#!/usr/bin/env python
import os,re,sys,math,numpy,string,damask
from scipy import ndimage
from optparse import OptionParser, Option
# -----------------------------
class extendableOption(Option):
# -----------------------------
# used for definition of new option parser action 'extend', which enables to take multiple option arguments
# taken from online tutorial http://docs.python.org/library/optparse.html
ACTIONS = Option.ACTIONS + ("extend",)
STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
def take_action(self, action, dest, opt, value, values, parser):
if action == "extend":
lvalue = value.split(",")
values.ensure_value(dest, []).extend(lvalue)
else:
Option.take_action(self, action, dest, opt, value, values, parser)
def periodic_3Dpad(array, rimdim=(1,1,1)):
rimdim = numpy.array(rimdim,'i')
size = numpy.array(array.shape,'i')
padded = numpy.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 = numpy.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':numpy.array([
[-1, 0, 0],
[ 1, 0, 0],
[ 0,-1, 0],
[ 0, 1, 0],
[ 0, 0,-1],
[ 0, 0, 1],
]),
'moore':numpy.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=extendableOption, usage='%prog options [file[s]]', description = """
Add column(s) containing Euclidean distance to grain structural features:
boundaries, triple lines, and quadruple points.
""" + string.replace('$Id$','\n','\\n')
)
parser.add_option('-i','--identifier', dest='id', action='store', type='string', \
help='heading of column containing grain identifier [%default]', \
metavar='<label>')
parser.add_option('-t','--type', dest='type', action='extend', type='string', \
help='feature type (%s)'%(', '.join(map(lambda x:', '.join(x['names']),features))))
parser.add_option('-n','--neighborhood', dest='neigborhood', action='store', type='string', \
help='type of neighborhood (%s)'%(', '.join(neighborhoods.keys())), \
metavar='<int>')
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 = []
if filenames == []:
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
else:
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:
if file['name'] != 'STDIN': file['croak'].write(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('$Id$','\n','\\n') + \
'\t' + ' '.join(sys.argv[1:]))
# ------------------------------------------ assemble header ---------------------------------------
if options.id not in table.labels:
file['croak'].write('column %s not found...\n'%options.id)
continue
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 ---------------------------------------
structure = table.data_asArray(['ip.x','ip.y','ip.z',options.id])
grid = [{},{},{}]
for i in xrange(len(structure)):
for j in xrange(3):
grid[j][str(structure[i,j])] = True
resolution = numpy.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 = numpy.empty([len(neighborhood)]+list(resolution+2),'i')
microstructure = periodic_3Dpad(numpy.array(structure[:,3].reshape(resolution),'i'))
for i,p in enumerate(neighborhood):
stencil = numpy.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 = numpy.ones((len(feature_list),resolution[0],resolution[1],resolution[2]),'d')
convoluted = numpy.sort(convoluted,axis=0)
uniques = numpy.zeros(resolution)
check = numpy.empty(resolution)
check[:,:,:] = numpy.nan
for i in xrange(len(neighborhood)):
uniques += numpy.where(convoluted[i,1:-1,1:-1,1:-1] == check,0,1)
check = convoluted[i,1:-1,1:-1,1:-1]
for i,feature_id in enumerate(feature_list):
distance[i,:,:,:] = numpy.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())
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
table.data_write() # output processed line
l += 1
# ------------------------------------------ output result ---------------------------------------
table.output_flush() # just in case of buffered ASCII table
if file['name'] != 'STDIN':
file['input'].close() # close input ASCII table
file['output'].close() # close output ASCII table
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new