199 lines
9.6 KiB
Python
Executable File
199 lines
9.6 KiB
Python
Executable File
#!/usr/bin/python
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import os,re,sys,math,string,numpy,damask
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from optparse import OptionParser, Option
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# -----------------------------
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class extendableOption(Option):
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# -----------------------------
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# used for definition of new option parser action 'extend', which enables to take multiple option arguments
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# taken from online tutorial http://docs.python.org/library/optparse.html
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ACTIONS = Option.ACTIONS + ("extend",)
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STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
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TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
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ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
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def take_action(self, action, dest, opt, value, values, parser):
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if action == "extend":
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lvalue = value.split(",")
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values.ensure_value(dest, []).extend(lvalue)
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else:
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Option.take_action(self, action, dest, opt, value, values, parser)
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def location(idx,res):
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return ( idx % res[0], \
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( idx // res[0]) % res[1], \
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( idx // res[0] // res[1]) % res[2] )
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def index(location,res):
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return ( location[0] % res[0] + \
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( location[1] % res[1]) * res[0] + \
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( location[2] % res[2]) * res[1] * res[0] )
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# --------------------------------------------------------------------
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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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Add column(s) containing divergence of requested column(s).
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Operates on periodic ordered three-dimensional data sets.
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Deals with both vector- and tensor-valued fields.
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""" + string.replace('$Id$','\n','\\n')
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)
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parser.add_option('--fdm', dest='accuracy', action='extend', type='string', \
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help='degree of central difference accuracy')
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parser.add_option('--fft', dest='fft', action='store_true', \
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help='calculate divergence in Fourier space [%default]')
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parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \
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help='heading of columns containing vector field values')
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parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \
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help='heading of columns containing tensor field values')
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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.set_defaults(accuracy = [])
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parser.set_defaults(fft = False)
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parser.set_defaults(vector = [])
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parser.set_defaults(tensor = [])
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parser.set_defaults(dim = [])
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accuracyChoices = [2,4,6,8]
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(options,filenames) = parser.parse_args()
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if len(options.vector) + len(options.tensor) == 0:
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parser.error('no data column specified...')
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if len(options.dim) < 3:
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parser.error('improper dimension specification...')
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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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for choice in options.accuracy:
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if int(choice) not in accuracyChoices:
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parser.error('accuracy must be chosen from %s...'%(', '.join(accuracyChoices)))
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if options.fft: options.accuracy.append('FFT')
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if not options.accuracy:
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parser.error('no accuracy selected')
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datainfo = { # list of requested labels per datatype
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'vector': {'len':3,
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'label':[]},
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'tensor': {'len':9,
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'label':[]},
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}
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if options.vector != None: datainfo['vector']['label'] += options.vector
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if options.tensor != None: datainfo['tensor']['label'] += options.tensor
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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', 'input':sys.stdin, 'output':sys.stdout})
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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, '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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if file['name'] != 'STDIN': print file['name']
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table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
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table.head_read() # read ASCII header info
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table.info_append(string.replace('$Id$','\n','\\n') + \
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'\t' + ' '.join(sys.argv[1:]))
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active = {}
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column = {}
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values = {}
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divergence = {}
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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 table.labels:
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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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if datatype not in values: values[datatype] = {}
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if datatype not in divergence: divergence[datatype] = {}
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if label not in divergence[datatype]: divergence[datatype][label] = {}
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active[datatype].append(label)
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column[datatype][label] = table.labels.index(key) # remember columns of requested data
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values[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\
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options.res[0]*options.res[1]*options.res[2])]).\
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reshape((options.res[0],options.res[1],options.res[2],\
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datainfo[datatype]['len']//3,3))
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for accuracy in options.accuracy:
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divergence[datatype][label][accuracy] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']//3*\
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options.res[0]*options.res[1]*options.res[2])]).\
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reshape((options.res[0],options.res[1],options.res[2],\
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datainfo[datatype]['len']//3))
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table.labels_append(['%i_div%s(%s)'%(i+1,accuracy,label)
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for i in xrange(datainfo[datatype]['len']//3)]) # extend ASCII header with new labels
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# ------------------------------------------ assemble header ---------------------------------------
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table.head_write()
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# ------------------------------------------ read value field ---------------------------------------
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idx = 0
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while table.data_read(): # read next data line of ASCII table
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(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count
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idx += 1
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested curls
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values[datatype][label][x,y,z] = numpy.array(
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map(float,table.data[column[datatype][label]:
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column[datatype][label]+datainfo[datatype]['len']]),'d').reshape(datainfo[datatype]['len']//3,3)
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# ------------------------------------------ process value field ---------------------------------------
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested divergencies
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for accuracy in options.accuracy:
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if accuracy == 'FFT':
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divergence[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
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else:
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divergence[datatype][label][accuracy] = damask.core.math.divergence_fdm(options.res,options.dim,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label])
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# ------------------------------------------ process data ---------------------------------------
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table.data_rewind()
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idx = 0
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while table.data_read(): # read next data line of ASCII table
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(x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count
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idx += 1
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested
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for accuracy in options.accuracy:
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table.data_append(list(divergence[datatype][label][accuracy][x,y,z].reshape(datainfo[datatype]['len']//3)))
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table.data_write() # output processed line
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# ------------------------------------------ output result ---------------------------------------
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table.output_flush() # just in case of buffered ASCII table
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file['input'].close() # close input ASCII table
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if file['name'] != 'STDIN':
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file['output'].close # close output ASCII table
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os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
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