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Merge remote-tracking branch 'upstream/develop' into mgxs_integration

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Adam Nelson 2016-05-12 05:16:31 -04:00
commit e8e5ccd1ad
26 changed files with 978 additions and 1304 deletions
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124
docs/source/pythonapi/examples/mgxs-part-i.ipynb
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@ -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 @@
" <td>6.250000e-07</td>\n",
" <td>total</td>\n",
" <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>0.007763</td>\n",
" </tr>\n",
" <tr>\n",
@ -1145,7 +1129,7 @@
" <td>2.000000e+01</td>\n",
" <td>total</td>\n",
" <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>0.003739</td>\n",
" </tr>\n",
" </tbody>\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,

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161
docs/source/pythonapi/examples/tally-arithmetic.ipynb
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@ -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
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@ -76,7 +62,7 @@
},
{
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"execution_count": 3,
"metadata": {
"collapsed": false
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@ -111,7 +97,7 @@
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{
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"execution_count": 4,
"metadata": {
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@ -134,7 +120,7 @@
},
{
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"metadata": {
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@ -163,7 +149,7 @@
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@ -200,7 +186,7 @@
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@ -227,7 +213,7 @@
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@ -240,7 +226,7 @@
},
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@ -259,7 +245,7 @@
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{
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@ -295,7 +281,7 @@
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{
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@ -323,7 +309,7 @@
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@ -334,7 +320,7 @@
"0"
]
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@ -346,19 +332,19 @@
},
{
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{
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@ -460,7 +446,7 @@
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@ -511,7 +497,7 @@
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{
"cell_type": "code",
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@ -530,7 +516,7 @@
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@ -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"
}

9
docs/source/usersguide/input.rst
View file

@ -1215,11 +1215,10 @@ Each ``material`` element can have the following attributes or sub-elements:
An element with attributes/sub-elements called ``value`` and ``units``. The
``value`` attribute is the numeric value of the density while the ``units``
can be "g/cm3", "kg/m3", "atom/b-cm", "atom/cm3", or "sum". The "sum" unit
indicates that values appearing in ``ao`` attributes for ``<nuclide>`` and
``<element>`` sub-elements are to be interpreted as nuclide/element
densities in atom/b-cm, and the total density of the material is taken as
the sum of all nuclides/elements. The "sum" option cannot be used in
conjunction with weight percents. The "macro" unit is used with
indicates that values appearing in ``ao`` or ``wo`` attributes for ``<nuclide>``
and ``<element>`` sub-elements are to be interpreted as absolute nuclide/element
densities in atom/b-cm or g/cm3, and the total density of the material is
taken as the sum of all nuclides/elements. The "macro" unit is used with
a ``macroscopic`` quantity to indicate that the density is already included
in the library and thus not needed here. However, if a value is provided
for the ``value``, then this is treated as a number density multiplier on

11
openmc/cell.py
View file

@ -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

205
openmc/filter.py
View file

@ -1,4 +1,4 @@
from collections import Iterable
from collections import Iterable, OrderedDict
import copy
from numbers import Real, Integral
import sys
@ -516,7 +516,7 @@ class Filter(object):
return filter_bin
def get_pandas_dataframe(self, data_size, summary=None):
def get_pandas_dataframe(self, data_size, distribcell_paths=True):
"""Builds a Pandas DataFrame for the Filter's bins.
This method constructs a Pandas DataFrame object for the filter with
@ -531,12 +531,13 @@ class Filter(object):
----------
data_size : Integral
The total number of bins in the tally corresponding to this filter
summary : None or openmc.Summary
An optional Summary object to be used to construct columns for
distribcell tally filters (default is None). The geometric
information in the Summary object is embedded into a Multi-index
column with a geometric "path" to each distribcell instance.
NOTE: This option requires the OpenCG Python package.
distribcell_paths : bool, optional
Construct columns for distribcell tally filters (default is True).
The geometric information in the Summary object is embedded into a
Multi-index column with a geometric "path" to each distribcell
instance. NOTE: This option assumes that all distribcell paths are
of the same length and do not have the same universes and cells but
different lattice cell indices.
Returns
-------
@ -554,7 +555,7 @@ class Filter(object):
1. a single column with the cell instance IDs (without summary info)
2. separate columns for the cell IDs, universe IDs, and lattice IDs
and x,y,z cell indices corresponding to each (with summary info).
and x,y,z cell indices corresponding to each (distribcell paths).
For 'energy' and 'energyout' filters, the DataFrame includes one
column for the lower energy bound and one column for the upper
@ -566,8 +567,7 @@ class Filter(object):
Raises
------
ImportError
When Pandas is not installed, or summary info is requested but
OpenCG is not installed.
When Pandas is not installed
See also
--------
@ -626,106 +626,117 @@ class Filter(object):
elif self.type == 'distribcell':
level_df = None
if isinstance(summary, Summary):
# Attempt to import the OpenCG package
try:
import opencg
except ImportError:
msg = 'The OpenCG package must be installed ' \
'to use a Summary for distribcell dataframes'
raise ImportError(msg)
# Create Pandas Multi-index columns for each level in CSG tree
if distribcell_paths:
# Extract the OpenCG geometry from the Summary
opencg_geometry = summary.opencg_geometry
openmc_geometry = summary.openmc_geometry
# Distribcell paths require linked metadata from the Summary
if self.distribcell_paths is None:
msg = 'Unable to construct distribcell paths since ' \
'the Summary is not linked to the StatePoint'
raise ValueError(msg)
# Use OpenCG to compute the number of regions
opencg_geometry.initialize_cell_offsets()
num_regions = opencg_geometry.num_regions
# Make copy of array of distribcell paths to use in
# Pandas Multi-index column construction
distribcell_paths = copy.deepcopy(self.distribcell_paths)
num_offsets = len(distribcell_paths)
# Initialize a dictionary mapping OpenMC distribcell
# offsets to OpenCG LocalCoords linked lists
offsets_to_coords = {}
for offset, path in enumerate(self.distribcell_paths):
region = opencg_geometry.get_region_from_path(path)
coords = opencg_geometry.find_region(region)
offsets_to_coords[offset] = coords
# Each distribcell offset is a DataFrame bin
# Unravel the paths into DataFrame columns
num_offsets = len(offsets_to_coords)
# Initialize termination condition for while loop
# Loop over CSG levels in the distribcell paths
level_counter = 0
levels_remain = True
counter = 0
# Iterate over each level in the CSG tree hierarchy
while levels_remain:
levels_remain = False
# Initialize dictionary to build Pandas Multi-index
# column for this level in the CSG tree hierarchy
level_dict = {}
# Use level key as first index in Pandas Multi-index column
level_counter += 1
level_key = 'level {}'.format(level_counter)
# Initialize prefix Multi-index keys
counter += 1
level_key = 'level {0}'.format(counter)
univ_key = (level_key, 'univ', 'id')
cell_key = (level_key, 'cell', 'id')
lat_id_key = (level_key, 'lat', 'id')
lat_x_key = (level_key, 'lat', 'x')
lat_y_key = (level_key, 'lat', 'y')
lat_z_key = (level_key, 'lat', 'z')
# Use the first distribcell path to determine if level
# is a universe/cell or lattice level
first_path = distribcell_paths[0]
next_index = first_path.index('-')
level = first_path[:next_index]
# Allocate NumPy arrays for each CSG level and
# each Multi-index column in the DataFrame
level_dict[univ_key] = np.empty(num_offsets)
level_dict[cell_key] = np.empty(num_offsets)
level_dict[lat_id_key] = np.empty(num_offsets)
level_dict[lat_x_key] = np.empty(num_offsets)
level_dict[lat_y_key] = np.empty(num_offsets)
level_dict[lat_z_key] = np.empty(num_offsets)
# Trim universe/lattice info from path
first_path = first_path[next_index+2:]
# Initialize Multi-index columns to NaN - this is
# necessary since some distribcell instances may
# have very different LocalCoords linked lists
level_dict[univ_key][:] = np.NAN
level_dict[cell_key][:] = np.NAN
level_dict[lat_id_key][:] = np.NAN
level_dict[lat_x_key][:] = np.NAN
level_dict[lat_y_key][:] = np.NAN
level_dict[lat_z_key][:] = np.NAN
# Create a dictionary for this level for Pandas Multi-index
level_dict = OrderedDict()
# Iterate over all regions (distribcell instances)
for offset in range(num_offsets):
coords = offsets_to_coords[offset]
# This level is a lattice (e.g., ID(x,y,z))
if '(' in level:
level_type = 'lattice'
# If entire LocalCoords has been unraveled into
# Multi-index columns already, continue
if coords is None:
continue
# Initialize prefix Multi-index keys
lat_id_key = (level_key, 'lat', 'id')
lat_x_key = (level_key, 'lat', 'x')
lat_y_key = (level_key, 'lat', 'y')
lat_z_key = (level_key, 'lat', 'z')
# Assign entry to Universe Multi-index column
if coords._type == 'universe':
level_dict[univ_key][offset] = coords._universe._id
level_dict[cell_key][offset] = coords._cell._id
# Allocate NumPy arrays for each CSG level and
# each Multi-index column in the DataFrame
level_dict[lat_id_key] = np.empty(num_offsets)
level_dict[lat_x_key] = np.empty(num_offsets)
level_dict[lat_y_key] = np.empty(num_offsets)
level_dict[lat_z_key] = np.empty(num_offsets)
# This level is a universe / cell (e.g., ID->ID)
else:
level_type = 'universe'
# Initialize prefix Multi-index keys
univ_key = (level_key, 'univ', 'id')
cell_key = (level_key, 'cell', 'id')
# Allocate NumPy arrays for each CSG level and
# each Multi-index column in the DataFrame
level_dict[univ_key] = np.empty(num_offsets)
level_dict[cell_key] = np.empty(num_offsets)
# Determine any levels remain in path
if '-' not in first_path:
levels_remain = False
# Populate Multi-index arrays with all distribcell paths
for i, path in enumerate(distribcell_paths):
if level_type == 'lattice':
# Extract lattice ID, indices from path
next_index = path.index('-')
lat_id_indices = path[:next_index]
# Trim lattice info from distribcell path
distribcell_paths[i] = path[next_index+2:]
# Extract the lattice cell indices from the path
i1 = lat_id_indices.index('(')
i2 = lat_id_indices.index(')')
i3 = lat_id_indices[i1+1:i2]
# Assign entry to Lattice Multi-index column
level_dict[lat_id_key][i] = path[:i1]
level_dict[lat_x_key][i] = int(i3.split(',')[0]) - 1
level_dict[lat_y_key][i] = int(i3.split(',')[1]) - 1
level_dict[lat_z_key][i] = int(i3.split(',')[2]) - 1
# Assign entry to Lattice Multi-index column
else:
# Reverse y index per lattice ordering in OpenCG
level_dict[lat_id_key][offset] = coords._lattice._id
level_dict[lat_x_key][offset] = coords._lat_x
level_dict[lat_y_key][offset] = \
coords._lattice.dimension[1] - coords._lat_y - 1
level_dict[lat_z_key][offset] = coords._lat_z
# Extract universe ID from path
next_index = path.index('-')
universe_id = int(path[:next_index])
# Move to next node in LocalCoords linked list
if coords._next is None:
offsets_to_coords[offset] = None
else:
offsets_to_coords[offset] = coords._next
levels_remain = True
# Trim universe info from distribcell path
path = path[next_index+2:]
# Extract cell ID from path
if '-' in path:
next_index = path.index('-')
cell_id = int(path[:next_index])
distribcell_paths[i] = path[next_index+2:]
else:
cell_id = int(path)
distribcell_paths[i] = ''
# Assign entry to Universe, Cell Multi-index columns
level_dict[univ_key][i] = universe_id
level_dict[cell_key][i] = cell_id
# Tile the Multi-index columns
for level_key, level_bins in level_dict.items():
@ -740,7 +751,7 @@ class Filter(object):
else:
level_df = pd.concat([level_df, pd.DataFrame(level_dict)], axis=1)
# Create DataFrame column for distribcell instances IDs
# Create DataFrame column for distribcell instance IDs
# NOTE: This is performed regardless of whether the user
# requests Summary geometric information
filter_bins = np.arange(self.num_bins)

26
openmc/lattice.py
View file

@ -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
"""
@ -635,7 +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 ' \

26
openmc/material.py
View file

@ -245,19 +245,25 @@ class Material(object):
"""
cv.check_type('the density for Material ID="{0}"'.format(self._id),
density, Real)
cv.check_value('density units', units, DENSITY_UNITS)
if density is None and units is not 'sum':
msg = 'Unable to set the density for Material ID="{0}" ' \
'because a density must be set when not using ' \
'sum unit'.format(self._id)
raise ValueError(msg)
self._density = density
self._density_units = units
if units is 'sum':
if density is not None:
msg = 'Density "{0}" for Material ID="{1}" is ignored ' \
'because the unit is "sum"'.format(density, self.id)
warnings.warn(msg)
else:
if density is None:
msg = 'Unable to set the density for Material ID="{0}" ' \
'because a density value must be given when not using ' \
'"sum" unit'.format(self.id)
raise ValueError(msg)
cv.check_type('the density for Material ID="{0}"'.format(self.id),
density, Real)
self._density = density
@distrib_otf_file.setter
def distrib_otf_file(self, filename):
# TODO: remove this when distributed materials are merged

44
openmc/mgxs/mgxs.py
View file

@ -1351,7 +1351,7 @@ class MGXS(object):
modified.write('\n\\end{document}')
def get_pandas_dataframe(self, groups='all', nuclides='all',
xs_type='macro', summary=None):
xs_type='macro', distribcell_paths=True):
"""Build a Pandas DataFrame for the MGXS data.
This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but
@ -1371,12 +1371,11 @@ class MGXS(object):
xs_type: {'macro', 'micro'}
Return macro or micro cross section in units of cm^-1 or barns.
Defaults to 'macro'.
summary : None or openmc.Summary
An optional Summary object to be used to construct columns for
distribcell tally filters (default is None). The geometric
information in the Summary object is embedded into a multi-index
column with a geometric "path" to each distribcell intance.
NOTE: This option requires the OpenCG Python package.
distribcell_paths : bool, optional
Construct columns for distribcell tally filters (default is True).
The geometric information in the Summary object is embedded into
a Multi-index column with a geometric "path" to each distribcell
instance.
Returns
-------
@ -1403,7 +1402,8 @@ class MGXS(object):
# Use tally summation to sum across all nuclides
query_nuclides = self.get_all_nuclides()
xs_tally = self.xs_tally.summation(nuclides=query_nuclides)
df = xs_tally.get_pandas_dataframe(summary=summary)
df = xs_tally.get_pandas_dataframe(
distribcell_paths=distribcell_paths)
# Remove nuclide column since it is homogeneous and redundant
df.drop('nuclide', axis=1, inplace=True)
@ -1411,17 +1411,16 @@ class MGXS(object):
# If the user requested a specific set of nuclides
elif self.by_nuclide and nuclides != 'all':
xs_tally = self.xs_tally.get_slice(nuclides=nuclides)
df = xs_tally.get_pandas_dataframe(summary=summary)
df = xs_tally.get_pandas_dataframe(
distribcell_paths=distribcell_paths)
# If the user requested all nuclides, keep nuclide column in dataframe
else:
df = self.xs_tally.get_pandas_dataframe(summary=summary)
df = self.xs_tally.get_pandas_dataframe(
distribcell_paths=distribcell_paths)
# Remove the score column since it is homogeneous and redundant
if summary and 'distribcell' in self.domain_type:
df = df.drop('score', level=0, axis=1)
else:
df = df.drop('score', axis=1)
df = df.drop('score', axis=1)
# Override energy groups bounds with indices
all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
@ -2572,7 +2571,7 @@ class Chi(MGXS):
return xs
def get_pandas_dataframe(self, groups='all', nuclides='all',
xs_type='macro', summary=None):
xs_type='macro', distribcell_paths=False):
"""Build a Pandas DataFrame for the MGXS data.
This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but
@ -2592,12 +2591,11 @@ class Chi(MGXS):
xs_type: {'macro', 'micro'}
Return macro or micro cross section in units of cm^-1 or barns.
Defaults to 'macro'.
summary : None or openmc.Summary
An optional Summary object to be used to construct columns for
distribcell tally filters (default is None). The geometric
information in the Summary object is embedded into a multi-index
column with a geometric "path" to each distribcell intance.
NOTE: This option requires the OpenCG Python package.
distribcell_paths : bool, optional
Construct columns for distribcell tally filters (default is True).
The geometric information in the Summary object is embedded into
a Multi-index column with a geometric "path" to each distribcell
instance.
Returns
-------
@ -2613,8 +2611,8 @@ class Chi(MGXS):
"""
# Build the dataframe using the parent class method
df = super(Chi, self).get_pandas_dataframe(groups, nuclides,
xs_type, summary)
df = super(Chi, self).get_pandas_dataframe(
groups, nuclides, xs_type, distribcell_paths=distribcell_paths)
# If user requested micro cross sections, multiply by the atom
# densities to cancel out division made by the parent class method

25
openmc/statepoint.py
View file

@ -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()
@ -612,6 +628,11 @@ class StatePoint(object):
"""
if self.summary is not None:
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 ' \
'is not a Summary object'.format(summary)

13
openmc/summary.py
View file

@ -67,9 +67,10 @@ 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_nuclides(self):
@ -391,11 +392,11 @@ 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'][...]

26
openmc/tallies.py
View file

@ -1538,8 +1538,8 @@ class Tally(object):
return data
def get_pandas_dataframe(self, filters=True, nuclides=True,
scores=True, summary=None, float_format='{:.2e}'):
def get_pandas_dataframe(self, filters=True, nuclides=True, scores=True,
distribcell_paths=True, float_format='{:.2e}'):
"""Build a Pandas DataFrame for the Tally data.
This method constructs a Pandas DataFrame object for the Tally data
@ -1557,12 +1557,11 @@ class Tally(object):
Include columns with nuclide bin information (default is True).
scores : bool
Include columns with score bin information (default is True).
summary : None or openmc.Summary
An optional Summary object to be used to construct columns for
distribcell tally filters (default is None). The geometric
information in the Summary object is embedded into a Multi-index
column with a geometric "path" to each distribcell intance.
NOTE: This option requires the OpenCG Python package.
distribcell_paths : bool, optional
Construct columns for distribcell tally filters (default is True).
The geometric information in the Summary object is embedded into a
Multi-index column with a geometric "path" to each distribcell
instance.
float_format : str
All floats in the DataFrame will be formatted using the given
format string before printing.
@ -1588,14 +1587,6 @@ class Tally(object):
msg = 'The Tally ID="{0}" has no data to return'.format(self.id)
raise KeyError(msg)
# If using Summary, ensure StatePoint.link_with_summary(...) was called
if summary and not self.with_summary:
msg = 'The Tally ID="{0}" has not been linked with the Summary. ' \
'Call the StatePoint.link_with_summary(...) method ' \
'before using Tally.get_pandas_dataframe(...) with ' \
'Summary info'.format(self.id)
raise KeyError(msg)
# Initialize a pandas dataframe for the tally data
import pandas as pd
df = pd.DataFrame()
@ -1608,7 +1599,8 @@ class Tally(object):
# Append each Filter's DataFrame to the overall DataFrame
for self_filter in self.filters:
filter_df = self_filter.get_pandas_dataframe(data_size, summary)
filter_df = self_filter.get_pandas_dataframe(
data_size, distribcell_paths)
df = pd.concat([df, filter_df], axis=1)
# Include DataFrame column for nuclides if user requested it

10
openmc/universe.py
View file

@ -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, optional
Cells to add to the universe. By default no cells are added.
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

2
src/output.F90
View file

@ -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

9
tests/test_asymmetric_lattice/test_asymmetric_lattice.py
View file

@ -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

5
tests/test_mgxs_library_condense/test_mgxs_library_condense.py
View file

@ -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)

5
tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py
View file

@ -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)

5
tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py
View file

@ -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)

5
tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py
View file

@ -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)

5
tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py
View file

@ -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)

5
tests/test_tally_aggregation/test_tally_aggregation.py
View file

@ -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')

5
tests/test_tally_arithmetic/test_tally_arithmetic.py
View file

@ -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')

5
tests/test_tally_slice_merge/test_tally_slice_merge.py
View file

@ -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')]