script to add the Euclidean distance map from boundaries, triple lines, and quadruple points to an ASCIItable of a regular grid.
requires numpy, scipy, and skfmm...
This commit is contained in:
Philip Eisenlohr
parent
dc746119e3
commit
a37250eef4
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#!/usr/bin/env python
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import os,re,sys,math,numpy,skfmm,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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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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Add column 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('-g','--geom', dest='geom', action='store', type='string', \
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help='optional geometry file', \
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metavar='<file>')
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parser.add_option('-i','--identifier', dest='id', action='store', type='string', \
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help='heading of column containing grain identifier [%default]', \
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metavar='<label>')
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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.set_defaults(type = [])
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parser.set_defaults(id = 'texture')
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parser.set_defaults(neighborhood = 'neumann')
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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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# ------------------------------------------ setup file handles ---------------------------------------
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files = []
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if filenames == []:
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files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
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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, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
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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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table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
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table.head_read() # read ASCII header info
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table.info_append(string.replace('$Id$','\n','\\n') + \
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'\t' + ' '.join(sys.argv[1:]))
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# ------------------------------------------ assemble header ---------------------------------------
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if options.id not in table.labels:
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file['croak'].write('column %s not found...\n'%options.id)
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continue
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for feature in feature_list:
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table.labels_append('ED_%s(%s)'%(features[feature]['names'][0],options.id)) # extend ASCII header with new labels
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table.head_write()
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# ------------------------------------------ process data ---------------------------------------
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structure = table.data_asArray(['ip.x','ip.y','ip.z',options.id])
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grid = [{},{},{}]
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for i in xrange(len(structure)):
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for j in xrange(3):
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grid[j][str(structure[i,j])] = True
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resolution = numpy.array(map(len,grid),'i')
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unitlength = 0.0
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for i,r in enumerate(resolution):
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if r > 1: unitlength = max(unitlength,(max(map(float,grid[i].keys()))-min(map(float,grid[i].keys())))/(r-1.0))
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neighborhood = neighborhoods[options.neighborhood]
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convoluted = numpy.empty([len(neighborhood)]+list(resolution+2),'i')
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microstructure = periodic_3Dpad(numpy.array(structure[:,3].reshape(resolution),'i'))
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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),resolution[0],resolution[1],resolution[2]),'d')
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for x in xrange(resolution[0]):
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for y in xrange(resolution[1]):
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for z in xrange(resolution[2]):
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uniques = len(numpy.unique(convoluted[:,x+1,y+1,z+1]))
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for i,feature_id in enumerate(feature_list):
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if uniques > features[feature_id]['aliens']:
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distance[i,x,y,z] = 0.0 # found a starting point
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for i in xrange(len(feature_list)):
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distance[i,:,:,:] = skfmm.distance(distance[i,:,:,:], dx=[unitlength]*3)
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distance.shape = (len(feature_list),resolution.prod())
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table.data_rewind()
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l = 0
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while table.data_read():
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for i in xrange(len(feature_list)):
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table.data_append(distance[i,l]) # add all distance fields
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table.data_write() # output processed line
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l += 1
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# ------------------------------------------ output result ---------------------------------------
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table.output_flush() # just in case of buffered ASCII table
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if file['name'] != 'STDIN':
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file['input'].close() # close input ASCII table
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file['output'].close() # close output ASCII table
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os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
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