223 lines
9.7 KiB
Python
Executable File
223 lines
9.7 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,itertools
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import numpy as np
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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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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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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, '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':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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#
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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=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['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 [neumann] {%s}'%(', '.join(neighborhoods.keys())))
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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(coords = 'ipinitialcoord')
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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 options.type == None:
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parser.error('no feature type selected.')
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if not set(options.type).issubset(set(list(itertools.chain(*map(lambda x: x['names'],features))))):
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parser.error('type must be chosen from (%s).'%(', '.join(map(lambda x:'|'.join(x['names']),features))) )
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if 'biplane' in options.type and 'boundary' in options.type:
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parser.error("only one from aliases 'biplane' and 'boundary' possible.")
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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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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name, buffered = False)
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except:
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continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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remarks = []
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column = {}
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if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
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else: coordCol = table.label_index(options.coords)
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if table.label_dimension(options.id) != 1: errors.append('grain identifier {} not found.'.format(options.id))
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else: idCol = table.label_index(options.id)
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header ---------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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for feature in feature_list:
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table.labels_append('ED_{}({})'.format(features[feature]['names'][0],options.id)) # extend ASCII header with new labels
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table.head_write()
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# --------------- figure out size and grid ---------------------------------------------------------
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table.data_readArray()
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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,coordCol+j])] = True
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grid = np.array(map(len,coords),'i')
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size = grid/np.maximum(np.ones(3,'d'),grid-1.0)* \
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np.array([max(map(float,coords[0].keys()))-min(map(float,coords[0].keys())),\
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max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
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max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
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],'d') # size from bounding box, corrected for cell-centeredness
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size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 equal to smallest among other spacings
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# ------------------------------------------ process value field -----------------------------------
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stack = [table.data]
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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[:,idCol].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.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) # seed with 0.0 when enough unique neighbor IDs are present
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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(),1])
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for i in xrange(len(feature_list)):
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stack.append(distance[i,:])
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# ------------------------------------------ output result -----------------------------------------
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if len(stack) > 1: table.data = np.hstack(tuple(stack))
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table.data_writeArray('%.12g')
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)
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