191 lines
8.5 KiB
Python
Executable File
191 lines
8.5 KiB
Python
Executable File
#!/usr/bin/env python
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import os,re,sys,math,string,numpy,damask,time
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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.add_option('-r','--resolution', dest='resolution', type='int', nargs=3, \
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help='resolution in x,y,z [autodetect]')
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parser.add_option('-d','--dimension', dest='dimension', type='float', nargs=3, \
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help='dimension in x,y,z [autodetect]')
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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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parser.set_defaults(resolution = [0,0,0])
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parser.set_defaults(dimension = [0.0,0.0,0.0])
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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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name = os.path.relpath(name)
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if os.path.exists(name):
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files.append({'name':name, 'input':open(name),
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'output':open(os.path.join(os.path.dirname(name),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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elemCol = table.labels.index('elem') # columns containing location data
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except ValueError:
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print 'no coordinate data or element data found...'
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continue
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if (any(options.resolution)==0 or any(options.dimension)==0.0):
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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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else:
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resolution = numpy.array(options.resolution,'i')
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dimension = numpy.array(options.dimension,'d')
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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]) # z spacing equal to smaller of x or y spacing
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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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data = numpy.zeros(resolution.tolist()+[len(table.labels)]).reshape(resolution[0],\
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resolution[1],\
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resolution[2],\
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[len(table.labels)])
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for z in xrange(resolution[2]):
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for y in xrange(resolution[1]):
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for x in xrange(resolution[0]):
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table.data_read()
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data[x,y,z,:] = numpy.array(table.data_asFloat(),'d') # convert to numpy array
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sum = numpy.zeros(numpy.shape(data))
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data = numpy.roll(data,axis=2,shift=-options.shift[2])
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data = numpy.roll(data,axis=1,shift=-options.shift[1])
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data = numpy.roll(data,axis=0,shift=-options.shift[0])
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for axis2 in xrange(options.packing[2]):
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shiftedZ = numpy.roll(data,shift=-axis2,axis=2)
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for axis1 in xrange(options.packing[1]):
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shiftedZY = numpy.roll(shiftedZ,shift=-axis1,axis=1)
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for axis0 in xrange(options.packing[0]):
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sum += numpy.roll(shiftedZY,shift=-axis0,axis=0)
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averagedDown = sum[::options.packing[0],::options.packing[1],::options.packing[2],::] / options.packing.prod() # normalize data by element count
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posOffset = (options.shift+[0.5,0.5,0.5])*dimension/resolution
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elementSize = dimension/resolution*options.packing
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elem = 1
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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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averagedDown[a,b,c,locationCol:locationCol+3] = posOffset + [a,b,c]*elementSize
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averagedDown[a,b,c,elemCol] = elem
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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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elem += 1
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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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