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improved performance for non-weighted Voronoi Tessellation

This commit is contained in:
Martin Diehl 2015-10-06 18:03:06 +00:00
parent 6e05082133
commit f014cef043
1 changed files with 20 additions and 16 deletions
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36
processing/pre/geom_fromVoronoiTessellation.py
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@ -5,6 +5,7 @@ import os,sys,math,string
import numpy as np import numpy as np
import multiprocessing import multiprocessing
from optparse import OptionParser from optparse import OptionParser
from scipy import spatial
import damask import damask
scriptID = string.replace('$Id$','\n','\\n') scriptID = string.replace('$Id$','\n','\\n')
@ -75,25 +76,30 @@ def laguerreTessellation(undeformed, coords, weights, grains, nonperiodic = Fals
]).astype(float) ]).astype(float)
squaredweights = np.power(np.tile(weights,len(copies)),2) # Laguerre weights (squared, size N*n) squaredweights = np.power(np.tile(weights,len(copies)),2) # Laguerre weights (squared, size N*n)
for i,vec in enumerate(copies): # periodic copies of seed points (size N*n) for i,vec in enumerate(copies): # periodic copies of seed points (size N*n)
try: seeds = np.append(seeds, coords+vec, axis=0) try: seeds = np.append(seeds, coords+vec, axis=0)
except NameError: seeds = coords+vec except NameError: seeds = coords+vec
arguments = [[arg] + [seeds,squaredweights] for arg in list(undeformed)] if all(squaredweights == 0.0): # standard Voronoi (no weights, KD tree)
myKDTree = spatial.cKDTree(seeds)
if cpus > 1: # use multithreading devNull,closestSeeds = myKDTree.query(undeformed)
pool = multiprocessing.Pool(processes = cpus) # initialize workers
result = pool.map_async(findClosestSeed, arguments) # evaluate function in parallel
pool.close()
pool.join()
closestSeeds = np.array(result.get()).flatten()
else: else:
closestSeeds = np.zeros(len(arguments),dtype='i') damask.util.croak('...using {} cpu{}'.format(options.cpus, 's' if options.cpus > 1 else ''))
for i,arg in enumerate(arguments): arguments = [[arg] + [seeds,squaredweights] for arg in list(undeformed)]
closestSeeds[i] = findClosestSeed(arg)
return grains[closestSeeds%coords.shape[0]] # closestSeed is modulo number of original seed points (i.e. excluding periodic copies) if cpus > 1: # use multithreading
pool = multiprocessing.Pool(processes = cpus) # initialize workers
result = pool.map_async(findClosestSeed, arguments) # evaluate function in parallel
pool.close()
pool.join()
closestSeeds = np.array(result.get()).flatten()
else:
closestSeeds = np.zeros(len(arguments),dtype='i')
for i,arg in enumerate(arguments):
closestSeeds[i] = findClosestSeed(arg)
return grains[closestSeeds%coords.shape[0]] # closestSeed is modulo number of original seed points (i.e. excluding periodic copies)
# -------------------------------------------------------------------- # --------------------------------------------------------------------
# MAIN # MAIN
@ -267,8 +273,6 @@ for name in filenames:
damask.util.croak('tessellating...') damask.util.croak('tessellating...')
damask.util.croak('...using {} cpu{}'.format(options.cpus, 's' if options.cpus > 1 else ''))
grid = np.vstack(meshgrid2(x, y, z)).reshape(3,-1).T grid = np.vstack(meshgrid2(x, y, z)).reshape(3,-1).T
indices = laguerreTessellation(grid, coords, weights, grains, options.nonperiodic, options.cpus) indices = laguerreTessellation(grid, coords, weights, grains, options.nonperiodic, options.cpus)