Hi Terri,

It might also be worth checking out the workshop from this years pycon from
Eria ma:
Best Testing Practices for Data Science, on yotube here -
https://www.youtube.com/watch?v=yACtdj1_IxE

The github repo is here:
https://github.com/ericmjl/data-testing-tutorial

Cheers,
Jeremy

On Fri, Jul 14, 2017 at 5:21 PM, Olav Vahtras <[email protected]>
wrote:

> Dear Terri
>
> In addition I can recommend the following resource:
>
> pythontesting.net has a podcast series on testing and more, check out the
> new book on pytest by the site maintainer Brian Okken
>
> Regards
> Olav
>
>
>
> Olav
> > 14 juli 2017 kl. 21:36 skrev Ashwin Srinath <[email protected]>:
> >
> > If you're using Python, numpy.testing has the tools you'll need:
> >
> > https://docs.scipy.org/doc/numpy/reference/routines.testing.html
> >
> > There's also pandas.testing for testing code that uses Pandas.
> >
> > Thanks,
> > Ashwin
> >
> >> On Fri, Jul 14, 2017 at 3:27 PM, Terri Yu <[email protected]> wrote:
> >> Hi everyone,
> >>
> >> Are there any resources that explain how to write unit tests for
> scientific
> >> software?  I'm writing some software that processes audio signals and
> there
> >> are many parameters.  I'm wondering what's the best way to test floating
> >> point numeric results.
> >>
> >> Do I need to test every single parameter?  How can I verify accuracy of
> >> numeric results... use a different language / library?  I would like to
> do a
> >> good job of testing, but I also don't want to write a bunch of
> semi-useless
> >> tests that take a long time to run.
> >>
> >> I would appreciate any thoughts you have.
> >>
> >> Thank you,
> >>
> >> Terri
> >>
> >> _______________________________________________
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