On Fri, 2021-03-12 at 21:36 +0100, PIERRE AUGIER wrote:
> Hi,
>
> I'm looking for a difference between Numpy 0.19.5 and 0.20 which
> could explain a performance regression (~15 %) with Pythran.
>
> I observe this regression with the script
>
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Hi,
I'm looking for a difference between Numpy 0.19.5 and 0.20 which could explain
a performance regression (~15 %) with Pythran.
I observe this regression with the script
https://github.com/paugier/nbabel/blob/master/py/bench.py
Pythran reimplements Numpy so it is not about Numpy code for
Einsum has a secret integer argument format that appears in the Examples
section of the `np.einsum` docs, but appears not to be mentioned at all in
the parameter listing.
Eric
On Sat, 13 Mar 2021 at 00:25, Michael Lamparski
wrote:
> Greetings,
>
> I have something in my code where I can
Hi,
I tried to compile Numpy with `pip install numpy==1.20.1 --no-binary numpy
--force-reinstall` and I can reproduce the regression.
Good news, I was able to reproduce the difference with only Numpy 1.20.1.
Arrays prepared with (`df` is a Pandas dataframe)
arr = df.values.copy()
or
arr =
On Sat, 2021-03-13 at 00:33 +0100, PIERRE AUGIER wrote:
> Hi,
>
> I tried to compile Numpy with `pip install numpy==1.20.1 --no-binary
> numpy --force-reinstall` and I can reproduce the regression.
>
> Good news, I was able to reproduce the difference with only Numpy
> 1.20.1.
>
> Arrays
On 2021/03/12 1:33 PM, PIERRE AUGIER wrote:
arr.copy() or np.copy(arr) do not give the same result, with arr obtained from a
Pandas dataframe with arr = df.values. It's strange because type(df.values) gives
so I would expect arr.copy() and np.copy(arr) to give
exactly the same result.
On Sat, Mar 13, 2021 at 1:32 AM Eric Wieser wrote:
>
> Einsum has a secret integer argument format that appears in the Examples
> section of the `np.einsum` docs, but appears not to be mentioned at all in
> the parameter listing.
It's mentioned (albeit somewhat cryptically) sooner in the
Greetings,
I have something in my code where I can receive an array M of unknown
dimensionality and a list of "labels" for each axis. E.g. perhaps I might
get an array of shape (2, 47, 3, 47, 3) with labels ['spin', 'atom',
'coord', 'atom', 'coord'].
For every axis that is labeled "coord", I
