On Tue, Sep 27, 2022 at 10:58 AM Filippo Tagliacarne <
filippotagliaca...@gmail.com> wrote:
> Hello everyone,
>
> I hope this is the correct place to post this as this is my first
> (potential) contribution to numpy.
>
Hi Filippo, yes this is the correct place. Thanks for your proposal, and
for being patient when no one replied the first time around.
>
> I would like to implement a function numpy.shift which would work
> similarly to numpy.roll, with one key difference. After shifting an array
> by a value `shift`, the function fills the missing values with a
> `fill_value`
> For example shifting the following array by 1 along axis 1with fill_value
> of 0
>
> >>> arr = numpy.arange(10).reshape((2,5))
> >>> arr
> array([[0, 1, 2, 3, 4],
> [5, 6, 7, 8, 9]])
> >>> numpy.shift(arr, 1, axis=1, fill_value=0)
>
> array([[0, 0, 1, 2, 3],
> [0, 5, 6, 7, 8]])
>
This shift function is straightforward to implement on top of roll:
>>> x = np.roll(arr, 1)
>>> x[:, 0] = 0 # fill_value
>>> x
array([[0, 0, 1, 2, 3],
[0, 5, 6, 7, 8]])
We prefer not to add new convenience functions like this to NumPy if they
can be implemented in a couple of lines of code. There are a huge amount of
such functions possible; it's better to implement those in the downstream
library or end user code where it is needed.
Cheers,
Ralf
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