DAMASK_EICMD/processing/post/addCalculation.py

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#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,re,sys,collections
import math,scipy,scipy.linalg # noqa
import numpy as np
from optparse import OptionParser
import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
def listify(x):
return x if isinstance(x, collections.Iterable) else [x]
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Add or alter column(s) with derived values according to user-defined arithmetic operation between column(s).
Column labels are tagged by '#label#' in formulas. Use ';' for ',' in functions.
Numpy is available as 'np'.
Special variables: #_row_# -- row index
Examples:
(1) magnitude of vector -- "np.linalg.norm(#vec#)"
(2) rounded root of row number -- "round(math.sqrt(#_row_#);3)"
""", version = scriptID)
parser.add_option('-l','--label',
dest = 'labels',
action = 'extend', metavar = '<string LIST>',
help = '(list of) new column labels')
parser.add_option('-f','--formula',
dest = 'formulas',
action = 'extend', metavar = '<string LIST>',
help = '(list of) formulas corresponding to labels')
parser.add_option('-c','--condition',
dest = 'condition', metavar='string',
help = 'condition to alter existing column data')
parser.set_defaults(condition = None,
)
(options,filenames) = parser.parse_args()
if options.labels is None or options.formulas is None:
parser.error('no formulas and/or labels specified.')
if len(options.labels) != len(options.formulas):
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)):
options.formulas[i] = options.formulas[i].replace(';',',')
# ------------------------------------- loop over input files --------------------------------------
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if filenames == []: filenames = [None]
for name in filenames:
try: table = damask.ASCIItable(name = name,
buffered = False)
except: continue
damask.util.report(scriptName,name)
# ------------------------------------------ read header -------------------------------------------
table.head_read()
# --------------------------------------------------------------------------------------------------
specials = { \
'_row_': 0,
}
# --------------------------------------- evaluate condition ---------------------------------------
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if options.condition is not None:
condition = options.condition # copy per file, since might be altered inline
breaker = False
for position,(all,marker,column) in enumerate(set(re.findall(r'#(([s]#)?(.+?))#',condition))): # find three groups
idx = table.label_index(column)
dim = table.label_dimension(column)
if idx < 0 and column not in specials:
damask.util.croak('column "{}" not found.'.format(column))
breaker = True
else:
if column in specials:
replacement = 'specials["{}"]'.format(column)
elif dim == 1: # scalar input
replacement = '{}(table.data[{}])'.format({ '':'float',
's#':'str'}[marker],idx) # take float or string value of data column
elif dim > 1: # multidimensional input (vector, tensor, etc.)
replacement = 'np.array(table.data[{}:{}],dtype=float)'.format(idx,idx+dim) # use (flat) array representation
condition = condition.replace('#'+all+'#',replacement)
if breaker: continue # found mistake in condition evaluation --> next file
# ------------------------------------------ build formulas ----------------------------------------
evaluator = {}
for label,formula in zip(options.labels,options.formulas):
for column in re.findall(r'#(.+?)#',formula): # loop over column labels in formula
idx = table.label_index(column)
dim = table.label_dimension(column)
if column in specials:
replacement = 'specials["{}"]'.format(column)
elif dim == 1: # scalar input
replacement = 'float(table.data[{}])'.format(idx) # take float value of data column
elif dim > 1: # multidimensional input (vector, tensor, etc.)
replacement = 'np.array(table.data[{}:{}],dtype=float)'.format(idx,idx+dim) # use (flat) array representation
else:
damask.util.croak('column {} not found, skipping {}...'.format(column,label))
options.labels.remove(label)
break
formula = formula.replace('#'+column+'#',replacement)
evaluator[label] = formula
# ---------------------------- separate requested labels into old and new --------------------------
veterans = list(set(options.labels)&set(table.labels(raw=False)+table.labels(raw=True)) ) # intersection of requested and existing
newbies = list(set(options.labels)-set(table.labels(raw=False)+table.labels(raw=True)) ) # requested but not existing
# ------------------------------------------ process data ------------------------------------------
firstLine = True
outputAlive = True
while outputAlive and table.data_read(): # read next data line of ASCII table
specials['_row_'] += 1 # count row
if firstLine:
firstLine = False
# ---------------------------- line 1: determine dimension of formulas -----------------------------
resultDim = {}
for label in list(options.labels): # iterate over stable copy
resultDim[label] = np.size(eval(evaluator[label])) # get dimension of formula[label]
if resultDim[label] == 0: options.labels.remove(label) # remove label if invalid result
for veteran in list(veterans):
if resultDim[veteran] != table.label_dimension(veteran):
damask.util.croak('skipping {} due to inconsistent dimension...'.format(veteran))
veterans.remove(veteran) # discard culprit
# ----------------------------------- line 1: assemble header --------------------------------------
for newby in newbies:
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table.labels_append(['{}_{}'.format(i+1,newby) for i in range(resultDim[newby])]
if resultDim[newby] > 1 else newby)
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
table.head_write()
# -------------------------------------- evaluate formulas -----------------------------------------
if options.condition is None or eval(condition): # condition for veteran replacement fulfilled
for veteran in veterans: # evaluate formulas that overwrite
table.data[table.label_index(veteran):
table.label_index(veteran)+table.label_dimension(veteran)] = \
listify(eval(evaluator[veteran]))
for newby in newbies: # evaluate formulas that append
table.data_append(listify(eval(evaluator[newby])))
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outputAlive = table.data_write() # output processed line
# ------------------------------------- output finalization ----------------------------------------
table.close() # close ASCII table