It is not new. It looks like it has a docstring. You can see it if you type
help(lambdify).
Aaron Meurer
On Apr 28, 2010, at 12:10 PM, janwillem wrote:
> 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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