diff --git a/processing/pre/geom_fromVoronoiTessellation.py b/processing/pre/geom_fromVoronoiTessellation.py index ad7b7aa49..f0f99dee6 100755 --- a/processing/pre/geom_fromVoronoiTessellation.py +++ b/processing/pre/geom_fromVoronoiTessellation.py @@ -229,7 +229,6 @@ for name in filenames: coords = damask.grid_filters.cell_coord0(grid,size,-origin).reshape(-1,3,order='F') - damask.util.croak('tessellating...') if options.laguerre: indices = Laguerre_tessellation(coords,seeds,grains,size,options.periodic, table.get(options.weight),options.cpus) diff --git a/processing/pre/seeds_fromRandom.py b/processing/pre/seeds_fromRandom.py index 3cc9abe13..f497518dc 100755 --- a/processing/pre/seeds_fromRandom.py +++ b/processing/pre/seeds_fromRandom.py @@ -6,6 +6,7 @@ import random from optparse import OptionParser,OptionGroup import numpy as np +from numpy import ma from scipy import spatial import damask @@ -15,24 +16,12 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0] scriptID = ' '.join([scriptName,damask.version]) -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 range(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 (a fraction of) the three-dimensional cube [0.0,0.0,0.0]--[1.0,1.0,1.0]. +Distribute given number of points randomly within rectangular cuboid. Reports positions with random crystal orientations in seeds file format to STDOUT. """, version = scriptID) @@ -41,11 +30,11 @@ parser.add_option('-N', dest = 'N', type = 'int', metavar = 'int', help = 'number of seed points [%default]') -parser.add_option('-f', - '--fraction', - dest = 'fraction', +parser.add_option('-s', + '--size', + dest = 'size', type = 'float', nargs = 3, metavar = 'float float float', - help='fractions along x,y,z of unit cube to fill %default') + help='size x,y,z of unit cube to fill %default') parser.add_option('-g', '--grid', dest = 'grid', @@ -86,8 +75,7 @@ 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', +group.add_option( '--selective', action = 'store_true', dest = 'selective', help = 'selective picking of seed points from random seed points') @@ -103,7 +91,7 @@ parser.add_option_group(group) parser.set_defaults(randomSeed = None, grid = (16,16,16), - fraction = (1.0,1.0,1.0), + size = (1.0,1.0,1.0), N = 20, weights = False, max = 0.0, @@ -118,62 +106,55 @@ parser.set_defaults(randomSeed = None, (options,filenames) = parser.parse_args() if filenames == []: filenames = [None] -fraction = np.array(options.fraction) -grid = np.array(options.grid) - -if options.randomSeed is None: options.randomSeed = int(os.urandom(4).hex(), 16) -np.random.seed(options.randomSeed) # init random generators -random.seed(options.randomSeed) +size = np.array(options.size) +grid = np.array(options.grid) +np.random.seed(int(os.urandom(4).hex(),16) if options.randomSeed is None else options.randomSeed) for name in filenames: damask.util.report(scriptName,name) - remarks = [] - errors = [] - if any(grid==0): - errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(grid == 0)[0]]))) - if options.N > grid.prod()/10.: - remarks.append('seed count exceeds 0.1 of grid points.') - if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5: - remarks.append('maximum recommended seed point count for given distance is {}'. - format(int(3./8./np.pi/(options.distance/2.)**3))) - - if remarks != []: damask.util.croak(remarks) - if errors != []: - damask.util.croak(errors) + if options.N > np.prod(grid): + damask.util.croak('More seeds than grid positions.') sys.exit() + if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5*np.prod(size): + vol = 4./3.*np.pi*(options.distance/2.)**3 + damask.util.croak('Recommended # of seeds is {}.'.format(int(0.5*np.prod(size)/vol))) eulers = np.random.rand(options.N,3) # create random Euler triplets eulers[:,0] *= 360.0 # phi_1 is uniformly distributed eulers[:,1] = np.degrees(np.arccos(2*eulers[:,1]-1.0)) # cos(Phi) is uniformly distributed eulers[:,2] *= 360.0 # phi_2 is uniformly distributed + coords = damask.grid_filters.cell_coord0(grid,size).reshape(-1,3) + if not options.selective: - n = np.maximum(np.ones(3),np.array(grid*fraction),dtype=int,casting='unsafe') # find max grid indices within fraction - meshgrid = np.meshgrid(*map(np.arange,n),indexing='ij') # create a meshgrid within fraction - coords = np.vstack((meshgrid[0],meshgrid[1],meshgrid[2])).reshape(n.prod(),3) # assemble list of 3D coordinates - seeds = (random.sample(coords.tolist(),options.N)+np.random.rand(options.N,3))/(n/fraction) # ... pick N of those, rattle position, - # ... and rescale to fall within fraction + seeds = coords[np.random.choice(coords.shape[0], options.N, replace=False)] else: - seeds = np.empty((options.N,3)) # seed positions array - seeds[0] = np.random.random(3)*grid/max(grid) - i = 1 # start out with one given point - if i%(options.N/100.) < 1: damask.util.croak('.',False) + seeds = np.empty((options.N,3)) + unpicked = ma.array(np.arange(coords.shape[0]),mask=np.zeros(coords.shape[0],dtype=bool)) + first_pick = np.random.randint(coords.shape[0]) + seeds[0] = coords[first_pick] + unpicked.mask[first_pick]=True + i = 1 + progress = damask.util._ProgressBar(options.N,'',50) while i < options.N: - candidates = np.random.rand(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('') + candidates = np.random.choice(unpicked[unpicked.mask==False],replace=False, + size=min(len(unpicked[unpicked.mask==False]),options.numCandidates)) + tree = spatial.cKDTree(seeds[:i]) + distances, dev_null = tree.query(coords[candidates]) + best = distances.argmax() + if distances[best] > options.distance: # require minimum separation + seeds[i] = coords[candidates[best]] # maximum separation to existing point cloud + unpicked.mask[candidates[best]]=True + i += 1 + progress.update(i) + seeds += np.broadcast_to(size/grid,seeds.shape)*(np.random.random(seeds.shape)*.5-.25) # wobble without leaving grid comments = [scriptID + ' ' + ' '.join(sys.argv[1:]), 'grid\ta {}\tb {}\tc {}'.format(*grid), + 'size\tx {}\ty {}\tz {}'.format(*size), 'randomSeed\t{}'.format(options.randomSeed), ]