Use a naive sparse histogram.

[delucchi-cmu]

We really just need the non-zero indexes and non-zero values, and the complexity of the general scipy sparse array is adding a lot of compute overhead, both in the perform_write call, and when aggregating histograms for the point_map.fits file.

Requires astronomy-commons/hats#446

[github-actions]

Before [c8082b6]	After [5819625]	Ratio	Benchmark (Parameter)
12.5±0.02s	12.7±0.03s	1.02	benchmarks.time_create_large_catalog
14.7±0.2ms	14.8±0.3ms	1.01	benchmarks.time_box_filter_on_partition
1.85±0.02s	1.86±0.01s	1.01	benchmarks.time_create_midsize_catalog
29.6±0.6ms	29.9±0.5ms	1.01	benchmarks.time_polygon_search
86.6±1ms	86.8±1ms	1	benchmarks.time_kdtree_crossmatch

Click here to view all benchmarks.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 97.38%. Comparing base (c8082b6) to head (37e3554).
Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #534      +/-   ##
==========================================
- Coverage   97.38%   97.38%   -0.01%     
==========================================
  Files          43       43              
  Lines        1720     1719       -1     
==========================================
- Hits         1675     1674       -1     
  Misses         45       45              

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