DAMASK_EICMD/processing/post/addEuclideanDistance.py

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#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys,string,re,math,itertools
import numpy as np
from scipy import ndimage
from optparse import OptionParser
import damask
scriptID = string.replace('$Id$','\n','\\n')
2014-12-19 00:56:52 +05:30
scriptName = os.path.splitext(scriptID.split()[1])[0]
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')
for side in xrange(3):
for p[(side+2)%3] in xrange(padded.shape[(side+2)%3]):
for p[(side+1)%3] in xrange(padded.shape[(side+1)%3]):
for p[side%3] in xrange(rimdim[side%3]):
spot = (p-rimdim)%size
padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
for p[side%3] in xrange(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('-c','--coordinates', dest='coords', metavar='string',
help='column heading for coordinates [%default]')
parser.add_option('-i','--identifier', dest='id', metavar = 'string',
help='heading of column containing grain identifier [%default]')
parser.add_option('-t','--type', dest = 'type', action = 'extend', type = 'string', metavar = '<string LIST>',
help = 'feature type (%s) '%(', '.join(map(lambda x:'|'.join(x['names']),features))) )
parser.add_option('-n','--neighborhood', dest='neighborhood', choices = neighborhoods.keys(), metavar = 'string',
help = 'type of neighborhood (%s) [neumann]'%(', '.join(neighborhoods.keys())))
parser.add_option('-s', '--scale', dest = 'scale', type = 'float', metavar='float',
help = 'voxel size [%default]')
parser.set_defaults(type = [])
parser.set_defaults(coords = 'ip')
parser.set_defaults(id = 'texture')
parser.set_defaults(neighborhood = 'neumann')
parser.set_defaults(scale = 1.0)
(options,filenames) = parser.parse_args()
if len(options.type) == 0: parser.error('please select a feature type')
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("please select only one alias for 'biplane' and 'boundary'")
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
files = []
for name in filenames:
if os.path.exists(name):
files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
# ------------------------------------------ loop over input files ---------------------------------
for file in files:
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
# --------------- figure out position of labels and coordinates ------------------------------------
try:
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
except ValueError:
file['croak'].write('no coordinate data (%s.x) found...\n'%options.coords)
continue
if options.id not in table.labels:
file['croak'].write('column %s not found...\n'%options.id)
continue
# ------------------------------------------ assemble header ---------------------------------------
for feature in feature_list:
table.labels_append('ED_%s(%s)'%(features[feature]['names'][0],options.id)) # extend ASCII header with new labels
table.head_write()
# ------------------------------------------ process data ------------------------------------------
table.data_readArray([options.coords+'.x',options.coords+'.y',options.coords+'.z',options.id])
coords = [{},{},{}]
for i in xrange(len(table.data)):
for j in xrange(3):
coords[j][str(table.data[i,j])] = True
grid = np.array(map(len,coords),'i')
unitlength = 0.0
for i,r in enumerate(grid):
if r > 1: unitlength = max(unitlength,(max(map(float,coords[i].keys()))-min(map(float,coords[i].keys())))/(r-1.0))
neighborhood = neighborhoods[options.neighborhood]
convoluted = np.empty([len(neighborhood)]+list(grid+2),'i')
microstructure = periodic_3Dpad(np.array(table.data[:,3].reshape(grid),'i'))
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
convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil)
distance = np.ones((len(feature_list),grid[0],grid[1],grid[2]),'d')
convoluted = np.sort(convoluted,axis = 0)
uniques = np.where(convoluted[0,1:-1,1:-1,1:-1] != 0, 1,0) # initialize unique value counter (exclude myself [= 0])
for i in xrange(1,len(neighborhood)): # check remaining points in neighborhood
uniques += np.where(np.logical_and(
convoluted[i,1:-1,1:-1,1:-1] != convoluted[i-1,1:-1,1:-1,1:-1], # flip of ID difference detected?
convoluted[i,1:-1,1:-1,1:-1] != 0), # not myself?
1,0) # count flip
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
for i in xrange(len(feature_list)):
distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3
distance.shape = (len(feature_list),grid.prod())
# ------------------------------------------ process data ------------------------------------------
table.data_rewind()
l = 0
while table.data_read():
for i in xrange(len(feature_list)):
table.data_append(distance[i,l]) # add all distance fields
l += 1
outputAlive = table.data_write() # output processed line
# ------------------------------------------ output result -----------------------------------------
outputAlive and table.output_flush() # just in case of buffered ASCII table
table.input_close() # close input ASCII table
table.output_close() # close output ASCII table
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new