not needed anymore
This commit is contained in:
Martin Diehl
parent
77b68ab86f
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#!/usr/bin/env python3
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import os
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import sys
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from optparse import OptionParser
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import re
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from collections.abc import Iterable
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import math # noqa
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import scipy # noqa
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import scipy.linalg # noqa
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import numpy as np
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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def listify(x):
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return x if isinstance(x, Iterable) else [x]
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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 [ASCIItable(s)]', description = """
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Add or alter column(s) with derived values according to user-defined arithmetic operation between column(s).
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Column labels are tagged by '#label#' in formulas. Use ';' for ',' in functions.
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Numpy is available as 'np'.
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Special variables: #_row_# -- row index
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Examples:
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(1) magnitude of vector -- "np.linalg.norm(#vec#)"
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(2) rounded root of row number -- "round(math.sqrt(#_row_#);3)"
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""", version = scriptID)
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parser.add_option('-l','--label',
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dest = 'labels',
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action = 'extend', metavar = '<string LIST>',
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help = '(list of) new column labels')
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parser.add_option('-f','--formula',
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dest = 'formulas',
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action = 'extend', metavar = '<string LIST>',
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help = '(list of) formulas corresponding to labels')
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parser.add_option('-c','--condition',
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dest = 'condition', metavar='string',
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help = 'condition to alter existing column data (optional)')
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(options,filenames) = parser.parse_args()
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if options.labels is None or options.formulas is None:
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parser.error('no formulas and/or labels specified.')
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if len(options.labels) != len(options.formulas):
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parser.error('number of labels ({}) and formulas ({}) do not match.'.format(len(options.labels),len(options.formulas)))
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for i in range(len(options.formulas)):
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options.formulas[i] = options.formulas[i].replace(';',',')
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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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except IOError:
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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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# --------------------------------------------------------------------------------------------------
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specials = { \
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'_row_': 0,
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}
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# --------------------------------------- evaluate condition ---------------------------------------
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if options.condition is not None:
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condition = options.condition # copy per file, since might be altered inline
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breaker = False
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for position,(all,marker,column) in enumerate(set(re.findall(r'#(([s]#)?(.+?))#',condition))): # find three groups
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idx = table.label_index(column)
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dim = table.label_dimension(column)
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if idx < 0 and column not in specials:
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damask.util.croak('column "{}" not found.'.format(column))
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breaker = True
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else:
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if column in specials:
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replacement = 'specials["{}"]'.format(column)
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elif dim == 1: # scalar input
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replacement = '{}(table.data[{}])'.format({ '':'float',
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's#':'str'}[marker],idx) # take float or string value of data column
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elif dim > 1: # multidimensional input (vector, tensor, etc.)
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replacement = 'np.array(table.data[{}:{}],dtype=float)'.format(idx,idx+dim) # use (flat) array representation
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condition = condition.replace('#'+all+'#',replacement)
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if breaker: continue # found mistake in condition evaluation --> next file
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# ------------------------------------------ build formulas ----------------------------------------
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evaluator = {}
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for label,formula in zip(options.labels,options.formulas):
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for column in re.findall(r'#(.+?)#',formula): # loop over column labels in formula
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idx = table.label_index(column)
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dim = table.label_dimension(column)
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if column in specials:
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replacement = 'specials["{}"]'.format(column)
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elif dim == 1: # scalar input
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replacement = 'float(table.data[{}])'.format(idx) # take float value of data column
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elif dim > 1: # multidimensional input (vector, tensor, etc.)
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replacement = 'np.array(table.data[{}:{}],dtype=float)'.format(idx,idx+dim) # use (flat) array representation
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else:
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damask.util.croak('column {} not found, skipping {}...'.format(column,label))
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options.labels.remove(label)
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break
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formula = formula.replace('#'+column+'#',replacement)
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evaluator[label] = formula
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# ---------------------------- separate requested labels into old and new --------------------------
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veterans = list(set(options.labels)&set(table.labels(raw=False)+table.labels(raw=True)) ) # intersection of requested and existing
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newbies = list(set(options.labels)-set(table.labels(raw=False)+table.labels(raw=True)) ) # requested but not existing
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# ------------------------------------------ process data ------------------------------------------
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firstLine = True
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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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specials['_row_'] += 1 # count row
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if firstLine:
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firstLine = False
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# ---------------------------- line 1: determine dimension of formulas -----------------------------
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resultDim = {}
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for label in list(options.labels): # iterate over stable copy
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resultDim[label] = np.size(eval(evaluator[label])) # get dimension of formula[label]
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if resultDim[label] == 0: options.labels.remove(label) # remove label if invalid result
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for veteran in list(veterans):
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if resultDim[veteran] != table.label_dimension(veteran):
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damask.util.croak('skipping {} due to inconsistent dimension...'.format(veteran))
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veterans.remove(veteran) # discard culprit
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# ----------------------------------- line 1: assemble header --------------------------------------
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for newby in newbies:
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table.labels_append(['{}_{}'.format(i+1,newby) for i in range(resultDim[newby])]
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if resultDim[newby] > 1 else newby)
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.head_write()
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# -------------------------------------- evaluate formulas -----------------------------------------
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if options.condition is None or eval(condition): # condition for veteran replacement fulfilled
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for veteran in veterans: # evaluate formulas that overwrite
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table.data[table.label_index(veteran):
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table.label_index(veteran)+table.label_dimension(veteran)] = \
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listify(eval(evaluator[veteran]))
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for newby in newbies: # evaluate formulas that append
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table.data_append(listify(eval(evaluator[newby])))
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outputAlive = table.data_write() # output processed line
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# ------------------------------------- output finalization ----------------------------------------
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table.close() # close ASCII table
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@ -1,75 +0,0 @@
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#!/usr/bin/env python3
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import os
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import sys
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from io import StringIO
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from optparse import OptionParser
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import numpy as np
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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# definition of element-wise p-norms for matrices
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# ToDo: better use numpy.linalg.norm
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def norm(which,object):
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if which == 'Abs': # p = 1
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return sum(map(abs, object))
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elif which == 'Frobenius': # p = 2
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return np.sqrt(sum([x*x for x in object]))
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elif which == 'Max': # p = inf
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return max(map(abs, object))
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else:
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return -1
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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 [ASCIItable(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 = scriptID)
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normChoices = ['abs','frobenius','max']
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parser.add_option('-n','--norm',
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dest = 'norm',
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type = 'choice', choices = normChoices, metavar='string',
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help = 'type of element-wise p-norm [frobenius] {%s}'%(','.join(map(str,normChoices))))
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parser.add_option('-l','--label',
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dest = 'labels',
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action = 'extend', metavar = '<string LIST>',
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help = 'heading of column(s) to calculate norm of')
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parser.set_defaults(norm = 'frobenius',
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)
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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if options.norm.lower() not in normChoices:
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parser.error('invalid norm ({}) specified.'.format(options.norm))
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if options.labels is None:
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parser.error('no data column specified.')
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for name in filenames:
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damask.util.report(scriptName,name)
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table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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for label in options.labels:
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data = table.get(label)
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data_norm = np.empty((data.shape[0],1))
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for i,d in enumerate(data):
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data_norm[i] = norm(options.norm.capitalize(),d)
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table.add('norm{}({})'.format(options.norm.capitalize(),label),
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data_norm,
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scriptID+' '+' '.join(sys.argv[1:]))
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table.to_ASCII(sys.stdout if name is None else name)
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