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DAMASK_EICMD
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put changes on algorithm from geom_fromEuclideanDistance into addEuclideanDistance

This commit is contained in:
Martin Diehl 2015-02-07 17:11:46 +00:00
parent f3bab46275
commit e4a94aa72b
2 changed files with 36 additions and 31 deletions
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63
processing/post/addEuclideanDistance.py
View File

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

2
processing/pre/geom_fromEuclideanDistance.py
View File

@ -105,7 +105,7 @@ parser.add_option('-t','--type', dest = 'type', action = 'extend', type
help = 'feature type (%s) '%(', '.join(map(lambda x:'|'.join(x['names']),features))) ) help = 'feature type (%s) '%(', '.join(map(lambda x:'|'.join(x['names']),features))) )
parser.add_option('-n','--neighborhood', dest='neighborhood', choices = neighborhoods.keys(), metavar = 'string', parser.add_option('-n','--neighborhood', dest='neighborhood', choices = neighborhoods.keys(), metavar = 'string',
help = 'type of neighborhood (%s) [neumann]'%(', '.join(neighborhoods.keys()))) help = 'type of neighborhood (%s) [neumann]'%(', '.join(neighborhoods.keys())))
parser.add_option('-s', '--scale', dest = 'scale', type = 'float', parser.add_option('-s', '--scale', dest = 'scale', type = 'float', metavar='float',
help = 'voxel size [%default]') help = 'voxel size [%default]')
parser.set_defaults(type = []) parser.set_defaults(type = [])