Matthieu Brucher wrote:
> Matthew B. will be working on converting SciPy tests to use nose per
> Fernando's email. If you are familiar with nose and want to help,
> please make sure to check with Matthew or Fernando first.
>
>
> I must have missed Fernando's email because I can't find the references
> for nose :(
Look in "SciPy Sprint Results". It's only a brief mention, though.
> What are its advantages against the current numpy.testing framework ?
Primarily:
* It is supported by someone else and gaining wide adoption by the rest of the
Python community.
Secondarily:
* More flexible organization of tests. For nose, if it looks like a test, it's a
test. numpy.testing collects test modules which are named like the module it is
testing. E.g. for module.py <=> tests/test_module.py.
* Test generators:
def test_evens():
for i in range(0, 5):
yield check_even, i, i*3
def check_even(n, nn):
assert n % 2 == 0 or nn % 2 == 0
* Package- and module-level setup() and teardown() functions.
* Test functions can be simple functions. They do not need to be organized into
classes if you don't need classes.
* Integrated doctest collection.
* Detailed error/failure reporting. nose can print out the values of variables
at the location of the error.
* Integrated code coverage and profiling.
* Dropping into pdb on errors and failures.
* More flexible running of specific tests. E.g. when I'm working on getting a
particular test function running, I can specify that exact test and not run the
rest of the test suite.
* Output capture. Tests can print out anything they like to be more informative,
but they won't appear unless if the test fails.
More thoroughly:
http://somethingaboutorange.com/mrl/projects/nose/
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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