155 lines
7.9 KiB
Python
Executable File
155 lines
7.9 KiB
Python
Executable File
#!/usr/bin/env python2.7
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# -*- coding: UTF-8 no BOM -*-
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import os,re,sys,fnmatch
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import math # noqa
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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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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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def sortingList(labels,whitelistitems):
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indices = []
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names = []
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for label in labels:
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if re.match('^\d+_',label):
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indices.append(int(label.split('_',1)[0]))
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names.append(label.split('_',1)[1])
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else:
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indices.append(0)
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names.append(label)
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return [indices,names,whitelistitems]
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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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Filter rows according to condition and columns by either white or black listing.
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Examples:
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Every odd row if x coordinate is positive -- " #ip.x# >= 0.0 and #_row_#%2 == 1 ).
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All rows where label 'foo' equals 'bar' -- " #s#foo# == 'bar' "
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""", version = scriptID)
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parser.add_option('-w','--white',
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dest = 'whitelist',
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action = 'extend', metavar = '<string LIST>',
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help = 'whitelist of column labels (a,b,c,...)')
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parser.add_option('-b','--black',
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dest = 'blacklist',
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action = 'extend', metavar='<string LIST>',
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help = 'blacklist of column labels (a,b,c,...)')
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parser.add_option('-c','--condition',
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dest = 'condition', metavar='string',
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help = 'condition to filter rows')
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parser.set_defaults(condition = None,
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)
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(options,filenames) = parser.parse_args()
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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: 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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# ------------------------------------------ assemble info ---------------------------------------
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table.head_read()
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# ------------------------------------------ process data ---------------------------------------
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specials = { \
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'_row_': 0,
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}
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labels = []
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positions = []
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for position,label in enumerate(table.labels(raw = True)):
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if (options.whitelist is None or any([ position in table.label_indexrange(needle) \
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or fnmatch.fnmatch(label,needle) for needle in options.whitelist])) \
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and (options.blacklist is None or not any([ position in table.label_indexrange(needle) \
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or fnmatch.fnmatch(label,needle) for needle in options.blacklist])): # a label to keep?
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labels.append(label) # remember name...
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positions.append(position) # ...and position
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if len(labels) > 0 and options.whitelist is not None and options.blacklist is None: # check whether reordering is possible
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whitelistitem = np.zeros(len(labels),dtype=int)
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for i,label in enumerate(labels): # check each selected label
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match = [ positions[i] in table.label_indexrange(needle) \
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or fnmatch.fnmatch(label,needle) for needle in options.whitelist] # which whitelist items do match it
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whitelistitem[i] = match.index(True) if np.sum(match) == 1 else -1 # unique match to a whitelist item --> store which
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order = range(len(labels)) if np.any(whitelistitem < 0) \
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else np.lexsort(sortingList(labels,whitelistitem)) # reorder if unique, i.e. no "-1" in whitelistitem
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else:
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order = range(len(labels)) # maintain original order of labels
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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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# ------------------------------------------ assemble header ---------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.labels_clear()
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table.labels_append(np.array(labels)[order]) # update with new label set
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table.head_write()
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# ------------------------------------------ process and output data ------------------------------------------
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positions = np.array(positions)[order]
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atOnce = options.condition is None
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if atOnce: # read full array and filter columns
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try:
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table.data_readArray(positions+1) # read desired columns (indexed 1,...)
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table.data_writeArray() # directly write out
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except:
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atOnce = False # data contains items that prevent array chunking
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if not atOnce: # read data line by line
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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 options.condition is None or eval(condition): # valid row ?
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table.data = [table.data[position] for position in positions] # retain filtered columns
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ finalize output -----------------------------------------
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table.close() # close input ASCII table (works for stdin)
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