"""Single entry point of the impact_window study.

    python3 code/run_all.py            # download (or reuse cached raw files), compute, draw
    python3 code/run_all.py --no-fetch # skip the download step and use the CSVs already in data/

Regenerates every table in data/, data/results.json and every figure in figures/, and runs
independent_check.py (a re-computation of one model that shares no code with model.py).
Runtime: about ten minutes on one free core (single-threaded by construction).  Needs numpy,
scipy, pandas, matplotlib and the `pdftotext` command (poppler) for the two PDF sources.
"""
from __future__ import annotations

import os
import sys
import time

# one thread only: the machine is shared, and oversubscribed BLAS threads slow the run tenfold
for _v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
    os.environ.setdefault(_v, "1")

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

import config as cfg  # noqa: E402


def main():
    t0 = time.time()
    if "--no-fetch" not in sys.argv:
        import fetch_data
        counts = fetch_data.run()
        print("data:", counts, flush=True)
    import analysis
    import figures
    res = analysis.main()
    figs = figures.make(res)
    print("figures:", figs)
    import independent_check
    print("\nIndependent check of Neukum 1983 x main-belt shape (shares no code with model.py):")
    print(independent_check.compare().to_string(index=False))
    s = res["summary"]
    for cond in cfg.HSE_MAIN:
        print(f"\nHeadline numbers, {cfg.HSE_LABEL[cond]} (mixture over 7 chronologies x 3 size distributions)")
        for D in (190, 440, 740, 2000):
            k = s["by_hse"][cond][f"ge{D}"]
            print(f"  last impactor >= {D} km: median {k['median_last_ALL']:.2f} Ga, 10% chance younger than "
                  f"{k['q90_ALL']:.2f} Ga (median by chronology {k['median_last_range_chron'][0]:.2f}-"
                  f"{k['median_last_range_chron'][1]:.2f}); P(none after 4.404 / 4.2 / 3.9 Ga) = "
                  f"{k['P_none_since_4p404_ALL']:.2f} / {k['P_none_ALL']['4.2']:.2f} / {k['P_none_ALL']['3.9']:.2f}")
        for lab, d in s["bayes"][cond].items():
            b = d["ge440"]
            print(f"  {lab}: B(m=1) survive {b['B_survive'][1]:.1f}, sterilise>=440 km {b['B_sterilise'][1]:.1f}, "
                  f"50:50 {b['B_mixed50'][1]:.1f}; sterilise>=190 km {d['ge190']['B_sterilise'][1]:.1f}; "
                  f">=740 km {d['ge740']['B_sterilise'][1]:.1f}; >=2000 km {d['ge2000']['B_sterilise'][1]:.1f}")
    print(f"done in {time.time() - t0:.0f} s; tables in {cfg.DATA}, figures in {cfg.FIGS}")


if __name__ == "__main__":
    main()