DAMASK_EICMD/processing/post/averageTable.py

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#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,re,sys,string
import numpy as np
from optparse import OptionParser
import damask
scriptID = string.replace('$Id$','\n','\\n')
scriptName = os.path.splitext(scriptID.split()[1])[0]
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Replace all rows for which column 'label' has identical values by a single row containing their average.
Output table will contain as many rows as there are different (unique) values in the grouping column.
Examples:
For grain averaged values, replace all rows of particular 'texture' with a single row containing their average.
""", version = scriptID)
parser.add_option('-l','--label',
dest = 'label',
type = 'string', metavar = 'string',
help = 'column label for grouping rows')
(options,filenames) = parser.parse_args()
if options.label == None:
parser.error('no grouping column specified.')
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = ['STDIN']
for name in filenames:
if not (name == 'STDIN' or os.path.exists(name)): continue
table = damask.ASCIItable(name = name,
outname = options.label+'_averaged_'+name,
buffered = False)
table.croak('\033[1m'+scriptName+'\033[0m'+(': '+name if name != 'STDIN' else ''))
# ------------------------------------------ sanity checks ---------------------------------------
table.head_read()
if table.label_dimension(options.label) != 1:
table.croak('column {} is not of scalar dimension.'.format(options.label))
table.close(dismiss = True) # close ASCIItable and remove empty file
continue
# ------------------------------------------ assemble info ---------------------------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
table.head_write()
# ------------------------------------------ process data --------------------------------
table.data_readArray()
rows,cols = table.data.shape
table.data = table.data[np.lexsort([table.data[:,table.label_index(options.label)]])]
values,index = np.unique(table.data[:,table.label_index(options.label)], return_index = True)
index = np.append(index,rows)
avgTable = np.empty((len(values), cols))
for j in xrange(cols) :
for i in xrange(len(values)) :
avgTable[i,j] = np.average(table.data[index[i]:index[i+1],j])
table.data = avgTable
# ------------------------------------------ output result -------------------------------
table.data_writeArray()
table.close() # close ASCII table