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Removed OpenCG dependency for distribcell paths in Pandas DataFrames
This commit is contained in:
Will Boyd
parent
ae083cf5d4
commit
7741b525bb
3 changed files with 208 additions and 484 deletions
File diff suppressed because one or more lines are too long
189
openmc/filter.py
189
openmc/filter.py
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@ -1,4 +1,4 @@
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from collections import Iterable
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from collections import Iterable, OrderedDict
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import copy
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from numbers import Real, Integral
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import sys
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@ -516,7 +516,7 @@ class Filter(object):
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return filter_bin
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def get_pandas_dataframe(self, data_size, summary=None):
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def get_pandas_dataframe(self, data_size, distribcell_paths=False):
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"""Builds a Pandas DataFrame for the Filter's bins.
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This method constructs a Pandas DataFrame object for the filter with
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@ -531,12 +531,10 @@ class Filter(object):
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----------
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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 openmc.Summary
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An optional Summary object to be used to construct columns for
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distribcell tally filters (default is None). The geometric
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distribcell_paths : bool
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Construct columns for distribcell tally filters. The geometric
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information in the Summary object is embedded into a Multi-index
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column with a geometric "path" to each distribcell instance.
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NOTE: This option requires the OpenCG Python package.
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Returns
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-------
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@ -554,7 +552,7 @@ class Filter(object):
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1. a single column with the cell instance IDs (without summary info)
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2. separate columns for the cell IDs, universe IDs, and lattice IDs
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and x,y,z cell indices corresponding to each (with summary info).
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and x,y,z cell indices corresponding to each (distribcell paths).
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For 'energy' and 'energyout' filters, the DataFrame includes one
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column for the lower energy bound and one column for the upper
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@ -566,8 +564,7 @@ class Filter(object):
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Raises
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------
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ImportError
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When Pandas is not installed, or summary info is requested but
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OpenCG is not installed.
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When Pandas is not installed
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See also
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--------
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@ -626,106 +623,108 @@ class Filter(object):
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elif self.type == 'distribcell':
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level_df = None
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if isinstance(summary, Summary):
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# Attempt to import the OpenCG package
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try:
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import opencg
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except ImportError:
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msg = 'The OpenCG package must be installed ' \
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'to use a Summary for distribcell dataframes'
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raise ImportError(msg)
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# Create Pandas Multi-index columns for each level in CSG tree
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if distribcell_paths:
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# Extract the OpenCG geometry from the Summary
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opencg_geometry = summary.opencg_geometry
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openmc_geometry = summary.openmc_geometry
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# FIXME: Make assumption that each path is the same length???
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# NOTE: Just state this caveat in the docstring
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# Use OpenCG to compute the number of regions
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opencg_geometry.initialize_cell_offsets()
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num_regions = opencg_geometry.num_regions
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# Initialize a dictionary mapping OpenMC distribcell
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# offsets to OpenCG LocalCoords linked lists
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offsets_to_coords = {}
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for offset, path in enumerate(self.distribcell_paths):
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region = opencg_geometry.get_region_from_path(path)
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coords = opencg_geometry.find_region(region)
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offsets_to_coords[offset] = coords
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# Each distribcell offset is a DataFrame bin
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# Unravel the paths into DataFrame columns
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num_offsets = len(offsets_to_coords)
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# Initialize termination condition for while loop
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distribcell_paths = copy.deepcopy(self.distribcell_paths)
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num_offsets = len(distribcell_paths)
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levels_remain = True
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counter = 0
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level_counter = 0
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# Iterate over each level in the CSG tree hierarchy
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# FIXME: Allocate NumPy arrays for each CSG level
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while levels_remain:
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levels_remain = False
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level_counter += 1
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level_key = 'level {}'.format(level_counter)
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first_path = distribcell_paths[0]
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level_dict = OrderedDict()
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# Initialize dictionary to build Pandas Multi-index
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# column for this level in the CSG tree hierarchy
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level_dict = {}
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next_index = first_path.index('-')
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level = first_path[:next_index]
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first_path = first_path[next_index+2:]
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# Initialize prefix Multi-index keys
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counter += 1
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level_key = 'level {0}'.format(counter)
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univ_key = (level_key, 'univ', 'id')
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cell_key = (level_key, 'cell', 'id')
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lat_id_key = (level_key, 'lat', 'id')
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lat_x_key = (level_key, 'lat', 'x')
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lat_y_key = (level_key, 'lat', 'y')
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lat_z_key = (level_key, 'lat', 'z')
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# This level is a lattice (e.g., ID(x,y,z))
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if '(' in level:
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level_type = 'lattice'
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# Allocate NumPy arrays for each CSG level and
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# each Multi-index column in the DataFrame
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level_dict[univ_key] = np.empty(num_offsets)
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level_dict[cell_key] = np.empty(num_offsets)
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level_dict[lat_id_key] = np.empty(num_offsets)
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level_dict[lat_x_key] = np.empty(num_offsets)
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level_dict[lat_y_key] = np.empty(num_offsets)
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level_dict[lat_z_key] = np.empty(num_offsets)
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# Initialize prefix Multi-index keys
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lat_id_key = (level_key, 'lat', 'id')
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lat_x_key = (level_key, 'lat', 'x')
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lat_y_key = (level_key, 'lat', 'y')
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lat_z_key = (level_key, 'lat', 'z')
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# Initialize Multi-index columns to NaN - this is
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# necessary since some distribcell instances may
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# have very different LocalCoords linked lists
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level_dict[univ_key][:] = np.NAN
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level_dict[cell_key][:] = np.NAN
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level_dict[lat_id_key][:] = np.NAN
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level_dict[lat_x_key][:] = np.NAN
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level_dict[lat_y_key][:] = np.NAN
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level_dict[lat_z_key][:] = np.NAN
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# Allocate NumPy arrays for each CSG level and
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# each Multi-index column in the DataFrame
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level_dict[lat_id_key] = np.empty(num_offsets)
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level_dict[lat_x_key] = np.empty(num_offsets)
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level_dict[lat_y_key] = np.empty(num_offsets)
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level_dict[lat_z_key] = np.empty(num_offsets)
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# Iterate over all regions (distribcell instances)
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for offset in range(num_offsets):
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coords = offsets_to_coords[offset]
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# This level is a universe / cell (e.g., ID->ID)
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else:
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level_type = 'universe'
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# If entire LocalCoords has been unraveled into
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# Multi-index columns already, continue
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if coords is None:
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continue
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# Assign entry to Universe Multi-index column
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if coords._type == 'universe':
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level_dict[univ_key][offset] = coords._universe._id
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level_dict[cell_key][offset] = coords._cell._id
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# Assign entry to Lattice Multi-index column
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# Pop off the cell ID from the path
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if '-' in first_path:
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next_index = first_path.index('-')
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level = first_path[:next_index]
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first_path = first_path[next_index+2:]
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else:
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# Reverse y index per lattice ordering in OpenCG
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level_dict[lat_id_key][offset] = coords._lattice._id
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level_dict[lat_x_key][offset] = coords._lat_x
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level_dict[lat_y_key][offset] = \
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coords._lattice.dimension[1] - coords._lat_y - 1
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level_dict[lat_z_key][offset] = coords._lat_z
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levels_remain = False
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# Initialize prefix Multi-index keys
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univ_key = (level_key, 'univ', 'id')
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cell_key = (level_key, 'cell', 'id')
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# Allocate NumPy arrays for each CSG level and
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# each Multi-index column in the DataFrame
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level_dict[univ_key] = np.empty(num_offsets)
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level_dict[cell_key] = np.empty(num_offsets)
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# Populate Multi-index arrays with all distribcell paths
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for i, path in enumerate(distribcell_paths):
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if level_type == 'lattice':
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# Extract lattice ID, indices from path
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next_index = path.index('-')
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lat_id_indices = path[:next_index]
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# Trim lattice info from distribcell path
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distribcell_paths[i] = path[next_index+2:]
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# Extract the lattice cell indices from the path
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i1 = lat_id_indices.index('(')
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i2 = lat_id_indices.index(')')
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i3 = lat_id_indices[i1+1:i2]
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# Assign entry to Lattice Multi-index column
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level_dict[lat_id_key][i] = path[:i1]
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level_dict[lat_x_key][i] = int(i3.split(',')[0]) - 1
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level_dict[lat_y_key][i] = int(i3.split(',')[1]) - 1
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level_dict[lat_z_key][i] = int(i3.split(',')[2]) - 1
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# Move to next node in LocalCoords linked list
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if coords._next is None:
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offsets_to_coords[offset] = None
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else:
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offsets_to_coords[offset] = coords._next
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levels_remain = True
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# Extract universe ID from path
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next_index = path.index('-')
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universe_id = int(path[:next_index])
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# Trim universe info from distribcell path
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path = path[next_index+2:]
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# Extract cell ID from path
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if '-' in path:
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next_index = path.index('-')
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cell_id = int(path[:next_index])
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distribcell_paths[i] = path[next_index+2:]
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else:
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cell_id = int(path)
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distribcell_paths[i] = ''
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# Assign entry to Universe, Cell Multi-index columns
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level_dict[univ_key][i] = universe_id
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level_dict[cell_key][i] = cell_id
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# Tile the Multi-index columns
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for level_key, level_bins in level_dict.items():
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@ -1538,8 +1538,8 @@ class Tally(object):
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return data
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def get_pandas_dataframe(self, filters=True, nuclides=True,
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scores=True, summary=None, float_format='{:.2e}'):
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def get_pandas_dataframe(self, filters=True, nuclides=True, scores=True,
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distribcell_paths=False, float_format='{:.2e}'):
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"""Build a Pandas DataFrame for the Tally data.
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This method constructs a Pandas DataFrame object for the Tally data
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@ -1557,12 +1557,10 @@ class Tally(object):
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Include columns with nuclide bin information (default is True).
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scores : bool
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Include columns with score bin information (default is True).
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summary : None or openmc.Summary
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An optional Summary object to be used to construct columns for
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distribcell tally filters (default is None). The geometric
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distribcell_paths : bool
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Construct columns for distribcell tally filters. The geometric
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information in the Summary object is embedded into a Multi-index
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column with a geometric "path" to each distribcell intance.
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NOTE: This option requires the OpenCG Python package.
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column with a geometric "path" to each distribcell instance.
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float_format : str
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All floats in the DataFrame will be formatted using the given
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format string before printing.
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@ -1588,13 +1586,15 @@ class Tally(object):
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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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'''
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# If using Summary, ensure StatePoint.link_with_summary(...) was called
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if summary and not self.with_summary:
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if distribcell_pathssummary and not self.with_summary:
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msg = 'The Tally ID="{0}" has not been linked with the Summary. ' \
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'Call the StatePoint.link_with_summary(...) method ' \
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'before using Tally.get_pandas_dataframe(...) with ' \
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'Summary info'.format(self.id)
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raise KeyError(msg)
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'''
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# Initialize a pandas dataframe for the tally data
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import pandas as pd
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@ -1608,7 +1608,8 @@ class Tally(object):
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# Append each Filter's DataFrame to the overall DataFrame
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for self_filter in self.filters:
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filter_df = self_filter.get_pandas_dataframe(data_size, summary)
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filter_df = self_filter.get_pandas_dataframe(
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data_size, distribcell_paths)
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df = pd.concat([df, filter_df], axis=1)
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# Include DataFrame column for nuclides if user requested it
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