On Mon, Dec 17, 2012 at 12:24 +0200, Tomi Pieviläinen wrote:
> I have a a simulation that has different kinds of forces to simulate,
> each a different function. I have implemented those functions in
> several modules: baseline python (Numpy), C (accessed via ctypes) and
> PyCUDA. I need to make sure I get the same results from the optimized
> versions compared to the slow but correct python version. Later I will
> be adding modules/implementations for cython, numba etc. too. Not all
> modules are always available. For example cuda depends on the current
> system hardware, so I need to skip some tests.
>
> For each opimized module I want to take the func1, func2 and func3,
> and compare them to the baseline func1, func2 and func3. So I was
> hoping I could do test functions with
>
> @parametrize(('basefunc', 'fastfunc'),
> [(func1, mod.func1),
> (func2, mod.func2),
> (func3, mod.func3)])
> @parametrize(('x, 'y'),
> [(numpy.randn(8), numpy.randn(8)),
> (numpy.randn(16), numpy.randn(16)),
> ... ])
> def test_random_arrays(x, y, basefunc, fastfunc)
> assert_arrays_almost_equal(basefunc(x, y), fastfunc(x, y))
>
>
> and have the mod parametrized. But I can't use @parametrize for
> that, since the modules can't be always imported.
>
> So I was hoping then to have a fixture for the module or the fastfunc,
> that would call pytest.skip() if the import fails, but I would need
> the fixture return a bunch of parameters for just one call which
> I've understood is not possible.
>
> I'm not really sure if what I'm aiming at makes any sense, but hopefully
> someone has an idea on how to do this in a clean way.
I came up with a rather simple approach how to handle "optional" imports.
See here:
http://pytest.org/latest/example/parametrize.html#indirect-parametrization-of-optional-implementations-imports
Works for you?
Btw, you might also want to checkout the pytest-quickcheck plugin:
http://pypi.python.org/pypi/pytest-quickcheck/
best,
holger
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