165 lines
8.0 KiB
Python
Executable File
165 lines
8.0 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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# --------------------------------------------------------------------
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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', 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', dest='grid', 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('-r', '--rnd', dest='randomSeed', type='int', metavar='int', \
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help='seed of random number generator [%default]')
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parser.add_option('-w', '--weights', dest='weights', action='store_true',
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help = 'assign random weigts (Gaussian Distribution) to seed points for laguerre tessellation [%default]')
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parser.add_option('-m', '--microstructure', dest='microstructure', type='int',
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help='first microstructure index [%default]', metavar='int')
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parser.add_option('-s','--selective', dest='selective', action='store_true',
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help = 'selective picking of seed points from random seed points [%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('--mean', dest='mean', type='float', metavar='float', \
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help='mean of Gaussian Distribution for weights [%default]')
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group.add_option('--sigma', dest='sigma', type='float', metavar='float', \
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help='standard deviation of Gaussian 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('--distance', dest='bestDistance', type='float', metavar='float', \
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help='minimum distance to the next neighbor [%default]')
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group.add_option('--numCandidates', dest='numCandidates', type='int', metavar='int', \
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help='maximum number of point to consider for initial random points generation [%default]')
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parser.add_option_group(group)
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parser.set_defaults(randomSeed = None)
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parser.set_defaults(grid = (16,16,16))
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parser.set_defaults(N = 20)
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parser.set_defaults(weights=False)
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parser.set_defaults(mean = 0.0)
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parser.set_defaults(sigma = 1.0)
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parser.set_defaults(microstructure = 1)
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parser.set_defaults(selective = False)
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parser.set_defaults(bestDistance = 0.2)
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parser.set_defaults(numCandidates = 10)
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(options,filename) = parser.parse_args()
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options.grid = np.array(options.grid)
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labels = "1_coords\t2_coords\t3_coords\tphi1\tPhi\tphi2\tmicrostructure"
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# ------------------------------------------ Functions Definitions ---------------------------------
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def kdtree_search(xyz, point) :
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dist, index = spatial.cKDTree(xyz).query(np.array(point))
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return dist
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def generatePoint() :
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return np.array([random.uniform(0,float(options.grid[0])/float(max(options.grid))), \
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random.uniform(0,float(options.grid[1])/float(max(options.grid))), \
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random.uniform(0,float(options.grid[2])/float(max(options.grid)))])
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# ------------------------------------------ setup file handle -------------------------------------
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if filename == []:
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file = {'output':sys.stdout, 'croak':sys.stderr}
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else:
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file = {'output':open(filename[0],'w'), 'croak':sys.stderr}
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gridSize = options.grid.prod()
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if gridSize == 0:
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file['croak'].write('zero grid dimension for %s.\n'%(', '.join([['a','b','c'][x] for x in np.where(options.grid == 0)[0]])))
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sys.exit()
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if options.N > gridSize:
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file['croak'].write('accommodating only %i seeds on grid.\n'%gridSize)
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options.N = gridSize
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randomSeed = int(os.urandom(4).encode('hex'), 16) if options.randomSeed == None else options.randomSeed
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np.random.seed(randomSeed) # init random generators
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random.seed(randomSeed)
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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.arccos(2*grainEuler[1,:]-1)*180.0/math.pi # cos(Phi) is uniformly distributed
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grainEuler[2,:] *= 360.0 # phi_2 is uniformly distributed
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microstructure=np.arange(options.microstructure,options.microstructure+options.N).reshape(1,options.N)
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if options.selective == False :
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seedpoints = -np.ones(options.N,dtype='int') # init grid positions of seed points
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if options.N * 1024 < gridSize: # heuristic limit for random search
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i = 0
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while i < options.N: # until all (unique) points determined
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p = np.random.randint(gridSize) # pick a location
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if p not in seedpoints: # not yet taken?
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seedpoints[i] = p # take it
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i += 1 # advance stepper
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else:
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seedpoints = np.array(random.sample(range(gridSize),options.N)) # create random permutation of all grid positions and choose first N
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seeds = np.zeros((3,options.N),float) # init seed positions
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seeds[0,:] = (np.mod(seedpoints ,options.grid[0])\
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+np.random.random())/options.grid[0]
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seeds[1,:] = (np.mod(seedpoints// options.grid[0] ,options.grid[1])\
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+np.random.random())/options.grid[1]
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seeds[2,:] = (np.mod(seedpoints//(options.grid[1]*options.grid[0]),options.grid[2])\
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+np.random.random())/options.grid[2]
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table = np.transpose(np.concatenate((seeds,grainEuler,microstructure),axis = 0))
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else :
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samples = generatePoint().reshape(1,3)
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while samples.shape[0] < options.N :
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bestDistance = options.bestDistance
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for i in xrange(options.numCandidates) :
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c = generatePoint()
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d = kdtree_search(samples, c)
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if (d > bestDistance) :
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bestDistance = d
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bestCandidate = c
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if kdtree_search(samples,bestCandidate) != 0.0 :
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samples = np.append(samples,bestCandidate.reshape(1,3),axis=0)
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else :
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continue
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table = np.transpose(np.concatenate((samples.T,grainEuler,microstructure),axis = 0))
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if options.weights :
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weight = np.random.normal(loc=options.mean, scale=options.sigma, size=options.N)
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table = np.append(table, weight.reshape(options.N,1), axis=1)
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labels += "\tweight"
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# -------------------------------------- Write Data --------------------------------------------------
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header = ["5\theader",
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scriptID + " " + " ".join(sys.argv[1:]),
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"grid\ta {}\tb {}\tc {}".format(options.grid[0],options.grid[1],options.grid[2]),
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"microstructures\t{}".format(options.N),
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"randomSeed\t{}".format(randomSeed),
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"%s"%labels,
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]
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for line in header:
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file['output'].write(line+"\n")
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np.savetxt(file['output'], table, fmt='%10.6f', delimiter='\t')
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