DAMASK_EICMD/processing/post/addEuclideanDistance.py

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#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,re,sys,math,string
import numpy as np
from optparse import OptionParser
from scipy import ndimage
import damask
scriptID = '$Id$'
scriptName = scriptID.split()[1]
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 = """
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Add column(s) containing Euclidean distance to grain structural features:
boundaries, triple lines, and quadruple points.
""", version = string.replace(scriptID,'\n','\\n')
)
parser.add_option('-c','--coordinates', dest='coords', action='store', type='string', metavar='string', \
help='column heading for coordinates [%default]')
parser.add_option('-i','--identifier', dest='id', action='store', type='string', 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='neigborhood', action='store', type='string', metavar='int', \
help='type of neighborhood (%s)'%(', '.join(neighborhoods.keys())))
parser.set_defaults(coords = 'ip')
parser.set_defaults(type = [])
parser.set_defaults(id = 'texture')
parser.set_defaults(neighborhood = 'neumann')
(options,filenames) = parser.parse_args()
options.neighborhood = options.neighborhood.lower()
if options.neighborhood not in neighborhoods:
parser.error('unknown neighborhood %s!'%options.neighborhood)
feature_list = []
for i,feature in enumerate(features):
for name in feature['names']:
for type in options.type:
if name.startswith(type):
feature_list.append(i) # remember valid features
break
# ------------------------------------------ setup file handles -----------------------------------
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(string.replace(scriptID,'\n','\\n') + '\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 found...\n'%key)
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()
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# ------------------------------------------ process data ---------------------------------------
table.data_readArray([options.coords+'.x',options.coords+'.y',options.coords+'.z',options.id])
grid = [{},{},{}]
for i in xrange(len(table.data)):
for j in xrange(3):
grid[j][str(table.data[i,j])] = True
resolution = np.array(map(len,grid),'i')
unitlength = 0.0
for i,r in enumerate(resolution):
if r > 1: unitlength = max(unitlength,(max(map(float,grid[i].keys()))-min(map(float,grid[i].keys())))/(r-1.0))
neighborhood = neighborhoods[options.neighborhood]
convoluted = np.empty([len(neighborhood)]+list(resolution+2),'i')
microstructure = periodic_3Dpad(np.array(table.data[:,3].reshape(resolution),'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),resolution[0],resolution[1],resolution[2]),'d')
convoluted = np.sort(convoluted,axis=0)
uniques = np.zeros(resolution)
check = np.empty(resolution)
check[:,:,:] = np.nan
for i in xrange(len(neighborhood)):
uniques += np.where(convoluted[i,1:-1,1:-1,1:-1] == check,0,1)
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check = convoluted[i,1:-1,1:-1,1:-1]
for i,feature_id in enumerate(feature_list):
distance[i,:,:,:] = np.where(uniques > features[feature_id]['aliens'],0.0,1.0)
for i in xrange(len(feature_list)):
distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[unitlength]*3
distance.shape = (len(feature_list),resolution.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
outputAlive = table.data_write() # output processed line
l += 1
# ------------------------------------------ output result ---------------------------------------
outputAlive and table.output_flush() # just in case of buffered ASCII table
file['input'].close() # close input ASCII table (works for stdin)
file['output'].close() # close output ASCII table (works for stdout)
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new