gaia = GaiaCatalog().get_catalog_in_wcs(targetwcs)
print('Got Gaia stars:', gaia)
gaia.about()
gaia.G = gaia.phot_g_mean_mag
gaia.pointsource = np.logical_or(
(gaia.G <= 18.) * (gaia.astrometric_excess_noise < 10.**0.5),
(gaia.G >= 18.) * (gaia.astrometric_excess_noise < 10.**(0.5 + 1.25 / 4. *
(gaia.G - 18.))))
gaia.writeto('test-gaia.fits')
gaiacat = []
for g in gaia:
gaiacat.append(GaiaSource.from_catalog(g, bands))
print('Created source:', gaiacat[-1])
print()
print()
# Select the largest-parallax source.
gaia.parallax[np.logical_not(np.isfinite(gaia.parallax))] = 0.
i = np.argmax(gaia.parallax)
gsrc = gaia[i]
src = GaiaSource.from_catalog(gsrc, bands)
print('Source:', src)
ra, dec = src.pos.ra, src.pos.dec
def read_tycho2(survey, targetwcs, bands):
from astrometry.libkd.spherematch import tree_open, tree_search_radec
from legacypipe.survey import GaiaSource
tycho2fn = survey.find_file('tycho2')
radius = 1.
ra, dec = targetwcs.radec_center()
# John added the "isgalaxy" flag 2018-05-10, from the Metz &
# Geffert (04) catalog.
# Eddie added the "zguess" column 2019-03-06, by matching with
# 2MASS and estimating z based on APASS.
# The "tycho2.kd.fits" file read here was produced by:
#
# fitscopy ~schlafly/legacysurvey/tycho-isgalaxyflag-2mass.fits"[col \
# tyc1;tyc2;tyc3;ra;dec;sigma_ra;sigma_dec;mean_ra;mean_dec;pm_ra;pm_dec; \
# sigma_pm_ra;sigma_pm_dec;epoch_ra;epoch_dec;mag_bt;mag_vt;mag_hp; \
# isgalaxy;Jmag;Hmag;Kmag,zguess]" /tmp/tycho2-astrom.fits
# startree -P -k -n stars -T -i /tmp/tycho2-astrom.fits \
# -o /global/project/projectdirs/cosmo/staging/tycho2/tycho2.kd.fits
kd = tree_open(tycho2fn, 'stars')
I = tree_search_radec(kd, ra, dec, radius)
debug(len(I), 'Tycho-2 stars within', radius,
'deg of RA,Dec (%.3f, %.3f)' % (ra, dec))
if len(I) == 0:
return None
# Read only the rows within range.
tycho = fits_table(tycho2fn, rows=I)
del kd
if 'isgalaxy' in tycho.get_columns():
tycho.cut(tycho.isgalaxy == 0)
debug('Cut to', len(tycho), 'Tycho-2 stars on isgalaxy==0')
else:
print('Warning: no "isgalaxy" column in Tycho-2 catalog')
tycho.ref_cat = np.array(['T2'] * len(tycho))
# tyc1: [1,9537], tyc2: [1,12121], tyc3: [1,3]
tycho.ref_id = (tycho.tyc1.astype(np.int64) * 1000000 +
tycho.tyc2.astype(np.int64) * 10 +
tycho.tyc3.astype(np.int64))
with np.errstate(divide='ignore'):
# In our Tycho-2 catalog, sigma_pm_* are in *arcsec/yr*, Gaia is in mas/yr.
tycho.pmra_ivar = 1. / (tycho.sigma_pm_ra * 1000.)**2
tycho.pmdec_ivar = 1. / (tycho.sigma_pm_dec * 1000.)**2
tycho.ra_ivar = 1. / tycho.sigma_ra**2
tycho.dec_ivar = 1. / tycho.sigma_dec**2
tycho.rename('pm_ra', 'pmra')
tycho.rename('pm_dec', 'pmdec')
for c in ['pmra', 'pmdec', 'pmra_ivar', 'pmdec_ivar']:
X = tycho.get(c)
X[np.logical_not(np.isfinite(X))] = 0.
tycho.mag = tycho.mag_vt
# Patch missing mag values...
tycho.mag[tycho.mag == 0] = tycho.mag_hp[tycho.mag == 0]
tycho.mag[tycho.mag == 0] = tycho.mag_bt[tycho.mag == 0]
# Use zguess
tycho.mask_mag = tycho.mag
with np.errstate(invalid='ignore'):
I = np.flatnonzero(
np.isfinite(tycho.zguess) * (tycho.zguess + 1. < tycho.mag))
tycho.mask_mag[I] = tycho.zguess[I]
# Per discussion in issue #306 -- cut on mag < 13.
# This drops only 13k/2.5M stars.
tycho.cut(tycho.mask_mag < 13.)
tycho.radius = mask_radius_for_mag(tycho.mask_mag)
tycho.keep_radius = 2. * tycho.radius
for c in [
'tyc1', 'tyc2', 'tyc3', 'mag_bt', 'mag_vt', 'mag_hp', 'mean_ra',
'mean_dec', 'sigma_pm_ra', 'sigma_pm_dec', 'sigma_ra', 'sigma_dec'
]:
tycho.delete_column(c)
# add Gaia-style columns
# No parallaxes in Tycho-2
tycho.parallax = np.zeros(len(tycho), np.float32)
# Tycho-2 "supplement" stars, from Hipparcos and Tycho-1 catalogs, have
# ref_epoch = 0. Fill in with the 1991.25 epoch of those catalogs.
tycho.epoch_ra[tycho.epoch_ra == 0] = 1991.25
tycho.epoch_dec[tycho.epoch_dec == 0] = 1991.25
# Tycho-2 has separate epoch_ra and epoch_dec.
# Move source to the mean epoch.
tycho.ref_epoch = (tycho.epoch_ra + tycho.epoch_dec) / 2.
cosdec = np.cos(np.deg2rad(tycho.dec))
tycho.ra += (tycho.ref_epoch -
tycho.epoch_ra) * tycho.pmra / 3600. / cosdec
tycho.dec += (tycho.ref_epoch - tycho.epoch_dec) * tycho.pmdec / 3600.
# Tycho-2 proper motions are in arcsec/yr; Gaia are mas/yr.
tycho.pmra *= 1000.
tycho.pmdec *= 1000.
# We already cut on John's "isgalaxy" flag
tycho.pointsource = np.ones(len(tycho), bool)
# phot_g_mean_mag -- for initial brightness of source
tycho.phot_g_mean_mag = tycho.mag
tycho.delete_column('epoch_ra')
tycho.delete_column('epoch_dec')
tycho.istycho = np.ones(len(tycho), bool)
tycho.isbright = np.ones(len(tycho), bool)
tycho.ismedium = np.ones(len(tycho), bool)
tycho.donotfit = np.zeros(len(tycho), bool)
tycho.sources = np.empty(len(tycho), object)
if bands is not None:
for i, t in enumerate(tycho):
tycho.sources[i] = GaiaSource.from_catalog(t, bands)
return tycho
def get_reference_sources(survey,
targetwcs,
pixscale,
bands,
tycho_stars=True,
gaia_stars=True,
large_galaxies=True,
star_clusters=True):
from legacypipe.survey import GaiaSource
from legacypipe.survey import LegacyEllipseWithPriors
from tractor import NanoMaggies, RaDecPos
from tractor.galaxy import ExpGalaxy
from tractor.ellipses import EllipseESoft
H, W = targetwcs.shape
H, W = int(H), int(W)
# How big of a margin to search for bright stars and star clusters --
# this should be based on the maximum radius they are considered to
# affect.
ref_margin = 0.125
mpix = int(np.ceil(ref_margin * 3600. / pixscale))
marginwcs = targetwcs.get_subimage(-mpix, -mpix, W + 2 * mpix,
H + 2 * mpix)
refs = []
# Tycho-2 stars
if tycho_stars:
tycho = read_tycho2(survey, marginwcs)
if len(tycho):
refs.append(tycho)
# Add Gaia stars
gaia = None
if gaia_stars:
from astrometry.libkd.spherematch import match_radec
gaia = read_gaia(marginwcs)
if gaia is not None:
gaia.isbright = (gaia.phot_g_mean_mag < 13.)
gaia.ismedium = (gaia.phot_g_mean_mag < 16.)
gaia.donotfit = np.zeros(len(gaia), bool)
# Handle sources that appear in both Gaia and Tycho-2 by
# dropping the entry from Tycho-2.
if len(gaia) and len(tycho):
# Before matching, apply proper motions to bring them to
# the same epoch. We want to use the more-accurate Gaia
# proper motions, so rewind Gaia positions to the
# approximate epoch of Tycho-2: 1991.5.
cosdec = np.cos(np.deg2rad(gaia.dec))
gra = gaia.ra + (1991.5 - gaia.ref_epoch) * gaia.pmra / (
3600. * 1000.) / cosdec
gdec = gaia.dec + (1991.5 - gaia.ref_epoch) * gaia.pmdec / (3600. *
1000.)
I, J, _ = match_radec(tycho.ra,
tycho.dec,
gra,
gdec,
1. / 3600.,
nearest=True)
debug('Matched', len(I), 'Tycho-2 stars to Gaia stars.')
if len(I):
keep = np.ones(len(tycho), bool)
keep[I] = False
tycho.cut(keep)
gaia.isbright[J] = True
if gaia is not None and len(gaia) > 0:
refs.append(gaia)
# Read the catalog of star (open and globular) clusters and add them to the
# set of reference stars (with the isbright bit set).
if star_clusters:
clusters = read_star_clusters(marginwcs)
if clusters is not None:
debug('Found', len(clusters), 'star clusters nearby')
clusters.iscluster = np.ones(len(clusters), bool)
refs.append(clusters)
# Read large galaxies nearby.
if large_galaxies:
galaxies = read_large_galaxies(survey, targetwcs)
if galaxies is not None:
# Resolve possible Gaia-large-galaxy duplicates
if gaia and len(gaia):
I, J, _ = match_radec(galaxies.ra,
galaxies.dec,
gaia.ra,
gaia.dec,
2. / 3600.,
nearest=True)
print('Matched', len(I), 'large galaxies to Gaia stars.')
if len(I):
gaia.donotfit[J] = True
refs.append(galaxies)
refcat = None
if len(refs):
refs = merge_tables([r for r in refs if r is not None],
columns='fillzero')
if len(refs) == 0:
return None, None
refs.radius_pix = np.ceil(refs.radius * 3600. / pixscale).astype(int)
ok, xx, yy = targetwcs.radec2pixelxy(refs.ra, refs.dec)
# ibx = integer brick coords
refs.ibx = np.round(xx - 1.).astype(int)
refs.iby = np.round(yy - 1.).astype(int)
# cut ones whose position + radius are outside the brick bounds.
refs.cut((xx > -refs.radius_pix) * (xx < W + refs.radius_pix) *
(yy > -refs.radius_pix) * (yy < H + refs.radius_pix))
# mark ones that are actually inside the brick area.
refs.in_bounds = ((refs.ibx >= 0) * (refs.ibx < W) * (refs.iby >= 0) *
(refs.iby < H))
for col in [
'isbright', 'ismedium', 'islargegalaxy', 'iscluster', 'donotfit'
]:
if not col in refs.get_columns():
refs.set(col, np.zeros(len(refs), bool))
## Create Tractor sources from reference stars
refcat = []
for g in refs:
if g.donotfit or g.iscluster:
refcat.append(None)
elif g.islargegalaxy:
fluxes = dict([(band, NanoMaggies.magToNanomaggies(g.mag))
for band in bands])
assert (np.all(np.isfinite(list(fluxes.values()))))
rr = g.radius * 3600. / 0.5 # factor of two accounts for R(25)-->reff
pa = 180 - g.pa
if not np.isfinite(pa):
pa = 0.
logr, ee1, ee2 = EllipseESoft.rAbPhiToESoft(rr, g.ba, pa)
gal = ExpGalaxy(RaDecPos(g.ra, g.dec),
NanoMaggies(order=bands, **fluxes),
LegacyEllipseWithPriors(logr, ee1, ee2))
refcat.append(gal)
else:
# Gaia star -- which we want to create a source for, regardless of
# whether it is marked medium | bright (or neither).
refcat.append(GaiaSource.from_catalog(g, bands))
for src in refcat:
if src:
src.is_reference_source = True
return refs, refcat
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