86 lines
3.4 KiB
Python
Executable File
86 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python2.7
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# -*- coding: UTF-8 no BOM -*-
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import os,sys
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import numpy as np
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from optparse import OptionParser
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Add column(s) containing Cauchy stress based on given column(s) of deformation gradient and first Piola--Kirchhoff stress.
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""", version = scriptID)
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parser.add_option('-f','--defgrad',
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dest = 'defgrad',
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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',
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dest = 'stress',
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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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stress = 'p',
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)
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(options,filenames) = parser.parse_args()
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name, buffered = False)
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except:
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continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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column = {}
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for tensor in [options.defgrad,options.stress]:
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dim = table.label_dimension(tensor)
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if dim < 0: errors.append('column {} not found.'.format(tensor))
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elif dim != 9: errors.append('column {} is not a tensor.'.format(tensor))
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else:
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column[tensor] = table.label_index(tensor)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header --------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.labels_append(['{}_Cauchy'.format(i+1) for i in range(9)]) # extend ASCII header with new labels
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table.head_write()
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# ------------------------------------------ process data ------------------------------------------
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outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
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F = np.array(map(float,table.data[column[options.defgrad]:column[options.defgrad]+9]),'d').reshape(3,3)
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P = np.array(map(float,table.data[column[options.stress ]:column[options.stress ]+9]),'d').reshape(3,3)
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table.data_append(list(1.0/np.linalg.det(F)*np.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 finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)
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