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/