Homogenize return type of Lattice.get_points_in_sphere to always be np.array(s)

[janosh]

closes #3794

[coderabbitai]

Walkthrough

The updates to the pymatgen codebase focus on refining the return types of the get_points_in_sphere() method in the Lattice module to ensure consistency, especially in scenarios where no points are found within a specified radius. This adjustment aligns the method's behavior with user expectations, preventing errors related to unexpected return types.

Changes

File Path	Change Summary
.../core/lattice.py	Standardized return types in get_points_in_sphere() to numpy arrays for empty results.
.../core/test_lattice.py	Updated variable names and added checks for consistent return types in test_get_points_in_sphere().

Assessment against linked issues

Objective	Addressed	Explanation
Ensure consistent return types in get_points_in_sphere() [#3794]	✅
Prevent type errors due to unexpected empty return types [#3794]	✅
Align method behavior with user expectations [#3794]	✅

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Recent Review Details

Configuration used: .coderabbit.yaml
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 9a3f714 and 650ee0c.

Files selected for processing (2)

• pymatgen/core/lattice.py (2 hunks)
• tests/core/test_lattice.py (1 hunks)

Files skipped from review due to trivial changes (1)

• pymatgen/core/lattice.py

Additional comments not posted (4)

tests/core/test_lattice.py (4)

355-356: Ensure that the fractional coordinates are correctly calculated from the points.

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359-366: The test case for get_points_in_sphere correctly checks for the number of neighbors and their distances. However, ensure that the method now consistently returns numpy arrays as per the PR's objective.

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369-372: The test case for the more complex scenario using zip_results=True is well-implemented. It checks the tuple's length and the number of coordinates, distances, indices, and supercell information.

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376-388: This test case effectively checks the consistent return type when no points are found in the sphere, addressing the issue #3794 directly. The use of isinstance and checking the types within the tuple to ensure they are all numpy arrays is a good practice.

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