139 lines
6.1 KiB
Python
Executable File
139 lines
6.1 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,itertools
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import numpy as np
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from optparse import OptionParser
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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#--------------------------------------------------------------------------------------------------
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# MAIN
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#--------------------------------------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Create seed file by taking microstructure indices from given ASCIItable column.
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White and black-listing of microstructure indices is possible.
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Examples:
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--white 1,2,5 --index grainID isolates grainID entries of value 1, 2, and 5;
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--black 1 --index grainID takes all grainID entries except for value 1.
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""", version = scriptID)
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parser.add_option('-p',
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'--pos', '--seedposition',
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dest = 'pos',
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type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
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parser.add_option('--boundingbox',
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dest = 'box',
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type = 'float', nargs = 6, metavar = ' '.join(['float']*6),
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help = 'min (x,y,z) and max (x,y,z) coordinates of bounding box [tight]')
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parser.add_option('-m',
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'--microstructure',
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dest = 'microstructure',
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type = 'string', metavar = 'string',
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help = 'label of microstructures [%default]')
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parser.add_option('--weight',
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dest = 'weight',
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type = 'string', metavar = 'string',
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help = 'label of weights [%default]')
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parser.add_option('-w',
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'--white',
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dest = 'whitelist',
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action = 'extend', metavar = '<int LIST>',
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help = 'whitelist of microstructure indices')
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parser.add_option('-b',
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'--black',
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dest = 'blacklist',
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action = 'extend', metavar = '<int LIST>',
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help = 'blacklist of microstructure indices')
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parser.set_defaults(pos = 'pos',
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microstructure = 'microstructure',
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weight = None,
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)
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(options,filenames) = parser.parse_args()
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if options.whitelist is not None: options.whitelist = map(int,options.whitelist)
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if options.blacklist is not None: options.blacklist = map(int,options.blacklist)
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try: table = damask.ASCIItable(name = name,
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outname = os.path.splitext(name)[0]+'.seeds' if name else name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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table.head_read() # read ASCII header info
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# ------------------------------------------ sanity checks ---------------------------------------
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missing_labels = table.data_readArray([options.pos,options.microstructure] +
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([options.weight] if options.weight else []))
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errors = []
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if len(missing_labels) > 0:
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errors.append('column{} {} not found'.format('s' if len(missing_labels) > 1 else '',
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', '.join(missing_labels)))
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input = {options.pos: 3,
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options.microstructure: 1,}
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if options.weight: input.update({options.weight: 1})
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for label, dim in input.iteritems():
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if table.label_dimension(label) != dim:
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errors.append('column {} has wrong dimension'.format(label))
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True) # close ASCII table file handles and delete output file
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continue
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# ------------------------------------------ process data ------------------------------------------
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# --- finding bounding box -------------------------------------------------------------------------
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boundingBox = np.array((np.amin(table.data[:,0:3],axis = 0),np.amax(table.data[:,0:3],axis = 0)))
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if options.box:
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boundingBox[0,:] = np.minimum(options.box[0:3],boundingBox[0,:])
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boundingBox[1,:] = np.maximum(options.box[3:6],boundingBox[1,:])
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# --- rescaling coordinates ------------------------------------------------------------------------
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table.data[:,0:3] -= boundingBox[0,:]
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table.data[:,0:3] /= boundingBox[1,:]-boundingBox[0,:]
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# --- filtering of grain voxels --------------------------------------------------------------------
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mask = np.logical_and(
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np.ones_like(table.data[:,3],bool) if options.whitelist is None \
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else np.in1d(table.data[:,3].ravel(), options.whitelist).reshape(table.data[:,3].shape),
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np.ones_like(table.data[:,3],bool) if options.blacklist is None \
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else np.invert(np.in1d(table.data[:,3].ravel(), options.blacklist).reshape(table.data[:,3].shape))
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)
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table.data = table.data[mask]
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# ------------------------------------------ assemble header ---------------------------------------
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table.info = [
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scriptID,
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'size {}'.format(' '.join(list(itertools.chain.from_iterable(zip(['x','y','z'],
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map(str,boundingBox[1,:]-boundingBox[0,:])))))),
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]
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table.labels_clear()
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table.labels_append(['1_pos','2_pos','3_pos','microstructure'] +
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['weight'] if options.weight else []) # implicitly switching label processing/writing on
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table.head_write()
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# ------------------------------------------ output result ---------------------------------------
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table.data_writeArray()
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table.close() # close ASCII tables
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