205 lines
9.2 KiB
Python
Executable File
205 lines
9.2 KiB
Python
Executable File
#!/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 numpy as np
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from optparse import OptionParser
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from scipy import ndimage
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import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = scriptID.split()[1][:-3]
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def periodic_3Dpad(array, rimdim=(1,1,1)):
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rimdim = np.array(rimdim,'i')
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size = np.array(array.shape,'i')
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padded = np.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 = np.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, '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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]
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neighborhoods = {
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'neumann':np.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':np.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=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Add column(s) containing Euclidean distance to grain structural features: boundaries, triple lines, and quadruple points.
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""", version = scriptID)
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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.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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(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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files = []
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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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file['croak'].write('\033[1m'+scriptName+'\033[0m: '+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(scriptID + '\t' + ' '.join(sys.argv[1:]))
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# --------------- figure out position of labels and coordinates ------------------------------------
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try:
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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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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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# ------------------------------------------ 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.head_write()
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# ------------------------------------------ process data ------------------------------------------
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table.data_readArray([options.coords+'.x',options.coords+'.y',options.coords+'.z',options.id])
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coords = [{},{},{}]
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for i in xrange(len(table.data)):
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for j in xrange(3):
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coords[j][str(table.data[i,j])] = True
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grid = np.array(map(len,coords),'i')
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unitlength = 0.0
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for i,r in enumerate(grid):
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if r > 1: unitlength = max(unitlength,(max(map(float,coords[i].keys()))-min(map(float,coords[i].keys())))/(r-1.0))
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neighborhood = neighborhoods[options.neighborhood]
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convoluted = np.empty([len(neighborhood)]+list(grid+2),'i')
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microstructure = periodic_3Dpad(np.array(table.data[:,3].reshape(grid),'i'))
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for i,p in enumerate(neighborhood):
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stencil = np.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 = np.ones((len(feature_list),grid[0],grid[1],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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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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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.shape = (len(feature_list),grid.prod())
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# ------------------------------------------ process data ------------------------------------------
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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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l += 1
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ output result -----------------------------------------
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outputAlive and table.output_flush() # just in case of buffered ASCII table
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table.input_close() # close input ASCII table
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table.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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