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 <[email protected]> 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 <[email protected]>
> 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 <[email protected]>
>> wrote:
>>
>>> Cool
>>>
>>>
>>> On Wed, Aug 27, 2014 at 8:07 PM, James Crist <[email protected]> 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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