I am tryin to "vectorize" 3 nested for loops but I am not having much success. Here is the code I use:
import numpy import numpy.ma as masked
grid = numpy.zeros((nx, ny), dtype=numpy.float32) xOut = numpy.zeros((nx, ny), dtype=numpy.float32) yOut = numpy.zeros((nx, ny), dtype=numpy.float32)
z = GetCentroids() # Some vector z values prop = GetValue() # Some other vector values
NaN = numpy.NaN
for I in xrange(1, nx+1):
for J in xrange(1, ny+1):
theSum = 
for K in xrange(1, nz+1):
cellPos = I-1 + nx*(J-1) + nx*ny*(K-1) centroid = z[cellPos]
if low <= centroid <= high and actnum[cellPos] > 0: theSum.append(prop[cellPos])
if theSum: grid[I-1, J-1] = sum(theSum)/len(theSum) else: grid[I-1, J-1] = NaN
xOut[I-1, J-1], yOut[I-1, J-1] = x[cellPos], y[cellPos]
grid = masked.masked_where(numpy.isnan(grid), grid)
1) "z" is a vector of nx*ny*nz components, where nx = 100, ny = 73, nz = 23, which represents 3D hexahedron cell centroids; 2) "prop" is a vector like z, with the same shape, with some floating point values in it; 3) "actnum" is a vector of integers (0 or 1) with the same shape as z, and indicates if a cell should be considered in the loop or not; 4) low and high are 2 floating point values with low < high: if the cell centroid fall between low and high and the cell is active (as stated in "actnum"), then I take the value of "prop" in that cell and I append it to the "theSum" list; 5) At the end of the K loop, I just take an arithmetic mean of the values in "theSum" list.
I think I may be able to figure out how to vectorize the part regarding the "grid" variable, but I have no idea on what to do for the xOut and yOut variables, and I need them because I use them in a later call to matplotlib.contourf.
If you could drop some hint on how to proceed, ot would be very appreciated. Thank you for your suggestions.
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