jseabold wrote > IIUC, > > [~/] > [1]: np.logical_and([True, False, True], [False, False, True]) > [1]: array([False, False, True], dtype=bool) > > You can avoid looping over k since they're all the same length > > [~/] > [3]: np.logical_and([[True, False],[False, True],[False, True]], > [[False, False], [False, True], [True, True]]) > [3]: > array([[False, False], > [False, True], > [False, True]], dtype=bool) > > [~/] > [4]: np.sum(np.logical_and([[True, False],[False, True],[False, > True]], [[False, False], [False, True], [True, True]]), axis=0) > [4]: array([0, 2])
Well, yes, if you work with the pure f_k and g_k that is true, but this two-dimensional array will have 4*10^14 elements and will exhaust my memory. That is why I have found a more efficient method for finding only the much fewer changes_at elements for each k, and these arrays have unequal length, and has to be considered for eack k (which is tolerable as long as I avoid a further inner loop for each k in explicit Python). I could implement this in C and get it done sufficiently efficient. I just like to make a point in demonstrating this is also doable in finite time in Python/numpy. -- View this message in context: http://numpy-discussion.10968.n7.nabble.com/Is-there-a-pure-numpy-recipe-for-this-tp37077p37081.html Sent from the Numpy-discussion mailing list archive at Nabble.com. _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
