#!/usr/bin/env python import os,re,sys,math,string,numpy,damask from optparse import OptionParser, Option # ----------------------------- class extendableOption(Option): # ----------------------------- # used for definition of new option parser action 'extend', which enables to take multiple option arguments # taken from online tutorial http://docs.python.org/library/optparse.html ACTIONS = Option.ACTIONS + ("extend",) STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",) TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",) ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",) def take_action(self, action, dest, opt, value, values, parser): if action == "extend": lvalue = value.split(",") values.ensure_value(dest, []).extend(lvalue) else: Option.take_action(self, action, dest, opt, value, values, parser) def location(idx,res): return ( idx % res[0], \ ( idx // res[0]) % res[1], \ ( idx // res[0] // res[1]) % res[2] ) def index(location,res): return ( location[0] % res[0] + \ ( location[1] % res[1]) * res[0] + \ ( location[2] % res[2]) * res[1] * res[0] ) # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """ Add column(s) containing curl of requested column(s). Operates on periodic ordered three-dimensional data sets. Deals with both vector- and tensor-valued fields. """ + string.replace('$Id$','\n','\\n') ) parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \ help='heading of columns containing vector field values') parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \ help='heading of columns containing tensor field values') parser.add_option('-d','--dimension', dest='dim', type='float', nargs=3, \ help='physical dimension of data set in x (fast) y z (slow) [%default]') parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \ help='resolution of data set in x (fast) y z (slow)') parser.set_defaults(vector = []) parser.set_defaults(tensor = []) parser.set_defaults(dim = []) parser.set_defaults(skip = [0,0,0]) (options,filenames) = parser.parse_args() if len(options.vector) + len(options.tensor) == 0: parser.error('no data column specified...') if len(options.dim) < 3: parser.error('improper dimension specification...') if not options.res or len(options.res) < 3: parser.error('improper resolution specification...') resSkip = map(lambda (a,b): a+b,zip(options.res,options.skip)) datainfo = { # list of requested labels per datatype 'vector': {'len':3, 'label':[]}, 'tensor': {'len':9, 'label':[]}, } if options.vector != None: datainfo['vector']['label'] += options.vector if options.tensor != None: datainfo['tensor']['label'] += options.tensor # ------------------------------------------ setup file handles --------------------------------------- files = [] if filenames == []: files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout}) else: for name in filenames: if os.path.exists(name): files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w')}) # ------------------------------------------ loop over input files --------------------------------------- for file in files: if file['name'] != 'STDIN': print file['name'] table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table table.head_read() # read ASCII header info table.info_append(string.replace('$Id$','\n','\\n') + \ '\t' + ' '.join(sys.argv[1:])) active = {} column = {} values = {} curl = {} head = [] for datatype,info in datainfo.items(): for label in info['label']: key = {True :'1_%s', False:'%s' }[info['len']>1]%label if key not in table.labels: sys.stderr.write('column %s not found...\n'%key) else: if datatype not in active: active[datatype] = [] if datatype not in column: column[datatype] = {} if datatype not in values: values[datatype] = {} if datatype not in curl: curl[datatype] = {} active[datatype].append(label) column[datatype][label] = table.labels.index(key) # remember columns of requested data values[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\ options.res[0]*options.res[1]*options.res[2])]).\ reshape((options.res[0],options.res[1],options.res[2],\ datainfo[datatype]['len']//3,3)) curl[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\ options.res[0]*options.res[1]*options.res[2])]).\ reshape((options.res[0],options.res[1],options.res[2],\ datainfo[datatype]['len']//3,3)) table.labels_append(['%i_curlFFT(%s)'%(i+1,label) for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels # ------------------------------------------ assemble header --------------------------------------- table.head_write() # ------------------------------------------ read value field --------------------------------------- idx = 0 while table.data_read(): # read next data line of ASCII table (x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count idx += 1 for datatype,labels in active.items(): # loop over vector,tensor for label in labels: # loop over all requested curls values[datatype][label][x,y,z] = numpy.array( map(float,table.data[column[datatype][label]: column[datatype][label]+datainfo[datatype]['len']]),'d').reshape(datainfo[datatype]['len']//3,3) # ------------------------------------------ process value field --------------------------------------- for datatype,labels in active.items(): # loop over vector,tensor for label in labels: # loop over all requested curls curl[datatype][label] = damask.core.math.curl_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label]) # ------------------------------------------ process data --------------------------------------- table.data_rewind() idx = 0 while table.data_read(): # read next data line of ASCII table (x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count idx += 1 for datatype,labels in active.items(): # loop over vector,tensor for label in labels: # loop over all requested norms table.data_append(list(curl[datatype][label][x,y,z].reshape(datainfo[datatype]['len']))) table.data_write() # output processed line # ------------------------------------------ output result --------------------------------------- table.output_flush() # just in case of buffered ASCII table file['input'].close() # close input ASCII table if file['name'] != 'STDIN': file['output'].close # close output ASCII table os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new