Status of fixing bugs for the 1.7.0rc1 release
Hi, I've finally finished review of https://github.com/numpy/numpy/pull/439 which back-ports all the PRs from master into the release branch and pushed it in. Here is the current status of bugs for the 1.7.0 release: https://github.com/numpy/numpy/issues/396 I believe that for example a lot of the Debian based bugs were fixed by now (in the 1.7.0 branch). Can I release 1.7.0b2? So that others can try it out, while we work on the rest of the issues. I don't think it's ready for rc1 yet, but we've done a lot of work since beta1 I think. Ondrej
On Sun, Sep 16, 2012 at 9:19 AM, Ondřej Čertík <ondrej.certik@gmail.com>wrote:
Hi,
I've finally finished review of
https://github.com/numpy/numpy/pull/439
which back-ports all the PRs from master into the release branch and pushed it in. Here is the current status of bugs for the 1.7.0 release:
https://github.com/numpy/numpy/issues/396
I believe that for example a lot of the Debian based bugs were fixed by now (in the 1.7.0 branch). Can I release 1.7.0b2? So that others can try it out, while we work on the rest of the issues. I don't think it's ready for rc1 yet, but we've done a lot of work since beta1 I think.
Sounds like a good idea to me. Ralf
On 9/16/2012 12:19 AM, Ondřej Čertík wrote:
Hi,
I've finally finished review of
https://github.com/numpy/numpy/pull/439
which back-ports all the PRs from master into the release branch and pushed it in. Here is the current status of bugs for the 1.7.0 release:
https://github.com/numpy/numpy/issues/396
I believe that for example a lot of the Debian based bugs were fixed by now (in the 1.7.0 branch). Can I release 1.7.0b2? So that others can try it out, while we work on the rest of the issues. I don't think it's ready for rc1 yet, but we've done a lot of work since beta1 I think.
Ondrej
Hello, I ran some compatibility tests on Windows, using numpy-MKL-1.7.x.dev.win-amd64-py2.7 with packages built against numpy-MKL-1.6.2. There are new test failures in scipy, bottleneck, pymc, and mvpa2 of the following types: IndexError: too many indices ValueError: negative dimensions are not allowed The test results are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120916-win-amd64-py2.7-num...> Christoph
[snip]
Hello,
I ran some compatibility tests on Windows, using numpy-MKL-1.7.x.dev.win-amd64-py2.7 with packages built against numpy-MKL-1.6.2.
There are new test failures in scipy, bottleneck, pymc, and mvpa2 of the following types:
IndexError: too many indices ValueError: negative dimensions are not allowed
The test results are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120916-win-amd64-py2.7-num...>
Christoph
Hi, https://github.com/numpy/numpy/pull/445 should fix "negative dimensions are not allowed", the other one I have not yet been able to pinpoint. Regards, Han
On 9/16/2012 3:06 PM, Han Genuit wrote:
[snip]
Hello,
I ran some compatibility tests on Windows, using numpy-MKL-1.7.x.dev.win-amd64-py2.7 with packages built against numpy-MKL-1.6.2.
There are new test failures in scipy, bottleneck, pymc, and mvpa2 of the following types:
IndexError: too many indices ValueError: negative dimensions are not allowed
The test results are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120916-win-amd64-py2.7-num...>
Christoph
Hi,
https://github.com/numpy/numpy/pull/445 should fix "negative dimensions are not allowed", the other one I have not yet been able to pinpoint.
Regards, Han
I just tracked the "IndexError: too many indices" errors to
list(np.ndindex(*())) [(0,)]
I'll check your PR. It might fix this too. Christoph
participants (4)
-
Christoph Gohlke -
Han Genuit -
Ondřej Čertík -
Ralf Gommers