#!/usr/bin/env python # -*- coding: UTF-8 no BOM -*- import os,sys,string,itertools import numpy as np from optparse import OptionParser from collections import defaultdict import damask scriptID = string.replace('$Id$','\n','\\n') scriptName = os.path.splitext(scriptID.split()[1])[0] #-------------------------------------------------------------------------------------------------- # MAIN #-------------------------------------------------------------------------------------------------- identifiers = { 'grid': ['a','b','c'], 'size': ['x','y','z'], 'origin': ['x','y','z'], } mappings = { 'grid': lambda x: int(x), 'size': lambda x: float(x), 'origin': lambda x: float(x), 'homogenization': lambda x: int(x), 'microstructures': lambda x: int(x), } parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ Create seed file by taking microstructure indices from given ASCIItable column. White and black-listing of microstructure indices is possible. Examples: --white 1,2,5 --index grainID isolates grainID entries of value 1, 2, and 5; --black 1 --index grainID takes all grainID entries except for value 1. """, version = scriptID) parser.add_option('-p', '--positions', dest = 'pos', type = 'string', help = 'coordinate label') parser.add_option('--boundingbox', dest = 'box', type = 'float', nargs = 6, help = 'min (x,y,z) and max (x,y,z) to specify bounding box [auto]') parser.add_option('-i', '--index', dest = 'index', type = 'string', help = 'microstructure index label') parser.add_option('-w','--white', dest = 'whitelist', action = 'extend', type = 'string', \ help = 'white list of microstructure indices', metavar = '') parser.add_option('-b','--black', dest = 'blacklist', action = 'extend', type = 'string', \ help = 'black list of microstructure indices', metavar = '') parser.set_defaults(pos = 'pos') parser.set_defaults(index = 'microstructure') parser.set_defaults(box = ()) parser.set_defaults(whitelist = []) parser.set_defaults(blacklist = []) (options,filenames) = parser.parse_args() datainfo = { # list of requested labels per datatype 'scalar': {'len':1, 'label':[]}, 'vector': {'len':3, 'label':[]}, } if options.pos != None: datainfo['vector']['label'] += [options.pos] if options.index != None: datainfo['scalar']['label'] += [options.index] options.whitelist = map(int,options.whitelist) options.blacklist = map(int,options.blacklist) #--- setup file handles -------------------------------------------------------------------------- files = [] if filenames == []: files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr, }) else: for name in filenames: if os.path.exists(name): files.append({'name':name, 'input':open(name), 'output':open(os.path.splitext(name)[0]+'.seeds','w'), 'croak':sys.stdout, }) #--- loop over input files ------------------------------------------------------------------------ for file in files: file['croak'].write('\033[1m' + scriptName + '\033[0m: ' + (file['name'] if file['name'] != 'STDIN' else '') + '\n') table = damask.ASCIItable(file['input'],file['output'],buffered = False) table.head_read() # --------------- figure out columns to process active = defaultdict(list) column = defaultdict(dict) for datatype,info in datainfo.items(): for label in info['label']: foundIt = False for key in ['1_'+label,label]: if key in table.labels: foundIt = True active[datatype].append(label) column[datatype][label] = table.labels.index(key) # remember columns of requested data if not foundIt: file['croak'].write('column %s not found...\n'%label) break # ------------------------------------------ process data --------------------------------------- table.data_readArray(list(itertools.chain.from_iterable(map(lambda x:[x+i for i in range(datainfo['vector']['len'])], [column['vector'][label] for label in active['vector']]))) + [column['scalar'][label] for label in active['scalar']]) #--- finding bounding box ------------------------------------------------------------------------------------ boundingBox = np.array((np.amin(table.data[:,0:3],axis = 0),np.amax(table.data[:,0:3],axis = 0))) if len(options.box) == 6: boundingBox[0,:] = np.minimum(options.box[0:3],boundingBox[0,:]) boundingBox[1,:] = np.maximum(options.box[3:6],boundingBox[1,:]) #--- rescaling coordinates ------------------------------------------------------------------------------------ table.data[:,0:3] -= boundingBox[0,:] table.data[:,0:3] /= boundingBox[1,:]-boundingBox[0,:] #--- filtering of grain voxels ------------------------------------------------------------------------------------ mask = np.logical_and(\ np.ones_like(table.data[:,3],bool) \ if options.whitelist == [] \ else np.in1d(table.data[:,3].ravel(), options.whitelist).reshape(table.data[:,3].shape), np.ones_like(table.data[:,3],bool) \ if options.blacklist == [] \ else np.invert(np.in1d(table.data[:,3].ravel(), options.blacklist).reshape(table.data[:,3].shape)) ) table.data = table.data[mask] # ------------------------------------------ output result --------------------------------------- # ------------------------------------------ assemble header --------------------------------------- table.info = [ scriptID, 'size %s'%(' '.join(list(itertools.chain.from_iterable(zip(['x','y','z'], map(str,boundingBox[1,:]-boundingBox[0,:])))))), ] table.labels_clear() table.labels_append(['1_coords','2_coords','3_coords','microstructure']) # implicitly switching label processing/writing on table.head_write() table.data_writeArray() table.output_flush() table.input_close() # close input ASCII table if file['name'] != 'STDIN': table.output_close() # close output ASCII table