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DAMASK_EICMD
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fixed systematic drift of grain structure along -[1,1,1] 

correction of periodic Gauss kernel extension, inclusion of ndimage.gaussian_filter alternative (same runtime as FFT), proper output handling of grids <3D
This commit is contained in:
Philip Eisenlohr 2016-11-30 09:39:13 -05:00
parent 40de6910b8
commit cb95f3b244
1 changed files with 103 additions and 68 deletions
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171
processing/pre/geom_grainGrowth.py
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@ -22,32 +22,46 @@ The final geometry is assembled by selecting at each voxel that grain index for
""", version = scriptID) """, version = scriptID)
parser.add_option('-d', '--distance', dest='d', type='int', metavar='int', parser.add_option('-d', '--distance',
help='diffusion distance in voxels [%default]') dest = 'd',
parser.add_option('-N', '--smooth', dest='N', type='int', metavar='int', type = 'float', metavar = 'float',
help='N for curvature flow [%default]') help = 'diffusion distance in voxels [%default]')
parser.add_option('-r', '--renumber', dest='renumber', action='store_true', parser.add_option('-N', '--iterations',
help='renumber microstructure indices from 1...N [%default]') dest = 'N',
parser.add_option('-i', '--immutable', action='extend', dest='immutable', metavar = '<int LIST>', type = 'int', metavar = 'int',
help='list of immutable microstructures') 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('-r', '--renumber',
dest = 'renumber', action='store_true',
help = 'output consecutive 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) parser.set_defaults(d = 1,
parser.set_defaults(N = 1) N = 1,
parser.set_defaults(renumber = False) immutable = [],
parser.set_defaults(immutable = []) renumber = False,
ndimage = False,
)
(options, filenames) = parser.parse_args() (options, filenames) = parser.parse_args()
options.immutable = map(int,options.immutable) options.immutable = map(int,options.immutable)
# --- loop over input files ------------------------------------------------------------------------- getInterfaceEnergy = lambda A,B: (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
# --- loop over input files -----------------------------------------------------------------------
if filenames == []: filenames = [None] if filenames == []: filenames = [None]
for name in filenames: for name in filenames:
try: try: table = damask.ASCIItable(name = name,
table = damask.ASCIItable(name = name, buffered = False,
buffered = False, labeled = False) labeled = False)
except: continue except: continue
damask.util.report(scriptName,name) damask.util.report(scriptName,name)
@ -56,12 +70,12 @@ for name in filenames:
table.head_read() table.head_read()
info,extra_header = table.head_getGeom() info,extra_header = table.head_getGeom()
damask.util.croak(['grid a b c: %s'%(' x '.join(map(str,info['grid']))), damask.util.croak(['grid a b c: {}'.format(' x '.join(map(str,info['grid']))),
'size x y z: %s'%(' x '.join(map(str,info['size']))), 'size x y z: {}'.format(' x '.join(map(str,info['size']))),
'origin x y z: %s'%(' : '.join(map(str,info['origin']))), 'origin x y z: {}'.format(' : '.join(map(str,info['origin']))),
'homogenization: %i'%info['homogenization'], 'homogenization: {}'.format(info['homogenization']),
'microstructures: %i'%info['microstructures'], 'microstructures: {}'.format(info['microstructures']),
]) ])
errors = [] errors = []
if np.any(info['grid'] < 1): errors.append('invalid grid a b c.') if np.any(info['grid'] < 1): errors.append('invalid grid a b c.')
@ -71,39 +85,35 @@ for name in filenames:
table.close(dismiss = True) table.close(dismiss = True)
continue continue
# --- read data ------------------------------------------------------------------------------------ # --- read data -----------------------------------------------------------------------------------
microstructure = np.tile(np.array(table.microstructure_read(info['grid']),'i').reshape(info['grid'],order='F'), microstructure = np.tile(table.microstructure_read(info['grid']).reshape(info['grid'],order='F'),
np.where(info['grid'] == 1, 2,1)) # make one copy along dimensions with grid == 1 np.where(info['grid'] == 1, 2,1)) # make one copy along dimensions with grid == 1
grid = np.array(microstructure.shape) grid = np.array(microstructure.shape)
#--- initialize support data ----------------------------------------------------------------------- # --- initialize support data ---------------------------------------------------------------------
# store a copy the initial microstructure to find locations of immutable indices # store a copy the initial microstructure to find locations of immutable indices
microstructure_original = np.copy(microstructure) microstructure_original = np.copy(microstructure)
X,Y,Z = np.mgrid[0:grid[0],0:grid[1],0:grid[2]] 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 # Calculates gaussian weights for simulating 3d diffusion
gauss = np.exp(-(X*X + Y*Y + Z*Z)/(2.0*options.d*options.d))/math.pow(2.0*np.pi*options.d*options.d,1.5) gauss = np.exp(-(X*X + Y*Y + Z*Z)/(2.0*options.d*options.d)) \
gauss[:,:,grid[2]/2::] = gauss[:,:,int(round(grid[2]/2.))-1::-1] # trying to cope with uneven (odd) grid size /math.pow(2.0*np.pi*options.d*options.d,(3.0 - np.count_nonzero(info['grid'] == 1))/2.)
gauss[:,grid[1]/2::,:] = gauss[:,int(round(grid[1]/2.))-1::-1,:]
gauss[grid[0]/2::,:,:] = gauss[int(round(grid[0]/2.))-1::-1,:,:] gauss[:,:,:grid[2]/2:-1] = gauss[:,:,1:(grid[2]+1)/2] # trying to cope with uneven (odd) grid size
gauss = np.fft.rfftn(gauss) gauss[:,:grid[1]/2:-1,:] = gauss[:,1:(grid[1]+1)/2,:]
gauss[:grid[0]/2:-1,:,:] = gauss[1:(grid[0]+1)/2,:,:]
getInterfaceEnergy = lambda A,B: (A*B != 0)*(A != B)*1.0 # 1.0 if A & B are distinct & nonzero, 0.0 otherwise gauss = np.fft.rfftn(gauss)
struc = ndimage.generate_binary_structure(3,1) # 3D von Neumann neighborhood
for smoothIter in range(options.N): for smoothIter in range(options.N):
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
interfaceEnergy = np.zeros(microstructure.shape) interfaceEnergy = np.zeros(microstructure.shape)
for i in (-1,0,1): for i in (-1,0,1):
for j in (-1,0,1): for j in (-1,0,1):
for k in (-1,0,1): for k in (-1,0,1):
# assign interfacial energy to all voxels that have a differing neighbor (in Moore neighborhood) # assign interfacial energy to all voxels that have a differing neighbor (in Moore neighborhood)
interfaceEnergy = np.maximum(interfaceEnergy, interfaceEnergy = np.maximum(interfaceEnergy,
getInterfaceEnergy(microstructure,np.roll(np.roll(np.roll( getInterfaceEnergy(microstructure,np.roll(np.roll(np.roll(
microstructure,i,axis=0), j,axis=1), k,axis=2))) microstructure,i,axis=0), j,axis=1), k,axis=2)))
@ -112,44 +122,65 @@ for name in filenames:
periodic_interfaceEnergy = np.tile(interfaceEnergy,(3,3,3))[grid[0]/2:-grid[0]/2, periodic_interfaceEnergy = np.tile(interfaceEnergy,(3,3,3))[grid[0]/2:-grid[0]/2,
grid[1]/2:-grid[1]/2, grid[1]/2:-grid[1]/2,
grid[2]/2:-grid[2]/2] grid[2]/2:-grid[2]/2]
# transform bulk volume (i.e. where interfacial energy is zero)
# 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., index = ndimage.morphology.distance_transform_edt(periodic_interfaceEnergy == 0.,
return_distances = False, return_distances = False,
return_indices = True) return_indices = True)
# want array index of nearest voxel on periodically extended boundary # want array index of nearest voxel on periodically extended boundary
periodic_bulkEnergy = periodic_interfaceEnergy[index[0], periodic_bulkEnergy = periodic_interfaceEnergy[index[0],
index[1], index[1],
index[2]].reshape(2*grid) # fill bulk with energy of nearest interface index[2]].reshape(2*grid) # fill bulk with energy of nearest interface
diffusedEnergy = np.fft.irfftn(np.fft.rfftn( if options.ndimage:
np.where( periodic_diffusedEnergy = ndimage.gaussian_filter(
ndimage.morphology.binary_dilation(interfaceEnergy > 0., np.where(ndimage.morphology.binary_dilation(periodic_interfaceEnergy > 0.,
structure = struc, structure = struc,
iterations = options.d/2 + 1), # fat boundary | PE: why 2d-1? I would argue for d/2 + 1 iterations = int(round(options.d*2.)), # fat boundary
periodic_bulkEnergy[grid[0]/2:-grid[0]/2, # retain filled energy on fat boundary... ),
grid[1]/2:-grid[1]/2, periodic_bulkEnergy, # ...and zero everywhere else
grid[2]/2:-grid[2]/2], # ...and zero everywhere else 0.),
0.))*gauss) sigma = options.d)
periodic_diffusedEnergy = np.tile(diffusedEnergy,(3,3,3))[grid[0]/2:-grid[0]/2, else:
grid[1]/2:-grid[1]/2, diffusedEnergy = np.fft.irfftn(np.fft.rfftn(
grid[2]/2:-grid[2]/2] # periodically extend the smoothed bulk energy np.where(
# transform voxels close to interface region | question PE: what motivates 1/2 (could be any small number, or)? ndimage.morphology.binary_dilation(interfaceEnergy > 0.,
index = ndimage.morphology.distance_transform_edt(periodic_diffusedEnergy >= 0.5, structure = struc,
iterations = int(round(options.d*2.))),# 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.))*gauss)
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 >= 0.5*np.amax(periodic_diffusedEnergy),
return_distances = False, return_distances = False,
return_indices = True) # want index of closest bulk grain 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], microstructure = periodic_microstructure[index[0],
index[1], index[1],
index[2]].reshape(2*grid)[grid[0]/2:-grid[0]/2, index[2]].reshape(2*grid)[grid[0]/2:-grid[0]/2,
grid[1]/2:-grid[1]/2, grid[1]/2:-grid[1]/2,
grid[2]/2:-grid[2]/2] # extent grains into interface region grid[2]/2:-grid[2]/2] # extent grains into interface region
immutable = np.zeros(microstructure.shape, dtype=bool) immutable = np.zeros(microstructure.shape, dtype=bool)
# find locations where immutable microstructures have been or are now # find locations where immutable microstructures have been or are now
for micro in options.immutable: for micro in options.immutable:
immutable += np.logical_or(microstructure == micro, microstructure_original == micro) immutable += np.logical_or(microstructure == micro, microstructure_original == micro)
# undo any changes involving immutable microstructures
microstructure = np.where(immutable, microstructure_original,microstructure)
# --- renumber to sequence 1...Ngrains if requested ------------------------------------------------ # undo any changes involving immutable microstructures
microstructure = np.where(immutable, microstructure_original,microstructure)
# --- renumber to sequence 1...Ngrains if requested -----------------------------------------------
# http://stackoverflow.com/questions/10741346/np-frequency-counts-for-unique-values-in-an-array # http://stackoverflow.com/questions/10741346/np-frequency-counts-for-unique-values-in-an-array
if options.renumber: if options.renumber:
@ -162,13 +193,14 @@ for name in filenames:
newInfo = {'microstructures': 0,} newInfo = {'microstructures': 0,}
newInfo['microstructures'] = microstructure.max() newInfo['microstructures'] = microstructure.max()
# --- report --------------------------------------------------------------------------------------- # --- report --------------------------------------------------------------------------------------
remarks = [] remarks = []
if (newInfo['microstructures'] != info['microstructures']): remarks.append('--> microstructures: %i'%newInfo['microstructures']) if newInfo['microstructures'] != info['microstructures']:
remarks.append('--> microstructures: {}'.format(newInfo['microstructures']))
if remarks != []: damask.util.croak(remarks) if remarks != []: damask.util.croak(remarks)
# --- write header --------------------------------------------------------------------------------- # --- write header --------------------------------------------------------------------------------
table.labels_clear() table.labels_clear()
table.info_clear() table.info_clear()
@ -185,8 +217,11 @@ for name in filenames:
# --- write microstructure information ------------------------------------------------------------ # --- write microstructure information ------------------------------------------------------------
formatwidth = int(math.floor(math.log10(microstructure.max())+1)) formatwidth = int(math.floor(math.log10(microstructure.max())+1))
table.data = microstructure.reshape((info['grid'][0],info['grid'][1]*info['grid'][2]),order='F').transpose() table.data = microstructure[::1 if info['grid'][0]>1 else 2,
table.data_writeArray('%%%ii'%(formatwidth),delimiter = ' ') ::1 if info['grid'][1]>1 else 2,
::1 if info['grid'][2]>1 else 2,].\
reshape((info['grid'][0],info['grid'][1]*info['grid'][2]),order='F').transpose()
table.data_writeArray('%{}i'.format(formatwidth),delimiter = ' ')
# --- output finalization -------------------------------------------------------------------------- # --- output finalization --------------------------------------------------------------------------