DAMASK_EICMD/processing/post/addCauchy.py

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#!/usr/bin/env python
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import os,re,sys,math,numpy,string,damask
from collections import defaultdict
from optparse import OptionParser, Option
scriptID = '$Id$'
scriptName = scriptID.split()[1]
# -----------------------------
class extendableOption(Option):
# -----------------------------
# used for definition of new option parser action 'extend', which enables to take multiple option arguments
# taken from online tutorial http://docs.python.org/library/optparse.html
ACTIONS = Option.ACTIONS + ("extend",)
STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
def take_action(self, action, dest, opt, value, values, parser):
if action == "extend":
lvalue = value.split(",")
values.ensure_value(dest, []).extend(lvalue)
else:
Option.take_action(self, action, dest, opt, value, values, parser)
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
Add column(s) containing Cauchy stress based on given column(s) of
deformation gradient and first Piola--Kirchhoff stress.
""" + string.replace(scriptID,'\n','\\n')
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)
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parser.add_option('-f','--defgrad', dest='defgrad', type='string', metavar='string', \
help='heading of columns containing deformation gradient [%default]')
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parser.add_option('-p','--stress', dest='stress', type='string', metavar='string', \
help='heading of columns containing first Piola--Kirchhoff stress [%default]')
parser.set_defaults(defgrad = 'f')
parser.set_defaults(stress = 'p')
(options,filenames) = parser.parse_args()
if options.defgrad == None or options.stress == None:
parser.error('missing data column...')
datainfo = { # list of requested labels per datatype
'defgrad': {'mandatory': True,
'len':9,
'label':[]},
'stress': {'mandatory': True,
'len':9,
'label':[]},
}
datainfo['defgrad']['label'].append(options.defgrad)
datainfo['stress']['label'].append(options.stress)
# ------------------------------------------ setup file handles ---------------------------------------
files = []
if filenames == []:
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
else:
for name in filenames:
if os.path.exists(name):
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
# ------------------------------------------ loop over input files ---------------------------------------
for file in files:
if file['name'] != 'STDIN': file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
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table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
table.info_append(string.replace(scriptID,'\n','\\n') + '\t' + ' '.join(sys.argv[1:]))
active = defaultdict(list)
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column = defaultdict(dict)
missingColumns = False
for datatype,info in datainfo.items():
for label in info['label']:
key = {True :'1_%s',
False:'%s' }[info['len']>1]%label
if key not in table.labels:
file['croak'].write('column %s not found...\n'%key)
missingColumns |= info['mandatory'] # break if label is mandatory
else:
active[datatype].append(label)
column[datatype][label] = table.labels.index(key) # remember columns of requested data
if missingColumns:
continue
table.labels_append(['%i_Cauchy'%(i+1)
for i in xrange(datainfo['stress']['len'])]) # extend ASCII header with new labels
# ------------------------------------------ assemble header ---------------------------------------
table.head_write()
# ------------------------------------------ process data ---------------------------------------
outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
F = numpy.array(map(float,table.data[column['defgrad'][active['defgrad'][0]]:
column['defgrad'][active['defgrad'][0]]+datainfo['defgrad']['len']]),'d').reshape(3,3)
P = numpy.array(map(float,table.data[column['stress'][active['stress'][0]]:
column['stress'][active['stress'][0]]+datainfo['stress']['len']]),'d').reshape(3,3)
table.data_append(list(1.0/numpy.linalg.det(F)*numpy.dot(P,F.T).reshape(9))) # [Cauchy] = (1/det(F)) * [P].[F_transpose]
outputAlive = table.data_write() # output processed line
# ------------------------------------------ output result ---------------------------------------
table.output_flush() # just in case of buffered ASCII table
file['input'].close() # close input ASCII table
if file['name'] != 'STDIN':
file['output'].close # close output ASCII table
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new