DAMASK_EICMD/processing/pre/geom_grainGrowth.py

176 lines
9.0 KiB
Python
Executable File

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