201 lines
9.0 KiB
Python
Executable File
201 lines
9.0 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 random
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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 kdtree_search(cloud, queryPoints):
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"""Find distances to nearest neighbor among cloud (N,d) for each of the queryPoints (n,d)."""
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n = queryPoints.shape[0]
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distances = np.zeros(n,dtype=float)
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tree = spatial.cKDTree(cloud)
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for i in range(n):
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distances[i], index = tree.query(queryPoints[i])
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return distances
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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', description = """
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Distribute given number of points randomly within (a fraction of) the three-dimensional cube [0.0,0.0,0.0]--[1.0,1.0,1.0].
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Reports positions with random crystal orientations in seeds file format to STDOUT.
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""", version = scriptID)
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parser.add_option('-N',
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dest = 'N',
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type = 'int', metavar = 'int',
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help = 'number of seed points [%default]')
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parser.add_option('-f',
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'--fraction',
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dest = 'fraction',
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type = 'float', nargs = 3, metavar = 'float float float',
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help='fractions along x,y,z of unit cube to fill %default')
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parser.add_option('-g',
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'--grid',
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dest = 'grid',
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type = 'int', nargs = 3, metavar = 'int int int',
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help='min a,b,c grid of hexahedral box %default')
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parser.add_option('-m',
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'--microstructure',
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dest = 'microstructure',
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type = 'int', metavar = 'int',
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help = 'first microstructure index [%default]')
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parser.add_option('-r',
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'--rnd',
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dest = 'randomSeed', type = 'int', metavar = 'int',
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help = 'seed of random number generator [%default]')
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group = OptionGroup(parser, "Laguerre Tessellation",
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"Parameters determining shape of weight distribution of seed points"
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)
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group.add_option( '-w',
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'--weights',
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action = 'store_true',
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dest = 'weights',
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help = 'assign random weights to seed points for Laguerre tessellation [%default]')
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group.add_option( '--max',
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dest = 'max',
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type = 'float', metavar = 'float',
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help = 'max of uniform distribution for weights [%default]')
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group.add_option( '--mean',
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dest = 'mean',
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type = 'float', metavar = 'float',
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help = 'mean of normal distribution for weights [%default]')
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group.add_option( '--sigma',
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dest = 'sigma',
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type = 'float', metavar = 'float',
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help='standard deviation of normal distribution for weights [%default]')
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parser.add_option_group(group)
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group = OptionGroup(parser, "Selective Seeding",
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"More uniform distribution of seed points using Mitchell's Best Candidate Algorithm"
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)
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group.add_option( '-s',
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'--selective',
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action = 'store_true',
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dest = 'selective',
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help = 'selective picking of seed points from random seed points')
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group.add_option( '--distance',
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dest = 'distance',
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type = 'float', metavar = 'float',
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help = 'minimum distance to next neighbor [%default]')
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group.add_option( '--numCandidates',
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dest = 'numCandidates',
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type = 'int', metavar = 'int',
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help = 'size of point group to select best distance from [%default]')
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parser.add_option_group(group)
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parser.set_defaults(randomSeed = None,
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grid = (16,16,16),
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fraction = (1.0,1.0,1.0),
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N = 20,
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weights = False,
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max = 0.0,
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mean = 0.2,
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sigma = 0.05,
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microstructure = 1,
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selective = False,
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distance = 0.2,
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numCandidates = 10,
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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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fraction = np.array(options.fraction)
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grid = np.array(options.grid)
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if options.randomSeed is None: options.randomSeed = int(os.urandom(4).hex(), 16)
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np.random.seed(options.randomSeed) # init random generators
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random.seed(options.randomSeed)
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for name in filenames:
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damask.util.report(scriptName,name)
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# --- sanity checks -------------------------------------------------------------------------
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remarks = []
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errors = []
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if any(grid==0):
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errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(grid == 0)[0]])))
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if options.N > grid.prod()/10.:
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remarks.append('seed count exceeds 0.1 of grid points.')
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if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5:
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remarks.append('maximum recommended seed point count for given distance is {}'.
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format(int(3./8./np.pi/(options.distance/2.)**3)))
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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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sys.exit()
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# --- do work ------------------------------------------------------------------------------------
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grainEuler = np.random.rand(3,options.N) # create random Euler triplets
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grainEuler[0,:] *= 360.0 # phi_1 is uniformly distributed
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grainEuler[1,:] = np.degrees(np.arccos(2*grainEuler[1,:]-1)) # cos(Phi) is uniformly distributed
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grainEuler[2,:] *= 360.0 # phi_2 is uniformly distributed
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if not options.selective:
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n = np.maximum(np.ones(3),np.array(grid*fraction),
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dtype=int,casting='unsafe') # find max grid indices within fraction
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meshgrid = np.meshgrid(*map(np.arange,n),indexing='ij') # create a meshgrid within fraction
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coords = np.vstack((meshgrid[0],meshgrid[1],meshgrid[2])).reshape(3,n.prod()).T # assemble list of 3D coordinates
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seeds = ((random.sample(list(coords),options.N)+np.random.random(options.N*3).reshape(options.N,3))\
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/ \
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(n/fraction)).T # pick options.N of those, rattle position,
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# and rescale to fall within fraction
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else:
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seeds = np.zeros((options.N,3),dtype=float) # seed positions array
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seeds[0] = np.random.random(3)*grid/max(grid)
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i = 1 # start out with one given point
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if i%(options.N/100.) < 1: damask.util.croak('.',False)
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while i < options.N:
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candidates = np.random.random(options.numCandidates*3).reshape(options.numCandidates,3)
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distances = kdtree_search(seeds[:i],candidates)
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best = distances.argmax()
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if distances[best] > options.distance: # require minimum separation
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seeds[i] = candidates[best] # maximum separation to existing point cloud
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i += 1
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if i%(options.N/100.) < 1: damask.util.croak('.',False)
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damask.util.croak('')
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seeds = seeds.T # prepare shape for stacking
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if options.weights:
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weights = [np.random.uniform(low = 0, high = options.max, size = options.N)] if options.max > 0.0 \
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else [np.random.normal(loc = options.mean, scale = options.sigma, size = options.N)]
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data = np.transpose(np.vstack(tuple([seeds,
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grainEuler,
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np.arange(options.microstructure,options.microstructure + options.N),
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] + (weights if options.weights else [])
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)))
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comments = [scriptID + ' ' + ' '.join(sys.argv[1:]),
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'grid\ta {}\tb {}\tc {}'.format(*grid),
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'randomSeed\t{}'.format(options.randomSeed),
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]
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shapes = {'pos':(3,),'euler':(3,),'microstructure':(1,)}
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if options.weights: shapes['weight'] = (1,)
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table = damask.Table(data,shapes,comments)
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table.to_ASCII(sys.stdout if name is None else name)
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