140 lines
6.9 KiB
Python
Executable File
140 lines
6.9 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 = scriptID.split()[1][:-3]
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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', dest='coords', metavar='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, metavar='int int int',
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help='size of packed group %default')
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parser.add_option('--shift', dest='shift', type='int', nargs=3, metavar='int int int',
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help='shift vector of packing stencil %default')
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parser.add_option('-g', '--grid', dest='grid', type='int', nargs=3, 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 = '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(grid = [0,0,0])
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parser.set_defaults(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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# ------------------------------------------ setup file handles ------------------------------------
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files = []
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for name in filenames:
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if os.path.exists(name):
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files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
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#--- loop over input files -------------------------------------------------------------------------
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for file in files:
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file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
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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(scriptID + '\t' + ' '.join(sys.argv[1:]))
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# --------------- figure out size and grid ---------------------------------------------------------
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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')
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except ValueError:
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file['croak'].write('no coordinate (%s.x) and/or elem data found...\n'%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 result -----------------------------------------
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outputAlive and table.output_flush() # just in case of buffered ASCII table
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table.input_close() # close input ASCII table
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table.output_close() # close output ASCII table
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os.rename(file['name']+'_tmp',\
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os.path.join(os.path.dirname(file['name']),prefix+os.path.basename(file['name'])))
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