286 lines
11 KiB
Python
Executable File
286 lines
11 KiB
Python
Executable File
#!/usr/bin/env python
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import os,re,sys,math,numpy,string,damask
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from scipy import ndimage
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from optparse import OptionParser, Option
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# -----------------------------
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class extendableOption(Option):
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# -----------------------------
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# used for definition of new option parser action 'extend', which enables to take multiple option arguments
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# taken from online tutorial http://docs.python.org/library/optparse.html
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ACTIONS = Option.ACTIONS + ("extend",)
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STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
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TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
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ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
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def take_action(self, action, dest, opt, value, values, parser):
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if action == "extend":
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lvalue = value.split(",")
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values.ensure_value(dest, []).extend(lvalue)
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else:
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Option.take_action(self, action, dest, opt, value, values, parser)
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def periodic_3Dpad(array, rimdim=(1,1,1)):
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rimdim = numpy.array(rimdim,'i')
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size = numpy.array(array.shape,'i')
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padded = numpy.empty(size+2*rimdim,array.dtype)
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padded[rimdim[0]:rimdim[0]+size[0],
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rimdim[1]:rimdim[1]+size[1],
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rimdim[2]:rimdim[2]+size[2]] = array
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p = numpy.zeros(3,'i')
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for side in xrange(3):
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for p[(side+2)%3] in xrange(padded.shape[(side+2)%3]):
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for p[(side+1)%3] in xrange(padded.shape[(side+1)%3]):
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for p[side%3] in xrange(rimdim[side%3]):
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spot = (p-rimdim)%size
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padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
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for p[side%3] in xrange(rimdim[side%3]+size[side%3],size[side%3]+2*rimdim[side%3]):
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spot = (p-rimdim)%size
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padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]]
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return padded
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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identifiers = {
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'resolution': ['a','b','c'],
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'dimension': ['x','y','z'],
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'origin': ['x','y','z'],
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}
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mappings = {
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'resolution': lambda x: int(x),
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'dimension': lambda x: float(x),
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'origin': lambda x: float(x),
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}
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features = [ \
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{'aliens': 1, 'names': ['boundary','biplane'],},
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{'aliens': 2, 'names': ['tripleline',],},
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{'aliens': 3, 'names': ['quadruplepoint',],}
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]
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neighborhoods = {
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'neumann':numpy.array([
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[-1, 0, 0],
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[ 1, 0, 0],
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[ 0,-1, 0],
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[ 0, 1, 0],
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[ 0, 0,-1],
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[ 0, 0, 1],
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]),
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'moore':numpy.array([
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[-1,-1,-1],
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[ 0,-1,-1],
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[ 1,-1,-1],
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[-1, 0,-1],
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[ 0, 0,-1],
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[ 1, 0,-1],
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[-1, 1,-1],
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[ 0, 1,-1],
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[ 1, 1,-1],
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[-1,-1, 0],
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[ 0,-1, 0],
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[ 1,-1, 0],
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[-1, 0, 0],
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#
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[ 1, 0, 0],
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[-1, 1, 0],
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[ 0, 1, 0],
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[ 1, 1, 0],
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[-1,-1, 1],
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[ 0,-1, 1],
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[ 1,-1, 1],
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[-1, 0, 1],
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[ 0, 0, 1],
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[ 1, 0, 1],
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[-1, 1, 1],
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[ 0, 1, 1],
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[ 1, 1, 1],
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])
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}
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parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
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Produce geom files containing Euclidean distance to grain structural features:
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boundaries, triple lines, and quadruple points.
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""" + string.replace('$Id$','\n','\\n')
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)
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parser.add_option('-t','--type', dest='type', action='extend', type='string', \
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help='feature type (%s)'%(', '.join(map(lambda x:', '.join(x['names']),features))))
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parser.add_option('-n','--neighborhood', dest='neigborhood', action='store', type='string', \
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help='type of neighborhood (%s)'%(', '.join(neighborhoods.keys())), \
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metavar='<int>')
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parser.add_option('-2', '--twodimensional', dest='twoD', action='store_true', \
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help='output geom file with two-dimensional data arrangement')
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parser.set_defaults(type = [])
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parser.set_defaults(neighborhood = 'neumann')
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parser.set_defaults(twoD = False)
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(options,filenames) = parser.parse_args()
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options.neighborhood = options.neighborhood.lower()
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if options.neighborhood not in neighborhoods:
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parser.error('unknown neighborhood %s!'%options.neighborhood)
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feature_list = []
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for i,feature in enumerate(features):
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for name in feature['names']:
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for type in options.type:
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if name.startswith(type):
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feature_list.append(i) # remember valid features
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break
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print feature_list
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# ------------------------------------------ setup file handles ---------------------------------------
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files = []
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if filenames == []:
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files.append({'name':'STDIN',
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'input':sys.stdin,
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'output':sys.stdout,
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'croak':sys.stderr,
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})
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else:
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for name in filenames:
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if os.path.exists(name):
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files.append({'name':name,
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'input':open(name),
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'output':[open(features[feature]['names'][0]+'_'+name,'w') for feature in feature_list],
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'croak':sys.stdout,
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})
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# ------------------------------------------ loop over input files ---------------------------------------
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for file in files:
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if file['name'] != 'STDIN': file['croak'].write(file['name']+'\n')
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# get labels by either read the first row, or - if keyword header is present - the last line of the header
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firstline = file['input'].readline()
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m = re.search('(\d+)\s*head', firstline.lower())
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if m:
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headerlines = int(m.group(1))
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headers = [firstline]+[file['input'].readline() for i in range(headerlines)]
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else:
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headerlines = 1
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headers = firstline
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content = file['input'].readlines()
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file['input'].close()
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info = {'resolution': numpy.array([0,0,0]),
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'dimension': numpy.array([0.0,0.0,0.0]),
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'origin': numpy.array([0.0,0.0,0.0]),
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'homogenization': 1,
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}
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new_header = []
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for header in headers:
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headitems = map(str.lower,header.split())
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if headitems[0] in mappings.keys():
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if headitems[0] in identifiers.keys():
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for i in xrange(len(identifiers[headitems[0]])):
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info[headitems[0]][i] = \
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mappings[headitems[0]](headitems[headitems.index(identifiers[headitems[0]][i])+1])
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else:
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info[headitems[0]] = mappings[headitems[0]](headitems[1])
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if numpy.all(info['resolution'] == 0):
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file['croak'].write('no resolution info found.\n')
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continue
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if numpy.all(info['dimension'] == 0.0):
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file['croak'].write('no dimension info found.\n')
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continue
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file['croak'].write('resolution: %s\n'%(' x '.join(map(str,info['resolution']))) + \
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'dimension: %s\n'%(' x '.join(map(str,info['dimension']))) + \
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'origin: %s\n'%(' : '.join(map(str,info['origin']))) + \
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'homogenization: %i\n'%info['homogenization'])
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new_header.append("resolution\ta %i\tb %i\tc %i\n"%(
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info['resolution'][0],
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info['resolution'][1],
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info['resolution'][2],))
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new_header.append("dimension\tx %f\ty %f\tz %f\n"%(
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info['dimension'][0],
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info['dimension'][1],
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info['dimension'][2],))
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new_header.append("origin\tx %f\ty %f\tz %f\n"%(
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info['origin'][0],
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info['origin'][1],
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info['origin'][2],))
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new_header.append("homogenization\t%i\n"%info['homogenization'])
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structure = numpy.zeros(info['resolution'],'i')
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i = 0
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for line in content:
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for item in map(int,line.split()):
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structure[i%info['resolution'][0],
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(i/info['resolution'][0])%info['resolution'][1],
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i/info['resolution'][0] /info['resolution'][1]] = item
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i += 1
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neighborhood = neighborhoods[options.neighborhood]
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convoluted = numpy.empty([len(neighborhood)]+list(info['resolution']+2),'i')
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microstructure = periodic_3Dpad(structure)
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for i,p in enumerate(neighborhood):
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stencil = numpy.zeros((3,3,3),'i')
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stencil[1,1,1] = -1
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stencil[p[0]+1,
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p[1]+1,
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p[2]+1] = 1
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convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil)
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distance = numpy.ones((len(feature_list),info['resolution'][0],info['resolution'][1],info['resolution'][2]),'d')
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convoluted = numpy.sort(convoluted,axis=0)
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uniques = numpy.zeros(info['resolution'])
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check = numpy.empty(info['resolution'])
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check[:,:,:] = numpy.nan
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for i in xrange(len(neighborhood)):
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uniques += numpy.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]
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for i,feature_id in enumerate(feature_list):
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distance[i,:,:,:] = numpy.where(uniques > features[feature_id]['aliens'],0.0,1.0)
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for i in xrange(len(feature_list)):
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distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[max(info['dimension']/info['resolution'])]*3
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for i,feature in enumerate(feature_list):
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formatwidth = int(math.floor(math.log10(distance[i,:,:,:].max())+1))
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# ------------------------------------------ assemble header ---------------------------------------
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output = '%i\theader\n'%(len(new_header))
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output += ''.join(new_header)
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# ------------------------------------- regenerate texture information ----------------------------------
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for z in xrange(info['resolution'][2]):
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for y in xrange(info['resolution'][1]):
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output += {True:' ',False:'\n'}[options.twoD].join(map(lambda x: ('%%%ii'%formatwidth)%(round(x)), distance[i,:,y,z])) + '\n'
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# ------------------------------------------ output result ---------------------------------------
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file['output'][i].write(output)
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if file['name'] != 'STDIN':
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file['output'][i].close()
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if file['name'] != 'STDIN':
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file['input'].close() # close input geom file
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