polishing of variable names, comments, and some of the programming structure.
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Philip Eisenlohr
parent
fec50e424e
commit
72e9c512bd
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@ -57,22 +57,23 @@ The final geometry is assembled by selecting at each voxel that grain index for
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""" + string.replace(scriptID,'\n','\\n')
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)
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parser.add_option('-d', '--distance', dest='d', type='int', metavar='int', \
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parser.add_option('-d', '--distance', dest='d', type='int', metavar='int',
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help='diffusion distance in voxels [%default]')
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parser.add_option('-N', '--smooth', dest='N', type='int', metavar='int', \
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parser.add_option('-N', '--smooth', dest='N', type='int', metavar='int',
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help='N for curvature flow [%default]')
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parser.add_option('-r', '--renumber', dest='renumber', action='store_true', \
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parser.add_option('-r', '--renumber', dest='renumber', action='store_true',
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help='renumber microstructure indices from 1...N [%default]')
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parser.add_option('-i', '--immutable', action='extend', dest='immutable', type='string', \
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help='immutable microstructures')
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parser.add_option('-i', '--immutable', action='extend', dest='immutable', type='string', metavar = '<LIST>',
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help='list of immutable microstructures')
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parser.set_defaults(d = 1)
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parser.set_defaults(N = 1)
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parser.set_defaults(renumber = False)
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parser.set_defaults(immutable = [0])
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parser.set_defaults(immutable = [])
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(options, filenames) = parser.parse_args()
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options.immutable = map(int,options.immutable)
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#--- setup file handles --------------------------------------------------------------------------
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files = []
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@ -157,73 +158,83 @@ for file in files:
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i += s
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#--- reshape, if 2D make copy ---------------------------------------------------------------------
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expandedGrid = numpy.array([2 if i == 1 else i for i in info['grid']],'i')
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nMicrostuctures = numpy.prod(expandedGrid)
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if nMicrostuctures > info['grid'].prod():
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microstructure[info['grid'].prod():nMicrostuctures] = microstructure[0:info['grid'].prod()]
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microstructure = microstructure.reshape([2 if i == 1 else i for i in info['grid']],order='F')
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grid = numpy.array([2 if i == 1 else i for i in info['grid']],'i')
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microstructure = numpy.tile(microstructure.reshape(info['grid'],order='F'),
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numpy.where(info['grid'] == 1, 2,1)) # make one copy along dimensions with grid == 1
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grid = numpy.array(microstructure.shape)
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#--- initialize helper data -----------------------------------------------------------------------
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X,Y,Z = numpy.mgrid[0:expandedGrid[0],0:expandedGrid[1],0:expandedGrid[2]]
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gauss = numpy.exp(-(X*X+Y*Y+Z*Z)/(2.0*options.d*options.d))/math.pow(2.0*numpy.pi*options.d*options.d,1.5)
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gauss[:,:,(expandedGrid[2])/2::] = gauss[:,:,(expandedGrid[2])/2-1::-1]
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gauss[:,(expandedGrid[1])/2::,:] = gauss[:,(expandedGrid[1])/2-1::-1,:]
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gauss[(expandedGrid[0])/2::,:,:] = gauss[(expandedGrid[0])/2-1::-1,:,:]
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#--- initialize support data -----------------------------------------------------------------------
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periodic_microstructure = numpy.tile(microstructure,(3,3,3))[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2] # periodically extend the microstructure
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microstructure_original = numpy.copy(microstructure) # store a copy the initial microstructure to find locations of immutable indices
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X,Y,Z = numpy.mgrid[0:grid[0],0:grid[1],0:grid[2]]
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gauss = numpy.exp(-(X*X + Y*Y + Z*Z)/(2.0*options.d*options.d))/math.pow(2.0*numpy.pi*options.d*options.d,1.5)
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gauss[:,:,grid[2]/2::] = gauss[:,:,round(grid[2]/2.)-1::-1] # trying to cope with uneven (odd) grid size
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gauss[:,grid[1]/2::,:] = gauss[:,round(grid[1]/2.)-1::-1,:]
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gauss[grid[0]/2::,:,:] = gauss[round(grid[0]/2.)-1::-1,:,:]
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gauss = numpy.fft.rfftn(gauss)
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interfacialEnergy = lambda A,B: (A*B != 0)*(A != B)*1.0
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struc = ndimage.generate_binary_structure(3,1)
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microstructure_original = numpy.copy(microstructure)
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struc = ndimage.generate_binary_structure(3,1) # 3D von Neumann neighborhood
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for smoothIter in xrange(options.N):
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boundary = numpy.zeros(microstructure.shape)
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for i in range(3):
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for j in range(3):
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for k in range(3):
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boundary = numpy.maximum(boundary,
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interfacialEnergy(microstructure,numpy.roll(numpy.roll(numpy.roll(
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microstructure,i-1,axis=0),j-1,axis=1),k-1,axis=2)))
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boundaryExt = numpy.tile(boundary,(3,3,3))
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boundaryExt = boundaryExt[(expandedGrid[0])/2:-(expandedGrid[0])/2,
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(expandedGrid[1])/2:-(expandedGrid[1])/2,
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(expandedGrid[2])/2:-(expandedGrid[2])/2]
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index = ndimage.morphology.distance_transform_edt(boundaryExt == 0.,return_distances = False,return_indices = True) # array index of nearest voxel on periodically extended boundary
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boundaryExt = boundaryExt[index[0].flatten(),index[1].flatten(),index[2].flatten()].reshape(boundaryExt.shape) # fill bulk with energy of nearest interface
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boundary = numpy.fft.irfftn(numpy.fft.rfftn(numpy.where(ndimage.morphology.binary_dilation(boundary > 0.,
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structure = struc,
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iterations = 2*options.d-1), # fat boundary
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boundaryExt[(expandedGrid[0])/2:-(expandedGrid[0])/2, # retain filled energy on fat boundary
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(expandedGrid[1])/2:-(expandedGrid[1])/2,
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(expandedGrid[2])/2:-(expandedGrid[2])/2],
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0.))*gauss) # zero everywhere else
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boundaryExt = numpy.tile(boundary,(3,3,3))
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boundaryExt = boundaryExt[(expandedGrid[0])/2:-(expandedGrid[0])/2,
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(expandedGrid[1])/2:-(expandedGrid[1])/2,
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(expandedGrid[2])/2:-(expandedGrid[2])/2]
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microstructureExt = numpy.tile(microstructure,(3,3,3))
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microstructureExt = microstructureExt[(expandedGrid[0])/2:-(expandedGrid[0])/2,
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(expandedGrid[1])/2:-(expandedGrid[1])/2,
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(expandedGrid[2])/2:-(expandedGrid[2])/2]
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index = ndimage.morphology.distance_transform_edt(boundaryExt >= 0.5,return_distances=False,return_indices=True)
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microstructureExt = microstructureExt[index[0].flatten(),index[1].flatten(),index[2].flatten()].reshape(microstructureExt.shape)
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microstructure = microstructureExt[(expandedGrid[0])/2:-(expandedGrid[0])/2,
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(expandedGrid[1])/2:-(expandedGrid[1])/2,
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(expandedGrid[2])/2:-(expandedGrid[2])/2]
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for i in (-1,0,1):
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for j in (-1,0,1):
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for k in (-1,0,1):
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interfaceEnergy = numpy.maximum(boundary,
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interfacialEnergy(microstructure,numpy.roll(numpy.roll(numpy.roll(
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microstructure,i,axis=0), j,axis=1), k,axis=2))) # assign interfacial energy to all voxels that have a differing neighbor (in Moore neighborhood)
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periodic_interfaceEnergy = numpy.tile(interfaceEnergy,(3,3,3))[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2] # periodically extend interfacial energy array by half a grid size in positive and negative directions
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index = ndimage.morphology.distance_transform_edt(periodic_interfaceEnergy == 0., # transform bulk volume (i.e. where interfacial energy is zero)
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return_distances = False,
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return_indices = True) # want array index of nearest voxel on periodically extended boundary
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# boundaryExt = boundaryExt[index[0].flatten(),index[1].flatten(),index[2].flatten()].reshape(boundaryExt.shape) # fill bulk with energy of nearest interface | question PE: what "flatten" for?
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periodic_bulkEnergy = periodic_interfaceEnergy[index[0],
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index[1],
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index[2]].reshape(2*grid) # fill bulk with energy of nearest interface
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diffusedEnergy = numpy.fft.irfftn(numpy.fft.rfftn(numpy.where(ndimage.morphology.binary_dilation(interfaceEnergy > 0.,
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structure = struc,
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iterations = options.d/2 + 1), # fat boundary | question PE: why 2d - 1? I would argue for d/2 + 1 !!
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periodic_bulkEnergy[grid[0]/2:-grid[0]/2, # retain filled energy on fat boundary...
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2], # ...and zero everywhere else
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0.)\
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)*gauss)
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periodic_diffusedEnergy = numpy.tile(diffusedEnergy,(3,3,3))[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2] # periodically extend the smoothed bulk energy
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index = ndimage.morphology.distance_transform_edt(periodic_diffusedEnergy >= 0.5, # transform voxels close to interface region | question PE: what motivates 1/2 (could be any small number, or)?
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return_distances = False,
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return_indices = True) # want index of closest bulk grain
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microstructure = periodic_microstructure[index[0],
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index[1],
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index[2]].reshape(2*grid)[grid[0]/2:-grid[0]/2,
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grid[1]/2:-grid[1]/2,
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grid[2]/2:-grid[2]/2] # extent grains into interface region
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immutable = numpy.zeros(microstructure.shape, dtype=bool)
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for micro in options.immutable:
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immutable = numpy.logical_or(immutable, numpy.logical_or(microstructure == int(micro), microstructure_original == int(micro)))
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immutable += numpy.logical_or(microstructure == micro, microstructure_original == micro) # find locations where immutable microstructures have been or are now
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microstructure = numpy.where(immutable, microstructure_original,microstructure)
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microstructure = numpy.where(immutable, microstructure_original,microstructure) # undo any changes involving immutable microstructures
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# --- renumber to sequence 1...Ngrains if requested ------------------------------------------------
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# http://stackoverflow.com/questions/10741346/numpy-frequency-counts-for-unique-values-in-an-array
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if options.renumber:
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newID=0
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for microstructureID,count in enumerate(numpy.bincount(microstructure.reshape(info['grid'].prod()))):
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newID = 0
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for microstructureID,count in enumerate(numpy.bincount(microstructure.flatten())):
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if count != 0:
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newID+=1
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microstructure=numpy.where(microstructure==microstructureID,newID,microstructure).reshape(microstructure.shape)
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newID += 1
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microstructure = numpy.where(microstructure == microstructureID, newID, microstructure)
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# --- assemble header -----------------------------------------------------------------------------
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newInfo['microstructures'] = microstructure[0:info['grid'][0],0:info['grid'][1],0:info['grid'][2]].max()
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@ -246,11 +257,11 @@ for file in files:
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# --- write microstructure information ------------------------------------------------------------
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formatwidth = int(math.floor(math.log10(microstructure.max())+1))
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theTable.data = microstructure[0:info['grid'][0],0:info['grid'][1],0:info['grid'][2]].reshape(numpy.prod(info['grid']),order='F').transpose()
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theTable.data = microstructure[0:info['grid'][0],0:info['grid'][1],0:info['grid'][2]].reshape(numpy.prod(info['grid']),order='F').transpose() # question PE: this assumes that only the Z dimension can be 1!
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theTable.data_writeArray('%%%ii'%(formatwidth),delimiter=' ')
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#--- output finalization --------------------------------------------------------------------------
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if file['name'] != 'STDIN':
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file['input'].close()
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file['output'].close()
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theTable.input_close()
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theTable.output_close()
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os.rename(file['name']+'_tmp',file['name'])
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