110 lines
5.8 KiB
Python
Executable File
110 lines
5.8 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 = '$Id$'
|
|
scriptName = scriptID.split()[1][:-3]
|
|
|
|
# --------------------------------------------------------------------
|
|
# MAIN
|
|
# --------------------------------------------------------------------
|
|
|
|
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
|
|
Uniformly scale values scalar/special, vector, or tensor columns by given factor.
|
|
|
|
""", version = scriptID)
|
|
|
|
parser.add_option('-s','--special', dest='special', action='extend', type='string', metavar='<string LIST>',
|
|
help='heading of columns containing field values of special dimension')
|
|
parser.add_option('-d','--dimension',dest='N', type='int', metavar='int',
|
|
help='dimension of special field values [%default]')
|
|
parser.add_option('-v','--vector', dest='vector', action='extend', metavar='<string LIST>',
|
|
help='column heading of vector to scale')
|
|
parser.add_option('-t','--tensor', dest='tensor', action='extend', metavar='<string LIST>',
|
|
help='column heading of tensor to scale')
|
|
parser.add_option('-f','--factor', dest='factor', action='extend', metavar='<float LIST>',
|
|
help='list of scalar/special, vector, and tensor scaling factors (in this order!)')
|
|
|
|
parser.set_defaults(special = [])
|
|
parser.set_defaults(vector = [])
|
|
parser.set_defaults(tensor = [])
|
|
parser.set_defaults(factor = [])
|
|
parser.set_defaults(N = 1)
|
|
|
|
(options,filenames) = parser.parse_args()
|
|
|
|
options.factor = np.array(options.factor,'d')
|
|
datainfo = { # list of requested labels per datatype
|
|
'special': {'len':options.N,
|
|
'label':[]},
|
|
'vector': {'len':3,
|
|
'label':[]},
|
|
'tensor': {'len':9,
|
|
'label':[]},
|
|
}
|
|
|
|
length = 0
|
|
if options.special != []: datainfo['special']['label'] += options.special; length += len(options.special)
|
|
if options.vector != []: datainfo['vector']['label'] += options.vector; length += len(options.vector)
|
|
if options.tensor != []: datainfo['tensor']['label'] += options.tensor; length += len(options.tensor)
|
|
if len(options.factor) != length:
|
|
parser.error('length of scaling vector does not match column count...')
|
|
|
|
# ------------------------------------------ setup file handles ------------------------------------
|
|
files = []
|
|
if filenames == []:
|
|
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'), 'croak':sys.stderr})
|
|
|
|
# ------------------------------------------ loop over input files ---------------------------------
|
|
for file in files:
|
|
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(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
|
|
|
# --------------- figure out columns to process ---------------------------------------------------
|
|
active = defaultdict(list)
|
|
column = defaultdict(dict)
|
|
|
|
for datatype,info in datainfo.items():
|
|
for label in info['label']:
|
|
key = '1_'+label if info['len'] > 1 else label
|
|
if key in table.labels:
|
|
active[datatype].append(label)
|
|
column[datatype][label] = table.labels.index(key) # remember columns of requested data
|
|
else:
|
|
file['croak'].write('column %s not found...\n'%label)
|
|
|
|
# ------------------------------------------ assemble header ---------------------------------------
|
|
table.head_write()
|
|
|
|
# ------------------------------------------ process data ------------------------------------------
|
|
outputAlive = True
|
|
while outputAlive and table.data_read(): # read next data line of ASCII table
|
|
i = 0
|
|
for datatype,labels in sorted(active.items(),key=lambda x:datainfo[x[0]]['len']): # loop over special,vector,tensor
|
|
for label in labels: # loop over all requested labels
|
|
for j in xrange(datainfo[datatype]['len']): # loop over entity elements
|
|
table.data[column[datatype][label]+j] = float(table.data[column[datatype][label]+j]) * options.factor[i]
|
|
i += 1
|
|
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
|