[Numpy-discussion] slicing an n-dimensional array
Hello, I'm sure there's a simple solution, but I'm not seeing it so any hints would be greatly appreciated. I have an array A of shape (NX, NY, NZ), and then I have a second array B of shape (NX, NY) that ranges from 0 to NZ in value. I want to create a third array C of shape (NX, NY) that holds the B-th slice for each (NX, NY). For example: A = numpy.zeros((NX,NY,NZ)) A[:,:,0] = numpy.arange(0, NX*NY).reshape(NX,NY) A[:,:,1] = numpy.arange(1, NX*NY+1).reshape(NX,NY) and so on B = numpy.zeros((NX,NY)) B[0,0] = 0 B[0,1] = 1 so C[0,0] = A[0,0,B[0,0]] C[0,1] = A[0,1,B[0,1]] ... C[NX-1,NY-1] = A[NX-1,NY-1,B[NX-1,NY-1]] and C has shape(NX,NY) How do I accomplish this without loops? Thank-you for any advice, Catherine ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] median filtering a masked array
Hello, I have to perform a median filter on only selected elements in an array and am wondering if using a masked array will speed things up by preventing the computation of the median on those elements where it's not needed. I'm using a Fortran code to fill up a (NX,NY,NZ) array where (NZ) is the number of points in the median filter window and NX and NY are the array dimensions themselves. If a filter is not required at a certain (I,J) element, I set all the elements along the NZ dimension to a fill value, and then create a masked array where the mask argument is set to True for all fill values. So, I am hoping that the numpy.median(data, axis=2) call will not even try to compute the median for those (I,J) elements where all the data in the NZ direction are masked out, thus saving me valuable computational time. Is this a valid assumption? Or does numpy go ahead and calculate the median for all (I,J) elements irregardless of whether they're masked out or not? What is the recommended way of doing a fast median filter on an array where only certain elements of the array need to be calculated? I'm trying to avoid a nested loop over all (I,J) elements. Catherine ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
