On Mo, 2015-05-25 at 21:02 +1000, Andrew Nelson wrote:
I have a function that operates over a 1D array, to return an array of a similar size. To use it in a 2D fashion I would have to do something like the following:
for row in range(np.size(arr, 0): arr_out[row] = func(arr[row]) for col in range(np.size(arr, 1): arr_out[:, col] = func(arr[:, col])
I would like to generalise this to N dimensions. Does anyone have any suggestions of how to achieve this? Presumably what I need to do is build an iterator, and then remove an axis:
# arr.shape=(2, 3, 4) it = np.nditer(arr, flags=['multi_index']) it.remove_axis(2) while not it.finished: arr_out[it.multi_index] = func(arr[it.multi_index]) it.iternext()
Just warning that nditer is pretty low level (i.e. can be a bit mind boggling since it is close to the C-side of things). Anyway, you can of course do this just iterating the result. Since you have no buffering, etc. this should work fine. There is also `np.nesterd_iters` but since I am a bit lazy to look it up, you would have to actually check some examples for it from the numpy tests to see how it works probably. - Sebastian
If I have an array with shape (2, 3, 4) this would allow me to iterate over the 6 1D arrays that are 4 elements long. However, how do I then construct the iterator for the preceding axes? _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion