On Mon, Jun 9, 2014 at 6:10 PM, Charles R Harris <charlesr.har...@gmail.com> wrote:
> > > > On Mon, Jun 9, 2014 at 5:21 PM, Christoph Gohlke <cgoh...@uci.edu> wrote: > >> On 6/8/2014 1:34 PM, Julian Taylor wrote: >> >>> Hello, >>> >>> I'm happy to announce the fist beta release of Numpy 1.9.0. >>> 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2 >>> - 3.4. >>> Due to low demand windows binaries for the beta are only available for >>> Python 2.7, 3.3 and 3.4. >>> Please try it and report any issues to the numpy-discussion mailing list >>> or on github. >>> >>> The 1.9 release will consists of mainly of many small improvements and >>> bugfixes. The highlights are: >>> >>> * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray >>> subclasses. Please note that there are still some known issues with this >>> mechanism which we hope to resolve before the final release (e.g. #4753) >>> * Numerous performance improvements in various areas, most notably >>> indexing and operations on small arrays are significantly faster. >>> Indexing operations now also release the GIL. >>> * Addition of nanmedian and nanpercentile rounds out the nanfunction set. >>> >>> The changes involve a lot of small changes that might affect some >>> applications, please read the release notes for the full details on all >>> changes: >>> https://github.com/numpy/numpy/blob/maintenance/1.9.x/ >>> doc/release/1.9.0-notes.rst >>> Please also take special note of the future changes section which will >>> apply to the following release 1.10.0 and make sure to check if your >>> applications would be affected by them. >>> >>> Source tarballs, windows installers and release notes can be found at >>> https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1 >>> >>> Cheers, >>> Julian Taylor >>> >>> >> Hello, >> >> I tested numpy-MKL-1.9.0b1 (msvc9, Intel MKL build) on win-amd64-py2.7 >> against a few other packages that were built against numpy-MKL-1.8.x. >> >> While numpy and scipy pass all tests, some other packages (matplotlib, >> statsmodels, skimage, pandas, pytables, sklearn...) show a few new test >> failures (compared to testing with numpy-MKL-1.8.1). Many test errors are >> of kind: >> >> ValueError: shape mismatch: value array of shape (24,) could not be >> broadcast to indexing result of shape (8,3) >> >> I have attached a list of failing tests. The full test results are at < >> http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7- >> numpy-1.9.0b1/> (compare to <http://www.lfd.uci.edu/~ >> gohlke/pythonlibs/tests/20140609-win-amd64-py2.7/>) >> >> I have not investigated any further... >> > > One of the matplotlib failures, and I suspect the others, comes from the > assignment > > found_index[refi_triangles[ancestor_mask, :] > ] = np.repeat(ancestors[ancestor_mask], 3) > > This fails with the error > > ValueError: shape mismatch: value array of shape (13824,) > > could not be broadcast to indexing result of shape (4608,3) > > I confess I find the construction odd, but the error probably results from > stricter indexing rules. Indeed, (13824,) does not broadcast to (4608,3). > Apart from considerations of backward compatibility, should it? > > Other errors are of the type: TypeError: NumPy boolean array indexing assignment requires a 0 or 1-dimensionalinput, input has 2 dimensions This one looks to arise is from stricter rules for boolean indexing. Chuck
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