Numba-enhanced Data Averaging (and more!)

[kartographer]

Description

A new numba kernel for averaging data (with arbitrary grouping along one axis) has been added and integrated into UVData.frequency_average and UVData.downsample_in_time, which should substantially increase its speed (via multithreaded processing) and significantly reduce the memory footprint of the averaging step.

Motivation and Context

The primary motivation for this was addressing the speed and memory issue with frequency averaging, given the prior report about MWA data suffering a ~6x increase in memory footprint, although because the new kernel is somewhat flexibly designed to handle irregularly grouped arrays, I took the opportunity to add a few additional features to frequency_average (including an option to sum across nsample_array, which is appropriate for when it's being used to carry weights, as is the case in a few different filetypes).

Closes #730
Closes #1311
Closes #1624

Types of changes

• New feature (non-breaking change which adds functionality)

Checklist:

• I have read the contribution guide.
• My code follows the code style of this project.

New feature checklist:

• I have added or updated the docstrings associated with my feature using the numpy docstring format.
• I have updated the tutorial to highlight my new feature (if appropriate).
• I have added/updated tests to cover my new feature.
• I have updated the CHANGELOG.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 99.94%. Comparing base (a12cae9) to head (d934328).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1694      +/-   ##
==========================================
+ Coverage   99.84%   99.94%   +0.09%     
==========================================
  Files          68       70       +2     
  Lines       23506    23562      +56     
==========================================
+ Hits        23470    23548      +78     
+ Misses         36       14      -22     

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[bhazelton]

Looks fantastic, thank you! I just have a couple comments on docstrings. My student is going to test this soon on a full MWA dataset.

[bhazelton]

@ldress reports that it works excellently for her. Thank you so much @kartographer !