Biggus also has such a function:
https://github.com/SciTools/biggus/blob/master/biggus/__init__.py#L2878
It handles newaxis outside of that function in:
https://github.com/SciTools/biggus/blob/master/biggus/__init__.py#L537.
Again, it only aims to deal with orthogonal array indexing, not numpy fan
Nice idea. Just a repository of courses would be a great first step.
For example, I know Jake Vanderplas's course at
https://github.com/jakevdp/2013_fall_ASTR599 is useful, and I have a few
introduction (3hr) courses at https://github.com/SciTools/courses.
On 3 July 2014 16:59, Chris Barker wro
I just wanted to let you know that there is currently a vacancy for a
full-time developer at the Met Office, the UK's National Weather Service,
within our Analysis, Visualisation and Data (AVD) team.
I'm posting on this list as the Met Office's AVD team are heavily involved
in the development of P
For the record, I started a discussion about 6 months ago about a
"find_first" type function which avoided running the logic over the whole
array (using lambdas instead). This spilled into a discussion about
implementing a short-cutted "any" or "all" function:
http://numpy-discussion.10968.n7.nabbl
I didn't find the rollaxis solution particularly obvious and also had to
think about what rollaxis did before understanding its usefulness for
iteration.
Now that I've understood it, I'm +1 for the statement that, as it stands,
the proposed iteraxis method doesn't add enough to warrant its inclusio
r could then be
exposed to limit the maximum chunk size to give the user control of the
maximum memory overhead that the routine could use.
I'll submit a PR and we can discuss inline.
Thanks for the response Nathaniel.
On 27 March 2013 12:19, Nathaniel Smith wrote:
> On Tue, Mar 26
rinciple isn't desirable in the core of numpy.
Cheers,
On 8 March 2013 17:38, Phil Elson wrote:
> Interesting. I hadn't thought of those. I've implemented (very roughly
> without a sound logic check) and benchmarked:
>
> def my_any(a, predicate, chunk_size=2048):
&g
1).all()
1 loops, best of 3: 978 ms per loop
In [26]: %timeit my_all(a, lambda a: np.abs(a) < 1)
1 loops, best of 3: 73.6 us per loop
On 6 March 2013 21:16, Benjamin Root wrote:
>
>
> On Tue, Mar 5, 2013 at 9:15 AM, Phil Elson wrote:
>
>> The ticket http
The ticket https://github.com/numpy/numpy/issues/2269 discusses the
possibility of implementing a "find first" style function which can
optimise the process of finding the first value(s) which match a predicate
in a given 1D array. For example:
>>> a = np.sin(np.linspace(0, np.pi, 200))
>>> print
I tried to suggest this for our matplotlib development cycle, but it didn't
get the roaring response I was hoping for (even though I was being
conservative by suggesting a 8-9 month release time):
http://matplotlib.1069221.n5.nabble.com/strategy-for-1-2-x-master-PEP8-changes-tp39453p39465.html
In
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