adopted ASCIItable class and checked correctness of results.
This commit is contained in:
Philip Eisenlohr
parent
acf7c86531
commit
b134ec7a39
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@ -29,11 +29,8 @@ def location(idx,res):
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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[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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@ -87,121 +84,97 @@ 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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curl_field ={}
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curl = {}
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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 curl_field: curl_field[datatype] = {}
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if datatype not in curl: curl[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] = 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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curl[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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table.labels_append(['%i_curlFFT(%s)'%(i+1,label)
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for i in xrange(datainfo[datatype]['len'])]) # extend ASCII header with new labels
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head += prefixMultiply('curlfft(%s)'%(label),datainfo[datatype]['len'])
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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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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 curl_field[datatype]: curl_field[datatype][label] = {}
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curl_field[datatype][label] = numpy.array([0.0 for i in range((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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curl_field[datatype][label] = damask.core.math.curl_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
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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 curls
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curl[datatype][label] = damask.core.math.curl_fft(options.res,options.dim,datainfo[datatype]['len']//3,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 i in range(3):
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for j in range(datainfo[datatype]['len']//3):
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output += '\t%f'%curl_field[datatype][label][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]][i][j]
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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 norms
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table.data_append(list(curl[datatype][label][x,y,z].reshape(datainfo[datatype]['len'])))
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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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