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/mo_check.py

@@ -1,85 +0,0 @@
-"""Diagnostic: how good is the coordinate registration, and how many
-detections survive removal of the static sky?
-
-astroalign only reports a few dozen matched stars, which is enough to define a
-similarity transform but says nothing about how well it holds across a
-4788 x 3194 field. This refines each frame's transform by nearest-neighbour
-matching every detection against the reference frame's detections and refitting
-a full affine, then quotes the residual. It also counts, per frame, how many
-detections are left once everything coincident with a master-stack source is
-removed - that residual population is the input to the tracklet search.
-"""
-import os
-
-import numpy as np
-from astropy.io import fits
-from scipy.spatial import cKDTree
-from skimage.transform import AffineTransform
-
-import layout
-
-SRC = layout.SESSION
-OUT = layout.SESSION
-CACHE = layout.path("_mo_dets.npz")
-
-
-def load():
-    z = np.load(CACHE, allow_pickle=True)
-    meta = {r[0]: r for r in z["meta"]}
-    dets = {k: z[k] for k in meta}
-    return meta, dets
-
-
-def refine(dets, ref_key="Luminance_002", tol=3.0):
-    """Refit each frame's transform on all cross-matched detections."""
-    ref = dets[ref_key]
-    tree = cKDTree(ref[:, :2])
-    out = {}
-    for key, d in dets.items():
-        src = d[:, 2:4].astype(float)          # native coords
-        cur = d[:, :2].astype(float)           # astroalign-transformed
-        for t in (tol, 1.5):
-            dist, idx = tree.query(cur, distance_upper_bound=t)
-            ok = np.isfinite(dist)
-            if ok.sum() < 50:
-                break
-            tf = AffineTransform()
-            tf.estimate(src[ok], ref[idx[ok], :2].astype(float))
-            cur = tf(src)
-        dist, idx = tree.query(cur, distance_upper_bound=1.5)
-        ok = np.isfinite(dist)
-        out[key] = (cur, np.median(dist[ok]), np.percentile(dist[ok], 90),
-                    ok.sum())
-    return out
-
-
-def main():
-    meta, dets = load()
-    lum = [k for k in dets if k.startswith("Luminance")]
-    ref = refine(dets)
-
-    print("registration residual against reference frame (px):")
-    for k in sorted(dets):
-        _, med, p90, n = ref[k]
-        print(f"  {k:16s} n={n:5d}  median={med:.3f}  p90={p90:.3f}")
-
-    # Static sky = every source in the deep luminance master.
-    with fits.open(layout.path("master-Luminance.fit")) as hd:
-        master = hd[0].data.astype(np.float32)
-    import sep
-    bkg = sep.Background(master, bw=64, bh=64, fw=3, fh=3)
-    m = sep.extract(master - bkg.back(), 2.5, err=bkg.globalrms, minarea=4,
-                    deblend_cont=0.005)
-    print(f"\nmaster detections (static sky): {len(m)}")
-    mtree = cKDTree(np.column_stack([m["x"], m["y"]]))
-
-    print("\nresiduals after removing anything within 4 px of a master source:")
-    for k in sorted(lum):
-        cur = ref[k][0]
-        d, _ = mtree.query(cur, distance_upper_bound=4.0)
-        left = ~np.isfinite(d)
-        print(f"  {k:16s} {left.sum():5d} / {len(cur)}")
-
-
-if __name__ == "__main__":
-    main()