Hi,
Thanks to everybody for all you valuable responses. This approach by
Rick White seems to nail it all down:
>> b = np.array([
>> [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 7, 8, 9, 10,
>> 11],
>> [5, 6, 1, 0, 2, 7, 3, 8, 1, 4, 9, 2, 10, 5, 3, 4, 11, 0, 0, 1, 2, 3, 4, 5]
>
Hi,
I'm happy to announce the availability of the scipy 0.13.3 release. This is
a bugfix only release; it contains fixes for regressions in ndimage and
weave.
Source tarballs can be found at
https://sourceforge.net/projects/scipy/files/scipy/0.13.3/ and on PyPi.
Release notes copied below, binari
On Mon, Feb 3, 2014 at 2:32 PM, Travis Oliphant wrote:
> Hey Sebastien,
>
> I didn't mean to imply that you would need to necessarily work on it.
> But the work Jay has done could use review.
>
> There are also conversations to have about what to do to resolve the
> ambiguity that led to the curr
Hey Sebastien,
I didn't mean to imply that you would need to necessarily work on it. But
the work Jay has done could use review.
There are also conversations to have about what to do to resolve the
ambiguity that led to the current behavior.
Thank you or all the great work on the indexing code
On Sun, 2014-02-02 at 13:11 -0600, Travis Oliphant wrote:
> This sounds like a great and welcome work and improvements.
>
> Does it make sense to also do something about the behavior of advanced
> indexing when slices are interleaved between lists and integers.
>
> I know that jay borque has some
On Mon, 2014-02-03 at 00:41 -0800, Dinesh Vadhia wrote:
> Does the numpy indexing refactorizing address the performance of fancy
> indexing highlighted in wes mckinney's blog some years back -
> http://wesmckinney.com/blog/?p=215 - where numpy.take() was shown to
> be preferable than fancy indexing
I think you'll find the algorithm below to be a lot faster, especially if the
arrays are big. Checking each array index against the list of included or
excluded elements is must slower than simply creating a secondary array and
looking up whether the elements are included or not.
b = np.array(
Seconding Jaime; I use this trick in mesh manipulations a lot as well.
There are a lot of graph-type manipulations you can express effectively in
numpy using np.unique and related functionality.
On Sun, Feb 2, 2014 at 11:57 PM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> Cannot test
On 2 February 2014 20:58, Mads Ipsen wrote:
> ie. bond 0 connects atoms 0 and 5, bond 1 connects atom 0 and 6, etc. In
> practical examples, the list can be much larger (N > 100.000 connections.
>
Perhaps you should consider an alternative approach. You could consider it
a graph, and you could
Does the numpy indexing refactorizing address the performance of fancy indexing
highlighted in wes mckinney's blog some years back -
http://wesmckinney.com/blog/?p=215 - where numpy.take() was shown to be
preferable than fancy indexing?
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