DAMASK_EICMD/processing/pre/seeds_fromRandom.py

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#!/usr/bin/env python3
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import os
import sys
from optparse import OptionParser,OptionGroup
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import numpy as np
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from numpy import ma
from scipy import spatial
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import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
# --------------------------------------------------------------------
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# MAIN
# --------------------------------------------------------------------
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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 rectangular cuboid.
Reports positions with random crystal orientations in seeds file format to STDOUT.
""", 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 [%default]')
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parser.add_option('-s',
'--size',
dest = 'size',
type = 'float', nargs = 3, metavar = 'float float float',
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help='size x,y,z of unit cube to fill %default')
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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')
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parser.add_option('-m',
'--microstructure',
dest = 'microstructure',
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type = 'int', metavar = 'int',
help = 'first microstructure index [%default]')
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parser.add_option('-r',
'--rnd',
dest = 'randomSeed', type = 'int', metavar = 'int',
help = 'seed of random number generator [%default]')
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group = OptionGroup(parser, "Laguerre Tessellation",
"Parameters determining shape of weight distribution of seed points"
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)
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group.add_option( '-w',
'--weights',
action = 'store_true',
dest = 'weights',
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help = 'assign random weights 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]')
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group.add_option( '--mean',
dest = 'mean',
type = 'float', metavar = 'float',
help = 'mean of normal distribution for weights [%default]')
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group.add_option( '--sigma',
dest = 'sigma',
type = 'float', metavar = 'float',
help='standard deviation of normal distribution for weights [%default]')
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parser.add_option_group(group)
group = OptionGroup(parser, "Selective Seeding",
"More uniform distribution of seed points using Mitchell's Best Candidate Algorithm"
)
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group.add_option( '--selective',
action = 'store_true',
dest = 'selective',
help = 'selective picking of seed points from random seed points')
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group.add_option( '--distance',
dest = 'distance',
type = 'float', metavar = 'float',
help = 'minimum distance to next neighbor [%default]')
group.add_option( '--numCandidates',
dest = 'numCandidates',
type = 'int', metavar = 'int',
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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),
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size = (1.0,1.0,1.0),
N = 20,
weights = False,
max = 0.0,
mean = 0.2,
sigma = 0.05,
microstructure = 1,
selective = False,
distance = 0.2,
numCandidates = 10,
)
(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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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)
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if options.N > np.prod(grid):
damask.util.croak('More seeds than grid positions.')
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):
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
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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
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coords = damask.grid_filters.cell_coord0(grid,size).reshape(-1,3)
if not options.selective:
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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))
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:
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candidates = np.random.choice(unpicked[not unpicked.mask],replace=False,
size=min(len(unpicked[not unpicked.mask]),options.numCandidates))
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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
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comments = [scriptID + ' ' + ' '.join(sys.argv[1:]),
'grid\ta {}\tb {}\tc {}'.format(*grid),
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'size\tx {}\ty {}\tz {}'.format(*size),
'randomSeed\t{}'.format(options.randomSeed),
]
table = damask.Table(np.hstack((seeds,eulers)),{'pos':(3,),'euler':(3,)},comments)
table.add('microstructure',np.arange(options.microstructure,options.microstructure + options.N,dtype=int))
if options.weights:
weights = np.random.uniform(low = 0, high = options.max, size = options.N) if options.max > 0.0 \
else np.random.normal(loc = options.mean, scale = options.sigma, size = options.N)
table.add('weight',weights)
table.to_ASCII(sys.stdout if name is None else name)