150 lines
6.5 KiB
Python
Executable File
150 lines
6.5 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string
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import numpy as np
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from optparse import OptionParser
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import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.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 grid to grid/packing.
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""", version = scriptID)
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parser.add_option('-c','--coordinates',
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dest='coords',
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metavar='string',
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help='column heading for coordinates [%default]')
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parser.add_option('-p','--packing',
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dest='packing',
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type='int', nargs=3,
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metavar='int int int',
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help='size of packed group [%default]')
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parser.add_option('--shift',
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dest='shift',
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type='int', nargs=3,
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metavar='int int int',
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help='shift vector of packing stencil [%default]')
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parser.add_option('-g', '--grid',
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dest='grid',
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type='int', nargs=3,
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metavar='int int int',
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help='grid in x,y,z [autodetect]')
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parser.add_option('-s', '--size', dest='size', type='float', nargs=3, metavar='float float float',
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help='size in x,y,z [autodetect]')
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parser.set_defaults(coords = 'ipinitialcoord',
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packing = (2,2,2),
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shift = (0,0,0),
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grid = (0,0,0),
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size = (0.0,0.0,0.0))
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(options,filenames) = parser.parse_args()
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options.packing = np.array(options.packing)
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options.shift = np.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 np.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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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = ['STDIN']
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for name in filenames:
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if not (name == 'STDIN' or os.path.exists(name)): continue
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table = damask.ASCIItable(name = name, outname = prefix+name,
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buffered = False)
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table.croak('\033[1m'+scriptName+'\033[0m'+(': '+name if name != 'STDIN' else ''))
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# ------------------------------------------ read header -------------------------------------------
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table.head_read()
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# --------------- figure out size and grid ---------------------------------------------------------
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try:
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elemCol = table.labels.index('elem')
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locationCol = table.labels.index('1_%s'%options.coords) # columns containing location data
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except ValueError:
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try:
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locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data (legacy naming scheme)
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except ValueError:
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table.croak('no coordinate (1_%s/%s.x) and/or elem data found...\n'%(options.coords,options.coords))
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continue
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if (any(options.grid)==0 or any(options.size)==0.0):
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coords = [{},{},{}]
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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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coords[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
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grid = np.array([len(coords[0]),\
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len(coords[1]),\
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len(coords[2]),],'i') # resolution is number of distinct coordinates found
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size = grid/np.maximum(np.ones(3,'d'),grid-1.0)* \
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np.array([max(map(float,coords[0].keys()))-min(map(float,coords[0].keys())),\
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max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
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max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
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],'d') # size from bounding box, corrected for cell-centeredness
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origin = np.array([min(map(float,coords[0].keys())),\
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min(map(float,coords[1].keys())),\
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min(map(float,coords[2].keys())),\
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],'d') - 0.5 * size / grid
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else:
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grid = np.array(options.grid,'i')
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size = np.array(options.size,'d')
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origin = np.zeros(3,'d')
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for i, res in enumerate(grid):
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if res == 1:
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options.packing[i] = 1
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options.shift[i] = 0
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mask = np.ones(3,dtype=bool)
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mask[i]=0
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size[i] = min(size[mask]/grid[mask]) # third spacing equal to smaller of other spacing
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packing = np.array(options.packing,'i')
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shift = np.array(options.shift,'i')
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downSized = np.maximum(np.ones(3,'i'),grid//packing)
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outSize = np.ceil(np.array(grid,'d')/np.array(packing,'d'))
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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 = np.zeros(outSize.tolist()+[len(table.labels)])
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p = np.zeros(3,'i')
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for p[2] in xrange(grid[2]):
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for p[1] in xrange(grid[1]):
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for p[0] in xrange(grid[0]):
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d = ((p-shift)%grid)//packing
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table.data_read()
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data[d[0],d[1],d[2],:] += np.array(table.data_asFloat(),'d') # convert to np array
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data /= packing.prod()
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elementSize = size/grid*packing
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posOffset = (shift+[0.5,0.5,0.5])*elementSize
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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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for i,x in enumerate([a,b,c]):
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data[a,b,c,locationCol+i] = posOffset[i] + x*elementSize[i] + origin[i]
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data[a,b,c,elemCol] = elem
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table.data = data[a,b,c,:].tolist()
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outputAlive = table.data_write() # output processed line
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elem += 1
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close ASCII tables
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