improved performance (hopefully)
now each new element gets a new ID, running from 1 to N for N elements
This commit is contained in:
Martin Diehl
parent
2477225c73
commit
6c7affc43f
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@ -1,6 +1,6 @@
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#!/usr/bin/env python
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import os,re,sys,math,string,numpy,damask
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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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@ -53,10 +53,15 @@ 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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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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@ -94,29 +99,36 @@ for file in files:
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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 found...'
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print 'no coordinate data element 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 (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 = options.resolution
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dimension = options.dimension
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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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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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@ -127,28 +139,45 @@ for file in files:
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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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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 = 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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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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averagedDown /= options.packing.prod() # normalize data by element count
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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 axis3 in xrange(options.packing[2]):
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shiftedZ = numpy.roll(data,shift=axis3,axis=2)
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for axis2 in xrange(options.packing[1]):
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shiftedZY = numpy.roll(shiftedZ,shift=axis2,axis=1)
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for axis1 in xrange(options.packing[0]):
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sum += numpy.roll(shiftedZY,shift=axis1,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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