[yt-dev] Testing Intervention

[Casey W. Stark]

Hi Matt.

I would like to help with this, but it's difficult to figure out where to
start.

Say I want to test projections. I make a fake 3D density field, maybe
something as simple as np.arange(4**3).reshape((4, 4, 4)). I write down the
answer to the x-projection. Now all I need to do is call
assert_allclose(yt_result, answer, rtol=1e-15), but I don't know what
pieces of low-level yt stuff to call to get to `yt_result`. Hopefully
that's clear...

Maybe this comes down to creating a fake frontend we can attach fields to?

- Casey


On Fri, Sep 21, 2012 at 2:42 PM, Matthew Turk <matthewturk at gmail.com> wrote:

> Hi all,
>
> As some of you have seen (at least Stephen), I filed a ticket this
> morning about increasing testing coverage.  The other night Anthony
> and I met up in NYC and he had something of an "intervention" about
> the sufficiency of answer testing for yt; it didn't take too much work
> on his part to convince me that we should be testing not just against
> a gold standard, but also performing unit tests.  In the past I had
> eschewed unit testing simply because the task of mocking data was
> quite tricky, and by adding tests that use smaller bits we could cover
> unit testable areas with answer testing.
>
> But, this isn't really a good strategy.  Let's move to having both.
> The testing infrastructure he recommends is the nearly-omnipresent
> nose:
>
> http://nose.readthedocs.org/en/latest/
>
> The ticket to track this is here:
>
> https://bitbucket.org/yt_analysis/yt/issue/426/increase-unit-test-coverage
>
> There are a couple sub-items here:
>
> 1) NumPy's nose test plugins provide a lot of necessary functionality
> that we have reimplemented in the answer testing utilities.  I'd like
> to start using the numpy plugins, which include things like
> conditional test execution, array comparisons, "slow" tests, etc etc.
> 2) We can evaluate, using conditional test execution, moving to nose
> for answer testing.  But that's not on the agenda now.
> 3) Writing tests for nose is super easy, and running them is too.  Just do:
>
> nosetest -w yt/
>
> when in your source directory.
>
> 4) I've written a simple sample here:
>
>
> https://bitbucket.org/yt_analysis/yt-3.0/src/da10ffc17f6d/yt/utilities/tests/test_interpolators.py
>
> 5) I'll handle writing up some mock data that doesn't require shipping
> lots of binary files, which can then be used for checking things that
> absolutely require hierarchies.
>
> --
>
> The way to organize tests is easy.  Inside each directory with
> testable items create a new directory called "tests", and in here toss
> some scripts.  You can stick a bunch of functions in those scripts.
>
> Anyway, I'm going to start writing more of these (in the main yt repo,
> and this change will be grafted there as well) and I'll write back
> once the data mocking is ready.  I'd like it if we started encouraging
> or even mandating simple tests (and/or answer tests) for functionality
> that gets added, but that's a discussion that should be held
> separately.
>
> The items on the ticket:
>
>  * kD-tree for nearest neighbor
>  * Geometric selection routines
>  * Profiles
>  * Projections -- underlying quadtree
>  * Data object selection of data containers
>  * Data object selection of points
>  * Orientation class
>  * Pixelization
>  * Color maps
>  * PNG writing
>
> Is anyone willing to claim any additional items that they will help
> write unit tests for?
>
> -Matt
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