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.

pipeline/analyse.py

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