put changes on algorithm from geom_fromEuclideanDistance into addEuclideanDistance
This commit is contained in:
Martin Diehl
parent
f3bab46275
commit
e4a94aa72b
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@ -1,10 +1,10 @@
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#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string
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import os,sys,string,re,math
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import numpy as np
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from optparse import OptionParser
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from scipy import ndimage
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from optparse import OptionParser
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import damask
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scriptID = string.replace('$Id$','\n','\\n')
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@ -35,11 +35,10 @@ def periodic_3Dpad(array, rimdim=(1,1,1)):
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# MAIN
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# --------------------------------------------------------------------
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features = [ \
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{'aliens': 1, 'name': 'biplane'},
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{'aliens': 1, 'name': 'boundary'},
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{'aliens': 2, 'name': 'tripleline'},
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{'aliens': 3, 'name': 'quadruplepoint'}
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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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@ -61,6 +60,7 @@ neighborhoods = {
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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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@ -70,6 +70,7 @@ neighborhoods = {
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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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@ -91,28 +92,31 @@ parser.add_option('-c','--coordinates', dest='coords', metavar='string',
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help='column heading for coordinates [%default]')
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parser.add_option('-i','--identifier', dest='id', metavar = 'string',
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help='heading of column containing grain identifier [%default]')
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parser.add_option('-t','--type', dest='type', action='extend', metavar='<string LIST>',
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help='feature type (%s)'%(', '.join(map(lambda x:', '.join([x['name']]),features))))
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parser.add_option('-n','--neighborhood',dest='neigborhood', type='choice',
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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('-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(coords = 'ip')
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parser.set_defaults(id = 'texture')
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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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if len(options.type) == 0: parser.error('please select a feature type')
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if not set(options.type).issubset(set(map(lambda x: x['name'],features))):
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parser.error('type must be chosen from (%s)...'%(', '.join(map(lambda x:', '.join([x['name']]),features))))
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if 'biplane' in options.type and 'boundary' in options.type:
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parser.error("please select only one alias for 'biplane' and 'boundary'")
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feature_list = []
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for i,feature in enumerate(features):
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if feature['name'] in options.type: feature_list.append(i) # remember valid features
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# ------------------------------------------ setup file handles ------------------------------------
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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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files = []
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for name in filenames:
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@ -129,7 +133,7 @@ for file in files:
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# --------------- figure out position of labels and coordinates ------------------------------------
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try:
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locationCol = table.labels.index('1_%s'%options.coords) # columns containing location data
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locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
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except ValueError:
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file['croak'].write('no coordinate data (%s.x) found...\n'%options.coords)
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continue
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@ -140,7 +144,7 @@ for file in files:
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# ------------------------------------------ assemble header ---------------------------------------
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for feature in feature_list:
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table.labels_append('ED_%s(%s)'%(features[feature]['name'],options.id)) # extend ASCII header with new labels
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table.labels_append('ED_%s(%s)'%(features[feature]['names'],options.id)) # extend ASCII header with new labels
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table.head_write()
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@ -168,23 +172,24 @@ for file in files:
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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 = np.ones((len(feature_list),grid[0],grid[1],grid[2]),'d')
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distance = np.ones((len(feature_list),info['grid'][0],info['grid'][1],info['grid'][2]),'d')
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convoluted = np.sort(convoluted,axis=0)
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uniques = np.zeros(grid)
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check = np.empty(grid)
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check[:,:,:] = np.nan
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for i in xrange(len(neighborhood)):
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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]
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convoluted = np.sort(convoluted,axis = 0)
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uniques = np.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 += np.where(np.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,:,:,:] = np.where(uniques > features[feature_id]['aliens'],0.0,1.0)
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distance[i,:,:,:] = np.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,:,:,:])*[unitlength]*3
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distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3
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distance.shape = (len(feature_list),grid.prod())
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# ------------------------------------------ process data ------------------------------------------
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@ -105,7 +105,7 @@ parser.add_option('-t','--type', dest = 'type', action = 'extend', type
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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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parser.add_option('-s', '--scale', dest = 'scale', type = 'float', metavar='float',
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help = 'voxel size [%default]')
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parser.set_defaults(type = [])
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