Maybe they should have written their code with **kwargs that consumes all
keyword arguments rather than assuming that no keyword arguments would be
added? The problem with this approach in general is that it makes writing
code unnecessarily convoluted.
On Tue, May 5, 2015 at 1:55 PM, Nathaniel Sm
ementation
to make it fast. What I want is for that conversion to be automated. I'm
still evaluating how to best achieve that.
On Tue, Apr 28, 2015 at 6:08 AM, Francesc Alted wrote:
> 2015-04-28 4:59 GMT+02:00 Neil Girdhar :
>
>> I don't think I'm asking for so much.
bits of numexpr that
I like with my code. For my purpose, this would have been the more ideal
design.
On Mon, Apr 27, 2015 at 10:47 PM, Nathaniel Smith wrote:
> On Apr 27, 2015 5:30 PM, "Neil Girdhar" wrote:
> >
> >
> >
> > On Mon, Apr 27, 2015 at 7:42 PM, Na
Wow, cool! Are there any users of this package?
On Mon, Apr 27, 2015 at 9:07 PM, Alexander Belopolsky
wrote:
>
> On Mon, Apr 27, 2015 at 7:14 PM, Nathaniel Smith wrote:
>
>> There's no way to access the ast reliably at runtime in python -- it gets
>> thrown away during compilation.
>
>
> The "
On Mon, Apr 27, 2015 at 7:42 PM, Nathaniel Smith wrote:
> On Mon, Apr 27, 2015 at 4:23 PM, Neil Girdhar
> wrote:
> > I was told that numba did similar ast parsing, but maybe that's not true.
> > Regarding the ast, I don't know about reliability, but take a look
Also, FYI: http://numba.pydata.org/numba-doc/0.6/doc/modules/transforms.html
It appears that numba does get the ast similar to pyautodiff and only get
the ast from source code as a fallback?
On Mon, Apr 27, 2015 at 7:23 PM, Neil Girdhar wrote:
> I was told that numba did similar ast pars
2015 at 7:14 PM, Nathaniel Smith wrote:
> On Apr 27, 2015 1:44 PM, "Neil Girdhar" wrote:
> >
> > I've always wondered why numexpr accepts strings rather than looking a
> function's source code, using ast to parse it, and then transforming the
> AST. I just
I've always wondered why numexpr accepts strings rather than looking a
function's source code, using ast to parse it, and then transforming the
AST. I just looked at another project, pyautodiff, which does that. And I
think numba does that for llvm code generation. Wouldn't it be nicer to
just a
On Fri, Apr 17, 2015 at 12:09 PM, wrote:
> On Fri, Apr 17, 2015 at 11:22 AM, Neil Girdhar
> wrote:
> >
> >
> > On Fri, Apr 17, 2015 at 10:47 AM, wrote:
> >>
> >> On Fri, Apr 17, 2015 at 10:07 AM, Sebastian Berg
> >> wrote:
> >
On Fri, Apr 17, 2015 at 12:09 PM, wrote:
> On Fri, Apr 17, 2015 at 11:22 AM, Neil Girdhar
> wrote:
> >
> >
> > On Fri, Apr 17, 2015 at 10:47 AM, wrote:
> >>
> >> On Fri, Apr 17, 2015 at 10:07 AM, Sebastian Berg
> >> wrote:
> >
This relationship between outer an dot only holds for vectors. For
tensors, and other kinds of vector spaces, I'm not sure if outer products
and dot products have anything to do with each other.
On Fri, Apr 17, 2015 at 11:11 AM, wrote:
> On Fri, Apr 17, 2015 at 10:59 AM, Sebastian Berg
> wrote
On Fri, Apr 17, 2015 at 10:47 AM, wrote:
> On Fri, Apr 17, 2015 at 10:07 AM, Sebastian Berg
> wrote:
> > On Do, 2015-04-16 at 15:28 -0700, Matthew Brett wrote:
> >> Hi,
> >>
> >
> >>
> >> So, how about a slight modification of your proposal?
> >>
> >> 1) Raise deprecation warning for np.outer f
Right.
On Thu, Apr 16, 2015 at 6:44 PM, Nathaniel Smith wrote:
> On Thu, Apr 16, 2015 at 6:37 PM, Neil Girdhar
> wrote:
> > I can always put np.outer = np.multiply.outer at the start of my code to
> get
> > what I want. Or could that break things?
>
> Please don
On Thu, Apr 16, 2015 at 6:32 PM, Nathaniel Smith wrote:
> On Thu, Apr 16, 2015 at 6:19 PM, Neil Girdhar
> wrote:
> > Actually, looking at the docs, numpy.outer is *only* defined for 1-d
> > vectors. Should anyone who used it with multi-dimensional arrays have an
> >
That sounds good to me.
I can always put np.outer = np.multiply.outer at the start of my code to
get what I want. Or could that break things?
On Thu, Apr 16, 2015 at 6:28 PM, Matthew Brett
wrote:
> Hi,
>
> On Thu, Apr 16, 2015 at 3:19 PM, Neil Girdhar
> wrote:
> > Actual
Actually, looking at the docs, numpy.outer is *only* defined for 1-d
vectors. Should anyone who used it with multi-dimensional arrays have an
expectation that it will keep working in the same way?
On Thu, Apr 16, 2015 at 10:53 AM, Neil Girdhar
wrote:
> Would it be possible to deprec
On Wed, Apr 15, 2015 at 6:08 PM, wrote:
> >> On Wed, Apr 15, 2015 at 5:31 PM, Neil Girdhar
> wrote:
> >>> Does it work for you to set
> >>>
> >>> outer = np.multiply.outer
> >>>
> >>> ?
> >>>
> >>> It&#x
Fernández del Río <
jaime.f...@gmail.com> wrote:
> On Wed, Apr 15, 2015 at 8:06 AM, Neil Girdhar
> wrote:
>
>> You got it. I remember this from when I worked at Google and we would
>> process (many many) logs. With enough bins, the approximation is still
>> really
I don't understand. Are you at pycon by any chance?
On Wed, Apr 15, 2015 at 6:12 PM, wrote:
> On Wed, Apr 15, 2015 at 6:08 PM, wrote:
> > On Wed, Apr 15, 2015 at 5:31 PM, Neil Girdhar
> wrote:
> >> Does it work for you to set
> >>
> >> outer =
Does it work for you to set
outer = np.multiply.outer
?
It's actually faster on my machine.
On Wed, Apr 15, 2015 at 5:29 PM, wrote:
> On Wed, Apr 15, 2015 at 7:35 AM, Neil Girdhar
> wrote:
> > Yes, I totally agree. If I get started on the PR to deprecate np.outer,
> &g
ith n=100 bins. I don't think it does O(n) computations per point. I
think it's more like O(log(n)).
Best,
Neil
On Wed, Apr 15, 2015 at 10:02 AM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> On Wed, Apr 15, 2015 at 4:36 AM, Neil Girdhar
> wrote:
>
>
Yeah, I'm not arguing, I'm just curious about your reasoning. That
explains why not C++. Why would you want to do this in C and not Python?
On Wed, Apr 15, 2015 at 1:48 AM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> On Tue, Apr 14, 2015 at 6:16 PM, Neil Girdha
22:18 -0400, Nathaniel Smith wrote:
> > I am, yes.
> >
> > On Apr 14, 2015 9:17 PM, "Neil Girdhar" wrote:
> > Ok, I didn't know that. Are you at pycon by any chance?
> >
> > On Tue, Apr 14, 2015 at 7:16 PM, Nathaniel Smith
> >
PM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>> On Tue, Apr 14, 2015 at 4:12 PM, Nathaniel Smith wrote:
>>
>>> On Mon, Apr 13, 2015 at 8:02 AM, Neil Girdhar
>>> wrote:
>>> > Can I suggest that we instead add the P-squar
Ok, I didn't know that. Are you at pycon by any chance?
On Tue, Apr 14, 2015 at 7:16 PM, Nathaniel Smith wrote:
> On Tue, Apr 14, 2015 at 3:48 PM, Neil Girdhar
> wrote:
> > Yes, I totally agree with you regarding np.sum and np.product, which is
> why
> > I di
t;
>> On Mon, Apr 13, 2015 at 8:02 AM, Neil Girdhar
>> wrote:
>> > Can I suggest that we instead add the P-square algorithm for the dynamic
>> > calculation of histograms?
>> > (
>> http://pierrechainais.ec-lille.fr/Centrale/Option_DAD/IMPACT_fil
Yes, you're right. Although in practice, people almost always want
adaptive bins.
On Tue, Apr 14, 2015 at 5:08 PM, Chris Barker wrote:
> On Mon, Apr 13, 2015 at 5:02 AM, Neil Girdhar
> wrote:
>
>> Can I suggest that we instead add the P-square algorithm for the dynam
Can I suggest that we instead add the P-square algorithm for the dynamic
calculation of histograms? (
http://pierrechainais.ec-lille.fr/Centrale/Option_DAD/IMPACT_files/Dynamic%20quantiles%20calcultation%20-%20P2%20Algorythm.pdf
)
This is already implemented in C++'s boost library (
http://www.bo
Apr 14, 2015 2:48 PM, "Neil Girdhar" wrote:
> >
> > Okay, but by the same token, why do we have cumsum? Isn't it identical
> to
> >
> > np.add.accumulate
> >
> > — or if you're passing in multidimensional data —
> >
> > np.ad
ocumenting.
Similarly, cumprod is just np.multiply.accumulate.
Best,
Neil
On Sat, Apr 11, 2015 at 12:49 PM, Nathaniel Smith wrote:
> Documentation and a call to warnings.warn(DeprecationWarning(...)), I
> guess.
>
> On Sat, Apr 11, 2015 at 12:39 PM, Neil Girdhar
> wrote:
> > I
run took 25.59 times longer than the fastest. This could mean
that an intermediate result is being cached
100 loops, best of 3: 834 ns per loop
On Tue, Apr 14, 2015 at 7:42 AM, Neil Girdhar wrote:
> Okay, but by the same token, why do we have cumsum? Isn't it iden
Hello,
Is this desired behaviour or a regression or a bug?
http://stackoverflow.com/questions/26497656/how-do-i-align-a-numpy-record-array-recarray
Thanks,
Neil
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Is this what I want? https://github.com/numpy/numpy/pull/3987
On Sun, Oct 27, 2013 at 9:42 PM, Neil Girdhar wrote:
> Yeah, I realized that I missed that and figured it wouldn't matter since
> it was my own master and I don't plan on making other changes to numpy. If
> you
, Oct 27, 2013 at 9:38 PM, Charles R Harris wrote:
>
>
>
> On Sun, Oct 27, 2013 at 7:23 PM, Neil Girdhar wrote:
>
>> This is my first code review request, so I may have done some things
>> wrong. I think the following URL should work?
>> https://github.com/MisterShe
This is my first code review request, so I may have done some things wrong.
I think the following URL should work?
https://github.com/MisterSheik/numpy/compare
Best,
Neil
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Since I am trying to add a "printoptions" context manager, I would like to
test it. Should I add tests, or can I somehow use it from an ipython shell?
On Sun, Oct 27, 2013 at 7:12 PM, Charles R Harris wrote:
>
>
>
> On Sun, Oct 27, 2013 at 4:59 PM, Neil Girdhar wrote
Ah, sorry, didn't see that I can do that from runtests!! Thanks!!
On Sun, Oct 27, 2013 at 7:13 PM, Neil Girdhar wrote:
> Since I am trying to add a "printoptions" context manager, I would like to
> test it. Should I add tests, or can I somehow use it from an ipython she
How do I test a patch that I've made locally? I can't seem to import numpy
locally:
Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and
relaunch
your python intepreter from there.
_
Why not replace get_printoptions/set_printoptions with a context manager
accessed using numpy.printoptions in the same way that numpy.errstate
exposes a context manager to seterr/geterr? This would make the set method
redundant.
Also, the context manager returned by numpy.errstate, numpy.printopt
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