"""Pass 1: measure every calibrated frame and cache its star list.

Frames are 4788x3194 float32 (61 MB each) and the machine has little free RAM,
so each frame is opened, measured and released one at a time. The star lists are
cached to an npz because both the registration pass and the plate solve need
them, and re-detecting costs more than re-reading a small array.

Recorded per frame: sky background and its rms, the number of detections, and a
median FWHM derived from sep's half-flux radius. The FWHM is the seeing metric
used later to pick the registration reference and to weight the stack.
"""
import glob
import os
import re

import numpy as np
import sep
from astropy.io import fits

import layout

SRC = layout.SESSION
CACHE = layout.path("_stars.npz")
os.makedirs(os.path.dirname(CACHE), exist_ok=True)

NAME_RE = re.compile(r"-(Luminance|Red|Green|Blue)-BIN2-W-300-(\d+)\.fit$")


def frame_list():
    out = []
    for path in sorted(glob.glob(layout.path("calibrated-*.fit"))):
        m = NAME_RE.search(path)
        if m:
            out.append((path, m.group(1), int(m.group(2))))
    return out


if __name__ == "__main__":
    frames = frame_list()
    print(f"{len(frames)} calibrated frames")
    store, meta = {}, []
    for path, filt, idx in frames:
        with fits.open(path, memmap=False) as hd:
            data = hd[0].data.astype(np.float32)
        bkg = sep.Background(data, bw=64, bh=64, fw=3, fh=3)
        back_med = float(np.median(bkg.back()))
        rms = float(bkg.globalrms)
        sub = data - bkg.back()
        objs = sep.extract(sub, 5.0, err=rms, minarea=9, deblend_cont=0.005)
        objs = objs[(objs["flag"] == 0) & (objs["npix"] > 12) &
                    (objs["npix"] < 2000)]
        objs = objs[np.argsort(objs["flux"])[::-1][:400]]
        rad, _ = sep.flux_radius(sub, objs["x"], objs["y"], 6.0 * objs["a"],
                                 0.5, normflux=objs["flux"])
        fwhm = float(np.median(rad) * 2.0)
        key = f"{filt}_{idx:03d}"
        store[key + "_xy"] = np.column_stack([objs["x"], objs["y"]])
        store[key + "_flux"] = objs["flux"]
        meta.append((key, os.path.basename(path), filt, idx, len(objs), fwhm,
                     back_med, rms))
        print(f"{key:16s} stars={len(objs):4d} fwhm={fwhm:5.2f}px "
              f"bg={back_med:8.1f} rms={rms:6.1f}")
        del data, sub, bkg, objs

    np.savez_compressed(
        CACHE,
        meta=np.array(meta, dtype=object),
        **store,
    )
    print("cached ->", CACHE)