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https://github.com/openmc-dev/openmc.git
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Fixed bugs in pandas dataframe construction for AggregateFilter
This commit is contained in:
wbinventor@gmail.com
parent
077eff7f19
commit
184ed3733b
5 changed files with 146 additions and 51 deletions
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@ -1,5 +1,7 @@
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import sys
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import copy
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from numbers import Integral
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from collections import Iterable
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import numpy as np
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@ -430,7 +432,7 @@ class CrossFilter(object):
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filter_index = left_index * self.right_filter.num_bins + right_index
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return filter_index
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def get_pandas_dataframe(self, datasize, summary=None):
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def get_pandas_dataframe(self, data_size, summary=None):
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"""Builds a Pandas DataFrame for the CrossFilter's bins.
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This method constructs a Pandas DataFrame object for the CrossFilter
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@ -445,7 +447,7 @@ class CrossFilter(object):
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Parameters
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----------
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datasize : Integral
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data_size : Integral
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The total number of bins in the tally corresponding to this filter
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summary : None or Summary
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An optional Summary object to be used to construct columns for
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@ -472,12 +474,12 @@ class CrossFilter(object):
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# If left and right filters are identical, do not combine bins
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if self.left_filter == self.right_filter:
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df = self.left_filter.get_pandas_dataframe(datasize, summary)
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df = self.left_filter.get_pandas_dataframe(data_size, summary)
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# If left and right filters are different, combine their bins
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else:
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left_df = self.left_filter.get_pandas_dataframe(datasize, summary)
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right_df = self.right_filter.get_pandas_dataframe(datasize, summary)
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left_df = self.left_filter.get_pandas_dataframe(data_size, summary)
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right_df = self.right_filter.get_pandas_dataframe(data_size, summary)
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left_df = left_df.astype(str)
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right_df = right_df.astype(str)
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df = '(' + left_df + ' ' + self.binary_op + ' ' + right_df + ')'
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@ -713,6 +715,13 @@ class AggregateFilter(object):
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def __ne__(self, other):
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return not self == other
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def __gt__(self, other):
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# FIXME
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return False
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def __lt__(self, other):
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return not self > other
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def __repr__(self):
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string = 'AggregateFilter\n'
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string += '{0: <16}{1}{2}\n'.format('\tType', '=\t', self.type)
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@ -757,7 +766,7 @@ class AggregateFilter(object):
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@property
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def num_bins(self):
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return 1 if self.aggregate_filter else 0
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return len(self.bins) if self.aggregate_filter else 0
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@property
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def stride(self):
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@ -779,8 +788,10 @@ class AggregateFilter(object):
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@bins.setter
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def bins(self, bins):
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cv.check_iterable_type('bins', bins, (Integral, tuple))
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self._bins = bins
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cv.check_iterable_type('bins', bins, Iterable)
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self._bins = []
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for bin in bins:
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self._bins.append(tuple(bin))
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@aggregate_op.setter
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def aggregate_op(self, aggregate_op):
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@ -825,9 +836,9 @@ class AggregateFilter(object):
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'"{0}" is not one of the bins'.format(filter_bin)
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raise ValueError(msg)
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else:
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return 0
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return self.bins.index(filter_bin)
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def get_pandas_dataframe(self, datasize, summary=None):
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def get_pandas_dataframe(self, data_size, summary=None):
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"""Builds a Pandas DataFrame for the AggregateFilter's bins.
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This method constructs a Pandas DataFrame object for the AggregateFilter
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@ -836,7 +847,7 @@ class AggregateFilter(object):
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Parameters
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----------
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datasize : Integral
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data_size : Integral
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The total number of bins in the tally corresponding to this filter
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summary : None or Summary
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An optional Summary object to be used to construct columns for
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@ -864,14 +875,85 @@ class AggregateFilter(object):
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import pandas as pd
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# Construct a sring representing the filter aggregation
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aggregate_bin = '{0}('.format(self.aggregate_op)
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aggregate_bin += ', '.join(map(str, self.bins)) + ')'
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# Create NumPy array of the bin tuples for repeating / tiling
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filter_bins = np.empty(self.num_bins, dtype=tuple)
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for i, bin in enumerate(self.bins):
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filter_bins[i] = bin
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# Construct NumPy array of bin repeated for each element in dataframe
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aggregate_bin_array = np.array([aggregate_bin])
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aggregate_bin_array = np.repeat(aggregate_bin_array, datasize)
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# Repeat and tile bins as needed for DataFrame
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filter_bins = np.repeat(filter_bins, self.stride)
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tile_factor = data_size / len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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# Construct Pandas DataFrame for the AggregateFilter
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df = pd.DataFrame({self.type: aggregate_bin_array})
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# Create DataFrame with aggregated bins
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df = pd.DataFrame({self.type: filter_bins})
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return df
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def can_merge(self, other):
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"""Determine if AggregateFilter can be merged with another.
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Parameters
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----------
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other : AggregateFilter
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Filter to compare with
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Returns
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-------
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bool
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Whether the filter can be merged
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"""
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if not isinstance(other, AggregateFilter):
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return False
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# Filters must be of the same type
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elif self.type != other.type:
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return False
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# None of the bins in this filter should match in the other filter
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for bin in self.bins:
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if bin in other.bins:
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return False
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# None of the bins in the other filter should match in this filter
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for bin in other.bins:
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if bin in self.bins:
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return False
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# If all conditional checks passed then filters are mergeable
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return True
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def merge(self, other):
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"""Merge this aggregatefilter with another.
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Parameters
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----------
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other : AggregateFilter
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Filter to merge with
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Returns
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-------
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merged_filter : AggregateFilter
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Filter resulting from the merge
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"""
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if not self.can_merge(other):
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msg = 'Unable to merge "{0}" with "{1}" ' \
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'filters'.format(self.type, other.type)
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raise ValueError(msg)
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# Create deep copy of filter to return as merged filter
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merged_filter = copy.deepcopy(self)
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# Merge unique filter bins
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merged_bins = self.bins + other.bins
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# Sort energy bin edges
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if 'energy' in self.type:
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merged_bins = sorted(merged_bins)
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# Assign merged bins to merged filter
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merged_filter.bins = list(merged_bins)
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return merged_filter
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@ -263,11 +263,11 @@ class Filter(object):
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return False
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# Filters must be of the same type
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elif self.type != other.type:
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if self.type != other.type:
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return False
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# Distribcell filters cannot have more than one bin
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elif self.type == 'distribcell':
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if self.type == 'distribcell':
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return False
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# Mesh filters cannot have more than one bin
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@ -289,6 +289,24 @@ class Filter(object):
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else:
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return True
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'''
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# FIXME: Should all bins be completely separate???
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# FIMXE: This is necessary if merging will choose out unique bins
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else:
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# None of the bins in this filter should match in the other filter
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for bin in self.bins:
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if bin in other.bins:
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return False
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# None of the bins in the other filter should match in this filter
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for bin in other.bins:
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if bin in self.bins:
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return False
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# If all conditional checks pass then filters are mergeable
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return True
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'''
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def merge(self, other):
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"""Merge this filter with another.
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@ -313,7 +331,8 @@ class Filter(object):
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merged_filter = copy.deepcopy(self)
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# Merge unique filter bins
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merged_bins = set(np.concatenate((self.bins, other.bins)))
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merged_bins = np.concatenate((self.bins, other.bins))
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merged_bins = np.unique(merged_bins)
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# Sort energy bin edges
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if 'energy' in self.type:
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@ -557,14 +576,8 @@ class Filter(object):
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"""
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# Attempt to import Pandas
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try:
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import pandas as pd
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except ImportError:
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msg = 'The Pandas Python package must be installed on your system'
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raise ImportError(msg)
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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# mesh filters
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@ -743,7 +756,6 @@ class Filter(object):
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filter_bins = np.repeat(filter_bins, self.stride)
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tile_factor = data_size / len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_bins = filter_bins
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df = pd.DataFrame({self.type : filter_bins})
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# If OpenCG level info DataFrame was created, concatenate
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@ -1368,7 +1368,6 @@ class MGXS(object):
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# Sort the dataframe by domain type id (e.g., distribcell id) and
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# energy groups such that data is from fast to thermal
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df.sort([self.domain_type] + columns, inplace=True)
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return df
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@ -301,7 +301,7 @@ class Tally(object):
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@property
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def sum(self):
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if not self._sp_filename:
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if not self._sp_filename or self.derived:
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return None
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if not self._results_read:
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@ -924,6 +924,17 @@ class Tally(object):
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if score not in merged_tally.scores:
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merged_tally.add_score(score)
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# Add triggers from other tally to merged tally
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for trigger in other.triggers:
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merged_tally.add_trigger(trigger)
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# If results have not been read, then return tally for input generation
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if self._sp_filename is None:
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return merged_tally
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#Otherwise, this is a derived tally which needs merged results arrays
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else:
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self._derived = True
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# Update filter strides in merged tally
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merged_tally._update_filter_strides()
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@ -986,10 +997,6 @@ class Tally(object):
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# Sparsify merged tally if both tallies are sparse
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merged_tally.sparse = self.sparse and other.sparse
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# Add triggers from other tally to merged tally
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for trigger in other.triggers:
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merged_tally.add_trigger(trigger)
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return merged_tally
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def get_tally_xml(self):
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@ -1538,14 +1545,8 @@ class Tally(object):
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'Summary info'.format(self.id)
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raise KeyError(msg)
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# Attempt to import Pandas
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try:
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import pandas as pd
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except ImportError:
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msg = 'The Pandas Python package must be installed on your system'
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raise ImportError(msg)
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# Initialize a pandas dataframe for the tally data
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import pandas as pd
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df = pd.DataFrame()
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# Find the total length of the tally data array
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@ -2189,8 +2190,8 @@ class Tally(object):
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# 'since it does not contain any results.'.format(self.id)
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# raise ValueError(msg)
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cv.check_type('filter1', filter1, Filter)
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cv.check_type('filter2', filter2, Filter)
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cv.check_type('filter1', filter1, (Filter, CrossFilter, AggregateFilter))
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cv.check_type('filter2', filter2, (Filter, CrossFilter, AggregateFilter))
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# Check that the filters exist in the tally and are not the same
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if filter1 == filter2:
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@ -2923,6 +2924,7 @@ class Tally(object):
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# Create deep copy of tally to return as sliced tally
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new_tally = copy.deepcopy(self)
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new_tally._derived = True
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# Differentiate Tally with a new auto-generated Tally ID
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new_tally.id = None
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@ -3094,7 +3096,7 @@ class Tally(object):
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# Add AggregateFilter to the tally sum
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if not remove_filter:
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filter_sum = \
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AggregateFilter(self_filter, filter_bins, 'sum')
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AggregateFilter(self_filter, [tuple(filter_bins)], 'sum')
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tally_sum.add_filter(filter_sum)
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# Add a copy of each filter not summed across to the tally sum
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@ -3243,7 +3245,7 @@ class Tally(object):
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# Add AggregateFilter to the tally avg
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if not remove_filter:
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filter_sum = \
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AggregateFilter(self_filter, filter_bins, 'avg')
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AggregateFilter(self_filter, [tuple(filter_bins)], 'avg')
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tally_avg.add_filter(filter_sum)
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# Add a copy of each filter not averaged across to the tally avg
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@ -1,5 +1,5 @@
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sum(distribcell) group in nuclide mean std. dev.
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0 sum(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ... 1 total 0.720213 1.424323 sum(distribcell) group in nuclide mean std. dev.
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0 sum(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ... 1 total 0 0 sum(distribcell) group in group out nuclide mean std. dev.
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0 sum(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ... 1 1 total 0.70466 1.403916 sum(distribcell) group out nuclide mean std. dev.
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0 sum(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ... 1 total 0 0
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.720213 1.424323 sum(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0 0 sum(distribcell) group in group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.70466 1.403916 sum(distribcell) group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0 0
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