Bump numpy from 1.21.6 to 1.22.0

[dependabot]

Bumps numpy from 1.21.6 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

• Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
• A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
• NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
• New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
• A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

• 4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release
• fd66547 REL: Prepare for the NumPy 1.22.0 release.
• 125304b wip
• c283859 Merge pull request #20682 from charris/backport-20416
• 5399c03 Merge pull request #20681 from charris/backport-20954
• f9c45f8 Merge pull request #20680 from charris/backport-20663
• 794b36f Update armccompiler.py
• d93b14e Update test_public_api.py
• 7662c07 Update init.py
• 311ab52 Update armccompiler.py
• Additional commits viewable in compare view

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[coveralls]

Pull Request Test Coverage Report for Build 6524393016

• 3 of 3 (100.0%) changed or added relevant lines in 2 files are covered.
• 2 unchanged lines in 1 file lost coverage.
• Overall coverage decreased (-0.05%) to 90.541%

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Files with Coverage Reduction	New Missed Lines	%
sed/calibrator/energy.py	2	91.86%

Totals
Change from base Build 6483984354:	-0.05%
Covered Lines:	4288
Relevant Lines:	4736

---

💛 - Coveralls

[rettigl]

I have no idea why mypy thinks _bin is of tpye "object", and why the TypeGuard narrowing to int does not work. Both seem bugs to me.

[rettigl]

Related:
microsoft/pyright#2852 (comment)

[zain-sohail]

I went through the document about TypeGuard not working that you posted.
It seems that you have managed to fix the issue at hand with the linter so I will approve it.

One thing I could suggest is to relax the mypy linting requirements perhaps?

[rettigl]

One thing I could suggest is to relax the mypy linting requirements perhaps?

These is nothing wrong with the mypy requirements, I think they are good as they are. There just seem to be some bugs.
The first apparently is a feature, that is that np.isscalar(x) changes the type of x (which is completely unintuitive, and is not explained in the docstring)
The second is that if you enumerate (Union[List[int], List[np.ndarray]), you get type objects as iterators, rather than Union[int, np.ndarray]. Not sure though, how and why this depends on the numpy version.

Both are circumnavigated by the fixes I posted...