#!/usr/bin/env python # -*- coding: UTF-8 no BOM -*- import os,sys,string import numpy as np from optparse import OptionParser import damask scriptID = string.replace('$Id$','\n','\\n') scriptName = os.path.splitext(scriptID.split()[1])[0] # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.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. """, version = scriptID) parser.add_option('-f','--defgrad', dest = 'defgrad', type = 'string', metavar = 'string', help = 'heading of columns containing deformation gradient [%default]') 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', stress = 'p', ) (options,filenames) = parser.parse_args() # --- loop over input files ------------------------------------------------------------------------- if filenames == []: filenames = [None] for name in filenames: try: table = damask.ASCIItable(name = name, buffered = False) except: continue table.report_name(scriptName,name) # ------------------------------------------ read header ------------------------------------------ table.head_read() # ------------------------------------------ sanity checks ---------------------------------------- errors = [] column = {} for tensor in [options.defgrad,options.stress]: dim = table.label_dimension(tensor) if dim < 0: errors.append('column {} not found.'.format(tensor)) elif dim != 9: errors.append('column {} is not a tensor.'.format(tensor)) else: column[tensor] = table.label_index(tensor) if errors != []: table.croak(errors) table.close(dismiss = True) continue # ------------------------------------------ assemble header -------------------------------------- table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:])) table.labels_append(['%i_Cauchy'%(i+1) for i in xrange(9)]) # extend ASCII header with new labels table.head_write() # ------------------------------------------ process data ------------------------------------------ outputAlive = True while outputAlive and table.data_read(): # read next data line of ASCII table F = np.array(map(float,table.data[column[options.defgrad]:column[options.defgrad]+9]),'d').reshape(3,3) P = np.array(map(float,table.data[column[options.stress ]:column[options.stress ]+9]),'d').reshape(3,3) table.data_append(list(1.0/np.linalg.det(F)*np.dot(P,F.T).reshape(9))) # [Cauchy] = (1/det(F)) * [P].[F_transpose] outputAlive = table.data_write() # output processed line # ------------------------------------------ output finalization ----------------------------------- table.close() # close input ASCII table (works for stdin)