def __init__(self):
""" CLASS CONSTRUCTOR
Arguments:
None
Returns:
Nothing
"""
# Initialize dust map if desired and share with class
self.dustmapJ = mwdust.Combined15(filter='2MASS J')
self.dustmapH = mwdust.Combined15(filter='2MASS H')
self.dustmapKs = mwdust.Combined15(filter='2MASS Ks')
return
def compute_AK_mwdust(ls, bs, dist, edist):
'''Using the EB-V map from 2014MNRAS.443.2907S and the extinction vector
RK = 0.31 from Schlafly and Finkbeiner 2011 (ApJ 737, 103)'''
dustmap = mwdust.Combined15(filter='2MASS Ks')
dist_kpc, edist_kpc = dist * 1e-3, edist * 1e-3
AK, eAK = np.zeros(ls.size), np.zeros(ls.size)
for i in range(ls.size):
v = dustmap(ls[i], bs[i],
np.array([dist_kpc[i], dist_kpc[i] + edist_kpc[i]]))
AK[i], eAK[i] = v[0], abs(np.diff(v))
return AK, eAK
def query_dustmodel_coords_allsky(ra,dec):
reddenMap = mwdust.Combined15()
sightLines = SkyCoord(ra*units.deg,dec*units.deg,frame='galactic')
distanceSamples = np.array([0.06309573,0.07943284,0.1,0.12589255,0.15848933,0.19952627,0.25118864,0.31622776,0.3981072,0.50118726,0.6309574,0.7943282 ,1.,1.2589258,1.5848933,1.9952621,2.511887,3.1622777,3.981073,5.011873,6.3095727,7.943284,10.,12.589258,15.848933,19.952621,25.11887,31.622776,39.81073,50.11873,63.095726])*1000. # In pc, from bayestar2017 map distance samples
reddenContainer=reddenMap(sightLines.l.value,sightLines.b.value,distanceSamples/1000.)
del reddenMap # To clear reddenMap from memory
dustModelDF = pd.DataFrame({'ra': [ra], 'dec': [dec]})
for index in range(len(reddenContainer)):
dustModelDF['av_'+str(round(distanceSamples[index],6))] = reddenContainer[index]
return dustModelDF
def compute_AK_mwdust(ls, bs, dist, edist, eAK_frac=.3):
'''Using the EB-V map from 2014MNRAS.443.2907S and the extinction vector
RK = 0.31 from Schlafly and Finkbeiner 2011 (ApJ 737, 103)'''
dustmap = mwdust.Combined15(filter='2MASS Ks')
dist_kpc, edist_kpc = np.ascontiguousarray(dist)*1e-3, \
np.ascontiguousarray(edist)*1e-3
ls, bs = np.ascontiguousarray(ls), np.ascontiguousarray(bs)
AK, eAK = np.zeros(ls.size), np.zeros(ls.size)
for i in range(ls.size):
v = dustmap(ls[i], bs[i],
np.array([dist_kpc[i], dist_kpc[i] + edist_kpc[i]]))
AK[i], eAK[i] = v[0], np.sqrt(
abs(np.diff(v))**2 + (eAK_frac * v[0])**2)
return unp.uarray(AK, eAK)
def run(self):
self.print_constraints()
bcmodel = '/Users/petigura/code/isoclassify/direct/bcgrid.h5'
dustmodel = mwdust.Combined15()
x = direct.classify_direct.obsdata()
self.addspec(x)
self.addjhk(x)
self.addgriz(x)
self.addplx(x)
self.paras = direct.classify_direct.stparas(input=x,
bcmodel=bcmodel,
dustmodel=dustmodel,
useav=0,
plot=1)
# from APASS DR9
vmag_s = star['Vmag'][sindx]
bmag_s = star['Bmag'][sindx]
feh_s = star['feh'][sindx]
rv_s = star['rv'][sindx]
rverr_s = star['rv_err'][sindx]
glon_s = glon[sindx]
glat_s = glat[sindx]
# absolute R mag
mvmag_s = np.interp(teff_s, teffmv, mvmag)
# extinction
# using mwdust
# need to do
# > export DUST_DIR=/Users/dkawata/work/pops/mwdust/DUST_DATA
combined = mwdust.Combined15(filter='CTIO V')
avmag = np.zeros_like(glon_s)
mod0_s = vmag_s - mvmag_s + avmag
dist0_s = np.power(10.0, (mod0_s + 5.0) / 5.0) * 0.001
dist_s = np.power(10.0, (mod0_s + 5.0) / 5.0) * 0.001
for i in range(0):
# distance modulus
mod_s = vmag_s - mvmag_s - avmag
dist_s = np.power(10.0, (mod_s + 5.0) / 5.0) * 0.001
# calculate extinction
for j in range(len(glon_s)):
avmag[j] = combined(glon_s[j], glat_s[j], dist_s[j])
print ' mwdust iteration ', i, ' finished'
# photometry V and V-I
# dwarf http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt
#bv_cyl = cut_flow(bv, vol_cut_indx)
gaia_cyl = cut_flow(gaia, vol_cut_indx)
print("Vol-reduced count: ", len(gaia_cyl))
#-------------------------------------------------volumn cut---------------------------------------------------------------
#-------------------------------------------------extinction---------------------------------------------------------------
#bv_corr = np.empty_like(bv_cyl)
jk_corr = np.zeros_like(jk_cyl)
mj_corr = np.zeros_like(mj_cyl)
if (dereddening) and (not load_dereddened_data):
print("loading dust map...")
#dust_combine = mwdust.Combined15() #b-v filter
dust_combine_J = mwdust.Combined15("2MASS J")
dust_combine_K = mwdust.Combined15("2MASS Ks")
print("Preparing dereddening...")
for i in tqdm.tqdm(range(len(jk_cyl))):
ldeg = (gaia_cyl['l'])[i]
bdeg = (gaia_cyl['b'])[i]
plx = (gaia_cyl['parallax'])[i]
ej = dust_combine_J(ldeg, bdeg, np.divide(1000., plx))
ek = dust_combine_K(ldeg, bdeg, np.divide(1000., plx))
ejk = ej - ek
#ebv = dust_combine(ldeg,bdeg,np.divide(1000.,plx))
#bv_corr[i] = bv_cyl[i] - ebv
jk_corr[i] = jk_cyl[i] - ejk
mj_corr[i] = mj_cyl[i] - ej
dereddened_ouput_file = "temp_ext.txt"
np.savetxt(dereddened_ouput_file,
def procedure(rank, size):
steps = 2643.0 - 635.0
chunky = int(steps/size)
rest = steps - chunky*size
mini = chunky*rank
maxi = chunky*(rank + 1)
if rank >= (size - 1 - rest):
maxi += 2 + rank - size + rest
mini += rank - size + 1 + rest
mini += 635
maxi += 635
mini = int(mini)
maxi = int(maxi)
if color == "iband":
filter_ = "SDSS i"
elif color == "red":
filter_ = "SDSS r"
elif color == "green":
filter_ = "SDSS g"
else:
print("Defined filter not available. Choose red, green or iband")
return
imap = mwdust.Combined15(filter = filter_, sf10 = True)
print("Creating Grid...")
foos = np.arange(mini, maxi, 1)
full = tools.create_grid(foos)
print("Kernel %s doing %s up to %s" % (rank, mini, maxi))
print("Calculating borders of the cells")
minras, maxras, mindecs, maxdecs = tools.get_centers(full[:,0], full[:,1], borders = True, maskdetect = True)
print("Using %s objects in Kernel %s" % (full.shape[0], rank))
print("Converting coordinates to galactic system")
idy = np.where(np.abs(minras + 99.0) < 0.0001))[0]
idy2 = np.where(np.abs(minras + 999.0) < 0.0001))[0]
idy = np.append(idy, idy2)
idy = np.unique(idy)
for it in idy:
minras[it] = 0.0
mindecs[it] = 0.0
maxras[it] = 0.0
maxdecs[it] = 0.0
c_maxs = SkyCoord(ra = maxras*u.degree, dec = maxdecs*u.degree, frame = 'icrs')
c_mins = SkyCoord(ra = minras*u.degree, dec = mindecs*u.degree, frame = 'icrs')
c_maxs = c_maxs.galactic
c_mins = c_mins.galactic
ramins = c_mins.l.degree
ramaxs = c_maxs.l.degree
decmaxs = c_mins.b.degree
decmins = c_maxs.b.degree
print("Calculating extinction...")
dustyness = np.zeros(0)
for idx in frogress.bar(range(full.shape[0])):
if idx in idy:
dustyness = np.append(dustyness, -99.0)
continue
dustynesscolor = np.zeros(0)
for b in np.linspace(decmins[idx], decmaxs[idx], 5):
for l in np.linspace(ramins[idx], ramaxs[idx], 5):
if color == "red":
foo = rmap(l, b, depth)
elif color == "iband":
foo = imap(l, b, depth)
elif color == "green":
foo = gmap(l, b, depth)
dustynesscolor = np.append(dustynesscolor, foo)
dustynesscolor = np.average(dustynesscolor)
dustyness = np.append(dustyness, dustynesscolor)
np.savetxt(str(outdir)+str(color)+"parts/dust_catalog_"+str(color)+"_"+str(rank)+".csv", np.hstack((full, dustyness.reshape(dustyness.size, 1))))
def stparas(input,
dnumodel=0,
bcmodel=0,
dustmodel=0,
dnucor=0,
useav=0,
plot=0):
# IAU XXIX Resolution, Mamajek et al. (2015)
r_sun = 6.957e10
gconst = 6.67408e-8
gm = 1.3271244e26
m_sun = gm / gconst
rho_sun = m_sun / (4. / 3. * np.pi * r_sun**3)
g_sun = gconst * m_sun / r_sun**2.
# solar constants
numaxsun = 3090.
dnusun = 135.1
teffsun = 5777.
Msun = 4.74 # NB this is fixed to MESA BCs!
# assumed uncertainty in bolometric corrections
err_bc = 0.02
# assumed uncertainty in extinction
err_ext = 0.02
# load model if they're not passed on
if (dnumodel == 0):
dnumodel = asfgrid.Seism()
if (bcmodel == 0):
bcmodel = h5py.File('bcgrid.h5', 'r')
if (dustmodel == 0.):
dustmodel = mwdust.Combined15()
# object containing output values
out = resdata()
## extinction coefficients
extfactors = extinction()
########################################################################################
# case 1: input is parallax + colors
########################################################################################
if ((input.plx > 0.)):
# only K-band for now
teffgrid = np.array(bcmodel['teffgrid'])
avgrid = np.array(bcmodel['avgrid'])
interp = RegularGridInterpolator((np.array(bcmodel['teffgrid']),\
np.array(bcmodel['logggrid']),np.array(bcmodel['fehgrid']),\
np.array(bcmodel['avgrid'])),np.array(bcmodel['bc_k']))
### Monte Carlo starts here
# number of samples
nsample = 1e5
# length scale for exp decreasing vol density prior in pc
L = 1350.
# get a rough maximum distance
tempdis = 1. / input.plx
tempdise = input.plxe / input.plx**2
maxds = tempdis + 5. * tempdise
ds = np.arange(1., 10000, 1.)
lh = np.exp((-1. / (2. * input.plxe**2)) * (input.plx - 1. / ds)**2)
prior = (ds**2 / (2. * L**3.)) * np.exp(-ds / L)
dis = lh * prior
dis2 = dis / np.sum(dis)
norm = dis2 / np.max(dis2)
um = np.where((ds > tempdis) & (norm < 0.001))[0]
if (len(um) > 0):
maxds = np.min(ds[um])
else:
maxds = 10000.
print 'using max distance:', maxds
ds = np.linspace(1., maxds, 10000)
lh = (1./(np.sqrt(2.*np.pi)*input.plxe))*\
np.exp( (-1./(2.*input.plxe**2))*(input.plx-1./ds)**2)
prior = (ds**2 / (2. * L**3.)) * np.exp(-ds / L)
prior = np.zeros(len(lh)) + 1.
dis = lh * prior
dis2 = dis / np.sum(dis)
# sample distances following the discrete distance posterior
np.random.seed(seed=10)
dsamp = np.random.choice(ds, p=dis2, size=nsample)
equ = ephem.Equatorial(input.ra * np.pi / 180.,
input.dec * np.pi / 180.,
epoch=ephem.J2000)
gal = ephem.Galactic(equ)
lon_deg = gal.lon * 180. / np.pi
lat_deg = gal.lat * 180. / np.pi
avs = 3.1 * dustmodel(lon_deg, lat_deg, dsamp / 1000.)
ext = avs * extfactors.ak
ext = 0. # already in BC
if (input.teff == -99.):
teff = casagrande(jkmag, 0.0)
teffe = 100.
else:
teff = input.teff
teffe = input.teffe
np.random.seed(seed=11)
teffsamp = teff + np.random.randn(nsample) * teffe
map = input.kmag
mape = input.kmage
np.random.seed(seed=12)
map_samp = map + np.random.randn(nsample) * mape
absmag = -5. * np.log10(dsamp) - ext + map_samp + 5.
if (input.teff < np.min(teffgrid)):
return out
if (input.teff > np.max(teffgrid)):
return out
#if (out.av > np.max(avgrid)):
# return out
#if (out.av < np.min(avgrid)):
# return out
if ((input.teff > -99.) & (input.logg > -99.) & (input.feh > -99.)):
#bc = interp(np.array([input.teff,input.logg,input.feh,0.]))[0]
arr = np.zeros((len(avs), 4))
arr[:, 0] = np.zeros(len(avs)) + input.teff
arr[:, 1] = np.zeros(len(avs)) + input.logg
arr[:, 2] = np.zeros(len(avs)) + input.feh
arr[:, 3] = np.zeros(len(avs)) + avs
um = np.where(arr[:, 3] < 0.)[0]
arr[um, 3] = 0.
#pdb.set_trace()
bc = interp(arr)
#pdb.set_trace()
#pdb.set_trace()
Mvbol = absmag + bc
lum = 10**((Mvbol - Msun) / (-2.5))
t = teffsamp / teffsun
rad = (lum * t**(-4.))**0.5
#pdb.set_trace()
out.teff = input.teff
out.teffe = input.teffe
'''
out.lum=np.median(lum)
out.lumep=np.percentile(lum,84.1)-out.lum
out.lumem=out.lum-np.percentile(lum,15.9)
out.rad=np.median(rad)
out.radep=np.percentile(rad,84.1)-out.rad
out.radem=out.rad-np.percentile(rad,15.9)
out.dis=np.median(dsamp)
out.disep=np.percentile(dsamp,84.1)-out.dis
out.disem=out.dis-np.percentile(dsamp,15.9)
'''
out.avs = np.median(avs)
out.avsep = np.percentile(avs, 84.1) - out.avs
out.avsem = out.avs - np.percentile(avs, 15.9)
#pdb.set_trace()
out.rad, out.radep, out.radem, radbn = getstat(rad)
out.lum, out.lumep, out.lumem, lumbn = getstat(lum)
out.dis, out.disep, out.disem, disbn = getstat(dsamp)
#out.avs,out.avsep,out.avsem=getstat(avs)
#pdb.set_trace()
out.teff = input.teff
out.teffep = input.teffe
out.teffem = input.teffe
out.logg = input.logg
out.loggep = input.logge
out.loggem = input.logge
out.feh = input.feh
out.fehep = input.fehe
out.fehem = input.fehe
if (plot == 1):
plt.ion()
plt.clf()
plt.subplot(3, 2, 1)
plt.hist(teffsamp, bins=100)
plt.title('Teff')
plt.subplot(3, 2, 2)
plt.hist(lum, bins=lumbn)
plt.title('Lum')
plt.subplot(3, 2, 3)
plt.hist(rad, bins=radbn)
plt.title('Rad')
plt.subplot(3, 2, 4)
plt.hist(absmag, bins=100)
plt.title('absmag')
plt.subplot(3, 2, 5)
plt.hist(dsamp, bins=disbn)
plt.title('distance')
plt.subplot(3, 2, 6)
plt.hist(avs, bins=100)
plt.title('Av')
#pdb.set_trace()
print ' '
print 'teff(K):', out.teff, '+/-', out.teffe
print 'dis(pc):', out.dis, '+', out.disep, '-', out.disem
print 'av(mag):', out.avs, '+', out.avsep, '-', out.avsem
print 'rad(rsun):', out.rad, '+', out.radep, '-', out.radem
print 'lum(lsun):', out.lum, '+', out.lumep, '-', out.lumem
print '-----'
#raw_input(':')
#pdb.set_trace()
########################################################################################
# case 1: input is spectroscopy + seismology
########################################################################################
if ((input.dnu > -99.) & (input.teff > -99.)):
# seismic logg, density, M and R from scaling relations; this is iterated,
# since Dnu scaling relation correction depends on M
dmass = 1.
fdnu = 1.
dnuo = input.dnu
oldmass = 1.0
nit = 0.
while (nit < 5):
numaxn = input.numax / numaxsun
numaxne = input.numaxe / numaxsun
dnun = (dnuo / fdnu) / dnusun
dnune = input.dnue / dnusun
teffn = input.teff / teffsun
teffne = input.teffe / teffsun
out.rad = (numaxn) * (dnun)**(-2.) * np.sqrt(teffn)
out.rade = np.sqrt( (input.numaxe/input.numax)**2. + \
4.*(input.dnue/input.dnu)**2. + \
0.25*(input.teffe/input.teff)**2.)*out.rad
out.mass = out.rad**3. * (dnun)**2.
out.masse = np.sqrt( 9.*(out.rade/out.rad)**2. + \
4.*(input.dnue/input.dnu)**2. )*out.mass
out.rho = rho_sun * (dnun**2.)
out.rhoe = np.sqrt(4. * (input.dnue / input.dnu)**2.) * out.rho
g = g_sun * numaxn * teffn**0.5
ge = np.sqrt ( (input.numaxe/input.numax)**2. + \
(0.5*input.teffe/input.teff)**2. ) * g
out.logg = np.log10(g)
out.logge = ge / (g * np.log(10.))
# Dnu scaling relation correction from Sharma et al. 2016
if (dnucor == 1):
if (input.clump == 1):
evstate = 2
else:
evstate = 1
#pdb.set_trace()
dnu, numax, fdnu = dnumodel.get_dnu_numax(evstate,
input.feh,
input.teff,
out.mass,
out.mass,
out.logg,
isfeh=True)
#print out.mass,fdnu
dmass = abs((oldmass - out.mass) / out.mass)
oldmass = out.mass
nit = nit + 1
print fdnu
#pdb.set_trace()
out.lum = out.rad**2. * teffn**4.
out.lume = np.sqrt((2. * out.rade / out.rad)**2. +
(4. * input.teffe / input.teff)**2.) * out.lum
print ' '
print 'teff(K):', input.teff, '+/-', input.teffe
print 'feh(dex):', input.feh, '+/-', input.fehe
print 'logg(dex):', out.logg, '+/-', out.logge
print 'rho(cgs):', out.rho, '+/-', out.rhoe
print 'rad(rsun):', out.rad, '+/-', out.rade
print 'mass(msun):', out.mass, '+/-', out.masse
print 'lum(lsun):', out.lum, '+/-', out.lume
print '-----'
out.teff = input.teff
out.teffep = input.teffe
out.teffem = input.teffe
out.feh = input.feh
out.fehep = input.fehe
out.fehem = input.fehe
out.loggep = out.logge
out.loggem = out.logge
out.radep = out.rade
out.radem = out.rade
out.rhoep = out.rhoe
out.rhoem = out.rhoe
out.massep = out.masse
out.massem = out.masse
out.lumep = out.lume
out.lumem = out.lume
ddis = 1.
ext = 0.0
err_ = 0.01
olddis = 100.0
# pick an apparent magnitude from input
map = -99.
if (input.vmag > -99.):
map = input.vmag
mape = input.vmage
str = 'bc_v'
avtoext = extfactors.av
if (input.vtmag > -99.):
map = input.vtmag
mape = input.vtmage
str = 'bc_vt'
avtoext = extfactors.avt
if (input.jmag > -99.):
map = input.jmag
mape = input.jmage
str = 'bc_j'
avtoext = extfactors.aj
if (input.kmag > -99.):
map = input.kmag
mape = input.kmage
str = 'bc_k'
avtoext = extfactors.ak
if (input.gamag > -99.):
map = input.gamag
mape = input.gamage
str = 'bc_ga'
avtoext = extfactors.aga
# if apparent mag is given, calculate distance
if (map > -99.):
print 'using ' + str
print 'using coords: ', input.ra, input.dec
equ = ephem.Equatorial(input.ra * np.pi / 180.,
input.dec * np.pi / 180.,
epoch=ephem.J2000)
gal = ephem.Galactic(equ)
lon_deg = gal.lon * 180. / np.pi
lat_deg = gal.lat * 180. / np.pi
# iterated since BC depends on extinction
nit = 0
while (nit < 5):
# bolometric correction interpolated from MESA
interp = RegularGridInterpolator((np.array(bcmodel['teffgrid']),\
np.array(bcmodel['logggrid']),np.array(bcmodel['fehgrid']),\
np.array(bcmodel['avgrid'])),np.array(bcmodel[str]))
#pdb.set_trace()
bc = interp(
np.array([input.teff, out.logg, input.feh, out.avs]))[0]
#bc = interp(np.array([input.teff,out.logg,input.feh,0.]))[0]
Mvbol = -2.5 * (np.log10(out.lum)) + Msun
Mvbole = np.sqrt(
(-2.5 / (out.lum * np.log(10.)))**2 * out.lume**2)
Mabs = Mvbol - bc
Mabse = np.sqrt(Mvbole**2 + err_bc**2)
ext = 0. # ext already applied in BC
logplx = (Mabs - 5. - map + ext) / 5.
logplxe = np.sqrt((Mabse / 5.)**2. + (mape / 5.)**2. +
(err_ext / 5.)**2.)
out.plx = 10.**logplx
out.plxe = np.log(10) * 10.**logplx * logplxe
out.dis = 1. / out.plx
out.dise = out.plxe / out.plx**2.
ddis = abs((olddis - out.dis) / out.dis)
#print olddis,out.dis,ddis,ext
olddis = out.dis
out.avs = 3.1 * dustmodel(lon_deg, lat_deg, out.dis / 1000.)[0]
if (useav != 0.):
out.avs = useav
if (out.avs < 0.):
out.avs = 0.0
#pdb.set_trace()
ext = out.avs * avtoext
nit = nit + 1
#pdb.set_trace()
print 'Av(mag):', out.avs
print 'plx(mas):', out.plx * 1e3, '+/-', out.plxe * 1e3
print 'dis(pc):', out.dis, '+/-', out.dise
out.disep = out.dise
out.disem = out.dise
return out
def reddening(data,
file_loc=os.getcwd() + os.sep + 'TESS_telecon3' + os.sep +
'reddening' + os.sep,
v=False):
if v: print data.shape, 'before reddening'
# check if the reddening file exists
if (os.path.isfile(file_loc + 'reddening.txt') == True):
if v: print 'reddening file located.'
reds = pd.read_csv(file_loc + 'reddening.txt')
# if it does exist, add extinction (Av) to the dataframe
if len(reds) == len(data):
reds = reds.round({'GLon': 4, 'GLat': 4})
data = data.round({'GLon': 4, 'GLat': 4})
data = pd.merge(data,
reds[list(['GLon', 'GLat', 'E(B-V)', 'Av'])],
how='inner')
# define the extinction coefficient in I band.
# from (12) eqn 1 and table 3 a(x) + b(x)/Rv value for Imag
data['Ai'] = data['Av'] * 0.479
if v: print data.shape, 'after reddening'
else:
if v:
print 'the reddening file is a different length to the data! recalculate reddening.'
# calculate reddening values using the dataframe inputs
# mwdust info at (9). E(B-V) TO Av info at (11)
import mwdust
combined15 = mwdust.Combined15(filter='2MASS H')
a = timeit.default_timer()
if v: print 'calculating extinction coefficients...'
GLon, GLat, Dist = np.split(
data[['GLon', 'GLat', 'Dist']].as_matrix(), 3, 1)
comb15 = np.full(len(GLon), -5.)
Av = np.full(len(GLon), -5.)
# calculate extinction using galactic longitude, latitude and distance (in pc)
for i in range(len(GLon)):
comb15[i] = combined15(float(GLon[i]), float(GLat[i]),
float(Dist[i]))
if (i % 1000. == 0): print i
Av = comb15 * 3.2 # convert from reddening E(B-V) to extinction Av
reds = np.c_[GLon, GLat, Dist, comb15, Av]
h = 'GLon,GLat,Dist,E(B-V),Av'
np.savetxt(file_loc + 'reddening.txt',
reds,
comments='',
header=h,
delimiter=',')
b = timeit.default_timer()
if v: print b - a, 'seconds'
else:
if v: print 'calculating reddening.'
# calculate reddening values using the dataframe inputs
# mwdust info at (9). E(B-V) TO Av info at (11)
import mwdust
combined15 = mwdust.Combined15(filter='2MASS H')
a = timeit.default_timer()
if v: print 'calculating extinction coefficients...'
GLon, GLat, Dist = np.split(data[['GLon', 'GLat', 'Dist']].as_matrix(),
3, 1)
comb15 = np.full(len(GLon), -5.)
Av = np.full(len(GLon), -5.)
# calculate extinction using galactic longitude, latitude and distance (in pc)
for i in range(len(GLon)):
comb15[i] = combined15(float(GLon[i]), float(GLat[i]),
float(Dist[i]))
if (i % 1000. == 0):
print i # only print the code's progress every 100 stars
Av = comb15 * 3.2 # convert from reddening E(B-V) to extinction Av
reds = np.c_[GLon, GLat, Dist, comb15, Av]
h = 'GLon,GLat,Dist,E(B-V),Av'
np.savetxt(file_loc + 'reddening.txt',
reds,
comments='',
header=h,
delimiter=',')
b = timeit.default_timer()
if v: print b - a, 'seconds'
sys.exit()
return data
