[SciPy-Dev] Consensus on the future of integrate.ode
Mon Sep 9 04:53:16 CDT 2013
2013/9/9 Benny Malengier <firstname.lastname@example.org>
> 2013/9/8 email@example.com <firstname.lastname@example.org>
> Hello Juan Luis,
>> Yes, I saw your pull request and this, in part, motivated me to start
>> this discussion. Looks like you put a lot of work into your odeint
>> I completely agree with you, that ode should be fixed first, then odeint
>> should be a nice interface wrapped around it. integrate.ode becomes a
>> package aimed at people who understand the complexities of solving ODE
>> problems and integrate.odeint is for people who just want an answer.
>> Also, thank you for doing a nice summary, that was really helpful.
>> Remark 1: Backend.
>> I don't think we need to reinvent the wheel, Benny's work looks excellent
>> so I think it make sense to base a new integrate.ode off scikits.odes. It
>> has a nice array of modern solvers, in both C and Fortran.
> Thanks for the thumbs up.
> However, odes is only one of 3 implementations, and then the one that
> exposes least of the C solvers. Odes was based originally on the previous
> pysundials which was no longer maintained and was a non-cython wrapper. The
> other implementations are geared towards a specific problem domain though.
> One is: https://github.com/casadi/casadi/wiki (LGPL tough)
> The other: http://www.jmodelica.org/assimulo (annoying copyright transfer
> contribution license though to a company,http://www.jmodelica.org/page/14)
> The non sundials solvers in odes are also not as state of the art as some
> that are added in above two interfaces.
> The problem with above packages is their license and the fact that they
> are packages with parsing language, ..., see eg:
> Note that you can pass an array of times to sundials at which you want
> output, and then all iteration is done inside sundials, so the point of
> time step outside of sundials is not needed (though in many problems you'll
> want to do stepping controlled in a python loop).
What would really be interesting is find some way to use the parallel
implementation of sundals in python. Some mapping of a numpy array to the
parallel sundial vector (
would then be needed. Has there ever been work to contruct a parallel aware
Would be nice to find funding for that somewhere
>> Remark 2 Frontend.
>> We can define a new integrate.ode interface that wraps scikits.odes. It
>> seems that you already have some ideas in that area, mentioning the MATLAB
>> style. I looked at the MATLAB docs just now and spend 20 minutes thinking
>> how this might look if it were more pythonic. You can take a look here,
>> fork your own version to make changes or add comments,
>> Best wishes,
>> SciPy-Dev mailing list
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