On Wed, Jul 16, 2014 at 6:37 AM, Tony Yu tsy...@gmail.com wrote:
Is there any reason why the defaults for `allclose` and `assert_allclose`
differ? This makes debugging a broken test much more difficult. More
importantly, using an absolute tolerance of 0 causes failures for some
common cases.
On 16 Jul 2014 10:26, Tony Yu tsy...@gmail.com wrote:
Is there any reason why the defaults for `allclose` and `assert_allclose`
differ? This makes debugging a broken test much more difficult. More
importantly, using an absolute tolerance of 0 causes failures for some
common cases. For example,
Le 15/07/2014 18:18, Chris Barker a écrit :
(or does HDF support var-length
elements?)
It does: http://www.hdfgroup.org/HDF5/doc/TechNotes/VLTypes.html
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Hi,
good argument for ASCII, but utf-8 is a bad idea, as there is no 1:1
correspondence between length of string in bytes and length in characters
-- as numpy needs to pre-allocate a defined number of bytes for a dtype,
there is a disconnect between the user and numpy as to how long a string is
Hi all,
sorry for not posting earlier, post-conference InboxInfinity blues and all
that...
The BoF did go as planned, and it was a good discussion, mostly following
the tentative agenda outlined here:
https://github.com/numpy/numpy/wiki/Numpy-BoF-at-Scipy-2014
Various folks were kind enough to
On Di, 2014-07-15 at 10:22 +0100, Neil Hodgson wrote:
Hi,
We came across this bug while using np.cross on 3D arrays of 2D
vectors.
Hi,
which numpy version are you using? Until recently, the cross product
simply did *not* work in a broadcasting manner (3d arrays of 2d
vectors), it did
On Mi, 2014-07-16 at 09:07 +0100, Nathaniel Smith wrote:
Weirdly, I never received Chuck's original email in this thread.
Should some list admin be informed?
I send some mails yesterday and they never arrived... Not sure if it is
a problem on my side or not.
I also am not sure what/where
On Tue, Jul 15, 2014 at 5:26 AM, Sebastian Berg sebast...@sipsolutions.net
wrote:
On Sa, 2014-07-12 at 12:17 -0500, Charles R Harris wrote:
As previous posts have pointed out, Numpy's `S` type is currently
treated as a byte string, which leads to more complicated code in
python3. OTOH, the
On Jul 16, 2014 11:43 AM, Chris Barker chris.bar...@noaa.gov wrote:
So numpy should have dtypes to match these. We're a bit stuck, however,
because 'S' mapped to the py2 string type, which no longer exists in py3.
Sorry not running py3 to see what 'S' does now, but I know it's bit broken,
and may
Hi,
15.07.2014 21:06, Julian Taylor kirjoitti:
[clip: __numpy_ufunc__]
So I'm wondering if we should delay the introduction of this
feature to 1.10 or is it important enough to wait until there is a
consensus on the remaining issues?
My 10c:
The feature is not so much in hurry that it alone
On 17 Jul 2014 11:51, Sebastian Berg sebast...@sipsolutions.net wrote:
On Mi, 2014-07-16 at 09:07 +0100, Nathaniel Smith wrote:
Weirdly, I never received Chuck's original email in this thread.
Should some list admin be informed?
I send some mails yesterday and they never arrived... Not
Hi,
We came across this bug while using np.cross on 3D arrays of 2D vectors.
What version of numpy are you using? This should already be solved in numpy
master, and be part of the 1.9 release. Here's the relevant commit,
although the code has been cleaned up a bit in later ones:
On Wed, Jul 16, 2014 at 6:48 AM, Todd toddr...@gmail.com wrote:
On Jul 16, 2014 11:43 AM, Chris Barker chris.bar...@noaa.gov wrote:
So numpy should have dtypes to match these. We're a bit stuck, however,
because 'S' mapped to the py2 string type, which no longer exists in py3.
Sorry not
On Tue, Jul 15, 2014 at 4:26 AM, Sebastian Berg sebast...@sipsolutions.net
wrote:
Just wondering, couldn't we have a type which actually has an
(arbitrary, python supported) encoding (and bytes might even just be a
special case of no encoding)?
well, then we're back to the core issue here:
On Mon, Jul 14, 2014 at 10:00 AM, Olivier Grisel olivier.gri...@ensta.org
wrote:
2014-07-13 19:05 GMT+02:00 Alexander Belopolsky ndar...@mac.com:
I've been toying with the idea of creating an array type for interned
strings. In many applications dealing with large arrays of variable size
Hi,
We came across this bug while using np.cross on 3D arrays of 2D vectors.
What version of numpy are you using? This should already be solved in numpy
master, and be part of the 1.9 release. Here's the relevant commit,
although the code has been cleaned up a bit in later ones:
On Tue, Jul 15, 2014 at 7:06 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
hi,
as you may know we want to release numpy 1.9 soon. We should have solved
most indexing regressions the first beta showed.
The remaining blockers are finishing the new __numpy_ufunc__ feature.
This feature
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