#!/usr/bin/env python3
"""Single entry point: regenerates every table and figure of the mir_quiescent study.

    python3 code/run_all.py                  # full run; downloads are cached in $MIRQ_RAW
    python3 code/run_all.py --no-download    # reuse the download cache (fails if it is incomplete)
    python3 code/run_all.py --analysis-only  # start from data/master_slim.csv.gz and data/model_grid.npz
                                             # (no catalogue service needed; add --no-images to skip everything
                                             # that needs image cut-outs: the vetting contact sheets, the
                                             # neighbour lists and the image-level profile-fit test, whose
                                             # tables in data/ are then reused as they are)

Raw downloads go to fermi_raw/mir_quiescent (override with MIRQ_RAW); derived tables to data/,
figures to figures/.  Cold-cache runtime is dominated by the network (IRSA upload matches ~10-40 min,
SDSS SkyServer ~20-40 min, NOIRLab Data Lab ~40-60 min, ~900 unWISE cut-outs ~10 min); with a warm cache the
whole chain takes about 20 minutes single-threaded (limits and variants ~8 min, image-level test ~10 min).
The only non-automatic input is code/vetting_manual.csv (visual classes of the 'clean' candidates, entered by
hand after looking at the contact sheets written to $MIRQ_RAW/cutouts); it affects only
data/limits_vetted.csv and the class labels in the candidate tables.  data/report_numbers.txt collects the
numbers quoted in report.md.

Step order: download -> signal model -> master table -> image-level profile-fit response test (s09; writes
data/psf_response_grid.csv, which the 'psf_response_*' variants of the limits step read) -> baseline ->
limits and variants -> candidate accounting -> digest of numbers and limit curves -> figures.
"""
import sys
import time


def main():
    t0 = time.time()
    args = set(sys.argv[1:])
    import s01_download
    import s02_build
    import s03_model
    import s04_baseline
    import s05_limits
    import s06_vetting
    import s07_figures
    import s08_report_numbers
    import s09_image_injection
    images = "--no-images" not in args
    steps = [("download", s01_download.main), ("signal model", s03_model.main), ("build master table", s02_build.main),
             ("image-level profile-fit response test", s09_image_injection.main),
             ("baseline, photometry checks, caps, stacks", s04_baseline.main),
             ("sensitivity fractions, candidate counts, limits, variants", s05_limits.main),
             ("candidate accounting / vetting", lambda: s06_vetting.main(make_sheets=images, fetch_neighbours=images)),
             ("digest of numbers and limit curves", s08_report_numbers.main), ("figures", s07_figures.main)]
    skip = set()
    if "--no-download" in args or "--analysis-only" in args:
        skip.add("download")
    if "--analysis-only" in args:
        skip |= {"signal model", "build master table"}
    if not images:
        skip.add("image-level profile-fit response test")
    for name, fn in steps:
        if name in skip:
            continue
        print("\n==== %s ====" % name, flush=True)
        fn()
        print("---- %s done, %.0f s elapsed" % (name, time.time() - t0), flush=True)


if __name__ == "__main__":
    main()