162 lines
7.0 KiB
Python
Executable File
162 lines
7.0 KiB
Python
Executable File
#!/usr/bin/env python
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import os,re,sys,math,string,numpy,damask
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from optparse import OptionParser, Option
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# -----------------------------
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class extendableOption(Option):
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# -----------------------------
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# used for definition of new option parser action 'extend', which enables to take multiple option arguments
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# taken from online tutorial http://docs.python.org/library/optparse.html
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ACTIONS = Option.ACTIONS + ("extend",)
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STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
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TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
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ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
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def take_action(self, action, dest, opt, value, values, parser):
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if action == "extend":
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lvalue = value.split(",")
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values.ensure_value(dest, []).extend(lvalue)
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else:
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Option.take_action(self, action, dest, opt, value, values, parser)
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def location(idx,res):
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return numpy.array([ idx % res[0], \
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(idx // res[0]) % res[1], \
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(idx // res[0] // res[1]) % res[2] ])
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def index(location,res):
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return ( location[0] % res[0] + \
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(location[1] % res[1]) * res[0] + \
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(location[2] % res[2]) * res[0] * res[1] )
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=extendableOption, usage='%prog [options] [file[s]]', description = """
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Average each data block of size 'packing' into single values thus reducing the former resolution
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to resolution/packing. (Requires numpy.)
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""" + string.replace('$Id$','\n','\\n')
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)
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parser.add_option('-c','--coordinates', dest='coords', type='string',\
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help='column heading for coordinates [%default]')
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parser.add_option('-p','--packing', dest='packing', type='int', nargs=3, \
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help='dimension of packed group %default')
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parser.add_option('-s','--shift', dest='shift', type='int', nargs=3, \
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help='shift vector of packing stencil %default')
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parser.set_defaults(coords = 'ip')
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parser.set_defaults(packing = [2,2,2])
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parser.set_defaults(shift = [0,0,0])
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(options,filenames) = parser.parse_args()
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if len(options.packing) < 3:
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parser.error('packing needs three parameters...')
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if len(options.shift) < 3:
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parser.error('shift needs three parameters...')
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options.packing = numpy.array(options.packing)
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options.shift = numpy.array(options.shift)
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prefix = 'averagedDown%ix%ix%i'%(options.packing[0],options.packing[1],options.packing[2])
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if numpy.any(options.shift != 0):
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prefix += '_shift%+i%+i%+i'%(options.shift[0],options.shift[1],options.shift[2])
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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})
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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(os.path.dirname(name)+os.sep+prefix+'_'+os.path.basename(name),'w')})
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# ------------------------------------------ loop over input files ---------------------------------------
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for file in files:
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if file['name'] != 'STDIN': print file['name'],
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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('$Id$','\n','\\n') + \
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'\t' + ' '.join(sys.argv[1:]))
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try:
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locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
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except ValueError:
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print 'no coordinate data found...'
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continue
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grid = [{},{},{}]
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while table.data_read(): # read next data line of ASCII table
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for j in xrange(3):
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grid[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
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resolution = numpy.array([len(grid[0]),\
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len(grid[1]),\
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len(grid[2]),],'i') # resolution is number of distinct coordinates found
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dimension = resolution/numpy.maximum(numpy.ones(3,'d'),resolution-1.0)* \
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numpy.array([max(map(float,grid[0].keys()))-min(map(float,grid[0].keys())),\
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max(map(float,grid[1].keys()))-min(map(float,grid[1].keys())),\
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max(map(float,grid[2].keys()))-min(map(float,grid[2].keys())),\
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],'d') # dimension from bounding box, corrected for cell-centeredness
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if resolution[2] == 1:
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options.packing[2] = 1
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options.shift[2] = 0
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dimension[2] = min(dimension[:2]/resolution[:2])
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downSized = numpy.maximum(numpy.ones(3,'i'),resolution//options.packing)
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print '\t%s @ %s --> %s'%(dimension,resolution,downSized)
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# ------------------------------------------ assemble header ---------------------------------------
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table.head_write()
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# ------------------------------------------ process data ---------------------------------------
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table.data_rewind()
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averagedDown = numpy.zeros(downSized.tolist()+[len(table.labels)])
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for z in xrange(-options.shift[2],-options.shift[2]+resolution[2]):
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for y in xrange(-options.shift[1],-options.shift[1]+resolution[1]):
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for x in xrange(-options.shift[0],-options.shift[0]+resolution[0]):
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table.data_read()
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data = numpy.array(table.data_asFloat(),'d') # convert to numpy array
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me = numpy.array((x,y,z),'i') # my location as array
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data[locationCol:locationCol+3] -= dimension*(me//resolution) # shift coordinates if periodic image
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(a,b,c) = (me%resolution)//options.packing # bin to condense my location into
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averagedDown[a,b,c,:] += data # store the (coord-updated) data there
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averagedDown /= options.packing.prod() # normalize data by element count
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for c in xrange(downSized[2]):
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for b in xrange(downSized[1]):
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for a in xrange(downSized[0]):
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table.data = averagedDown[a,b,c,:].tolist()
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table.data_write() # output processed line
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# ------------------------------------------ output result ---------------------------------------
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table.output_flush() # just in case of buffered ASCII table
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# ------------------------------------------ close file handles ---------------------------------------
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for file in files:
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file['input'].close() # close input ASCII table
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if file['name'] != 'STDIN':
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file['output'].close() # close output ASCII table
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