On Fri, Jan 9, 2009 at 5:11 AM, Christopher Hanley <chanley@stsci.edu> wrote:
David Cournapeau wrote:
On Fri, Jan 9, 2009 at 4:29 AM, Christopher Hanley <chanley@stsci.edu> wrote:
David Cournapeau wrote:
On Fri, Jan 9, 2009 at 1:37 AM, Christopher Hanley <chanley@stsci.edu> wrote:
Hi,
I've committed the following change to test_print.py to fix one of the tests.
Hi Christopher,
Please do not modify those tests - they are supposed to fail,
David _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion Hi David,
Sorry. Should these tests be generating a "known failures" then?
No. The problem are known, and are being fixed (in a branch). Since the problem is only in the development trunk, I don't see any problem with having failures for some time,
David _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
I would disagree. If you were to attempt the following:
n = numpy.test() n.wasSuccessful()
You expect the result to be 'True'. If not it is necessary to find out why. Right now the following occurs:
n.wasSuccessful() False
I have no way of knowing that you wanted those tests to fail unless you have them marked as KNOWNFAIL. Since we use numpy in our production systems I need to determine why numpy is failing. We track the changes on the trunk because we need to know how changes will effect our code prior to our customers downloading the latest numpy release.
I don't understand: you can't expect the trunk to always work. We try not to break it - but sometimes it does not work. Personally, I don't like knownfailure much anyway: I feel like it is too easy to tag one test known failure, and then nobody cares about it anymore. Those formatting problems were already problems before - the tests only show the problem, it does not cause the problem, so I don't understand why it is so important: a 100 % running test suite with a problem which is not shown or a 95 % running test suite with the problem is the same thing; the code in numpy itself is exactly the same. David