#!/usr/bin/env python # -*- coding: UTF-8 no BOM -*- import os,sys,math,random import numpy as np import damask from optparse import OptionParser,OptionGroup from scipy import spatial scriptName = os.path.splitext(os.path.basename(__file__))[0] scriptID = ' '.join([scriptName,damask.version]) # ------------------------------------------ aux functions --------------------------------- def kdtree_search(cloud, queryPoints): """find distances to nearest neighbor among cloud (N,d) for each of the queryPoints (n,d)""" n = queryPoints.shape[0] distances = np.zeros(n,dtype=float) tree = spatial.cKDTree(cloud) for i in xrange(n): distances[i], index = tree.query(queryPoints[i]) return distances # -------------------------------------------------------------------- # 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('-m', '--microstructure', dest = 'microstructure', type = 'int', metavar='int', help = 'first microstructure index [%default]') parser.add_option('-r', '--rnd', dest = 'randomSeed', type = 'int', metavar = 'int', help = 'seed of random number generator [%default]') parser.add_option('--format', dest = 'format', type = 'string', metavar = 'string', help = 'number format of output [auto]') group = OptionGroup(parser, "Laguerre Tessellation", "Parameters determining shape of weight distribution of seed points" ) group.add_option('-w', '--weights', action = 'store_true', dest = 'weights', help = 'assign random weigts to seed points for Laguerre tessellation [%default]') group.add_option('--max', dest = 'max', type = 'float', metavar = 'float', help = 'max of uniform distribution for weights [%default]') group.add_option('--mean', dest = 'mean', type = 'float', metavar = 'float', help = 'mean of normal distribution for weights [%default]') group.add_option('--sigma', dest = 'sigma', type = 'float', metavar = 'float', help='standard deviation of normal distribution for weights [%default]') parser.add_option_group(group) group = OptionGroup(parser, "Selective Seeding", "More uniform distribution of seed points using Mitchell's Best Candidate Algorithm" ) group.add_option('-s','--selective', action = 'store_true', dest = 'selective', help = 'selective picking of seed points from random seed points [%default]') group.add_option('-f','--force', action = 'store_true', dest = 'force', help = 'try selective picking despite large seed point number [%default]') group.add_option('--distance', dest = 'distance', type = 'float', metavar = 'float', help = 'minimum distance to the next neighbor [%default]') group.add_option('--numCandidates', dest = 'numCandidates', type = 'int', metavar = 'int', help = 'size of point group to select best distance from [%default]') parser.add_option_group(group) parser.set_defaults(randomSeed = None, grid = (16,16,16), N = 20, weights = False, max = 0.0, mean = 0.2, sigma = 0.05, microstructure = 1, selective = False, force = False, distance = 0.2, numCandidates = 10, format = None, ) (options,filenames) = parser.parse_args() options.grid = np.array(options.grid) gridSize = options.grid.prod() if options.randomSeed is None: options.randomSeed = int(os.urandom(4).encode('hex'), 16) np.random.seed(options.randomSeed) # init random generators random.seed(options.randomSeed) # --- loop over output files ------------------------------------------------------------------------- if filenames == []: filenames = [None] for name in filenames: try: table = damask.ASCIItable(outname = name, buffered = False) except: continue damask.util.report(scriptName,name) # --- sanity checks ------------------------------------------------------------------------- remarks = [] errors = [] if gridSize == 0: errors.append('zero grid dimension for %s.'%(', '.join([['a','b','c'][x] for x in np.where(options.grid == 0)[0]]))) if options.N > gridSize/10.: errors.append('seed count exceeds 0.1 of grid points.') if options.selective and 4./3.*math.pi*(options.distance/2.)**3*options.N > 0.5: (remarks if options.force else errors).append('maximum recommended seed point count for given distance is {}.{}'. format(int(3./8./math.pi/(options.distance/2.)**3),'..'*options.force)) if remarks != []: damask.util.croak(remarks) if errors != []: damask.util.croak(errors) sys.exit() # --- do work ------------------------------------------------------------------------------------ grainEuler = np.random.rand(3,options.N) # create random Euler triplets grainEuler[0,:] *= 360.0 # phi_1 is uniformly distributed grainEuler[1,:] = np.degrees(np.arccos(2*grainEuler[1,:]-1)) # cos(Phi) is uniformly distributed grainEuler[2,:] *= 360.0 # phi_2 is uniformly distributed if not options.selective: seeds = np.zeros((3,options.N),dtype='d') # seed positions array gridpoints = random.sample(range(gridSize),options.N) # choose first N from random permutation of grid positions seeds[0,:] = (np.mod(gridpoints ,options.grid[0])\ +np.random.random(options.N)) /options.grid[0] seeds[1,:] = (np.mod(gridpoints// options.grid[0] ,options.grid[1])\ +np.random.random(options.N)) /options.grid[1] seeds[2,:] = (np.mod(gridpoints//(options.grid[1]*options.grid[0]),options.grid[2])\ +np.random.random(options.N)) /options.grid[2] else: seeds = np.zeros((options.N,3),dtype=float) # seed positions array seeds[0] = np.random.random(3)*options.grid/max(options.grid) i = 1 # start out with one given point if i%(options.N/100.) < 1: damask.util.croak('.',False) while i < options.N: candidates = np.random.random(options.numCandidates*3).reshape(options.numCandidates,3) distances = kdtree_search(seeds[:i],candidates) best = distances.argmax() if distances[best] > options.distance: # require minimum separation seeds[i] = candidates[best] # maximum separation to existing point cloud i += 1 if i%(options.N/100.) < 1: damask.util.croak('.',False) damask.util.croak('') seeds = seeds.T # prepare shape for stacking if options.weights: if options.max > 0.0: weights = [np.random.uniform(low = 0, high = options.max, size = options.N)] else: weights = [np.random.normal(loc = options.mean, scale = options.sigma, size = options.N)] else: weights = [] seeds = np.transpose(np.vstack(tuple([seeds, grainEuler, np.arange(options.microstructure, options.microstructure + options.N), ] + weights ))) # ------------------------------------------ assemble header --------------------------------------- table.info_clear() table.info_append([ scriptID + ' ' + ' '.join(sys.argv[1:]), "grid\ta {grid[0]}\tb {grid[1]}\tc {grid[2]}".format(grid=options.grid), "microstructures\t{}".format(options.N), "randomSeed\t{}".format(options.randomSeed), ]) table.labels_clear() table.labels_append( ['{dim}_{label}'.format(dim = 1+k,label = 'pos') for k in xrange(3)] + ['{dim}_{label}'.format(dim = 1+k,label = 'eulerangles') for k in xrange(3)] + ['microstructure'] + (['weight'] if options.weights else [])) table.head_write() table.output_flush() # --- write seeds information ------------------------------------------------------------ table.data = seeds table.data_writeArray(fmt = options.format) # --- output finalization -------------------------------------------------------------------------- table.close() # close ASCII table