Chaining apply_over_axis for multiple axes.
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()
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?
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
On May 25, 2015 4:05 AM, "Andrew Nelson" andyfaff@gmail.com 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?
The crude but effective way is
tmp_in = arr.reshape((-1, arr.shape[- 1])) tmp_out = np.empty(tmp_in.shape) for i in range(tmp_in.shape[0]): tmp_out[i, :] = func(tmp_in[i, :]) out = tmp_out.reshape(arr.shape)
This won't produce any unnecessary copies if your input array is contiguous.
This also assumes you want to apply the function on the last axis. If not you can do something like
arr = arr.swapaxes(axis, -1) ... call the code above ... out = out.swapaxes(axis, -1)
This will result in an extra copy of the input array though if it's >2d and the requested axis is not the last one.
-n
participants (3)
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Andrew Nelson -
Nathaniel Smith -
Sebastian Berg