skip file if mandatory column not found in ASCIItable
This commit is contained in:
Philip Eisenlohr
parent
ad203d32c1
commit
7f08c976c3
|
|
@ -1,8 +1,12 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
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):
|
||||
# -----------------------------
|
||||
|
|
@ -31,7 +35,7 @@ parser = OptionParser(option_class=extendableOption, usage='%prog options [file[
|
|||
Add column(s) containing Cauchy stress based on given column(s) of
|
||||
deformation gradient and first Piola--Kirchhoff stress.
|
||||
|
||||
""" + string.replace('$Id$','\n','\\n')
|
||||
""" + string.replace(scriptID,'\n','\\n')
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -49,9 +53,11 @@ if options.defgrad == None or options.stress == None:
|
|||
parser.error('missing data column...')
|
||||
|
||||
datainfo = { # list of requested labels per datatype
|
||||
'defgrad': {'len':9,
|
||||
'defgrad': {'mandatory': True,
|
||||
'len':9,
|
||||
'label':[]},
|
||||
'stress': {'len':9,
|
||||
'stress': {'mandatory': True,
|
||||
'len':9,
|
||||
'label':[]},
|
||||
}
|
||||
|
||||
|
|
@ -60,52 +66,55 @@ datainfo['defgrad']['label'].append(options.defgrad)
|
|||
datainfo['stress']['label'].append(options.stress)
|
||||
|
||||
|
||||
# ------------------------------------------ setup file handles ---------------------------------------
|
||||
# ------------------------------------------ setup file handles ---------------------------------------
|
||||
|
||||
files = []
|
||||
if filenames == []:
|
||||
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout})
|
||||
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')})
|
||||
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
|
||||
|
||||
# ------------------------------------------ loop over input files ---------------------------------------
|
||||
# ------------------------------------------ loop over input files ---------------------------------------
|
||||
|
||||
for file in files:
|
||||
if file['name'] != 'STDIN': print file['name']
|
||||
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')
|
||||
|
||||
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
|
||||
table.head_read() # read ASCII header info
|
||||
table.info_append(string.replace('$Id$','\n','\\n') + \
|
||||
'\t' + ' '.join(sys.argv[1:]))
|
||||
|
||||
active = {}
|
||||
column = {}
|
||||
head = []
|
||||
table.info_append(string.replace(scriptID,'\n','\\n') + '\t' + ' '.join(sys.argv[1:]))
|
||||
|
||||
active = defaultdict(list)
|
||||
column = defalutdict(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:
|
||||
sys.stderr.write('column %s not found...\n'%key)
|
||||
file['croak'].write('column %s not found...\n'%key)
|
||||
missingColumns |= info['mandatory'] # break if label is mandatory
|
||||
else:
|
||||
if datatype not in active: active[datatype] = []
|
||||
if datatype not in column: column[datatype] = {}
|
||||
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['defgrad']['len'])]) # extend ASCII header with new labels
|
||||
for i in xrange(datainfo['stress']['len'])]) # extend ASCII header with new labels
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
outputAlive = True
|
||||
while outoutAlive 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)
|
||||
|
|
@ -113,9 +122,9 @@ for file in files:
|
|||
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]
|
||||
table.data_write() # output processed line
|
||||
outputAlive = table.data_write() # output processed line
|
||||
|
||||
# ------------------------------------------ output result ---------------------------------------
|
||||
# ------------------------------------------ output result ---------------------------------------
|
||||
|
||||
table.output_flush() # just in case of buffered ASCII table
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue