def read_gaia(targetwcs):
'''
*targetwcs* here should include margin
'''
from legacypipe.gaiacat import GaiaCatalog
gaia = GaiaCatalog().get_catalog_in_wcs(targetwcs)
debug('Got', len(gaia), 'Gaia stars nearby')
# DJS, [decam-chatter 5486] Solved! GAIA separation of point sources
# from extended sources
# Updated for Gaia DR2 by Eisenstein,
# [decam-data 2770] Re: [desi-milkyway 639] GAIA in DECaLS DR7
# But shifted one mag to the right in G.
gaia.G = gaia.phot_g_mean_mag
gaia.pointsource = np.logical_or(
(gaia.G <= 19.) * (gaia.astrometric_excess_noise < 10.**0.5),
(gaia.G >= 19.) *
(gaia.astrometric_excess_noise < 10.**(0.5 + 0.2 * (gaia.G - 19.))))
# in our catalog files, this is in float32; in the Gaia data model it's
# a byte, with only values 3 and 31 in DR2.
gaia.astrometric_params_solved = gaia.astrometric_params_solved.astype(
np.uint8)
# Gaia version?
gaiaver = int(os.getenv('GAIA_CAT_VER', '1'))
#print('Assuming Gaia catalog Data Release', gaiaver)
gaia_release = 'G%i' % gaiaver
gaia.ref_cat = np.array([gaia_release] * len(gaia))
gaia.ref_id = gaia.source_id
gaia.pmra_ivar = 1. / gaia.pmra_error**2
gaia.pmdec_ivar = 1. / gaia.pmdec_error**2
gaia.parallax_ivar = 1. / gaia.parallax_error**2
# mas -> deg
gaia.ra_ivar = 1. / (gaia.ra_error / 1000. / 3600.)**2
gaia.dec_ivar = 1. / (gaia.dec_error / 1000. / 3600.)**2
for c in [
'ra_error', 'dec_error', 'parallax_error', 'pmra_error',
'pmdec_error'
]:
gaia.delete_column(c)
for c in [
'pmra', 'pmdec', 'parallax', 'pmra_ivar', 'pmdec_ivar',
'parallax_ivar'
]:
X = gaia.get(c)
X[np.logical_not(np.isfinite(X))] = 0.
# radius to consider affected by this star --
# FIXME -- want something more sophisticated here!
# (also see tycho.radius below)
# This is in degrees and the magic 0.262 (indeed the whole
# relation) is from eyeballing a radius-vs-mag plot that was in
# pixels; that is unrelated to the present targetwcs pixel scale.
gaia.radius = np.minimum(1800., 150. * 2.5**(
(11. - gaia.G) / 3.)) * 0.262 / 3600.
gaia.delete_column('G')
return gaia
def read_gaia(wcs, bands):
'''
*wcs* here should include margin
'''
from legacypipe.gaiacat import GaiaCatalog
from legacypipe.survey import GaiaSource
gaia = GaiaCatalog().get_catalog_in_wcs(wcs)
debug('Got', len(gaia), 'Gaia stars nearby')
gaia.G = gaia.phot_g_mean_mag
# Sort by brightness (for reference-*.fits output table)
gaia.cut(np.argsort(gaia.G))
# Gaia to DECam color transformations for stars
color = gaia.phot_bp_mean_mag - gaia.phot_rp_mean_mag
# From Rongpu, 2020-04-12
# no BP-RP color: use average color
color[np.logical_not(np.isfinite(color))] = 1.4
# clip to reasonable range for the polynomial fit
color = np.clip(color, -0.6, 4.1)
for b, coeffs in [
('g', [
-0.1178631039, 0.3650113495, 0.5608615360, -0.2850687702,
-1.0243473939, 1.4378375491, 0.0679401731, -1.1713172509,
0.9107811975, -0.3374324004, 0.0683946390, -0.0073089582,
0.0003230170
]),
('r', [
0.1139078673, -0.2868955307, 0.0013196434, 0.1029151074,
0.1196710702, -0.3729031390, 0.1859874242, 0.1370162451,
-0.1808580848, 0.0803219195, -0.0180218196, 0.0020584707,
-0.0000953486
]),
('z', [
0.4811198057, -0.9990015041, 0.1403990019, 0.2150988888,
-0.2917655866, 0.1326831887, -0.0259205004, 0.0018548776
])
]:
mag = gaia.G.copy()
for order, c in enumerate(coeffs):
mag += c * color**order
gaia.set('decam_mag_%s' % b, mag)
del color
# For possible future use:
# BASS/MzLS:
# coeffs = dict(
# g = [-0.1299895823, 0.3120393968, 0.5989482686, 0.3125882487,
# -1.9401592247, 1.1011670449, 2.0741304659, -3.3930306403,
# 2.1857291197, -0.7674676232, 0.1542300648, -0.0167007725,
# 0.0007573720],
# r = [0.0901464643, -0.2463711147, 0.0094963025, -0.1187138789,
# 0.4131107392, -0.1832183301, -0.6015486252, 0.9802538471,
# -0.6613809948, 0.2426395251, -0.0505867727, 0.0056462458,
# -0.0002625789],
# z = [0.4862049092, -1.0278704657, 0.1220984456, 0.3000129189,
# -0.3770662617, 0.1696090596, -0.0331679127, 0.0023867628])
# force this source to remain a point source?
# Long history here, starting DJS, [decam-chatter 5486] Solved! GAIA separation
# of point sources from extended sources
# Updated for Gaia DR2 by Eisenstein,
# [decam-data 2770] Re: [desi-milkyway 639] GAIA in DECaLS DR7
# And made far more restrictive following BGS feedback.
gaia.pointsource = np.logical_or(
(gaia.G <= 18.) * (gaia.astrometric_excess_noise < 10.**0.5),
(gaia.G <= 13.))
# in our catalog files, this is in float32; in the Gaia data model it's
# a byte, with only values 3 and 31 in DR2.
gaia.astrometric_params_solved = gaia.astrometric_params_solved.astype(
np.uint8)
# Gaia version?
gaiaver = int(os.getenv('GAIA_CAT_VER', '1'))
gaia_release = 'G%i' % gaiaver
gaia.ref_cat = np.array([gaia_release] * len(gaia))
gaia.ref_id = gaia.source_id
gaia.pmra_ivar = 1. / gaia.pmra_error**2
gaia.pmdec_ivar = 1. / gaia.pmdec_error**2
gaia.parallax_ivar = 1. / gaia.parallax_error**2
# mas -> deg
gaia.ra_ivar = 1. / (gaia.ra_error / 1000. / 3600.)**2
gaia.dec_ivar = 1. / (gaia.dec_error / 1000. / 3600.)**2
for c in [
'ra_error', 'dec_error', 'parallax_error', 'pmra_error',
'pmdec_error'
]:
gaia.delete_column(c)
for c in [
'pmra', 'pmdec', 'parallax', 'pmra_ivar', 'pmdec_ivar',
'parallax_ivar'
]:
X = gaia.get(c)
X[np.logical_not(np.isfinite(X))] = 0.
# uniform name w/ Tycho-2
gaia.zguess = gaia.decam_mag_z
gaia.mag = gaia.G
# Take the brighter of G, z to expand masks around red stars.
gaia.mask_mag = np.minimum(gaia.G, gaia.zguess + 1.)
# radius to consider affected by this star, for MASKBITS
gaia.radius = mask_radius_for_mag(gaia.mask_mag)
# radius for keeping this source in the ref catalog
# (eg, for halo subtraction)
gaia.keep_radius = 4. * gaia.radius
gaia.delete_column('G')
gaia.isgaia = np.ones(len(gaia), bool)
gaia.istycho = np.zeros(len(gaia), bool)
gaia.isbright = (gaia.mask_mag < 13.)
gaia.ismedium = (gaia.mask_mag < 16.)
gaia.donotfit = np.zeros(len(gaia), bool)
# NOTE, must initialize gaia.sources array this way, or else numpy
# will try to be clever and create a 2-d array, because GaiaSource is
# iterable.
gaia.sources = np.empty(len(gaia), object)
if bands is not None:
for i, g in enumerate(gaia):
gaia.sources[i] = GaiaSource.from_catalog(g, bands)
return gaia