I've put up a pull request implementing a new function, np.moveaxis, as an
alternative to np.transpose and np.rollaxis:
https://github.com/numpy/numpy/pull/6630
This functionality has been discussed (even the exact function name)
several times over the years, but it never made it into a pull reque
On Thu, Nov 5, 2015 at 5:11 AM, Nathaniel Smith wrote:
> On Wed, Nov 4, 2015 at 4:40 PM, Stefan Seefeld
> wrote:
> > Hello,
> >
> > is there a way to query Numpy for information about backends (BLAS,
> > LAPACK, etc.) that it was compiled against, including compiler / linker
> > flags that were
On Wed, Nov 4, 2015 at 4:40 PM, Stefan Seefeld wrote:
> Hello,
>
> is there a way to query Numpy for information about backends (BLAS,
> LAPACK, etc.) that it was compiled against, including compiler / linker
> flags that were used ?
> Consider the use-case where instead of calling a function such
Hello,
is there a way to query Numpy for information about backends (BLAS,
LAPACK, etc.) that it was compiled against, including compiler / linker
flags that were used ?
Consider the use-case where instead of calling a function such as
numpy.dot() I may want to call the appropriate backend directl
On 3 Nov 2015, at 6:03 pm, Chris Barker - NOAA Federal
wrote:
>
> I was more aiming to point out a situation where the NumPy's text file reader
> was significantly better than the Pandas version, so we would want to make
> sure that we properly benchmark any significant changes to NumPy's text
Yup, https://github.com/numpy/numpy/issues/6196
On Wed, Nov 4, 2015 at 1:45 PM, Charles R Harris
wrote:
>
>
> On Wed, Nov 4, 2015 at 12:42 PM, Nathan Goldbaum
> wrote:
>
>> Oh oops, this is about np.allcose, not np.assert_allclose. Sorry for the
>> noise...
>>
>
> Probably related ;) Did you op
On Wed, Nov 4, 2015 at 12:42 PM, Nathan Goldbaum
wrote:
> Oh oops, this is about np.allcose, not np.assert_allclose. Sorry for the
> noise...
>
Probably related ;) Did you open an issue for it?
Chuck
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On Wed, Nov 4, 2015 at 12:40 PM, Benjamin Root wrote:
> I am not sure I understand what you mean. Specifically that np.isclose
> will return a memmap if one of the inputs is a memmap. The result is a
> brand new array, right? So, what is that result memmapping from? Also, how
> does this impact n
Oh oops, this is about np.allcose, not np.assert_allclose. Sorry for the
noise...
On Wed, Nov 4, 2015 at 1:36 PM, Nathan Goldbaum
wrote:
> I actually brought this up before 1.10 came out:
> https://github.com/numpy/numpy/issues/6196
>
> The behavior change brought out a bug in our use of allclos
I am not sure I understand what you mean. Specifically that np.isclose will
return a memmap if one of the inputs is a memmap. The result is a brand new
array, right? So, what is that result memmapping from? Also, how does this
impact np.allclose()? That function returns a scalar True/False, so what
I actually brought this up before 1.10 came out:
https://github.com/numpy/numpy/issues/6196
The behavior change brought out a bug in our use of allclose, so while it
was annoying in the sense that our test suite started failing in a new way,
it was good in that our tests are now more correct.
On
Hi All,
This is to open a discussion of a change of behavior of `np.allclose`. That
function uses `isclose` in numpy 1.10 with the result that array subtypes
are preserved whereas before they were not. In particular, memmaps are
returned when at least one of the inputs is a memmap. By and large I
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