DAMASK_EICMD/processing/pre/seeds_fromTable.py

139 lines
6.2 KiB
Python
Executable File

#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys,string,itertools
import numpy as np
from optparse import OptionParser
from collections import defaultdict
import damask
scriptID = string.replace('$Id$','\n','\\n')
scriptName = os.path.splitext(scriptID.split()[1])[0]
#--------------------------------------------------------------------------------------------------
# MAIN
#--------------------------------------------------------------------------------------------------
identifiers = {
'grid': ['a','b','c'],
'size': ['x','y','z'],
'origin': ['x','y','z'],
}
mappings = {
'grid': lambda x: int(x),
'size': lambda x: float(x),
'origin': lambda x: float(x),
'homogenization': lambda x: int(x),
'microstructures': lambda x: int(x),
}
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Create seed file by taking microstructure indices from given ASCIItable column.
White and black-listing of microstructure indices is possible.
Examples:
--white 1,2,5 --index grainID isolates grainID entries of value 1, 2, and 5;
--black 1 --index grainID takes all grainID entries except for value 1.
""", version = scriptID)
parser.add_option('-p', '--positions', dest = 'pos', metavar = 'string',
help = 'coordinate label')
parser.add_option('--boundingbox', dest = 'box', type = 'float', nargs = 6, metavar = ' '.join(['float']*6),
help = 'min (x,y,z) and max (x,y,z) coordinates of bounding box [auto]')
parser.add_option('-i', '--index', dest = 'index', type = 'string', metavar = 'string',
help = 'microstructure index label')
parser.add_option('-w','--white', dest = 'whitelist', action = 'extend',
help = 'white list of microstructure indices', metavar = '<LIST>')
parser.add_option('-b','--black', dest = 'blacklist', action = 'extend',
help = 'black list of microstructure indices', metavar = '<LIST>')
parser.set_defaults(pos = 'pos',
index ='microstructure',
)
(options,filenames) = parser.parse_args()
if options.whitelist != None: options.whitelist = map(int,options.whitelist)
if options.blacklist != None: options.blacklist = map(int,options.blacklist)
# --- loop over input files -------------------------------------------------------------------------
if filenames == []:
filenames = ['STDIN']
for name in filenames:
if name == 'STDIN':
file = {'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr}
file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
else:
if not os.path.exists(name): continue
file = {'name':name,
'input':open(name),
'output':open(os.path.splitext(name)[0]+ \
('' if options.label == None else '_'+options.label)+ \
'.png','w'),
'croak':sys.stderr}
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
table = damask.ASCIItable(file['input'],file['output'],
buffered = False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
# ------------------------------------------ process data ------------------------------------------
errors = []
missing_labels = table.data_readArray(options.pos,options.label)
if len(missing_labels) > 0:
errors.append('column%s %s not found'%('s' if len(missing_labels) > 1 else '',
', '.join(missing_labels)))
for label, dim in {options.pos: 3,
options.label: 1}.iteritems():
if table.label_dimension(label) != dim:
errors.append('column %s has wrong dimension'%label)
if errors != []:
file['croak'].write('\n'.join(errors))
table.close(dismiss = True) # close ASCII table file handles and delete output file
continue
#--- finding bounding box ------------------------------------------------------------------------------------
boundingBox = np.array((np.amin(table.data[:,0:3],axis = 0),np.amax(table.data[:,0:3],axis = 0)))
if options.box:
boundingBox[0,:] = np.minimum(options.box[0:3],boundingBox[0,:])
boundingBox[1,:] = np.maximum(options.box[3:6],boundingBox[1,:])
#--- rescaling coordinates ------------------------------------------------------------------------------------
table.data[:,0:3] -= boundingBox[0,:]
table.data[:,0:3] /= boundingBox[1,:]-boundingBox[0,:]
#--- filtering of grain voxels ------------------------------------------------------------------------------------
mask = np.logical_and(\
np.ones_like(table.data[:,3],bool) \
if options.whitelist == None \
else np.in1d(table.data[:,3].ravel(), options.whitelist).reshape(table.data[:,3].shape),
np.ones_like(table.data[:,3],bool) \
if options.blacklist == None \
else np.invert(np.in1d(table.data[:,3].ravel(), options.blacklist).reshape(table.data[:,3].shape))
)
table.data = table.data[mask]
# ------------------------------------------ output result ---------------------------------------
# ------------------------------------------ assemble header ---------------------------------------
table.info = [
scriptID,
'size %s'%(' '.join(list(itertools.chain.from_iterable(zip(['x','y','z'],
map(str,boundingBox[1,:]-boundingBox[0,:])))))),
]
table.labels_clear()
table.labels_append(['1_coords','2_coords','3_coords','microstructure']) # implicitly switching label processing/writing on
table.head_write()
table.data_writeArray()
table.output_flush()
table.close() # close ASCII tables