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DAMASK_EICMD
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Merged branch development into development

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chen 2016-09-14 19:50:18 -04:00
parent ae16ccae61 eb69d6f4f1
commit 38d1a2c254
1 changed files with 26 additions and 11 deletions
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37
processing/pre/seeds_fromRandom.py
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@ -29,7 +29,7 @@ def kdtree_search(cloud, queryPoints):
# --------------------------------------------------------------------
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].
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].
Reports positions with random crystal orientations in seeds file format to STDOUT.
""", version = scriptID)
@ -38,6 +38,11 @@ parser.add_option('-N',
dest = 'N',
type = 'int', metavar = 'int',
help = 'number of seed points [%default]')
parser.add_option('-f',
'--fraction',
dest = 'fraction',
type = 'float', nargs = 3, metavar = 'float float float',
help='fractions along x,y,z of unit cube to fill %default')
parser.add_option('-g',
'--grid',
dest = 'grid',
@ -86,8 +91,7 @@ group.add_option( '-s',
action = 'store_true',
dest = 'selective',
help = 'selective picking of seed points from random seed points [%default]')
group.add_option( '-f',
'--force',
group.add_option( '--force',
action = 'store_true',
dest = 'force',
help = 'try selective picking despite large seed point number [%default]')
@ -103,6 +107,7 @@ parser.add_option_group(group)
parser.set_defaults(randomSeed = None,
grid = (16,16,16),
fraction = (1.0,1.0,1.0),
N = 20,
weights = False,
max = 0.0,
@ -118,6 +123,7 @@ parser.set_defaults(randomSeed = None,
(options,filenames) = parser.parse_args()
options.fraction = np.array(options.fraction)
options.grid = np.array(options.grid)
gridSize = options.grid.prod()
@ -160,16 +166,25 @@ for name in filenames:
grainEuler[2,:] *= 360.0 # phi_2 is uniformly distributed
if not options.selective:
seeds = np.array([])
while len(seeds) < options.N:
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
theSeeds = np.zeros((options.N,3),dtype=float) # 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]
theSeeds[:,0] = (np.mod(gridpoints ,options.grid[0])\
+np.random.random(options.N)) /options.grid[0]
theSeeds[:,1] = (np.mod(gridpoints// options.grid[0] ,options.grid[1])\
+np.random.random(options.N)) /options.grid[1]
theSeeds[:,2] = (np.mod(gridpoints//(options.grid[1]*options.grid[0]),options.grid[2])\
+np.random.random(options.N)) /options.grid[2]
goodSeeds = theSeeds[np.all(theSeeds<=options.fraction,axis=1)] # pick seeds within threshold fraction
seeds = goodSeeds if len(seeds) == 0 else np.vstack((seeds,goodSeeds))
if len(seeds) > options.N: seeds = seeds[:min(options.N,len(seeds))]
seeds = seeds.T # switch layout to point index as last index
else: