Optimize opacity data reading performance

[natashabatalha]

This PR addresses the performance bottleneck in reading monochromatic opacities from SQLite databases.

Key improvements:

1. SQLite Indexing: New databases now include indexes on (molecule, ptid) and (molecule, temperature), significantly speeding up targeted queries.
2. NumPy Vectorization: Replaced slow Python loops for PT point matching in optics.py with vectorized broadcasting and np.argmin, reducing overhead for many-layer atmospheres.
3. HDF5 Backend: Introduced HDF5 as an alternative storage format for monochromatic opacities. HDF5 is ~3-4x faster than indexed SQLite for this use case and handles large numerical arrays more efficiently.
4. Migration Utility: Provided convert_sqlite_to_h5 to allow users to upgrade their existing databases to the faster format.
5. Robustness: Fixed file handle management and redundant file opening in the HDF5 implementation.

Benchmarks showed up to 17x speedup over the original unindexed SQLite implementation and ~4x speedup over the optimized indexed SQLite for realistic monochromatic opacity reads.

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PR created automatically by Jules for task 2624491173451506681 started by @natashabatalha

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