Add DBSCAN clustering step to Sophronia city

[camacortespar]

Summary

This PR integrates a hit clustering step (using DBSCAN) into the Sophronia city workflow.

Key Changes

1. New Logic (reco/hits_functions.py)

• Implemented cluster_tagger: A function that applies DBSCAN on an event-by-event basis.
• It handles spatial anisotropy via scale_xy and scale_z parameters.
• It preserves data structure and alignment perfectly.

2. Integration (cities/sophronia.py)

• Added clustering_params to the city configuration.
• Inserted the clustering step in the dataflow.
• Backward Compatibility: If clustering_params is None (default), the city skips clustering and produces the exact same output structure as before (no cluster column).

3. Testing (cities/sophronia_test.py & reco/hits_functions_test.py)

• Added unit tests for structure preservation, row alignment, and noise rejection in DBSCAN.
• Added integration tests ensuring cluster column appears only when enabled.
• Verified that enabling clustering does not corrupt other physical variables.

4. Reference Update

• Updated database/test_data/228Th_10evt_hits.h5.
• Re-generated this reference file using the current master logic to ensure test_sophronia_exact_result passes with the new code structure.

Configuration Example

To use this feature, add the following to the configuration file (these values are optimized for NEXT-100 detector geometry):

clustering_params = dict(
    eps = 3,
    min_samples = 5,
    scale_xy = 14.55,
    scale_z = 3.7
)