150 lines
6.3 KiB
Python
Executable File
150 lines
6.3 KiB
Python
Executable File
#!/usr/bin/env python2.7
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# -*- coding: UTF-8 no BOM -*-
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import os,sys
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import numpy as np
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import scipy.ndimage
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from optparse import OptionParser
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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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 = 'pos',
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type = 'string', metavar = 'string',
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help = 'column label of 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, 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, 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, metavar = 'int int int',
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help = 'grid in x,y,z [autodetect]')
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parser.add_option('-s', '--size',
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dest = 'size',
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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(pos = 'pos',
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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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)
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(options,filenames) = parser.parse_args()
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packing = np.array(options.packing,dtype = int)
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shift = np.array(options.shift, dtype = int)
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prefix = 'averagedDown{}x{}x{}_'.format(*packing)
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if any(shift != 0): prefix += 'shift{:+}{:+}{:+}_'.format(*shift)
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# --- loop over input files ------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try: table = damask.ASCIItable(name = name,
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outname = os.path.join(os.path.dirname(name),
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prefix+os.path.basename(name)) if name else name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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remarks = []
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if table.label_dimension(options.pos) != 3: errors.append('coordinates {} are not a vector.'.format(options.pos))
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else: colCoord = table.label_index(options.pos)
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header ---------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.head_write()
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# --------------- figure out size and grid ---------------------------------------------------------
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table.data_readArray()
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if (any(options.grid) == 0 or any(options.size) == 0.0):
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coords = [np.unique(table.data[:,colCoord+i]) for i in range(3)]
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mincorner = np.array(map(min,coords))
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maxcorner = np.array(map(max,coords))
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grid = np.array(map(len,coords),'i')
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size = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner) # size from edge to edge = dim * n/(n-1)
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size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 set to smallest among other spacings
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delta = size/np.maximum(np.ones(3,'d'), grid)
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origin = mincorner - 0.5*delta # shift from cell center to corner
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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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packing = np.where(grid == 1,1,packing) # reset packing to 1 where grid==1
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shift = np.where(grid == 1,0,shift) # reset shift to 0 where grid==1
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packedGrid = np.maximum(np.ones(3,'i'),grid//packing)
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averagedDown = scipy.ndimage.filters.uniform_filter( \
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np.roll(
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np.roll(
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np.roll(table.data.reshape(list(grid)+[table.data.shape[1]],order = 'F'),
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-shift[0],axis = 0),
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-shift[1],axis = 1),
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-shift[2],axis = 2),
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size = list(packing) + [1],
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mode = 'wrap',
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origin = list(-(packing/2)) + [0])\
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[::packing[0],::packing[1],::packing[2],:].reshape((packedGrid.prod(),table.data.shape[1]),order = 'F')
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table.data = averagedDown
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#--- generate grid --------------------------------------------------------------------------------
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if colCoord:
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x = (0.5 + shift[0] + np.arange(packedGrid[0],dtype=float))/packedGrid[0]*size[0] + origin[0]
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y = (0.5 + shift[1] + np.arange(packedGrid[1],dtype=float))/packedGrid[1]*size[1] + origin[1]
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z = (0.5 + shift[2] + np.arange(packedGrid[2],dtype=float))/packedGrid[2]*size[2] + origin[2]
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xx = np.tile( x, packedGrid[1]* packedGrid[2])
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yy = np.tile(np.repeat(y,packedGrid[0] ),packedGrid[2])
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zz = np.repeat(z,packedGrid[0]*packedGrid[1])
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table.data[:,colCoord:colCoord+3] = np.squeeze(np.dstack((xx,yy,zz)))
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# ------------------------------------------ output result -----------------------------------------
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table.data_writeArray()
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close ASCII tables
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