def get_MG_BPRP(g, dust_correct=False):
global bayestar
if dust_correct:
if bayestar is None:
bayestar = BayestarQuery()
c = g.get_skycoord()
ebv = bayestar.query(c)
mg = g.get_G0(ebv=ebv) - g.distmod
bprp = g.get_BP0(ebv=ebv) - g.get_RP0(ebv=ebv)
else:
mg = g.phot_g_mean_mag - g.distmod
bprp = g.phot_bp_mean_mag - g.phot_rp_mean_mag
return mg, bprp
def __init__(self, with_extinction_maps=False):
self.redd_maps = {}
with open(dir_path + 'extinction/extinction_coeffs_2017.dat') as f:
self.R_V_grid = np.fromstring(f.readline(),
dtype=np.float64,
sep=' ')
for l in f.readlines():
spl = l.split(" ", 1)
self.redd_maps[spl[0]] = np.fromstring(spl[1],
dtype=np.float64,
sep=' ')
self.interp_maps = {
n: interp1d(self.R_V_grid, self.redd_maps[n])
for n in self.redd_maps
}
self.R_G = np.zeros((75, len(self.R_V_grid)))
with open(dir_path + 'extinction/extinction_coeffs_G_2017.dat') as f:
self.logTeffgrid = np.fromstring(f.readline(),
dtype=np.float64,
sep=' ')
for n, l in enumerate(f.readlines()):
self.R_G[n] = np.fromstring(l, dtype=np.float64, sep=' ')
self.interp_G_maps = interp2d(self.R_V_grid, self.logTeffgrid,
self.R_G)
if (with_extinction_maps):
self.sfd = SFDQuery()
self.bayestar = BayestarQuery(max_samples=10)
self.marshall = MarshallQuery()
self.drimmel = mwdust.Drimmel03(filter='Landolt V')
def dust(l, b, distance, plot=False, max_samples=2, mode='median', model='bayes'):
if model == 'sfd':
c = SkyCoord(l, b,
frame='galactic')
sfd = SFDQuery()
dust = sfd(c)
if model == 'bayes':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
bayes = BayestarQuery(max_samples=max_samples)
dust = bayes(c, mode=mode)
if model == 'iphas':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
iphas = IPHASQuery()
dust = iphas(c, mode=mode)
if model == 'marshall':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
marshall = MarshallQuery()
dust = marshall(c)
if model == 'chen':
c = SkyCoord(l, b,
distance = distance,
frame='galactic')
chen = Chen2014Query()
dust = chen(c)
#cNoDist = SkyCoord(l, b,
# frame='galactic')
#bayesDustNoDist = bayes(cNoDist, mode=mode)
#!!!!! Do something else than setting it equal to 0 !!!!!
#if len(bayesDust) > 1: bayesDust[np.isnan(bayesDust)] = 0.0
if plot:
fig, ax = plt.subplots(3, figsize=(5, 7.5))
ax[0].hist(np.log10(sfd(c)), bins=100, log=True, histtype='step')
ax[1].hist(np.log10(bayesDustNoDist[bayesDustNoDist>0]), bins=100, log=True, histtype='step')
ax[2].hist(np.log10(bayesDust[bayesDust >0]), bins=100, log=True, histtype='step')
ax[0].set_xlabel('SFD Dust Attenuation')
ax[1].set_xlabel('Bayestar Dust Attenuation No Distance')
ax[2].set_xlabel('Bayestar Dust Attenuation')
for a in ax: a.set_xlim(-4, 0.0)
hist, bins = np.histogram(magsMatched['bmag'], bins=100)
plt.hist(magsMatched['bmag'], bins=bins, histtype='step')
plt.hist(magsMatched['bmag'] - B_RedCoeff*bayesDust, bins=bins, histtype='step')
plt.tight_layout()
return dust
def main():
w, h = (2056, 1024)
l_0 = 130.
# Set up Bayestar query object
print('Loading bayestar map...')
bayestar = BayestarQuery(max_samples=1)
# Create a grid of coordinates
print('Creating grid of coordinates...')
l = np.linspace(-180. + l_0, 180. + l_0, 2 * w)
b = np.linspace(-90., 90., 2 * h + 2)
b = b[1:-1]
l, b = np.meshgrid(l, b)
l += (np.random.random(l.shape) - 0.5) * 360. / (2. * w)
b += (np.random.random(l.shape) - 0.5) * 180. / (2. * h)
ebv = np.empty(l.shape + (3, ), dtype='f8')
for k, d in enumerate([0.5, 1.5, 5.]):
# d = 5. # We'll query integrated reddening to a distance of 5 kpc
coords = SkyCoord(l * u.deg, b * u.deg, d * u.kpc, frame='galactic')
# Get the dust median reddening at each coordinate
print('Querying map...')
ebv[:, :, k] = bayestar.query(coords, mode='median')
ebv[:, :, 2] -= ebv[:, :, 1]
ebv[:, :, 1] -= ebv[:, :, 0]
# Convert the output array to a PIL image and save
print('Saving image...')
img = numpy2pil(ebv[::-1, ::-1, :], 0., 1.5)
img = img.resize((w, h), resample=PIL.Image.LANCZOS)
fname = 'bayestar.png'
img.save(fname)
return 0
def main():
w,h = (2056,1024)
l_0 = 130.
# Set up Bayestar query object
print('Loading bayestar map...')
bayestar = BayestarQuery(max_samples=1)
# Create a grid of coordinates
print('Creating grid of coordinates...')
l = np.linspace(-180.+l_0, 180.+l_0, 2*w)
b = np.linspace(-90., 90., 2*h+2)
b = b[1:-1]
l,b = np.meshgrid(l, b)
l += (np.random.random(l.shape) - 0.5) * 360./(2.*w)
b += (np.random.random(l.shape) - 0.5) * 180./(2.*h)
ebv = np.empty(l.shape+(3,), dtype='f8')
for k,d in enumerate([0.5, 1.5, 5.]):
# d = 5. # We'll query integrated reddening to a distance of 5 kpc
coords = SkyCoord(l*u.deg, b*u.deg, d*u.kpc, frame='galactic')
# Get the dust median reddening at each coordinate
print('Querying map...')
ebv[:,:,k] = bayestar.query(coords, mode='median')
ebv[:,:,2] -= ebv[:,:,1]
ebv[:,:,1] -= ebv[:,:,0]
# Convert the output array to a PIL image and save
print('Saving image...')
img = numpy2pil(ebv[::-1,::-1,:], 0., 1.5)
img = img.resize((w,h), resample=PIL.Image.LANCZOS)
fname = 'bayestar.png'
img.save(fname)
return 0
def InitializeDustmap(config):
# Fetch the planck data if we don't have it yet.
if not os.path.exists(os.path.join(config['DataDirectory'], 'planck')):
print('downloading planck data')
from dustmaps.config import config as dustconfig
dustconfig['data_dir'] = config['DataDirectory']
from dustmaps.planck import fetch
fetch()
planck = PlanckQuery()
# Fetch the bayestar data if we don't have it yet.
if not os.path.exists(os.path.join(config['DataDirectory'], 'bayestar')):
print('downloading bayestar data')
from dustmaps.config import config as dustconfig
dustconfig['data_dir'] = config['DataDirectory']
from dustmaps.bayestar import fetch
fetch()
bayestar = BayestarQuery(version='bayestar2015')
return planck, bayestar
def main():
c0 = SkyCoord.from_name('orion a', frame='galactic')
print(c0)
# l = np.arange(c0.l.deg - 5., c0.l.deg + 5., 0.05)
# b = np.arange(c0.b.deg - 5., c0.b.deg + 5., 0.05)
l0, b0 = (37., -16.)
l = np.arange(l0 - 5., l0 + 5., 0.05)
b = np.arange(b0 - 5., b0 + 5., 0.05)
l, b = np.meshgrid(l, b)
coords = SkyCoord(l * units.deg,
b * units.deg,
distance=1. * units.kpc,
frame='galactic')
sfd = SFDQuery()
Av_sfd = 2.742 * sfd(coords)
planck = PlanckQuery()
Av_planck = 3.1 * planck(coords)
bayestar = BayestarQuery(max_samples=1)
Av_bayestar = 2.742 * bayestar(coords)
fig = plt.figure(figsize=(12, 4), dpi=150)
for k, (Av, title) in enumerate([(Av_sfd, 'SFD'), (Av_planck, 'Planck'),
(Av_bayestar, 'Bayestar')]):
ax = fig.add_subplot(1, 3, k + 1)
ax.imshow(np.sqrt(Av)[::, ::-1],
vmin=0.,
vmax=2.,
origin='lower',
interpolation='nearest',
cmap='binary',
aspect='equal')
ax.axis('off')
ax.set_title(title)
fig.subplots_adjust(wspace=0., hspace=0.)
plt.savefig('comparison.png', dpi=150)
return 0
def calc_extinct():
t = Table.read('lamost_rc_wise_gaia_PS1_2mass.fits')
from astropy.coordinates import SkyCoord
import astropy.units as units
import dustmaps.bayestar
from dustmaps.bayestar import BayestarQuery
dustmaps.bayestar.fetch()
bayestar = BayestarQuery(version='bayestar2019',max_samples=1) # 'bayestar2019' is the default
print('here')
avs = np.full(len(t),np.nan)
inds = np.where(~np.isnan(t['dist']))[0]
coords = SkyCoord(t['GLON'][inds]*units.deg, t['GLAT'][inds]*units.deg,distance=t['dist'][inds]*units.kpc, frame='galactic')
reddening = bayestar(coords, mode='median')
AK = .3026*reddening
AV = AK/.05
avs[inds] = AV
t['A_V'] = avs
t.write('lamost_rc_wise_gaia_PS1_2mass_ext.fits',overwrite=True)
print("Loading Gaia catalog")
with fits.open("kepler_dr2_1arcsec.fits") as data:
gaia = pd.DataFrame(data[1].data, dtype="float64")
gaia_mc = pd.merge(mc, gaia, on="kepid", how="left")
# S/N cuts
sn = gaia_mc.parallax.values / gaia_mc.parallax_error.values
m = (sn > 10)
m &= (gaia_mc.parallax.values > 0) * np.isfinite(gaia_mc.parallax.values)
m &= gaia_mc.astrometric_excess_noise.values < 5
gaia_mc = gaia_mc.iloc[m]
print("Loading Dustmaps")
bayestar = BayestarQuery(max_samples=2, version='bayestar2019')
print("Calculating Ebv")
coords = SkyCoord(gaia_mc.ra.values * units.deg,
gaia_mc.dec.values * units.deg,
distance=gaia_mc.r_est.values * units.pc)
ebv, flags = bayestar(coords,
mode='percentile',
pct=[16., 50., 84.],
return_flags=True)
# Calculate Av
Av_bayestar = 2.742 * ebv
Av = Av_bayestar[:, 1]
Av_errm = Av - Av_bayestar[:, 0]
l*units.deg,
b*units.deg,
distance=1.*units.kpc,
frame='galactic'
)
#--------------------------------------------
# Then, we’ll load up and query three different dust maps:
sfd = SFDQuery()
Av_sfd = 2.742 * sfd(coords)
planck = PlanckQuery()
Av_planck = 3.1 * planck(coords)
bayestar = BayestarQuery(max_samples=1)
Av_bayestar = 2.742 * bayestar(coords)
#--------------------------------------------
# We’ve assumed RV=3.1, and used the coefficient from Table 6 of Schlafly & Finkbeiner (2011)
# to convert SFD and Bayestar reddenings to magnitudes of AV.
#
# Finally, we create the figure using matplotlib:
fig = plt.figure(figsize=(12,4), dpi=150)
for k,(Av,title) in enumerate([(Av_sfd, 'SFD'),
(Av_planck, 'Planck'),
(Av_bayestar, 'Bayestar')]):
ax = fig.add_subplot(1,3,k+1)
ax.imshow(
def InitializeDustmap():
# Initialize dustmaps using Bayestar2019
print('Initializing Bayestar2019.')
#dustmaps.bayestar.fetch()
bayestar = BayestarQuery(max_samples=1)
return bayestar
def extinction(spec, red, coord):
"""
:param spec: (numpy array) XSpectrum1D objects: use clamato_read.py
:param red: (numpy array) redshift values
:param coord: (numpy array) coordinates
:return:
unred_spec: (numpy array) de-reddened spec
"""
import numpy as np
from numpy.lib.polynomial import poly1d
import astropy.units as u
from astropy.coordinates import SkyCoord
from dustmaps.bayestar import BayestarQuery
from astropy.cosmology import WMAP9 as cosmo
from linetools.spectra.xspectrum1d import XSpectrum1D
r = range(len(spec))
Mpc = cosmo.comoving_distance(red)
bayestar = BayestarQuery()
coords = [
SkyCoord(coord[i][0] * u.deg,
coord[i][1] * u.deg,
distance=Mpc[i],
frame='fk5') for i in r
]
ebv = [bayestar(i) for i in coords] #to get the ebv values for each galaxy
unred_spec = []
for i in r:
x = 10000. / np.array(spec[i].wavelength) # Convert to inverse microns
npts = x.size
a = np.zeros(npts, dtype=np.float)
b = np.zeros(npts, dtype=np.float)
r_v = 3.1
good = np.where((x >= 0.3) & (x < 1.1))
if len(good[0]) > 0:
a[good] = 0.574 * x[good]**(1.61)
b[good] = -0.527 * x[good]**(1.61)
good = np.where((x >= 1.1) & (x < 3.3))
if len(good[0]) > 0: # Use new constants from O'Donnell (1994)
y = x[good] - 1.82
c1 = np.array([
1., 0.104, -0.609, 0.701, 1.137, -1.718, -0.827, 1.647, -0.505
]) # from O'Donnell
c2 = np.array([
0., 1.952, 2.908, -3.989, -7.985, 11.102, 5.491, -10.805, 3.347
])
a[good] = poly1d(c1[::-1])(y)
b[good] = poly1d(c2[::-1])(y)
good = np.where((x >= 3.3) & (x < 8))
if len(good[0]) > 0:
y = x[good]
a[good] = 1.752 - 0.316 * y - (0.104 /
((y - 4.67)**2 + 0.341)) # + f_a
b[good] = -3.090 + 1.825 * y + (1.206 /
((y - 4.62)**2 + 0.263)) # + f_b
good = np.where((x >= 8) & (x <= 11))
if len(good[0]) > 0:
y = x[good] - 8.
c1 = np.array([-1.073, -0.628, 0.137, -0.070])
c2 = np.array([13.670, 4.257, -0.420, 0.374])
a[good] = poly1d(c1[::-1])(y)
b[good] = poly1d(c2[::-1])(y)
# Now apply extinction correction to input flux vector
a_v = r_v * ebv[i]
a_lambda = a_v * (a + b / r_v)
funred = spec[i].flux * 10.**(0.4 * a_lambda) # Derive unreddened flux
funred = np.asarray(funred)
unred_spec.append(XSpectrum1D(spec[i].wavelength, funred, spec[i].sig))
return np.asarray(unred_spec)
distance = eval(sys.argv[3])
else:
print('wrong number of parameters')
print('Example: $ python dustmap_script.py 15h28m17.53s -58d35m13.9s 2.3')
sys.exit(1)
#source_ra = '15h28m17.53s'
#source_dec = '-58d35m13.9s'
#distance = 2.3
#print(source_ra, source_dec, distance)
print('Estimating E(B-V) using dust maps:')
coords = SkyCoord(source_ra, source_dec, distance=distance*units.kpc, frame='icrs')
bayestar = BayestarQuery(max_samples=5, version='bayestar2017')
print('## Bayestar (3D):', bayestar(coords, mode='median'))
chen = Chen2014Query()
print('## Chen 2014 (3D):', chen(coords))
iphas = IPHASQuery()
print('## IPHAS (3D):', iphas(coords, mode='median'))
marshal = MarshallQuery()
print('## Marshall 2006 (3D):', marshal(coords))
sfdebv = SFDQuery()
print('## SFD 1998/2011 (2D):', sfdebv(coords))
lenz = Lenz2017Query()
