273 lines
10 KiB
Python
Executable File
273 lines
10 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 io import StringIO
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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 Laguerre_tessellation(grid, seeds, grains, size, periodic, weights, cpus):
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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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],dtype=np.float)*size if periodic else \
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np.array([
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[ 0, 0, 0 ],
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],dtype=np.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 vec in copies: # periodic copies of seed points ...
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try: seeds = np.append(seeds, seeds+vec, axis=0) # ... (1+a,2+a,3+a,...,1+z,2+z,3+z)
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except NameError: seeds = seeds+vec
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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(grid)]
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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%seeds.shape[0]]
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def Voronoi_tessellation(grid, seeds, grains, size, periodic = True):
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KDTree = spatial.cKDTree(seeds,boxsize=size) if periodic else spatial.cKDTree(seeds)
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devNull,closestSeeds = KDTree.query(grid)
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return grains[closestSeeds]
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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.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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size = np.zeros(3)
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origin = np.zeros(3)
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for line in table.comments:
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items = line.lower().strip().split()
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key = items[0] if items else ''
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if key == 'grid':
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grid = np.array([ int(dict(zip(items[1::2],items[2::2]))[i]) for i in ['a','b','c']])
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elif key == 'size':
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size = np.array([float(dict(zip(items[1::2],items[2::2]))[i]) for i in ['x','y','z']])
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elif key == 'origin':
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origin = np.array([float(dict(zip(items[1::2],items[2::2]))[i]) for i in ['x','y','z']])
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if options.grid: grid = np.array(options.grid)
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if options.size: size = np.array(options.size)
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if options.origin: origin = np.array(options.origin)
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size = np.where(size <= 0.0,grid/grid.max(),size)
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seeds = table.get(options.pos) * size if options.normalized else table.get(options.pos) - origin
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if options.eulers in table.labels:
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eulers = table.get(options.eulers)
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grains = table.get(options.microstructure) if options.microstructure in table.labels else np.arange(len(seeds))+1
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grainIDs = np.unique(grains).astype('i')
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NgrainIDs = len(grainIDs)
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coords = damask.grid_filters.cell_coord0(grid,size).reshape(-1,3)
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damask.util.croak('tessellating...')
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if options.laguerre:
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indices = Laguerre_tessellation(coords,seeds,grains,size,options.periodic,
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table.get(options.weight),options.cpus)
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else:
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indices = Voronoi_tessellation (coords,seeds,grains,size,options.periodic)
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config_header = []
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if options.config:
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if options.eulers in table.labels:
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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: 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(grid,order='F'),size,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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