Hi Robert, This solution works beautifully! Thanks for sending it along. I need to learn and understand more about fancy indexing for multi-dimensional arrays, especially your clever trick of np.newaxis for broadcasting.
Daran -- > Hello list, > > This didn't seem to get through last time round, and my > first version was poorly written. > > I have a rather pedestrian question about fancy indexing > for multi-dimensional arrays. > > Suppose I have two 3-D arrays, one named "A" and the other "B", > where both arrays have identical dimensions of time, longitude, > and latitude. I wish to use data from A to conditionally select > values from array B. Specifically, I first find the time where > the values at each point in A are at their maximum. This is > accomplished with: > > ?>>> tmax_idx = np.argsort(A, axis=0) > > I now wish to use this tmax_idx array to conditionally select > the values from B. In essence, I want to pick values from B for > times where the values at A are at their max. Can this be done > with fancy indexing? Or is there a smarter way to do this? I've > certainly done this sort of selection before, but the index > selection array is 1D. I've carefully studied the excellent > indexing documentation and examples on-line, but can't sort out > whether what I want to do is even possible, without doing the > brute force looping method, similar to: > > max_B = np.zeros((nlon, nlat), dtype=np.float32) > > for i in xrange(nlon): > ? ?for j in xrange(nlat): > ? ? ? ?max_B[i,j] = B[tmax_idx[i,j],i,j] All of the index arrays need to be broadcastable to the same shape. Thus, you want the "i" index array to be a column vector. max_B = B[tmax_idx, np.arange(nlon)[:,np.newaxis], np.arange(nlat)] -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
