Fix to_segmented_index() for pandas 3.0

[hagenw]

Closes #487

This add tests and fixes for the new pandas==3.0.0 timdelta[s] instead of timedelta[ns] default.

This required the following fixes:

Location	Fix
audformat.utils.to_segmented_index()	Convert ends to timedelta64[ns] before iloc assignment
audformat.utils.union()	Normalize timedelta dtypes to timedelta64[ns] in all code paths
audformat.utils.intersect()	Normalize timedelta dtypes to timedelta64[ns]
audformat.utils.set_index_dtypes()	Add .astype(dtype) after pd.to_timedelta() for empty levels
audformat.segmented_index()	Call set_index_dtypes to ensure timedelta64[ns]
audformat.testing.add_table()	Remove unnecessary pd.to_timedelta() call
audformat.utils.hash()	Enforce object dtype for string columns to get same hash under Python 3.14

Summary by Sourcery

Ensure segmented index duration handling preserves sub-second precision with pandas 3.0 and later.

Bug Fixes:

• Fix to_segmented_index to always use nanosecond timedelta precision so assigning high-precision durations no longer raises type or FutureWarning errors with pandas 3.0.

Tests:

• Add regression test verifying to_segmented_index correctly handles sub-second duration values when the index uses second-level timedelta precision and that no FutureWarning is raised.

[sourcery-ai]

Reviewer's Guide

Adjusts audformat.utils.to_segmented_index() to preserve nanosecond timedelta precision under pandas 3.0’s new default of second precision, and adds a regression test to guard against FutureWarnings and precision loss when filling NaT segment ends from file durations.

File-Level Changes

Change	Details	Files
Ensure to_segmented_index preserves sub-second timedelta precision under pandas 3.0 and add a regression test for the behavior.	Add a pytest that constructs a MultiIndex with timedelta64[s] start/end levels, applies to_segmented_index with sub-second file durations, and asserts no warnings are raised and precise end times are preserved. Update the logic that replaces NaT entries in the segmented index end level to first convert the ends array to a Series and then cast it to timedelta64[ns] before assigning duration values, preventing dtype incompatibilities and precision loss with pandas 3.0 timedelta defaults.	tests/test_utils.py audformat/core/utils.py

Assessment against linked issues

Issue	Objective	Addressed	Explanation
#487	Update audformat.utils.to_segmented_index() so it correctly handles pandas 3.0.0's changed default timedelta precision (seconds instead of nanoseconds), preserving sub-second precision and avoiding type/warning issues.	✅
#487	Add tests that verify to_segmented_index() works with pandas 3.0.0, including sub-second duration handling and absence of FutureWarning/TypeError due to timedelta dtype incompatibilities.	✅

Possibly linked issues

• Pandas 3.0.0 breaking changes #487: The PR updates to_segmented_index() to use nanosecond timedelta precision, fixing the pandas 3.0.0 regression.

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[sourcery-ai]

Hey - I've left some high level feedback:

• Before calling ends = ends.astype("timedelta64[ns]"), consider guarding with a check that ends has a timedelta-like dtype (e.g., using is_timedelta64_dtype) to avoid surprising failures if the index level type changes in the future.
• In test_to_segmented_index_timedelta_precision, you can simplify and make the duration comparison more robust by using pandas.testing.assert_index_equal (or assert_series_equal) for result_ends vs expected_ends instead of the manual loop.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Before calling `ends = ends.astype("timedelta64[ns]")`, consider guarding with a check that `ends` has a timedelta-like dtype (e.g., using `is_timedelta64_dtype`) to avoid surprising failures if the index level type changes in the future.
- In `test_to_segmented_index_timedelta_precision`, you can simplify and make the duration comparison more robust by using `pandas.testing.assert_index_equal` (or `assert_series_equal`) for `result_ends` vs `expected_ends` instead of the manual loop.

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[hagenw]

We have now too many changes here, and introduced a lot of new issues that were not present before, so we should maybe better target the updates step by step in several pull requests.