#!/usr/bin/env python # -*- coding: UTF-8 no BOM -*- import os,sys,string,math,random import numpy as np import damask from optparse import OptionParser,OptionGroup from scipy import spatial scriptID = string.replace('$Id$','\n','\\n') scriptName = os.path.splitext(scriptID.split()[1])[0] # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.extendableOption, usage='%prog [options]', description = """ Distribute given number of points randomly within the three-dimensional cube [0.0,0.0,0.0]--[1.0,1.0,1.0]. Reports positions with random crystal orientations in seeds file format to STDOUT. """, version = scriptID) parser.add_option('-N', dest='N', type='int', metavar='int', \ help='number of seed points to distribute [%default]') parser.add_option('-g','--grid', dest='grid', type='int', nargs=3, metavar='int int int', \ help='min a,b,c grid of hexahedral box %default') parser.add_option('-r', '--rnd', dest='randomSeed', type='int', metavar='int', \ help='seed of random number generator [%default]') parser.add_option('-w', '--weights', dest='weights', action='store_true', help = 'assign random weigts (Gaussian Distribution) to seed points for laguerre tessellation [%default]') parser.add_option('--mean', dest='mean', type='float', metavar='float', \ help='mean of Gaussian Distribution for weights [%default]') parser.add_option('--sigma', dest='sigma', type='float', metavar='float', \ help='standard deviation of Gaussian Distribution for weights [%default]') parser.add_option('-m', '--microstructure', dest='microstructure', type='int', help='first microstructure index [%default]', metavar='int') parser.add_option('-s','--selective', dest='selective', action='store_true', help = 'selective picking of seed points from random seed points [%default]') group = OptionGroup(parser, "Selective Seeding Options", "More uniform distribution of seed points using Mitchells Best Candidate Algorithm" ) group.add_option('--distance', dest='bestDistance', type='float', metavar='float', \ help='minimum distance to the next neighbor [%default]') group.add_option('--numCandidates', dest='numCandidates', type='int', metavar='int', \ help='maximum number of point to consider for initial random points generation [%default]') parser.add_option_group(group) parser.set_defaults(randomSeed = None) parser.set_defaults(grid = (16,16,16)) parser.set_defaults(N = 20) parser.set_defaults(weights=False) parser.set_defaults(mean = 0.0) parser.set_defaults(sigma = 1.0) parser.set_defaults(microstructure = 1) parser.set_defaults(selective = False) parser.set_defaults(bestDistance = 0.2) parser.set_defaults(numCandidates = 10) (options,filename) = parser.parse_args() options.grid = np.array(options.grid) labels = "1_coords\t2_coords\t3_coords\tphi1\tPhi\tphi2\tmicrostructure" # ------------------------------------------ Functions Definitions --------------------------------- def kdtree_search(xyz, point) : dist, index = spatial.cKDTree(xyz).query(np.array(point)) return dist def generatePoint() : return np.array([random.uniform(0,options.grid[0]/max(options.grid)), \ random.uniform(0,options.grid[1]/max(options.grid)), \ random.uniform(0,options.grid[2]/max(options.grid))]) # ------------------------------------------ setup file handle ------------------------------------- if filename == []: file = {'output':sys.stdout, 'croak':sys.stderr} else: file = {'output':open(filename[0],'w'), 'croak':sys.stderr} gridSize = options.grid.prod() if gridSize == 0: file['croak'].write('zero grid dimension for %s.\n'%(', '.join([['a','b','c'][x] for x in np.where(options.grid == 0)[0]]))) sys.exit() if options.N > gridSize: file['croak'].write('accommodating only %i seeds on grid.\n'%gridSize) options.N = gridSize randomSeed = int(os.urandom(4).encode('hex'), 16) if options.randomSeed == None else options.randomSeed np.random.seed(randomSeed) # init random generators random.seed(randomSeed) grainEuler = np.random.rand(3,options.N) # create random Euler triplets grainEuler[0,:] *= 360.0 # phi_1 is uniformly distributed grainEuler[1,:] = np.arccos(2*grainEuler[1,:]-1)*180.0/math.pi # cos(Phi) is uniformly distributed grainEuler[2,:] *= 360.0 # phi_2 is uniformly distributed microstructure=np.arange(options.microstructure,options.microstructure+options.N).reshape(1,options.N) if options.selective == False : seedpoints = -np.ones(options.N,dtype='int') # init grid positions of seed points if options.N * 1024 < gridSize: # heuristic limit for random search i = 0 while i < options.N: # until all (unique) points determined p = np.random.randint(gridSize) # pick a location if p not in seedpoints: # not yet taken? seedpoints[i] = p # take it i += 1 # advance stepper else: seedpoints = np.array(random.sample(range(gridSize),options.N)) # create random permutation of all grid positions and choose first N seeds = np.zeros((3,options.N),float) # init seed positions seeds[0,:] = (np.mod(seedpoints ,options.grid[0])\ +np.random.random())/options.grid[0] seeds[1,:] = (np.mod(seedpoints// options.grid[0] ,options.grid[1])\ +np.random.random())/options.grid[1] seeds[2,:] = (np.mod(seedpoints//(options.grid[1]*options.grid[0]),options.grid[2])\ +np.random.random())/options.grid[2] table = np.transpose(np.concatenate((seeds,grainEuler,microstructure),axis = 0)) else : samples = generatePoint().reshape(1,3) while samples.shape[0] < options.N : bestDistance = options.bestDistance for i in xrange(options.numCandidates) : c = generatePoint() d = kdtree_search(samples, c) if (d > bestDistance) : bestDistance = d bestCandidate = c if kdtree_search(samples,bestCandidate) != 0.0 : samples = np.append(samples,bestCandidate.reshape(1,3),axis=0) else : continue table = np.transpose(np.concatenate((samples.T,grainEuler,microstructure),axis = 0)) if options.weights : weight = np.random.normal(loc=options.mean, scale=options.sigma, size=options.N) weight /= np.sum(weight) table = np.append(table, weight.reshape(options.N,1), axis=1) labels += "\tweight" # -------------------------------------- Write Data -------------------------------------------------- header = ["5\theader", scriptID + " " + " ".join(sys.argv[1:]), "grid\ta {}\tb {}\tc {}".format(options.grid[0],options.grid[1],options.grid[2]), "microstructures\t{}".format(options.N), "randomSeed\t{}".format(randomSeed), "%s"%labels, ] for line in header: file['output'].write(line+"\n") np.savetxt(file['output'], table, fmt='%10.6f', delimiter='\t')