major restructuring. packing stencil can be shifted to allow for element or nodal value averaging.
This commit is contained in:
Philip Eisenlohr
parent
7e23f84d2c
commit
bffc22fbe1
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@ -1,6 +1,6 @@
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#!/usr/bin/python
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import os,re,sys,math,string,numpy
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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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@ -47,25 +47,30 @@ 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('-m','--memory', dest='memory', action='store_true', \
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help='load complete file into memory [%default]')
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parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \
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help='resolution in fast, medium, and slow dimension [%default]')
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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='number of data points to average down in each dimension [%default]')
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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(memory = False)
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parser.set_defaults(resolution = [32,32,32])
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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.resolution) < 3:
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parser.error('resolution needs three parameters...')
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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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@ -75,74 +80,82 @@ if filenames == []:
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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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(head,tail) = os.path.split(name)
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files.append({'name':name, 'input':open(name), 'output':open(os.path.join(head,'avgDown_%s'%tail),'w')})
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files.append({'name':name, 'input':open(name), 'output':open(prefix+'_'+name,'w')})
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# ------------------------------------------ loop over input files ---------------------------------------
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for file in files:
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print file['name']
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if file['name'] != 'STDIN': print file['name'],
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# get labels by either read the first row, or - if keyword header is present - the last line of the header
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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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firstline = file['input'].readline()
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m = re.search('(\d+)\s*head', firstline.lower())
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if m:
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headerlines = int(m.group(1))
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passOn = [file['input'].readline() for i in range(1,headerlines)]
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headers = file['input'].readline().split()
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else:
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headerlines = 1
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passOn = []
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headers = firstline.split()
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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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if options.memory:
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data = file['input'].readlines()
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else:
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data = []
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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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output = '%i\theader'%(headerlines+1) + '\n' + \
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''.join(passOn) + \
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string.replace('$Id$','\n','\\n')+ '\t' + \
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' '.join(sys.argv[1:]) + '\n' + \
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'\t'.join(headers) + '\n' # build extended header
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table.head_write()
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if not options.memory:
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file['output'].write(output)
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output = ''
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# ------------------------------------------ process data ---------------------------------------
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# ------------------------------------------ read file ---------------------------------------
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table.data_rewind()
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averagedDown = numpy.zeros([options.res[2]/options.packing[2],
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options.res[1]/options.packing[1],
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options.res[0]/options.packing[0],
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len(headers)])
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averagedDown = numpy.zeros(downSized.tolist()+[len(table.labels)])
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idx = 0
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for line in {True : data,
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False : file['input']}[options.memory]:
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items = numpy.array(map(float,line.split()[:len(headers)]))
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if len(items) < len(headers):
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continue
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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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loc = location(idx,options.res)//options.packing
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averagedDown[loc[2],loc[1],loc[0],:] += items
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averagedDown /= options.packing.prod() # normalize data by element count
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idx += 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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table.data = averagedDown[a,b,c,:].tolist()
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table.data_write() # output processed line
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averagedDown /= options.packing[0]*options.packing[1]*options.packing[2]
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for z in range(options.res[2]/options.packing[2]):
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for y in range(options.res[1]/options.packing[1]):
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for x in range(options.res[0]/options.packing[0]):
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output += '\t'.join(map(str,averagedDown[z,y,x,:])) + '\n'
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file['input'].close()
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# ------------------------------------------ output result ---------------------------------------
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file['output'].write(output)
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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
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file['output'].close # close output ASCII table
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