288 lines
12 KiB
Python
Executable File
288 lines
12 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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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, OptionGroup, Option, SUPPRESS_HELP
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scriptID = '$Id$'
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scriptName = scriptID.split()[1]
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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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synonyms = {
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'grid': ['resolution'],
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'size': ['dimension'],
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}
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identifiers = {
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'grid': ['a','b','c'],
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'size': ['x','y','z'],
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'origin': ['x','y','z'],
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}
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mappings = {
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'grid': lambda x: int(x),
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'size': lambda x: float(x),
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'origin': lambda x: float(x),
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'homogenization': lambda x: int(x),
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'microstructures': lambda x: int(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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#
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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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#
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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(scriptID,'\n','\\n')
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)
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parser.add_option('-t','--type', dest = 'type', action = 'extend', type = 'string', metavar = '<string LIST>',
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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='neighborhood', choices = neighborhoods.keys(), metavar = 'string',
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help = 'type of neighborhood (%s) [neumann]'%(', '.join(neighborhoods.keys())))
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parser.add_option('-s', '--scale', dest = 'scale', type = 'float',
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help = 'voxel size [%default]')
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parser.set_defaults(type = [])
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parser.set_defaults(neighborhood = 'neumann')
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parser.set_defaults(scale = 1.0)
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(options,filenames) = parser.parse_args()
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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 myType in options.type:
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if name.startswith(myType):
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feature_list.append(i) # remember valid features
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break
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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('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
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else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
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theTable = damask.ASCIItable(file['input'],file['output'][0],labels = False)
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theTable.head_read()
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#--- interpret header ----------------------------------------------------------------------------
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info = {
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'grid': numpy.zeros(3,'i'),
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'size': numpy.zeros(3,'d'),
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'origin': numpy.zeros(3,'d'),
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'homogenization': 0,
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'microstructures': 0,
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}
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newInfo = {
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'grid': numpy.zeros(3,'i'),
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'origin': numpy.zeros(3,'d'),
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'microstructures': 0,
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}
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extra_header = []
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for header in theTable.info:
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headitems = map(str.lower,header.split())
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if len(headitems) == 0: continue # skip blank lines
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for synonym,alternatives in synonyms.iteritems():
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if headitems[0] in alternatives: headitems[0] = synonym
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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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else:
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extra_header.append(header)
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file['croak'].write('grid a b c: %s\n'%(' x '.join(map(str,info['grid']))) + \
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'size x y z: %s\n'%(' x '.join(map(str,info['size']))) + \
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'origin x y z: %s\n'%(' : '.join(map(str,info['origin']))) + \
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'homogenization: %i\n'%info['homogenization'] + \
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'microstructures: %i\n'%info['microstructures'])
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if numpy.any(info['grid'] < 1):
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file['croak'].write('invalid grid a b c.\n')
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continue
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if numpy.any(info['size'] <= 0.0):
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file['croak'].write('invalid size x y z.\n')
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continue
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#--- read data ------------------------------------------------------------------------------------
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microstructure = numpy.zeros(info['grid'].prod(),'i') # initialize as flat array
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i = 0
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while theTable.data_read():
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items = theTable.data
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if len(items) > 2:
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if items[1].lower() == 'of': items = [int(items[2])]*int(items[0])
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elif items[1].lower() == 'to': items = xrange(int(items[0]),1+int(items[2]))
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else: items = map(int,items)
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else: items = map(int,items)
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s = len(items)
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microstructure[i:i+s] = items
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i += s
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neighborhood = neighborhoods[options.neighborhood]
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convoluted = numpy.empty([len(neighborhood)]+list(info['grid']+2),'i')
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structure = periodic_3Dpad(microstructure.reshape(info['grid'],order='F'))
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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(structure,stencil)
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distance = numpy.ones((len(feature_list),info['grid'][0],info['grid'][1],info['grid'][2]),'d')
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convoluted = numpy.sort(convoluted,axis = 0)
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uniques = numpy.where(convoluted[0,1:-1,1:-1,1:-1] != 0, 1,0) # initialize unique value counter (exclude myself [= 0])
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for i in xrange(1,len(neighborhood)): # check remaining points in neighborhood
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uniques += numpy.where(numpy.logical_and(
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convoluted[i,1:-1,1:-1,1:-1] != convoluted[i-1,1:-1,1:-1,1:-1], # flip of ID difference detected?
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convoluted[i,1:-1,1:-1,1:-1] != 0), # not myself?
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1,0) # count flip
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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) # seed with 0.0 when enough unique neighbor IDs are present
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for i in xrange(len(feature_list)):
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distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3
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for i,feature in enumerate(feature_list):
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newInfo['microstructures'] = int(math.ceil(distance[i,:,:,:].max()))
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#--- write header ---------------------------------------------------------------------------------
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theTable = damask.ASCIItable(file['input'],file['output'][i],labels = False)
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theTable.labels_clear()
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theTable.info_clear()
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theTable.info_append(extra_header+[
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scriptID + ' ' + ' '.join(sys.argv[1:]),
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"grid\ta %i\tb %i\tc %i"%(info['grid'][0],info['grid'][1],info['grid'][2],),
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"size\tx %f\ty %f\tz %f"%(info['size'][0],info['size'][1],info['size'][2],),
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"origin\tx %f\ty %f\tz %f"%(info['origin'][0],info['origin'][1],info['origin'][2],),
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"homogenization\t%i"%info['homogenization'],
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"microstructures\t%i"%(newInfo['microstructures']),
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])
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theTable.head_write()
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theTable.output_flush()
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# --- write microstructure information ------------------------------------------------------------
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formatwidth = int(math.floor(math.log10(distance[i,:,:,:].max())+1))
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theTable.data = distance[i,:,:,:].reshape((info['grid'][0],info['grid'][1]*info['grid'][2]),order='F').transpose()
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theTable.data_writeArray('%%%ii'%(formatwidth),delimiter=' ')
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file['output'][i].close()
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#--- output finalization --------------------------------------------------------------------------
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if file['name'] != 'STDIN':
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table.input_close()
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