I am searching for the exact git hash corresponding to numpy 1.12.1
Looking at the result of `git tag -l` yielded tags for v1.11.0, but
nothing higher.
Also the documentation site
https://docs.scipy.org/doc/numpy/release.html does not seem to list
1.12.1, but I guess that is a separate issue
Congrats to all on the release.Two questions:
Is there a guide to building standard wheels for NumPy?
Assuming I can build standardized PyPy 2.7 wheels for Ubuntu, Win32 and
OSX64, how can I get them blessed and uploaded to PyPI?
Matti
On 17/11/16 07:47, numpy-discussion-requ...@scipy.org w
On 07/11/16 10:19, numpy-discussion-requ...@scipy.org wrote:
Date: Sun, 06 Nov 2016 17:56:12 +0100
From: Sebastian Berg
To:numpy-discussion@scipy.org
Subject: Re: [Numpy-discussion] Numpy 1.12.x branched
Message-ID:<1478451372.3875.5.ca...@sipsolutions.net>
Content-Type: text/plain; charset="utf-
test_closing_fid essentially calls this to ensure close() is called when
a NpzFile object goes out of context:
for i in range(1, 1025):
np.load(tmp)["data"]
This raises a ResourceWarning on python 3, and fails on pypy since the
garbage collector works differently.
It seems to be a clas
Am I missing something simple? I
- install git, subversion, gcc, gfortran (Ubuntu 16.04)
- create a clean python2 virtual env (no numpy)
- activate it
- git clone numpy
- cd into it
- python runtests.py
- wait
And it fails tests because it cannot find f2py.
Then I
- python setup.py install # to i
Hi. This is a heads up and RFC about a pull request I am preparing for
PyArray_Scalar, within the framework of getting NumPy working properly
on PyPy. For those who don't know, the numpy HEAD builds and runs on
PyPy2.7 HEAD (otherwise known as nightly default). However there are a
number of tes
I am trying to
understand how nditer(ops, order='K') handles C and F order. In
the documentation it states
"âKâ means as close to the order the array elements appear in
memory as possible"
but I seem to be getting inconsistent results (numpy 1.9):
The algorithms for cubics and quadratics come from Numerical Recipes
(3rd ed.), and the quartic problem is internally reduced to a cubic and
two quadratics, using well-known standard tricks.Nice, wll documented code.
Just to be sure you are on safe ground, you took the *algorithms* but
In general, you will find more help on pypy-related questions in a
pypy-related communication channel like pypy-...@python.org or
visiting us on IRC at #pypy.
This is a known issue with the numpy-status page, since you are not
the first to notice I will try to fix it soon
11:19 +0100
> From: Nathaniel Smith
> Subject: Re: [Numpy-discussion] NumPy-Discussion OpenBLAS and dotblas
> To: Discussion of Numerical Python
>
> On Sat, Aug 9, 2014 at 8:35 PM, Matti Picus wrote:
>> Hi. I am working on numpy in pypy. It would be much more challenging for
&g
Hi. I am working on numpy in pypy. It would be much more challenging for
me if you merged more code into the core of numpy, that means even more
must be duplicated when using a different underlying ndarray
implementation.
If you are thinking of touching linalg/lapack_lite/blas_lite, I would
pre
approach interests anyone, the code is on the cffi-random fork
of numpy at bitbucket.org/pypy/numpy, which is a partial implementation
of pure-python numpy for pypy.
Comments are welcome
Matti Picus
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NumPy-Discussion mailing list
NumPy-Discussion
Hi. I started to port the stdlib cmath C99 compatible complex number
tests to numpy, after noticing that numpy seems to have different
complex number routines than cmath. The work is available on a
"retest_complex" branch of numpy
https://github.com/mattip/numpy/tree/retest_complex
The tests can
t;iso-8859-1"
On Thu, Jul 4, 2013 at 9:43 PM, Matti Picus wrote:
> round() does not consistently preserve subtype of the ndarray,
> is this known behaviour or should I file a bug for it?
>
That looks like a bug to me. The docstring explicitly says that
round() does not consistently preserve subtype of the ndarray,
is this known behaviour or should I file a bug for it?
Python 2.7.3 (default, Sep 26 2012, 21:51:14)
[GCC 4.7.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.version.v
as a lurker, may I say that this discussion seems to have become
non-productive?
It seems all agree that docs needs improvement, perhaps a first step would
be to suggest doc improvements, and then the need for renaming may become
self-evident, or not.
aww darn, ruined my lurker status.
Matti
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