changed to new structure (using damask module)
This commit is contained in:
Martin Diehl
parent
5a658d1b82
commit
27a976c04f
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@ -23,29 +23,20 @@ class extendableOption(Option):
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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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( 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[0] * res[1] )
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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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def prefixMultiply(what,len):
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return {True: ['%i_%s'%(i+1,what) for i in range(len)],
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False:[what]}[len>1]
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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FDcoefficients = [ \
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[1.0/2.0, 0.0, 0.0, 0.0],\
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[2.0/3.0,-1.0/12.0, 0.0, 0.0],\
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[3.0/4.0,-3.0/20.0,1.0/ 60.0, 0.0],\
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[4.0/5.0,-1.0/ 5.0,4.0/105.0,-1.0/280.0],\
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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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@ -59,8 +50,6 @@ parser.add_option('--fdm', dest='accuracy', action='extend', type='
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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('-m','--memory', dest='memory', action='store_true', \
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help='memory efficient calculation (not possible for FFT based divergency [%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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@ -69,16 +58,13 @@ 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('-s','--skip', dest='skip', type='int', nargs=3, \
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help='items skipped due to periodicity in x (fast) y z (slow)')
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parser.set_defaults(accuracy = [])
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parser.set_defaults(memory = False)
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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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parser.set_defaults(skip = [0,0,0])
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accuracyChoices = [2,4,6,8]
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(options,filenames) = parser.parse_args()
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@ -89,12 +75,14 @@ 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 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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resSkip = map(lambda (a,b): a+b,zip(options.res,options.skip))
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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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@ -109,174 +97,102 @@ if options.tensor != None: datainfo['tensor']['label'] += options.tensor
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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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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, '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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if file['name'] != 'STDIN': 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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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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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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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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div_field ={}
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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 headers:
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print 'column %s not found...'%key
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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 div_field: div_field[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] = headers.index(key)
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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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3,datainfo[datatype]['len']//3))
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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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for what in options.accuracy: # loop over all requested degrees of accuracy (plus potentially fft)
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if not options.memory or what != 'fft': # FFT divergence excluded in memory saving mode
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head += prefixMultiply('div%s(%s)'%(what,label),datainfo[datatype]['len']//3)
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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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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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table.head_write()
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# ------------------------------------------ read value field ---------------------------------------
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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): # skip too short lines (probably comments or invalid)
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continue
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locSkip = location(idx,resSkip)
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if ( locSkip[0] < options.res[0]
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and locSkip[1] < options.res[1]
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and locSkip[2] < options.res[2] ): # only take values that are not periodic images
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for datatype,labels in active.items():
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for label in labels:
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values[datatype][label][locSkip[0]][locSkip[1]][locSkip[2]]\
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= numpy.reshape(items[column[datatype][label]:
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column[datatype][label]+datainfo[datatype]['len']],(3,datainfo[datatype]['len']//3))
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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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# ------------------------------------------ read file ---------------------------------------
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if options.memory:
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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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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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output += '\t'.join(items)
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(x,y,z) = location(idx,options.res)
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for datatype,labels in active.items():
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for label in labels:
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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': continue
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for k in range(datainfo[datatype]['len']/3): # formulas from Mikhail Itskov: Tensor Algebra and Tensor Analysis for Engineers, Springer 2009, p 52
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theDiv = 0.0
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for a in range(int(accuracy)//2):
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theDiv += FDcoefficients[int(accuracy)//2-1][a] * \
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( \
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(values[datatype][label][location(index([x+1+a,y,z],options.res),options.res)[0]] \
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[location(index([x+1+a,y,z],options.res),options.res)[1]] \
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[location(index([x+1+a,y,z],options.res),options.res)[2]][k][0] - \
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values[datatype][label][location(index([x-1-a,y,z],options.res),options.res)[0]] \
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[location(index([x-1-a,y,z],options.res),options.res)[1]] \
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[location(index([x-1-a,y,z],options.res),options.res)[2]][k][0]) * options.res[0] / options.dim[0] + \
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(values[datatype][label][location(index([x,y+1+a,z],options.res),options.res)[0]] \
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[location(index([x,y+1+a,z],options.res),options.res)[1]] \
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[location(index([x,y+1+a,z],options.res),options.res)[2]][k][1] - \
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values[datatype][label][location(index([x,y-1-a,z],options.res),options.res)[0]] \
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[location(index([x,y-1-a,z],options.res),options.res)[1]] \
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[location(index([x,y-1-a,z],options.res),options.res)[2]][k][1]) * options.res[1] / options.dim[1] + \
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(values[datatype][label][location(index([x,y,z+1+a],options.res),options.res)[0]] \
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[location(index([x,y,z+1+a],options.res),options.res)[1]] \
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[location(index([x,y,z+1+a],options.res),options.res)[2]][k][2]- \
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values[datatype][label][location(index([x,y,z-1-a],options.res),options.res)[0]] \
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[location(index([x,y,z-1-a],options.res),options.res)[1]] \
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[location(index([x,y,z-1-a],options.res),options.res)[2]][k][2]) * options.res[2] / options.dim[2] \
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)
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output += '\t%f'%theDiv
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output += '\n'
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idx += 1
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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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for datatype,labels in active.items():
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for label in labels:
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if label not in div_field[datatype]: div_field[datatype][label] = {}
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for accuracy in options.accuracy:
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div_field[datatype][label][accuracy] = numpy.array([0.0 for i in range((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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if accuracy == 'fft':
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div_field[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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div_field[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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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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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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for accuracy in options.accuracy:
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for i in range(datainfo[datatype]['len']/3):
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output += '\t%f'%div_field[datatype][label][accuracy][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]][i]
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output += '\n'
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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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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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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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