Yes, Julian is doing an amazing work on getting rid of temporaries inside
NumPy. However, NumExpr still has the advantage of using multi-threading
right out of the box, as well as integration with Intel VML. Hopefully
these features will eventually arrive to NumPy, but meanwhile there is
still va
Hi All,
Just a side note that at a smaller scale some of the benefits of
numexpr are coming to numpy: Julian Taylor has been working on
identifying temporary arrays in
https://github.com/numpy/numpy/pull/7997. Julian also commented
(https://github.com/numpy/numpy/pull/7997#issuecomment-246118772)
Hi Juan,
A guy on reddit suggested looking at SymPy for just such a thing. I know
that Dask also represents its process as a graph.
https://www.reddit.com/r/Python/comments/5um04m/numexpr3/
I'll think about it some more but it seems a little abstract still. To a
certain extent the NE3 compiler a
Hi everyone,
Thanks for this. It looks absolutely fantastic. I've been putting off using
numexpr but it looks like I don't have a choice anymore. ;)
Regarding feature requests, I've always found it off putting that I have to
wrap my expressions in a string to speed them up. Has anyone explored
Hi David,
Thanks for your comments, reply below the fold.
On Fri, Feb 17, 2017 at 4:34 PM, Daπid wrote:
> This is very nice indeed!
>
> On 17 February 2017 at 12:15, Robert McLeod wrote:
> > * bytes and unicode support
> > * reductions (mean, sum, prod, std)
>
> I use both a lot, maybe I can h
This is very nice indeed!
On 17 February 2017 at 12:15, Robert McLeod wrote:
> * bytes and unicode support
> * reductions (mean, sum, prod, std)
I use both a lot, maybe I can help you get them working.
Also, regarding "Vectorization hasn't been done yet with cmath
functions for real numbers (su
Yay! This looks really exciting. Thanks for all the hard work!
Francesc
2017-02-17 12:15 GMT+01:00 Robert McLeod :
> Hi everyone,
>
> I'm pleased to announce that a new branch of NumExpr has been developed
> that will hopefully lead to a new major version release in the future. You
> can find
Hi everyone,
I'm pleased to announce that a new branch of NumExpr has been developed
that will hopefully lead to a new major version release in the future. You
can find the branch on the PyData github repository, and installation is as
follows:
git clone https://github.com/pydata/numexpr.git
cd