You can use sympy objects inside numpy arrays, see sympy/test_external/
test_numpy.py.

However, to use subs with numpy arrays you would have to implement
this operation first. Did you have actually a look at lambdify? It
supports numpy.

Vinzent

On Jan 28, 6:17 pm, Nikolas Tezak <[email protected]> wrote:
> This goes a lot further than what I would need. I would not even need  
> the array indices to be symbolic expressions (although it certainly  
> would be nice to have that option). I would only like to substitute a  
> very large numpy array into a symbolic expression that is then  
> completely determined and should be evaluated (as efficiently as  
> possible). I understand of course that this is a bit specialized.
>
> Nikolas
>
> Am 27.01.2010 um 23:47 schrieb Aaron S. Meurer:
>
> > If I understand you correctly, it is related to this:  
> > http://code.google.com/p/sympy/issues/detail?id=16
>
> > Symbols with indices is something that would be nice to have  
> > implemented, but unfortunately isn't yet.  So maybe the information  
> > on that page will help you.
>
> > Aaron Meurer
> > On Jan 27, 2010, at 3:44 PM, Nikolas Tezak wrote:
>
> >> Hi,
>
> >> I am wondering if it were in principle possible to create subclass  
> >> of Symbol, that could handle array indices, so that at a later time  
> >> (after having performed symbolic calculations) I could just  
> >> substitute a numpy array in for that symbol.
>
> >> e.g. (10 + a[:,0] *a[:,1]).subst(a, np.array([[...]])
> >> Of course I could simply create symbols for individual array  
> >> elements, but I am working with a large number of coefficients  
> >> (~1000+) for which it would seem inefficient (at least to me) to  
> >> create individual symbols.
>
> >> I am still trying to get a good feeling for the code and I will try  
> >> to implement it myself, but maybe some of you already know of some  
> >> reason why this would not work (efficiently).
>
> >> Nikolas
>
>

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