reworked to match former script layouts and logics. (not yet tested, use at your own risk)
This commit is contained in:
Philip Eisenlohr
parent
282d2d7b97
commit
7aba05ed9f
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@ -36,7 +36,7 @@ def index(location,res):
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
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parser = OptionParser(option_class=extendableOption, usage='%prog options file[s]', description = """
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Add column containing debug information
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Operates on periodic ordered three-dimensional data sets.
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@ -45,137 +45,149 @@ Operates on periodic ordered three-dimensional data sets.
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parser.add_option('--no-shape','-s', dest='shape', action='store_false', \
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help='calcuate mismatch of shape [%default]')
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help='do not calcuate shape mismatch [%default]')
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parser.add_option('--no-volume','-v', dest='volume', action='store_false', \
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help='calculate mismatch of volume [%default]')
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help='do not calculate volume mismatch [%default]')
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parser.add_option('-d','--dimension', dest='dim', type='float', nargs=3, \
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help='physical dimension of data set in x (fast) y z (slow) [%default]')
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parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \
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help='resolution of data set in x (fast) y z (slow)')
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parser.add_option('-f','--deformation', dest='defgrad', action='extend', type='string', \
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help='heading(s) of columns containing deformation tensor values %default')
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parser.set_defaults(volume = True)
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parser.set_defaults(shape = True)
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parser.set_defaults(defgrad = ['f'])
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(options,filenames) = parser.parse_args()
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if not options.res or len(options.res) < 3:
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parser.error('improper resolution specification...')
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if not options.dim or len(options.dim) < 3:
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parser.error('improper resolution specification...')
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parser.error('improper dimension specification...')
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defgrad = {}
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defgrad_av = {}
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centroids = {}
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nodes = {}
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datainfo = { # list of requested labels per datatype
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'tensor': {'len':9,
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'label':[]},
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'defgrad': {'len':9,
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'label':[]},
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}
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datainfo['tensor']['label'] += 'f'
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if options.defgrad != None: datainfo['defgrad']['label'] += options.defgrad
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# ------------------------------------------ setup file handles ---------------------------------------
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files = []
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if filenames == []:
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files.append({'name':'STDIN', 'handle':sys.stdin})
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parser.error('no data file specified')
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else:
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for name in filenames:
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if os.path.exists(name):
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files.append({'name':name, 'handle':open(name)})
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files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w')})
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# ------------------------------------------ loop over input files ---------------------------------------
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for file in files:
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print file['name']
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content = file['handle'].readlines()
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file['handle'].close()
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# get labels by either read the first row, or - if keyword header is present - the last line of the header
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headerlines = 1
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m = re.search('(\d+)\s*head', content[0].lower())
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firstline = file['input'].readline()
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m = re.search('(\d+)\s*head', firstline.lower())
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if m:
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headerlines = int(m.group(1))
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passOn = content[1:headerlines]
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headers = content[headerlines].split()
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data = content[headerlines+1:]
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regexp = re.compile('1_\d+_')
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passOn = [file['input'].readline() for i in range(1,headerlines)]
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headers = file['input'].readline().split()
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else:
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headerlines = 1
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passOn = []
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headers = firstline.split()
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data = file['input'].readlines()
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for i,l in enumerate(headers):
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if regexp.match(l):
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headers[i] = l[2:]
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if l.startswith('1_'):
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if re.match('\d+_',l[2:]) or i == len(headers)-1 or not headers[i+1].endswith(l[2:]):
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headers[i] = l[2:]
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active = {}
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column = {}
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values = {}
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head = []
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for datatype,info in datainfo.items():
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for label in info['label']:
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key = {True :'1_%s',
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False:'%s' }[info['len']>1]%label
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if key not in headers:
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print 'column %s not found...'%key
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sys.stderr.write('column %s not found...\n'%key)
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else:
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if datatype not in active: active[datatype] = []
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if datatype not in column: column[datatype] = {}
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active[datatype].append(label)
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column[datatype][label]=headers.index(key)
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column[datatype][label] = headers.index(key)
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if options.shape: head += 'mismatch_shape(%s)'%label
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if options.volume: head += 'mismatch_volume(%s)'%label
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defgrad = numpy.array([0.0 for i in range(9*options.res[0]*options.res[1]*options.res[2])]).\
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reshape((options.res[0],options.res[1],options.res[2],3,3))
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if options.shape: head += ['shape_mismatch']
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if options.volume: head += ['volume_mismatch']
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# ------------------------------------------ assemble header ---------------------------------------
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output = '%i\theader'%(headerlines+1) + '\n' + \
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''.join(passOn) + \
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''.join(passOn) + \
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string.replace('$Id$','\n','\\n')+ '\t' + \
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' '.join(sys.argv[1:]) + '\n' + \
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'\t'.join(headers + head) + '\n' # build extended header
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# ------------------------------------------ read value field ---------------------------------------
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# ------------------------------------------ read deformation tensors ---------------------------------------
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for datatype,labels in active.items():
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for label in labels:
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defgrad[label] = numpy.array([0.0 for i in xrange(9*options.res[0]*options.res[1]*options.res[2])],'d').\
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reshape((options.res[0],options.res[1],options.res[2],3,3))
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idx = 0
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for line in data:
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items = line.split()[:len(headers)] # take only valid first items
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if len(items) < len(headers): # too short lines get dropped
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continue
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defgrad[label][location(idx,options.res)[0]]\
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[location(idx,options.res)[1]]\
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[location(idx,options.res)[2]]\
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= numpy.array(map(float,items[column[datatype][label]:
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column[datatype][label]+datainfo[datatype]['len']]),'d').reshape(3,3)
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defgrad_av[label] = postprocessingMath.tensor_avg(options.res[0],options.res[1],options.res[2],defgrad[label])
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centroids[label] = postprocessingMath.deformed_fft(options.res[0],options.res[1],options.res[2],options.dim,defgrad[label],defgrad_av[label],1.0)
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nodes[label] = postprocessingMath.mesh(options.res[0],options.res[1],options.res[2],options.dim,defgrad_av[label],centroids[label])
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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])
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if options.volume: volume_mismatch[label] = postprocessingMath.volume_compare(options.res[0],options.res[1],options.res[2],options.dim,nodes[label], defgrad[label])
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idx += 1
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# ------------------------------------------ read file ---------------------------------------
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idx = 0
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for line in data:
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items = line.split()[:len(headers)]
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if len(items) < len(headers):
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continue
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output += '\t'.join(items)
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for datatype,labels in active.items():
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for label in labels:
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defgrad[location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]]\
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= numpy.reshape(items[column[datatype][label]:column[datatype][label]+9],(3,3))
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idx += 1
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defgrad_av = postprocessingMath.tensor_avg(options.res[0],options.res[1],options.res[2],defgrad)
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centroids = postprocessingMath.deformed_fft(options.res[0],options.res[1],options.res[2],options.dim,defgrad,defgrad_av,1.0)
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nodes = postprocessingMath.mesh(options.res[0],options.res[1],options.res[2],options.dim,defgrad_av,centroids)
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# ------------------------------------------ read file ---------------------------------------
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if options.shape:
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shape_mismatch = postprocessingMath.shape_compare(options.res[0],options.res[1],options.res[2],options.dim,nodes,centroids,defgrad)
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if options.volume:
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volume_mismatch = postprocessingMath.volume_compare(options.res[0],options.res[1],options.res[2],options.dim,nodes,defgrad)
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idx = 0
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for line in data:
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items = line.split()[:len(headers)]
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if len(items) < len(headers):
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continue
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output += '\t'.join(items)
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for datatype,labels in active.items():
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if options.shape:
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output += '\t%f'%shape_mismatch[location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]]
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if options.volume:
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output += '\t%f'%volume_mismatch[location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]]
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output += '\n'
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idx += 1
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if options.shape: output += '\t%f'%shape_mismatch[label][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]]
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if options.volume: output += '\t%f'%volume_mismatch[label][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]]
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output += '\n'
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idx += 1
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file['input'].close()
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# ------------------------------------------ output result ---------------------------------------
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if file['name'] == 'STDIN':
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print output
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else:
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file['handle'] = open(file['name']+'_tmp','w')
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try:
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file['handle'].write(output)
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file['handle'].close()
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os.rename(file['name']+'_tmp',file['name'])
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except:
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print 'error during writing',file['name']+'_tmp'
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file['output'].write(output)
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file['output'].close
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os.rename(file['name']+'_tmp',file['name'])
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