laurence
5286a2e81b
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.
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
"""Fetch Gaia DR3 BP-RP colours for the field, cached for colour calibration.
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The first composite balanced colour on "the average field star is grey", which
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is a workable fudge but is biased by whatever mix of spectral types the field
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happens to contain. With real colours available, a much better anchor exists:
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pick the stars that actually ARE solar-coloured (BP-RP near 0.82) and force
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those to neutral. That is the same principle as a photometric colour
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calibration, without needing the filters' response curves.
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ESA's archive was down during this work, so this uses the VizieR mirror of the
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identical catalogue.
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"""
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import os
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import numpy as np
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from astropy import units as u
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from astropy.coordinates import SkyCoord
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import layout
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OUT = layout.SESSION
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CACHE = layout.path("_gaia_colours.npz")
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CENTRE = SkyCoord("13h25m27.37s", "-43d01m10.9s")
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if os.path.exists(CACHE):
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z = np.load(CACHE)
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print(f"cached: {len(z['ra'])} stars with colours")
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else:
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from astroquery.vizier import Vizier
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v = Vizier(columns=["RA_ICRS", "DE_ICRS", "Gmag", "BP-RP"],
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column_filters={"Gmag": "<18", "BP-RP": ">-1"},
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row_limit=50000)
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res = v.query_region(CENTRE, radius=0.45 * u.deg, catalog="I/355/gaiadr3")
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t = res[0]
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ok = ~np.isnan(np.asarray(t["BP-RP"], float))
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ra = np.asarray(t["RA_ICRS"], float)[ok]
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dec = np.asarray(t["DE_ICRS"], float)[ok]
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g = np.asarray(t["Gmag"], float)[ok]
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bprp = np.asarray(t["BP-RP"], float)[ok]
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np.savez_compressed(CACHE, ra=ra, dec=dec, g=g, bprp=bprp)
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print(f"fetched {len(ra)} stars with BP-RP")
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z = dict(ra=ra, dec=dec, g=g, bprp=bprp)
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bprp = z["bprp"]
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solar = np.abs(bprp - 0.82) < 0.15
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print(f"BP-RP range {bprp.min():.2f} to {bprp.max():.2f}, "
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f"median {np.median(bprp):.2f}")
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print(f"solar-coloured stars (BP-RP 0.67-0.97): {solar.sum()}")
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print(f"G range {z['g'].min():.1f} to {z['g'].max():.1f}")
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