#!/usr/bin/env python # -*- coding: UTF-8 no BOM -*- import os,sys,itertools import numpy as np from scipy import ndimage from optparse import OptionParser import damask scriptName = os.path.splitext(os.path.basename(__file__))[0] scriptID = ' '.join([scriptName,damask.version]) def periodic_3Dpad(array, rimdim=(1,1,1)): rimdim = np.array(rimdim,'i') size = np.array(array.shape,'i') padded = np.empty(size+2*rimdim,array.dtype) padded[rimdim[0]:rimdim[0]+size[0], rimdim[1]:rimdim[1]+size[1], rimdim[2]:rimdim[2]+size[2]] = array p = np.zeros(3,'i') for side in xrange(3): for p[(side+2)%3] in xrange(padded.shape[(side+2)%3]): for p[(side+1)%3] in xrange(padded.shape[(side+1)%3]): for p[side%3] in xrange(rimdim[side%3]): spot = (p-rimdim)%size padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]] for p[side%3] in xrange(rimdim[side%3]+size[side%3],size[side%3]+2*rimdim[side%3]): spot = (p-rimdim)%size padded[p[0],p[1],p[2]] = array[spot[0],spot[1],spot[2]] return padded # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- features = [ {'aliens': 1, 'names': ['boundary','biplane'],}, {'aliens': 2, 'names': ['tripleline',],}, {'aliens': 3, 'names': ['quadruplepoint',],} ] neighborhoods = { 'neumann':np.array([ [-1, 0, 0], [ 1, 0, 0], [ 0,-1, 0], [ 0, 1, 0], [ 0, 0,-1], [ 0, 0, 1], ]), 'moore':np.array([ [-1,-1,-1], [ 0,-1,-1], [ 1,-1,-1], [-1, 0,-1], [ 0, 0,-1], [ 1, 0,-1], [-1, 1,-1], [ 0, 1,-1], [ 1, 1,-1], # [-1,-1, 0], [ 0,-1, 0], [ 1,-1, 0], [-1, 0, 0], # [ 1, 0, 0], [-1, 1, 0], [ 0, 1, 0], [ 1, 1, 0], # [-1,-1, 1], [ 0,-1, 1], [ 1,-1, 1], [-1, 0, 1], [ 0, 0, 1], [ 1, 0, 1], [-1, 1, 1], [ 0, 1, 1], [ 1, 1, 1], ]) } parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ Add column(s) containing Euclidean distance to grain structural features: boundaries, triple lines, and quadruple points. """, version = scriptID) parser.add_option('-c','--coordinates', dest='coords', metavar='string', help='column label of coordinates [%default]') parser.add_option('-i','--identifier', dest='id', metavar = 'string', help='column label of grain identifier [%default]') parser.add_option('-t','--type', dest = 'type', action = 'extend', metavar = '', help = 'feature type {%s} '%(', '.join(map(lambda x:'/'.join(x['names']),features))) ) parser.add_option('-n','--neighborhood',dest='neighborhood', choices = neighborhoods.keys(), metavar = 'string', help = 'type of neighborhood [neumann] {%s}'%(', '.join(neighborhoods.keys()))) parser.add_option('-s', '--scale', dest = 'scale', type = 'float', metavar = 'float', help = 'voxel size [%default]') parser.set_defaults(coords = 'pos', id = 'texture', neighborhood = 'neumann', scale = 1.0, ) (options,filenames) = parser.parse_args() if options.type is None: parser.error('no feature type selected.') if not set(options.type).issubset(set(list(itertools.chain(*map(lambda x: x['names'],features))))): parser.error('type must be chosen from (%s).'%(', '.join(map(lambda x:'|'.join(x['names']),features))) ) if 'biplane' in options.type and 'boundary' in options.type: parser.error("only one from aliases 'biplane' and 'boundary' possible.") feature_list = [] for i,feature in enumerate(features): for name in feature['names']: for myType in options.type: if name.startswith(myType): feature_list.append(i) # remember valid features break # --- loop over input files ------------------------------------------------------------------------- if filenames == []: filenames = [None] for name in filenames: try: table = damask.ASCIItable(name = name, buffered = False) except: continue damask.util.report(scriptName,name) # ------------------------------------------ read header ------------------------------------------ table.head_read() # ------------------------------------------ sanity checks ---------------------------------------- errors = [] remarks = [] column = {} coordDim = table.label_dimension(options.coords) if not 3 >= coordDim >= 1: errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.coords)) else: coordCol = table.label_index(options.coords) if table.label_dimension(options.id) != 1: errors.append('grain identifier {} not found.'.format(options.id)) else: idCol = table.label_index(options.id) if remarks != []: damask.util.croak(remarks) if errors != []: damask.util.croak(errors) table.close(dismiss = True) continue # ------------------------------------------ assemble header --------------------------------------- table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:])) for feature in feature_list: table.labels_append('ED_{}({})'.format(features[feature]['names'][0],options.id)) # extend ASCII header with new labels table.head_write() # --------------- figure out size and grid --------------------------------------------------------- table.data_readArray() coords = [np.unique(table.data[:,coordCol+i]) for i in xrange(coordDim)] mincorner = np.array(map(min,coords)) maxcorner = np.array(map(max,coords)) grid = np.array(map(len,coords)+[1]*(3-len(coords)),'i') N = grid.prod() if N != len(table.data): errors.append('data count {} does not match grid '.format(N) + 'x'.join(map(str,grid)) + '.') if errors != []: damask.util.croak(errors) table.close(dismiss = True) continue # ------------------------------------------ process value field ----------------------------------- stack = [table.data] neighborhood = neighborhoods[options.neighborhood] convoluted = np.empty([len(neighborhood)]+list(grid+2),'i') microstructure = periodic_3Dpad(np.array(table.data[:,idCol].reshape(grid),'i')) for i,p in enumerate(neighborhood): stencil = np.zeros((3,3,3),'i') stencil[1,1,1] = -1 stencil[p[0]+1, p[1]+1, p[2]+1] = 1 convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil) distance = np.ones((len(feature_list),grid[0],grid[1],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 for i,feature_id in enumerate(feature_list): distance[i,:,:,:] = np.where(uniques >= features[feature_id]['aliens'],0.0,1.0) # seed with 0.0 when enough unique neighbor IDs are present distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3 distance.shape = ([len(feature_list),grid.prod(),1]) for i in xrange(len(feature_list)): stack.append(distance[i,:]) # ------------------------------------------ output result ----------------------------------------- if len(stack) > 1: table.data = np.hstack(tuple(stack)) table.data_writeArray('%.12g') # ------------------------------------------ output finalization ----------------------------------- table.close() # close input ASCII table (works for stdin)