I'll need to break it out of a more specialized package first, but this all 
sounds very promising. It'll be a few weeks before I can really get started 
(dissertation writing...), but I think it should only take a day or so 
after that, at least to have something to share. Maybe longer to flesh out 
the feature set and add a cleaner interface. Mostly I just want it to be 
available in a place where it'll get use.

N

On Tuesday, February 24, 2015 at 10:28:13 AM UTC-7, Jason Moore wrote:
>
> Nathan,
>
> Can you show us the code? It may help us understand what you are doing.
>
> Also, we have code in sympy that optional depends on scipy as do we other 
> packages: cython, numpy, theano, matplotlib, etc. The code generation and 
> tightly coupled symboli/numeric code is in that blurry zone about what we'd 
> add.
>
> But if you make a working separate package it can be a good start. For 
> example the PyDy project depends on sympy but deals with generating 
> specific numerical codes. If you make the package and we want to include 
> it, then great. If not, you have an easily installable package that others 
> can use and we can link to it from our docs/website.
>
>
> Jason
> moorepants.info
> +01 530-601-9791
>
> On Tue, Feb 24, 2015 at 9:08 AM, Nathan Woods <[email protected] 
> <javascript:>> wrote:
>
>> The decoupling is actually pretty easy. Unfortunately, the Sympy-specific 
>> stuff isn't especially useful without the context of a numerical 
>> integrator. 
>>
>> Maybe an example will help. Say you want to numerically integrate a step 
>> function H(x) over the interval [-1, 1]. Most numerical integrators will 
>> try to fit a polynomial to this, but polynomials can't handle the jump at 
>> x=0 gracefully, so you end up wasting a lot of processing time trying to 
>> refine it. The fix is to break up the integral, and do it in two pieces, 
>> one from [-1, 0), and one from [0, 1]. The integrator has no trouble with 
>> this, so you get a very fast evaluation. Many integrators, and in 
>> particular scipy.integrate.quad, have this capability.
>>
>> What I've done is figure out a way to get the same benefits for 
>> multidimensional functions. So, you could integrate H(x**2 + y**2 - z) over 
>> a volume, and get the same kind of efficiency. Without this, not only is 
>> evaluation of the integral very slow, but the results can sometimes be very 
>> inaccurate too. 
>>
>> Anyway, from what I can tell, a similar effect could be achieved using 
>> mpmath, rather than SciPy. Maybe that project is a better venue. 
>>
>> Thanks again!
>>
>> N
>>
>>
>> On Tuesday, February 24, 2015 at 9:17:41 AM UTC-7, Joachim Durchholz 
>> wrote:
>>>
>>> Am 24.02.2015 um 17:03 schrieb Nathan Woods: 
>>> > Anyway, I would like to package this up in a way that would be 
>>> publicly 
>>> > useful, but I'm not sure where it fits. Sympy seemed a likely guess, 
>>> but 
>>> > the SciPy dependence is problematic. 
>>>
>>> Out of the box, a separate project with a dependency on both SymPy and 
>>> SciPy would probably fit best. 
>>>
>>>  > Alternatively, the 
>>> > discontinuity-processing could be decoupled and used with any iterated 
>>> > integrator that supports manually specified points of discontinuity. 
>>>
>>> Decoupling and putting the modules into their respective projects would 
>>> work, too. 
>>> It's much more work though. 
>>>
>>>  > If 
>>> > Sympy isn't a good fit for this, I would appreciate suggestions about 
>>> other 
>>> > places that might be better. 
>>>
>>> I did not fully understand your description of what your code does, so I 
>>> can't be very specific. 
>>> In general, anything that analyzes mathematical objects for properties 
>>> of interest that go beyond a one-shot task would be a worthy addition to 
>>> SymPy. 
>>>
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