George Dahl <george.dahl <at> gmail.com> writes: > > Hi everyone, > I was wondering if anyone had insight on the best way to solve the > following problem. > > Suppose I have a numpy array called U. > U has shape (N,M) > Suppose further that I have another array called dU and that > dU has shape (P,M) and that P has no particular relationship to N, it > could be larger or smaller. > Additionally, I have a one dimensional array called idx and > idx has shape (P,). Furthermore idx holds integers that are all valid > indices into the rows of U. idx almost certainly contains some > duplicates. > > I want, > > for k in range(P): > U[idx[k],:] += dU[k,:] > > Is there a nice vectorized and efficient way to do this without making > the obvious python for loop I included above? For what I am doing, P > is usually quite large. I am most interested in a clever use of > numpy/scipy commands and Python and not Cython. >
I'm also interested to see if there are any answers to this; I came across a similar problem recently. It would have been convenient to do something like U[idx] += dU, but this didn't work because there were repeated indices in idx. Here's a short example that shows the problem: In [1]: U = np.array([1., 2., 3., 4.]) In [2]: dU = np.array([0.1, 0.1, 0.1, 0.1]) In [3]: idx = np.array([0, 1, 2, 0]) In [4]: U Out[4]: array([ 1., 2., 3., 4.]) In [5]: U[idx] Out[5]: array([ 1., 2., 3., 1.]) In [6]: U[idx] += dU In [7]: U Out[7]: array([ 1.1, 2.1, 3.1, 4. ]) Ideally U would end up as array([ 1.2, 2.1, 3.1, 4. ]) Neil _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
