#!/usr/bin/env python2.7 # -*- 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 range(3): for p[(side+2)%3] in range(padded.shape[(side+2)%3]): for p[(side+1)%3] in range(padded.shape[(side+1)%3]): for p[side%3] in range(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 range(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('-p', '--pos', '--position', dest = 'pos', metavar = 'string', help = 'label of coordinates [%default]') parser.add_option('-i', '--id', '--identifier', dest = 'id', metavar = 'string', help='label of grain identifier [%default]') parser.add_option('-t', '--type', dest = 'type', action = 'extend', metavar = '', help = 'feature type {{{}}} '.format(', '.join(map(lambda x:'/'.join(x['names']),features))) ) parser.add_option('-n', '--neighborhood', dest = 'neighborhood', choices = neighborhoods.keys(), metavar = 'string', help = 'neighborhood type [neumann] {{{}}}'.format(', '.join(neighborhoods.keys()))) parser.add_option('-s', '--scale', dest = 'scale', type = 'float', metavar = 'float', help = 'voxel size [%default]') parser.set_defaults(pos = '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.pos) if not 3 >= coordDim >= 1: errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.pos)) else: coordCol = table.label_index(options.pos) 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) 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 range(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)))) else: remarks.append('grid: {}x{}x{}'.format(*grid)) if remarks != []: damask.util.croak(remarks) if errors != []: damask.util.croak(errors) table.close(dismiss = True) continue # ------------------------------------------ process value field ----------------------------------- stack = [table.data] neighborhood = neighborhoods[options.neighborhood] diffToNeighbor = np.empty(list(grid+2)+[len(neighborhood)],'i') microstructure = periodic_3Dpad(table.data[:,idCol].astype('i').reshape(grid,order='F')) 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 diffToNeighbor[:,:,:,i] = ndimage.convolve(microstructure,stencil) # compare ID at each point... # ...to every one in the specified neighborhood # for same IDs at both locations ==> 0 diffToNeighbor = np.sort(diffToNeighbor) # sort diff such that number of changes in diff (steps)... # ...reflects number of unique neighbors uniques = np.where(diffToNeighbor[1:-1,1:-1,1:-1,0] != 0, 1,0) # initialize unique value counter (exclude myself [= 0]) for i in range(1,len(neighborhood)): # check remaining points in neighborhood uniques += np.where(np.logical_and( diffToNeighbor[1:-1,1:-1,1:-1,i] != 0, # not myself? diffToNeighbor[1:-1,1:-1,1:-1,i] != diffToNeighbor[1:-1,1:-1,1:-1,i-1], ), # flip of ID difference detected? 1,0) # count that flip distance = np.ones((len(feature_list),grid[0],grid[1],grid[2]),'d') 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 = distance.reshape([len(feature_list),grid.prod(),1],order='F') for i in range(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)