01.05.19 00:24, Chris Withers пише:
I have a crazy idea of getting unittest.mock up to 100% code coverage.
I noticed at the bottom of all of the test files in testmock/, there's a:
if __name__ == '__main__':
unittest.main()
...block.
How would people feel about these going away? I don't
On 01/05/2019 06:12, Terry Reedy wrote:
Such blocks should be excluded from coverage by the default .coveragerc
file. Mine came with
exclude_lines =
# Don't complain if non-runnable code isn't run:
if 0:
if __name__ == .__main__.:
if DEBUG:
Which .coveragerc are you refer
On 4/30/2019 5:24 PM, Chris Withers wrote:
Hi All,
I have a crazy idea of getting unittest.mock up to 100% code coverage.
I noticed at the bottom of all of the test files in testmock/, there's a:
if __name__ == '__main__':
unittest.main()
...block.
Such blocks should be excluded from c
Cross-posting to jython-users for obvious reasons.
Jeff Allen
On 30/04/2019 10:26, Chris Withers wrote:
[resending to python-dev in case there are Jython users here...]
Hi All,
If you need Jython support in the mock backport, please shout now:
https://github.com/testing-cabal/mock/issues/453
They were never needed 😁
Removal is fine with me.
On Wed, 1 May 2019, 09:27 Chris Withers, wrote:
> Hi All,
>
> I have a crazy idea of getting unittest.mock up to 100% code coverage.
>
> I noticed at the bottom of all of the test files in testmock/, there's a:
>
> if __name__ == '__main__':
>
Hi All,
I have a crazy idea of getting unittest.mock up to 100% code coverage.
I noticed at the bottom of all of the test files in testmock/, there's a:
if __name__ == '__main__':
unittest.main()
...block.
How would people feel about these going away? I don't *think* they're
needed now t
[resending to python-dev in case there are Jython users here...]
Hi All,
If you need Jython support in the mock backport, please shout now:
https://github.com/testing-cabal/mock/issues/453
cheers,
Chris
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Hello,
I've put the final touches to PEP 574 - Pickle protocol 5 with
out-of-band data (*). It is now ready for review. The implementation
is fully functional, as well as its PyPI backport (**), and has
regression tests against Numpy. Numpy and PyArrow have their own tests
against the pickle5
