ok from pandas we test with numpy master on Travis (which does pick up things!)
thanks > On Jul 4, 2014, at 7:07 PM, Charles R Harris <charlesr.har...@gmail.com> > wrote: > > > > >> On Fri, Jul 4, 2014 at 3:33 PM, Nathaniel Smith <n...@pobox.com> wrote: >> On Fri, Jul 4, 2014 at 10:31 PM, Charles R Harris >> <charlesr.har...@gmail.com> wrote: >> > >> > On Fri, Jul 4, 2014 at 3:15 PM, Nathaniel Smith <n...@pobox.com> wrote: >> >> >> >> On Fri, Jul 4, 2014 at 9:48 PM, Charles R Harris >> >> <charlesr.har...@gmail.com> wrote: >> >> > >> >> > On Fri, Jul 4, 2014 at 2:41 PM, Nathaniel Smith <n...@pobox.com> wrote: >> >> >> >> >> >> On Fri, Jul 4, 2014 at 9:33 PM, Charles R Harris >> >> >> <charlesr.har...@gmail.com> wrote: >> >> >> > >> >> >> > On Fri, Jul 4, 2014 at 2:09 PM, Nathaniel Smith <n...@pobox.com> >> >> >> > wrote: >> >> >> >> >> >> >> >> On Fri, Jul 4, 2014 at 9:02 PM, Ralf Gommers >> >> >> >> <ralf.gomm...@gmail.com> >> >> >> >> wrote: >> >> >> >> > >> >> >> >> > On Fri, Jul 4, 2014 at 10:00 PM, Charles R Harris >> >> >> >> > <charlesr.har...@gmail.com> wrote: >> >> >> >> >> >> >> >> >> >> On Fri, Jul 4, 2014 at 1:42 PM, Charles R Harris >> >> >> >> >> <charlesr.har...@gmail.com> wrote: >> >> >> >> >>> >> >> >> >> >>> Sebastian Seberg has fixed one class of test failures due to the >> >> >> >> >>> indexing >> >> >> >> >>> changes in numpy 1.9.0b1. There are some remaining errors, and >> >> >> >> >>> in >> >> >> >> >>> the >> >> >> >> >>> case >> >> >> >> >>> of the Matplotlib failures, they look to me to be Matplotlib >> >> >> >> >>> bugs. >> >> >> >> >>> The >> >> >> >> >>> 2-d >> >> >> >> >>> arrays that cause the error are returned by the overloaded >> >> >> >> >>> _interpolate_single_key function in CubicTriInterpolator that is >> >> >> >> >>> documented >> >> >> >> >>> in the base class to return a 1-d array, whereas the actual >> >> >> >> >>> dimensions >> >> >> >> >>> are >> >> >> >> >>> of the form (n, 1). The question is, what is the best work >> >> >> >> >>> around >> >> >> >> >>> here >> >> >> >> >>> for >> >> >> >> >>> these sorts errors? Can we afford to break Matplotlib and other >> >> >> >> >>> packages on >> >> >> >> >>> account of a bug that was previously accepted by Numpy? >> >> >> >> > >> >> >> >> > >> >> >> >> > It depends how bad the break is, but in principle I'd say that >> >> >> >> > breaking >> >> >> >> > Matplotlib is not OK. >> >> >> >> >> >> >> >> I agree. If it's easy to hack around it and issue a warning for now, >> >> >> >> and doesn't have other negative consequences, then IMO we should >> >> >> >> give >> >> >> >> matplotlib a release or so worth of grace period to fix things. >> >> >> > >> >> >> > >> >> >> > Here is another example, from skimage. >> >> >> > >> >> >> > >> >> >> > ====================================================================== >> >> >> > ERROR: test_join.test_relabel_sequential_offset1 >> >> >> > >> >> >> > ---------------------------------------------------------------------- >> >> >> > Traceback (most recent call last): >> >> >> > File "X:\Python27-x64\lib\site-packages\nose\case.py", line 197, in >> >> >> > runTest >> >> >> > self.test(*self.arg) >> >> >> > File >> >> >> > >> >> >> > >> >> >> > "X:\Python27-x64\lib\site-packages\skimage\segmentation\tests\test_join.py", >> >> >> > line 30, in test_relabel_sequential_offset1 >> >> >> > ar_relab, fw, inv = relabel_sequential(ar) >> >> >> > File >> >> >> > "X:\Python27-x64\lib\site-packages\skimage\segmentation\_join.py", >> >> >> > line 127, in relabel_sequential >> >> >> > forward_map[labels0] = np.arange(offset, offset + len(labels0) + >> >> >> > 1) >> >> >> > ValueError: shape mismatch: value array of shape (6,) could not be >> >> >> > broadcast >> >> >> > to indexing result of shape (5,) >> >> >> > >> >> >> > Which is pretty clearly a coding error. Unfortunately, the error is >> >> >> > in >> >> >> > the >> >> >> > package rather than the test. >> >> >> > >> >> >> > The only easy way to fix all of these sorts of things is to revert >> >> >> > the >> >> >> > indexing changes, and I'm loathe to do that. Grrr... >> >> >> >> >> >> Ugh, that's pretty bad :-/. Do you really think we can't use a >> >> >> band-aid over the new indexing code, though? >> >> > >> >> > >> >> > Yeah, we can. But Sebastian doesn't have time and I'm unfamiliar with >> >> > the >> >> > code, so it may take a while... >> >> >> >> Fair enough! >> >> >> >> I guess that if what are (arguably) bugs in matplotlib and >> >> scikit-image are holding up the numpy release, then it's worth CC'ing >> >> their mailing lists in case someone feels like volunteering to fix >> >> it... ;-). >> > >> > I can do that ;) Doesn't help with the release though unless we want to >> > document the errors in the release notes and tell folks to wait on the next >> > release of the packages. >> >> Oh, I meant, in case they want to fix numpy so that their packages >> don't break :-). > > I've filed issues with all the affected projects. Here is the current status. > > matplotlib -- Reported, being fixed, should be in 1.4 in a few days. > skimage -- Reported. > scikit-learn -- Reported. > tables -- Reported. > statsmodels -- Reported, fixed in master. > bottleneck -- Reported. IIRC, kwgoodman already knew of the changes. > pyfits -- Reported to astropy. > milk -- Reported. > pandas -- Reportedly fixed in master. > > If the issues are fixed in matplotlib and pandas I'd be inclined to release > as is with a mention of versions in the release notes. > > Chuck > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion
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