diff --git a/processing/pre/geom_fromEuclideanDistance.py b/processing/pre/geom_fromEuclideanDistance.py index a640ef9d0..1eedeac07 100755 --- a/processing/pre/geom_fromEuclideanDistance.py +++ b/processing/pre/geom_fromEuclideanDistance.py @@ -51,12 +51,15 @@ def periodic_3Dpad(array, rimdim=(1,1,1)): #-------------------------------------------------------------------------------------------------- # MAIN #-------------------------------------------------------------------------------------------------- +synonyms = { + 'grid': ['resolution'], + 'size': ['dimension'], + } identifiers = { 'grid': ['a','b','c'], 'size': ['x','y','z'], 'origin': ['x','y','z'], } - mappings = { 'grid': lambda x: int(x), 'size': lambda x: float(x), @@ -66,9 +69,9 @@ mappings = { } features = [ - {'aliens': 1, 'names': ['boundary(biplane)'],}, - {'aliens': 2, 'names': ['tripleline'],}, - {'aliens': 3, 'names': ['quadruplepoint'],} + {'aliens': 1, 'names': ['boundary','biplane'],}, + {'aliens': 2, 'names': ['tripleline',],}, + {'aliens': 3, 'names': ['quadruplepoint',],} ] neighborhoods = { @@ -90,6 +93,7 @@ neighborhoods = { [-1, 1,-1], [ 0, 1,-1], [ 1, 1,-1], +# [-1,-1, 0], [ 0,-1, 0], [ 1,-1, 0], @@ -99,6 +103,7 @@ neighborhoods = { [-1, 1, 0], [ 0, 1, 0], [ 1, 1, 0], +# [-1,-1, 1], [ 0,-1, 1], [ 1,-1, 1], @@ -117,16 +122,16 @@ boundaries, triple lines, and quadruple points. """ + string.replace(scriptID,'\n','\\n') ) -parser.add_option('-t','--type', dest='type', action='extend', type='string', metavar = '', \ - help='feature type (%s) '%(', '.join(map(lambda x:', '.join(x['names']),features))) ) -parser.add_option('-n','--neighborhood', dest='neigborhood', choices=neighborhoods.keys(), metavar = 'string', \ - help='type of neighborhood (%s) [neumann]'%(', '.join(neighborhoods.keys()))) -parser.add_option('-2', '--twodimensional', dest='twoD', action='store_true', \ - help='output geom file with two-dimensional data arrangement [%default]') +parser.add_option('-t','--type', dest = 'type', action = 'extend', type = 'string', 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 (%s) [neumann]'%(', '.join(neighborhoods.keys()))) +parser.add_option('-s', '--scale', dest = 'scale', type = 'float', + help = 'voxel size [%default]') parser.set_defaults(type = []) parser.set_defaults(neighborhood = 'neumann') -parser.set_defaults(twoD = False) +parser.set_defaults(scale = 1.0) (options,filenames) = parser.parse_args() @@ -152,8 +157,7 @@ else: if os.path.exists(name): files.append({'name':name, 'input':open(name), - 'output':[open(string.split(''.join((features[feature]['names'])),sep='(')[0]+'_'+name,'w') - for feature in feature_list], + 'output':[open(features[feature]['names'][0]+'_'+name,'w') for feature in feature_list], 'croak':sys.stdout, }) @@ -162,35 +166,29 @@ for file in files: if file['name'] != 'STDIN': file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n') else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n') - firstline = file['input'].readline() - m = re.search('(\d+)\s*head', firstline.lower()) - if m: - headerlines = int(m.group(1)) - headers = [file['input'].readline() for i in range(headerlines)] - else: - headerlines = 1 - headers = firstline + theTable = damask.ASCIItable(file['input'],file['output'][0],labels = False) + theTable.head_read() - content = file['input'].readlines() - file['input'].close() - -#--- interprete header ---------------------------------------------------------------------------- +#--- interpret header ---------------------------------------------------------------------------- info = { - 'grid': numpy.zeros(3,'i'), - 'size': numpy.zeros(3,'d'), - 'origin': numpy.zeros(3,'d'), - 'microstructures': 0, - 'homogenization': 0 + 'grid': numpy.zeros(3,'i'), + 'size': numpy.zeros(3,'d'), + 'origin': numpy.zeros(3,'d'), + 'homogenization': 0, + 'microstructures': 0, } newInfo = { - 'microstructures': 0, - } + 'grid': numpy.zeros(3,'i'), + 'origin': numpy.zeros(3,'d'), + 'microstructures': 0, + } + extra_header = [] - new_header = [] - for header in headers: + for header in theTable.info: headitems = map(str.lower,header.split()) - if headitems[0] == 'resolution': headitems[0] = 'grid' - if headitems[0] == 'dimension': headitems[0] = 'size' + if len(headitems) == 0: continue # skip blank lines + for synonym,alternatives in synonyms.iteritems(): + if headitems[0] in alternatives: headitems[0] = synonym if headitems[0] in mappings.keys(): if headitems[0] in identifiers.keys(): for i in xrange(len(identifiers[headitems[0]])): @@ -199,40 +197,41 @@ for file in files: else: info[headitems[0]] = mappings[headitems[0]](headitems[1]) else: - new_header.append(header) + extra_header.append(header) file['croak'].write('grid a b c: %s\n'%(' x '.join(map(str,info['grid']))) + \ 'size x y z: %s\n'%(' x '.join(map(str,info['size']))) + \ 'origin x y z: %s\n'%(' : '.join(map(str,info['origin']))) + \ 'homogenization: %i\n'%info['homogenization'] + \ 'microstructures: %i\n'%info['microstructures']) - + if numpy.any(info['grid'] < 1): file['croak'].write('invalid grid a b c.\n') - sys.exit() + continue if numpy.any(info['size'] <= 0.0): file['croak'].write('invalid size x y z.\n') - sys.exit() + continue - new_header.append(scriptID + ' ' + ' '.join(sys.argv[1:]) + '\n') - new_header.append("grid\ta %i\tb %i\tc %i\n"%(info['grid'][0],info['grid'][1],info['grid'][2],)) - new_header.append("size\tx %f\ty %f\tz %f\n"%(info['size'][0],info['size'][1],info['size'][2],)) - new_header.append("origin\tx %f\ty %f\tz %f\n"%(info['origin'][0],info['origin'][1],info['origin'][2],)) - new_header.append("homogenization\t%i\n"%info['homogenization']) - -#--- process input -------------------------------------------------------------------------------- - structure = numpy.zeros(info['grid'],'i') +#--- read data ------------------------------------------------------------------------------------ + microstructure = numpy.zeros(info['grid'].prod(),'i') # initialize as flat array i = 0 - for line in content: - for item in map(int,line.split()): - structure[i%info['grid'][0], - (i/info['grid'][0])%info['grid'][1], - i/info['grid'][0] /info['grid'][1]] = item - i += 1 + + while theTable.data_read(): + items = theTable.data + if len(items) > 2: + if items[1].lower() == 'of': items = [int(items[2])]*int(items[0]) + elif items[1].lower() == 'to': items = xrange(int(items[0]),1+int(items[2])) + else: items = map(int,items) + else: items = map(int,items) + + s = len(items) + microstructure[i:i+s] = items + i += s + neighborhood = neighborhoods[options.neighborhood] convoluted = numpy.empty([len(neighborhood)]+list(info['grid']+2),'i') - microstructure = periodic_3Dpad(structure) + structure = periodic_3Dpad(microstructure.reshape(info['grid'],order='F')) for i,p in enumerate(neighborhood): stencil = numpy.zeros((3,3,3),'i') @@ -240,43 +239,49 @@ for file in files: stencil[p[0]+1, p[1]+1, p[2]+1] = 1 - - convoluted[i,:,:,:] = ndimage.convolve(microstructure,stencil) + convoluted[i,:,:,:] = ndimage.convolve(structure,stencil) distance = numpy.ones((len(feature_list),info['grid'][0],info['grid'][1],info['grid'][2]),'d') - convoluted = numpy.sort(convoluted,axis=0) - uniques = numpy.zeros(info['grid']) - check = numpy.empty(info['grid']) - check[:,:,:] = numpy.nan - for i in xrange(len(neighborhood)): - uniques += numpy.where(convoluted[i,1:-1,1:-1,1:-1] == check,0,1) - check = convoluted[i,1:-1,1:-1,1:-1] + convoluted = numpy.sort(convoluted,axis = 0) + uniques = numpy.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 += numpy.where(numpy.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,:,:,:] = numpy.where(uniques > features[feature_id]['aliens'],0.0,1.0) + distance[i,:,:,:] = numpy.where(uniques >= features[feature_id]['aliens'],0.0,1.0) # seed with 0.0 when enough unique neighbor IDs are present + for i in xrange(len(feature_list)): - distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*\ - [max(info['size']/info['grid'])]*3 + distance[i,:,:,:] = ndimage.morphology.distance_transform_edt(distance[i,:,:,:])*[options.scale]*3 + for i,feature in enumerate(feature_list): newInfo['microstructures'] = int(math.ceil(distance[i,:,:,:].max())) + +#--- write header --------------------------------------------------------------------------------- + theTable = damask.ASCIItable(file['input'],file['output'][i],labels = False) + theTable.labels_clear() + theTable.info_clear() + theTable.info_append(extra_header+[ + scriptID + ' ' + ' '.join(sys.argv[1:]), + "grid\ta %i\tb %i\tc %i"%(info['grid'][0],info['grid'][1],info['grid'][2],), + "size\tx %f\ty %f\tz %f"%(info['size'][0],info['size'][1],info['size'][2],), + "origin\tx %f\ty %f\tz %f"%(info['origin'][0],info['origin'][1],info['origin'][2],), + "homogenization\t%i"%info['homogenization'], + "microstructures\t%i"%(newInfo['microstructures']), + ]) + theTable.head_write() + theTable.output_flush() + +# --- write microstructure information ------------------------------------------------------------ formatwidth = int(math.floor(math.log10(distance[i,:,:,:].max())+1)) - -#--- assemble header and report changes ----------------------------------------------------------- - output = '%i\theader\n'%(len(new_header)+1)+''.join(new_header) - output += "microstructures\t%i\n"%newInfo['microstructures'] - file['croak'].write('\n'+features[i]['names'][0]+'\n') - if (newInfo['microstructures'] != info['microstructures']): - file['croak'].write('--> microstructures: %i\n'%newInfo['microstructures']) - -#--- write new data ------------------------------------------------------------------------------- - for z in xrange(info['grid'][2]): - for y in xrange(info['grid'][1]): - output += {True:' ',False:'\n'}[options.twoD].join(map(lambda x: \ - ('%%%ii'%formatwidth)%(round(x)), distance[i,:,y,z])) + '\n' - file['output'][i].write(output) - if file['name'] != 'STDIN': - file['output'][i].close() - -#--- output finalization -------------------------------------------------------------------------- + theTable.data = distance[i,:,:,:].reshape((info['grid'][0],info['grid'][1]*info['grid'][2]),order='F').transpose() + theTable.data_writeArray('%%%ii'%(formatwidth),delimiter=' ') + file['output'][i].close() + +#--- output finalization -------------------------------------------------------------------------- if file['name'] != 'STDIN': file['input'].close()