Anne,

Thanks for the info. I don't see anything... [wow, 14 month delay; Dec 2015 to Feb 2017]  in this documentation that explains how to use the dense output (interpolation coefficients)  for the last step when using vode, as you've mentioned.

I have a few work-arounds available, but I'd really like to try the following. If I detect a result sufficiently close to a target, I'd like to call the interpolation from a minimizer to find the moment and location of closest approach within this step. If it is not close enough, I'd then like to take the next step and test again.

ultimately this may not be the best way to do this, but I'd like to try this none-the-less.

How can I do this with vode - as you described in December 2015? Is there a tutorial or example of something like this being done within Python 2.7 / SciPy?

Thanks!
David

On Fri, Dec 4, 2015 at 9:47 PM, Anne Archibald wrote:

Dense output is actually already available from some of the integrators - I believe vode in particular. It is limited to evaluation within the last step, so the most reliable way to use it is to allow the adaptive stepper to take a single step then evaluate within it.

For many purposes, for example stopping criteria, evaluation within the last step is cheap and sufficient, and many solver implementations provide an interface for it. But it would occasionally be useful to have a solution object that can be evaluated anywhere - preferably including one or more derivatives.

Anne

On Mon, Nov 23, 2015, 12:43 David Mikolas <david.mikolas1@gmail.com> wrote:
OK, Jim Martin has started this and I'm trying to add momentum and any help that I can. I'm already using this feature (access to interpolation coefficients) by installing an environment with his changes, so I can run the integration flat out, yet get results at arbitrary density with nearly the same precision.

I'm new to SciPy-dev and mail lists in general, appreciate any/all suggestions. Thanks!

David

On Mon, Nov 23, 2015 at 4:28 PM, Evgeni Burovski wrote:

Hi David,

This sounds like a useful feature.
I guess a pull request would be welcome. (even though I'm not a user, and I won't be able to review it).

Evgeni

23.11.2015 10:47 пользователь "David Mikolas" <david.mikolas1@gmail.com> написал:

One way that dense output for dopri5 and dop853 can be very useful is if the integration is expensive/long and you don't want to repeat it, but you want to obtain results at new time points at a later date, or even iterate on it - for example, find the time of closest approac. This is done by saving the interpolation coefficients. I put a simple example here, though there is no saving to disk yet.

http://pastebin.com/e6qNjbL9   dendop_test_v00.py

I wonder if this could be developed into an option to return an interpolator object or function. If I can help let me know.

On Sat, Nov 14, 2015 at 12:19 AM, Evgeni Burovski wrote:
Hi David, Hi Jim,

> I am new to SciPy-dev. Will the dense output option in scipy.integrate.ode
> become available in 0.17.0? This is a feature already available in the
> original FORTAN, but wasn't implemented in the wrapper.

If the feature is sent as a pull request against the scipy master
branch, the PR is reviewed by the maintainers of the integrate package
and merged into master before the release split, then yes, it would be
available in 0.17.0.

So far I do not see any progress towards it.

> I wrote these dense output extensions that you listed:
>
> https://github.com/jddmartin/scipy/tree/dense_output_from_dopri5_and_dop853
> https://github.com/jddmartin/dense_output_example_usage
>
> but I didn't issue any pull request to scipy.  I sent this message to the
> scipy developers list:
>   http://article.gmane.org/gmane.comp.python.scientific.devel/19635/
> explaining the changes.  I was hoping for some feedback before issuing a
> pull request.

Ah, I see that the email likely fell through the cracks back in April.
You might want to ping that email thread once more or send a pull
request on github (or both).

<snip>

Cheers,

Evgeni
_______________________________________________
SciPy-Dev mailing list
SciPy-Dev@scipy.org
https://mail.scipy.org/mailman/listinfo/scipy-dev

_______________________________________________
SciPy-Dev mailing list
SciPy-Dev@scipy.org
https://mail.scipy.org/mailman/listinfo/scipy-dev

_______________________________________________
SciPy-Dev mailing list
SciPy-Dev@scipy.org
https://mail.scipy.org/mailman/listinfo/scipy-dev

_______________________________________________
SciPy-Dev mailing list
SciPy-Dev@scipy.org
https://mail.scipy.org/mailman/listinfo/scipy-dev

_______________________________________________
SciPy-Dev mailing list
SciPy-Dev@scipy.org
https://mail.scipy.org/mailman/listinfo/scipy-dev