I was wondering about that. I wasn't sure if the overhead from looping
through the inputs multiple times would outweigh improvements from fast C
loops. Glad that in your case it does.

I've thrown a WIP PR up: https://github.com/sympy/sympy/pull/7929

For some reason, creating the functions in python with numpy calls still
seems to be faster (for micro-benchmarks). This probably has something to
do with function complexity (the example function above is simple), but I'd
still think it'd be faster in pure C. I tried inlining the call, which was
a small improvement, but it was still slower than the pure numpy-python
version. Something to look into.


On Wed, Aug 27, 2014 at 10:28 PM, Jason Moore <moorepa...@gmail.com> wrote:

> Yeh, but if you simply create a ufunc for each expression in a matrix you
> still get substantial speedups. I wrote a bunch of test cases that I'll
> post to my blog tomorrow.
>
>
> Jason
> moorepants.info
> +01 530-601-9791
>
>
> On Wed, Aug 27, 2014 at 11:26 PM, James Crist <crist...@umn.edu> wrote:
>
>> Not yet. I wrote it this morning during an extremely boring meeting, and
>> haven't had a chance to clean it up. This doesn't solve your problem about
>> broadcasting a matrix calculation though...
>>
>>
>> On Wed, Aug 27, 2014 at 10:23 PM, Jason Moore <moorepa...@gmail.com>
>> wrote:
>>
>>> Awesome. I was working on this today but it looks like you've by passed
>>> what I had working. Do you have a PR with this?
>>>
>>>
>>> Jason
>>> moorepants.info
>>> +01 530-601-9791
>>>
>>>
>>> On Wed, Aug 27, 2014 at 11:11 PM, Matthew Rocklin <mrock...@gmail.com>
>>> wrote:
>>>
>>>> Cool
>>>>
>>>>
>>>> On Wed, Aug 27, 2014 at 8:07 PM, James Crist <crist...@umn.edu> wrote:
>>>>
>>>>> I still need to do some cleanups and add tests, but I finally have
>>>>> this working and thought I'd share. I'm really happy with this:
>>>>>
>>>>> In [1]: from sympy import *
>>>>>
>>>>> In [2]: a, b, c = symbols('a, b, c')
>>>>>
>>>>> In [3]: expr = (sin(a) + sqrt(b)*c**2)/2
>>>>>
>>>>> In [4]: from sympy.utilities.autowrap import ufuncify
>>>>>
>>>>> In [5]: func = ufuncify((a, b, c), expr)
>>>>>
>>>>> In [6]: func(1, 2, 3)
>>>>> Out[6]: 6.7846965230828769
>>>>>
>>>>> In [7]: func([1, 2, 3, 4, 5], [6, 7, 8, 9, 10], 3)
>>>>> Out[7]: array([ 11.44343933,  12.36052961,  12.79848207,  13.12159875,
>>>>>  13.75078733])
>>>>>
>>>>> In [8]: from numpy import arange
>>>>>
>>>>> In [9]: a = arange(10).reshape((2, 5))
>>>>>
>>>>> In [10]: c = arange(10, 20).reshape((2, 5))
>>>>>
>>>>> In [11]: b = 25
>>>>>
>>>>> In [12]: func(a, b, c)
>>>>> Out[12]:
>>>>> array([[ 250.        ,  302.92073549,  360.45464871,  422.57056   ,
>>>>>          489.62159875],
>>>>>        [ 562.02053786,  639.86029225,  722.8284933 ,  810.49467912,
>>>>>          902.70605924]])
>>>>>
>>>>> In [13]: type(func)
>>>>> Out[13]: numpy.ufunc
>>>>>
>>>>> This now does everything a numpy `ufunc` does normally, as it *is* a
>>>>> ufunc. Codegen is hooked up to numpy api. Type conversion and broadcasting
>>>>> are done automagically.
>>>>>
>>>>> Caveats: only functions with a single output are accepted (this could
>>>>> be changed to accept multi-output without much effort though). Also, as
>>>>> with all unfuncs, input/outputs must all be scalars (no matrix/Indexed
>>>>> operations allowed).
>>>>>
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