From 7aba05ed9f611141431b2082dd3c4d98f2df7921 Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Thu, 25 Aug 2011 17:55:36 +0000 Subject: [PATCH] reworked to match former script layouts and logics. (not yet tested, use at your own risk) --- processing/post/addDebugInformation | 152 +++++++++++++++------------- 1 file changed, 82 insertions(+), 70 deletions(-) mode change 100644 => 100755 processing/post/addDebugInformation diff --git a/processing/post/addDebugInformation b/processing/post/addDebugInformation old mode 100644 new mode 100755 index a00883181..89ded7696 --- a/processing/post/addDebugInformation +++ b/processing/post/addDebugInformation @@ -36,7 +36,7 @@ def index(location,res): # MAIN # -------------------------------------------------------------------- -parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """ +parser = OptionParser(option_class=extendableOption, usage='%prog options file[s]', description = """ Add column containing debug information Operates on periodic ordered three-dimensional data sets. @@ -45,137 +45,149 @@ Operates on periodic ordered three-dimensional data sets. parser.add_option('--no-shape','-s', dest='shape', action='store_false', \ - help='calcuate mismatch of shape [%default]') + help='do not calcuate shape mismatch [%default]') parser.add_option('--no-volume','-v', dest='volume', action='store_false', \ - help='calculate mismatch of volume [%default]') + help='do not calculate volume mismatch [%default]') 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.add_option('-f','--deformation', dest='defgrad', action='extend', type='string', \ + help='heading(s) of columns containing deformation tensor values %default') parser.set_defaults(volume = True) parser.set_defaults(shape = True) +parser.set_defaults(defgrad = ['f']) (options,filenames) = parser.parse_args() if not options.res or len(options.res) < 3: parser.error('improper resolution specification...') if not options.dim or len(options.dim) < 3: - parser.error('improper resolution specification...') - + parser.error('improper dimension specification...') + +defgrad = {} +defgrad_av = {} +centroids = {} +nodes = {} + datainfo = { # list of requested labels per datatype - 'tensor': {'len':9, - 'label':[]}, + 'defgrad': {'len':9, + 'label':[]}, } - -datainfo['tensor']['label'] += 'f' + +if options.defgrad != None: datainfo['defgrad']['label'] += options.defgrad + # ------------------------------------------ setup file handles --------------------------------------- files = [] if filenames == []: - files.append({'name':'STDIN', 'handle':sys.stdin}) + parser.error('no data file specified') else: for name in filenames: if os.path.exists(name): - files.append({'name':name, 'handle':open(name)}) + files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w')}) # ------------------------------------------ loop over input files --------------------------------------- for file in files: print file['name'] - content = file['handle'].readlines() - file['handle'].close() - # get labels by either read the first row, or - if keyword header is present - the last line of the header - headerlines = 1 - m = re.search('(\d+)\s*head', content[0].lower()) + firstline = file['input'].readline() + m = re.search('(\d+)\s*head', firstline.lower()) if m: headerlines = int(m.group(1)) - passOn = content[1:headerlines] - headers = content[headerlines].split() - data = content[headerlines+1:] - - regexp = re.compile('1_\d+_') + passOn = [file['input'].readline() for i in range(1,headerlines)] + headers = file['input'].readline().split() + else: + headerlines = 1 + passOn = [] + headers = firstline.split() + + data = file['input'].readlines() + for i,l in enumerate(headers): - if regexp.match(l): - headers[i] = l[2:] + if l.startswith('1_'): + if re.match('\d+_',l[2:]) or i == len(headers)-1 or not headers[i+1].endswith(l[2:]): + headers[i] = l[2:] active = {} column = {} - values = {} 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 headers: - print 'column %s not found...'%key + sys.stderr.write('column %s not found...\n'%key) else: if datatype not in active: active[datatype] = [] if datatype not in column: column[datatype] = {} active[datatype].append(label) - column[datatype][label]=headers.index(key) + column[datatype][label] = headers.index(key) + if options.shape: head += 'mismatch_shape(%s)'%label + if options.volume: head += 'mismatch_volume(%s)'%label - defgrad = numpy.array([0.0 for i in range(9*options.res[0]*options.res[1]*options.res[2])]).\ - reshape((options.res[0],options.res[1],options.res[2],3,3)) - - if options.shape: head += ['shape_mismatch'] - if options.volume: head += ['volume_mismatch'] - # ------------------------------------------ assemble header --------------------------------------- output = '%i\theader'%(headerlines+1) + '\n' + \ - ''.join(passOn) + \ + ''.join(passOn) + \ string.replace('$Id$','\n','\\n')+ '\t' + \ ' '.join(sys.argv[1:]) + '\n' + \ '\t'.join(headers + head) + '\n' # build extended header -# ------------------------------------------ read value field --------------------------------------- + +# ------------------------------------------ read deformation tensors --------------------------------------- + + for datatype,labels in active.items(): + for label in labels: + defgrad[label] = numpy.array([0.0 for i in xrange(9*options.res[0]*options.res[1]*options.res[2])],'d').\ + reshape((options.res[0],options.res[1],options.res[2],3,3)) + + idx = 0 + for line in data: + items = line.split()[:len(headers)] # take only valid first items + if len(items) < len(headers): # too short lines get dropped + continue + + defgrad[label][location(idx,options.res)[0]]\ + [location(idx,options.res)[1]]\ + [location(idx,options.res)[2]]\ + = numpy.array(map(float,items[column[datatype][label]: + column[datatype][label]+datainfo[datatype]['len']]),'d').reshape(3,3) + defgrad_av[label] = postprocessingMath.tensor_avg(options.res[0],options.res[1],options.res[2],defgrad[label]) + centroids[label] = postprocessingMath.deformed_fft(options.res[0],options.res[1],options.res[2],options.dim,defgrad[label],defgrad_av[label],1.0) + nodes[label] = postprocessingMath.mesh(options.res[0],options.res[1],options.res[2],options.dim,defgrad_av[label],centroids[label]) + if options.shape: shape_mismatch[label] = postprocessingMath.shape_compare( options.res[0],options.res[1],options.res[2],options.dim,nodes[label],centroids[label],defgrad[label]) + if options.volume: volume_mismatch[label] = postprocessingMath.volume_compare(options.res[0],options.res[1],options.res[2],options.dim,nodes[label], defgrad[label]) + idx += 1 + +# ------------------------------------------ read file --------------------------------------- idx = 0 for line in data: items = line.split()[:len(headers)] if len(items) < len(headers): continue + + output += '\t'.join(items) + for datatype,labels in active.items(): for label in labels: - defgrad[location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]]\ - = numpy.reshape(items[column[datatype][label]:column[datatype][label]+9],(3,3)) - idx += 1 - defgrad_av = postprocessingMath.tensor_avg(options.res[0],options.res[1],options.res[2],defgrad) - centroids = postprocessingMath.deformed_fft(options.res[0],options.res[1],options.res[2],options.dim,defgrad,defgrad_av,1.0) - nodes = postprocessingMath.mesh(options.res[0],options.res[1],options.res[2],options.dim,defgrad_av,centroids) -# ------------------------------------------ read file --------------------------------------- - if options.shape: - shape_mismatch = postprocessingMath.shape_compare(options.res[0],options.res[1],options.res[2],options.dim,nodes,centroids,defgrad) - if options.volume: - volume_mismatch = postprocessingMath.volume_compare(options.res[0],options.res[1],options.res[2],options.dim,nodes,defgrad) - idx = 0 - for line in data: - items = line.split()[:len(headers)] - if len(items) < len(headers): - continue - - output += '\t'.join(items) - - for datatype,labels in active.items(): - if options.shape: - output += '\t%f'%shape_mismatch[location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]] - if options.volume: - output += '\t%f'%volume_mismatch[location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]] - output += '\n' - idx += 1 + + if options.shape: output += '\t%f'%shape_mismatch[label][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]] + if options.volume: output += '\t%f'%volume_mismatch[label][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]] + + output += '\n' + idx += 1 + + file['input'].close() + # ------------------------------------------ output result --------------------------------------- - if file['name'] == 'STDIN': - print output - else: - file['handle'] = open(file['name']+'_tmp','w') - try: - file['handle'].write(output) - file['handle'].close() - os.rename(file['name']+'_tmp',file['name']) - except: - print 'error during writing',file['name']+'_tmp' + file['output'].write(output) + file['output'].close + os.rename(file['name']+'_tmp',file['name'])