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Fixed some bugs in tally sparsification - tally arithmetic ipython notebook now working
This commit is contained in:
wbinventor@gmail.com
parent
e613658930
commit
963238194b
1 changed files with 127 additions and 70 deletions
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@ -83,6 +83,11 @@ class Tally(object):
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An array containing the sample mean for each bin
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std_dev : ndarray
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An array containing the sample standard deviation for each bin
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derived : bool
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Whether or not the tally is derived from one or more other tallies
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sparse : bool
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Whether or not the tally uses SciPy's LIL sparse matrix format for
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compressed data storage
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"""
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@ -246,6 +251,10 @@ class Tally(object):
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def scores(self):
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return self._scores
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@property
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def shape(self):
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return (self.num_filter_bins, self.num_nuclides, self.num_score_bins)
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@property
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def num_scores(self):
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return len(self._scores)
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@ -308,21 +317,18 @@ class Tally(object):
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return 1 if not val else val
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# Reshape the results arrays
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new_shape = (nonzero(self.num_filter_bins),
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nonzero(self.num_nuclides),
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nonzero(self.num_score_bins))
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sum = np.reshape(sum, new_shape)
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sum_sq = np.reshape(sum_sq, new_shape)
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sum = np.reshape(sum, self.shape)
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sum_sq = np.reshape(sum_sq, self.shape)
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# Set the data for this Tally
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self._sum = sum
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self._sum_sq = sum_sq
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# Convert NumPy arrays to SciPy sparse matrices
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# Convert NumPy arrays to SciPy sparse LIL matrices
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if self.sparse:
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self._sum = sps.lil_matrix(self._sum)
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self._sum_sq = sps.lil_matrix(self._sum_sq)
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self._sum = sps.lil_matrix(self._sum.flatten(), self._sum.shape)
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self._sum_sq = \
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sps.lil_matrix(self._sum_sq.flatten(), self._sum_sq.shape)
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# Indicate that Tally results have been read
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self._results_read = True
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@ -331,7 +337,7 @@ class Tally(object):
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f.close()
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if self.sparse:
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return self._sum.toarray()
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return np.reshape(self._sum.toarray(), self.shape)
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else:
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return self._sum
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@ -345,7 +351,7 @@ class Tally(object):
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self.sum
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if self.sparse:
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return self._sum_sq.toarray()
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return np.reshape(self._sum_sq.toarray(), self.shape)
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else:
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return self._sum_sq
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@ -357,12 +363,13 @@ class Tally(object):
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self._mean = self.sum / self.num_realizations
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# Convert NumPy arrays to SciPy sparse matrices
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# Convert NumPy array to SciPy sparse LIL matrix
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if self.sparse:
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self._mean = sps.lil_matrix(self._mean)
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self._mean = \
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sps.lil_matrix(self._mean.flatten(), self._mean.shape)
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if self.sparse:
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return self._mean.toarray()
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return np.reshape(self._mean.toarray(), self.shape)
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else:
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return self._mean
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@ -378,14 +385,15 @@ class Tally(object):
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self._std_dev[nonzero] = np.sqrt((self.sum_sq[nonzero]/n -
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self.mean[nonzero]**2)/(n - 1))
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# Convert NumPy arrays to SciPy sparse matrices
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# Convert NumPy array to SciPy sparse LIL matrix
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if self.sparse:
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self._std_dev = sps.lil_matrix(self._std_dev)
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self._std_dev = \
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sps.lil_matrix(self._std_dev.flatten(), self._std_dev.shape)
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self.with_batch_statistics = True
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if self.sparse:
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return self._std_dev.toarray()
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return np.reshape(self._std_dev.toarray(), self.shape)
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else:
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return self._std_dev
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@ -1099,22 +1107,19 @@ class Tally(object):
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Raises
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------
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ValueError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method. ValueError is also thrown
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if the input parameters do not correspond to the Tally's attributes,
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When this method is called before the Tally is populated with data,
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or the input parameters do not correspond to the Tally's attributes,
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e.g., if the score(s) do not match those in the Tally.
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"""
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# Ensure that StatePoint.read_results() was called first
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# Ensure that the tally has data
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if (value == 'mean' and self.mean is None) or \
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(value == 'std_dev' and self.std_dev is None) or \
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(value == 'rel_err' and self.mean is None) or \
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(value == 'sum' and self.sum is None) or \
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(value == 'sum_sq' and self.sum_sq is None):
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msg = 'The Tally ID="{0}" has no data to return. Call the ' \
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'StatePoint.read_results() method before using ' \
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'Tally.get_values(...)'.format(self.id)
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msg = 'The Tally ID="{0}" has no data to return'.format(self.id)
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raise ValueError(msg)
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# Get filter, nuclide and score indices
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@ -1148,20 +1153,41 @@ class Tally(object):
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"""
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"""
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# FIXME: pandas dataframes, summation
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if self._sum is not None:
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self._sum = \
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sps.lil_matrix(self._sum.flatten(), self._sum.shape)
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if self._sum_sq is not None:
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self._sum_sq = \
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sps.lil_matrix(self._sum_sq.flatten(), self._sum_sq.shape)
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if self._mean is not None:
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self._mean = \
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sps.lil_matrix(self._mean.flatten(), self._mean.shape)
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if self._std_dev is not None:
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self._std_dev = \
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sps.lil_matrix(self._std_dev.flatten(), self._std_dev.shape)
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self._sparse = True
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# FIXME: get_values
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# FIXME: each of the properties which load in the data
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# FIXME: pandas dataframes
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# summation, slicing
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if self._sum:
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self._sum = sps.lil_matrix(self._sum)
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if self._sum_sq:
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self._sum_sq = sps.lil_matrix(self._sum_sq)
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if self._mean:
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self._mean = sps.lil_matrix(self._mean)
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if self._std_dev:
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self._std_dev = sps.lil_matrix(self._std_dev)
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def densify(self):
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"""
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"""
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# If the tally is already dense, simply return
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if not self._sparse:
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return
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# FIXME: pandas dataframes, summation
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if self._sum is not None:
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self._sum = np.reshape(self._sum.toarray(), self.shape)
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if self._sum_sq is not None:
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self._sum_sq = np.reshape(self._sum_sq.toarray(), self.shape)
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if self._mean is not None:
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self._mean = np.reshape(self._mean.toarray(), self.shape)
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if self._std_dev is not None:
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self._std_dev = np.reshape(self._std_dev.toarray(), self.shape)
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self._sparse = False
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def get_pandas_dataframe(self, filters=True, nuclides=True,
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scores=True, summary=None):
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@ -1200,17 +1226,14 @@ class Tally(object):
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------
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KeyError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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ImportError
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When Pandas can not be found on the caller's system
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"""
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# Ensure that StatePoint.read_results() was called first
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# Ensure that the tally has data
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if self.mean is None or self.std_dev is None:
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msg = 'The Tally ID="{0}" has no data to return. Call the ' \
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'StatePoint.read_results() method before using ' \
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'Tally.get_pandas_dataframe(...)'.format(self.id)
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msg = 'The Tally ID="{0}" has no data to return'.format(self.id)
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raise KeyError(msg)
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# If using Summary, ensure StatePoint.link_with_summary(...) was called
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@ -1356,16 +1379,13 @@ class Tally(object):
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Raises
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------
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KeyError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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When this method is called before the Tally is populated with data.
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"""
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# Ensure that StatePoint.read_results() was called first
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# Ensure that the tally has data
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if self._sum is None or self._sum_sq is None and not self.derived:
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msg = 'The Tally ID="{0}" has no data to export. Call the ' \
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'StatePoint.read_results() method before using ' \
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'Tally.export_results(...)'.format(self.id)
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msg = 'The Tally ID="{0}" has no data to export'.format(self.id)
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raise KeyError(msg)
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if not isinstance(filename, basestring):
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@ -1527,7 +1547,7 @@ class Tally(object):
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------
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ValueError
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When this method is called before the other tally is populated
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with data by the StatePoint.read_results() method.
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with data.
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"""
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@ -1708,6 +1728,12 @@ class Tally(object):
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"""
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# Use dense NumPy arrays for data alignment operations
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self_sparse = self.sparse
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other_sparse = other.sparse
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self.densify()
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other.densify()
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# Get the set of filters that each tally is missing
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other_missing_filters = set(self.filters).difference(set(other.filters))
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self_missing_filters = set(other.filters).difference(set(self.filters))
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@ -1769,7 +1795,7 @@ class Tally(object):
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# If necessary, swap other nuclide
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if other_index != i:
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other.swap_nuclides(nuclide, other.nuclides[i])
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other.swap_nuclides(nuclide, other.nuclides[i])
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# Repeat and tile the data by score in preparation for performing
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# the tensor product across scores.
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@ -1820,6 +1846,12 @@ class Tally(object):
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filter.stride = stride
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stride *= filter.num_bins
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# Restore tally operands to sparse storage
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if self.sparse:
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self.sparsify()
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if other.sparse:
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other.sparsify()
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# Deep copy the mean and std dev data
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self_mean = copy.deepcopy(self.mean)
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self_std_dev = copy.deepcopy(self.std_dev)
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@ -1864,7 +1896,7 @@ class Tally(object):
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------
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ValueError
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If this is a derived tally or this method is called before the tally
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is populated with data by the StatePoint.read_results() method.
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is populated with data.
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"""
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@ -1981,7 +2013,7 @@ class Tally(object):
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------
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ValueError
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If this is a derived tally or this method is called before the tally
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is populated with data by the StatePoint.read_results() method.
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is populated with data.
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"""
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@ -2036,7 +2068,7 @@ class Tally(object):
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------
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ValueError
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If this is a derived tally or this method is called before the tally
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is populated with data by the StatePoint.read_results() method.
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is populated with data.
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"""
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@ -2103,8 +2135,7 @@ class Tally(object):
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Raises
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------
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ValueError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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When this method is called before the Tally is populated with data.
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"""
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@ -2136,6 +2167,10 @@ class Tally(object):
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for score in self.scores:
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new_tally.add_score(score)
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# If original tally operands were sparse, sparsify the sliced tally
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if self.sparse and other.sparse:
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new_tally.sparsify()
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else:
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msg = 'Unable to add "{0}" to Tally ID="{1}"'.format(other, self.id)
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raise ValueError(msg)
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@ -2173,8 +2208,7 @@ class Tally(object):
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Raises
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------
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ValueError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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When this method is called before the Tally is populated with data.
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"""
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@ -2205,6 +2239,10 @@ class Tally(object):
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for score in self.scores:
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new_tally.add_score(score)
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# If original tally operands were sparse, sparsify the sliced tally
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if self.sparse and other.sparse:
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new_tally.sparsify()
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else:
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msg = 'Unable to subtract "{0}" from Tally ' \
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'ID="{1}"'.format(other, self.id)
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@ -2243,8 +2281,7 @@ class Tally(object):
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Raises
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------
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ValueError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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When this method is called before the Tally is populated with data.
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"""
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@ -2275,6 +2312,10 @@ class Tally(object):
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for score in self.scores:
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new_tally.add_score(score)
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# If original tally operands were sparse, sparsify the sliced tally
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if self.sparse and other.sparse:
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new_tally.sparsify()
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else:
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msg = 'Unable to multiply Tally ID="{0}" ' \
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'by "{1}"'.format(self.id, other)
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@ -2313,8 +2354,7 @@ class Tally(object):
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Raises
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------
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ValueError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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When this method is called before the Tally is populated with data.
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"""
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@ -2345,6 +2385,10 @@ class Tally(object):
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for score in self.scores:
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new_tally.add_score(score)
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# If original tally operands were sparse, sparsify the sliced tally
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if self.sparse and other.sparse:
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new_tally.sparsify()
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else:
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msg = 'Unable to divide Tally ID="{0}" ' \
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'by "{1}"'.format(self.id, other)
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@ -2386,8 +2430,7 @@ class Tally(object):
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Raises
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------
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ValueError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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When this method is called before the Tally is populated with data.
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"""
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@ -2419,6 +2462,10 @@ class Tally(object):
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for score in self.scores:
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new_tally.add_score(score)
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# If original tally was sparse, sparsify the exponentiated tally
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if self.sparse:
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new_tally.sparsify()
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else:
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msg = 'Unable to raise Tally ID="{0}" to ' \
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'power "{1}"'.format(self.id, power)
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@ -2569,12 +2616,11 @@ class Tally(object):
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Raises
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------
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ValueError
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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When this method is called before the Tally is populated with data.
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"""
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# Ensure that StatePoint.read_results() was called first
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# Ensure that the tally has data
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if not self.derived and self.sum is None:
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msg = 'Unable to use tally arithmetic with Tally ID="{0}" ' \
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'since it does not contain any results.'.format(self.id)
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@ -2657,6 +2703,10 @@ class Tally(object):
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filter.stride = stride
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stride *= filter.num_bins
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# If original tally was sparse, sparsify the sliced tally
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if self.sparse:
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new_tally.sparsify()
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return new_tally
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def summation(self, scores=[], filter_type=None,
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@ -2759,6 +2809,10 @@ class Tally(object):
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filters[i] = CrossFilter(filters[i-1], filters[i], '+')
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tally_sum.add_filter(filters[-1])
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# If original tally was sparse, sparsify the tally summation
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if self.sparse:
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tally_sum.sparsify()
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return tally_sum
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def diagonalize_filter(self, new_filter):
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@ -2799,7 +2853,6 @@ class Tally(object):
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num_filter_bins = new_tally.num_filter_bins
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num_nuclides = new_tally.num_nuclides
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num_score_bins = new_tally.num_score_bins
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new_shape = (num_filter_bins, num_nuclides, num_score_bins)
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# Determine "base" indices along the new "diagonal", and the factor
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# by which the "base" indices should be repeated to account for all
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@ -2816,16 +2869,16 @@ class Tally(object):
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# Inject this Tally's data along the diagonal of the diagonalized Tally
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if self.sum is not None:
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new_tally._sum = np.zeros(new_shape, dtype=np.float64)
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new_tally._sum = np.zeros(self.shape, dtype=np.float64)
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new_tally._sum[diag_indices, :, :] = self.sum
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if self.sum_sq is not None:
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||||
new_tally._sum_sq = np.zeros(new_shape, dtype=np.float64)
|
||||
new_tally._sum_sq = np.zeros(self.shape, dtype=np.float64)
|
||||
new_tally._sum_sq[diag_indices, :, :] = self.sum_sq
|
||||
if self.mean is not None:
|
||||
new_tally._mean = np.zeros(new_shape, dtype=np.float64)
|
||||
new_tally._mean = np.zeros(self.shape, dtype=np.float64)
|
||||
new_tally._mean[diag_indices, :, :] = self.mean
|
||||
if self.std_dev is not None:
|
||||
new_tally._std_dev = np.zeros(new_shape, dtype=np.float64)
|
||||
new_tally._std_dev = np.zeros(self.shape, dtype=np.float64)
|
||||
new_tally._std_dev[diag_indices, :, :] = self.std_dev
|
||||
|
||||
# Correct each Filter's stride
|
||||
|
|
@ -2834,6 +2887,10 @@ class Tally(object):
|
|||
filter.stride = stride
|
||||
stride *= filter.num_bins
|
||||
|
||||
# If original tally was sparse, sparsify the diagonalized tally
|
||||
if self.sparse:
|
||||
new_tally.sparsify
|
||||
|
||||
return new_tally
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue