Geomag ctao sorting

[GernotMaier]

No description provided.

[Copilot]

Pull request overview

This PR adds CTAO-specific support for telescope indexing/sorting and geomagnetic angle calculation by introducing an observatory configuration, new geomagnetic field presets, and updated sorting behavior (mirror area first, then size).

Changes:

• Add observatory plumbing through config/model application into feature flattening and geomagnetic angle computation.
• Introduce CTAO-NORTH/CTAO-SOUTH geomagnetic field components and rename the geomag API parameter to observatory.
• Adjust telescope sorting logic (area desc, then size desc) and add tests around CTAO/ImgSel_list behavior.

Reviewed changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 6 comments.

Show a summary per file

File	Description
tests/test_sort_logic.py	Adds unit tests for the intended sort ordering (currently re-implements the algorithm inline).
tests/test_imgsel_debug.py	Updates the CTAO ImgSel_list-mode test to pass observatory.
src/eventdisplay_ml/models.py	Propagates observatory into flatten_feature_data for apply-time inference.
src/eventdisplay_ml/hyper_parameters.py	Changes default regression pre-cut to require DispNImages >= 2.
src/eventdisplay_ml/geomag.py	Adds CTAO field presets and changes geomag angle API to use observatory.
src/eventdisplay_ml/features.py	Adds ImgSel_list to feature/branch lists for regression/classification.
src/eventdisplay_ml/data_processing.py	Adds observatory parameter, switches indexing-mode selection logic, and changes sorting implementation.
src/eventdisplay_ml/config.py	Adds CLI --observatory to training/apply configuration.

Comments suppressed due to low confidence (1)

src/eventdisplay_ml/data_processing.py:404

• The indexing-mode selection is now driven solely by the observatory string. This is brittle: if observatory is mis-set (or defaults) for a dataset, the code will try to read ImgSel_list and fail (or pick the wrong normalization). The previous data-driven detection via presence of R_core/ImgSel_list is safer; consider using column/field presence as the primary signal and observatory only as an override.

    # Index remapping mode for CTAO-style variable-length indexing
    index_list_for_remapping = None  # None = telescope-ID-indexed (VERITAS R_core mode)
    if observatory.lower() == "veritas":
        _logger.info("Detected VERITAS R_core fixed telescope-ID indexing mode.")
    else:
        _logger.info("Detected CTAO ImgSel_list variable-length indexing mode.")
        index_list_for_remapping = _to_dense_array(df["ImgSel_list"])

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