Hi, I am also interested in this. In my application there is a large 2d array, lets call it 'b' to keep the notation consistent in the thread. b's columns need to be recomputed often. Ideally this re-computation happens in a function. Lets call that function updater(b, col_index): The simplest example is where updater(b, col_index) is a matrix vector multiply, where the matrix or the vector changes.
Is there anyway apart from using ufuncs that I can make updater() write the result directly in b and not create a new temporary column that is then copied into b ? Say for the matrix vector multiply example. I can write the matrix vector product in terms of ufuncs but will lose out in terms of speed. In the best case scenario I would like to maintain 'b' in a csr sparse matrix form, as 'b' participates in a matrix vector multiply. I think csr would be asking for too much, but even ccs should help. I dont want to clutter this thread with the sparsity issues though, any solution to the original question or pointers to solutions would be appreciated. Thanks --srean On Sat, Mar 26, 2011 at 12:10 PM, Hugo Gagnon < [email protected]> wrote: > Hello, > > Say I have a few 1d arrays and one 2d array which columns I want to be > the 1d arrays. > I also want all the a's arrays to share the *same data* with the b > array. > If I call my 1d arrays a1, a2, etc. and my 2d array b, then > > b[:,0] = a1[:] > b[:,1] = a2[:] > ... > > won't work because apparently copying occurs. > I tried it the other way around i.e. > > a1 = b[:,0] > a2 = b[:,1] > ... > > and it works but that doesn't help me for my problem. > Is there a way to reformulate the first code snippet above but with > shallow copying? > > Thanks, > -- > Hugo Gagnon > -- > Hugo Gagnon > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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