DAMASK_EICMD/processing/post/addEuclideanDistance.py

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#!/usr/bin/env python2.7
# -*- coding: UTF-8 no BOM -*-
import os,sys,itertools
import numpy as np
from scipy import ndimage
from optparse import OptionParser
import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
def periodic_3Dpad(array, rimdim=(1,1,1)):
rimdim = np.array(rimdim,'i')
size = np.array(array.shape,'i')
padded = np.empty(size+2*rimdim,array.dtype)
padded[rimdim[0]:rimdim[0]+size[0],
rimdim[1]:rimdim[1]+size[1],
rimdim[2]:rimdim[2]+size[2]] = array
p = np.zeros(3,'i')
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for side in range(3):
for p[(side+2)%3] in range(padded.shape[(side+2)%3]):
for p[(side+1)%3] in range(padded.shape[(side+1)%3]):
for p[side%3] in range(rimdim[side%3]):
spot = (p-rimdim)%size
padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
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for p[side%3] in range(rimdim[side%3]+size[side%3],size[side%3]+2*rimdim[side%3]):
spot = (p-rimdim)%size
padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
return padded
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
features = [
{'aliens': 1, 'names': ['boundary','biplane'],},
{'aliens': 2, 'names': ['tripleline',],},
{'aliens': 3, 'names': ['quadruplepoint',],}
]
neighborhoods = {
'neumann':np.array([
[-1, 0, 0],
[ 1, 0, 0],
[ 0,-1, 0],
[ 0, 1, 0],
[ 0, 0,-1],
[ 0, 0, 1],
]),
'moore':np.array([
[-1,-1,-1],
[ 0,-1,-1],
[ 1,-1,-1],
[-1, 0,-1],
[ 0, 0,-1],
[ 1, 0,-1],
[-1, 1,-1],
[ 0, 1,-1],
[ 1, 1,-1],
#
[-1,-1, 0],
[ 0,-1, 0],
[ 1,-1, 0],
[-1, 0, 0],
#
[ 1, 0, 0],
[-1, 1, 0],
[ 0, 1, 0],
[ 1, 1, 0],
#
[-1,-1, 1],
[ 0,-1, 1],
[ 1,-1, 1],
[-1, 0, 1],
[ 0, 0, 1],
[ 1, 0, 1],
[-1, 1, 1],
[ 0, 1, 1],
[ 1, 1, 1],
])
}
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Add column(s) containing Euclidean distance to grain structural features: boundaries, triple lines, and quadruple points.
""", version = scriptID)
parser.add_option('-p',
'--pos', '--position',
dest = 'pos', metavar = 'string',
help = 'label of coordinates [%default]')
parser.add_option('-i',
'--id', '--identifier',
dest = 'id', metavar = 'string',
help='label of grain identifier [%default]')
parser.add_option('-t',
'--type',
dest = 'type', action = 'extend', metavar = '<string LIST>',
help = 'feature type {{{}}} '.format(', '.join(map(lambda x:'/'.join(x['names']),features))) )
parser.add_option('-n',
'--neighborhood',
dest = 'neighborhood', choices = neighborhoods.keys(), metavar = 'string',
help = 'neighborhood type [neumann] {{{}}}'.format(', '.join(neighborhoods.keys())))
parser.add_option('-s',
'--scale',
dest = 'scale', type = 'float', metavar = 'float',
help = 'voxel size [%default]')
parser.set_defaults(pos = 'pos',
id = 'texture',
neighborhood = 'neumann',
scale = 1.0,
)
(options,filenames) = parser.parse_args()
if options.type is None:
parser.error('no feature type selected.')
if not set(options.type).issubset(set(list(itertools.chain(*map(lambda x: x['names'],features))))):
parser.error('type must be chosen from (%s).'%(', '.join(map(lambda x:'|'.join(x['names']),features))) )
if 'biplane' in options.type and 'boundary' in options.type:
parser.error('only one from aliases "biplane" and "boundary" possible.')
feature_list = []
for i,feature in enumerate(features):
for name in feature['names']:
for myType in options.type:
if name.startswith(myType):
feature_list.append(i) # remember valid features
break
# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
for name in filenames:
try: table = damask.ASCIItable(name = name, buffered = False)
except: continue
damask.util.report(scriptName,name)
# ------------------------------------------ read header ------------------------------------------
table.head_read()
# ------------------------------------------ sanity checks ----------------------------------------
errors = []
remarks = []
column = {}
coordDim = table.label_dimension(options.pos)
if not 3 >= coordDim >= 1:
errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.pos))
else: coordCol = table.label_index(options.pos)
if table.label_dimension(options.id) != 1: errors.append('grain identifier {} not found.'.format(options.id))
else: idCol = table.label_index(options.id)
if remarks != []:
damask.util.croak(remarks)
remarks = []
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ assemble header ---------------------------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for feature in feature_list:
table.labels_append('ED_{}({})'.format(features[feature]['names'][0],options.id)) # extend ASCII header with new labels
table.head_write()
# --------------- figure out size and grid ---------------------------------------------------------
table.data_readArray()
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coords = [np.unique(table.data[:,coordCol+i]) for i in range(coordDim)]
mincorner = np.array(map(min,coords))
maxcorner = np.array(map(max,coords))
grid = np.array(map(len,coords)+[1]*(3-len(coords)),'i')
N = grid.prod()
if N != len(table.data): errors.append('data count {} does not match grid {}.'.format(N,'x'.join(map(str,grid))))
else: remarks.append('grid: {}x{}x{}'.format(*grid))
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ process value field -----------------------------------
stack = [table.data]
neighborhood = neighborhoods[options.neighborhood]
diffToNeighbor = np.empty(list(grid+2)+[len(neighborhood)],'i')
microstructure = periodic_3Dpad(table.data[:,idCol].astype('i').reshape(grid,order='F'))
for i,p in enumerate(neighborhood):
stencil = np.zeros((3,3,3),'i')
stencil[1,1,1] = -1
stencil[p[0]+1,
p[1]+1,
p[2]+1] = 1
diffToNeighbor[:,:,:,i] = ndimage.convolve(microstructure,stencil) # compare ID at each point...
# ...to every one in the specified neighborhood
# for same IDs at both locations ==> 0
diffToNeighbor = np.sort(diffToNeighbor) # sort diff such that number of changes in diff (steps)...
# ...reflects number of unique neighbors
uniques = np.where(diffToNeighbor[1:-1,1:-1,1:-1,0] != 0, 1,0) # initialize unique value counter (exclude myself [= 0])
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for i in range(1,len(neighborhood)): # check remaining points in neighborhood
uniques += np.where(np.logical_and(
diffToNeighbor[1:-1,1:-1,1:-1,i] != 0, # not myself?
diffToNeighbor[1:-1,1:-1,1:-1,i] != diffToNeighbor[1:-1,1:-1,1:-1,i-1],
), # flip of ID difference detected?
1,0) # count that flip
distance = np.ones((len(feature_list),grid[0],grid[1],grid[2]),'d')
for i,feature_id in enumerate(feature_list):
distance[i,:,:,:] = np.where(uniques >= features[feature_id]['aliens'],0.0,1.0) # seed with 0.0 when enough unique neighbor IDs are present
distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3
distance = distance.reshape([len(feature_list),grid.prod(),1],order='F')
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for i in range(len(feature_list)):
stack.append(distance[i,:])
# ------------------------------------------ output result -----------------------------------------
if len(stack) > 1: table.data = np.hstack(tuple(stack))
table.data_writeArray('%.12g')
# ------------------------------------------ output finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)