DAMASK_EICMD/processing/post/groupTable.py

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#!/usr/bin/env python2.7
# -*- coding: UTF-8 no BOM -*-
import os,sys
import math # noqa
import numpy as np
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from optparse import OptionParser, OptionGroup
import damask
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#"https://en.wikipedia.org/wiki/Center_of_mass#Systems_with_periodic_boundary_conditions"
def periodicAverage(Points, Box):
theta = (Points/Box[1]) * (2.0*np.pi)
xi = np.cos(theta)
zeta = np.sin(theta)
theta_avg = np.arctan2(-1.0*zeta.mean(), -1.0*xi.mean()) + np.pi
Pmean = Box[1] * theta_avg/(2.0*np.pi)
return Pmean
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',
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action = 'store_true',
help = 'apply mapping function also to grouping column')
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group = OptionGroup(parser, "periodic averaging", "")
group.add_option('-p','--periodic',
dest = 'periodic',
action = 'store_true',
help = 'calculate average in periodic space defined by periodic length [%default]')
group.add_option('--boundary',
dest = 'boundary', metavar = 'MIN MAX',
type = 'float', nargs = 2,
help = 'define periodic box end points %default')
parser.add_option_group(group)
parser.set_defaults(function = 'np.average',
all = False,
periodic = False,
boundary = [0.0, 1.0])
(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
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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)
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if options.periodic :
grpTable[i] = periodicAverage(table.data[index[i]:index[i+1]],options.boundary) # apply periodicAverage mapping function
else :
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