Dask support for first, last, first_n and last_n reductions

[ianthomas23]

This fixes the Dask parts of issues #1182 and #1207. CUDA support will follow in a separate PR, this is already too big.

Full list of example reductions this supports on CPU with and without Dask:

• first('value') - returns single value per pixel
• last('value') - ditto
• first_n('value', n=3) - returns n=3 values per pixel
• last_n('value', n=3) - ditto
• where(first('value')) - returns single row index per pixel
• where(last('value')) - ditto
• where(first_n('value', n=3)) - returns n=3 row indexes per pixel
• where(last_n('value', n=3)) - ditto
• where(first('value'), 'other') - returns single other per pixel
• where(last('value'), 'other') - ditto
• where(first_n('value', n=3), 'other') - returns n=3 others per pixel
• where(last_n('value', n=3), 'other') - ditto

Summary of changes:

1. Some reduction behaviour is now dependent on whether supplied data is partitioned (dask or CUDA) or not, so extra arguments cuda and partitioned are passed through the class hierarchy.

2. I have factored out some common first and last code into a private base class _first_or_last to simplify, and similar applies to _first_n_or_last_n.

3. The fundamental addition is that first('value') using Dask is implemented as where(_min_row_index(), 'value'). This takes advantage of the existing where machinery and a new class _min_row_index which is very similar to a min reduction except that it accepts a virtual row index column, not a real column supplied by the user. I could have extended the min class to cover this, but I thought it better to keep the new functionality in a new private class. It is possible to instantiate the private class of course, although no user code should do this, and I have done this in the test suite. There are equivalent new private classes for last, first_n and last_n.

I do need to recheck that trying to use CUDA with these reductions raises informative error messages not crashes.

[codecov[bot]]

Codecov Report

Merging #1214 (25bd257) into main (8092f4d) will increase coverage by 0.31%. The diff coverage is 95.09%.

@@            Coverage Diff             @@
##             main    #1214      +/-   ##
==========================================
+ Coverage   84.52%   84.83%   +0.31%     
==========================================
  Files          35       35              
  Lines        8369     8561     +192     
==========================================
+ Hits         7074     7263     +189     
- Misses       1295     1298       +3     

Impacted Files	Coverage Δ
datashader/data_libraries/pandas.py	100.00% <ø> (ø)
datashader/reductions.py	84.68% <93.15%> (+1.56%)	:arrow_up:
datashader/compiler.py	91.07% <100.00%> (+0.50%)	:arrow_up:
datashader/data_libraries/dask.py	95.23% <100.00%> (+0.07%)	:arrow_up:
datashader/data_libraries/dask_xarray.py	98.95% <100.00%> (ø)
datashader/utils.py	81.86% <100.00%> (+2.61%)	:arrow_up:

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[ianthomas23]

I may or may not have glazed over when reviewing certain bits. :-)

It hadn't occurred to me to be entertaining. I can try that approach, but it may come at the expense of code accuracy :grin:

[ianthomas23]

I've rebased it on main to pick up the fast CUDA mutex PR, and added a couple of error messages. There are no functional code changes so I will merge then CI is green.