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 2012-10-16 18:06:02 +00:00
parent 2477225c73
commit 6c7affc43f
1 changed files with 60 additions and 31 deletions

View File

@ -1,6 +1,6 @@
#!/usr/bin/env python
import os,re,sys,math,string,numpy,damask
import os,re,sys,math,string,numpy,damask,time
from optparse import OptionParser, Option
# -----------------------------
@ -53,10 +53,15 @@ parser.add_option('-p','--packing', dest='packing', type='int', nargs=3, \
help='dimension of packed group %default')
parser.add_option('-s','--shift', dest='shift', type='int', nargs=3, \
help='shift vector of packing stencil %default')
parser.set_defaults(coords = 'ip')
parser.set_defaults(packing = [2,2,2])
parser.set_defaults(shift = [0,0,0])
parser.add_option('-r','--resolution', dest='resolution', type='int', nargs=3, \
help='resolution in x,y,z [autodetect]')
parser.add_option('-d','--dimension', dest='dimension', type='float', nargs=3, \
help='dimension in x,y,z [autodetect]')
parser.set_defaults(coords = 'ip')
parser.set_defaults(packing = [2,2,2])
parser.set_defaults(shift = [0,0,0])
parser.set_defaults(resolution = [0,0,0])
parser.set_defaults(dimension = [0.0,0.0,0.0])
(options,filenames) = parser.parse_args()
@ -95,28 +100,35 @@ for file in files:
table.info_append(string.replace('$Id$','\n','\\n') + \
'\t' + ' '.join(sys.argv[1:]))
try:
locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
elemCol = table.labels.index('elem') # columns containing location data
except ValueError:
print 'no coordinate data found...'
print 'no coordinate data element data found...'
continue
grid = [{},{},{}]
while table.data_read(): # read next data line of ASCII table
for j in xrange(3):
grid[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
resolution = numpy.array([len(grid[0]),\
len(grid[1]),\
len(grid[2]),],'i') # resolution is number of distinct coordinates found
dimension = resolution/numpy.maximum(numpy.ones(3,'d'),resolution-1.0)* \
numpy.array([max(map(float,grid[0].keys()))-min(map(float,grid[0].keys())),\
max(map(float,grid[1].keys()))-min(map(float,grid[1].keys())),\
max(map(float,grid[2].keys()))-min(map(float,grid[2].keys())),\
],'d') # dimension from bounding box, corrected for cell-centeredness
if (any(options.resolution)==0 or any(options.dimension)==0.0):
grid = [{},{},{}]
while table.data_read(): # read next data line of ASCII table
for j in xrange(3):
grid[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
resolution = numpy.array([len(grid[0]),\
len(grid[1]),\
len(grid[2]),],'i') # resolution is number of distinct coordinates found
dimension = resolution/numpy.maximum(numpy.ones(3,'d'),resolution-1.0)* \
numpy.array([max(map(float,grid[0].keys()))-min(map(float,grid[0].keys())),\
max(map(float,grid[1].keys()))-min(map(float,grid[1].keys())),\
max(map(float,grid[2].keys()))-min(map(float,grid[2].keys())),\
],'d') # dimension from bounding box, corrected for cell-centeredness
else:
resolution = options.resolution
dimension = options.dimension
if resolution[2] == 1:
options.packing[2] = 1
options.shift[2] = 0
dimension[2] = min(dimension[:2]/resolution[:2])
dimension[2] = min(dimension[:2]/resolution[:2]) # z spacing equal to smaller of x or y spacing
downSized = numpy.maximum(numpy.ones(3,'i'),resolution//options.packing)
@ -127,28 +139,45 @@ for file in files:
table.head_write()
# ------------------------------------------ process data ---------------------------------------
table.data_rewind()
averagedDown = numpy.zeros(downSized.tolist()+[len(table.labels)])
for z in xrange(-options.shift[2],-options.shift[2]+resolution[2]):
for y in xrange(-options.shift[1],-options.shift[1]+resolution[1]):
for x in xrange(-options.shift[0],-options.shift[0]+resolution[0]):
data = numpy.zeros(resolution.tolist()+[len(table.labels)]).reshape(resolution[0],\
resolution[1],\
resolution[2],\
[len(table.labels)])
for z in xrange(resolution[2]):
for y in xrange(resolution[1]):
for x in xrange(resolution[0]):
table.data_read()
data = numpy.array(table.data_asFloat(),'d') # convert to numpy array
me = numpy.array((x,y,z),'i') # my location as array
data[locationCol:locationCol+3] -= dimension*(me//resolution) # shift coordinates if periodic image
(a,b,c) = (me%resolution)//options.packing # bin to condense my location into
averagedDown[a,b,c,:] += data # store the (coord-updated) data there
data[x,y,z,:] = numpy.array(table.data_asFloat(),'d') # convert to numpy array
averagedDown /= options.packing.prod() # normalize data by element count
sum = numpy.zeros(numpy.shape(data))
data = numpy.roll(data,axis=2,shift=options.shift[2])
data = numpy.roll(data,axis=1,shift=options.shift[1])
data = numpy.roll(data,axis=0,shift=options.shift[0])
for axis3 in xrange(options.packing[2]):
shiftedZ = numpy.roll(data,shift=axis3,axis=2)
for axis2 in xrange(options.packing[1]):
shiftedZY = numpy.roll(shiftedZ,shift=axis2,axis=1)
for axis1 in xrange(options.packing[0]):
sum += numpy.roll(shiftedZY,shift=axis1,axis=0)
averagedDown = sum[::options.packing[0],::options.packing[1],::options.packing[2]] / options.packing.prod() # normalize data by element count
posOffset = (options.shift+[0.5,0.5,0.5])*dimension/resolution
elementSize = dimension/resolution*options.packing
elem = 1
for c in xrange(downSized[2]):
for b in xrange(downSized[1]):
for a in xrange(downSized[0]):
averagedDown[a,b,c,locationCol:locationCol+3] = posOffset + [a,b,c]*elementSize
averagedDown[a,b,c,elemCol] = elem
table.data = averagedDown[a,b,c,:].tolist()
table.data_write() # output processed line
elem += 1
# ------------------------------------------ output result ---------------------------------------