130 lines
5.7 KiB
Python
Executable File
130 lines
5.7 KiB
Python
Executable File
#!/usr/bin/env python2.7
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# -*- coding: UTF-8 no BOM -*-
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import os,sys
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import math # noqa
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import numpy as np
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from optparse import OptionParser, OptionGroup
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import damask
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def periodicAverage(coords, limits):
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"""Centroid in periodic domain, see https://en.wikipedia.org/wiki/Center_of_mass#Systems_with_periodic_boundary_conditions"""
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theta = 2.0*np.pi * (coords - limits[0])/(limits[1] - limits[0])
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theta_avg = np.pi + np.arctan2(-np.sin(theta).mean(axis=0), -np.cos(theta).mean(axis=0))
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return limits[0] + theta_avg * (limits[1] - limits[0])/2.0/np.pi
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Apply a user-specified function to condense all rows for which column 'label' has identical values into a single row.
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Output table will contain as many rows as there are different (unique) values in the grouping column.
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Periodic domain averaging of coordinate values is supported.
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Examples:
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For grain averaged values, replace all rows of particular 'texture' with a single row containing their average.
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""", version = scriptID)
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parser.add_option('-l','--label',
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dest = 'label',
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type = 'string', metavar = 'string',
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help = 'column label for grouping rows')
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parser.add_option('-f','--function',
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dest = 'function',
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type = 'string', metavar = 'string',
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help = 'mapping function [%default]')
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parser.add_option('-a','--all',
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dest = 'all',
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action = 'store_true',
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help = 'apply mapping function also to grouping column')
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group = OptionGroup(parser, "periodic averaging", "")
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group.add_option ('-p','--periodic',
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dest = 'periodic',
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action = 'extend', metavar = '<string LIST>',
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help = 'coordinate label(s) to average across periodic domain')
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group.add_option ('--limits',
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dest = 'boundary',
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type = 'float', metavar = 'float float', nargs = 2,
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help = 'min and max of periodic domain %default')
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parser.add_option_group(group)
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parser.set_defaults(function = 'np.average',
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all = False,
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boundary = [0.0, 1.0])
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(options,filenames) = parser.parse_args()
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funcModule,funcName = options.function.split('.')
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try:
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mapFunction = getattr(locals().get(funcModule) or
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globals().get(funcModule) or
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__import__(funcModule),
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funcName)
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except:
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mapFunction = None
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if options.label is None:
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parser.error('no grouping column specified.')
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if not hasattr(mapFunction,'__call__'):
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parser.error('function "{}" is not callable.'.format(options.function))
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try: table = damask.ASCIItable(name = name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ sanity checks ---------------------------------------
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table.head_read()
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if table.label_dimension(options.label) != 1:
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damask.util.croak('column {} is not of scalar dimension.'.format(options.label))
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table.close(dismiss = True) # close ASCIItable and remove empty file
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continue
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else:
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grpColumn = table.label_index(options.label)
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# ------------------------------------------ assemble info ---------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.head_write()
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# ------------------------------------------ process data --------------------------------
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table.data_readArray()
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indexrange = table.label_indexrange(options.periodic) if options.periodic is not None else []
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rows,cols = table.data.shape
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table.data = table.data[np.lexsort([table.data[:,grpColumn]])] # sort data by grpColumn
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values,index = np.unique(table.data[:,grpColumn], return_index = True) # unique grpColumn values and their positions
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index = np.append(index,rows) # add termination position
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grpTable = np.empty((len(values), cols)) # initialize output
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for i in range(len(values)): # iterate over groups (unique values in grpColumn)
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grpTable[i] = np.apply_along_axis(mapFunction,0,table.data[index[i]:index[i+1]]) # apply (general) mapping function
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grpTable[i,indexrange] = \
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periodicAverage(table.data[index[i]:index[i+1],indexrange],options.boundary) # apply periodicAverage mapping function
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if not options.all: grpTable[i,grpColumn] = table.data[index[i],grpColumn] # restore grouping column value
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table.data = grpTable
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# ------------------------------------------ output result -------------------------------
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table.data_writeArray()
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table.close() # close ASCII table
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