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DAMASK_EICMD
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Vectorized checking of neighbourhood for unique microstructure 

(faster) 

M    addEuclideanDistance.py
This commit is contained in:
Pratheek Shanthraj 2012-12-07 10:50:34 +00:00
parent 98d02dcffb
commit eaf7072bc1
1 changed files with 15 additions and 12 deletions
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15
processing/post/addEuclideanDistance.py
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@ -180,6 +180,9 @@ for file in files:
convoluted = numpy.empty([len(neighborhood)]+list(resolution+2),'i') convoluted = numpy.empty([len(neighborhood)]+list(resolution+2),'i')
microstructure = periodic_3Dpad(numpy.array(structure[:,3].reshape(resolution),'i')) microstructure = periodic_3Dpad(numpy.array(structure[:,3].reshape(resolution),'i'))
print 'setup | time = '+repr(time.clock()-cputime)
cputime = time.clock()
for i,p in enumerate(neighborhood): for i,p in enumerate(neighborhood):
stencil = numpy.zeros((3,3,3),'i') stencil = numpy.zeros((3,3,3),'i')
stencil[1,1,1] = -1 stencil[1,1,1] = -1
@ -190,13 +193,13 @@ for file in files:
convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil) convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil)
distance = numpy.ones((len(feature_list),resolution[0],resolution[1],resolution[2]),'d') distance = numpy.ones((len(feature_list),resolution[0],resolution[1],resolution[2]),'d')
for x in xrange(resolution[0]):
for y in xrange(resolution[1]): uniques = numpy.ones(resolution)
for z in xrange(resolution[2]): for i in xrange(len(neighborhood)):
uniques = len(numpy.unique(convoluted[:,x+1,y+1,z+1])) for j in xrange(len(neighborhood)):
uniques += numpy.where(convoluted[i,1:-1,1:-1,1:-1] == convoluted[j,1:-1,1:-1,1:-1],1,0)
for i,feature_id in enumerate(feature_list): for i,feature_id in enumerate(feature_list):
if uniques > features[feature_id]['aliens']: distance[i,:,:,:] = numpy.where(uniques > features[feature_id]['aliens'],0.0,1.0)
distance[i,x,y,z] = 0.0 # found a starting point
for i in xrange(len(feature_list)): for i in xrange(len(feature_list)):
distance[i,:,:,:] = skfmm.distance(distance[i,:,:,:], dx=[unitlength]*3) distance[i,:,:,:] = skfmm.distance(distance[i,:,:,:], dx=[unitlength]*3)