now fully anisotropic grain growth possible by setting up the function "interfacialEnergy"
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#!/usr/bin/env python
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#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string,re,math,numpy
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import os,sys,string,re,math,numpy,itertools
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import damask
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import damask
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from optparse import OptionParser, OptionGroup, Option, SUPPRESS_HELP
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from optparse import OptionParser, OptionGroup, Option, SUPPRESS_HELP
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from scipy import ndimage
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from scipy import ndimage
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@ -26,18 +26,42 @@ class extendedOption(Option):
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Option.take_action(self, action, dest, opt, value, values, parser)
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Option.take_action(self, action, dest, opt, value, values, parser)
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def grainCoarsenLocal(microLocal,ix,iy,iz,window):
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def grainCoarsenLocal(microLocal,ix,iy,iz,window):
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interfacialEnergy = lambda A,B: 1.0
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winner = numpy.zeros(microLocal.shape).astype(int)
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winner = numpy.zeros(microLocal.shape).astype(int)
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winner = numpy.where(numpy.reshape(numpy.in1d(microLocal,options.black),microLocal.shape),
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winner = numpy.where(numpy.reshape(numpy.in1d(microLocal,options.black),microLocal.shape),
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microLocal,0) # zero out non-blacklisted microstructures
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microLocal,0) # zero out non-blacklisted microstructures
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diffusedMax = (winner > 0).astype(float) # concentration of immutable microstructures
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diffusedMax = (winner > 0).astype(float) # concentration of immutable microstructures
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boundingSlice = ndimage.measurements.find_objects(microLocal) # bounding boxes of each distinct microstructure region
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boundingSlice = ndimage.measurements.find_objects(microLocal) # bounding boxes of each distinct microstructure region
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for grain in set(numpy.unique(microLocal)).difference(set(options.black).union(set([0]))): # diffuse all microstructures except immutable ones
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for grain in set(numpy.unique(microLocal)) - set(options.black) - (set([0])): # diffuse all microstructures except immutable ones
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mini = [max(0, boundingSlice[grain-1][i].start - window) for i in range(3)] # upper right of expanded bounding box
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mini = [max(0, boundingSlice[grain-1][i].start - window) for i in range(3)] # upper right of expanded bounding box
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maxi = [min(microLocal.shape[i], boundingSlice[grain-1][i].stop + window) for i in range(3)] # lower left of expanded bounding box
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maxi = [min(microLocal.shape[i], boundingSlice[grain-1][i].stop + window) for i in range(3)] # lower left of expanded bounding box
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diffused = ndimage.filters.gaussian_filter((microLocal[mini[0]:maxi[0],\
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microWindow = microLocal[mini[0]:maxi[0],mini[1]:maxi[1],mini[2]:maxi[2]]
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mini[1]:maxi[1],\
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neighbours = set(numpy.unique(ndimage.morphology.binary_dilation(microWindow==grain,\
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mini[2]:maxi[2]]==grain).astype(float),\
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structure=ndimage.generate_binary_structure(3,3))))\
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options.d) # diffuse microstructure inside extended bounding box
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- set([grain]) - set(options.black) # who is on my boundary?
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if len(neighbours) == 0: # no neighbours
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diffused = numpy.ones(microWindow.shape)
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elif len(neighbours) == 1: # 1 neighbour
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speed = interfacialEnergy(grain,neighbours.pop()) # speed proportional to interfacial energy between me and only neighbour
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diffused = ndimage.filters.gaussian_filter((microWindow==grain).astype(float),speed*options.d)# diffuse microstructure inside extended bounding box
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else: # more than 1 neighbour ie junctions
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numerator = numpy.zeros(microWindow.shape)
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denominator = numpy.zeros(microWindow.shape)
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diffused = ndimage.filters.gaussian_filter((microWindow==grain).astype(float),options.d) # diffuse microstructure inside extended bounding box
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weights = {}
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weights[grain] = diffused
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for i in neighbours:
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weights[i] = ndimage.filters.gaussian_filter((microWindow==i).astype(float),options.d) # partition of unity around me
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for grainA,grainB in itertools.combinations(neighbours,2): # combinations of triple junctions possible
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speed = interfacialEnergy(grain,grainA) +\
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interfacialEnergy(grain,grainB) -\
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interfacialEnergy(grainA,grainB) # speed of the triple junction
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weight = weights[grain] + weights[grainA] + weights[grainB]
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numerator += weight*(speed*diffused + (1.-speed)*(microWindow==grain))
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denominator += weight
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diffused = (numerator)/(denominator+1e-30)
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isMax = diffused > diffusedMax[mini[0]:maxi[0],\
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isMax = diffused > diffusedMax[mini[0]:maxi[0],\
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mini[1]:maxi[1],\
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mini[1]:maxi[1],\
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mini[2]:maxi[2]] # me at highest concentration?
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mini[2]:maxi[2]] # me at highest concentration?
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