using central functionality
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@ -122,20 +122,10 @@ for name in filenames:
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#--- generate grid --------------------------------------------------------------------------------
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#--- generate grid --------------------------------------------------------------------------------
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x = (0.5 + shift[0] + np.arange(packedGrid[0],dtype=float))/packedGrid[0]*size[0]
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coords = damask.grid_filters.cell_coord0(packedGrid,size,shift/packedGrid*size+origin)
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y = (0.5 + shift[1] + np.arange(packedGrid[1],dtype=float))/packedGrid[1]*size[1]
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table.data[:,table.label_indexrange(options.pos)] = coords.reshape((-1,3))
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z = (0.5 + shift[2] + np.arange(packedGrid[2],dtype=float))/packedGrid[2]*size[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[:,table.label_indexrange(options.pos)] = 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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# ------------------------------------------ output finalization -----------------------------------
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table.data_writeArray()
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table.close() # close ASCII tables
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table.close()
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@ -4,6 +4,7 @@ import os
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import sys
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import sys
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from optparse import OptionParser
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from optparse import OptionParser
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from scipy import ndimage
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import numpy as np
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import numpy as np
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import damask
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import damask
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@ -94,30 +95,13 @@ for name in filenames:
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table.head_write()
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table.head_write()
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# ------------------------------------------ process data -------------------------------------------
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# ------------------------------------------ process data -------------------------------------------
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table.data_readArray()
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data = table.data.reshape(tuple(grid)+(-1,))
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table.data = ndimage.interpolation.zoom(data,tuple(packing)+(1,),order=0,mode='nearest').reshape((outSize.prod(),-1))
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coords = damask.grid_filters.cell_coord0(outSize,size,origin)
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table.data[:,table.label_indexrange(options.pos)] = coords.reshape((-1,3))
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table.data[:,table.label_index('elem')] = np.arange(1,outSize.prod()+1)
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data = np.zeros(outSize.tolist()+[len(table.labels(raw = True))])
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# ------------------------------------------ output finalization -----------------------------------
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p = np.zeros(3,'i')
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table.data_writeArray()
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table.close()
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for p[2] in range(grid[2]):
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for p[1] in range(grid[1]):
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for p[0] in range(grid[0]):
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d = p*packing
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table.data_read()
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data[d[0]:d[0]+packing[0],
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d[1]:d[1]+packing[1],
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d[2]:d[2]+packing[2],
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: ] = np.tile(np.array(table.data_asFloat(),'d'),packing.tolist()+[1]) # tile to match blowUp voxel size
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elementSize = size/grid/packing
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elem = 1
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for c in range(outSize[2]):
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for b in range(outSize[1]):
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for a in range(outSize[0]):
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data[a,b,c,table.label_indexrange(options.pos)] = [a+0.5,b+0.5,c+0.5]*elementSize
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if colElem != -1: data[a,b,c,colElem] = 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 input ASCII table (works for stdin)
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