Bug 2226736 - incompatible numpy update (1.22.0->1.24.3) caused test failures in mrcfile
Summary: incompatible numpy update (1.22.0->1.24.3) caused test failures in mrcfile
Keywords:
Status: CLOSED ERRATA
Alias: None
Product: Fedora
Classification: Fedora
Component: numpy
Version: 37
Hardware: Unspecified
OS: Linux
unspecified
medium
Target Milestone: ---
Assignee: Gwyn Ciesla
QA Contact: Fedora Extras Quality Assurance
URL:
Whiteboard:
Depends On:
Blocks:
TreeView+ depends on / blocked
 
Reported: 2023-07-26 11:36 UTC by Dominik 'Rathann' Mierzejewski
Modified: 2023-07-26 20:00 UTC (History)
5 users (show)

Fixed In Version:
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Last Closed: 2023-07-26 20:00:57 UTC
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Description Dominik 'Rathann' Mierzejewski 2023-07-26 11:36:52 UTC
Since numpy-1.24.3-1.fc37 update was pushed to F37+ back in May, python-mrcfile has been failing in its test suite:
https://koschei.fedoraproject.org/package/python-mrcfile?collection=f37


Reproducible: Always

Steps to Reproduce:
1. Run python-mrcfile test suite with numpy 1.24.0 or later installed

Actual Results:  
> ======================================================================
> FAIL: test_data_is_not_copied_unnecessarily (tests.test_bzip2mrcfile.Bzip2MrcFileTest)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "/<<PKGBUILDDIR>>/tests/test_mrcobject.py", line 341, in test_data_is_not_copied_unnecessarily
>     assert self.mrcobject.data is data
> AssertionError
and other failures.

Expected Results:  
Tests pass.

Upstream issue points to incompatible behaviour change in numpy.
https://github.com/ccpem/mrcfile/issues/49

Could you test all numpy consumers in COPR before doing updates in the future? That can be easily automated, see Karolina Surma's talk at the last DevConf for details:
https://devconfcz2023.sched.com/event/1MYlI/fedora-package-update-assess-its-impact-in-copr
https://www.youtube.com/watch?v=nS0Z1OilOas (starts around 07:37:00 mark)

Comment 1 Gwyn Ciesla 2023-07-26 20:00:57 UTC
Apologies, I hadn't run into something like this with numpy before. I'll do that in the future.


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