104 lines
4.3 KiB
Python
Executable File
104 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python2.7
|
|
# -*- coding: UTF-8 no BOM -*-
|
|
|
|
import os,sys
|
|
import math # noqa
|
|
import numpy as np
|
|
from optparse import OptionParser
|
|
import damask
|
|
|
|
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
|
scriptID = ' '.join([scriptName,damask.version])
|
|
|
|
# --------------------------------------------------------------------
|
|
# MAIN
|
|
# --------------------------------------------------------------------
|
|
|
|
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
|
|
Apply a user-specified function to condense all rows for which column 'label' has identical values into a single row.
|
|
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')
|
|
parser.add_option('-f','--function',
|
|
dest = 'function',
|
|
type = 'string', metavar = 'string',
|
|
help = 'mapping function [%default]')
|
|
parser.add_option('-a','--all',
|
|
dest = 'all',
|
|
action = 'store_true',
|
|
help = 'apply mapping function also to grouping column')
|
|
|
|
parser.set_defaults(function = 'np.average')
|
|
|
|
(options,filenames) = parser.parse_args()
|
|
|
|
funcModule,funcName = options.function.split('.')
|
|
|
|
try:
|
|
mapFunction = getattr(locals().get(funcModule) or
|
|
globals().get(funcModule) or
|
|
__import__(funcModule),
|
|
funcName)
|
|
except:
|
|
mapFunction = None
|
|
|
|
if options.label is None:
|
|
parser.error('no grouping column specified.')
|
|
if not hasattr(mapFunction,'__call__'):
|
|
parser.error('function "{}" is not callable.'.format(options.function))
|
|
|
|
|
|
# --- loop over input files -------------------------------------------------------------------------
|
|
|
|
if filenames == []: filenames = [None]
|
|
|
|
for name in filenames:
|
|
try: table = damask.ASCIItable(name = name,
|
|
buffered = False)
|
|
except: continue
|
|
damask.util.report(scriptName,name)
|
|
|
|
# ------------------------------------------ sanity checks ---------------------------------------
|
|
|
|
table.head_read()
|
|
if table.label_dimension(options.label) != 1:
|
|
damask.util.croak('column {} is not of scalar dimension.'.format(options.label))
|
|
table.close(dismiss = True) # close ASCIItable and remove empty file
|
|
continue
|
|
else:
|
|
grpColumn = table.label_index(options.label)
|
|
|
|
# ------------------------------------------ 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[:,grpColumn]])] # sort data by grpColumn
|
|
|
|
values,index = np.unique(table.data[:,grpColumn], return_index = True) # unique grpColumn values and their positions
|
|
index = np.append(index,rows) # add termination position
|
|
grpTable = np.empty((len(values), cols)) # initialize output
|
|
|
|
for i in xrange(len(values)): # iterate over groups (unique values in grpColumn)
|
|
grpTable[i] = np.apply_along_axis(mapFunction,0,table.data[index[i]:index[i+1]]) # apply mapping function
|
|
if not options.all: grpTable[i,grpColumn] = table.data[index[i],grpColumn] # restore grouping column value
|
|
|
|
table.data = grpTable
|
|
|
|
# ------------------------------------------ output result -------------------------------
|
|
|
|
table.data_writeArray()
|
|
table.close() # close ASCII table
|