DAMASK_EICMD/processing/post/addSpectralDecomposition.py

87 lines
4.3 KiB
Python
Executable File

#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys,string
import numpy as np
from collections import defaultdict
from optparse import OptionParser
import damask
scriptID = string.replace('$Id$','\n','\\n')
scriptName = scriptID.split()[1][:-3]
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Add column(s) containing eigenvalues and eigenvectors of requested tensor column(s).
""", version = scriptID)
parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', metavar='<string LIST>',
help='heading of columns containing tensor field values')
parser.set_defaults(tensor = [])
(options,filenames) = parser.parse_args()
if len(options.tensor) == 0:
parser.error('no data column specified...')
datainfo = { # list of requested labels per datatype
'tensor': {'len':9,
'label':[]},
}
datainfo['tensor']['label'] += options.tensor
# ------------------------------------------ setup file handles ------------------------------------
files = []
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:
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
table = damask.ASCIItable(file['input'],file['output'],True) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
active = []
column = defaultdict(dict)
for label in datainfo['tensor']['label']:
key = '1_%s'%label
if key not in table.labels:
file['croak'].write('column %s not found...\n'%key)
else:
active.append(label)
column[label] = table.labels.index(key) # remember columns of requested data
# ------------------------------------------ assemble header ---------------------------------------
for labels in active:
table.labels_append(['%i_eigval(%s)'%(i+1,label) for i in xrange(3)]) # extend ASCII header with new labels
table.labels_append(['%i_eigvec(%s)'%(i+1,label) 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
for labels in active: # loop over requested data
tensor = np.array(map(float,table.data[column[label]:column[label]+datainfo['tensor']['len']])).\
reshape((datainfo['tensor']['len']//3,3))
(u,v) = np.linalg.eig(tensor)
table.data_append(list(u))
table.data_append(list(v.transpose().reshape(datainfo['tensor']['len'])))
outputAlive = table.data_write() # output processed line
# ------------------------------------------ output result -----------------------------------------
outputAlive and table.output_flush() # just in case of buffered ASCII table
table.input_close() # close input ASCII table (works for stdin)
table.output_close() # close output ASCII table (works for stdout)
if file['name'] != 'STDIN':
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new