81 lines
3.0 KiB
Python
Executable File
81 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys
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import numpy as np
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from optparse import OptionParser
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import damask
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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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Replace all rows for which column 'label' has identical values by a single row containing their average.
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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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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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(options,filenames) = parser.parse_args()
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if options.label is None:
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parser.error('no grouping column specified.')
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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:
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table = damask.ASCIItable(name = name,
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outname = options.label+'_averaged_'+name if name else 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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# ------------------------------------------ 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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rows,cols = table.data.shape
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table.data = table.data[np.lexsort([table.data[:,table.label_index(options.label)]])]
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values,index = np.unique(table.data[:,table.label_index(options.label)], return_index = True)
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index = np.append(index,rows)
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avgTable = np.empty((len(values), cols))
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for j in xrange(cols) :
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for i in xrange(len(values)) :
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avgTable[i,j] = np.average(table.data[index[i]:index[i+1],j])
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table.data = avgTable
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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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