"""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}")