TST: check total seconds of segmented index

[audeerington]

This adds a test that checks whether the total seconds of the start and end levels of an index created with audformat.segmented_index() are as expected. (See #529)

Summary by Sourcery

Tests:

• Add parametrized tests ensuring segmented_index start and end levels match expected total seconds for various floating-point inputs.

[sourcery-ai]

Reviewer's Guide

Adds a new parametrized test ensuring that the total seconds derived from the start/end levels of segmented indexes match the original numeric inputs, and documents the related GitHub issue in the existing dtype test.

File-Level Changes

Change	Details	Files
Extend the existing segmented index dtype test with an inline reference to the related GitHub issue.	Update the docstring of the segmented index dtype test to mention the relevant GitHub issue for additional context.	tests/test_index.py
Add a regression test that validates total_seconds correctness for start and end levels in segmented indexes for various float/integer second inputs.	Introduce a new parametrized pytest function that constructs segmented indexes from different combinations of file paths and numeric start/end values. Convert the MultiIndex start and end levels to series, compute their total seconds, and compare them against the original numeric starts/ends using NumPy's assert_almost_equal to allow for floating point tolerance.	tests/test_index.py

Possibly linked issues

• audformat.segmented_index() not working as expected for pandas>=3.0 #529: PR adds regression tests to verify segmented_index produces correct total seconds, directly targeting the reported pandas>=3.0 issue.

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[sourcery-ai]

Hey - I've left some high level feedback:

• In test_segmented_index_seconds, consider converting the starts and ends parameters to np.array(..., dtype=float) explicitly before comparison to avoid any ambiguity from mixing ints and floats across parametrizations.
• You could add ids= to the @pytest.mark.parametrize for test_segmented_index_seconds to make it easier to see which specific case fails when a regression occurs.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- In `test_segmented_index_seconds`, consider converting the `starts` and `ends` parameters to `np.array(..., dtype=float)` explicitly before comparison to avoid any ambiguity from mixing ints and floats across parametrizations.
- You could add `ids=` to the `@pytest.mark.parametrize` for `test_segmented_index_seconds` to make it easier to see which specific case fails when a regression occurs.

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[hagenw]

Looks good.

I tested locally, and it passes when using pandas<3.0.0.

I would propose we merge this failing test first and afterwards implement a solution for it.