This is a useful idea certainly. I would recommended extending it to an
arbitrary number of axes. You could either raise an error if the ndim of
the two arrays are unequal, or allow a broadcast of a lesser ndimmed src
array.
- Joe
On Jun 29, 2017 20:17, "Mikhail V" wrote:
To add to Allan's message: point (2), the printing of 0-d arrays, is
the one that is the most important in the sense that it rectifies a
really strange situation, where the printing cannot be logically
controlled by the same mechanism that controls >=1-d arrays (see PR).
While point 3 can also be
> Convert the doctest in `numpy/lib/tests/test_polynomial.py` to regular
tests. Might be tricky as it mostly checks print formatting.
Port scipy's refguide-check and enhance/fix/improve code examples in
docstrings?
Also somewhat janitorial though.
___
Charles R Harris kirjoitti 29.06.2017 klo 20:45:
> Here's a random idea: how about building a NumPy gallery?
> scikit-{image,learn} has it, and while those projects may have more
> visual datasets, I can imagine something along the lines of Nicolas
> Rougier's beautiful book:
>
>
On Thu, Jun 29, 2017 at 12:15 PM, Stefan van der Walt
wrote:
> On Thu, Jun 29, 2017, at 11:09, Charles R Harris wrote:
>
>
> On Thu, Jun 29, 2017 at 12:07 PM, Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
> I will be running the NumPy sprint at Scipy 2017 and I'm
On Thu, Jun 29, 2017, at 11:09, Charles R Harris wrote:
>
> On Thu, Jun 29, 2017 at 12:07 PM, Charles R Harris
> wrote:>> I will be running the NumPy sprint at
> Scipy 2017 and I'm trying to
>> put together a suitable list of things to sprint on. In my
>> experience,
On Thu, Jun 29, 2017 at 12:07 PM, Charles R Harris <
charlesr.har...@gmail.com> wrote:
> Hi All,
>
> I will be running the NumPy sprint at Scipy 2017 and I'm trying to put
> together a suitable list of things to sprint on. In my experience,
> sprinting on NumPy is hard, enhancements generally
Hi All,
I will be running the NumPy sprint at Scipy 2017 and I'm trying to put
together a suitable list of things to sprint on. In my experience,
sprinting on NumPy is hard, enhancements generally need lengthy review and
even finding and doing simple bug fixes can take time. What I have in mind