faster version of smoother using the scipy gaussian filter to get the diffused microstructure and vectorized sharpening of the diffused microstructure
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@ -3,6 +3,7 @@
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import os,sys,string,re,math,numpy
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from optparse import OptionParser, OptionGroup, Option, SUPPRESS_HELP
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from scipy import ndimage
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#--------------------------------------------------------------------------------------------------
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class extendedOption(Option):
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@ -160,38 +161,14 @@ for file in files:
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i/info['grid'][0] /info['grid'][1]] = item
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i += 1
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for i in range(options.N):
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active = []
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for z in xrange(info['grid'][2]):
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for y in xrange(info['grid'][1]):
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for x in xrange(info['grid'][0]):
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me = microstructure[x,y,z]
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others = me*numpy.ones(1+len(neighborhood),'i')
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hot = False
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o = 0
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for offset in neighborhood:
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otherX = (x+offset[0])%info['grid'][0]
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otherY = (y+offset[1])%info['grid'][1]
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otherZ = (z+offset[2])%info['grid'][2]
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other = microstructure[otherX,otherY,otherZ]
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o += 1
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others[o] = other
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if other != me:
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hot = True
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if hot:
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active.insert(0,numpy.array([x,y,z,0,0],'i')) # remember current position, best candidate, and best change
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# append might be not a good idea in linked lists. try to put at the start, not end!
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for o in xrange(len(others)):
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howMany = numpy.array(others[1:] == others[o],'i').sum() # count number of particular others in neighborhood
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if active[0][3] < howMany:
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active[0][3:5] = [howMany,others[o]]
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for spot in active:
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microstructure[spot[0],spot[1],spot[2]] = spot[4]
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maxMicro = numpy.zeros(info['grid'])
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microstructureNew = numpy.zeros(info['grid'],'i')
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for i in range(numpy.amax(microstructure)):
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diffusedMicro = ndimage.filters.gaussian_filter((microstructure == i).astype(float),numpy.sqrt(options.N))
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microstructureNew = numpy.where(diffusedMicro > maxMicro,i,microstructureNew)
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maxMicro = numpy.where(diffusedMicro > maxMicro,diffusedMicro,maxMicro)
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microstructure = microstructureNew
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formatwidth = int(math.floor(math.log10(microstructure.max())+1))
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