179 lines
9.0 KiB
Python
Executable File
179 lines
9.0 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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import sys
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from io import StringIO
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from optparse import OptionParser
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import numpy as np
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from scipy import ndimage
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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getInterfaceEnergy = lambda A,B: np.float32((A != B)*1.0) # 1.0 if A & B are distinct, 0.0 otherwise
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struc = ndimage.generate_binary_structure(3,1) # 3D von Neumann neighborhood
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#--------------------------------------------------------------------------------------------------
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# MAIN
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#--------------------------------------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog option(s) [geomfile(s)]', description = """
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Smoothen interface roughness by simulated curvature flow.
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This is achieved by the diffusion of each initially sharply bounded grain volume within the periodic domain
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up to a given distance 'd' voxels.
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The final geometry is assembled by selecting at each voxel that grain index for which the concentration remains largest.
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References 10.1073/pnas.1111557108 (10.1006/jcph.1994.1105)
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""", version = scriptID)
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parser.add_option('-d', '--distance',
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dest = 'd',
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type = 'float', metavar = 'float',
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help = 'diffusion distance in voxels [%default]')
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parser.add_option('-N', '--iterations',
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dest = 'N',
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type = 'int', metavar = 'int',
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help = 'curvature flow iterations [%default]')
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parser.add_option('-i', '--immutable',
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action = 'extend', dest = 'immutable', metavar = '<int LIST>',
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help = 'list of immutable material indices')
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parser.add_option('--ndimage',
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dest = 'ndimage', action='store_true',
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help = 'use ndimage.gaussian_filter in lieu of explicit FFT')
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parser.set_defaults(d = 1,
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N = 1,
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immutable = [],
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ndimage = False,
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)
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(options, filenames) = parser.parse_args()
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options.immutable = list(map(int,options.immutable))
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if filenames == []: filenames = [None]
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for name in filenames:
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damask.util.report(scriptName,name)
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geom = damask.Grid.load(StringIO(''.join(sys.stdin.read())) if name is None else name)
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grid_original = geom.cells
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damask.util.croak(geom)
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material = np.tile(geom.material,np.where(grid_original == 1, 2,1)) # make one copy along dimensions with grid == 1
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grid = np.array(material.shape)
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# --- initialize support data ---------------------------------------------------------------------
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# store a copy of the initial material indices to find locations of immutable indices
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material_original = np.copy(material)
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if not options.ndimage:
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X,Y,Z = np.mgrid[0:grid[0],0:grid[1],0:grid[2]]
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# Calculates gaussian weights for simulating 3d diffusion
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gauss = np.exp(-(X*X + Y*Y + Z*Z)/(2.0*options.d*options.d),dtype=np.float32) \
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/np.power(2.0*np.pi*options.d*options.d,(3.0 - np.count_nonzero(grid_original == 1))/2.,dtype=np.float32)
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gauss[:,:,:grid[2]//2:-1] = gauss[:,:,1:(grid[2]+1)//2] # trying to cope with uneven (odd) grid size
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gauss[:,:grid[1]//2:-1,:] = gauss[:,1:(grid[1]+1)//2,:]
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gauss[:grid[0]//2:-1,:,:] = gauss[1:(grid[0]+1)//2,:,:]
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gauss = np.fft.rfftn(gauss).astype(np.complex64)
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for smoothIter in range(options.N):
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interfaceEnergy = np.zeros(material.shape,dtype=np.float32)
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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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# assign interfacial energy to all voxels that have a differing neighbor (in Moore neighborhood)
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interfaceEnergy = np.maximum(interfaceEnergy,
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getInterfaceEnergy(material,np.roll(np.roll(np.roll(
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material,i,axis=0), j,axis=1), k,axis=2)))
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# periodically extend interfacial energy array by half a grid size in positive and negative directions
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periodic_interfaceEnergy = np.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]
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# transform bulk volume (i.e. where interfacial energy remained zero), store index of closest boundary voxel
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index = ndimage.morphology.distance_transform_edt(periodic_interfaceEnergy == 0.,
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return_distances = False,
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return_indices = True)
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# want array index of nearest voxel on periodically extended boundary
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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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if options.ndimage:
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periodic_diffusedEnergy = ndimage.gaussian_filter(
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np.where(ndimage.morphology.binary_dilation(periodic_interfaceEnergy > 0.,
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structure = struc,
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iterations = int(round(options.d*2.))-1, # fat boundary
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),
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periodic_bulkEnergy, # ...and zero everywhere else
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0.),
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sigma = options.d)
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else:
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diffusedEnergy = np.fft.irfftn(np.fft.rfftn(
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np.where(
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ndimage.morphology.binary_dilation(interfaceEnergy > 0.,
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structure = struc,
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iterations = int(round(options.d*2.))-1),# fat boundary
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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.)).astype(np.complex64) *
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gauss).astype(np.float32)
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periodic_diffusedEnergy = np.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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# transform voxels close to interface region
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index = ndimage.morphology.distance_transform_edt(periodic_diffusedEnergy >= 0.95*np.amax(periodic_diffusedEnergy),
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return_distances = False,
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return_indices = True) # want index of closest bulk grain
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periodic_material = np.tile(material,(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 geometry
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material = periodic_material[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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# replace immutable materials with closest mutable ones
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index = ndimage.morphology.distance_transform_edt(np.in1d(material,options.immutable).reshape(grid),
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return_distances = False,
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return_indices = True)
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material = material[index[0],
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index[1],
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index[2]]
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immutable = np.zeros(material.shape, dtype=np.bool)
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# find locations where immutable materials have been in original structure
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for micro in options.immutable:
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immutable += material_original == micro
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# undo any changes involving immutable materials
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material = np.where(immutable, material_original,material)
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damask.Grid(material = material[0:grid_original[0],0:grid_original[1],0:grid_original[2]],
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size = geom.size,
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origin = geom.origin,
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comments = geom.comments + [scriptID + ' ' + ' '.join(sys.argv[1:])],
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)\
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.save(sys.stdout if name is None else name)
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