208 lines
9.6 KiB
Python
Executable File
208 lines
9.6 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string,math,random
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import numpy as np
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import damask
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from optparse import OptionParser,OptionGroup
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from scipy import spatial
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
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# ------------------------------------------ aux functions ---------------------------------
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def kdtree_search(cloud, queryPoints):
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'''
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find distances to nearest neighbor among cloud (N,d) for each of the queryPoints (n,d)
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'''
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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 xrange(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 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', dest='N',
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type = 'int', metavar = 'int',
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help = 'number of seed points to distribute [%default]')
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parser.add_option('-g','--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', '--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', '--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 Options",
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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', '--weights',
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action = 'store_true',
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dest = 'weights',
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help = 'assign random weigts (normal distribution) to seed points for Laguerre tessellation [%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 Options",
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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','--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 [%default]')
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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 the 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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N = 20,
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weights = False,
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mean = 0.0,
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sigma = 1.0,
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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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options.grid = np.array(options.grid)
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gridSize = options.grid.prod()
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if options.randomSeed == None: options.randomSeed = int(os.urandom(4).encode('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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# --- loop over output files -------------------------------------------------------------------------
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if filenames == []: filenames = ['STDIN']
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for name in filenames:
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table = damask.ASCIItable(name = name, outname = None,
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buffered = False, writeonly = True)
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table.croak('\033[1m'+scriptName+'\033[0m'+(': '+name if name != 'STDIN' else ''))
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# --- sanity checks -------------------------------------------------------------------------
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errors = []
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if gridSize == 0: errors.append('zero grid dimension for %s.'%(', '.join([['a','b','c'][x] for x in np.where(options.grid == 0)[0]])))
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if options.N > gridSize/10.: errors.append('seed count exceeds 0.1 of grid points.')
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if options.selective and 4./3.*math.pi*(options.distance/2.)**3*options.N > 0.5:
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errors.append('maximum recommended seed point count for given distance is {}.'.format(int(3./8./math.pi/(options.distance/2.)**3)))
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if errors != []:
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table.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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seeds = np.zeros((3,options.N),dtype=float) # seed positions array
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gridpoints = random.sample(range(gridSize),options.N) # create random permutation of all grid positions and choose first N
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seeds[0,:] = (np.mod(gridpoints ,options.grid[0])\
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+np.random.random()) /options.grid[0]
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seeds[1,:] = (np.mod(gridpoints// options.grid[0] ,options.grid[1])\
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+np.random.random()) /options.grid[1]
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seeds[2,:] = (np.mod(gridpoints//(options.grid[1]*options.grid[0]),options.grid[2])\
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+np.random.random()) /options.grid[2]
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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)*options.grid/max(options.grid)
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i = 1 # start out with one given point
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if i%(options.N/100.) < 1: table.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] # take candidate with maximum separation to existing point cloud
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i += 1
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if i%(options.N/100.) < 1: table.croak('.',False)
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table.croak('')
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seeds = np.transpose(seeds) # prepare shape for stacking
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if options.weights:
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seeds = np.transpose(np.vstack((seeds,
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grainEuler,
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np.arange(options.microstructure,
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options.microstructure + options.N),
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np.random.normal(loc=options.mean, scale=options.sigma, size=options.N),
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)))
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else:
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seeds = np.transpose(np.vstack((seeds,
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grainEuler,
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np.arange(options.microstructure,
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options.microstructure + options.N),
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)))
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# ------------------------------------------ assemble header ---------------------------------------
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table.info_clear()
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table.info_append([
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scriptID + ' ' + ' '.join(sys.argv[1:]),
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"grid\ta {grid[0]}\tb {grid[1]}\tc {grid[2]}".format(grid=options.grid),
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"microstructures\t{}".format(options.N),
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"randomSeed\t{}".format(options.randomSeed),
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])
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table.labels_clear()
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table.labels_append( ['{dim}_{label}'.format(dim = 1+i,label = 'pos') for i in xrange(3)] +
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['{dim}_{label}'.format(dim = 1+i,label = 'Euler') for i in xrange(3)] +
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['microstructure'] +
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(['weight'] if options.weights else []))
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table.head_write()
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table.output_flush()
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# --- write seeds information ------------------------------------------------------------
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table.data = seeds
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table.data_writeArray()
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# --- output finalization --------------------------------------------------------------------------
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table.close() # close ASCII table
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