119 lines
6.4 KiB
Python
Executable File
119 lines
6.4 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,re,sys,math,string
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from collections import defaultdict
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from optparse import OptionParser
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import damask
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scriptID = '$Id$'
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scriptName = scriptID.split()[1]
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# definition of element-wise p-norms for matrices
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def normAbs(object): # p = 1
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return sum(map(abs, object))
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def normFrobenius(object): # p = 2
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return math.sqrt(sum([x*x for x in object]))
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def normMax(object): # p = infinity
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return max(map(abs, object))
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Add column(s) containing norm of requested column(s) being either vectors or tensors.
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""", version = string.replace(scriptID,'\n','\\n')
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)
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normChoices = ['abs','frobenius','max']
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parser.add_option('-n','--norm', dest='norm', action='store', type='choice', choices=normChoices, metavar='string',
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help='type of element-wise p-norm (%s) [frobenius]'%(','.join(map(str,normChoices))))
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parser.add_option('-v','--vector', dest='vector', action='extend', type='string', metavar='<string LIST>',
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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', metavar='<string LIST>',
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help='heading of columns containing tensor field values')
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parser.add_option('-s','--special', dest='special', action='extend', type='string', metavar='<string LIST>',
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help='heading of columns containing field values of special dimension')
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parser.add_option('-d','--dimension', dest='N', action='store', type='int', metavar='int',
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help='dimension of special field values [%default]')
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parser.set_defaults(norm = 'frobenius')
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parser.set_defaults(vector = [])
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parser.set_defaults(tensor = [])
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parser.set_defaults(special = [])
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parser.set_defaults(N = 12)
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(options,filenames) = parser.parse_args()
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if len(options.vector) + len(options.tensor) + len(options.special)== 0:
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parser.error('no data column specified...')
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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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'special': {'len':options.N,
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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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if options.special != None: datainfo['special']['label'] += options.special
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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, 'croak':sys.stderr})
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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'), 'croak':sys.stderr})
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#--- loop over input files ------------------------------------------------------------------------
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for file in files:
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if file['name'] != 'STDIN': file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
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else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
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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(scriptID,'\n','\\n') + '\t' + ' '.join(sys.argv[1:]))
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active = defaultdict(list)
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column = defaultdict(dict)
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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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file['croak'].write('column %s not found...\n'%key)
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else:
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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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# ------------------------------------------ assemble header ---------------------------------------
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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 determinants
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table.labels_append('norm%s(%s)'%(options.norm.capitalize(),label)) # extend ASCII header with new labels
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table.head_write()
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# ------------------------------------------ process data ---------------------------------------
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outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
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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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eval("table.data_append(norm%s(map(float,table.data[column[datatype][label]:column[datatype][label]+datainfo[datatype]['len']])))"%options.norm.capitalize())
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ output result ---------------------------------------
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outputAlive and table.output_flush() # just in case of buffered ASCII table
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file['input'].close() # close input ASCII table (works for stdin)
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file['output'].close() # close output ASCII table (works for stdout)
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if file['name'] != 'STDIN':
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os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
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