96 lines
3.3 KiB
Python
Executable File
96 lines
3.3 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string
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import numpy as np
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from optparse import OptionParser
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import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
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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 cumulative (sum of first to current row) values for given label(s).
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""", version = scriptID)
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parser.add_option('-l','--label',
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dest='label',
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action = 'extend', metavar = '<string LIST>',
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help = 'columns to cumulate')
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parser.set_defaults(label = [],
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)
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(options,filenames) = parser.parse_args()
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if len(options.label) == 0:
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parser.error('no data column(s) specified.')
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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remarks = []
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columns = []
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dims = []
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for what in options.label:
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dim = table.label_dimension(what)
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if dim < 0: remarks.append('column {} not found...'.format(what))
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else:
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dims.append(dim)
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columns.append(table.label_index(what))
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table.labels_append('cum({})'.format(what) if dim == 1 else
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['{}_cum({})'.format(i+1,what) for i in xrange(dim)] ) # extend ASCII header with new labels
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header ---------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.head_write()
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# ------------------------------------------ process data ------------------------------------------
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table.data_readArray()
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mask = []
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for col,dim in zip(columns,dims): mask += range(col,col+dim) # isolate data columns to cumulate
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cumulated = np.zeros((len(table.data),len(mask))) # prepare output field
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for i,values in enumerate(table.data[:,mask]):
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cumulated[i,:] = cumulated[max(0,i-1),:] + values # cumulate values
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table.data = np.hstack((table.data,cumulated))
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# ------------------------------------------ output result -----------------------------------------
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table.data_writeArray()
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close ASCII tables
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