Fix code that raises new numpy deprecation warnings

[bhazelton]

Description

Changes:

• Fixed a bunch of places that produced new DeprecationWarnings with numpy 1.25. I think that a few of these were real bugs.
• Improve check_warnings to give the file and line number where warnings occur to help with debugging.
• Don't issue a warning in efield to beam when fixing auto pols to be real if imaginary values are very small (i.e. caused by numerical precision issues). We started getting a bunch of these warnings when we didn't use to.
• Made some hard to read one-line if statements in utility methods multiline. This resulted in a drop in coverage because we haven't set branch=True for coverage (see Check for coverage on all branches #1309). So I added some tests to cover all the branches.
• Made a new conda environment for python 3.11. This is needed because we can't get h5py>=3.9 to resolve with all the other dependencies in python 3.11. We need h5py>=3.9 if we have numpy>=1.25 so we don't get flooded by deprecation warnings. So in the new env for 3.11 I restricted numpy to less than 1.25. This should be reverted in the future (Revert restriction on numpy<1.25 for python == 3.11 #1310).

Motivation and Context

Fix CI errors due to new numpy deprecation warnings.

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation change (documentation changes only)
• Version change
• Build or continuous integration change

Checklist:

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

Bug fix checklist:

• My fix includes a new test that breaks as a result of the bug (if possible).
• All new and existing tests pass.
• I have updated the CHANGELOG.

[codecov]

Codecov Report

Merging #1308 (cf37631) into main (986976c) will increase coverage by 0.00%.
The diff coverage is 100.00%.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1308   +/-   ##
=======================================
  Coverage   99.92%   99.92%           
=======================================
  Files          36       36           
  Lines       20064    20091   +27     
=======================================
+ Hits        20048    20075   +27     
  Misses         16       16           

Impacted Files	Coverage Δ
pyuvdata/utils.py	100.00% <100.00%> (ø)
pyuvdata/uvbeam/mwa_beam.py	100.00% <100.00%> (ø)
pyuvdata/uvbeam/uvbeam.py	100.00% <100.00%> (ø)
pyuvdata/uvcal/fhd_cal.py	100.00% <100.00%> (ø)
pyuvdata/uvdata/mir.py	100.00% <100.00%> (ø)
pyuvdata/uvdata/mir_meta_data.py	100.00% <100.00%> (ø)
pyuvdata/uvdata/uvdata.py	100.00% <100.00%> (ø)
pyuvdata/uvdata/uvfits.py	100.00% <100.00%> (ø)

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[mkolopanis]

Looking good to me. I think my only question is why are we adding a lot of [0] indices in places? Did we used to have length 1 dimensions that we are collapsing?

[bhazelton]

I think my only question is why are we adding a lot of [0] indices in places? Did we used to have length 1 dimensions that we are collapsing?

Yes. Numpy now complains about converting single element arrays to scalars:
Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)

[steven-murray]

Just the one small typo