TST: Add arithmetic compatibility tests.

[genedan]

Summary of Changes

Add some tests for uncovered lines in dunders.py, dealing with arithmetic operations on Triangles with different grain and backends.

Related GitHub Issue(s)

Additional Context for Reviewers

• I passed tests locally for both code (uv run pytest) and documentation changes (uv run jb build docs --builder=custom --custom-builder=doctest)

---

Note

Low Risk
Test-only changes with no production code modifications.

Overview
Adds regression tests in test_arithmetic.py for dunders.py arithmetic behavior that was previously untested.

Backend promotion: sparse Triangle + numpy.ndarray, and numpy Triangle + sparse COO, should both yield a sparse backend and match raa + 1 when converted to numpy.

Grain guard: adding triangles with mismatched grain (e.g. raa vs qtr) must raise ValueError with the message that both triangles need the same grain.

Existing arithmetic tests are unchanged aside from new imports and typing (TYPE_CHECKING, Triangle fixture annotations).

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 89.35%. Comparing base (468f11a) to head (b024736).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1038      +/-   ##
==========================================
+ Coverage   89.21%   89.35%   +0.14%     
==========================================
  Files          89       89              
  Lines        5165     5169       +4     
  Branches      661      661              
==========================================
+ Hits         4608     4619      +11     
+ Misses        390      386       -4     
+ Partials      167      164       -3     

Flag	Coverage Δ
unittests	89.35% <100.00%> (+0.14%)	⬆️

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[henrydingliu]

ah, splendid. i've had an issue with sparse arithmetic forever.

the behavior below seems counter-intuitive when adding any two sparse triangles return a sparse triangle. would you consider this to already covered by these new test?

clrd = cl.load_sample('clrd').set_backend('sparse')
clrd_gr = clrd.groupby('GRNAME').sum()
print(clrd.array_backend)
print(clrd_gr.array_backend)

sparse
numpy