"""Single entry point: regenerates every table, number and figure of the tf_kiii study.

    python3 code/run_all.py            # from the study directory (or from anywhere)

Raw downloads are cached in fermi_raw/tf_kiii (see common.RAW); with a warm cache the whole
pipeline takes a few minutes single-threaded.  From a cold cache step s02 (SDSS sizes, ~8 min) and step s08
(852 small cone queries and ~1,200 image cutouts, roughly an hour) are limited by the remote services.

Hand-entered inputs that are NOT regenerated (all in data/):
  * visual_classification.csv : blind by-eye classification of the 135 contact-sheet objects of step s13.
                                Used only for the "with visual vetting" VARIANT, not for the primary limits.
  * no_photometry_notes.csv   : by-eye class of the 35 broad-line HI sources with a counterpart but no usable
                                SDSS photometry (used only in the "by-eye ordinary sources removed" accounting row).
  * dark_visual_notes.csv     : notes on the 9 broad-line HI sources without a counterpart (not used in any number).
The write-up is prepared by hand from the tables in data/ and the log of this script; no step writes it.
"""
import importlib
import os
import sys
import time

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

STEPS = [
    ("s01_download", "catalogue downloads (VizieR TAP)"),
    ("s02_build_alfalfa", "ALFALFA-SDSS sample, cuts, confusion flags, SDSS sizes"),
    ("s03_fit_tf", "ALFALFA-SDSS Tully-Fisher fits and residuals"),
    ("s04_xmatch", "2MASS XSC / AllWISE cross-match (CDS XMatch)"),
    ("s06_cf4", "Cosmicflows-4 sample, fits and residuals"),
    ("s07_union", "union sample, SIMBAD neighbours (s05), flags, dimming bookkeeping"),
    ("s08_vet_download", "per-object vetting downloads (cones, cutouts)"),
    ("s09_aperture_phot", "independent aperture photometry"),
    ("s10_vet_table", "vetting table and mid-infrared test"),
    ("s11_dark", "ALFALFA sources without optical counterparts"),
    ("s12_clustering", "3D clustering of residuals"),
    ("s13_contact_sheets", "blind contact sheets for visual vetting"),
    ("s14_limits", "candidate bookkeeping and upper limits"),
    ("s16_robustness", "robustness table, CF4 membership check"),
    ("s15_figures", "figures"),
]


def main():
    t00 = time.time()
    for mod, what in STEPS:
        t0 = time.time()
        print(f"=== {mod}: {what}", flush=True)
        importlib.import_module(mod).main()
        print(f"    done in {time.time() - t0:.0f} s", flush=True)
    print(f"all steps finished in {(time.time() - t00) / 60:.1f} min")


if __name__ == "__main__":
    main()