From ed5505cd979b72ee6a32f5e4624ec614ef34e802 Mon Sep 17 00:00:00 2001 From: Will Boyd Date: Fri, 29 Apr 2016 22:13:41 -0400 Subject: [PATCH 01/13] Added option to transpose array returned by ScatterMatrixXS.get_xs(...) method --- openmc/mgxs/mgxs.py | 25 +++++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 33255de30..90b956b21 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -654,7 +654,8 @@ class MGXS(object): self.tallies[tally_type] = sp_tally def get_xs(self, groups='all', subdomains='all', nuclides='all', - xs_type='macro', order_groups='increasing', value='mean'): + xs_type='macro', order_groups='increasing', + value='mean', **kwargs): """Returns an array of multi-group cross sections. This method constructs a 2D NumPy array for the requested multi-group @@ -1143,7 +1144,7 @@ class MGXS(object): def build_hdf5_store(self, filename='mgxs.h5', directory='mgxs', subdomains='all', nuclides='all', - xs_type='macro', append=True): + xs_type='macro', row_column='inout', append=True): """Export the multi-group cross section data to an HDF5 binary file. This method constructs an HDF5 file which stores the multi-group @@ -1172,6 +1173,9 @@ class MGXS(object): xs_type: {'macro', 'micro'} Store the macro or micro cross section in units of cm^-1 or barns. Defaults to 'macro'. + row_column: {'inout', 'outin'} + Store scattering matrices indexed first by incoming group and second + by outgoing group ('inout'), or vice versa ('outin'). append : bool If true, appends to an existing HDF5 file with the same filename directory (if one exists). Defaults to True. @@ -1258,9 +1262,9 @@ class MGXS(object): # Extract the cross section for this subdomain and nuclide average = self.get_xs(subdomains=[subdomain], nuclides=[nuclide], - xs_type=xs_type, value='mean') + xs_type=xs_type, value='mean', row_column=row_column) std_dev = self.get_xs(subdomains=[subdomain], nuclides=[nuclide], - xs_type=xs_type, value='std_dev') + xs_type=xs_type, value='std_dev', row_column=row_column) average = average.squeeze() std_dev = std_dev.squeeze() @@ -1973,7 +1977,8 @@ class ScatterMatrixXS(MGXS): def get_xs(self, in_groups='all', out_groups='all', subdomains='all', nuclides='all', xs_type='macro', - order_groups='increasing', value='mean'): + order_groups='increasing', row_column='inout', + value='mean', **kwargs): """Returns an array of multi-group cross sections. This method constructs a 2D NumPy array for the requested scattering @@ -1999,6 +2004,9 @@ class ScatterMatrixXS(MGXS): Return the cross section indexed according to increasing or decreasing energy groups (decreasing or increasing energies). Defaults to 'increasing'. + row_column: {'inout', 'outin'} + Return the cross section indexed first by incoming group and second + by outgoing group ('inout'), or vice versa ('outin'). value : str A string for the type of value to return - 'mean', 'std_dev', or 'rel_err' are accepted. Defaults to the empty string. @@ -2092,6 +2100,10 @@ class ScatterMatrixXS(MGXS): new_shape += xs.shape[1:] xs = np.reshape(xs, new_shape) + # Transpose the scattering matrix if requested by user + if row_column == 'outin': + xs = np.swapaxes(xs, 1, 2) + # Reverse energies to align with increasing energy groups xs = xs[:, ::-1, ::-1, :] @@ -2422,7 +2434,8 @@ class Chi(MGXS): return merged_mgxs def get_xs(self, groups='all', subdomains='all', nuclides='all', - xs_type='macro', order_groups='increasing', value='mean'): + xs_type='macro', order_groups='increasing', + value='mean', **kwargs): """Returns an array of the fission spectrum. This method constructs a 2D NumPy array for the requested multi-group From 879728ea5d5b5b46c1f1b15b87357dc3acfa170c Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 3 May 2016 14:46:52 -0600 Subject: [PATCH 02/13] Add ability to expand natural elements in Python API --- openmc/data/__init__.py | 1 + openmc/data/data.py | 101 ++++++++++++++++++++++++++++++++++++++++ openmc/element.py | 21 +++++++++ openmc/material.py | 29 ++++++++++-- setup.py | 2 +- 5 files changed, 149 insertions(+), 5 deletions(-) create mode 100644 openmc/data/__init__.py create mode 100644 openmc/data/data.py diff --git a/openmc/data/__init__.py b/openmc/data/__init__.py new file mode 100644 index 000000000..df22d8bbb --- /dev/null +++ b/openmc/data/__init__.py @@ -0,0 +1 @@ +from .data import * diff --git a/openmc/data/data.py b/openmc/data/data.py new file mode 100644 index 000000000..c6dd81ba6 --- /dev/null +++ b/openmc/data/data.py @@ -0,0 +1,101 @@ +# Isotopic abundances from M. Berglund and M. E. Wieser, "Isotopic compositions +# of the elements 2009 (IUPAC Technical Report)", Pure. Appl. Chem. 83 (2), +# pp. 397--410 (2011). +natural_abundance = { + 'H-1': 0.999885, 'H-2': 0.000115, 'He-3': 1.34e-06, + 'He-4': 0.99999866, 'Li-6': 0.0759, 'Li-7': 0.9241, + 'Be-9': 1.0, 'B-10': 0.199, 'B-11': 0.801, + 'C-12': 0.9893, 'C-13': 0.0107, 'N-14': 0.99636, + 'N-15': 0.00364, 'O-16': 0.99757, 'O-17': 0.00038, + 'O-18': 0.00205, 'F-19': 1.0, 'Ne-20': 0.9048, + 'Ne-21': 0.0027, 'Ne-22': 0.0925, 'Na-23': 1.0, + 'Mg-24': 0.7899, 'Mg-25': 0.1, 'Mg-26': 0.1101, + 'Al-27': 1.0, 'Si-28': 0.92223, 'Si-29': 0.04685, + 'Si-30': 0.03092, 'P-31': 1.0, 'S-32': 0.9499, + 'S-33': 0.0075, 'S-34': 0.0425, 'S-36': 0.0001, + 'Cl-35': 0.7576, 'Cl-37': 0.2424, 'Ar-36': 0.003336, + 'Ar-38': 0.000629, 'Ar-40': 0.996035, 'K-39': 0.932581, + 'K-40': 0.000117, 'K-41': 0.067302, 'Ca-40': 0.96941, + 'Ca-42': 0.00647, 'Ca-43': 0.00135, 'Ca-44': 0.02086, + 'Ca-46': 4e-05, 'Ca-48': 0.00187, 'Sc-45': 1.0, + 'Ti-46': 0.0825, 'Ti-47': 0.0744, 'Ti-48': 0.7372, + 'Ti-49': 0.0541, 'Ti-50': 0.0518, 'V-50': 0.0025, + 'V-51': 0.9975, 'Cr-50': 0.04345, 'Cr-52': 0.83789, + 'Cr-53': 0.09501, 'Cr-54': 0.02365, 'Mn-55': 1.0, + 'Fe-54': 0.05845, 'Fe-56': 0.91754, 'Fe-57': 0.02119, + 'Fe-58': 0.00282, 'Co-59': 1.0, 'Ni-58': 0.68077, + 'Ni-60': 0.26223, 'Ni-61': 0.011399, 'Ni-62': 0.036346, + 'Ni-64': 0.009255, 'Cu-63': 0.6915, 'Cu-65': 0.3085, + 'Zn-64': 0.4917, 'Zn-66': 0.2773, 'Zn-67': 0.0404, + 'Zn-68': 0.1845, 'Zn-70': 0.0061, 'Ga-69': 0.60108, + 'Ga-71': 0.39892, 'Ge-70': 0.2057, 'Ge-72': 0.2745, + 'Ge-73': 0.0775, 'Ge-74': 0.365, 'Ge-76': 0.0773, + 'As-75': 1.0, 'Se-74': 0.0089, 'Se-76': 0.0937, + 'Se-77': 0.0763, 'Se-78': 0.2377, 'Se-80': 0.4961, + 'Se-82': 0.0873, 'Br-79': 0.5069, 'Br-81': 0.4931, + 'Kr-78': 0.00355, 'Kr-80': 0.02286, 'Kr-82': 0.11593, + 'Kr-83': 0.115, 'Kr-84': 0.56987, 'Kr-86': 0.17279, + 'Rb-85': 0.7217, 'Rb-87': 0.2783, 'Sr-84': 0.0056, + 'Sr-86': 0.0986, 'Sr-87': 0.07, 'Sr-88': 0.8258, + 'Y-89': 1.0, 'Zr-90': 0.5145, 'Zr-91': 0.1122, + 'Zr-92': 0.1715, 'Zr-94': 0.1738, 'Zr-96': 0.028, + 'Nb-93': 1.0, 'Mo-92': 0.1453, 'Mo-94': 0.0915, + 'Mo-95': 0.1584, 'Mo-96': 0.1667, 'Mo-97': 0.096, + 'Mo-98': 0.2439, 'Mo-100': 0.0982, 'Ru-96': 0.0554, + 'Ru-98': 0.0187, 'Ru-99': 0.1276, 'Ru-100': 0.126, + 'Ru-101': 0.1706, 'Ru-102': 0.3155, 'Ru-104': 0.1862, + 'Rh-103': 1.0, 'Pd-102': 0.0102, 'Pd-104': 0.1114, + 'Pd-105': 0.2233, 'Pd-106': 0.2733, 'Pd-108': 0.2646, + 'Pd-110': 0.1172, 'Ag-107': 0.51839, 'Ag-109': 0.48161, + 'Cd-106': 0.0125, 'Cd-108': 0.0089, 'Cd-110': 0.1249, + 'Cd-111': 0.128, 'Cd-112': 0.2413, 'Cd-113': 0.1222, + 'Cd-114': 0.2873, 'Cd-116': 0.0749, 'In-113': 0.0429, + 'In-115': 0.9571, 'Sn-112': 0.0097, 'Sn-114': 0.0066, + 'Sn-115': 0.0034, 'Sn-116': 0.1454, 'Sn-117': 0.0768, + 'Sn-118': 0.2422, 'Sn-119': 0.0859, 'Sn-120': 0.3258, + 'Sn-122': 0.0463, 'Sn-124': 0.0579, 'Sb-121': 0.5721, + 'Sb-123': 0.4279, 'Te-120': 0.0009, 'Te-122': 0.0255, + 'Te-123': 0.0089, 'Te-124': 0.0474, 'Te-125': 0.0707, + 'Te-126': 0.1884, 'Te-128': 0.3174, 'Te-130': 0.3408, + 'I-127': 1.0, 'Xe-124': 0.000952, 'Xe-126': 0.00089, + 'Xe-128': 0.019102, 'Xe-129': 0.264006, 'Xe-130': 0.04071, + 'Xe-131': 0.212324, 'Xe-132': 0.269086, 'Xe-134': 0.104357, + 'Xe-136': 0.088573, 'Cs-133': 1.0, 'Ba-130': 0.00106, + 'Ba-132': 0.00101, 'Ba-134': 0.02417, 'Ba-135': 0.06592, + 'Ba-136': 0.07854, 'Ba-137': 0.11232, 'Ba-138': 0.71698, + 'La-138': 0.0008881, 'La-139': 0.9991119, 'Ce-136': 0.00185, + 'Ce-138': 0.00251, 'Ce-140': 0.8845, 'Ce-142': 0.11114, + 'Pr-141': 1.0, 'Nd-142': 0.27152, 'Nd-143': 0.12174, + 'Nd-144': 0.23798, 'Nd-145': 0.08293, 'Nd-146': 0.17189, + 'Nd-148': 0.05756, 'Nd-150': 0.05638, 'Sm-144': 0.0307, + 'Sm-147': 0.1499, 'Sm-148': 0.1124, 'Sm-149': 0.1382, + 'Sm-150': 0.0738, 'Sm-152': 0.2675, 'Sm-154': 0.2275, + 'Eu-151': 0.4781, 'Eu-153': 0.5219, 'Gd-152': 0.002, + 'Gd-154': 0.0218, 'Gd-155': 0.148, 'Gd-156': 0.2047, + 'Gd-157': 0.1565, 'Gd-158': 0.2484, 'Gd-160': 0.2186, + 'Tb-159': 1.0, 'Dy-156': 0.00056, 'Dy-158': 0.00095, + 'Dy-160': 0.02329, 'Dy-161': 0.18889, 'Dy-162': 0.25475, + 'Dy-163': 0.24896, 'Dy-164': 0.2826, 'Ho-165': 1.0, + 'Er-162': 0.00139, 'Er-164': 0.01601, 'Er-166': 0.33503, + 'Er-167': 0.22869, 'Er-168': 0.26978, 'Er-170': 0.1491, + 'Tm-169': 1.0, 'Yb-168': 0.00123, 'Yb-170': 0.02982, + 'Yb-171': 0.1409, 'Yb-172': 0.2168, 'Yb-173': 0.16103, + 'Yb-174': 0.32026, 'Yb-176': 0.12996, 'Lu-175': 0.97401, + 'Lu-176': 0.02599, 'Hf-174': 0.0016, 'Hf-176': 0.0526, + 'Hf-177': 0.186, 'Hf-178': 0.2728, 'Hf-179': 0.1362, + 'Hf-180': 0.3508, 'Ta-180': 0.0001201, 'Ta-181': 0.9998799, + 'W-180': 0.0012, 'W-182': 0.265, 'W-183': 0.1431, + 'W-184': 0.3064, 'W-186': 0.2843, 'Re-185': 0.374, + 'Re-187': 0.626, 'Os-184': 0.0002, 'Os-186': 0.0159, + 'Os-187': 0.0196, 'Os-188': 0.1324, 'Os-189': 0.1615, + 'Os-190': 0.2626, 'Os-192': 0.4078, 'Ir-191': 0.373, + 'Ir-193': 0.627, 'Pt-190': 0.00012, 'Pt-192': 0.00782, + 'Pt-194': 0.3286, 'Pt-195': 0.3378, 'Pt-196': 0.2521, + 'Pt-198': 0.07356, 'Au-197': 1.0, 'Hg-196': 0.0015, + 'Hg-198': 0.0997, 'Hg-199': 0.1687, 'Hg-200': 0.231, + 'Hg-201': 0.1318, 'Hg-202': 0.2986, 'Hg-204': 0.0687, + 'Tl-203': 0.2952, 'Tl-205': 0.7048, 'Pb-204': 0.014, + 'Pb-206': 0.241, 'Pb-207': 0.221, 'Pb-208': 0.524, + 'Bi-209': 1.0, 'Th-232': 1.0, 'Pa-231': 1.0, + 'U-234': 5.4e-05, 'U-235': 0.007204, 'U-238': 0.992742 +} diff --git a/openmc/element.py b/openmc/element.py index 219aafbdf..39564add4 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -1,6 +1,8 @@ import sys +import openmc from openmc.checkvalue import check_type +from openmc.data import natural_abundance if sys.version_info[0] >= 3: basestring = str @@ -109,3 +111,22 @@ class Element(object): raise ValueError(msg) self._scattering = scattering + + def expand(self): + """Expand natural element into its naturally-occurring isotopes. + + Returns + ------- + isotopes : list + Naturally-occurring isotopes of the element. Each item of the list + is a tuple consisting of an openmc.Nuclide instance and the natural + abundance of the isotope. + + """ + + isotopes = [] + for isotope, abundance in natural_abundance.items(): + if isotope.startswith(self.name): + nuc = openmc.Nuclide(isotope, self.xs) + isotopes.append((nuc, abundance)) + return isotopes diff --git a/openmc/material.py b/openmc/material.py index b3c281341..c617015a3 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -10,6 +10,7 @@ if sys.version_info[0] >= 3: import openmc import openmc.checkvalue as cv from openmc.clean_xml import * +from openmc.data import natural_abundance # A static variable for auto-generated Material IDs @@ -382,7 +383,7 @@ class Material(object): if macroscopic._name == self._macroscopic.name: self._macroscopic = None - def add_element(self, element, percent, percent_type='ao'): + def add_element(self, element, percent, percent_type='ao', expand=False): """Add a natural element to the material Parameters @@ -391,8 +392,12 @@ class Material(object): Element to add percent : float Atom or weight percent - percent_type : {'ao', 'wo'} - 'ao' for atom percent and 'wo' for weight percent + percent_type : {'ao', 'wo'}, optional + 'ao' for atom percent and 'wo' for weight percent. Defaults to atom + percent. + expand : bool, optional + Whether to expand the natural element into its naturally-occurring + isotopes. Defaults to False. """ @@ -422,7 +427,15 @@ class Material(object): else: element = openmc.Element(element) - self._elements[element._name] = (element, percent, percent_type) + if expand: + if percent_type == 'wo': + raise NotImplementedError('Expanding natural element based on ' + 'weight percent is not yet supported.') + for isotope, abundance in element.expand(): + self._nuclides[isotope.name] = ( + isotope, percent*abundance, percent_type) + else: + self._elements[element.name] = (element, percent, percent_type) def remove_element(self, element): """Remove a natural element from the material @@ -491,6 +504,14 @@ class Material(object): density = nuclide_tuple[1] nuclides[nuclide._name] = (nuclide, density) + for element_name, element_tuple in self._elements.items(): + element = element_tuple[0] + density = element_tuple[1] + + # Expand natural element into isotopes + for isotope, abundance in element.expand(): + nuclides[isotope.name] = (isotope, density*abundance) + return nuclides def _get_nuclide_xml(self, nuclide, distrib=False): diff --git a/setup.py b/setup.py index e66b0b7a0..770f280ad 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ except ImportError: kwargs = {'name': 'openmc', 'version': '0.7.1', - 'packages': ['openmc', 'openmc.mgxs', 'openmc.stats'], + 'packages': ['openmc', 'openmc.data', 'openmc.mgxs', 'openmc.stats'], 'scripts': glob.glob('scripts/openmc-*'), # Metadata From 7eae7a629d5599f5f157ebf28aa6f9faf89583ba Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 4 May 2016 11:32:23 -0600 Subject: [PATCH 03/13] Add nuclides and elements properties on openmc.Material --- openmc/material.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/openmc/material.py b/openmc/material.py index c617015a3..ff690aa9a 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -50,6 +50,14 @@ class Material(object): Units used for `density`. Can be one of 'g/cm3', 'g/cc', 'kg/cm3', 'atom/b-cm', 'atom/cm3', 'sum', or 'macro'. The 'macro' unit only applies in the case of a multi-group calculation. + elements : collections.OrderedDict + Dictionary whose keys are element names and values are 3-tuples + consisting of an :class:`openmc.Element` instance, the percent density, + and the percent type (atom or weight fraction). + nuclides : collections.OrderedDict + Dictionary whose keys are nuclide names and values are 3-tuples + consisting of an :class:`openmc.Nuclide` instance, the percent density, + and the percent type (atom or weight fraction). """ @@ -187,6 +195,14 @@ class Material(object): def density_units(self): return self._density_units + @property + def elements(self): + return self._elements + + @property + def nuclides(self): + return self._nuclides + @property def convert_to_distrib_comps(self): return self._convert_to_distrib_comps From ff198abf3a704767f8195e002b8b5a4e6c2b4f04 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 2 May 2016 10:41:45 -0600 Subject: [PATCH 04/13] Improve constructors for Universe and Cell --- openmc/cell.py | 11 ++++++++++- openmc/universe.py | 10 ++++++---- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index ed1f3178b..37828d8fc 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -33,6 +33,10 @@ class Cell(object): automatically be assigned. name : str, optional Name of the cell. If not specified, the name is the empty string. + fill : openmc.Material or openmc.Universe or openmc.Lattice or 'void' or iterable of openmc.Material, optional + Indicates what the region of space is filled with + region : openmc.Region, optional + Region of space that is assigned to the cell. Attributes ---------- @@ -58,7 +62,7 @@ class Cell(object): """ - def __init__(self, cell_id=None, name=''): + def __init__(self, cell_id=None, name='', fill=None, region=None): # Initialize Cell class attributes self.id = cell_id self.name = name @@ -70,6 +74,11 @@ class Cell(object): self._offsets = None self._distribcell_index = None + if fill is not None: + self.fill = fill + if region is not None: + self.region = region + def __eq__(self, other): if not isinstance(other, Cell): return False diff --git a/openmc/universe.py b/openmc/universe.py index eb6d13233..8834eaa52 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -36,6 +36,8 @@ class Universe(object): automatically be assigned name : str, optional Name of the universe. If not specified, the name is the empty string. + cells : Iterable of openmc.Cell + Cells to add to the universe Attributes ---------- @@ -49,7 +51,7 @@ class Universe(object): """ - def __init__(self, universe_id=None, name=''): + def __init__(self, universe_id=None, name='', cells=None): # Initialize Cell class attributes self.id = universe_id self.name = name @@ -61,7 +63,9 @@ class Universe(object): # Keys - Cell IDs # Values - Offsets self._cell_offsets = OrderedDict() - self._num_regions = 0 + + if cells is not None: + self.add_cells(cells) def __eq__(self, other): if not isinstance(other, Universe): @@ -87,8 +91,6 @@ class Universe(object): string += '{0: <16}{1}{2}\n'.format('\tName', '=\t', self._name) string += '{0: <16}{1}{2}\n'.format('\tCells', '=\t', list(self._cells.keys())) - string += '{0: <16}{1}{2}\n'.format('\t# Regions', '=\t', - self._num_regions) return string @property From 744ed3c5f81712416a6144b5a7f598475dc17682 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 2 May 2016 10:42:53 -0600 Subject: [PATCH 05/13] Fix up docstrings for Lattice and its subclasses --- openmc/lattice.py | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/openmc/lattice.py b/openmc/lattice.py index baccbad90..f1e775920 100644 --- a/openmc/lattice.py +++ b/openmc/lattice.py @@ -32,11 +32,11 @@ class Lattice(object): Name of the lattice pitch : float Pitch of the lattice in cm - outer : int - The unique identifier of a universe to fill all space outside the - lattice - universes : numpy.ndarray of openmc.Universe - An array of universes filling each element of the lattice + outer : openmc.Universe + A universe to fill all space outside the lattice + universes : Iterable of Iterable of openmc.Universe + A two- or three-dimensional list/array of universes filling each element + of the lattice """ @@ -259,6 +259,13 @@ class RectLattice(Lattice): lower_left : Iterable of float The coordinates of the lower-left corner of the lattice. If the lattice is two-dimensional, only the x- and y-coordinates are specified. + pitch : float + Pitch of the lattice in cm + outer : openmc.Universe + A universe to fill all space outside the lattice + universes : Iterable of Iterable of openmc.Universe + A two- or three-dimensional list/array of universes filling each element + of the lattice """ @@ -505,6 +512,13 @@ class HexLattice(Lattice): center : Iterable of float Coordinates of the center of the lattice. If the lattice does not have axial sections then only the x- and y-coordinates are specified + pitch : float + Pitch of the lattice in cm + outer : openmc.Universe + A universe to fill all space outside the lattice + universes : Iterable of Iterable of openmc.Universe + A two- or three-dimensional list/array of universes filling each element + of the lattice """ From e1a1e081fd7e84c38ed74e1a96f4b02484a056c1 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 2 May 2016 10:50:56 -0600 Subject: [PATCH 06/13] Automatically link summary.h5 by default when present --- openmc/statepoint.py | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 7b75ac767..6c8af88a7 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -1,5 +1,6 @@ import sys import re +import os import numpy as np import openmc @@ -14,6 +15,14 @@ class StatePoint(object): of a given batch). Statepoints can be used to analyze tally results as well as restart a simulation. + Parameters + ---------- + filename : str + Path to file to load + autolink : bool, optional + Whether to automatically link in metadata from a summary.h5 + file. Defaults to True. + Attributes ---------- cmfd_on : bool @@ -93,7 +102,7 @@ class StatePoint(object): """ - def __init__(self, filename): + def __init__(self, filename, autolink=True): import h5py self._f = h5py.File(filename, 'r') @@ -116,10 +125,17 @@ class StatePoint(object): # Set flags for what data has been read self._meshes_read = False self._tallies_read = False - self._summary = False + self._summary = None self._global_tallies = None self._sparse = False + # Automatically link in a summary file if one exists + if autolink: + path_summary = os.path.join(os.path.dirname(filename), 'summary.h5') + if os.path.exists(path_summary): + su = openmc.Summary(path_summary) + self.link_with_summary(su) + def close(self): self._f.close() @@ -606,12 +622,17 @@ class StatePoint(object): Raises ------ + RuntimeError + If a Summary object has already been linked. ValueError An error when the argument passed to the 'summary' parameter is not an openmc.Summary object. """ + if self.summary is not None: + raise RuntimeError('A Summary object has already been linked.') + if not isinstance(summary, openmc.summary.Summary): msg = 'Unable to link statepoint with "{0}" which ' \ 'is not a Summary object'.format(summary) From 6558fd8a34032e143a34b9ffdcf06e9acb4ee0a7 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 3 May 2016 10:44:16 -0600 Subject: [PATCH 07/13] Fix reading hexagonal lattices in Summary --- openmc/summary.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/openmc/summary.py b/openmc/summary.py index 9b1c451f3..ea13284bd 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -378,11 +378,11 @@ class Summary(object): self.lattices[index] = lattice if lattice_type == 'hexagonal': - n_rings = self._f['geometry/lattices'][key]['n_rings'][0] - n_axial = self._f['geometry/lattices'][key]['n_axial'][0] + n_rings = self._f['geometry/lattices'][key]['n_rings'].value + n_axial = self._f['geometry/lattices'][key]['n_axial'].value center = self._f['geometry/lattices'][key]['center'][...] pitch = self._f['geometry/lattices'][key]['pitch'][...] - outer = self._f['geometry/lattices'][key]['outer'][0] + outer = self._f['geometry/lattices'][key]['outer'].value universe_ids = self._f[ 'geometry/lattices'][key]['universes'][...] From 5947bbefd2f833b03dfb78453cee296810a9ee4f Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 4 May 2016 14:09:12 -0600 Subject: [PATCH 08/13] Don't read eigenvalue-related data in summary.h5 if fixed source --- openmc/summary.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/openmc/summary.py b/openmc/summary.py index ea13284bd..34c51bc51 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -65,9 +65,10 @@ class Summary(object): self.n_batches = self._f['n_batches'].value self.n_particles = self._f['n_particles'].value - self.n_active = self._f['n_active'].value - self.n_inactive = self._f['n_inactive'].value - self.gen_per_batch = self._f['gen_per_batch'].value + if 'n_inactive' in self._f: + self.n_active = self._f['n_active'].value + self.n_inactive = self._f['n_inactive'].value + self.gen_per_batch = self._f['gen_per_batch'].value self.n_procs = self._f['n_procs'].value def _read_geometry(self): From 049b04d99595b4115ce3170dc7c83bec426423f6 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 5 May 2016 13:40:34 -0600 Subject: [PATCH 09/13] Fix tests which autolink summary metadata now --- tests/test_asymmetric_lattice/test_asymmetric_lattice.py | 9 ++------- .../test_mgxs_library_condense.py | 5 ----- .../test_mgxs_library_distribcell.py | 5 ----- tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py | 5 ----- .../test_mgxs_library_no_nuclides.py | 5 ----- .../test_mgxs_library_nuclides.py | 5 ----- tests/test_tally_aggregation/test_tally_aggregation.py | 5 ----- tests/test_tally_arithmetic/test_tally_arithmetic.py | 5 ----- tests/test_tally_slice_merge/test_tally_slice_merge.py | 5 ----- 9 files changed, 2 insertions(+), 47 deletions(-) diff --git a/tests/test_asymmetric_lattice/test_asymmetric_lattice.py b/tests/test_asymmetric_lattice/test_asymmetric_lattice.py index 03e55d32f..504cc4746 100644 --- a/tests/test_asymmetric_lattice/test_asymmetric_lattice.py +++ b/tests/test_asymmetric_lattice/test_asymmetric_lattice.py @@ -82,11 +82,6 @@ class AsymmetricLatticeTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Extract the tally of interest tally = sp.get_tally(name='distribcell tally') @@ -96,8 +91,8 @@ class AsymmetricLatticeTestHarness(PyAPITestHarness): outstr += ', '.join(map(str, tally.std_dev.flatten())) + '\n' # Extract fuel assembly lattices from the summary - core = su.get_cell_by_id(1) - fuel = su.get_cell_by_id(80) + core = sp.summary.get_cell_by_id(1) + fuel = sp.summary.get_cell_by_id(80) fuel = fuel.fill core = core.fill diff --git a/tests/test_mgxs_library_condense/test_mgxs_library_condense.py b/tests/test_mgxs_library_condense/test_mgxs_library_condense.py index 97bb853b6..3ca98904f 100644 --- a/tests/test_mgxs_library_condense/test_mgxs_library_condense.py +++ b/tests/test_mgxs_library_condense/test_mgxs_library_condense.py @@ -43,11 +43,6 @@ class MGXSTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Load the MGXS library from the statepoint self.mgxs_lib.load_from_statepoint(sp) diff --git a/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py b/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py index 681266186..d488e8ec9 100644 --- a/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py +++ b/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py @@ -46,11 +46,6 @@ class MGXSTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Load the MGXS library from the statepoint self.mgxs_lib.load_from_statepoint(sp) diff --git a/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py b/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py index 30be46b4c..91bb036e3 100644 --- a/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py +++ b/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py @@ -44,11 +44,6 @@ class MGXSTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Load the MGXS library from the statepoint self.mgxs_lib.load_from_statepoint(sp) diff --git a/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py b/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py index 381b5b87c..15f90cb87 100644 --- a/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py +++ b/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py @@ -43,11 +43,6 @@ class MGXSTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Load the MGXS library from the statepoint self.mgxs_lib.load_from_statepoint(sp) diff --git a/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py b/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py index c3e4f5f77..113f2aa41 100644 --- a/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py +++ b/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py @@ -43,11 +43,6 @@ class MGXSTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Load the MGXS library from the statepoint self.mgxs_lib.load_from_statepoint(sp) diff --git a/tests/test_tally_aggregation/test_tally_aggregation.py b/tests/test_tally_aggregation/test_tally_aggregation.py index 359afbe34..fdc086e68 100644 --- a/tests/test_tally_aggregation/test_tally_aggregation.py +++ b/tests/test_tally_aggregation/test_tally_aggregation.py @@ -43,11 +43,6 @@ class TallyAggregationTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Extract the tally of interest tally = sp.get_tally(name='distribcell tally') diff --git a/tests/test_tally_arithmetic/test_tally_arithmetic.py b/tests/test_tally_arithmetic/test_tally_arithmetic.py index 8e2d2b349..a5919909f 100644 --- a/tests/test_tally_arithmetic/test_tally_arithmetic.py +++ b/tests/test_tally_arithmetic/test_tally_arithmetic.py @@ -62,11 +62,6 @@ class TallyArithmeticTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Load the tallies tally_1 = sp.get_tally(name='tally 1') tally_2 = sp.get_tally(name='tally 2') diff --git a/tests/test_tally_slice_merge/test_tally_slice_merge.py b/tests/test_tally_slice_merge/test_tally_slice_merge.py index 85dd532c6..4dbb993d5 100644 --- a/tests/test_tally_slice_merge/test_tally_slice_merge.py +++ b/tests/test_tally_slice_merge/test_tally_slice_merge.py @@ -83,11 +83,6 @@ class TallySliceMergeTestHarness(PyAPITestHarness): statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0] sp = openmc.StatePoint(statepoint) - # Read the summary file. - summary = glob.glob(os.path.join(os.getcwd(), 'summary.h5'))[0] - su = openmc.Summary(summary) - sp.link_with_summary(su) - # Extract the cell tally tallies = [sp.get_tally(name='cell tally')] From 8923e1b8f2731cd21b66088c79916fa1c6ef3ad0 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 5 May 2016 15:14:26 -0600 Subject: [PATCH 10/13] Update Jupyter notebook examples --- .../pythonapi/examples/mgxs-part-i.ipynb | 124 ++- .../pythonapi/examples/mgxs-part-ii.ipynb | 690 ++++++++--------- .../pythonapi/examples/mgxs-part-iii.ipynb | 206 +++-- .../examples/pandas-dataframes.ipynb | 721 +++++++++--------- .../pythonapi/examples/post-processing.ipynb | 75 +- .../pythonapi/examples/tally-arithmetic.ipynb | 161 ++-- src/output.F90 | 2 +- 7 files changed, 927 insertions(+), 1052 deletions(-) diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index a450af97e..610e82ec1 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -417,24 +417,22 @@ "data": { "text/plain": [ "OrderedDict([('flux', Tally\n", - "\tID =\t10000\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n", - "), ('absorption', Tally\n", - "\tID =\t10001\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['absorption']\n", - "\tEstimator =\ttracklength\n", - ")])" + " \tID =\t10000\n", + " \tName =\t\n", + " \tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + " \tNuclides =\ttotal \n", + " \tScores =\t['flux']\n", + " \tEstimator =\ttracklength), ('absorption', Tally\n", + " \tID =\t10001\n", + " \tName =\t\n", + " \tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + " \tNuclides =\ttotal \n", + " \tScores =\t['absorption']\n", + " \tEstimator =\ttracklength)])" ] }, "execution_count": 13, @@ -508,12 +506,11 @@ " 888\n", " 888\n", "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", + " Copyright: 2011-2016 Massachusetts Institute of Technology\n", + " License: http://openmc.readthedocs.org/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: eeb5091ca3a34cc85df73a3318cae2b6c7097413\n", - " Date/Time: 2016-04-13 11:24:09\n", - " MPI Processes: 1\n", + " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", + " Date/Time: 2016-05-05 13:43:54\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -598,20 +595,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.6300E-01 seconds\n", - " Reading cross sections = 1.2100E-01 seconds\n", - " Total time in simulation = 1.6504E+01 seconds\n", - " Time in transport only = 1.6479E+01 seconds\n", - " Time in inactive batches = 1.9620E+00 seconds\n", - " Time in active batches = 1.4542E+01 seconds\n", - " Time synchronizing fission bank = 1.0000E-02 seconds\n", - " Sampling source sites = 4.0000E-03 seconds\n", - " SEND/RECV source sites = 3.0000E-03 seconds\n", + " Total time for initialization = 5.7300E-01 seconds\n", + " Reading cross sections = 1.7600E-01 seconds\n", + " Total time in simulation = 2.1188E+01 seconds\n", + " Time in transport only = 2.1173E+01 seconds\n", + " Time in inactive batches = 2.6880E+00 seconds\n", + " Time in active batches = 1.8500E+01 seconds\n", + " Time synchronizing fission bank = 3.0000E-03 seconds\n", + " Sampling source sites = 2.0000E-03 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 1.6977E+01 seconds\n", - " Calculation Rate (inactive) = 12742.1 neutrons/second\n", - " Calculation Rate (active) = 6876.63 neutrons/second\n", + " Total time elapsed = 2.1776E+01 seconds\n", + " Calculation Rate (inactive) = 9300.60 neutrons/second\n", + " Calculation Rate (active) = 5405.41 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -669,20 +666,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. This is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a `Summary` object and link it with the statepoint." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the summary file and link it with the statepoint\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" + "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data." ] }, { @@ -694,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -729,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -764,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -811,7 +795,7 @@ "0 1 2 total 1.292013 0.007642" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -830,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -848,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -875,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -932,7 +916,7 @@ "1 (((total / flux) - (absorption / flux)) - (sca... 1.44e-15 2.57e-03 " ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -954,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -1011,7 +995,7 @@ "1 ((absorption / flux) / (total / flux)) 1.93e-02 9.46e-05 " ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1026,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1083,7 +1067,7 @@ "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.74e-03 " ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1105,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -1135,7 +1119,7 @@ " 6.250000e-07\n", " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", - " 1\n", + " 1.0\n", " 0.007763\n", " \n", " \n", @@ -1145,7 +1129,7 @@ " 2.000000e+01\n", " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", - " 1\n", + " 1.0\n", " 0.003739\n", " \n", " \n", @@ -1162,7 +1146,7 @@ "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.74e-03 " ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1178,21 +1162,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.1" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index 793d88436..fd8d09052 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -13,7 +13,7 @@ "* The use of **[PyNE](http://pyne.io/) to plot** continuous-energy vs. multi-group cross sections\n", "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n", "\n", - "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)." + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data." ] }, { @@ -34,16 +34,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:884: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", - " warnings.warn(self.msg_depr % (key, alt_key))\n", - "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:1362: UserWarning: This call to matplotlib.use() has no effect\n", + "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", " warnings.warn(_use_error_msg)\n", - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.rxname is not yet QA compliant.\n", - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.ace is not yet QA compliant.\n" + "/home/romano/miniconda3/envs/default/lib/python3.5/importlib/_bootstrap.py:222: QAWarning: pyne.rxname is not yet QA compliant.\n", + " return f(*args, **kwds)\n", + "/home/romano/miniconda3/envs/default/lib/python3.5/importlib/_bootstrap.py:222: QAWarning: pyne.ace is not yet QA compliant.\n", + " return f(*args, **kwds)\n" ] } ], @@ -442,12 +442,11 @@ " 888\n", " 888\n", "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", + " Copyright: 2011-2016 Massachusetts Institute of Technology\n", + " License: http://openmc.readthedocs.org/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: eeb5091ca3a34cc85df73a3318cae2b6c7097413\n", - " Date/Time: 2016-04-13 11:59:39\n", - " MPI Processes: 1\n", + " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", + " Date/Time: 2016-05-05 15:00:51\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -523,7 +522,7 @@ " 48/1 1.21610 1.22612 +/- 0.00251\n", " 49/1 1.22199 1.22602 +/- 0.00245\n", " 50/1 1.20860 1.22558 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10050\n", + " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10056\n", " The estimated number of batches is 73\n", " Creating state point statepoint.050.h5...\n", " 51/1 1.21850 1.22541 +/- 0.00237\n", @@ -549,7 +548,7 @@ " 71/1 1.19720 1.22444 +/- 0.00195\n", " 72/1 1.23770 1.22465 +/- 0.00193\n", " 73/1 1.23894 1.22488 +/- 0.00191\n", - " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10050\n", + " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10056\n", " The estimated number of batches is 74\n", " 74/1 1.22437 1.22487 +/- 0.00188\n", " Triggers satisfied for batch 74\n", @@ -562,20 +561,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.0100E-01 seconds\n", - " Reading cross sections = 8.8000E-02 seconds\n", - " Total time in simulation = 2.3897E+02 seconds\n", - " Time in transport only = 2.3892E+02 seconds\n", - " Time in inactive batches = 1.6456E+01 seconds\n", - " Time in active batches = 2.2251E+02 seconds\n", - " Time synchronizing fission bank = 1.8000E-02 seconds\n", - " Sampling source sites = 1.3000E-02 seconds\n", - " SEND/RECV source sites = 4.0000E-03 seconds\n", + " Total time for initialization = 3.8600E-01 seconds\n", + " Reading cross sections = 1.1000E-01 seconds\n", + " Total time in simulation = 2.3697E+02 seconds\n", + " Time in transport only = 2.3690E+02 seconds\n", + " Time in inactive batches = 1.5640E+01 seconds\n", + " Time in active batches = 2.2133E+02 seconds\n", + " Time synchronizing fission bank = 3.0000E-02 seconds\n", + " Sampling source sites = 1.9000E-02 seconds\n", + " SEND/RECV source sites = 1.1000E-02 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.2000E-02 seconds\n", - " Total time elapsed = 2.3943E+02 seconds\n", - " Calculation Rate (inactive) = 6076.81 neutrons/second\n", - " Calculation Rate (active) = 1797.66 neutrons/second\n", + " Total time for finalization = 1.0000E-02 seconds\n", + " Total time elapsed = 2.3743E+02 seconds\n", + " Calculation Rate (inactive) = 6393.86 neutrons/second\n", + " Calculation Rate (active) = 1807.26 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -629,26 +628,6 @@ "sp = openmc.StatePoint('statepoint.074.h5')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. This is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a `Summary` object and link it with the statepoint." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the summary file and link it with the statepoint\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -658,7 +637,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -693,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -747,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -789,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -919,7 +898,7 @@ "119 10002 1 5 O-16 0.000000 0.000000" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -939,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -961,7 +940,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -1000,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -1083,7 +1062,7 @@ "2 10000 2 O-16 3.794859 0.011139" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1109,14 +1088,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create an OpenMOC Geometry from the OpenCG Geometry\n", - "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)" + "openmoc_geometry = get_openmoc_geometry(sp.summary.opencg_geometry)" ] }, { @@ -1128,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1173,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -1187,81 +1166,81 @@ "[ NORMAL ] Iteration 0:\tk_eff = 0.574672\tres = 0.000E+00\n", "[ NORMAL ] Iteration 1:\tk_eff = 0.679815\tres = 4.253E-01\n", "[ NORMAL ] Iteration 2:\tk_eff = 0.660826\tres = 1.830E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.658940\tres = 2.793E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.853E-03\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.658941\tres = 2.793E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.852E-03\n", "[ NORMAL ] Iteration 5:\tk_eff = 0.625810\tres = 2.417E-02\n", "[ NORMAL ] Iteration 6:\tk_eff = 0.606678\tres = 2.675E-02\n", "[ NORMAL ] Iteration 7:\tk_eff = 0.587485\tres = 3.057E-02\n", "[ NORMAL ] Iteration 8:\tk_eff = 0.569029\tres = 3.164E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.551707\tres = 3.142E-02\n", + "[ NORMAL ] Iteration 9:\tk_eff = 0.551708\tres = 3.142E-02\n", "[ NORMAL ] Iteration 10:\tk_eff = 0.536035\tres = 3.044E-02\n", "[ NORMAL ] Iteration 11:\tk_eff = 0.522275\tres = 2.841E-02\n", "[ NORMAL ] Iteration 12:\tk_eff = 0.510610\tres = 2.567E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.501106\tres = 2.234E-02\n", + "[ NORMAL ] Iteration 13:\tk_eff = 0.501107\tres = 2.233E-02\n", "[ NORMAL ] Iteration 14:\tk_eff = 0.493832\tres = 1.861E-02\n", "[ NORMAL ] Iteration 15:\tk_eff = 0.488781\tres = 1.452E-02\n", "[ NORMAL ] Iteration 16:\tk_eff = 0.485924\tres = 1.023E-02\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.485211\tres = 5.846E-03\n", - "[ NORMAL ] Iteration 18:\tk_eff = 0.486571\tres = 1.467E-03\n", - "[ NORMAL ] Iteration 19:\tk_eff = 0.489905\tres = 2.802E-03\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.495105\tres = 6.853E-03\n", + "[ NORMAL ] Iteration 17:\tk_eff = 0.485212\tres = 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get_openmoc_geometry(sp.summary.opencg_geometry)\n", "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", "\n", "# Inject multi-group cross sections into OpenMOC Materials\n", @@ -1444,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1456,14 +1435,14 @@ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", "[ NORMAL ] Iteration 0:\tk_eff = 0.495816\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.557477\tres = 5.042E-01\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.557478\tres = 5.042E-01\n", "[ NORMAL ] Iteration 2:\tk_eff = 0.518301\tres = 1.244E-01\n", "[ NORMAL ] Iteration 3:\tk_eff = 0.509212\tres = 7.027E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.496489\tres = 1.754E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.496490\tres = 1.754E-02\n", "[ NORMAL ] Iteration 5:\tk_eff = 0.488581\tres = 2.498E-02\n", "[ NORMAL ] Iteration 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1.138E-05\n", - "[ NORMAL ] Iteration 228:\tk_eff = 1.223234\tres = 1.094E-05\n", - "[ NORMAL ] Iteration 229:\tk_eff = 1.223246\tres = 1.052E-05\n", - "[ NORMAL ] Iteration 230:\tk_eff = 1.223258\tres = 1.012E-05\n" + "[ NORMAL ] Iteration 166:\tk_eff = 1.219937\tres = 1.223E-04\n", + "[ NORMAL ] Iteration 167:\tk_eff = 1.220075\tres = 1.174E-04\n", + "[ NORMAL ] Iteration 168:\tk_eff = 1.220207\tres = 1.131E-04\n", + "[ NORMAL ] Iteration 169:\tk_eff = 1.220335\tres = 1.085E-04\n", + "[ NORMAL ] Iteration 170:\tk_eff = 1.220458\tres = 1.044E-04\n", + "[ NORMAL ] Iteration 171:\tk_eff = 1.220576\tres = 1.008E-04\n", + "[ NORMAL ] Iteration 172:\tk_eff = 1.220689\tres = 9.680E-05\n", + "[ NORMAL ] Iteration 173:\tk_eff = 1.220799\tres = 9.304E-05\n", + "[ NORMAL ] Iteration 174:\tk_eff = 1.220904\tres = 8.975E-05\n", + "[ NORMAL ] Iteration 175:\tk_eff = 1.221006\tres = 8.643E-05\n", + "[ NORMAL ] Iteration 176:\tk_eff = 1.221104\tres = 8.326E-05\n", + "[ NORMAL ] Iteration 177:\tk_eff = 1.221197\tres = 7.985E-05\n", + "[ NORMAL ] Iteration 178:\tk_eff = 1.221287\tres = 7.662E-05\n", + "[ NORMAL ] Iteration 179:\tk_eff = 1.221374\tres = 7.371E-05\n", + "[ NORMAL ] Iteration 180:\tk_eff = 1.221457\tres = 7.101E-05\n", + "[ NORMAL ] Iteration 181:\tk_eff = 1.221538\tres = 6.821E-05\n", + "[ NORMAL ] Iteration 182:\tk_eff = 1.221615\tres = 6.577E-05\n", + "[ NORMAL ] Iteration 183:\tk_eff = 1.221689\tres = 6.307E-05\n", + "[ NORMAL ] Iteration 184:\tk_eff = 1.221760\tres = 6.069E-05\n", + "[ NORMAL ] Iteration 185:\tk_eff = 1.221829\tres = 5.831E-05\n", + "[ NORMAL ] Iteration 186:\tk_eff = 1.221895\tres = 5.641E-05\n", + "[ NORMAL ] Iteration 187:\tk_eff = 1.221958\tres = 5.382E-05\n", + "[ NORMAL ] Iteration 188:\tk_eff = 1.222019\tres = 5.206E-05\n", + "[ NORMAL ] Iteration 189:\tk_eff = 1.222077\tres = 4.967E-05\n", + "[ NORMAL ] Iteration 190:\tk_eff = 1.222134\tres = 4.811E-05\n", + "[ NORMAL ] Iteration 191:\tk_eff = 1.222188\tres = 4.618E-05\n", + "[ NORMAL ] Iteration 192:\tk_eff = 1.222241\tres = 4.456E-05\n", + "[ NORMAL ] Iteration 193:\tk_eff = 1.222291\tres = 4.293E-05\n", + "[ NORMAL ] Iteration 194:\tk_eff = 1.222339\tres = 4.122E-05\n", + "[ NORMAL ] Iteration 195:\tk_eff = 1.222386\tres = 3.982E-05\n", + "[ NORMAL ] Iteration 196:\tk_eff = 1.222431\tres = 3.788E-05\n", + "[ NORMAL ] Iteration 197:\tk_eff = 1.222474\tres = 3.663E-05\n", + "[ NORMAL ] Iteration 198:\tk_eff = 1.222515\tres = 3.522E-05\n", + "[ NORMAL ] Iteration 199:\tk_eff = 1.222555\tres = 3.381E-05\n", + "[ NORMAL ] Iteration 200:\tk_eff = 1.222593\tres = 3.264E-05\n", + "[ NORMAL ] Iteration 201:\tk_eff = 1.222629\tres = 3.117E-05\n", + "[ NORMAL ] Iteration 202:\tk_eff = 1.222665\tres = 2.985E-05\n", + "[ NORMAL ] Iteration 203:\tk_eff = 1.222699\tres = 2.901E-05\n", + "[ NORMAL ] Iteration 204:\tk_eff = 1.222732\tres = 2.770E-05\n", + "[ NORMAL ] Iteration 205:\tk_eff = 1.222763\tres = 2.679E-05\n", + "[ NORMAL ] Iteration 206:\tk_eff = 1.222793\tres = 2.560E-05\n", + "[ NORMAL ] Iteration 207:\tk_eff = 1.222823\tres = 2.487E-05\n", + "[ NORMAL ] Iteration 208:\tk_eff = 1.222851\tres = 2.412E-05\n", + "[ NORMAL ] Iteration 209:\tk_eff = 1.222878\tres = 2.298E-05\n", + "[ NORMAL ] Iteration 210:\tk_eff = 1.222904\tres = 2.194E-05\n", + "[ NORMAL ] Iteration 211:\tk_eff = 1.222929\tres = 2.131E-05\n", + "[ NORMAL ] Iteration 212:\tk_eff = 1.222953\tres = 2.030E-05\n", + "[ NORMAL ] Iteration 213:\tk_eff = 1.222976\tres = 1.963E-05\n", + "[ NORMAL ] Iteration 214:\tk_eff = 1.222998\tres = 1.889E-05\n", + "[ NORMAL ] Iteration 215:\tk_eff = 1.223020\tres = 1.812E-05\n", + "[ NORMAL ] Iteration 216:\tk_eff = 1.223041\tres = 1.764E-05\n", + "[ NORMAL ] Iteration 217:\tk_eff = 1.223060\tres = 1.691E-05\n", + "[ NORMAL ] Iteration 218:\tk_eff = 1.223079\tres = 1.629E-05\n", + "[ NORMAL ] Iteration 219:\tk_eff = 1.223098\tres = 1.571E-05\n", + "[ NORMAL ] Iteration 220:\tk_eff = 1.223115\tres = 1.508E-05\n", + "[ NORMAL ] Iteration 221:\tk_eff = 1.223132\tres = 1.429E-05\n", + "[ NORMAL ] Iteration 222:\tk_eff = 1.223149\tres = 1.394E-05\n", + "[ NORMAL ] Iteration 223:\tk_eff = 1.223165\tres = 1.330E-05\n", + "[ NORMAL ] Iteration 224:\tk_eff = 1.223180\tres = 1.299E-05\n", + "[ NORMAL ] Iteration 225:\tk_eff = 1.223194\tres = 1.241E-05\n", + "[ NORMAL ] Iteration 226:\tk_eff = 1.223208\tres = 1.167E-05\n", + "[ NORMAL ] Iteration 227:\tk_eff = 1.223221\tres = 1.151E-05\n", + "[ NORMAL ] Iteration 228:\tk_eff = 1.223234\tres = 1.073E-05\n", + "[ NORMAL ] Iteration 229:\tk_eff = 1.223246\tres = 1.051E-05\n", + "[ NORMAL ] Iteration 230:\tk_eff = 1.223258\tres = 1.000E-05\n" ] } ], @@ -1701,7 +1680,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1758,7 +1737,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -1784,7 +1763,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -1795,15 +1774,15 @@ "(9.9999999999999994e-12, 20.0)" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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QJiJHAEOBeSJSCOyS3GKpXGezwaRJzc+lXLvWxpIlmb+bbaKXz6irs7XoeUol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\n+KP1PP8hsU6nNWku3KqsyVy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GugNni0h3YC/ge+9p2bHgi1I7oVMnz05tMxruRl5S4uHw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\nCqHJQSmlVAhNDkoppUL8Pzlt5uQccjZkAAAAAElFTkSuQmCC\n", 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s//SZaStbolLd0ZyNidDkt7hJQUQeUNXTRGQ2zn0J4e0AqGonmuHGpEqwvEub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\nMFCsqjsBE3BulItm/z4mpXw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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1845,7 +1824,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -1877,16 +1856,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1913,34 +1892,25 @@ "# Show the plot on screen\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.1" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb index 34190371b..c39f21dfa 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb @@ -11,7 +11,7 @@ "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n", "* Steady-state pin-by-pin **fission rates comparison** between OpenMC and [OpenMOC](https://mit-crpg.github.io/OpenMOC/)\n", "\n", - "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)." + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data." ] }, { @@ -32,7 +32,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:1362: UserWarning: This call to matplotlib.use() has no effect\n", + "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", @@ -459,7 +459,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -721,12 +721,11 @@ " 888\n", " 888\n", "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", + " Copyright: 2011-2016 Massachusetts Institute of Technology\n", + " License: http://openmc.readthedocs.org/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: eeb5091ca3a34cc85df73a3318cae2b6c7097413\n", - " Date/Time: 2016-04-13 11:57:40\n", - " MPI Processes: 1\n", + " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", + " Date/Time: 2016-05-05 15:06:49\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -812,20 +811,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.3700E-01 seconds\n", - " Reading cross sections = 8.2000E-02 seconds\n", - " Total time in simulation = 4.7745E+01 seconds\n", - " Time in transport only = 4.7726E+01 seconds\n", - " Time in inactive batches = 3.8220E+00 seconds\n", - " Time in active batches = 4.3923E+01 seconds\n", + " Total time for initialization = 4.1500E-01 seconds\n", + " Reading cross sections = 1.1800E-01 seconds\n", + " Total time in simulation = 5.3686E+01 seconds\n", + " Time in transport only = 5.3657E+01 seconds\n", + " Time in inactive batches = 4.3970E+00 seconds\n", + " Time in active batches = 4.9289E+01 seconds\n", " Time synchronizing fission bank = 3.0000E-03 seconds\n", " Sampling source sites = 2.0000E-03 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 1.0000E-03 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 4.8198E+01 seconds\n", - " Calculation Rate (inactive) = 6541.08 neutrons/second\n", - " Calculation Rate (active) = 2276.71 neutrons/second\n", + " Total time elapsed = 5.4118E+01 seconds\n", + " Calculation Rate (inactive) = 5685.70 neutrons/second\n", + " Calculation Rate (active) = 2028.85 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -879,25 +878,6 @@ "sp = openmc.StatePoint('statepoint.50.h5')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. This is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a `Summary` object and link it with the statepoint." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -907,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -942,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -961,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -970,7 +950,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.1-py2.7.egg/openmc/tallies.py:1996: RuntimeWarning: invalid value encountered in true_divide\n" + "/home/romano/openmc/openmc/tallies.py:1996: RuntimeWarning: invalid value encountered in true_divide\n", + " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, { @@ -1051,7 +1032,7 @@ "2 10000 2 O-16 0.000000e+00 0.000000e+00" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1070,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -1116,7 +1097,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -1135,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "collapsed": true }, @@ -1147,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -1166,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "collapsed": true }, @@ -1181,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -1237,7 +1218,7 @@ "2 10000 1 O-16 0.000000 0.000000" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1266,7 +1247,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -1285,7 +1266,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -1304,7 +1285,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "collapsed": false, "scrolled": true @@ -1318,12 +1299,12 @@ "[ NORMAL ] Computing the eigenvalue...\n", "[ NORMAL ] Iteration 0:\tk_eff = 0.854370\tres = 0.000E+00\n", "[ NORMAL ] Iteration 1:\tk_eff = 0.801922\tres = 1.521E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.761746\tres = 6.349E-02\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.761745\tres = 6.349E-02\n", "[ NORMAL ] Iteration 3:\tk_eff = 0.732367\tres = 5.029E-02\n", "[ NORMAL ] Iteration 4:\tk_eff = 0.711075\tres = 3.869E-02\n", "[ NORMAL ] Iteration 5:\tk_eff = 0.696557\tres = 2.912E-02\n", "[ NORMAL ] Iteration 6:\tk_eff = 0.687673\tres = 2.044E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.683470\tres = 1.277E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.683469\tres = 1.277E-02\n", "[ NORMAL ] Iteration 8:\tk_eff = 0.683129\tres = 6.141E-03\n", "[ NORMAL ] Iteration 9:\tk_eff = 0.685949\tres = 7.889E-04\n", "[ NORMAL ] Iteration 10:\tk_eff = 0.691329\tres = 4.181E-03\n", @@ -1336,11 +1317,11 @@ "[ NORMAL ] Iteration 17:\tk_eff = 0.765800\tres = 1.655E-02\n", "[ NORMAL ] Iteration 18:\tk_eff = 0.778371\tres = 1.660E-02\n", "[ NORMAL ] Iteration 19:\tk_eff = 0.790897\tres = 1.643E-02\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.803273\tres = 1.611E-02\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.815415\tres = 1.566E-02\n", + "[ NORMAL ] Iteration 20:\tk_eff = 0.803272\tres = 1.611E-02\n", + "[ NORMAL ] Iteration 21:\tk_eff = 0.815414\tres = 1.566E-02\n", "[ NORMAL ] Iteration 22:\tk_eff = 0.827256\tres = 1.513E-02\n", "[ NORMAL ] Iteration 23:\tk_eff = 0.838747\tres = 1.453E-02\n", - "[ NORMAL ] Iteration 24:\tk_eff = 0.849847\tres = 1.390E-02\n", + "[ NORMAL ] Iteration 24:\tk_eff = 0.849846\tres = 1.390E-02\n", "[ NORMAL ] Iteration 25:\tk_eff = 0.860527\tres = 1.324E-02\n", "[ NORMAL ] Iteration 26:\tk_eff = 0.870770\tres = 1.258E-02\n", "[ NORMAL ] Iteration 27:\tk_eff = 0.880562\tres = 1.191E-02\n", @@ -1362,8 +1343,8 @@ "[ NORMAL ] Iteration 43:\tk_eff = 0.981021\tres = 4.104E-03\n", "[ NORMAL ] Iteration 44:\tk_eff = 0.984493\tres = 3.814E-03\n", "[ NORMAL ] Iteration 45:\tk_eff = 0.987729\tres = 3.543E-03\n", - "[ NORMAL ] Iteration 46:\tk_eff = 0.990742\tres = 3.290E-03\n", - "[ NORMAL ] Iteration 47:\tk_eff = 0.993546\tres = 3.053E-03\n", + "[ NORMAL ] Iteration 46:\tk_eff = 0.990741\tres = 3.290E-03\n", + "[ NORMAL ] Iteration 47:\tk_eff = 0.993545\tres = 3.053E-03\n", "[ NORMAL ] Iteration 48:\tk_eff = 0.996153\tres = 2.833E-03\n", "[ NORMAL ] Iteration 49:\tk_eff = 0.998577\tres = 2.627E-03\n", "[ NORMAL ] Iteration 50:\tk_eff = 1.000829\tres = 2.436E-03\n", @@ -1377,63 +1358,63 @@ "[ NORMAL ] Iteration 58:\tk_eff = 1.013868\tres = 1.314E-03\n", "[ NORMAL ] Iteration 59:\tk_eff = 1.015006\tres = 1.215E-03\n", "[ NORMAL ] Iteration 60:\tk_eff = 1.016059\tres = 1.124E-03\n", - "[ NORMAL ] Iteration 61:\tk_eff = 1.017033\tres = 1.039E-03\n", + "[ NORMAL ] Iteration 61:\tk_eff = 1.017033\tres = 1.038E-03\n", "[ NORMAL ] Iteration 62:\tk_eff = 1.017933\tres = 9.596E-04\n", "[ NORMAL ] Iteration 63:\tk_eff = 1.018766\tres = 8.865E-04\n", "[ NORMAL ] Iteration 64:\tk_eff = 1.019535\tres = 8.188E-04\n", - "[ NORMAL ] Iteration 65:\tk_eff = 1.020246\tres = 7.562E-04\n", + "[ NORMAL ] Iteration 65:\tk_eff = 1.020246\tres = 7.561E-04\n", "[ NORMAL ] Iteration 66:\tk_eff = 1.020903\tres = 6.981E-04\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.021509\tres = 6.445E-04\n", + "[ NORMAL ] Iteration 67:\tk_eff = 1.021509\tres = 6.444E-04\n", "[ NORMAL ] Iteration 68:\tk_eff = 1.022069\tres = 5.948E-04\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.022586\tres = 5.489E-04\n", + "[ NORMAL ] Iteration 69:\tk_eff = 1.022586\tres = 5.488E-04\n", "[ NORMAL ] Iteration 70:\tk_eff = 1.023063\tres = 5.064E-04\n", "[ NORMAL ] Iteration 71:\tk_eff = 1.023503\tres = 4.671E-04\n", "[ NORMAL ] Iteration 72:\tk_eff = 1.023909\tres = 4.308E-04\n", "[ NORMAL ] Iteration 73:\tk_eff = 1.024284\tres = 3.973E-04\n", "[ NORMAL ] Iteration 74:\tk_eff = 1.024629\tres = 3.663E-04\n", - "[ NORMAL ] Iteration 75:\tk_eff = 1.024948\tres = 3.377E-04\n", + "[ NORMAL ] Iteration 75:\tk_eff = 1.024947\tres = 3.377E-04\n", "[ NORMAL ] Iteration 76:\tk_eff = 1.025241\tres = 3.113E-04\n", "[ NORMAL ] Iteration 77:\tk_eff = 1.025512\tres = 2.869E-04\n", "[ NORMAL ] Iteration 78:\tk_eff = 1.025761\tres = 2.644E-04\n", "[ NORMAL ] Iteration 79:\tk_eff = 1.025991\tres = 2.436E-04\n", "[ NORMAL ] Iteration 80:\tk_eff = 1.026203\tres = 2.244E-04\n", "[ NORMAL ] Iteration 81:\tk_eff = 1.026398\tres = 2.067E-04\n", - "[ NORMAL ] Iteration 82:\tk_eff = 1.026578\tres = 1.904E-04\n", - "[ NORMAL ] Iteration 83:\tk_eff = 1.026743\tres = 1.754E-04\n", + "[ NORMAL ] Iteration 82:\tk_eff = 1.026577\tres = 1.904E-04\n", + "[ NORMAL ] Iteration 83:\tk_eff = 1.026743\tres = 1.753E-04\n", "[ NORMAL ] Iteration 84:\tk_eff = 1.026895\tres = 1.615E-04\n", "[ NORMAL ] Iteration 85:\tk_eff = 1.027036\tres = 1.487E-04\n", "[ NORMAL ] Iteration 86:\tk_eff = 1.027165\tres = 1.369E-04\n", "[ NORMAL ] Iteration 87:\tk_eff = 1.027284\tres = 1.260E-04\n", "[ NORMAL ] Iteration 88:\tk_eff = 1.027393\tres = 1.160E-04\n", - "[ NORMAL ] Iteration 89:\tk_eff = 1.027494\tres = 1.068E-04\n", - "[ NORMAL ] Iteration 90:\tk_eff = 1.027587\tres = 9.825E-05\n", - "[ NORMAL ] Iteration 91:\tk_eff = 1.027672\tres = 9.041E-05\n", - "[ NORMAL ] Iteration 92:\tk_eff = 1.027751\tres = 8.319E-05\n", - "[ NORMAL ] Iteration 93:\tk_eff = 1.027823\tres = 7.654E-05\n", - "[ NORMAL ] Iteration 94:\tk_eff = 1.027889\tres = 7.042E-05\n", - "[ NORMAL ] Iteration 95:\tk_eff = 1.027950\tres = 6.478E-05\n", - "[ NORMAL ] Iteration 96:\tk_eff = 1.028007\tres = 5.959E-05\n", - "[ NORMAL ] Iteration 97:\tk_eff = 1.028058\tres = 5.481E-05\n", - "[ NORMAL ] Iteration 98:\tk_eff = 1.028106\tres = 5.041E-05\n", - "[ NORMAL ] Iteration 99:\tk_eff = 1.028150\tres = 4.636E-05\n", - "[ NORMAL ] Iteration 100:\tk_eff = 1.028190\tres = 4.263E-05\n", - "[ NORMAL ] Iteration 101:\tk_eff = 1.028227\tres = 3.920E-05\n", - "[ NORMAL ] Iteration 102:\tk_eff = 1.028261\tres = 3.604E-05\n", - "[ NORMAL ] Iteration 103:\tk_eff = 1.028292\tres = 3.314E-05\n", - "[ NORMAL ] Iteration 104:\tk_eff = 1.028321\tres = 3.047E-05\n", - "[ NORMAL ] Iteration 105:\tk_eff = 1.028347\tres = 2.801E-05\n", - "[ NORMAL ] Iteration 106:\tk_eff = 1.028371\tres = 2.575E-05\n", - "[ NORMAL ] Iteration 107:\tk_eff = 1.028394\tres = 2.367E-05\n", - "[ NORMAL ] Iteration 108:\tk_eff = 1.028414\tres = 2.175E-05\n", - "[ NORMAL ] Iteration 109:\tk_eff = 1.028433\tres = 1.999E-05\n", - "[ NORMAL ] Iteration 110:\tk_eff = 1.028450\tres = 1.838E-05\n", + "[ NORMAL ] Iteration 89:\tk_eff = 1.027493\tres = 1.067E-04\n", + "[ NORMAL ] Iteration 90:\tk_eff = 1.027586\tres = 9.824E-05\n", + "[ NORMAL ] Iteration 91:\tk_eff = 1.027671\tres = 9.043E-05\n", + "[ NORMAL ] Iteration 92:\tk_eff = 1.027750\tres = 8.318E-05\n", + "[ NORMAL ] Iteration 93:\tk_eff = 1.027822\tres = 7.654E-05\n", + "[ NORMAL ] Iteration 94:\tk_eff = 1.027889\tres = 7.041E-05\n", + "[ NORMAL ] Iteration 95:\tk_eff = 1.027950\tres = 6.481E-05\n", + "[ NORMAL ] Iteration 96:\tk_eff = 1.028006\tres = 5.960E-05\n", + "[ NORMAL ] Iteration 97:\tk_eff = 1.028058\tres = 5.480E-05\n", + "[ NORMAL ] Iteration 98:\tk_eff = 1.028105\tres = 5.043E-05\n", + "[ NORMAL ] Iteration 99:\tk_eff = 1.028149\tres = 4.634E-05\n", + "[ NORMAL ] Iteration 100:\tk_eff = 1.028189\tres = 4.266E-05\n", + "[ NORMAL ] Iteration 101:\tk_eff = 1.028226\tres = 3.920E-05\n", + "[ NORMAL ] Iteration 102:\tk_eff = 1.028260\tres = 3.604E-05\n", + "[ NORMAL ] Iteration 103:\tk_eff = 1.028291\tres = 3.316E-05\n", + "[ NORMAL ] Iteration 104:\tk_eff = 1.028320\tres = 3.047E-05\n", + "[ NORMAL ] Iteration 105:\tk_eff = 1.028347\tres = 2.800E-05\n", + "[ NORMAL ] Iteration 106:\tk_eff = 1.028371\tres = 2.576E-05\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.028393\tres = 2.367E-05\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.028414\tres = 2.176E-05\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.028433\tres = 2.003E-05\n", + "[ NORMAL ] Iteration 110:\tk_eff = 1.028450\tres = 1.836E-05\n", "[ NORMAL ] Iteration 111:\tk_eff = 1.028466\tres = 1.689E-05\n", - "[ NORMAL ] Iteration 112:\tk_eff = 1.028481\tres = 1.552E-05\n", - "[ NORMAL ] Iteration 113:\tk_eff = 1.028494\tres = 1.426E-05\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.028507\tres = 1.310E-05\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.028518\tres = 1.204E-05\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.028528\tres = 1.106E-05\n", - "[ NORMAL ] Iteration 117:\tk_eff = 1.028538\tres = 1.017E-05\n" + "[ NORMAL ] Iteration 112:\tk_eff = 1.028481\tres = 1.553E-05\n", + "[ NORMAL ] Iteration 113:\tk_eff = 1.028494\tres = 1.427E-05\n", + "[ NORMAL ] Iteration 114:\tk_eff = 1.028507\tres = 1.309E-05\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.028518\tres = 1.202E-05\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.028528\tres = 1.107E-05\n", + "[ NORMAL ] Iteration 117:\tk_eff = 1.028538\tres = 1.015E-05\n" ] } ], @@ -1456,7 +1437,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": { "collapsed": false }, @@ -1509,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -1535,7 +1516,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": { "collapsed": false }, @@ -1567,7 +1548,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": { "collapsed": false }, @@ -1575,18 +1556,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 44, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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Mpk37fDP7hZn9q5nt0TKLhJh45NuibRnpglGfBy52dzezvwM+A7yvmfCCxQPH\nvVVN6JsDGgNtsr48LbMtsLLOz0rpqqbVSZHGyEBD49OJ83tH9FRwZ7mhL9C+HiJlMGAVO60PouJ+\nLnymlFHRCPvgVljcqmupZli+fX7huK90rrQuWiWR9tjIulmRBaOqynm0lB5qoaLhsC0gM1JdZfeK\n6JoRkIkEw0i3T3mn9fsrZCZX5D0KLOk/v2hR0/JHFLTdvfjofpmsD6Yp1x09cDzijsgXAnYdmJbZ\nsjgtM6XCC8odkZ2R5dkiHZGJXqZDI3qa0GBjOaJUEfHI8r2rwI4MlNNk1caGjsjAl57dG9A1DIbr\n258rHNexIxLq1xEJ9eqIhBF0RM6dy0Hd3ZXnos0jRqGdz8z2KZx7B9D8a0GI9ka+LWpF8k3bzLqA\nU4BZZvYEcBFwqpmdQPb+tgz44BjaKMSYIN8WdSQZtN29syL7qjGwRYhxRb4t6sj47FyzU0njTqXz\nLwuUEehpsYfSMru8PS3z7psb05Oeh85y70JgksmOB9IycxI9JKFdfSoaK20SWPHuvjRQzqsDMpE2\n5EjPWVWn8Toae2givUcTTLHbYnspXXaZkdKqdub9K/JmNskfikinZ6QNOVJOVYCaESy/SKRTuFX1\nXG6vnlyRl+ruquqo7EfT2IUQokYoaAshRI1Q0BZCiBqhoC2EEDVi3IP2g5FOqjbjwUiPSZvxYGAy\nUrvx4Ja0TDvz2EQbMAKemGgDRkDdbC7POh0t4x60F9cwaC+uYdBeHJnb22YsVtAed+oWAKF+Ni9J\niwwLNY8IIUSNGJ9x2i+bN3D82BJ42WGN5w8IlBFZXCGyTschAZnjSukHlsBxJZsj5QSYlBpAekSg\nkKrFtDYugd8p2BxZVyRyHyKLZRwUkKlaC2X5EjiiYPNQg1X7uf2+gNDYMX3egG9PXrKE6YcN2B9Z\nECkyFD1SDZF1M6rKmbJkCbsddljFmeZExjxHbB5pOSOxObL0Tnn6SKtkJi9Zwk4le1Nr/+50xBHQ\nZO2R5M41o8XMxlaB+K1npDvXjBb5thhrqnx7zIO2EEKI1qE2bSGEqBEK2kIIUSPGNWib2ZvM7CEz\n+7WZfWw8dY8UM1tmZg+Y2f1mVt7Upi0wsyvNbI2Z/bKQN9PMbjKzh83sxnbaNquJvReZ2Qozuy//\ne9NE2jgc5NdjQ938GsbHt8ctaJvZJLKNPs4AjgHeaWZHjZf+UdAHnOLuL3f3+RNtTBOqdhX/OHCL\nux8J3AobYXIlAAABsElEQVRcOO5WNedFswu6/HpMqZtfwzj49ni+ac8HHnH3x919O3At8NZx1D9S\njDZvRmqyq/hbgavz46uBt42rUUPwItsFXX49RtTNr2F8fHs8b9r+NK6ivILhL+U7EThws5ndY2bv\nn2hjhsFsd18D4O5PApGVuSeaOu6CLr8eX+ro19BC327rb9o24WR3nwe8Gfiwmb12og0aIe0+tvPz\nwEvd/QTgSbJd0MXYIb8eP1rq2+MZtFfSOFfugDyvrXH31fn/p4HryX4O14E1ZjYHfrNZbZP9z9sD\nd3/aByYNfBl41UTaMwzk1+NLrfwaWu/b4xm07wEON7ODzWwacC7w/XHUP2zMbBcz2zU/ngG8kfbd\nnbthV3Gyuj0vP34PcMN4G5TgxbILuvx6bKmbX8MY+/b4rD0CuHuvmZ0P3ET2ZXGluy8eL/0jZA5w\nfT5deQrwDXe/aYJtGkSTXcUvA75jZu8FHgfOmTgLG3kx7YIuvx476ubXMD6+rWnsQghRI9QRKYQQ\nNUJBWwghaoSCthBC1AgFbSGEqBEK2kIIUSMUtIUQokYoaAshRI1Q0BZCiBrx314M3U2ye2u1AAAA\nAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1604,34 +1585,25 @@ "plt.imshow(openmoc_fission_rates, interpolation='none', cmap='jet')\n", "plt.title('OpenMOC Fission Rates')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.1" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index d5e8b9861..ea71055a7 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -20,7 +20,7 @@ "%matplotlib inline\n", "import glob\n", "from IPython.display import Image\n", - "import matplotlib.pylab as pylab\n", + "import matplotlib.pyplot as plt\n", "import scipy.stats\n", "import numpy as np\n", "\n", @@ -370,7 +370,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -551,12 +551,11 @@ " 888\n", " 888\n", "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", + " Copyright: 2011-2016 Massachusetts Institute of Technology\n", + " License: http://openmc.readthedocs.org/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: eeb5091ca3a34cc85df73a3318cae2b6c7097413\n", - " Date/Time: 2016-04-13 11:40:02\n", - " MPI Processes: 1\n", + " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", + " Date/Time: 2016-05-05 14:39:34\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -619,20 +618,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.7900E-01 seconds\n", - " Reading cross sections = 8.6000E-02 seconds\n", - " Total time in simulation = 8.7310E+00 seconds\n", - " Time in transport only = 8.7200E+00 seconds\n", - " Time in inactive batches = 1.3230E+00 seconds\n", - " Time in active batches = 7.4080E+00 seconds\n", - " Time synchronizing fission bank = 2.0000E-03 seconds\n", - " Sampling source sites = 1.0000E-03 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", + " Total time for initialization = 4.6600E-01 seconds\n", + " Reading cross sections = 1.1100E-01 seconds\n", + " Total time in simulation = 1.1106E+01 seconds\n", + " Time in transport only = 1.1089E+01 seconds\n", + " Time in inactive batches = 1.7090E+00 seconds\n", + " Time in active batches = 9.3970E+00 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 3.0000E-03 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 9.1240E+00 seconds\n", - " Calculation Rate (inactive) = 9448.22 neutrons/second\n", - " Calculation Rate (active) = 5062.10 neutrons/second\n", + " Total time elapsed = 1.1590E+01 seconds\n", + " Calculation Rate (inactive) = 7314.22 neutrons/second\n", + " Calculation Rate (active) = 3990.64 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -686,20 +685,6 @@ "sp = openmc.StatePoint(statepoints[-1])" ] }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "# Load the summary file and link with statepoint\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -709,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -725,7 +710,7 @@ " \t\tmesh\t[1]\n", " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", "\tNuclides =\ttotal \n", - "\tScores =\t[u'fission', u'nu-fission']\n", + "\tScores =\t['fission', 'nu-fission']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -748,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -757,13 +742,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.1508711 ]]\n", + "[[[ 0.1501735 ]]\n", "\n", - " [[ 0.05389822]]\n", + " [[ 0.05936257]]\n", "\n", - " [[ 0.19633 ]]\n", + " [[ 0.21402727]]\n", "\n", - " [[ 0.12963172]]]\n" + " [[ 0.13436703]]]\n" ] } ], @@ -778,7 +763,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -819,8 +804,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 2.34e-04\n", - " 3.54e-05\n", + " 2.20e-04\n", + " 3.31e-05\n", " \n", " \n", " 1\n", @@ -830,8 +815,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 5.71e-04\n", - " 8.62e-05\n", + " 5.37e-04\n", + " 8.06e-05\n", " \n", " \n", " 2\n", @@ -841,8 +826,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 7.03e-05\n", - " 7.05e-06\n", + " 7.43e-05\n", + " 7.91e-06\n", " \n", " \n", " 3\n", @@ -852,8 +837,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 1.87e-04\n", - " 1.76e-05\n", + " 1.97e-04\n", + " 1.96e-05\n", " \n", " \n", " 4\n", @@ -863,8 +848,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 3.67e-04\n", - " 3.61e-05\n", + " 3.52e-04\n", + " 3.39e-05\n", " \n", " \n", " 5\n", @@ -874,8 +859,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 8.94e-04\n", - " 8.80e-05\n", + " 8.57e-04\n", + " 8.26e-05\n", " \n", " \n", " 6\n", @@ -885,8 +870,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.04e-04\n", - " 5.36e-06\n", + " 1.02e-04\n", + " 6.16e-06\n", " \n", " \n", " 7\n", @@ -896,8 +881,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 2.76e-04\n", - " 1.40e-05\n", + " 2.70e-04\n", + " 1.61e-05\n", " \n", " \n", " 8\n", @@ -907,8 +892,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.04e-04\n", - " 5.57e-05\n", + " 6.09e-04\n", + " 6.55e-05\n", " \n", " \n", " 9\n", @@ -918,8 +903,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.47e-03\n", - " 1.36e-04\n", + " 1.48e-03\n", + " 1.60e-04\n", " \n", " \n", " 10\n", @@ -929,8 +914,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.41e-04\n", - " 6.69e-06\n", + " 1.38e-04\n", + " 6.74e-06\n", " \n", " \n", " 11\n", @@ -940,8 +925,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 3.72e-04\n", - " 1.82e-05\n", + " 3.65e-04\n", + " 1.88e-05\n", " \n", " \n", " 12\n", @@ -951,8 +936,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.45e-04\n", - " 4.59e-05\n", + " 6.23e-04\n", + " 5.16e-05\n", " \n", " \n", " 13\n", @@ -962,8 +947,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.57e-03\n", - " 1.12e-04\n", + " 1.52e-03\n", + " 1.26e-04\n", " \n", " \n", " 14\n", @@ -973,8 +958,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.82e-04\n", - " 9.37e-06\n", + " 1.74e-04\n", + " 9.99e-06\n", " \n", " \n", " 15\n", @@ -984,8 +969,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.76e-04\n", - " 2.47e-05\n", + " 4.58e-04\n", + " 2.68e-05\n", " \n", " \n", " 16\n", @@ -995,8 +980,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 7.28e-04\n", - " 7.49e-05\n", + " 6.94e-04\n", + " 8.68e-05\n", " \n", " \n", " 17\n", @@ -1006,8 +991,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.77e-03\n", - " 1.83e-04\n", + " 1.69e-03\n", + " 2.12e-04\n", " \n", " \n", " 18\n", @@ -1017,8 +1002,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.81e-04\n", - " 1.04e-05\n", + " 1.75e-04\n", + " 1.10e-05\n", " \n", " \n", " 19\n", @@ -1028,8 +1013,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.72e-04\n", - " 2.67e-05\n", + " 4.55e-04\n", + " 2.80e-05\n", " \n", " \n", "\n", @@ -1038,52 +1023,52 @@ "text/plain": [ " mesh 1 energy low [MeV] energy high [MeV] score mean \\\n", " x y z \n", - "0 1 1 1 0.00e+00 6.25e-07 fission 2.34e-04 \n", - "1 1 1 1 0.00e+00 6.25e-07 nu-fission 5.71e-04 \n", - "2 1 1 1 6.25e-07 2.00e+01 fission 7.03e-05 \n", - "3 1 1 1 6.25e-07 2.00e+01 nu-fission 1.87e-04 \n", - "4 1 2 1 0.00e+00 6.25e-07 fission 3.67e-04 \n", - "5 1 2 1 0.00e+00 6.25e-07 nu-fission 8.94e-04 \n", - "6 1 2 1 6.25e-07 2.00e+01 fission 1.04e-04 \n", - "7 1 2 1 6.25e-07 2.00e+01 nu-fission 2.76e-04 \n", - "8 1 3 1 0.00e+00 6.25e-07 fission 6.04e-04 \n", - "9 1 3 1 0.00e+00 6.25e-07 nu-fission 1.47e-03 \n", - "10 1 3 1 6.25e-07 2.00e+01 fission 1.41e-04 \n", - "11 1 3 1 6.25e-07 2.00e+01 nu-fission 3.72e-04 \n", - "12 1 4 1 0.00e+00 6.25e-07 fission 6.45e-04 \n", - "13 1 4 1 0.00e+00 6.25e-07 nu-fission 1.57e-03 \n", - "14 1 4 1 6.25e-07 2.00e+01 fission 1.82e-04 \n", - "15 1 4 1 6.25e-07 2.00e+01 nu-fission 4.76e-04 \n", - "16 1 5 1 0.00e+00 6.25e-07 fission 7.28e-04 \n", - "17 1 5 1 0.00e+00 6.25e-07 nu-fission 1.77e-03 \n", - "18 1 5 1 6.25e-07 2.00e+01 fission 1.81e-04 \n", - "19 1 5 1 6.25e-07 2.00e+01 nu-fission 4.72e-04 \n", + "0 1 1 1 0.00e+00 6.25e-07 fission 2.20e-04 \n", + "1 1 1 1 0.00e+00 6.25e-07 nu-fission 5.37e-04 \n", + "2 1 1 1 6.25e-07 2.00e+01 fission 7.43e-05 \n", + "3 1 1 1 6.25e-07 2.00e+01 nu-fission 1.97e-04 \n", + "4 1 2 1 0.00e+00 6.25e-07 fission 3.52e-04 \n", + "5 1 2 1 0.00e+00 6.25e-07 nu-fission 8.57e-04 \n", + "6 1 2 1 6.25e-07 2.00e+01 fission 1.02e-04 \n", + "7 1 2 1 6.25e-07 2.00e+01 nu-fission 2.70e-04 \n", + "8 1 3 1 0.00e+00 6.25e-07 fission 6.09e-04 \n", + "9 1 3 1 0.00e+00 6.25e-07 nu-fission 1.48e-03 \n", + "10 1 3 1 6.25e-07 2.00e+01 fission 1.38e-04 \n", + "11 1 3 1 6.25e-07 2.00e+01 nu-fission 3.65e-04 \n", + "12 1 4 1 0.00e+00 6.25e-07 fission 6.23e-04 \n", + "13 1 4 1 0.00e+00 6.25e-07 nu-fission 1.52e-03 \n", + "14 1 4 1 6.25e-07 2.00e+01 fission 1.74e-04 \n", + "15 1 4 1 6.25e-07 2.00e+01 nu-fission 4.58e-04 \n", + "16 1 5 1 0.00e+00 6.25e-07 fission 6.94e-04 \n", + "17 1 5 1 0.00e+00 6.25e-07 nu-fission 1.69e-03 \n", + "18 1 5 1 6.25e-07 2.00e+01 fission 1.75e-04 \n", + "19 1 5 1 6.25e-07 2.00e+01 nu-fission 4.55e-04 \n", "\n", " std. dev. \n", " \n", - "0 3.54e-05 \n", - "1 8.62e-05 \n", - "2 7.05e-06 \n", - "3 1.76e-05 \n", - "4 3.61e-05 \n", - "5 8.80e-05 \n", - "6 5.36e-06 \n", - "7 1.40e-05 \n", - "8 5.57e-05 \n", - "9 1.36e-04 \n", - "10 6.69e-06 \n", - "11 1.82e-05 \n", - "12 4.59e-05 \n", - "13 1.12e-04 \n", - "14 9.37e-06 \n", - "15 2.47e-05 \n", - "16 7.49e-05 \n", - "17 1.83e-04 \n", - "18 1.04e-05 \n", - "19 2.67e-05 " + "0 3.31e-05 \n", + "1 8.06e-05 \n", + "2 7.91e-06 \n", + "3 1.96e-05 \n", + "4 3.39e-05 \n", + "5 8.26e-05 \n", + "6 6.16e-06 \n", + "7 1.61e-05 \n", + "8 6.55e-05 \n", + "9 1.60e-04 \n", + "10 6.74e-06 \n", + "11 1.88e-05 \n", + "12 5.16e-05 \n", + "13 1.26e-04 \n", + "14 9.99e-06 \n", + "15 2.68e-05 \n", + "16 8.68e-05 \n", + "17 2.12e-04 \n", + "18 1.10e-05 \n", + "19 2.80e-05 " ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1102,16 +1087,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1126,7 +1111,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -1134,18 +1119,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1160,11 +1145,11 @@ "# Extract mean and reshape as 2D NumPy arrays\n", "mean = fiss['mean'].reshape((17,17))\n", "\n", - "pylab.imshow(mean, interpolation='nearest')\n", - "pylab.title('fission rate')\n", - "pylab.xlabel('x')\n", - "pylab.ylabel('y')\n", - "pylab.colorbar()" + "plt.imshow(mean, interpolation='nearest')\n", + "plt.title('fission rate')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.colorbar()" ] }, { @@ -1176,7 +1161,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1191,7 +1176,7 @@ "\tFilters =\t\n", " \t\tcell\t[10000]\n", "\tNuclides =\tU-235 U-238 \n", - "\tScores =\t[u'scatter-Y0,0', u'scatter-Y1,-1', u'scatter-Y1,0', u'scatter-Y1,1', u'scatter-Y2,-2', u'scatter-Y2,-1', u'scatter-Y2,0', u'scatter-Y2,1', u'scatter-Y2,2']\n", + "\tScores =\t['scatter-Y0,0', 'scatter-Y1,-1', 'scatter-Y1,0', 'scatter-Y1,1', 'scatter-Y2,-2', 'scatter-Y2,-1', 'scatter-Y2,0', 'scatter-Y2,1', 'scatter-Y2,2']\n", "\tEstimator =\tanalog\n", "\n" ] @@ -1207,7 +1192,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1233,144 +1218,144 @@ " 10000\n", " U-235\n", " scatter-Y0,0\n", - " 3.86e-02\n", - " 1.11e-03\n", + " 3.84e-02\n", + " 1.32e-03\n", " \n", " \n", " 1\n", " 10000\n", " U-235\n", " scatter-Y1,-1\n", - " 2.75e-04\n", - " 2.96e-04\n", + " 3.61e-04\n", + " 3.13e-04\n", " \n", " \n", " 2\n", " 10000\n", " U-235\n", " scatter-Y1,0\n", - " -5.55e-05\n", - " 4.33e-04\n", + " -2.38e-04\n", + " 4.69e-04\n", " \n", " \n", " 3\n", " 10000\n", " U-235\n", " scatter-Y1,1\n", - " -4.22e-04\n", - " 3.51e-04\n", + " -5.08e-04\n", + " 3.83e-04\n", " \n", " \n", " 4\n", " 10000\n", " U-235\n", " scatter-Y2,-2\n", - " 5.88e-05\n", - " 2.04e-04\n", + " 6.68e-05\n", + " 2.46e-04\n", " \n", " \n", " 5\n", " 10000\n", " U-235\n", " scatter-Y2,-1\n", - " 1.00e-04\n", - " 2.49e-04\n", + " 6.47e-06\n", + " 2.84e-04\n", " \n", " \n", " 6\n", " 10000\n", " U-235\n", " scatter-Y2,0\n", - " -8.09e-05\n", - " 1.59e-04\n", + " -1.41e-04\n", + " 1.75e-04\n", " \n", " \n", " 7\n", " 10000\n", " U-235\n", " scatter-Y2,1\n", - " 1.93e-04\n", - " 2.14e-04\n", + " 1.61e-04\n", + " 2.33e-04\n", " \n", " \n", " 8\n", " 10000\n", " U-235\n", " scatter-Y2,2\n", - " 1.12e-04\n", - " 1.86e-04\n", + " -1.80e-05\n", + " 1.97e-04\n", " \n", " \n", " 9\n", " 10000\n", " U-238\n", " scatter-Y0,0\n", - " 2.34e+00\n", - " 1.34e-02\n", + " 2.33e+00\n", + " 1.35e-02\n", " \n", " \n", " 10\n", " 10000\n", " U-238\n", " scatter-Y1,-1\n", - " 2.32e-02\n", - " 2.97e-03\n", + " 2.53e-02\n", + " 3.23e-03\n", " \n", " \n", " 11\n", " 10000\n", " U-238\n", " scatter-Y1,0\n", - " 7.50e-04\n", - " 2.55e-03\n", + " 7.10e-04\n", + " 2.92e-03\n", " \n", " \n", " 12\n", " 10000\n", " U-238\n", " scatter-Y1,1\n", - " -2.73e-02\n", - " 3.28e-03\n", + " -2.49e-02\n", + " 3.52e-03\n", " \n", " \n", " 13\n", " 10000\n", " U-238\n", " scatter-Y2,-2\n", - " -2.36e-03\n", - " 1.21e-03\n", + " -1.43e-03\n", + " 1.17e-03\n", " \n", " \n", " 14\n", " 10000\n", " U-238\n", " scatter-Y2,-1\n", - " -1.80e-04\n", - " 1.49e-03\n", + " 6.84e-04\n", + " 1.63e-03\n", " \n", " \n", " 15\n", " 10000\n", " U-238\n", " scatter-Y2,0\n", - " 3.23e-03\n", - " 2.25e-03\n", + " 2.85e-03\n", + " 2.63e-03\n", " \n", " \n", " 16\n", " 10000\n", " U-238\n", " scatter-Y2,1\n", - " 3.75e-03\n", - " 1.97e-03\n", + " 3.97e-03\n", + " 2.24e-03\n", " \n", " \n", " 17\n", " 10000\n", " U-238\n", " scatter-Y2,2\n", - " 2.07e-03\n", - " 1.60e-03\n", + " 2.26e-03\n", + " 1.85e-03\n", " \n", " \n", "\n", @@ -1378,27 +1363,27 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 10000 U-235 scatter-Y0,0 3.86e-02 1.11e-03\n", - "1 10000 U-235 scatter-Y1,-1 2.75e-04 2.96e-04\n", - "2 10000 U-235 scatter-Y1,0 -5.55e-05 4.33e-04\n", - "3 10000 U-235 scatter-Y1,1 -4.22e-04 3.51e-04\n", - "4 10000 U-235 scatter-Y2,-2 5.88e-05 2.04e-04\n", - "5 10000 U-235 scatter-Y2,-1 1.00e-04 2.49e-04\n", - "6 10000 U-235 scatter-Y2,0 -8.09e-05 1.59e-04\n", - "7 10000 U-235 scatter-Y2,1 1.93e-04 2.14e-04\n", - "8 10000 U-235 scatter-Y2,2 1.12e-04 1.86e-04\n", - "9 10000 U-238 scatter-Y0,0 2.34e+00 1.34e-02\n", - "10 10000 U-238 scatter-Y1,-1 2.32e-02 2.97e-03\n", - "11 10000 U-238 scatter-Y1,0 7.50e-04 2.55e-03\n", - "12 10000 U-238 scatter-Y1,1 -2.73e-02 3.28e-03\n", - "13 10000 U-238 scatter-Y2,-2 -2.36e-03 1.21e-03\n", - "14 10000 U-238 scatter-Y2,-1 -1.80e-04 1.49e-03\n", - "15 10000 U-238 scatter-Y2,0 3.23e-03 2.25e-03\n", - "16 10000 U-238 scatter-Y2,1 3.75e-03 1.97e-03\n", - "17 10000 U-238 scatter-Y2,2 2.07e-03 1.60e-03" + "0 10000 U-235 scatter-Y0,0 3.84e-02 1.32e-03\n", + "1 10000 U-235 scatter-Y1,-1 3.61e-04 3.13e-04\n", + "2 10000 U-235 scatter-Y1,0 -2.38e-04 4.69e-04\n", + "3 10000 U-235 scatter-Y1,1 -5.08e-04 3.83e-04\n", + "4 10000 U-235 scatter-Y2,-2 6.68e-05 2.46e-04\n", + "5 10000 U-235 scatter-Y2,-1 6.47e-06 2.84e-04\n", + "6 10000 U-235 scatter-Y2,0 -1.41e-04 1.75e-04\n", + "7 10000 U-235 scatter-Y2,1 1.61e-04 2.33e-04\n", + "8 10000 U-235 scatter-Y2,2 -1.80e-05 1.97e-04\n", + "9 10000 U-238 scatter-Y0,0 2.33e+00 1.35e-02\n", + "10 10000 U-238 scatter-Y1,-1 2.53e-02 3.23e-03\n", + "11 10000 U-238 scatter-Y1,0 7.10e-04 2.92e-03\n", + "12 10000 U-238 scatter-Y1,1 -2.49e-02 3.52e-03\n", + "13 10000 U-238 scatter-Y2,-2 -1.43e-03 1.17e-03\n", + "14 10000 U-238 scatter-Y2,-1 6.84e-04 1.63e-03\n", + "15 10000 U-238 scatter-Y2,0 2.85e-03 2.63e-03\n", + "16 10000 U-238 scatter-Y2,1 3.97e-03 2.24e-03\n", + "17 10000 U-238 scatter-Y2,2 2.26e-03 1.85e-03" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1420,7 +1405,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1429,8 +1414,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00159927 0.01341406]\n", - " [ 0.00018637 0.00111048]]]\n" + "[[[ 0.00185463 0.01350521]\n", + " [ 0.00019723 0.00131654]]]\n" ] } ], @@ -1451,7 +1436,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -1466,7 +1451,7 @@ "\tFilters =\t\n", " \t\tdistribcell\t[10002]\n", "\tNuclides =\ttotal \n", - "\tScores =\t[u'absorption', u'scatter']\n", + "\tScores =\t['absorption', 'scatter']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -1489,7 +1474,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -1498,7 +1483,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.05767856]]]\n" + "[[[ 0.05468423]]]\n" ] } ], @@ -1519,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -1543,141 +1528,141 @@ " 558\n", " 279\n", " absorption\n", - " 8.19e-05\n", - " 7.82e-06\n", + " 8.72e-05\n", + " 8.13e-06\n", " \n", " \n", " 559\n", " 279\n", " scatter\n", - " 1.33e-02\n", - " 6.19e-04\n", + " 1.37e-02\n", + " 6.98e-04\n", " \n", " \n", " 560\n", " 280\n", " absorption\n", - " 1.00e-04\n", - " 7.93e-06\n", + " 1.03e-04\n", + " 9.17e-06\n", " \n", " \n", " 561\n", " 280\n", " scatter\n", - " 1.40e-02\n", - " 5.61e-04\n", + " 1.41e-02\n", + " 6.26e-04\n", " \n", " \n", " 562\n", " 281\n", " absorption\n", - " 9.52e-05\n", - " 7.08e-06\n", + " 9.41e-05\n", + " 8.40e-06\n", " \n", " \n", " 563\n", " 281\n", " scatter\n", - " 1.51e-02\n", - " 6.50e-04\n", + " 1.50e-02\n", + " 6.92e-04\n", " \n", " \n", " 564\n", " 282\n", " absorption\n", - " 9.85e-05\n", - " 9.47e-06\n", + " 9.56e-05\n", + " 1.03e-05\n", " \n", " \n", " 565\n", " 282\n", " scatter\n", - " 1.53e-02\n", - " 4.63e-04\n", + " 1.52e-02\n", + " 5.37e-04\n", " \n", " \n", " 566\n", " 283\n", " absorption\n", - " 1.08e-04\n", - " 1.34e-05\n", + " 1.06e-04\n", + " 1.49e-05\n", " \n", " \n", " 567\n", " 283\n", " scatter\n", - " 1.65e-02\n", - " 7.04e-04\n", + " 1.64e-02\n", + " 8.14e-04\n", " \n", " \n", " 568\n", " 284\n", " absorption\n", - " 1.13e-04\n", - " 7.91e-06\n", + " 1.16e-04\n", + " 9.02e-06\n", " \n", " \n", " 569\n", " 284\n", " scatter\n", - " 1.67e-02\n", - " 5.51e-04\n", + " 1.64e-02\n", + " 6.00e-04\n", " \n", " \n", " 570\n", " 285\n", " absorption\n", - " 1.23e-04\n", - " 9.53e-06\n", + " 1.25e-04\n", + " 1.12e-05\n", " \n", " \n", " 571\n", " 285\n", " scatter\n", - " 1.88e-02\n", - " 7.25e-04\n", + " 1.87e-02\n", + " 8.26e-04\n", " \n", " \n", " 572\n", " 286\n", " absorption\n", - " 1.44e-04\n", - " 1.34e-05\n", + " 1.47e-04\n", + " 1.49e-05\n", " \n", " \n", " 573\n", " 286\n", " scatter\n", - " 1.90e-02\n", - " 7.07e-04\n", + " 1.94e-02\n", + " 7.71e-04\n", " \n", " \n", " 574\n", " 287\n", " absorption\n", - " 1.26e-04\n", - " 8.66e-06\n", + " 1.31e-04\n", + " 9.84e-06\n", " \n", " \n", " 575\n", " 287\n", " scatter\n", " 1.97e-02\n", - " 7.23e-04\n", + " 7.93e-04\n", " \n", " \n", " 576\n", " 288\n", " absorption\n", - " 1.25e-04\n", - " 9.59e-06\n", + " 1.23e-04\n", + " 1.07e-05\n", " \n", " \n", " 577\n", " 288\n", " scatter\n", - " 2.01e-02\n", - " 6.75e-04\n", + " 1.97e-02\n", + " 7.34e-04\n", " \n", " \n", "\n", @@ -1685,29 +1670,29 @@ ], "text/plain": [ " distribcell score mean std. dev.\n", - "558 279 absorption 8.19e-05 7.82e-06\n", - "559 279 scatter 1.33e-02 6.19e-04\n", - "560 280 absorption 1.00e-04 7.93e-06\n", - "561 280 scatter 1.40e-02 5.61e-04\n", - "562 281 absorption 9.52e-05 7.08e-06\n", - "563 281 scatter 1.51e-02 6.50e-04\n", - "564 282 absorption 9.85e-05 9.47e-06\n", - "565 282 scatter 1.53e-02 4.63e-04\n", - "566 283 absorption 1.08e-04 1.34e-05\n", - "567 283 scatter 1.65e-02 7.04e-04\n", - "568 284 absorption 1.13e-04 7.91e-06\n", - "569 284 scatter 1.67e-02 5.51e-04\n", - "570 285 absorption 1.23e-04 9.53e-06\n", - "571 285 scatter 1.88e-02 7.25e-04\n", - "572 286 absorption 1.44e-04 1.34e-05\n", - "573 286 scatter 1.90e-02 7.07e-04\n", - "574 287 absorption 1.26e-04 8.66e-06\n", - "575 287 scatter 1.97e-02 7.23e-04\n", - "576 288 absorption 1.25e-04 9.59e-06\n", - "577 288 scatter 2.01e-02 6.75e-04" + "558 279 absorption 8.72e-05 8.13e-06\n", + "559 279 scatter 1.37e-02 6.98e-04\n", + "560 280 absorption 1.03e-04 9.17e-06\n", + "561 280 scatter 1.41e-02 6.26e-04\n", + "562 281 absorption 9.41e-05 8.40e-06\n", + "563 281 scatter 1.50e-02 6.92e-04\n", + "564 282 absorption 9.56e-05 1.03e-05\n", + "565 282 scatter 1.52e-02 5.37e-04\n", + "566 283 absorption 1.06e-04 1.49e-05\n", + "567 283 scatter 1.64e-02 8.14e-04\n", + "568 284 absorption 1.16e-04 9.02e-06\n", + "569 284 scatter 1.64e-02 6.00e-04\n", + "570 285 absorption 1.25e-04 1.12e-05\n", + "571 285 scatter 1.87e-02 8.26e-04\n", + "572 286 absorption 1.47e-04 1.49e-05\n", + "573 286 scatter 1.94e-02 7.71e-04\n", + "574 287 absorption 1.31e-04 9.84e-06\n", + "575 287 scatter 1.97e-02 7.93e-04\n", + "576 288 absorption 1.23e-04 1.07e-05\n", + "577 288 scatter 1.97e-02 7.34e-04" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1729,7 +1714,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -1791,8 +1776,8 @@ " 10000\n", " 279\n", " absorption\n", - " 8.19e-05\n", - " 7.82e-06\n", + " 8.72e-05\n", + " 8.13e-06\n", " \n", " \n", " 559\n", @@ -1806,8 +1791,8 @@ " 10000\n", " 279\n", " scatter\n", - " 1.33e-02\n", - " 6.19e-04\n", + " 1.37e-02\n", + " 6.98e-04\n", " \n", " \n", " 560\n", @@ -1821,8 +1806,8 @@ " 10000\n", " 280\n", " absorption\n", - " 1.00e-04\n", - " 7.93e-06\n", + " 1.03e-04\n", + " 9.17e-06\n", " \n", " \n", " 561\n", @@ -1836,8 +1821,8 @@ " 10000\n", " 280\n", " scatter\n", - " 1.40e-02\n", - " 5.61e-04\n", + " 1.41e-02\n", + " 6.26e-04\n", " \n", " \n", " 562\n", @@ -1851,8 +1836,8 @@ " 10000\n", " 281\n", " absorption\n", - " 9.52e-05\n", - " 7.08e-06\n", + " 9.41e-05\n", + " 8.40e-06\n", " \n", " \n", " 563\n", @@ -1866,8 +1851,8 @@ " 10000\n", " 281\n", " scatter\n", - " 1.51e-02\n", - " 6.50e-04\n", + " 1.50e-02\n", + " 6.92e-04\n", " \n", " \n", " 564\n", @@ -1881,8 +1866,8 @@ " 10000\n", " 282\n", " absorption\n", - " 9.85e-05\n", - " 9.47e-06\n", + " 9.56e-05\n", + " 1.03e-05\n", " \n", " \n", " 565\n", @@ -1896,8 +1881,8 @@ " 10000\n", " 282\n", " scatter\n", - " 1.53e-02\n", - " 4.63e-04\n", + " 1.52e-02\n", + " 5.37e-04\n", " \n", " \n", " 566\n", @@ -1911,8 +1896,8 @@ " 10000\n", " 283\n", " absorption\n", - " 1.08e-04\n", - " 1.34e-05\n", + " 1.06e-04\n", + " 1.49e-05\n", " \n", " \n", " 567\n", @@ -1926,8 +1911,8 @@ " 10000\n", " 283\n", " scatter\n", - " 1.65e-02\n", - " 7.04e-04\n", + " 1.64e-02\n", + " 8.14e-04\n", " \n", " \n", " 568\n", @@ -1941,8 +1926,8 @@ " 10000\n", " 284\n", " absorption\n", - " 1.13e-04\n", - " 7.91e-06\n", + " 1.16e-04\n", + " 9.02e-06\n", " \n", " \n", " 569\n", @@ -1956,8 +1941,8 @@ " 10000\n", " 284\n", " scatter\n", - " 1.67e-02\n", - " 5.51e-04\n", + " 1.64e-02\n", + " 6.00e-04\n", " \n", " \n", " 570\n", @@ -1971,8 +1956,8 @@ " 10000\n", " 285\n", " absorption\n", - " 1.23e-04\n", - " 9.53e-06\n", + " 1.25e-04\n", + " 1.12e-05\n", " \n", " \n", " 571\n", @@ -1986,8 +1971,8 @@ " 10000\n", " 285\n", " scatter\n", - " 1.88e-02\n", - " 7.25e-04\n", + " 1.87e-02\n", + " 8.26e-04\n", " \n", " \n", " 572\n", @@ -2001,8 +1986,8 @@ " 10000\n", " 286\n", " absorption\n", - " 1.44e-04\n", - " 1.34e-05\n", + " 1.47e-04\n", + " 1.49e-05\n", " \n", " \n", " 573\n", @@ -2016,8 +2001,8 @@ " 10000\n", " 286\n", " scatter\n", - " 1.90e-02\n", - " 7.07e-04\n", + " 1.94e-02\n", + " 7.71e-04\n", " \n", " \n", " 574\n", @@ -2031,8 +2016,8 @@ " 10000\n", " 287\n", " absorption\n", - " 1.26e-04\n", - " 8.66e-06\n", + " 1.31e-04\n", + " 9.84e-06\n", " \n", " \n", " 575\n", @@ -2047,7 +2032,7 @@ " 287\n", " scatter\n", " 1.97e-02\n", - " 7.23e-04\n", + " 7.93e-04\n", " \n", " \n", " 576\n", @@ -2061,8 +2046,8 @@ " 10000\n", " 288\n", " absorption\n", - " 1.25e-04\n", - " 9.59e-06\n", + " 1.23e-04\n", + " 1.07e-05\n", " \n", " \n", " 577\n", @@ -2076,8 +2061,8 @@ " 10000\n", " 288\n", " scatter\n", - " 2.01e-02\n", - " 6.75e-04\n", + " 1.97e-02\n", + " 7.34e-04\n", " \n", " \n", "\n", @@ -2111,36 +2096,36 @@ " mean std. dev. \n", " \n", " \n", - "558 8.19e-05 7.82e-06 \n", - "559 1.33e-02 6.19e-04 \n", - "560 1.00e-04 7.93e-06 \n", - "561 1.40e-02 5.61e-04 \n", - "562 9.52e-05 7.08e-06 \n", - "563 1.51e-02 6.50e-04 \n", - "564 9.85e-05 9.47e-06 \n", - "565 1.53e-02 4.63e-04 \n", - "566 1.08e-04 1.34e-05 \n", - "567 1.65e-02 7.04e-04 \n", - "568 1.13e-04 7.91e-06 \n", - "569 1.67e-02 5.51e-04 \n", - "570 1.23e-04 9.53e-06 \n", - "571 1.88e-02 7.25e-04 \n", - "572 1.44e-04 1.34e-05 \n", - "573 1.90e-02 7.07e-04 \n", - "574 1.26e-04 8.66e-06 \n", - "575 1.97e-02 7.23e-04 \n", - "576 1.25e-04 9.59e-06 \n", - "577 2.01e-02 6.75e-04 " + "558 8.72e-05 8.13e-06 \n", + "559 1.37e-02 6.98e-04 \n", + "560 1.03e-04 9.17e-06 \n", + "561 1.41e-02 6.26e-04 \n", + "562 9.41e-05 8.40e-06 \n", + "563 1.50e-02 6.92e-04 \n", + "564 9.56e-05 1.03e-05 \n", + "565 1.52e-02 5.37e-04 \n", + "566 1.06e-04 1.49e-05 \n", + "567 1.64e-02 8.14e-04 \n", + "568 1.16e-04 9.02e-06 \n", + "569 1.64e-02 6.00e-04 \n", + "570 1.25e-04 1.12e-05 \n", + "571 1.87e-02 8.26e-04 \n", + "572 1.47e-04 1.49e-05 \n", + "573 1.94e-02 7.71e-04 \n", + "574 1.31e-04 9.84e-06 \n", + "575 1.97e-02 7.93e-04 \n", + "576 1.23e-04 1.07e-05 \n", + "577 1.97e-02 7.34e-04 " ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a pandas dataframe for the distribcell tally data\n", - "df = tally.get_pandas_dataframe(summary=su, nuclides=False)\n", + "df = tally.get_pandas_dataframe(summary=sp.summary, nuclides=False)\n", "\n", "# Print the last twenty rows in the dataframe\n", "df.tail(20)" @@ -2148,7 +2133,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -2183,38 +2168,38 @@ " \n", " \n", " mean\n", - " 4.19e-04\n", - " 2.24e-05\n", + " 4.16e-04\n", + " 2.42e-05\n", " \n", " \n", " std\n", - " 2.42e-04\n", - " 9.14e-06\n", + " 2.39e-04\n", + " 1.03e-05\n", " \n", " \n", " min\n", " 1.90e-05\n", - " 3.44e-06\n", + " 3.80e-06\n", " \n", " \n", " 25%\n", - " 2.02e-04\n", - " 1.56e-05\n", + " 1.99e-04\n", + " 1.61e-05\n", " \n", " \n", " 50%\n", - " 4.05e-04\n", - " 2.20e-05\n", + " 4.09e-04\n", + " 2.37e-05\n", " \n", " \n", " 75%\n", - " 6.07e-04\n", - " 2.89e-05\n", + " 6.00e-04\n", + " 3.08e-05\n", " \n", " \n", " max\n", - " 9.19e-04\n", - " 4.95e-05\n", + " 9.07e-04\n", + " 5.38e-05\n", " \n", " \n", "\n", @@ -2225,16 +2210,16 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.19e-04 2.24e-05\n", - "std 2.42e-04 9.14e-06\n", - "min 1.90e-05 3.44e-06\n", - "25% 2.02e-04 1.56e-05\n", - "50% 4.05e-04 2.20e-05\n", - "75% 6.07e-04 2.89e-05\n", - "max 9.19e-04 4.95e-05" + "mean 4.16e-04 2.42e-05\n", + "std 2.39e-04 1.03e-05\n", + "min 1.90e-05 3.80e-06\n", + "25% 1.99e-04 1.61e-05\n", + "50% 4.09e-04 2.37e-05\n", + "75% 6.00e-04 3.08e-05\n", + "max 9.07e-04 5.38e-05" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -2257,7 +2242,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -2266,7 +2251,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.303583331507\n" + "Mann-Whitney Test p-value: 0.7234916721800682\n" ] } ], @@ -2295,7 +2280,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -2304,7 +2289,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 6.038663783e-42\n" + "Mann-Whitney Test p-value: 3.5054120724573393e-41\n" ] } ], @@ -2331,7 +2316,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -2340,7 +2325,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n", + "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -2350,18 +2335,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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MTGiGAH7z1Nx9921cf/3ClHDa9u3bLcxmGMagkveqMyM3JM9TEw+ndXQkhtOam5t9l7e1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\nyRsDKQZIfYpODDv5Ee+Ily6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2380,7 +2365,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -2388,18 +2373,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 39, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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DlnoznkkeyJPJ/qpSks3oorJstUPi00rHX03uFWEkUtbX3oa7Sn7FgYX/j5ML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\nlcvmVCblU7lsLlvKpCE+qXx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WDt9JxAMVCEm4Wetn0a1FN98xJBo/NYbvgPYTfScRD1QgJOFmrZvFSS10BXWl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\nEpEKhIiIRPT/Abz6PSTJ+oGTAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2410,29 +2395,29 @@ "# Plot a histogram and kernel density estimate for the scattering rates\n", "scatter['mean'].plot(kind='hist', bins=25)\n", "scatter['mean'].plot(kind='kde')\n", - "pylab.title('Scattering Rates')\n", - "pylab.xlabel('Mean')\n", - "pylab.legend(['KDE', 'Histogram'])" + "plt.title('Scattering Rates')\n", + "plt.xlabel('Mean')\n", + "plt.legend(['KDE', 'Histogram'])" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.1" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb index 36cf63c6a..de92c2bcb 100644 --- a/docs/source/pythonapi/examples/post-processing.ipynb +++ b/docs/source/pythonapi/examples/post-processing.ipynb @@ -339,7 +339,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nVDExOjMyOjU1LTA0OjAwoL4U5gAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN\nAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEX///9yEhLpgJFNv8Tq\nQYT7AAAAAWJLR0QAiAUdSAAAAAd0SU1FB+AFBRQpN8J6/ygAAALKSURBVGje7dpLcqQwDAbgHHE2\nYeEj+D4cwQucBUfo+3CEXoSp8OhuhF70T4qpKXmdr21LogK2Pj7A8QmNP+HDhw8fPnz48Kf6VH9G\n+66vy+je8k19jnf8C5dXIPv86ms56lPdjvaYbyodx3ze+XLE76cXFiD4zPji99z0/AJ4n1lfvJ6f\nnl0A6x+578efMSg1wPr172/jPO5yFXM+Ef78gdblM+WPHyguP//t1/g6pA0wfln+ho/fwgYYn19C\n/xwDvwHGc9OvC+hs37DTrwuwfWanXxdQTC9Mvyygs3wjTL8uwPJpn/tNDbSGz7T0SBEWw4vLXzbQ\n6b6RoveIoO6TvPxlA63qs7z8ZQPF9F+SH22vbX8OQKf5Rtv+EgDNJ3X58wZaxWd1+fMGiuFvir8b\nvjp8J/tGy/6jAmRvhW8fwL3vVT+o3grfPoB7r/IpALI3tz8FoJN84/NV873hB8UnM3xzANtf8nb4\ndwmg3grfFEDJO8JPE0i9Ff4pAYL3pI8mkHor/HMCeO9JH00g9SafEsh7T/ppARBvp48UwJnelT5S\nACd7O31TAlnvKx9SQCd7B58KgPO+8iMFuPWe9E8F8BveWX7bAjzX9y4//Jve+fhsH6Ctv7n8PTzj\nvY/v9gEOHz58+PBX+6v/f/wPvnd54f3j6venE/yl769Xv7+j3x/o98/V32/o9+fl389Xnx+g5x/o\n+Qt6/oOeP6HnX+j5G3z+h54/ouefV5/foufP6Pk3ev4On/+j9w/o/Qd6/4Le/6D3T/D9V67Y/ZsV\nQBq+s+8f0ftP+P41axXguP9NWgDuu/Cdfv+N3r/D9/9TAID+A7T/Ae2/gPs/0P4TtP8F7r9J3AIO\n9P+g/Udw/9Oygbf7r9D+L7j/DO1/Q/vv4P4/tP8Q7n9E+y/h/k+0/xTuf4X7b+H+X7T/+BPuf3aM\n8OHDhw8fPnz4w/4vzcvgeY10sY0AAAAldEVYdGRhdGU6Y3JlYXRlADIwMTYtMDUtMDVUMTQ6NDE6\nNTUtMDY6MDCnHFu9AAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE2LTA1LTA1VDE0OjQxOjU1LTA2OjAw\n1kHjAQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -445,12 +445,11 @@ " 888\n", " 888\n", "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", + " Copyright: 2011-2016 Massachusetts Institute of Technology\n", + " License: http://openmc.readthedocs.org/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: eeb5091ca3a34cc85df73a3318cae2b6c7097413\n", - " Date/Time: 2016-04-13 11:32:56\n", - " MPI Processes: 1\n", + " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", + " Date/Time: 2016-05-05 14:41:55\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -586,20 +585,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.8100E-01 seconds\n", - " Reading cross sections = 8.6000E-02 seconds\n", - " Total time in simulation = 2.4400E+02 seconds\n", - " Time in transport only = 2.4395E+02 seconds\n", - " Time in inactive batches = 8.3260E+00 seconds\n", - " Time in active batches = 2.3567E+02 seconds\n", - " Time synchronizing fission bank = 1.6000E-02 seconds\n", - " Sampling source sites = 6.0000E-03 seconds\n", - " SEND/RECV source sites = 7.0000E-03 seconds\n", + " Total time for initialization = 4.4900E-01 seconds\n", + " Reading cross sections = 1.2100E-01 seconds\n", + " Total time in simulation = 3.4132E+02 seconds\n", + " Time in transport only = 3.4128E+02 seconds\n", + " Time in inactive batches = 1.0748E+01 seconds\n", + " Time in active batches = 3.3057E+02 seconds\n", + " Time synchronizing fission bank = 1.1000E-02 seconds\n", + " Sampling source sites = 1.1000E-02 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", " Time accumulating tallies = 1.9000E-02 seconds\n", - " Total time for finalization = 1.7400E-01 seconds\n", - " Total time elapsed = 2.4458E+02 seconds\n", - " Calculation Rate (inactive) = 6005.28 neutrons/second\n", - " Calculation Rate (active) = 1909.46 neutrons/second\n", + " Total time for finalization = 1.5600E-01 seconds\n", + " Total time elapsed = 3.4196E+02 seconds\n", + " Calculation Rate (inactive) = 4652.03 neutrons/second\n", + " Calculation Rate (active) = 1361.27 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -674,11 +673,11 @@ "text": [ "Tally\n", "\tID =\t10000\n", - "\tName =\t\n", + "\tName =\tflux\n", "\tFilters =\t\n", " \t\tmesh\t[10000]\n", "\tNuclides =\ttotal \n", - "\tScores =\t[u'flux', u'fission']\n", + "\tScores =\t['flux', 'fission']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -809,11 +808,11 @@ "text": [ "Tally\n", "\tID =\t10001\n", - "\tName =\t\n", + "\tName =\tflux\n", "\tFilters =\t\n", " \t\tmesh\t[10000]\n", "\tNuclides =\ttotal \n", - "\tScores =\t[u'flux']\n", + "\tScores =\t['flux']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -856,7 +855,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -865,9 +864,9 @@ }, { "data": { - "image/png": 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dHYwxnwF+tv9cfcZSuEhS5aY53cGpqRLyYEtZLtFQm9jTLhqLe0EU5dVUATOh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\n0aUjYeXpp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5wWkCkSo++wGPu8scNqZo1ELYKYlKIcad965Tdkbw+Ft87h/9CVtjs+QPn6N+K4qRVFAm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\nHip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f4IwNxcCDBBQ9PrtKeqEFpeeTVHnX4DRG0sP2ME0UU8VWZNcO5OZtQhnsQ1et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qUWM9BKc/isdYh6muBNGcDzU6CErINWJzbqCHr4R7tq6hJPpEDCk7MK2qwx/f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qYxdsNyiiu3cKbqMY5G3EC/3cmxqy+nwGQlO3geid4iCp77AqkoG0U6hdrgxxm5BlUvZBMfMkFc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\niz1fo3ZfHG9C+VUB2d/SQvFNN5Hy5z9jrH8X9n8MShrknAZASdJw0Jtg8oX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1094,21 +1093,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.1" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index 25c57f3c5..56b3cb45c 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -4,9 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook shows the how tallies can be combined (added, subtracted, multiplied, etc.) using the Python API in order to create derived tallies. Since no covariance information is obtained, it is assumed that tallies are completely independent of one another when propagating uncertainties. The target problem is a simple pin cell.\n", - "\n", - "**Note:** that this Notebook was created using the latest Pandas v0.16.1. Everything in the Notebook will wun with older versions of Pandas, but the multi-indexing option in >v0.15.0 makes the tables look prettier." + "This notebook shows the how tallies can be combined (added, subtracted, multiplied, etc.) using the Python API in order to create derived tallies. Since no covariance information is obtained, it is assumed that tallies are completely independent of one another when propagating uncertainties. The target problem is a simple pin cell." ] }, { @@ -16,18 +14,6 @@ "collapsed": false }, "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], "source": [ "import glob\n", "from IPython.display import Image\n", @@ -52,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -76,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -111,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -134,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -163,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -200,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -227,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -240,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -259,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -295,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -323,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -334,7 +320,7 @@ "0" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -346,19 +332,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { - 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"execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -380,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -392,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -429,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -445,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -460,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -476,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -491,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -511,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -530,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true @@ -556,8 +542,8 @@ " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.org/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: ae083cf5d491e6a778d5b762dad19c8d5fe45238\n", - " Date/Time: 2016-04-30 06:37:41\n", + " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", + " Date/Time: 2016-05-05 14:51:45\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -613,20 +599,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 7.0900E-01 seconds\n", - " Reading cross sections = 4.0400E-01 seconds\n", - " Total time in simulation = 1.7108E+01 seconds\n", - " Time in transport only = 1.7093E+01 seconds\n", - " Time in inactive batches = 3.3970E+00 seconds\n", - " Time in active batches = 1.3711E+01 seconds\n", + " Total time for initialization = 7.2500E-01 seconds\n", + " Reading cross sections = 4.4400E-01 seconds\n", + " Total time in simulation = 1.5547E+01 seconds\n", + " Time in transport only = 1.5527E+01 seconds\n", + " Time in inactive batches = 2.2880E+00 seconds\n", + " Time in active batches = 1.3259E+01 seconds\n", " Time synchronizing fission bank = 1.0000E-03 seconds\n", - " Sampling source sites = 1.0000E-03 seconds\n", + " Sampling source sites = 0.0000E+00 seconds\n", " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 1.7835E+01 seconds\n", - " Calculation Rate (inactive) = 3679.72 neutrons/second\n", - " Calculation Rate (active) = 2735.03 neutrons/second\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", + " Total time for finalization = 2.0000E-03 seconds\n", + " Total time elapsed = 1.6291E+01 seconds\n", + " Calculation Rate (inactive) = 5463.29 neutrons/second\n", + " Calculation Rate (active) = 2828.27 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -644,7 +630,7 @@ "0" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -673,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false, "scrolled": true @@ -684,27 +670,6 @@ "sp = openmc.StatePoint('statepoint.20.h5')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may have also noticed we instructed OpenMC to create a summary file with lots of geometry information in it. This can help to produce more sensible output from the Python API, so we will use the summary file to link against." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "# Load the summary file and link with statepoint\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -716,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -752,7 +717,7 @@ "0 total (nu-fission / absorption) 1.04e+00 6.14e-03" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -776,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -816,7 +781,7 @@ "0 0.00e+00 6.25e-07 total absorption 6.93e-01 4.11e-03" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -838,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -878,7 +843,7 @@ "0 0.00e+00 6.25e-07 total nu-fission 1.20e+00 7.60e-03" ] }, - "execution_count": 28, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -901,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -946,7 +911,7 @@ "0 4.72e-03 " ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -967,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1012,7 +977,7 @@ "0 (nu-fission / absorption) 1.66e+00 1.13e-02 " ] }, - "execution_count": 30, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1032,7 +997,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1077,7 +1042,7 @@ "0 (((absorption * nu-fission) * absorption) * (n... 1.04e+00 1.32e-02 " ] }, - "execution_count": 31, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1098,7 +1063,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": { "collapsed": false, "scrolled": true @@ -1114,7 +1079,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -1243,7 +1208,7 @@ "7 (scatter / flux) 3.37e-03 1.44e-05 " ] }, - "execution_count": 33, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1262,7 +1227,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -1294,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -1318,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -1349,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -1430,7 +1395,7 @@ "3 7.32e-04 " ] }, - "execution_count": 37, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1443,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -1584,7 +1549,7 @@ "8 3.20e-03 " ] }, - "execution_count": 38, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } diff --git a/src/output.F90 b/src/output.F90 index 4b4b966dc..786d9a10e 100644 --- a/src/output.F90 +++ b/src/output.F90 @@ -54,7 +54,7 @@ contains write(UNIT=OUTPUT_UNIT, FMT=*) & ' Copyright: 2011-2016 Massachusetts Institute of Technology' write(UNIT=OUTPUT_UNIT, FMT=*) & - ' License: http://openmc.readthedocs.org/en/latest/license.html' + ' License: http://openmc.readthedocs.io/en/latest/license.html' write(UNIT=OUTPUT_UNIT, FMT='(6X,"Version:",8X,I1,".",I1,".",I1)') & VERSION_MAJOR, VERSION_MINOR, VERSION_RELEASE #ifdef GIT_SHA1 From cf45388d6e0e207e814a378708189e0b19778780 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Sat, 7 May 2016 15:38:59 -0400 Subject: [PATCH 11/13] Fix universe assignment for openmc.HexLattice --- openmc/lattice.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/lattice.py b/openmc/lattice.py index f1e775920..f78ec8e90 100644 --- a/openmc/lattice.py +++ b/openmc/lattice.py @@ -649,7 +649,7 @@ class HexLattice(Lattice): # Set the number of rings and make sure this number is consistent for # all axial positions. if n_dims == 3: - self.num_rings = len(self._universes) + self.num_rings = len(self._universes[0]) for rings in self._universes: if len(rings) != self._num_rings: msg = 'HexLattice ID={0:d} has an inconsistent number of ' \ From 8aada0af2977c12940ecf37dbb2b2bdaaf5e117a Mon Sep 17 00:00:00 2001 From: Will Boyd Date: Sun, 8 May 2016 18:43:43 -0400 Subject: [PATCH 12/13] Added row_column parameter to Library.build_hdf5_store(...)` --- openmc/mgxs/library.py | 11 ++++++++--- openmc/mgxs/mgxs.py | 10 ++++++---- 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py index 8d5e9854e..c3529fc98 100644 --- a/openmc/mgxs/library.py +++ b/openmc/mgxs/library.py @@ -575,7 +575,8 @@ class Library(object): return subdomain_avg_library def build_hdf5_store(self, filename='mgxs.h5', directory='mgxs', - subdomains='all', nuclides='all', xs_type='macro'): + subdomains='all', nuclides='all', xs_type='macro', + row_column='inout'): """Export the multi-group cross section library to an HDF5 binary file. This method constructs an HDF5 file which stores the library's @@ -605,6 +606,10 @@ class Library(object): xs_type: {'macro', 'micro'} Store the macro or micro cross section in units of cm^-1 or barns. Defaults to 'macro'. + row_column: {'inout', 'outin'} + Store scattering matrices indexed first by incoming group and + second by outgoing group ('inout'), or vice versa ('outin'). + Defaults to 'inout'. Raises ------ @@ -646,8 +651,8 @@ class Library(object): if subdomains == 'avg': mgxs = mgxs.get_subdomain_avg_xs() - mgxs.build_hdf5_store(filename, directory, - xs_type=xs_type, nuclides=nuclides) + mgxs.build_hdf5_store(filename, directory, xs_type=xs_type, + nuclides=nuclides, row_column=row_column) def dump_to_file(self, filename='mgxs', directory='mgxs'): """Store this Library object in a pickle binary file. diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 90b956b21..57f42c1ee 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -1174,8 +1174,9 @@ class MGXS(object): Store the macro or micro cross section in units of cm^-1 or barns. Defaults to 'macro'. row_column: {'inout', 'outin'} - Store scattering matrices indexed first by incoming group and second - by outgoing group ('inout'), or vice versa ('outin'). + Store scattering matrices indexed first by incoming group and + second by outgoing group ('inout'), or vice versa ('outin'). + Defaults to 'inout'. append : bool If true, appends to an existing HDF5 file with the same filename directory (if one exists). Defaults to True. @@ -2005,8 +2006,9 @@ class ScatterMatrixXS(MGXS): decreasing energy groups (decreasing or increasing energies). Defaults to 'increasing'. row_column: {'inout', 'outin'} - Return the cross section indexed first by incoming group and second - by outgoing group ('inout'), or vice versa ('outin'). + Return the cross section indexed first by incoming group and + second by outgoing group ('inout'), or vice versa ('outin'). + Defaults to 'inout'. value : str A string for the type of value to return - 'mean', 'std_dev', or 'rel_err' are accepted. Defaults to the empty string. From 838f84c963dbf5fe0f80b02bec2f056a3fd4036f Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 9 May 2016 08:33:18 -0500 Subject: [PATCH 13/13] Respond to @wbinventor comments on #642 --- openmc/statepoint.py | 6 +++--- openmc/universe.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 6c8af88a7..19aa3dbaf 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -622,8 +622,6 @@ class StatePoint(object): Raises ------ - RuntimeError - If a Summary object has already been linked. ValueError An error when the argument passed to the 'summary' parameter is not an openmc.Summary object. @@ -631,7 +629,9 @@ class StatePoint(object): """ if self.summary is not None: - raise RuntimeError('A Summary object has already been linked.') + warnings.warn('A Summary object has already been linked.', + RuntimeWarning) + return if not isinstance(summary, openmc.summary.Summary): msg = 'Unable to link statepoint with "{0}" which ' \ diff --git a/openmc/universe.py b/openmc/universe.py index 8834eaa52..770e789da 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -36,8 +36,8 @@ class Universe(object): automatically be assigned name : str, optional Name of the universe. If not specified, the name is the empty string. - cells : Iterable of openmc.Cell - Cells to add to the universe + cells : Iterable of openmc.Cell, optional + Cells to add to the universe. By default no cells are added. Attributes ----------