305 lines
12 KiB
Python
Executable File
305 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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import sys
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import multiprocessing
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from optparse import OptionParser,OptionGroup
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import numpy as np
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from scipy import spatial
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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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def laguerreTessellation(undeformed, coords, weights, grains, periodic = True, cpus = 2):
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def findClosestSeed(fargs):
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point, seeds, myWeights = fargs
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tmp = np.repeat(point.reshape(3,1), len(seeds), axis=1).T
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dist = np.sum((tmp - seeds)**2,axis=1) -myWeights
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return np.argmin(dist) # seed point closest to point
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copies = \
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np.array([
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[ -1,-1,-1 ],
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[ 0,-1,-1 ],
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[ 1,-1,-1 ],
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[ -1, 0,-1 ],
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[ 0, 0,-1 ],
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[ 1, 0,-1 ],
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[ -1, 1,-1 ],
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[ 0, 1,-1 ],
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[ 1, 1,-1 ],
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[ -1,-1, 0 ],
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[ 0,-1, 0 ],
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[ 1,-1, 0 ],
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[ -1, 0, 0 ],
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[ 0, 0, 0 ],
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[ 1, 0, 0 ],
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[ -1, 1, 0 ],
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[ 0, 1, 0 ],
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[ 1, 1, 0 ],
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[ -1,-1, 1 ],
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[ 0,-1, 1 ],
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[ 1,-1, 1 ],
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[ -1, 0, 1 ],
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[ 0, 0, 1 ],
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[ 1, 0, 1 ],
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[ -1, 1, 1 ],
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[ 0, 1, 1 ],
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[ 1, 1, 1 ],
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]).astype(float)*info['size'] if periodic else \
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np.array([
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[ 0, 0, 0 ],
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]).astype(float)
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repeatweights = np.tile(weights,len(copies)).flatten(order='F') # Laguerre weights (1,2,3,1,2,3,...,1,2,3)
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for i,vec in enumerate(copies): # periodic copies of seed points ...
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try: seeds = np.append(seeds, coords+vec, axis=0) # ... (1+a,2+a,3+a,...,1+z,2+z,3+z)
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except NameError: seeds = coords+vec
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if (repeatweights == 0.0).all(): # standard Voronoi (no weights, KD tree)
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myKDTree = spatial.cKDTree(seeds)
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devNull,closestSeeds = myKDTree.query(undeformed)
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else:
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damask.util.croak('...using {} cpu{}'.format(options.cpus, 's' if options.cpus > 1 else ''))
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arguments = [[arg,seeds,repeatweights] for arg in list(undeformed)]
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if cpus > 1: # use multithreading
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pool = multiprocessing.Pool(processes = cpus) # initialize workers
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result = pool.map_async(findClosestSeed, arguments) # evaluate function in parallel
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pool.close()
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pool.join()
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closestSeeds = np.array(result.get()).flatten()
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else:
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closestSeeds = np.zeros(len(arguments),dtype='i')
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for i,arg in enumerate(arguments):
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closestSeeds[i] = findClosestSeed(arg)
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# closestSeed is modulo number of original seed points (i.e. excluding periodic copies)
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return grains[closestSeeds%coords.shape[0]]
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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 option(s) [seedfile(s)]', description = """
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Generate geometry description and material configuration by tessellation of given seeds file.
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""", version = scriptID)
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group = OptionGroup(parser, "Tessellation","")
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group.add_option('-l',
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'--laguerre',
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dest = 'laguerre',
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action = 'store_true',
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help = 'use Laguerre (weighted Voronoi) tessellation')
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group.add_option('--cpus',
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dest = 'cpus',
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type = 'int', metavar = 'int',
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help = 'number of parallel processes to use for Laguerre tessellation [%default]')
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group.add_option('--nonperiodic',
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dest = 'periodic',
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action = 'store_false',
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help = 'nonperiodic tessellation')
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parser.add_option_group(group)
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group = OptionGroup(parser, "Geometry","")
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group.add_option('-g',
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'--grid',
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dest = 'grid',
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type = 'int', nargs = 3, metavar = ' '.join(['int']*3),
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help = 'a,b,c grid of hexahedral box')
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group.add_option('-s',
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'--size',
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dest = 'size',
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type = 'float', nargs = 3, metavar=' '.join(['float']*3),
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help = 'x,y,z size of hexahedral box')
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group.add_option('-o',
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'--origin',
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dest = 'origin',
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type = 'float', nargs = 3, metavar=' '.join(['float']*3),
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help = 'origin of grid')
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group.add_option('--nonnormalized',
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dest = 'normalized',
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action = 'store_false',
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help = 'seed coordinates are not normalized to a unit cube')
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parser.add_option_group(group)
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group = OptionGroup(parser, "Seeds","")
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group.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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group.add_option('-w',
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'--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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group.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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group.add_option('-e',
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'--eulers',
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dest = 'eulers',
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type = 'string', metavar = 'string',
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help = 'label of Euler angles [%default]')
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group.add_option('--axes',
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dest = 'axes',
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type = 'string', nargs = 3, metavar = ' '.join(['string']*3),
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help = 'orientation coordinate frame in terms of position coordinate frame')
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parser.add_option_group(group)
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group = OptionGroup(parser, "Configuration","")
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group.add_option('--without-config',
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dest = 'config',
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action = 'store_false',
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help = 'omit material configuration header')
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group.add_option('--homogenization',
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dest = 'homogenization',
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type = 'int', metavar = 'int',
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help = 'homogenization index to be used [%default]')
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group.add_option('--phase',
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dest = 'phase',
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type = 'int', metavar = 'int',
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help = 'phase index to be used [%default]')
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parser.add_option_group(group)
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parser.set_defaults(pos = 'pos',
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weight = 'weight',
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microstructure = 'microstructure',
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eulers = 'euler',
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homogenization = 1,
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phase = 1,
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cpus = 2,
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laguerre = False,
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periodic = True,
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normalized = True,
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config = True,
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)
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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for name in filenames:
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damask.util.report(scriptName,name)
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table = damask.ASCIItable(name = name, readonly = True)
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# --- read header ----------------------------------------------------------------------------
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table.head_read()
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info,extra_header = table.head_getGeom()
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if options.grid is not None: info['grid'] = options.grid
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if options.size is not None: info['size'] = options.size
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if options.origin is not None: info['origin'] = options.origin
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# ------------------------------------------ sanity checks ---------------------------------------
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remarks = []
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errors = []
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labels = []
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hasGrains = table.label_dimension(options.microstructure) == 1
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hasEulers = table.label_dimension(options.eulers) == 3
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hasWeights = table.label_dimension(options.weight) == 1 and options.laguerre
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for i in range(3):
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if info['size'][i] <= 0.0: # any invalid size?
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info['size'][i] = float(info['grid'][i])/max(info['grid']) # normalize to grid
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remarks.append('rescaling size {} to {}...'.format(['x','y','z'][i],info['size'][i]))
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if table.label_dimension(options.pos) != 3:
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errors.append('seed positions "{}" have dimension {}.'.format(options.pos,
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table.label_dimension(options.pos)))
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else:
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labels += [options.pos]
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if not options.normalized: remarks.append('using real-space seed coordinates...')
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if not hasEulers: remarks.append('missing seed orientations...')
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else: labels += [options.eulers]
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if not hasGrains: remarks.append('missing seed microstructure indices...')
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else: labels += [options.microstructure]
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if options.laguerre and not hasWeights: remarks.append('missing seed weights...')
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else: labels += [options.weight]
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss=True)
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continue
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# ------------------------------------------ read seeds ---------------------------------------
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table.data_readArray(labels)
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coords = table.data[:,table.label_indexrange(options.pos)] * info['size'] if options.normalized \
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else table.data[:,table.label_indexrange(options.pos)] - info['origin']
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eulers = table.data[:,table.label_indexrange(options.eulers)] if hasEulers \
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else np.zeros(3*len(coords))
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grains = table.data[:,table.label_indexrange(options.microstructure)].astype(int) if hasGrains \
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else 1+np.arange(len(coords))
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weights = table.data[:,table.label_indexrange(options.weight)] if hasWeights \
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else np.zeros(len(coords))
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grainIDs = np.unique(grains).astype('i')
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NgrainIDs = len(grainIDs)
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# --- tessellate microstructure ------------------------------------------------------------
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x = (np.arange(info['grid'][0])+0.5)*info['size'][0]/info['grid'][0]
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y = (np.arange(info['grid'][1])+0.5)*info['size'][1]/info['grid'][1]
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z = (np.arange(info['grid'][2])+0.5)*info['size'][2]/info['grid'][2]
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X,Y,Z = np.meshgrid(x, y, z,indexing='ij')
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grid = np.stack((X,Y,Z),axis=-1).reshape((np.prod(info['grid']),3),order='F')
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damask.util.croak('tessellating...')
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indices = laguerreTessellation(grid, coords, weights, grains, options.periodic, options.cpus)
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config_header = []
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if options.config:
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if hasEulers:
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config_header += ['<texture>']
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for ID in grainIDs:
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eulerID = np.nonzero(grains == ID)[0][0] # find first occurrence of this grain id
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config_header += ['[Grain{}]'.format(ID),
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'(gauss)\tphi1 {:.2f}\tPhi {:.2f}\tphi2 {:.2f}'.format(*eulers[eulerID])
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]
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if options.axes is not None: config_header += ['axes\t{} {} {}'.format(*options.axes)]
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config_header += ['<microstructure>']
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for ID in grainIDs:
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config_header += ['[Grain{}]'.format(ID),
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'(constituent)\tphase {}\ttexture {}\tfraction 1.0'.format(options.phase,ID)
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]
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config_header += ['<!skip>']
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header = [scriptID + ' ' + ' '.join(sys.argv[1:])]\
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+ config_header
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geom = damask.Geom(indices.reshape(info['grid'],order='F'),info['size'],info['origin'],
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homogenization=options.homogenization,comments=header)
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damask.util.croak(geom)
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geom.to_file(sys.stdout if name is None else os.path.splitext(name)[0]+'.geom',pack=False)
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