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Revised comments for Tally.get_pandas_dataframe(...)
This commit is contained in:
Will Boyd
parent
6aa48c921b
commit
c5f7f8efea
1 changed files with 92 additions and 39 deletions
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@ -1,13 +1,13 @@
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import copy
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import os
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from xml.etree import ElementTree as ET
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import numpy as np
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from openmc import Mesh, Filter, Trigger, Nuclide
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from openmc.summary import Summary
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from openmc.clean_xml import *
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from openmc.checkvalue import *
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from openmc.constants import *
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from openmc.summary import Summary
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# "Static" variable for auto-generated Tally IDs
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@ -689,26 +689,33 @@ class Tally(object):
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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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"""Exports tallly results to an HDF5 or Python pickle binary file.
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"""Build a Pandas DataFrame for the Tally data.
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This routine constructs a Pandas DataFrame object for the Tally data
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with columns annotated by filter, nuclide and score bin information.
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This capability has been tested for Pandas >=v0.13.1. However, it is
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recommended to use the v0.16 or newer versions of Pandas since this
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this routine uses the Multi-index Pandas feature, if possible.
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Parameters
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----------
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filename : str
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The name of the file for the results (default is 'tally-results')
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filters : bool
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Include columns with filter bin information (default is True).
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directory : str
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The name of the directory for the results (default is '.')
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nuclides : bool
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Include columns with nuclide bin information (default is True).
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format : str
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The format for the exported file - HDF5 ('hdf5', default) and
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Python pickle ('pkl') files are supported.
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scores : bool
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Include columns with score bin information (default is True).
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append : bool
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Whether or not to append the results to the file (default is True)
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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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for distribcell tally filters (default is None). 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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"""
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# FIXME: docstring this bitch
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# Attempt to import the pandas package
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try:
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import pandas as pd
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@ -719,13 +726,13 @@ class Tally(object):
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# Compute batch statistics if not yet computed
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self.compute_std_dev()
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# Attempt to import the pandas package
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data_size = self.sum.size
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# Initialize a pandas dataframe for the tally data
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df = pd.DataFrame()
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# Include columns for filters if user requested them
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# Find the total length of the tally data array
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data_size = self.sum.size
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# Build DataFrame columns for filters if user requested them
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if filters:
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for filter in self.filters:
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@ -733,16 +740,21 @@ class Tally(object):
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# mesh filters
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if filter.type == 'mesh':
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# Initialize dictionary to build Pandas Multi-index column
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filter_dict = {}
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# Append Mesh ID as outermost index of mult-index
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mesh_id = filter.mesh.id
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mesh_key = 'mesh {0}'.format(mesh_id)
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# Find mesh dimensions - use 3D indices for simplicity
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if (len(filter.mesh.dimension) == 3):
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nx, ny, nz = filter.mesh.dimension
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else:
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nx, ny = filter.mesh.dimension
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nz = 1
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filter_dict = {}
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mesh_id = filter.mesh.id
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mesh_key = 'mesh {0}'.format(mesh_id)
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# Generate multi-index sub-column for x-axis
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filter_bins = np.arange(1, nx+1)
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repeat_factor = ny * nz * filter.stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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@ -750,6 +762,7 @@ class Tally(object):
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'x')] = filter_bins
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# Generate multi-index sub-column for y-axis
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filter_bins = np.arange(1, ny+1)
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repeat_factor = nz * filter.stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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@ -757,6 +770,7 @@ class Tally(object):
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'y')] = filter_bins
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# Generate multi-index sub-column for z-axis
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filter_bins = np.arange(1, nz+1)
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repeat_factor = filter.stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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@ -764,6 +778,7 @@ class Tally(object):
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_dict[(mesh_key, 'z')] = filter_bins
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# Append the multi-index column to the DataFrame
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df = pd.concat([df, pd.DataFrame(filter_dict)], axis=1)
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# distribcell filters
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@ -771,13 +786,15 @@ class Tally(object):
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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 Python package must be installed ' \
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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 and extract the OpenCG geometry the Summary
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summary.make_opencg_geometry()
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opencg_goemetry = summary.opencg_geometry
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openmc_geometry = summary.openmc_geometry
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@ -786,27 +803,42 @@ class Tally(object):
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opencg_goemetry.initializeCellOffsets()
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num_regions = opencg_goemetry._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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# Use OpenCG to compute LocalCoords linked list for
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# each region and store in dictionary
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for region in range(num_regions):
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coords = opencg_goemetry.findRegion(region)
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path = opencg.get_path(coords)
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cell_id = path[-1]
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# If this region is in Cell corresponding to the
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# distribcell filter bin, store it in dictionary
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if cell_id == filter._bins[0]:
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offset = openmc_geometry.get_offset(path, filter.offset)
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offset = openmc_geometry.get_offset(path,
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filter.offset)
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offsets_to_coords[offset] = coords
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# The offset is the dataframe bin, the path we must unravel into columns
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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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levels_remain = True
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counter = 1
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counter = 0
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# Iterate over each level in the CSG tree hierarchy
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while(levels_remain):
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levels_remain = False
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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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# 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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@ -815,12 +847,18 @@ class Tally(object):
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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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# 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 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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@ -828,18 +866,23 @@ class Tally(object):
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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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# 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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# If entire LocalCoords has been unraveled into
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# Multi-index columns already, continue
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if coords == 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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univ_id = coords._universe._id
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cell_id = coords._cell._id
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level_dict[univ_key][offset] = univ_id
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level_dict[cell_key][offset] = cell_id
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# Assign entry to Lattice Multi-index column
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else:
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lat_id = coords._lattice._id
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lat_x = coords._lat_x
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@ -850,22 +893,26 @@ class Tally(object):
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level_dict[lat_y_key][offset] = lat_y
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level_dict[lat_z_key][offset] = lat_z
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# Move to next node in LocalCoords linked list
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if coords._next == 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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# FIXME: must tile, repeat these
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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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level_bins = np.repeat(level_bins, filter.stride)
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level_bins = np.repeat(level_bins,filter.stride)
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tile_factor = data_size / len(level_bins)
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level_bins = np.tile(level_bins, tile_factor)
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level_dict[level_key] = level_bins
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# Append the multi-index column to the DataFrame
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df = pd.concat([df,pd.DataFrame(level_dict)],axis=1)
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df = pd.concat([df, pd.DataFrame(level_dict)], axis=1)
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counter += 1
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# Create DataFrame column for distribcell instances IDs
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# NOTE: This is performed regardless of whether the user
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# requests Summary geomeric information
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filter_bins = np.arange(filter.num_bins)
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filter_bins = np.repeat(filter_bins, filter.stride)
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tile_factor = data_size / len(filter_bins)
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@ -876,32 +923,38 @@ class Tally(object):
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elif 'energy' in filter.type:
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bins = filter.bins
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num_bins = filter.num_bins
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template = '{0:.1e} - {1:.1e}'
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filter_bins = \
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[template.format(bins[i], bins[i+1]) for i in range(num_bins)]
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# Create strings for
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template = '{0:.1e} - {1:.1e}'
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filter_bins = []
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for i in range(num_bins):
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filter_bins.append(template.format(bins[i], bins[i+1]))
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# Tile the energy bins into a DataFrame column
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filter_bins = np.repeat(filter_bins, filter.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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df[filter.type + ' [MeV]'] = filter_bins
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# universe, material, surface, cell, cellborn, distribcell filters
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# universe, material, surface, cell, and cellborn filters
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else:
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filter_bins = np.repeat(filter.bins, filter.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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df[filter.type] = filter_bins
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# Include column for nuclides if user requested it
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# Include DataFrame column for nuclides if user requested it
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if nuclides:
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nuclides = []
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for nuclide in self.nuclides:
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if is_integer(nuclide):
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nuclides.append(nuclide)
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else:
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# Write Nuclide name if Summary info was linked with StatePoint
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if isinstance(nuclide, Nuclide):
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nuclides.append(nuclide.name)
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else:
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nuclides.append(nuclide)
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# Tile the nuclide bins into a DataFrame column
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nuclides = np.repeat(nuclides, len(self.scores))
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tile_factor = data_size / len(nuclides)
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df['nuclide'] = np.tile(nuclides, tile_factor)
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