Move the processing code under pipeline/

Preparing to merge this repository into a combined astrophotography
repo. session-scripts/ becomes pipeline/ because the scripts import
layout.py from their own directory and must stay together, and because
'pipeline' says what it is rather than how it came about. observing/
stays at the top level: observing plans are not processing code.

session-scripts/analyse.py

@@ -1,70 +0,0 @@
-"""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)