main.py

from __future__ import annotations

from tcrb.analysis import analyze_t_crb_aavso_archive, detect_precursor_state
from tcrb.loader import AAVSOLoader, summarize_alias_matches
from tcrb.service import build_monitoring_snapshot, build_target_profile


def main() -> None:
    target_name = "T CrB"
    asas_sn_id = "137439769436"
    vsx_oid = 10602

    profile = build_target_profile(
        target_name=target_name,
        asas_sn_id=asas_sn_id,
        vsx_oid=vsx_oid,
    )
    snapshot = build_monitoring_snapshot(
        target_name=target_name,
        asas_sn_id=asas_sn_id,
    )

    print(f"Target: {profile.canonical_name}")
    print("")

    print("Target Profile")
    print(f"  canonical_auid: {profile.canonical_auid}")
    print(f"  vsx_oid: {profile.vsx_oid}")
    print(f"  alias_count: {len(profile.aliases)}")
    print(f"  aliases_sample: {profile.aliases[:8]}")
    print("")

    print("Source Metadata")
    print(
        f"  VSP chart_id: {profile.vsp.chart_id if profile.vsp is not None else None}"
    )
    print(
        "  VSP comparison_star_count: "
        f"{len(profile.vsp.comparison_stars) if profile.vsp is not None else 0}"
    )
    print(
        f"  VSX observation_count: {profile.vsx.observation_count if profile.vsx is not None else None}"
    )
    print(
        f"  VSX period_days: {profile.vsx.period_days if profile.vsx is not None else None}"
    )
    print(
        f"  SIMBAD object_type: {profile.simbad.object_type if profile.simbad is not None else None}"
    )
    print(
        "  SIMBAD coordinates: "
        f"{profile.simbad.ra_hms if profile.simbad is not None else None} "
        f"{profile.simbad.dec_dms if profile.simbad is not None else None}"
    )
    print("")

    print("Monitoring Snapshot")
    print(f"  target_record_count: {len(snapshot.target_records)}")
    if snapshot.target_records:
        print(f"  first_target_record: {snapshot.target_records[0]}")
    if snapshot.series_summary is not None:
        print(f"  light_curve_point_count: {snapshot.series_summary.point_count}")
        print(f"  light_curve_filters: {snapshot.series_summary.unique_filters}")
        print(f"  latest_time_label: {snapshot.series_summary.latest_time_label}")
        print(f"  latest_mag: {snapshot.series_summary.latest_mag}")
    if snapshot.light_curve_error is not None:
        print(f"  light_curve_error: {snapshot.light_curve_error}")
    print("")

    print("AAVSO Archive")
    aavso_loader = AAVSOLoader(
        "data/aavso_1866_2026.txt", alias_catalog=profile.alias_catalog
    )
    aavso_frame = aavso_loader.load_dataframe()
    print(f"  record_count: {aavso_frame.height}")
    if not aavso_frame.is_empty():
        print(f"  alias_match_counts: {summarize_alias_matches(aavso_frame)}")
        print(f"  first_record: {aavso_frame.row(0, named=True)}")
        print(f"  last_record: {aavso_frame.row(aavso_frame.height - 1, named=True)}")
    print("")

    study = analyze_t_crb_aavso_archive(
        "data/aavso_1866_2026.txt",
        alias_catalog=profile.alias_catalog,
    )
    decision = detect_precursor_state(study)
    print("Precursor Decision")
    print(f"  level: {decision.level}")
    print(f"  rationale: {decision.rationale}")
    for assessment in decision.assessments:
        print(
            "  signal="
            f"{assessment.signal} "
            f"z_ratio={assessment.robust_zscore_ratio} "
            f"cusum_ratio={assessment.cusum_ratio} "
            f"warning={assessment.triggered_warning} "
            f"candidate={assessment.triggered_candidate} "
            f"strong={assessment.triggered_strong}"
        )


if __name__ == "__main__":
    main()