easier to specify size directly
This commit is contained in:
Martin Diehl
parent
42b9ccf99e
commit
ab1ab42e75
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@ -229,7 +229,6 @@ for name in filenames:
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coords = damask.grid_filters.cell_coord0(grid,size,-origin).reshape(-1,3,order='F')
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damask.util.croak('tessellating...')
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if options.laguerre:
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indices = Laguerre_tessellation(coords,seeds,grains,size,options.periodic,
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table.get(options.weight),options.cpus)
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@ -6,6 +6,7 @@ import random
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from optparse import OptionParser,OptionGroup
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import numpy as np
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from numpy import ma
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from scipy import spatial
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import damask
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@ -15,24 +16,12 @@ scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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def kdtree_search(cloud, queryPoints):
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"""Find distances to nearest neighbor among cloud (N,d) for each of the queryPoints (n,d)."""
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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 range(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 (a fraction of) the three-dimensional cube [0.0,0.0,0.0]--[1.0,1.0,1.0].
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Distribute given number of points randomly within rectangular cuboid.
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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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@ -41,11 +30,11 @@ parser.add_option('-N',
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dest = 'N',
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type = 'int', metavar = 'int',
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help = 'number of seed points [%default]')
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parser.add_option('-f',
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'--fraction',
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dest = 'fraction',
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parser.add_option('-s',
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'--size',
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dest = 'size',
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type = 'float', nargs = 3, metavar = 'float float float',
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help='fractions along x,y,z of unit cube to fill %default')
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help='size x,y,z of unit cube to fill %default')
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parser.add_option('-g',
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'--grid',
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dest = 'grid',
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@ -86,8 +75,7 @@ parser.add_option_group(group)
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group = OptionGroup(parser, "Selective Seeding",
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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',
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'--selective',
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group.add_option( '--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')
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@ -103,7 +91,7 @@ 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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fraction = (1.0,1.0,1.0),
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size = (1.0,1.0,1.0),
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N = 20,
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weights = False,
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max = 0.0,
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@ -118,62 +106,55 @@ parser.set_defaults(randomSeed = None,
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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fraction = np.array(options.fraction)
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grid = np.array(options.grid)
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if options.randomSeed is None: options.randomSeed = int(os.urandom(4).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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size = np.array(options.size)
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grid = np.array(options.grid)
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np.random.seed(int(os.urandom(4).hex(),16) if options.randomSeed is None else options.randomSeed)
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for name in filenames:
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damask.util.report(scriptName,name)
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remarks = []
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errors = []
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if any(grid==0):
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errors.append('zero grid dimension for {}.'.format(', '.join([['a','b','c'][x] for x in np.where(grid == 0)[0]])))
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if options.N > grid.prod()/10.:
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remarks.append('seed count exceeds 0.1 of grid points.')
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if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5:
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remarks.append('maximum recommended seed point count for given distance is {}'.
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format(int(3./8./np.pi/(options.distance/2.)**3)))
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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if options.N > np.prod(grid):
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damask.util.croak('More seeds than grid positions.')
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sys.exit()
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if options.selective and 4./3.*np.pi*(options.distance/2.)**3*options.N > 0.5*np.prod(size):
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vol = 4./3.*np.pi*(options.distance/2.)**3
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damask.util.croak('Recommended # of seeds is {}.'.format(int(0.5*np.prod(size)/vol)))
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eulers = np.random.rand(options.N,3) # create random Euler triplets
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eulers[:,0] *= 360.0 # phi_1 is uniformly distributed
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eulers[:,1] = np.degrees(np.arccos(2*eulers[:,1]-1.0)) # cos(Phi) is uniformly distributed
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eulers[:,2] *= 360.0 # phi_2 is uniformly distributed
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coords = damask.grid_filters.cell_coord0(grid,size).reshape(-1,3)
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if not options.selective:
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n = np.maximum(np.ones(3),np.array(grid*fraction),dtype=int,casting='unsafe') # find max grid indices within fraction
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meshgrid = np.meshgrid(*map(np.arange,n),indexing='ij') # create a meshgrid within fraction
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coords = np.vstack((meshgrid[0],meshgrid[1],meshgrid[2])).reshape(n.prod(),3) # assemble list of 3D coordinates
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seeds = (random.sample(coords.tolist(),options.N)+np.random.rand(options.N,3))/(n/fraction) # ... pick N of those, rattle position,
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# ... and rescale to fall within fraction
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seeds = coords[np.random.choice(coords.shape[0], options.N, replace=False)]
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else:
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seeds = np.empty((options.N,3)) # seed positions array
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seeds[0] = np.random.random(3)*grid/max(grid)
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i = 1 # start out with one given point
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if i%(options.N/100.) < 1: damask.util.croak('.',False)
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seeds = np.empty((options.N,3))
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unpicked = ma.array(np.arange(coords.shape[0]),mask=np.zeros(coords.shape[0],dtype=bool))
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first_pick = np.random.randint(coords.shape[0])
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seeds[0] = coords[first_pick]
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unpicked.mask[first_pick]=True
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i = 1
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progress = damask.util._ProgressBar(options.N,'',50)
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while i < options.N:
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candidates = np.random.rand(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] # maximum separation to existing point cloud
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i += 1
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if i%(options.N/100.) < 1: damask.util.croak('.',False)
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damask.util.croak('')
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candidates = np.random.choice(unpicked[unpicked.mask==False],replace=False,
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size=min(len(unpicked[unpicked.mask==False]),options.numCandidates))
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tree = spatial.cKDTree(seeds[:i])
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distances, dev_null = tree.query(coords[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] = coords[candidates[best]] # maximum separation to existing point cloud
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unpicked.mask[candidates[best]]=True
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i += 1
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progress.update(i)
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seeds += np.broadcast_to(size/grid,seeds.shape)*(np.random.random(seeds.shape)*.5-.25) # wobble without leaving grid
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comments = [scriptID + ' ' + ' '.join(sys.argv[1:]),
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'grid\ta {}\tb {}\tc {}'.format(*grid),
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'size\tx {}\ty {}\tz {}'.format(*size),
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'randomSeed\t{}'.format(options.randomSeed),
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]
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