Processing and analysis code for remote-telescope imaging sessions

The scripts that processed the NGC 5128 session of 2026-07-21 previously
lived inside the data directory and addressed it with absolute paths.
Code and data are now separated: the code lives here, and a session is
located at runtime through the ASTRO_SESSION environment variable.

layout.py is what makes that work. It maps a FILENAME to the
subdirectory that file belongs in, using the same rules the session
directories are organised with, so a script can go on asking for
'master-Red.fit' or '_stars.npz' without any call site knowing the
directory structure. Anything unrecognised resolves to the session root,
which is visible and correctable rather than silently wrong.

restructure.py reorganises a flat session directory into that layout. It
is idempotent and dry-run by default.

The 50 session scripts are kept as they were run rather than tidied into
a library. They were written in sequence as the work went along, several
of them by parallel agents, and they show it - but they are the honest
provenance of a published set of results, and the productionised pipeline
should be able to reproduce those results exactly.

Verified before committing: all 51 files compile without warnings, and
verify_core.py, closeup.py and triptych.py were run end to end against
the reorganised session, correctly finding inputs across calibrated/,
stacks/masters/ and final/ and writing outputs back to the right places.

session-scripts/gaia_colours.py

@@ -0,0 +1,49 @@
+"""Fetch Gaia DR3 BP-RP colours for the field, cached for colour calibration.
+
+The first composite balanced colour on "the average field star is grey", which
+is a workable fudge but is biased by whatever mix of spectral types the field
+happens to contain. With real colours available, a much better anchor exists:
+pick the stars that actually ARE solar-coloured (BP-RP near 0.82) and force
+those to neutral. That is the same principle as a photometric colour
+calibration, without needing the filters' response curves.
+
+ESA's archive was down during this work, so this uses the VizieR mirror of the
+identical catalogue.
+"""
+import os
+
+import numpy as np
+from astropy import units as u
+from astropy.coordinates import SkyCoord
+
+import layout
+
+OUT = layout.SESSION
+CACHE = layout.path("_gaia_colours.npz")
+CENTRE = SkyCoord("13h25m27.37s", "-43d01m10.9s")
+
+if os.path.exists(CACHE):
+    z = np.load(CACHE)
+    print(f"cached: {len(z['ra'])} stars with colours")
+else:
+    from astroquery.vizier import Vizier
+    v = Vizier(columns=["RA_ICRS", "DE_ICRS", "Gmag", "BP-RP"],
+               column_filters={"Gmag": "<18", "BP-RP": ">-1"},
+               row_limit=50000)
+    res = v.query_region(CENTRE, radius=0.45 * u.deg, catalog="I/355/gaiadr3")
+    t = res[0]
+    ok = ~np.isnan(np.asarray(t["BP-RP"], float))
+    ra = np.asarray(t["RA_ICRS"], float)[ok]
+    dec = np.asarray(t["DE_ICRS"], float)[ok]
+    g = np.asarray(t["Gmag"], float)[ok]
+    bprp = np.asarray(t["BP-RP"], float)[ok]
+    np.savez_compressed(CACHE, ra=ra, dec=dec, g=g, bprp=bprp)
+    print(f"fetched {len(ra)} stars with BP-RP")
+    z = dict(ra=ra, dec=dec, g=g, bprp=bprp)
+
+bprp = z["bprp"]
+solar = np.abs(bprp - 0.82) < 0.15
+print(f"BP-RP range {bprp.min():.2f} to {bprp.max():.2f}, "
+      f"median {np.median(bprp):.2f}")
+print(f"solar-coloured stars (BP-RP 0.67-0.97): {solar.sum()}")
+print(f"G range {z['g'].min():.1f} to {z['g'].max():.1f}")