[Bug 1358870] Re: update numpy to 1.8.2 in trusty

[Chris J Arges]

Hello Julian, or anyone else affected,

Accepted python-numpy into trusty-proposed. The package will build now
and be available at http://launchpad.net/ubuntu/+source/python-
numpy/1:1.8.2-0ubuntu0.1 in a few hours, and then in the -proposed
repository.

Please help us by testing this new package.  See
https://wiki.ubuntu.com/Testing/EnableProposed for documentation how to
enable and use -proposed.  Your feedback will aid us getting this update
out to other Ubuntu users.

If this package fixes the bug for you, please add a comment to this bug,
mentioning the version of the package you tested, and change the tag
from verification-needed to verification-done. If it does not fix the
bug for you, please add a comment stating that, and change the tag to
verification-failed.  In either case, details of your testing will help
us make a better decision.

Further information regarding the verification process can be found at
https://wiki.ubuntu.com/QATeam/PerformingSRUVerification .  Thank you in
advance!

** Changed in: python-numpy (Ubuntu Trusty)
       Status: In Progress => Fix Committed

** Tags added: verification-needed

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https://bugs.launchpad.net/bugs/1358870

Title:
  update numpy to 1.8.2 in trusty

Status in “python-numpy” package in Ubuntu:
  Fix Released
Status in “python-numpy” source package in Trusty:
  Fix Committed

Bug description:
  [Impact] numpy has recently released a bugfix release fixing several bugs that are severe or hard to workaround.
  Full changelog:

  * gh-4836: partition produces wrong results for multiple selections in equal ranges
  * gh-4656: Make fftpack._raw_fft threadsafe
  * gh-4628: incorrect argument order to _copyto in in np.nanmax, np.nanmin
  * gh-4642: Hold GIL for converting dtypes types with fields
  * gh-4733: fix np.linalg.svd(b, compute_uv=False)
  * gh-4853: avoid unaligned simd load on reductions on i386
  * gh-4722: Fix seg fault converting empty string to object
  * gh-4613: Fix lack of NULL check in array_richcompare
  * gh-4774: avoid unaligned access for strided byteswap
  * gh-650: Prevent division by zero when creating arrays from some buffers
  * gh-4602: ifort has issues with optimization flag O2, use O1

  
  [Test case]
  the partition issue is the most severe:

  python
  d = np.array([0, 1, 2, 3, 4, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,7, 7, 7, 7, 7, 9])
  kth = [0, 3, 19, 20]
  np.partition(d, kth)[kth]

  output:
  array([7, 3, 7, 7])

  expected:
  (0, 3, 7, 7)

  [Regression potential]
  low, the fixes where intentionally kept simple to avoid regressions.
  the test coverage of numpy is good and most of its dependencies (scipy, pandas, pytables, ..) have tested the release via their own extensive testsuites.

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