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