.23157 +/- 0.00030\n", - " 70/1 0.23008 0.23154 +/- 0.00029\n", - " 71/1 0.23157 0.23154 +/- 0.00029\n", - " 72/1 0.23126 0.23153 +/- 0.00028\n", - " 73/1 0.23377 0.23157 +/- 0.00028\n", - " 74/1 0.23105 0.23157 +/- 0.00028\n", - " 75/1 0.23654 0.23166 +/- 0.00029\n", - " 76/1 0.23198 0.23166 +/- 0.00028\n", - " 77/1 0.23390 0.23170 +/- 0.00028\n", - " 78/1 0.23455 0.23175 +/- 0.00028\n", - " 79/1 0.23245 0.23176 +/- 0.00027\n", - " 80/1 0.23121 0.23175 +/- 0.00027\n", - " 81/1 0.23183 0.23175 +/- 0.00026\n", - " 82/1 0.23496 0.23181 +/- 0.00027\n", - " 83/1 0.22763 0.23174 +/- 0.00027\n", - " 84/1 0.23184 0.23174 +/- 0.00027\n", - " 85/1 0.23074 0.23173 +/- 0.00026\n", - " 86/1 0.23178 0.23173 +/- 0.00026\n", - " 87/1 0.23135 0.23172 +/- 0.00025\n", - " 88/1 0.23117 0.23171 +/- 0.00025\n", - " 89/1 0.22815 0.23166 +/- 0.00025\n", - " 90/1 0.22852 0.23162 +/- 0.00025\n", - " 91/1 0.22910 0.23158 +/- 0.00025\n", - " 92/1 0.23143 0.23158 +/- 0.00025\n", - " 93/1 0.23097 0.23157 +/- 0.00024\n", - " 94/1 0.23348 0.23160 +/- 0.00024\n", - " 95/1 0.23068 0.23158 +/- 0.00024\n", - " 96/1 0.23089 0.23157 +/- 0.00024\n", - " 97/1 0.23373 0.23160 +/- 0.00024\n", - " 98/1 0.23336 0.23163 +/- 0.00023\n", - " 99/1 0.23084 0.23162 +/- 0.00023\n", - " 100/1 0.23116 0.23161 +/- 0.00023\n", - " Creating state point statepoint.100.h5...\n", - " Writing unstructured mesh tally_1.100.vtk...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 2.0470e+02 seconds\n", - " Reading cross sections = 6.4733e-01 seconds\n", - " Total time in simulation = 3.5637e+02 seconds\n", - " Time in transport only = 3.5039e+02 seconds\n", - " Time in inactive batches = 1.8166e+01 seconds\n", - " Time in active batches = 3.3821e+02 seconds\n", - " Time synchronizing fission bank = 6.9102e-01 seconds\n", - " Sampling source sites = 6.0920e-01 seconds\n", - " SEND/RECV source sites = 8.1712e-02 seconds\n", - " Time accumulating tallies = 2.2190e-01 seconds\n", - " Total time for finalization = 8.3563e-01 seconds\n", - " Total time elapsed = 5.6214e+02 seconds\n", - " Calculation Rate (inactive) = 110098.0 particles/second\n", - " Calculation Rate (active) = 23654.1 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " k-effective (Collision) = 0.23129 +/- 0.00020\n", - " k-effective (Track-length) = 0.23161 +/- 0.00023\n", - " k-effective (Absorption) = 0.23099 +/- 0.00019\n", - " Combined k-effective = 0.23119 +/- 0.00017\n", - " Leakage Fraction = 0.79477 +/- 0.00014\n", - "\n" - ] - }, { "data": { "text/plain": [ - "0.23118721242921098+/-0.0001701893231378034" + "0.2311872124292096+/-0.00017018932313754055" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -694,7 +490,7 @@ "model.settings.particles = 100_000\n", "model.settings.inactive = 20\n", "model.settings.batches = 100\n", - "model.run()" + "model.run(output=False)" ] }, { @@ -718,14 +514,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "manifold_flux.vtk tally_1.100.vtk tally_1.200.vtk tally_2.10.vtk\r\n" + "tally_1.100.vtk\r\n" ] } ], @@ -742,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -752,7 +548,7 @@ "" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": { "image/png": { "width": 600 @@ -775,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -785,7 +581,7 @@ "" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": { "image/png": { "width": 600 @@ -807,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -825,20 +621,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[1.43381086e-04]\n", - " [1.48043747e-04]\n", - " [1.60408339e-04]\n", - " ...\n", - " [7.04197023e-05]\n", - " [7.04197023e-05]\n", - " [7.04197023e-05]]\n", + "[1.43381086e-04 1.48043747e-04 1.60408339e-04 ... 7.04197023e-05\n", + " 7.04197023e-05 7.04197023e-05]\n", "[[ 2.88485691 -2.55429784 9.97768184]\n", " [ 2.87565092 -2.60469781 9.8884092 ]\n", " [ 2.85832254 -2.65291228 9.97768184]\n", @@ -858,19 +649,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The combination of these values can provide for an appoxmiate visualization of the unstructured mesh without its explicit representation or use of an additional mesh library." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "The combination of these values can provide for an appoxmiate visualization of the unstructured mesh without its explicit representation or use of an additional mesh library.\n", + "\n", "We hope you've found this example notebook useful!" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -880,7 +666,7 @@ "" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": { "image/png": { "width": 600 diff --git a/examples/jupyter/unstructured-mesh-part-ii.ipynb b/examples/jupyter/unstructured-mesh-part-ii.ipynb index 8373656db9..24df3cd051 100644 --- a/examples/jupyter/unstructured-mesh-part-ii.ipynb +++ b/examples/jupyter/unstructured-mesh-part-ii.ipynb @@ -40,8 +40,8 @@ "source": [ "import urllib.request\n", "\n", - "manifold_geom_url = 'https://tinyurl.com/rp7grox'\n", - "manifold_mesh_url = 'https://tinyurl.com/wojemuh'\n", + "manifold_geom_url = 'https://tinyurl.com/rp7grox' # 99 MB\n", + "manifold_mesh_url = 'https://tinyurl.com/wojemuh' # 5.4 MB\n", "\n", "def download(url, filename='dagmc.h5m'):\n", " \"\"\"\n", @@ -108,53 +108,41 @@ "source": [ "air = openmc.Material(name='air')\n", "air.set_density('g/cc', 0.001205)\n", - "air.add_nuclide('N14',0.781557629247)\n", - "air.add_nuclide('N15',0.002873370753)\n", - "air.add_nuclide('O16',0.210668126508)\n", - "air.add_nuclide('O17',7.9873492e-05)\n", - "air.add_nuclide('Ar36',1.53456e-05)\n", - "air.add_nuclide('Ar38',2.8934e-06)\n", - "air.add_nuclide('Ar40',0.004581761)\n", + "air.add_element('N', 0.784431)\n", + "air.add_element('O', 0.210748)\n", + "air.add_element('Ar',0.0046)\n", "\n", "steel = openmc.Material(name='steel')\n", "steel.set_density('g/cc', 8.0)\n", - "steel.add_nuclide('Si28',0.0092672382464)\n", - "steel.add_nuclide('Si29',0.00047056391679999997)\n", - "steel.add_nuclide('Si30',0.00031019783679999996)\n", + "steel.add_element('Si', 0.010048)\n", + "steel.add_element('S', 0.00023)\n", + "steel.add_element('Fe', 0.669)\n", + "steel.add_element('Ni', 0.12)\n", + "steel.add_element('Mo', 0.025)\n", "steel.add_nuclide('P31',0.00023)\n", - "steel.add_nuclide('S32',0.000218593702)\n", - "steel.add_nuclide('S33',1.721987e-06)\n", - "steel.add_nuclide('S34',9.650777000000001e-06)\n", - "steel.add_nuclide('S36',3.3534e-08)\n", "steel.add_nuclide('Mn55',0.011014)\n", - "steel.add_nuclide('Fe54',0.03910305)\n", - "steel.add_nuclide('Fe56',0.6138342600000001)\n", - "steel.add_nuclide('Fe57',0.01417611)\n", - "steel.add_nuclide('Fe58',0.0018865800000000001)\n", - "steel.add_nuclide('Ni58',0.08169227999999999)\n", - "steel.add_nuclide('Ni60',0.03146772)\n", - "steel.add_nuclide('Ni61',0.00136788)\n", - "steel.add_nuclide('Ni62',0.0043614000000000005)\n", - "steel.add_nuclide('Ni64',0.00111072)\n", - "steel.add_nuclide('Mo100',0.0024360000000000002)\n", - "steel.add_nuclide('Mo92',0.0036622500000000006)\n", - "steel.add_nuclide('Mo94',0.0022967499999999997)\n", - "steel.add_nuclide('Mo95',0.00396825)\n", - "steel.add_nuclide('Mo96',0.00416825)\n", - "steel.add_nuclide('Mo97',0.0023955)\n", - "steel.add_nuclide('Mo98',0.006073)\n", "\n", "materials = openmc.Materials([air, steel])\n", "materials.export_to_xml()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's download the geometry and mesh files.\n", + "(This may take some time.)" + ] + }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "download(manifold_geom_url)\n", + "# get the manifold DAGMC geometry file\n", + "download(manifold_geom_url) \n", + "# get the manifold tet mesh\n", "download(manifold_mesh_url, 'manifold.h5m')" ] }, @@ -246,9 +234,9 @@ " Copyright | 2011-2020 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.12.0-dev\n", - " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", - " Date/Time | 2020-03-19 10:10:21\n", - " OpenMP Threads | 8\n", + " Git SHA1 | e7eceff2aa3a9bbfd4dd553f585d1a31b051ddb3\n", + " Date/Time | 2020-03-27 10:25:39\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -269,12 +257,10 @@ " Reading Si28 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", " Reading Si29 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", " Reading Si30 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", - " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", " Reading S32 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", " Reading S33 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", " Reading S34 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", " Reading S36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", - " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", @@ -291,13 +277,13 @@ " Reading Mo96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", " Reading Mo97 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", " Reading Mo98 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", + " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", + " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", " Maximum neutron transport energy: 20000000.000000 eV for N15\n", " Minimum neutron data temperature: 294.000000 K\n", " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Initializing source particles...\n", "\n", " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", "\n", @@ -312,24 +298,22 @@ " Simulating batch 9\n", " Simulating batch 10\n", " Creating state point statepoint.10.h5...\n", - " Writing unstructured mesh tally_1.10.vtk...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.2053e+02 seconds\n", - " Reading cross sections = 2.1899e+00 seconds\n", - " Total time in simulation = 1.4710e+01 seconds\n", - " Time in transport only = 6.4195e+00 seconds\n", - " Time in active batches = 1.4710e+01 seconds\n", - " Time sampling source = 3.6842e+00 seconds\n", - " Time accumulating tallies = 1.9801e-02 seconds\n", - " Total time for finalization = 6.9415e-01 seconds\n", - " Total time elapsed = 2.3623e+02 seconds\n", - " Calculation Rate (active) = 3399.10 particles/second\n", + " Total time for initialization = 2.0935e+01 seconds\n", + " Reading cross sections = 2.3075e+00 seconds\n", + " Total time in simulation = 7.0885e+00 seconds\n", + " Time in transport only = 7.0872e+00 seconds\n", + " Time in active batches = 7.0885e+00 seconds\n", + " Time accumulating tallies = 3.8640e-06 seconds\n", + " Total time for finalization = 1.2420e-06 seconds\n", + " Total time elapsed = 2.8024e+01 seconds\n", + " Calculation Rate (active) = 7053.66 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " Leakage Fraction = 0.97422 +/- 0.00091\n", + " Leakage Fraction = 0.97472 +/- 0.00083\n", "\n" ] } @@ -342,7 +326,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's setup the unstructured mesh tally. We'll do this the same way we did in the previous notebook." + "Now let's setup the unstructured mesh tally. We'll do this the same way we did in the [previous notebook](./unstructured-mesh-part-i.ipynb)." ] }, { @@ -379,332 +363,16 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", - " Date/Time | 2020-03-19 10:14:19\n", - " OpenMP Threads | 8\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading DAGMC geometry...\n", - "Loading file dagmc.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.001\n", - "Building OBB Tree...\n", - " Reading N14 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", - " Reading N15 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading O17 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", - " Reading Ar36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", - " Reading Ar38 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", - " Reading Ar40 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", - " Reading Si28 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", - " Reading Si29 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", - " Reading Si30 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", - " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", - " Reading S32 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", - " Reading S33 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", - " Reading S34 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", - " Reading S36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", - " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", - " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", - " Reading Ni58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", - " Reading Mo100 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for N15\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Initializing source particles...\n", - "\n", - " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", - "\n", - " Simulating batch 1\n", - " Simulating batch 2\n", - " Simulating batch 3\n", - " Simulating batch 4\n", - " Simulating batch 5\n", - " Simulating batch 6\n", - " Simulating batch 7\n", - " Simulating batch 8\n", - " Simulating batch 9\n", - " Simulating batch 10\n", - " Simulating batch 11\n", - " Simulating batch 12\n", - " Simulating batch 13\n", - " Simulating batch 14\n", - " Simulating batch 15\n", - " Simulating batch 16\n", - 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" Simulating batch 192\n", - " Simulating batch 193\n", - " Simulating batch 194\n", - " Simulating batch 195\n", - " Simulating batch 196\n", - " Simulating batch 197\n", - " Simulating batch 198\n", - " Simulating batch 199\n", - " Simulating batch 200\n", - " Creating state point statepoint.200.h5...\n", - " Writing unstructured mesh tally_1.200.vtk...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 4.1748e+01 seconds\n", - " Reading cross sections = 2.1717e+00 seconds\n", - " Total time in simulation = 1.8229e+02 seconds\n", - " Time in transport only = 1.0991e+02 seconds\n", - " Time in active batches = 1.8229e+02 seconds\n", - " Time sampling source = 7.1488e+01 seconds\n", - " Time accumulating tallies = 4.7612e-02 seconds\n", - " Total time for finalization = 1.4539e-01 seconds\n", - " Total time elapsed = 2.2446e+02 seconds\n", - " Calculation Rate (active) = 5485.91 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " Leakage Fraction = 0.97394 +/- 0.00017\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "openmc.run()" + "openmc.run(output=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Again we should see that `tally_1.100.vtk` file which we can use to visualize our results in VisIt or another tool of your choice that supports VTK files." + "Again we should see that `tally_1.200.vtk` file which we can use to visualize our results in VisIt or another tool of your choice that supports VTK files." ] }, { @@ -716,7 +384,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tally_1.10.vtk\ttally_1.200.vtk\r\n" + "tally_1.200.vtk\r\n" ] } ], @@ -820,326 +488,9 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.12.0-dev\n", - " Git SHA1 | 34da5a1e2a3942428dfca6e8dfc2ce02d04333dc\n", - " Date/Time | 2020-03-19 10:18:04\n", - " OpenMP Threads | 8\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading DAGMC geometry...\n", - "Loading file dagmc.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.001\n", - "Building OBB Tree...\n", - " Reading N14 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n", - " Reading N15 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading O17 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n", - " Reading Ar36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n", - " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n", - " Reading Ar38 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n", - " Reading Ar40 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n", - " Reading Si28 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n", - " Reading Si29 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n", - " Reading Si30 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n", - " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n", - " Reading S32 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n", - " Reading S33 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n", - " Reading S34 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n", - " Reading S36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n", - " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n", - " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", - " Reading Ni58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n", - " Reading Ni60 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n", - " Reading Ni61 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n", - " Reading Ni62 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n", - " Reading Ni64 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n", - " Reading Mo100 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n", - " Reading Mo92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n", - " Reading Mo94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n", - " Reading Mo95 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n", - " Reading Mo96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n", - " Reading Mo97 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n", - " Reading Mo98 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for N15\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Initializing source particles...\n", - "\n", - " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", - "\n", - " Simulating batch 1\n", - " Simulating batch 2\n", - " Simulating batch 3\n", - " Simulating batch 4\n", - " Simulating batch 5\n", - " Simulating batch 6\n", - " Simulating batch 7\n", - " Simulating batch 8\n", - " Simulating batch 9\n", - " Simulating batch 10\n", - " Simulating batch 11\n", - " Simulating batch 12\n", - " Simulating batch 13\n", - " Simulating batch 14\n", - " Simulating batch 15\n", - " Simulating batch 16\n", - " Simulating batch 17\n", - " Simulating batch 18\n", - " Simulating batch 19\n", - " Simulating batch 20\n", - " Simulating batch 21\n", - " Simulating batch 22\n", - " Simulating batch 23\n", - " Simulating batch 24\n", - " Simulating batch 25\n", - " Simulating batch 26\n", - " Simulating batch 27\n", - " Simulating batch 28\n", - " Simulating batch 29\n", - 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" Simulating batch 172\n", - " Simulating batch 173\n", - " Simulating batch 174\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Simulating batch 175\n", - " Simulating batch 176\n", - " Simulating batch 177\n", - " Simulating batch 178\n", - " Simulating batch 179\n", - " Simulating batch 180\n", - " Simulating batch 181\n", - " Simulating batch 182\n", - " Simulating batch 183\n", - " Simulating batch 184\n", - " Simulating batch 185\n", - " Simulating batch 186\n", - " Simulating batch 187\n", - " Simulating batch 188\n", - " Simulating batch 189\n", - " Simulating batch 190\n", - " Simulating batch 191\n", - " Simulating batch 192\n", - " Simulating batch 193\n", - " Simulating batch 194\n", - " Simulating batch 195\n", - " Simulating batch 196\n", - " Simulating batch 197\n", - " Simulating batch 198\n", - " Simulating batch 199\n", - " Simulating batch 200\n", - " Creating state point statepoint.200.h5...\n", - " WARNING: Skipping unstructured mesh writing for tally 1. More than one filter\n", - " is present on the tally.\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 4.0432e+01 seconds\n", - " Reading cross sections = 1.9152e+00 seconds\n", - " Total time in simulation = 1.7878e+02 seconds\n", - " Time in transport only = 1.0807e+02 seconds\n", - " Time in active batches = 1.7878e+02 seconds\n", - " Time sampling source = 7.0570e+01 seconds\n", - " Time accumulating tallies = 9.5415e-02 seconds\n", - " Total time for finalization = 2.9952e-01 seconds\n", - " Total time elapsed = 2.1979e+02 seconds\n", - " Calculation Rate (active) = 5593.58 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " Leakage Fraction = 0.97394 +/- 0.00017\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "openmc.run()" + "openmc.run(output=False)" ] }, { @@ -1273,7 +624,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "manifold_flux.vtk tally_1.10.vtk tally_1.200.vtk\r\n" + "manifold_flux.vtk tally_1.200.vtk\r\n" ] } ], From e2f230263ff9860fc1be99e222b710cb61b7e357 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 27 Mar 2020 12:02:28 -0500 Subject: [PATCH 203/205] Generating the test model outside of the class definition. --- .../unstructured_mesh/inputs_true0.dat | 48 +-- .../unstructured_mesh/inputs_true1.dat | 48 +-- .../unstructured_mesh/inputs_true2.dat | 48 +-- .../unstructured_mesh/inputs_true3.dat | 48 +-- .../unstructured_mesh/inputs_true4.dat | 48 +-- .../unstructured_mesh/inputs_true5.dat | 48 +-- .../unstructured_mesh/inputs_true6.dat | 48 +-- .../unstructured_mesh/inputs_true7.dat | 48 +-- .../unstructured_mesh/test.py | 327 +++++++++--------- 9 files changed, 354 insertions(+), 357 deletions(-) diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat index 37cecc8c02..e598b673bc 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat index e378d6a98c..36a1e643ed 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat index 620fef6fde..89a5ac549f 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat index 863d2bdd85..0f438ad2ae 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true4.dat b/tests/regression_tests/unstructured_mesh/inputs_true4.dat index c1a60ea708..a1d54134eb 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true4.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true4.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true5.dat b/tests/regression_tests/unstructured_mesh/inputs_true5.dat index 030878dfd3..5ea502526d 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true5.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true5.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true6.dat b/tests/regression_tests/unstructured_mesh/inputs_true6.dat index d033657534..bd24cf4c2a 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true6.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true6.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/inputs_true7.dat b/tests/regression_tests/unstructured_mesh/inputs_true7.dat index 726455b0f5..d46313182c 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true7.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true7.dat @@ -1,34 +1,34 @@ - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + @@ -36,7 +36,7 @@ - + diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index a6ecb82745..3cd4deaf90 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -20,174 +20,13 @@ class UnstructuredMeshTest(PyAPITestHarness): def __init__(self, statepoint_name, + model, inputs_true, - estimator='collision', - external_geom=False, holes=None): - super().__init__(statepoint_name, inputs_true=inputs_true) + super().__init__(statepoint_name, model=model, inputs_true=inputs_true) - self.estimator = estimator # tally estimator type - self.external_geom = external_geom # geometry size matches mesh self.holes = holes # holes in the test mesh - if self.holes: - self.mesh_filename = "test_mesh_tets_w_holes.h5m" - else: - self.mesh_filename = "test_mesh_tets.h5m" # mesh file to use - - def _build_inputs(self): - ### Materials ### - materials = openmc.Materials() - - fuel_mat = openmc.Material(name="fuel") - fuel_mat.add_nuclide("U235", 1.0) - fuel_mat.set_density('g/cc', 4.5) - materials.append(fuel_mat) - - zirc_mat = openmc.Material(name="zircaloy") - zirc_mat.add_element("Zr", 1.0) - zirc_mat.set_density("g/cc", 5.77) - materials.append(zirc_mat) - - water_mat = openmc.Material(name="water") - water_mat.add_nuclide("H1", 2.0) - water_mat.add_nuclide("O16", 1.0) - water_mat.set_density("atom/b-cm", 0.07416) - materials.append(water_mat) - - materials.export_to_xml() - - ### Geometry ### - fuel_min_x = openmc.XPlane(-5.0, name="minimum x") - fuel_max_x = openmc.XPlane(5.0, name="maximum x") - - fuel_min_y = openmc.YPlane(-5.0, name="minimum y") - fuel_max_y = openmc.YPlane(5.0, name="maximum y") - - fuel_min_z = openmc.ZPlane(-5.0, name="minimum z") - fuel_max_z = openmc.ZPlane(5.0, name="maximum z") - - fuel_cell = openmc.Cell(name="fuel") - fuel_cell.region = +fuel_min_x & -fuel_max_x & \ - +fuel_min_y & -fuel_max_y & \ - +fuel_min_z & -fuel_max_z - fuel_cell.fill = fuel_mat - - clad_min_x = openmc.XPlane(-6.0, name="minimum x") - clad_max_x = openmc.XPlane(6.0, name="maximum x") - - clad_min_y = openmc.YPlane(-6.0, name="minimum y") - clad_max_y = openmc.YPlane(6.0, name="maximum y") - - clad_min_z = openmc.ZPlane(-6.0, name="minimum z") - clad_max_z = openmc.ZPlane(6.0, name="maximum z") - - clad_cell = openmc.Cell(name="clad") - clad_cell.region = (-fuel_min_x | +fuel_max_x | - -fuel_min_y | +fuel_max_y | - -fuel_min_z | +fuel_max_z) & \ - (+clad_min_x & -clad_max_x & - +clad_min_y & -clad_max_y & - +clad_min_z & -clad_max_z) - clad_cell.fill = zirc_mat - - if self.external_geom: - bounds = (15, 15, 15) - else: - bounds = (10, 10, 10) - - water_min_x = openmc.XPlane(x0=-bounds[0], - name="minimum x", - boundary_type='vacuum') - water_max_x = openmc.XPlane(x0=bounds[0], - name="maximum x", - boundary_type='vacuum') - - water_min_y = openmc.YPlane(y0=-bounds[1], - name="minimum y", - boundary_type='vacuum') - water_max_y = openmc.YPlane(y0=bounds[1], - name="maximum y", - boundary_type='vacuum') - - water_min_z = openmc.ZPlane(z0=-bounds[2], - name="minimum z", - boundary_type='vacuum') - water_max_z = openmc.ZPlane(z0=bounds[2], - name="maximum z", - boundary_type='vacuum') - - water_cell = openmc.Cell(name="water") - water_cell.region = (-clad_min_x | +clad_max_x | - -clad_min_y | +clad_max_y | - -clad_min_z | +clad_max_z) & \ - (+water_min_x & -water_max_x & - +water_min_y & -water_max_y & - +water_min_z & -water_max_z) - water_cell.fill = water_mat - - # create a containing universe - geom = openmc.Geometry([fuel_cell, clad_cell, water_cell]) - - geom.export_to_xml() - - ### Tallies ### - - # create meshes - regular_mesh = openmc.RegularMesh() - regular_mesh.dimension = (10, 10, 10) - regular_mesh.lower_left = (-10.0, -10.0, -10.0) - regular_mesh.upper_right = (10.0, 10.0, 10.0) - - regular_mesh_filter = openmc.MeshFilter(mesh=regular_mesh) - - uscd_mesh = openmc.UnstructuredMesh(self.mesh_filename) - uscd_mesh.mesh_lib = 'moab' - uscd_filter = openmc.MeshFilter(mesh=uscd_mesh) - - # create tallies - tallies = openmc.Tallies() - - regular_mesh_tally = openmc.Tally(name="regular mesh tally") - regular_mesh_tally.filters = [regular_mesh_filter] - regular_mesh_tally.scores = ['flux'] - regular_mesh_tally.estimator = self.estimator - tallies.append(regular_mesh_tally) - - uscd_tally = openmc.Tally(name="unstructured mesh tally") - uscd_tally.filters = [uscd_filter] - uscd_tally.scores = ['flux'] - uscd_tally.estimator = self.estimator - tallies.append(uscd_tally) - - tallies.export_to_xml() - - ### Settings ### - settings = openmc.Settings() - settings.run_mode = 'fixed source' - settings.particles = 100 - settings.batches = 10 - - # source setup - r = openmc.stats.Uniform(a=0.0, b=0.0) - theta = openmc.stats.Discrete(x=[0.0], p=[1.0]) - phi = openmc.stats.Discrete(x=[0.0], p=[1.0]) - origin = (0.0, 0.0, 0.0) - - space = openmc.stats.SphericalIndependent(r=r, - theta=theta, - phi=phi, - origin=origin) - - angle = openmc.stats.Monodirectional((-1.0, 0.0, 0.0)) - - energy = openmc.stats.Discrete(x=[15.e+06], p=[1.0]) - - source = openmc.Source(space=space, energy=energy, angle=angle) - - settings.source = source - - settings.export_to_xml() def _compare_results(self): with openmc.StatePoint(self._sp_name) as sp: @@ -203,11 +42,12 @@ class UnstructuredMeshTest(PyAPITestHarness): reg_mesh_data = np.delete(reg_mesh_data, self.holes) reg_mesh_std_dev = np.delete(reg_mesh_std_dev, self.holes) else: + umesh_tally = tally unstructured_data, unstructured_std_dev = self.get_mesh_tally_data(tally, True) # we expect these results to be the same to within at least ten # decimal places - decimals = 10 if self.estimator == 'collision' else 8 + decimals = 10 if umesh_tally.estimator == 'collision' else 8 np.testing.assert_array_almost_equal(unstructured_data, reg_mesh_data, decimals) @@ -245,5 +85,162 @@ for i, (estimator, ext_geom, holes) in enumerate(product(*param_values)): @pytest.mark.parametrize("opts", test_cases) def test_unstructured_mesh(opts): - harness = UnstructuredMeshTest('statepoint.10.h5', **opts) + + ### Materials ### + materials = openmc.Materials() + + fuel_mat = openmc.Material(name="fuel") + fuel_mat.add_nuclide("U235", 1.0) + fuel_mat.set_density('g/cc', 4.5) + materials.append(fuel_mat) + + zirc_mat = openmc.Material(name="zircaloy") + zirc_mat.add_element("Zr", 1.0) + zirc_mat.set_density("g/cc", 5.77) + materials.append(zirc_mat) + + water_mat = openmc.Material(name="water") + water_mat.add_nuclide("H1", 2.0) + water_mat.add_nuclide("O16", 1.0) + water_mat.set_density("atom/b-cm", 0.07416) + materials.append(water_mat) + + materials.export_to_xml() + + ### Geometry ### + fuel_min_x = openmc.XPlane(-5.0, name="minimum x") + fuel_max_x = openmc.XPlane(5.0, name="maximum x") + + fuel_min_y = openmc.YPlane(-5.0, name="minimum y") + fuel_max_y = openmc.YPlane(5.0, name="maximum y") + + fuel_min_z = openmc.ZPlane(-5.0, name="minimum z") + fuel_max_z = openmc.ZPlane(5.0, name="maximum z") + + fuel_cell = openmc.Cell(name="fuel") + fuel_cell.region = +fuel_min_x & -fuel_max_x & \ + +fuel_min_y & -fuel_max_y & \ + +fuel_min_z & -fuel_max_z + fuel_cell.fill = fuel_mat + + clad_min_x = openmc.XPlane(-6.0, name="minimum x") + clad_max_x = openmc.XPlane(6.0, name="maximum x") + + clad_min_y = openmc.YPlane(-6.0, name="minimum y") + clad_max_y = openmc.YPlane(6.0, name="maximum y") + + clad_min_z = openmc.ZPlane(-6.0, name="minimum z") + clad_max_z = openmc.ZPlane(6.0, name="maximum z") + + clad_cell = openmc.Cell(name="clad") + clad_cell.region = (-fuel_min_x | +fuel_max_x | + -fuel_min_y | +fuel_max_y | + -fuel_min_z | +fuel_max_z) & \ + (+clad_min_x & -clad_max_x & + +clad_min_y & -clad_max_y & + +clad_min_z & -clad_max_z) + clad_cell.fill = zirc_mat + + if opts['external_geom']: + bounds = (15, 15, 15) + else: + bounds = (10, 10, 10) + + water_min_x = openmc.XPlane(x0=-bounds[0], + name="minimum x", + boundary_type='vacuum') + water_max_x = openmc.XPlane(x0=bounds[0], + name="maximum x", + boundary_type='vacuum') + + water_min_y = openmc.YPlane(y0=-bounds[1], + name="minimum y", + boundary_type='vacuum') + water_max_y = openmc.YPlane(y0=bounds[1], + name="maximum y", + boundary_type='vacuum') + + water_min_z = openmc.ZPlane(z0=-bounds[2], + name="minimum z", + boundary_type='vacuum') + water_max_z = openmc.ZPlane(z0=bounds[2], + name="maximum z", + boundary_type='vacuum') + + water_cell = openmc.Cell(name="water") + water_cell.region = (-clad_min_x | +clad_max_x | + -clad_min_y | +clad_max_y | + -clad_min_z | +clad_max_z) & \ + (+water_min_x & -water_max_x & + +water_min_y & -water_max_y & + +water_min_z & -water_max_z) + water_cell.fill = water_mat + + # create a containing universe + geometry = openmc.Geometry([fuel_cell, clad_cell, water_cell]) + + ### Tallies ### + + # create meshes + regular_mesh = openmc.RegularMesh() + regular_mesh.dimension = (10, 10, 10) + regular_mesh.lower_left = (-10.0, -10.0, -10.0) + regular_mesh.upper_right = (10.0, 10.0, 10.0) + + regular_mesh_filter = openmc.MeshFilter(mesh=regular_mesh) + + if opts['holes']: + mesh_filename = "test_mesh_tets_w_holes.h5m" + else: + mesh_filename = "test_mesh_tets.h5m" + + uscd_mesh = openmc.UnstructuredMesh(mesh_filename) + uscd_mesh.mesh_lib = 'moab' + uscd_filter = openmc.MeshFilter(mesh=uscd_mesh) + + # create tallies + tallies = openmc.Tallies() + + regular_mesh_tally = openmc.Tally(name="regular mesh tally") + regular_mesh_tally.filters = [regular_mesh_filter] + regular_mesh_tally.scores = ['flux'] + regular_mesh_tally.estimator = opts['estimator'] + tallies.append(regular_mesh_tally) + + uscd_tally = openmc.Tally(name="unstructured mesh tally") + uscd_tally.filters = [uscd_filter] + uscd_tally.scores = ['flux'] + uscd_tally.estimator = opts['estimator'] + tallies.append(uscd_tally) + + ### Settings ### + settings = openmc.Settings() + settings.run_mode = 'fixed source' + settings.particles = 100 + settings.batches = 10 + + # source setup + r = openmc.stats.Uniform(a=0.0, b=0.0) + theta = openmc.stats.Discrete(x=[0.0], p=[1.0]) + phi = openmc.stats.Discrete(x=[0.0], p=[1.0]) + origin = (0.0, 0.0, 0.0) + + space = openmc.stats.SphericalIndependent(r=r, + theta=theta, + phi=phi, + origin=origin) + angle = openmc.stats.Monodirectional((-1.0, 0.0, 0.0)) + energy = openmc.stats.Discrete(x=[15.e+06], p=[1.0]) + source = openmc.Source(space=space, energy=energy, angle=angle) + settings.source = source + + model = openmc.model.Model(geometry=geometry, + materials=materials, + tallies=tallies, + settings=settings) + + harness = UnstructuredMeshTest('statepoint.10.h5', + model, + opts['inputs_true'], + holes=opts['holes']) harness.main() From b32514d7b88a27dbc2ea43fff29206b19e437a16 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 27 Mar 2020 12:12:06 -0500 Subject: [PATCH 204/205] Using clearer variable name for test parameter. --- .../regression_tests/unstructured_mesh/test.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index 3cd4deaf90..e2727cac74 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -22,7 +22,7 @@ class UnstructuredMeshTest(PyAPITestHarness): statepoint_name, model, inputs_true, - holes=None): + holes): super().__init__(statepoint_name, model=model, inputs_true=inputs_true) @@ -83,8 +83,8 @@ for i, (estimator, ext_geom, holes) in enumerate(product(*param_values)): 'inputs_true' : 'inputs_true{}.dat'.format(i)}) -@pytest.mark.parametrize("opts", test_cases) -def test_unstructured_mesh(opts): +@pytest.mark.parametrize("test_opts", test_cases) +def test_unstructured_mesh(test_opts): ### Materials ### materials = openmc.Materials() @@ -141,7 +141,7 @@ def test_unstructured_mesh(opts): +clad_min_z & -clad_max_z) clad_cell.fill = zirc_mat - if opts['external_geom']: + if test_opts['external_geom']: bounds = (15, 15, 15) else: bounds = (10, 10, 10) @@ -189,7 +189,7 @@ def test_unstructured_mesh(opts): regular_mesh_filter = openmc.MeshFilter(mesh=regular_mesh) - if opts['holes']: + if test_opts['holes']: mesh_filename = "test_mesh_tets_w_holes.h5m" else: mesh_filename = "test_mesh_tets.h5m" @@ -204,13 +204,13 @@ def test_unstructured_mesh(opts): regular_mesh_tally = openmc.Tally(name="regular mesh tally") regular_mesh_tally.filters = [regular_mesh_filter] regular_mesh_tally.scores = ['flux'] - regular_mesh_tally.estimator = opts['estimator'] + regular_mesh_tally.estimator = test_opts['estimator'] tallies.append(regular_mesh_tally) uscd_tally = openmc.Tally(name="unstructured mesh tally") uscd_tally.filters = [uscd_filter] uscd_tally.scores = ['flux'] - uscd_tally.estimator = opts['estimator'] + uscd_tally.estimator = test_opts['estimator'] tallies.append(uscd_tally) ### Settings ### @@ -241,6 +241,6 @@ def test_unstructured_mesh(opts): harness = UnstructuredMeshTest('statepoint.10.h5', model, - opts['inputs_true'], - holes=opts['holes']) + test_opts['inputs_true'], + test_opts['holes']) harness.main() From 0e3d58958e56b9c1532bd6522b6d50ac7eba1079 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 30 Mar 2020 10:35:33 -0500 Subject: [PATCH 205/205] Address PR suggestions and comments from @paulromano. --- include/openmc/mesh.h | 100 +++++++++--------- openmc/mesh.py | 15 ++- src/mesh.cpp | 12 +-- src/state_point.cpp | 6 +- .../unstructured_mesh/inputs_true0.dat | 2 +- .../unstructured_mesh/inputs_true1.dat | 2 +- .../unstructured_mesh/inputs_true2.dat | 2 +- .../unstructured_mesh/inputs_true3.dat | 2 +- .../unstructured_mesh/inputs_true4.dat | 2 +- .../unstructured_mesh/inputs_true5.dat | 2 +- .../unstructured_mesh/inputs_true6.dat | 2 +- .../unstructured_mesh/inputs_true7.dat | 2 +- .../unstructured_mesh/test.py | 15 +-- 13 files changed, 76 insertions(+), 88 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 6454563cc7..bb52cd8a59 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -260,6 +260,54 @@ public: std::vector& bins, std::vector& lengths) const override; + std::pair, std::vector> + plot(Position plot_ll, Position plot_ur) const override; + + //! Determine which surface bins were crossed by a particle. + // + //! \param[in] p Particle to check + //! \param[out] bins Surface bins that were crossed + void surface_bins_crossed(const Particle* p, std::vector& bins) const; + + //! Write mesh data to an HDF5 group. + // + //! \param[in] group HDF5 group + void to_hdf5(hid_t group) const; + + //! Get bin at a given position. + // + //! \param[in] r Position to get bin for + //! \return Mesh bin + int get_bin(Position r) const; + + int n_bins() const override; + + int n_surface_bins() const override; + + //! Retrieve a centroid for the mesh cell + // + // \param[in] tet MOAB EntityHandle of the tetrahedron + // \return The centroid of the element + Position centroid(moab::EntityHandle tet) const; + + //! Return a string represntation of the mesh bin + // + //! \param[in] bin Mesh bin to generate a label for + std::string bin_label(int bin) const override; + + //! Add a score to the mesh instance + void add_score(std::string score) const; + + //! Set data for a score + void set_score_data(const std::string& score, + std::vector values, + std::vector std_dev) const; + + //! Write the mesh with any current tally data + void write(std::string base_filename) const; + + std::string filename_; //!< Path to unstructured mesh file + private: //! Find all intersections with faces of the mesh. @@ -352,57 +400,7 @@ private: std::pair get_score_tags(std::string score) const; -public: - - std::pair, std::vector> - plot(Position plot_ll, Position plot_ur) const override; - - //! Determine which surface bins were crossed by a particle. - // - //! \param[in] p Particle to check - //! \param[out] bins Surface bins that were crossed - void surface_bins_crossed(const Particle* p, std::vector& bins) const; - - //! Write mesh data to an HDF5 group. - // - //! \param[in] group HDF5 group - void to_hdf5(hid_t group) const; - - //! Get bin at a given position. - // - //! \param[in] r Position to get bin for - //! \return Mesh bin - int get_bin(Position r) const; - - int n_bins() const override; - - int n_surface_bins() const override; - - //! Retrieve a centroid for the mesh cell - // - // \param[in] tet MOAB EntityHandle of the tetrahedron - // \return The centroid of the element - Position centroid(moab::EntityHandle tet) const; - - //! Return a string represntation of the mesh bin - // - //! \param[in] bin Mesh bin to generate a label for - std::string bin_label(int bin) const override; - - //! Add a score to the mesh instance - void add_score(std::string score) const; - - //! Set data for a score - void set_score_data(const std::string& score, - std::vector values, - std::vector std_dev) const; - - //! Write the mesh with any current tally data - void write(std::string base_filename) const; - - std::string filename_; //!< Path to unstructured mesh file - -private: + // data members moab::Range ehs_; //!< Range of tetrahedra EntityHandle's in the mesh moab::EntityHandle tetset_; //!< EntitySet containing all tetrahedra moab::EntityHandle kdtree_root_; //!< Root of the MOAB KDTree diff --git a/openmc/mesh.py b/openmc/mesh.py index dc48e1ca0f..fcc1f2ee2d 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -595,12 +595,12 @@ class UnstructuredMesh(MeshBase): Parameters ---------- + filename : str + Location of the unstructured mesh file mesh_id : int Unique identifier for the mesh name : str Name of the mesh - filename : str - Location of the unstructured mesh file Attributes ---------- @@ -608,7 +608,7 @@ class UnstructuredMesh(MeshBase): Unique identifier for the mesh name : str Name of the mesh - mesh_file : str + filename : str Name of the file containing the unstructured mesh volumes : Iterable of float Volumes of the unstructured mesh elements @@ -621,7 +621,7 @@ class UnstructuredMesh(MeshBase): def __init__(self, filename, mesh_id=None, name=''): super().__init__(mesh_id, name) - self._filename = filename + self.filename = filename self._volumes = [] self._centroids = [] @@ -631,8 +631,7 @@ class UnstructuredMesh(MeshBase): @filename.setter def filename(self, filename): - cv.check_type('Unstructured Mesh filename: {}'.format(filename), - filename, str) + cv.check_type('Unstructured Mesh filename', filename, str) self._filename = filename @property @@ -689,7 +688,7 @@ class UnstructuredMesh(MeshBase): element.set("id", str(self._id)) element.set("type", "unstructured") - subelement = ET.SubElement(element, "mesh_file") + subelement = ET.SubElement(element, "filename") subelement.text = self.filename return element @@ -709,7 +708,7 @@ class UnstructuredMesh(MeshBase): UnstructuredMesh generated from an XML element """ mesh_id = int(get_text(elem, 'id')) - filename = get_text(elem, 'mesh_file') + filename = get_text(elem, 'filename') mesh = cls(filename, mesh_id) diff --git a/src/mesh.cpp b/src/mesh.cpp index 5b4665cb73..f40ce30855 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1541,8 +1541,8 @@ UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) } // get the filename of the unstructured mesh to load - if (check_for_node(node, "mesh_file")) { - filename_ = get_node_value(node, "mesh_file"); + if (check_for_node(node, "filename")) { + filename_ = get_node_value(node, "filename"); } else { fatal_error("No filename supplied for unstructured mesh with ID: " + std::to_string(id_)); @@ -1652,7 +1652,8 @@ UnstructuredMesh::intersect_track(const moab::CartVect& start, void UnstructuredMesh::bins_crossed(const Particle* p, std::vector& bins, - std::vector& lengths) const { + std::vector& lengths) const +{ moab::ErrorCode rval; Position last_r{p->r_last_}; @@ -1855,9 +1856,8 @@ UnstructuredMesh::point_in_tet(const moab::CartVect& r, moab::EntityHandle tet) // first vertex is used as a reference point for the barycentric data - // retrieve its coordinates - moab::EntityHandle v_zero = verts[0]; moab::CartVect p_zero; - rval = mbi_->get_coords(&v_zero, 1, p_zero.array()); + rval = mbi_->get_coords(verts.data(), 1, p_zero.array()); if (rval != moab::MB_SUCCESS) { warning("Failed to get coordinates of a vertex in " "unstructured mesh: " + filename_); @@ -1956,7 +1956,7 @@ UnstructuredMesh::centroid(moab::EntityHandle tet) const { } // compute the centroid of the element vertices - moab::CartVect centroid(0.0); + moab::CartVect centroid(0.0, 0.0, 0.0); for(const auto& coord : coords) { centroid += coord; } diff --git a/src/state_point.cpp b/src/state_point.cpp index a74b1d6856..bf249aa983 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -702,7 +702,7 @@ void write_unstructured_mesh_results() { // warning and skip writing the mesh if (tally->filters().size() > 1) { warning(fmt::format("Skipping unstructured mesh writing for tally " - "{0}. More than one filter is present on the tally.", + "{}. More than one filter is present on the tally.", tally->id_)); break; } @@ -714,7 +714,7 @@ void write_unstructured_mesh_results() { for (int i_nuc = 0; i_nuc < tally->nuclides_.size(); i_nuc++) { // index for this nuclide and score - int nuc_score_idx = i_score + i_nuc * tally->scores_.size(); + int nuc_score_idx = i_score + i_nuc*tally->scores_.size(); // construct result vectors std::vector mean_vec, std_dev_vec; @@ -725,7 +725,7 @@ void write_unstructured_mesh_results() { // std. dev. double sum_sq = tally->results_(j , nuc_score_idx, TallyResult::SUM_SQ); if (n_realizations > 1) { - double std_dev = sum_sq / n_realizations - (mean* mean); + double std_dev = sum_sq/n_realizations - mean*mean; std_dev = std::sqrt(std_dev / (n_realizations - 1)); std_dev_vec.push_back(std_dev); } else { diff --git a/tests/regression_tests/unstructured_mesh/inputs_true0.dat b/tests/regression_tests/unstructured_mesh/inputs_true0.dat index e598b673bc..58d6f0b1ed 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true0.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true0.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets_w_holes.h5m + test_mesh_tets_w_holes.h5m 1 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true1.dat b/tests/regression_tests/unstructured_mesh/inputs_true1.dat index 36a1e643ed..1ccfda9ba0 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true1.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true1.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets.h5m + test_mesh_tets.h5m 3 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true2.dat b/tests/regression_tests/unstructured_mesh/inputs_true2.dat index 89a5ac549f..7f25eead89 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true2.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true2.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets_w_holes.h5m + test_mesh_tets_w_holes.h5m 5 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true3.dat b/tests/regression_tests/unstructured_mesh/inputs_true3.dat index 0f438ad2ae..d0e329b60f 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true3.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true3.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets.h5m + test_mesh_tets.h5m 7 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true4.dat b/tests/regression_tests/unstructured_mesh/inputs_true4.dat index a1d54134eb..d2fbe4197b 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true4.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true4.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets_w_holes.h5m + test_mesh_tets_w_holes.h5m 9 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true5.dat b/tests/regression_tests/unstructured_mesh/inputs_true5.dat index 5ea502526d..dd2d9d59bb 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true5.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true5.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets.h5m + test_mesh_tets.h5m 11 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true6.dat b/tests/regression_tests/unstructured_mesh/inputs_true6.dat index bd24cf4c2a..c14e9052b4 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true6.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true6.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets_w_holes.h5m + test_mesh_tets_w_holes.h5m 13 diff --git a/tests/regression_tests/unstructured_mesh/inputs_true7.dat b/tests/regression_tests/unstructured_mesh/inputs_true7.dat index d46313182c..6eff949061 100644 --- a/tests/regression_tests/unstructured_mesh/inputs_true7.dat +++ b/tests/regression_tests/unstructured_mesh/inputs_true7.dat @@ -71,7 +71,7 @@ 10.0 10.0 10.0 - test_mesh_tets.h5m + test_mesh_tets.h5m 15 diff --git a/tests/regression_tests/unstructured_mesh/test.py b/tests/regression_tests/unstructured_mesh/test.py index e2727cac74..f4461cd117 100644 --- a/tests/regression_tests/unstructured_mesh/test.py +++ b/tests/regression_tests/unstructured_mesh/test.py @@ -18,14 +18,9 @@ TETS_PER_VOXEL = 12 class UnstructuredMeshTest(PyAPITestHarness): - def __init__(self, - statepoint_name, - model, - inputs_true, - holes): - - super().__init__(statepoint_name, model=model, inputs_true=inputs_true) + def __init__(self, statepoint_name, model, inputs_true, holes): + super().__init__(statepoint_name, model, inputs_true) self.holes = holes # holes in the test mesh def _compare_results(self): @@ -223,12 +218,8 @@ def test_unstructured_mesh(test_opts): r = openmc.stats.Uniform(a=0.0, b=0.0) theta = openmc.stats.Discrete(x=[0.0], p=[1.0]) phi = openmc.stats.Discrete(x=[0.0], p=[1.0]) - origin = (0.0, 0.0, 0.0) - space = openmc.stats.SphericalIndependent(r=r, - theta=theta, - phi=phi, - origin=origin) + space = openmc.stats.SphericalIndependent(r, theta, phi) angle = openmc.stats.Monodirectional((-1.0, 0.0, 0.0)) energy = openmc.stats.Discrete(x=[15.e+06], p=[1.0]) source = openmc.Source(space=space, energy=energy, angle=angle)