Adding keepdims to linspace / logspace / geomspace
Hi all, We've created a PR (#14922 <https://github.com/numpy/numpy/pull/14922>) on adding keepdims to linspace / logspace / geomspace, which enables linspace to directly take the output of min and max with keepdims = True as the start and stop arguments. That is, the following two linspace calls return the same result. np.linspace( arr.min(axis=ax), arr.max(axis=ax), axis=ax ) np.linspace( arr.min(axis=ax, keepdims=True), arr.max(axis=ax, keepdims=True), axis=ax, keepdims=True ) Please let me know if you have any questions / suggestions. Regards, ZJ
On Tue, 2019-12-10 at 10:43 -0800, Zijie Poh wrote:
Hi all,
We've created a PR (#14922) on adding keepdims to linspace / logspace / geomspace, which enables linspace to directly take the output of min and max with keepdims = True as the start and stop arguments. That is, the following two linspace calls return the same result.
np.linspace( arr.min(axis=ax), arr.max(axis=ax), axis=ax )
np.linspace( arr.min(axis=ax, keepdims=True), arr.max(axis=ax, keepdims=True), axis=ax, keepdims=True )
I am a bit hesitant about the name `keepdims` being the best one. I realize it is nice to have the pattern to use the same name for symmetry. But on the other hand, there is no axes being "kept" here. In fact, `keepdims=True` returns fewer dims than `keepdims=False` :). Not sure I have a better idea though, `expand_axis` (or axis) might be closer to what happens? `keepdims` is currently used entirely for reduction-like operations (including complex reduce-like behaviour in `percentile`). However, the closest to an opposite of reduce-like operations are maybe broadcasts (they expand axis), but I cannot think of a way to use that for a parameter name ;). The change itself is small enough and I am good with adding it, I have some doubts it will be used much. But it is like a very natural thing to give input with the same number of dimensions/axis as the output will have. Maybe we should have had `new_axis=` and `expand_axis=` and you can only use one ;). - Sebastian
Please let me know if you have any questions / suggestions.
Regards, ZJ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
I'm not sure I understand the motivation here. Is the idea that you want to reuse min/max values computed with keepdims that you also want to use for other purposes? If so, this could be achieved with squeeze(), e.g., np.linspace( arr.min(axis=ax, keepdims=True).squeeze(ax), arr.max(axis=ax, keepdims=True).squeeze(ax), axis=ax, ) This is not the most elegant code, but it seems better than adding a new keyword argument, and the resulting code is likely about as efficient. On Tue, Dec 10, 2019 at 11:54 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
On Tue, 2019-12-10 at 10:43 -0800, Zijie Poh wrote:
Hi all,
We've created a PR (#14922) on adding keepdims to linspace / logspace / geomspace, which enables linspace to directly take the output of min and max with keepdims = True as the start and stop arguments. That is, the following two linspace calls return the same result.
np.linspace( arr.min(axis=ax), arr.max(axis=ax), axis=ax )
np.linspace( arr.min(axis=ax, keepdims=True), arr.max(axis=ax, keepdims=True), axis=ax, keepdims=True )
I am a bit hesitant about the name `keepdims` being the best one. I realize it is nice to have the pattern to use the same name for symmetry. But on the other hand, there is no axes being "kept" here. In fact, `keepdims=True` returns fewer dims than `keepdims=False` :).
Not sure I have a better idea though, `expand_axis` (or axis) might be closer to what happens?
`keepdims` is currently used entirely for reduction-like operations (including complex reduce-like behaviour in `percentile`). However, the closest to an opposite of reduce-like operations are maybe broadcasts (they expand axis), but I cannot think of a way to use that for a parameter name ;).
The change itself is small enough and I am good with adding it, I have some doubts it will be used much. But it is like a very natural thing to give input with the same number of dimensions/axis as the output will have.
Maybe we should have had `new_axis=` and `expand_axis=` and you can only use one ;).
- Sebastian
Please let me know if you have any questions / suggestions.
Regards, ZJ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
I wasn't sure whether the discussion would be on the list or on Github, so I posted to the latter: https://github.com/numpy/numpy/pull/14922#issuecomment-564211192 Eric On 2019/12/10 8:43 AM, Zijie Poh wrote:
Hi all,
We've created a PR (#14922 <https://github.com/numpy/numpy/pull/14922>) on adding keepdims to linspace / logspace / geomspace, which enables linspace to directly take the output of min and max with keepdims = True as the start and stop arguments. That is, the following two linspace calls return the same result.
np.linspace( arr.min(axis=ax), arr.max(axis=ax), axis=ax )
np.linspace( arr.min(axis=ax, keepdims=True), arr.max(axis=ax, keepdims=True), axis=ax, keepdims=True )
Please let me know if you have any questions / suggestions.
Regards, ZJ
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
I agree with Stephan and Eric especially it when can be done with existing method. I'll add it to the PR and close it. Thanks for the suggestions / comments! Regards, ZJ On Tue, Dec 10, 2019 at 2:05 PM Eric Firing <efiring@hawaii.edu> wrote:
I wasn't sure whether the discussion would be on the list or on Github, so I posted to the latter:
https://github.com/numpy/numpy/pull/14922#issuecomment-564211192
Eric
On 2019/12/10 8:43 AM, Zijie Poh wrote:
Hi all,
We've created a PR (#14922 <https://github.com/numpy/numpy/pull/14922>) on adding keepdims to linspace / logspace / geomspace, which enables linspace to directly take the output of min and max with keepdims = True as the start and stop arguments. That is, the following two linspace calls return the same result.
np.linspace( arr.min(axis=ax), arr.max(axis=ax), axis=ax )
np.linspace( arr.min(axis=ax, keepdims=True), arr.max(axis=ax, keepdims=True), axis=ax, keepdims=True )
Please let me know if you have any questions / suggestions.
Regards, ZJ
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
participants (4)
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Eric Firing -
Sebastian Berg -
Stephan Hoyer -
Zijie Poh