easier to specify size directly

This commit is contained in:
Martin Diehl 2020-03-21 15:17:02 +01:00
parent 42b9ccf99e
commit ab1ab42e75
2 changed files with 38 additions and 58 deletions

View File

@ -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)

View File

@ -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)
size = np.array(options.size)
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)
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
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
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)
coords = damask.grid_filters.cell_coord0(grid,size).reshape(-1,3)
if not options.selective:
seeds = coords[np.random.choice(coords.shape[0], options.N, replace=False)]
else:
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)
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] = candidates[best] # maximum separation to existing point cloud
seeds[i] = coords[candidates[best]] # maximum separation to existing point cloud
unpicked.mask[candidates[best]]=True
i += 1
if i%(options.N/100.) < 1: damask.util.croak('.',False)
damask.util.croak('')
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),
]