#!/usr/bin/env python # -*- coding: UTF-8 no BOM -*- import os,re,sys,math,string from collections import defaultdict from optparse import OptionParser import damask scriptID = '$Id$' scriptName = scriptID.split()[1] # definition of element-wise p-norms for matrices def normAbs(object): # p = 1 return sum(map(abs, object)) def normFrobenius(object): # p = 2 return math.sqrt(sum([x*x for x in object])) def normMax(object): # p = infinity return max(map(abs, object)) # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ Add column(s) containing norm of requested column(s) being either vectors or tensors. """, version = string.replace(scriptID,'\n','\\n') ) normChoices = ['abs','frobenius','max'] parser.add_option('-n','--norm', dest='norm', action='store', type='choice', choices=normChoices, metavar='string', help='type of element-wise p-norm (%s) [frobenius]'%(','.join(map(str,normChoices)))) parser.add_option('-v','--vector', dest='vector', action='extend', type='string', metavar='', help='heading of columns containing vector field values') parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', metavar='', help='heading of columns containing tensor field values') parser.add_option('-s','--special', dest='special', action='extend', type='string', metavar='', help='heading of columns containing field values of special dimension') parser.add_option('-d','--dimension', dest='N', action='store', type='int', metavar='int', help='dimension of special field values [%default]') parser.set_defaults(norm = 'frobenius') parser.set_defaults(vector = []) parser.set_defaults(tensor = []) parser.set_defaults(special = []) parser.set_defaults(N = 12) (options,filenames) = parser.parse_args() if len(options.vector) + len(options.tensor) + len(options.special)== 0: parser.error('no data column specified...') datainfo = { # list of requested labels per datatype 'vector': {'len':3, 'label':[]}, 'tensor': {'len':9, 'label':[]}, 'special': {'len':options.N, 'label':[]}, } if options.vector != None: datainfo['vector']['label'] += options.vector if options.tensor != None: datainfo['tensor']['label'] += options.tensor if options.special != None: datainfo['special']['label'] += options.special # ------------------------------------------ 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(name+'_tmp','w'), 'croak':sys.stderr}) #--- loop over input files ------------------------------------------------------------------------ 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') table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table table.head_read() # read ASCII header info table.info_append(string.replace(scriptID,'\n','\\n') + '\t' + ' '.join(sys.argv[1:])) active = defaultdict(list) column = defaultdict(dict) 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: file['croak'].write('column %s not found...\n'%key) else: active[datatype].append(label) column[datatype][label] = table.labels.index(key) # remember columns of requested data # ------------------------------------------ assemble header --------------------------------------- for datatype,labels in active.items(): # loop over vector,tensor for label in labels: # loop over all requested determinants table.labels_append('norm%s(%s)'%(options.norm.capitalize(),label)) # extend ASCII header with new labels table.head_write() # ------------------------------------------ process data --------------------------------------- outputAlive = True while outputAlive and table.data_read(): # read next data line of ASCII table for datatype,labels in active.items(): # loop over vector,tensor for label in labels: # loop over all requested norms eval("table.data_append(norm%s(map(float,table.data[column[datatype][label]:column[datatype][label]+datainfo[datatype]['len']])))"%options.norm.capitalize()) outputAlive = table.data_write() # output processed line # ------------------------------------------ output result --------------------------------------- outputAlive and table.output_flush() # just in case of buffered ASCII table file['input'].close() # close input ASCII table (works for stdin) file['output'].close() # close output ASCII table (works for stdout) if file['name'] != 'STDIN': os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new