You most likely don't need a scipy build against it. You should be able to use the oldest scipy our project supports. Numpy does try to not break its reverse dependencies, if stuff breaks it should only occur in edge cases not affecting functionality of real applications (like warnings or overzealous testing).
Of course that only works if people bother to test against the numpy prereleases. On 01/29/2016 06:45 PM, Andreas Mueller wrote: > Is this the point when scikit-learn should build against it? > Or do we wait for an RC? > Also, we need a scipy build against it. Who does that? > Our continuous integration doesn't usually build scipy or numpy, so it > will be a bit tricky to add to our config. > Would you run our master tests? [did we ever finish this discussion?] > > Andy > > On 01/28/2016 03:51 PM, Charles R Harris wrote: >> Hi All, >> >> I hope I am pleased to announce the Numpy 1.11.0b2 release. The first >> beta was a damp squib due to missing files in the released source >> files, this release fixes that. The new source filese may be >> downloaded from sourceforge, no binaries will be released until the >> mingw tool chain problems are sorted. >> >> Please test and report any problem. >> >> Chuck >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
