Works great, thanks. As far as benchmarks go exactly as efficient as
the "write and import" method and so much more elegant. However,
cann't find documentation, is lambdify brand new?
This is what I tried:
flambdify = sympy.lambdify((X, F, B), Y)
and used as
y = flambdify(x, f, b)
On Apr 28, 7:27 pm, Sebastian <basti...@gmail.com> wrote:
> On 04/28/2010 07:21 PM, janwillem wrote:
>
> > I have a function of a few variables that is the result of some
> > symbolic manipulations like differentiation and matrix operations. I
> > would like to do some Monte Carlo work on this to get the distribution
> > of the function result as a function of the distributions of the
> > inputs. However, the subs method (like /. in Mathematica) is much to
> > slow. Is there an elegant solution for that?
> > What came to my mind is writing the result to a file and then
> > importing that like for a more trivial case:
>
> > X, F, B = sympy.symbols('XFB')
> > Y = X/F - B
> > f = open('sympy_function.py' ,'w')
> > f.write('def fsympy(X,F,B):\n')
> > f.write(' return %s\n' % Y)
> > f.close()
> > from sympy_function import *
>
> > With numerical values for x, f and b:
> > y = fsympy(x, f, b) is 300 times faster than y = Y.subs({X:x, F:f,
> > B:b}) and only 10% slower than straightaway y = x/f -b
>
> > So that works but I do not consider it elegant. Something better?
>
> Take a look into lambdify - it basically does what you proposed without
> doing the file manipulation ;)
>
> Sebastian
>
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