def calc_effsel(args, options, sample=None):
# Work-horse function to compute the effective selection function,
# sample is a data sample of stars to consider for the (JK,Z) sampling
# Setup selection function
selectFile = '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile, 'rb') as savefile:
apo = pickle.load(savefile)
else:
# Setup selection function
apo = apogee.select.apogeeSelect()
# Delete these because they're big and we don't need them
del apo._specdata
del apo._photdata
save_pickles(selectFile, apo)
# Get the full data sample for the locations (need all locations where
# stars could be observed, so the whole sample, not just the subsample
# being analyzed)
data = get_rcsample()
locations = list(set(list(data['LOCATION_ID'])))
# Load the dust map and setup the effective selection function
if options.dmap.lower() == 'green15':
dmap3d = mwdust.Green15(filter='2MASS H')
elif options.dmap.lower() == 'marshall06':
dmap3d = mwdust.Marshall06(filter='2MASS H')
elif options.dmap.lower() == 'drimmel03':
dmap3d = mwdust.Drimmel03(filter='2MASS H')
elif options.dmap.lower() == 'sale14':
dmap3d = mwdust.Sale14(filter='2MASS H')
elif options.dmap.lower() == 'zero':
dmap3d = mwdust.Zero(filter='2MASS H')
# Sample the M_H distribution
if options.samplemh:
if sample is None: sample = data
MH = sample['H0'] - sample['RC_DM']
MH = numpy.random.permutation(MH)[:1000] # do 1,000 max
else:
MH = -1.49
apof = apogee.select.apogeeEffectiveSelect(apo, dmap3d=dmap3d, MH=MH)
# Distances at which to calculate the effective selection function
distmods = numpy.linspace(options.dm_min, options.dm_max, options.ndm)
ds = 10.**(distmods / 5 - 2.)
# Now compute all selection functions
out= multi.parallel_map((lambda x: _calc_effsel_onelocation(\
locations[x],apof,apo,ds)),
range(len(locations)),
numcores=numpy.amin([len(locations),
multiprocessing.cpu_count(),options.multi]))
# Save out
out = numpy.array(out)
save_pickles(args[0], locations, out, distmods, ds)
return None
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.Green15()
# 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.)
# NB the next line means that useav is not actually working yet
# avs = np.zeros(len(dsamp))+useav
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):
if (nit == 0.):
out.avs = 0.0
else:
out.avs = 3.1 * dustmodel(lon_deg, lat_deg,
out.dis / 1000.)[0]
#print lon_deg,lat_deg,out.dis
if (useav != 0.):
out.avs = useav
if (out.avs < 0.):
out.avs = 0.0
ext = out.avs * avtoext
# 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
nit = nit + 1
#print out.dis,out.avs
#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
out.mabs = Mabs
return out
def plot_effsel_location(location, plotname):
# Setup selection function
selectFile = '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile, 'rb') as savefile:
apo = pickle.load(savefile)
else:
# Setup selection function
apo = apogee.select.apogeeSelect()
# Delete these because they're big and we don't need them
del apo._specdata
del apo._photdata
save_pickles(selectFile, apo)
effselFile = '../savs/effselfunc-%i.sav' % location
if not os.path.exists(effselFile):
# Distances at which to calculate the effective selection function
distmods = numpy.linspace(7., 15.5, 301)
ds = 10.**(distmods / 5 - 2.)
# Setup default effective selection function
do_samples = True
gd = mwdust.Green15(filter='2MASS H', load_samples=do_samples)
apof = apogee.select.apogeeEffectiveSelect(apo, dmap3d=gd)
sf_default = apof(location, ds)
# Also calculate for a sample of MH
data = get_rcsample()
MH = data['H0'] - data['RC_DM']
MH = numpy.random.permutation(MH)[:1000]
sf_jkz = apof(location, ds, MH=MH)
# Go through the samples
sf_samples = numpy.zeros((20, len(ds)))
if do_samples:
for ii in range(20):
# Swap in a sample for bestfit in the Green et al. (2015) dmap
gd.substitute_sample(ii)
apof = apogee.select.apogeeEffectiveSelect(apo, dmap3d=gd)
sf_samples[ii] = apof(location, ds)
zerodust = mwdust.Zero(filter='2MASS H')
apof = apogee.select.apogeeEffectiveSelect(apo, dmap3d=zerodust)
sf_zero = apof(location, ds)
drimmel = mwdust.Drimmel03(filter='2MASS H')
apof = apogee.select.apogeeEffectiveSelect(apo, dmap3d=drimmel)
sf_drimmel = apof(location, ds)
marshall = mwdust.Marshall06(filter='2MASS H')
apof = apogee.select.apogeeEffectiveSelect(apo, dmap3d=marshall)
try:
sf_marshall = apof(location, ds)
except IndexError:
sf_marshall = -numpy.ones_like(ds)
sale = mwdust.Sale14(filter='2MASS H')
apof = apogee.select.apogeeEffectiveSelect(apo, dmap3d=sale)
try:
sf_sale = apof(location, ds)
except (TypeError, ValueError):
sf_sale = -numpy.ones_like(ds)
save_pickles(effselFile, distmods, sf_default, sf_jkz, sf_samples,
sf_zero, sf_drimmel, sf_marshall, sf_sale)
else:
with open(effselFile, 'rb') as savefile:
distmods = pickle.load(savefile)
sf_default = pickle.load(savefile)
sf_jkz = pickle.load(savefile)
sf_samples = pickle.load(savefile)
sf_zero = pickle.load(savefile)
sf_drimmel = pickle.load(savefile)
sf_marshall = pickle.load(savefile)
sf_sale = pickle.load(savefile)
# Now plot
bovy_plot.bovy_print(fig_height=3.)
rc('text.latex',
preamble=r'\usepackage{amsmath}' + '\n' + r'\usepackage{amssymb}' +
'\n' + r'\usepackage{yfonts}')
if _PLOTDIST:
distmods = 10.**(distmods / 5 - 2.)
xrange = [0., 12.]
xlabel = r'$D\,(\mathrm{kpc})$'
ylabel = r'$\textswab{S}(\mathrm{location},D)$'
else:
xrange = [7., 15.8],
xlabel = r'$\mathrm{distance\ modulus}\ \mu$'
ylabel = r'$\textswab{S}(\mathrm{location},\mu)$'
line_default = bovy_plot.bovy_plot(distmods,
sf_default,
'b-',
lw=_LW,
zorder=12,
xrange=xrange,
xlabel=xlabel,
yrange=[0., 1.2 * numpy.amax(sf_zero)],
ylabel=ylabel)
pyplot.fill_between(distmods,
sf_default-_EXAGGERATE_ERRORS\
*(sf_default-numpy.amin(sf_samples,axis=0)),
sf_default+_EXAGGERATE_ERRORS\
*(numpy.amax(sf_samples,axis=0)-sf_default),
color='0.65',zorder=0)
line_jkz = bovy_plot.bovy_plot(distmods,
sf_jkz,
'g-.',
lw=2. * _LW,
overplot=True,
zorder=13)
line_zero = bovy_plot.bovy_plot(distmods,
sf_zero,
'k--',
lw=_LW,
overplot=True,
zorder=7)
line_drimmel = bovy_plot.bovy_plot(distmods,
sf_drimmel,
'-',
color='gold',
lw=_LW,
overplot=True,
zorder=8)
line_marshall = bovy_plot.bovy_plot(distmods,
sf_marshall,
'r-',
lw=_LW,
overplot=True,
zorder=9)
line_sale = bovy_plot.bovy_plot(distmods,
sf_sale,
'c-',
lw=_LW,
overplot=True,
zorder=10)
if location == 4378:
pyplot.legend(
(line_default[0], line_jkz[0], line_zero[0]),
(r'$\mathrm{Green\ et\ al.\ (2015)}$',
r'$\mathrm{Green\ et\ al.} + p(M_H)$',
r'$\mathrm{zero\ extinction}$'),
loc='lower right', #bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size': 14},
frameon=False)
elif location == 4312:
pyplot.legend(
(line_sale[0], line_marshall[0], line_drimmel[0]),
(r'$\mathrm{Sale\ et\ al.\ (2014)}$',
r'$\mathrm{Marshall\ et\ al.\ (2006)}$',
r'$\mathrm{Drimmel\ et\ al.\ (2003)}$'),
loc='lower right', #bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size': 14},
frameon=False)
# Label
lcen, bcen = apo.glonGlat(location)
if numpy.fabs(bcen) < 0.1: bcen = 0.
bovy_plot.bovy_text(r'$(l,b) = (%.1f,%.1f)$' % (lcen, bcen),
top_right=True,
size=16.)
bovy_plot.bovy_end_print(plotname)
return None
def plot_dustnearplane(plotname, green=False):
if green: savefile = _SAVEFILE_GREEN
else: savefile = _SAVEFILE_MARSHALL
# Grid
ls = numpy.linspace(15., 70., _NL)
bs = numpy.linspace(-2., 2., _NB)
if not os.path.exists(savefile):
# Setup dust map
if green:
dmap = mwdust.Green15(filter='2MASS H')
else:
dmap = mwdust.Marshall06(filter='2MASS H')
plotthis = numpy.empty((_NL, _NB))
rad = 0.5 # deg
for jj in range(_NB):
print jj
for ii in range(_NL):
pa, ah = dmap.dust_vals_disk(ls[ii], bs[jj], 7., rad)
plotthis[ii, jj] = numpy.sum(pa * ah) / numpy.sum(pa)
save_pickles(savefile, plotthis)
else:
with open(savefile, 'rb') as f:
plotthis = pickle.load(f)
# Now plot
bovy_plot.bovy_print(fig_width=8.4, fig_height=4.)
bovy_plot.bovy_dens2d(
plotthis[::-1].T,
origin='lower',
cmap=cm.coolwarm,
# interpolation='nearest',
colorbar=True,
shrink=0.45,
vmin=0.,
vmax=2. - 0.75 * green,
aspect=3.,
xrange=[ls[-1] + (ls[1] - ls[0]) / 2., ls[0] - (ls[1] - ls[0]) / 2.],
yrange=[bs[0] - (bs[1] - bs[0]) / 2., bs[-1] + (bs[1] - bs[0]) / 2.],
xlabel=r'$l\,\mathrm{(deg)}$',
ylabel=r'$b\,\mathrm{(deg)}$',
zlabel=r'$A_H\,(\mathrm{mag})$',
zorder=0)
bovy_plot.bovy_text(r'$D = 7\,\mathrm{kpc}$',
top_left=True,
color='w',
size=16.)
# Overplot fields
glons = [34., 64., 27.]
glats = [0., 0., 0.]
colors = ['w', 'w', 'y']
xs = numpy.linspace(-1.5, 1.5, 201)
ys = numpy.sqrt(1.5**2. - xs**2.)
for glon, glat, c in zip(glons, glats, colors):
bovy_plot.bovy_plot(xs + glon,
ys + glat,
'-',
overplot=True,
zorder=1,
lw=2.,
color=c)
bovy_plot.bovy_plot(xs + glon,
-ys + glat,
'-',
overplot=True,
zorder=1,
lw=2.,
color=c)
bovy_plot.bovy_end_print(plotname)
return None
def plot_ah_location(location, plotname):
# Setup selection function
selectFile = '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile, 'rb') as savefile:
apo = pickle.load(savefile)
else:
# Setup selection function
apo = apogee.select.apogeeSelect()
# Delete these because they're big and we don't need them
del apo._specdata
del apo._photdata
save_pickles(selectFile, apo)
glon, glat = apo.glonGlat(location)
glon = glon[0]
glat = glat[0]
ahFile = '../savs/ah-%i.sav' % location
if not os.path.exists(ahFile):
# Distances at which to calculate the extinction
distmods = numpy.linspace(7., 15.5, 301)
ds = 10.**(distmods / 5 - 2.)
# Setup Green et al. (2015) dust map
gd = mwdust.Green15(filter='2MASS H')
pa, ah = gd.dust_vals_disk(glon, glat, ds, apo.radius(location))
meanah_default = numpy.sum(numpy.tile(pa, (len(ds), 1)).T * ah,
axis=0) / numpy.sum(pa)
stdah_default= numpy.sqrt(numpy.sum(numpy.tile(pa,(len(ds),1)).T\
*ah**2.,axis=0)\
/numpy.sum(pa)-meanah_default**2.)
# Marshall et al. (2006)
marshall = mwdust.Marshall06(filter='2MASS H')
try:
pa, ah = marshall.dust_vals_disk(glon, glat, ds,
apo.radius(location))
except IndexError:
meanah_marshall = -numpy.ones_like(ds)
stdah_marshall = -numpy.ones_like(ds)
else:
meanah_marshall = numpy.sum(numpy.tile(pa, (len(ds), 1)).T * ah,
axis=0) / numpy.sum(pa)
stdah_marshall= numpy.sqrt(numpy.sum(numpy.tile(pa,(len(ds),1)).T\
*ah**2.,axis=0)\
/numpy.sum(pa)-meanah_marshall**2.)
if True:
# Drimmel et al. (2003)
drimmel = mwdust.Drimmel03(filter='2MASS H')
pa, ah = drimmel.dust_vals_disk(glon, glat, ds,
apo.radius(location))
meanah_drimmel = numpy.sum(numpy.tile(pa, (len(ds), 1)).T * ah,
axis=0) / numpy.sum(pa)
stdah_drimmel= numpy.sqrt(numpy.sum(numpy.tile(pa,(len(ds),1)).T\
*ah**2.,axis=0)\
/numpy.sum(pa)-meanah_drimmel**2.)
else:
meanah_drimmel = -numpy.ones_like(ds)
stdah_drimmel = -numpy.ones_like(ds)
if True:
# Sale et al. (2014)
sale = mwdust.Sale14(filter='2MASS H')
try:
pa, ah = sale.dust_vals_disk(glon, glat, ds,
apo.radius(location))
meanah_sale = numpy.sum(numpy.tile(pa, (len(ds), 1)).T * ah,
axis=0) / numpy.sum(pa)
except (TypeError, ValueError):
meanah_sale = -numpy.ones_like(ds)
stdah_sale = -numpy.ones_like(ds)
else:
stdah_sale= numpy.sqrt(numpy.sum(numpy.tile(pa,(len(ds),1)).T\
*ah**2.,axis=0)\
/numpy.sum(pa)-meanah_sale**2.)
else:
meanah_sale = -numpy.ones_like(ds)
stdah_sale = -numpy.ones_like(ds)
save_pickles(ahFile, distmods, meanah_default, stdah_default,
meanah_marshall, stdah_marshall, meanah_drimmel,
stdah_drimmel, meanah_sale, stdah_sale)
else:
with open(ahFile, 'rb') as savefile:
distmods = pickle.load(savefile)
meanah_default = pickle.load(savefile)
stdah_default = pickle.load(savefile)
meanah_marshall = pickle.load(savefile)
stdah_marshall = pickle.load(savefile)
meanah_drimmel = pickle.load(savefile)
stdah_drimmel = pickle.load(savefile)
meanah_sale = pickle.load(savefile)
stdah_sale = pickle.load(savefile)
# Now plot
bovy_plot.bovy_print(fig_height=3.)
if _PLOTDIST:
distmods = 10.**(distmods / 5 - 2.)
xrange = [0., 12.]
xlabel = r'$D\,(\mathrm{kpc})$'
else:
xrange = [7., 15.8],
xlabel = r'$\mathrm{distance\ modulus}\ \mu$'
ylabel = r'$A_H$'
yrange = [
0., 1.2 * numpy.amax(
numpy.vstack(
(meanah_default + stdah_default,
meanah_marshall + stdah_marshall,
meanah_drimmel + stdah_drimmel, meanah_sale + stdah_sale)))
]
line_default = bovy_plot.bovy_plot(distmods,
meanah_default,
'b-',
lw=_LW,
zorder=12,
xrange=xrange,
xlabel=xlabel,
yrange=yrange,
ylabel=ylabel)
pyplot.fill_between(distmods,
meanah_default - stdah_default,
meanah_default + stdah_default,
hatch='/',
facecolor=(0, 0, 0, 0),
color='b',
lw=0.25,
zorder=4)
line_marshall = bovy_plot.bovy_plot(distmods,
meanah_marshall,
'r-',
lw=_LW,
overplot=True,
zorder=8)
pyplot.fill_between(distmods,
meanah_marshall - stdah_marshall,
meanah_marshall + stdah_marshall,
hatch='\\',
facecolor=(0, 0, 0, 0),
color='r',
lw=0.25,
zorder=2)
line_drimmel = bovy_plot.bovy_plot(distmods,
meanah_drimmel,
'-',
lw=_LW,
color='gold',
overplot=True,
zorder=7)
pyplot.fill_between(distmods,
meanah_drimmel - stdah_drimmel,
meanah_drimmel + stdah_drimmel,
hatch='///',
facecolor=(0, 0, 0, 0),
color='gold',
lw=0.25,
zorder=1)
line_sale = bovy_plot.bovy_plot(distmods,
meanah_sale,
'-',
lw=_LW,
color='c',
overplot=True,
zorder=9)
pyplot.fill_between(distmods,
meanah_sale - stdah_sale,
meanah_sale + stdah_sale,
hatch='//',
facecolor=(0, 0, 0, 0),
color='c',
lw=0.25,
zorder=3)
if True:
data = get_rcsample()
data = data[data['LOCATION_ID'] == location]
bovy_plot.bovy_plot(data['RC_DIST'],
data['AK_TARG'] * 1.55,
'ko',
zorder=20,
overplot=True,
ms=2.)
if location == 4318:
pyplot.legend(
(line_default[0], line_sale[0]),
(r'$\mathrm{Green\ et\ al.\ (2015)}$',
r'$\mathrm{Sale\ et\ al.\ (2014)}$'),
loc='lower right', #bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size': 14},
frameon=False)
elif location == 4242:
pyplot.legend(
(line_marshall[0], line_drimmel[0]),
(r'$\mathrm{Marshall\ et\ al.\ (2006)}$',
r'$\mathrm{Drimmel\ et\ al.\ (2003)}$'),
loc='lower right', #bbox_to_anchor=(.91,.375),
numpoints=8,
prop={'size': 14},
frameon=False)
# Label
lcen, bcen = apo.glonGlat(location)
if numpy.fabs(bcen) < 0.1: bcen = 0.
bovy_plot.bovy_text(r'$(l,b) = (%.1f,%.1f)$' % (lcen, bcen),
top_right=True,
size=16.)
bovy_plot.bovy_end_print(plotname,
dpi=300,
bbox_extra_artists=pyplot.gca().get_children(),
bbox_inches='tight')
return None
def generate(locations,
type='exp',
sample='lowlow',
extmap='green15',
nls=101,
nmock=1000,
H0=-1.49,
_dmapg15=None,
ncpu=1):
"""
NAME:
generate
PURPOSE:
generate mock data following a given density
INPUT:
locations - locations to be included in the sample
type= ('exp') type of density profile to sample from
sample= ('lowlow') for selecting mock parameters
extmap= ('green15') extinction map to use ('marshall06' and others use Green15 to fill in unobserved regions)
nls= (101) number of longitude bins to use for each field
nmock= (1000) number of mock data points to generate
H0= (-1.49) absolute magnitude (can be array w/ sampling spread)
ncpu= (1) number of cpus to use to compute the probability
OUTPUT:
mockdata recarray with tags 'RC_GALR_H', 'RC_GALPHI_H', 'RC_GALZ_H'
HISTORY:
2015-04-03 - Written - Bovy (IAS)
"""
if isinstance(H0, float): H0 = [H0]
# Setup the density function and its initial parameters
rdensfunc = fitDens._setup_densfunc(type)
mockparams = _setup_mockparams_densfunc(type, sample)
densfunc = lambda x, y, z: rdensfunc(x, y, z, params=mockparams)
# Setup the extinction map
global dmap
global dmapg15
if _dmapg15 is None: dmapg15 = mwdust.Green15(filter='2MASS H')
else: dmapg15 = _dmapg15
if isinstance(extmap, mwdust.DustMap3D.DustMap3D):
dmap = extmap
elif extmap.lower() == 'green15':
dmap = dmapg15
elif extmap.lower() == 'marshall06':
dmap = mwdust.Marshall06(filter='2MASS H')
elif extmap.lower() == 'sale14':
dmap = mwdust.Sale14(filter='2MASS H')
elif extmap.lower() == 'drimmel03':
dmap = mwdust.Drimmel03(filter='2MASS H')
# Use brute-force rejection sampling to make no approximations
# First need to estimate the max probability to use in rejection;
# Loop through all locations and compute sampling probability on grid in
# (l,b,D)
# First restore the APOGEE selection function (assumed pre-computed)
global apo
selectFile = '../savs/selfunc-nospdata.sav'
if os.path.exists(selectFile):
with open(selectFile, 'rb') as savefile:
apo = pickle.load(savefile)
# Now compute the necessary coordinate transformations and evaluate the
# maximum probability
distmods = numpy.linspace(7., 15.5, 301)
ds = 10.**(distmods / 5 - 2.)
nbs = nls
lnprobs = numpy.empty((len(locations), len(distmods), nbs, nls))
radii = []
lcens, bcens = [], []
lnprobs = multi.parallel_map(lambda x: _calc_lnprob(
locations[x], nls, nbs, ds, distmods, H0, densfunc),
range(len(locations)),
numcores=numpy.amin([
len(locations),
multiprocessing.cpu_count(), ncpu
]))
lnprobs = numpy.array(lnprobs)
for ll, loc in enumerate(locations):
lcen, bcen = apo.glonGlat(loc)
rad = apo.radius(loc)
radii.append(rad) # save for later
lcens.append(lcen[0])
bcens.append(bcen[0])
maxp = (numpy.exp(numpy.nanmax(lnprobs)) -
10.**-8.) * 1.1 # Just to be sure
# Now generate mock data using rejection sampling
nout = 0
arlocations = numpy.array(locations)
arradii = numpy.array(radii)
arlcens = numpy.array(lcens)
arbcens = numpy.array(bcens)
out = numpy.recarray((nmock, ),
dtype=[('RC_DIST_H', 'f8'), ('RC_DM_H', 'f8'),
('RC_GALR_H', 'f8'), ('RC_GALPHI_H', 'f8'),
('RC_GALZ_H', 'f8')])
while nout < nmock:
nnew = 2 * (nmock - nout)
# nnew new locations
locIndx = numpy.floor(
numpy.random.uniform(size=nnew) * len(locations)).astype('int')
newlocations = arlocations[locIndx]
# Point within these locations
newds_coord = numpy.random.uniform(size=nnew)
newds= 10.**((newds_coord*(numpy.amax(distmods)-numpy.amin(distmods))\
+numpy.amin(distmods))/5.-2.)
newdls_coord = numpy.random.uniform(size=nnew)
newdls= newdls_coord*2.*arradii[locIndx]\
-arradii[locIndx]
newdbs_coord = numpy.random.uniform(size=nnew)
newdbs= newdbs_coord*2.*arradii[locIndx]\
-arradii[locIndx]
newr2s = newdls**2. + newdbs**2.
keepIndx = newr2s < arradii[locIndx]**2.
newlocations = newlocations[keepIndx]
newds_coord = newds_coord[keepIndx]
newdls_coord = newdls_coord[keepIndx]
newdbs_coord = newdbs_coord[keepIndx]
newds = newds[keepIndx]
newdls = newdls[keepIndx]
newdbs = newdbs[keepIndx]
newls = newdls + arlcens[locIndx][keepIndx]
newbs = newdbs + arbcens[locIndx][keepIndx]
# Reject?
tps = numpy.zeros_like(newds)
for nloc in list(set(newlocations)):
lindx = newlocations == nloc
pindx = arlocations == nloc
coord = numpy.array([
newds_coord[lindx] * (len(distmods) - 1.),
newdbs_coord[lindx] * (nbs - 1.),
newdls_coord[lindx] * (nls - 1.)
])
tps[lindx]= \
numpy.exp(ndimage.interpolation.map_coordinates(\
lnprobs[pindx][0],
coord,cval=-10.,
order=1))-10.**-8.
XYZ = bovy_coords.lbd_to_XYZ(newls, newbs, newds, degree=True)
Rphiz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Xsun=define_rcsample._R0,
Ysun=0.,
Zsun=define_rcsample._Z0)
testp = numpy.random.uniform(size=len(newds)) * maxp
keepIndx = tps > testp
if numpy.sum(keepIndx) > nmock - nout:
rangeIndx = numpy.zeros(len(keepIndx), dtype='int')
rangeIndx[keepIndx] = numpy.arange(numpy.sum(keepIndx))
keepIndx *= (rangeIndx < nmock - nout)
out['RC_DIST_H'][nout:nout + numpy.sum(keepIndx)] = newds[keepIndx]
out['RC_DM_H'][nout:nout+numpy.sum(keepIndx)]= newds_coord[keepIndx]*(numpy.amax(distmods)-numpy.amin(distmods))\
+numpy.amin(distmods)
out['RC_GALR_H'][nout:nout + numpy.sum(keepIndx)] = Rphiz[0][keepIndx]
out['RC_GALPHI_H'][nout:nout +
numpy.sum(keepIndx)] = Rphiz[1][keepIndx]
out['RC_GALZ_H'][nout:nout + numpy.sum(keepIndx)] = Rphiz[2][keepIndx]
nout = nout + numpy.sum(keepIndx)
return (out, lnprobs)
import matplotlib.pyplot as plt
import mwdust
#import healpy as hp
#import time
from pylab import *
from matplotlib.colors import LogNorm
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
#mpl.rcParams['pdf.fonttype'] = 42
#mpl.rcParams['ps.fonttype'] = 42
#mpl.rcParams['text.usetex'] = True
#marshall = mwdust.Marshall06(sf10=True)
drimmel = mwdust.Drimmel03(sf10=True)
green = mwdust.Green15(sf10=True)
sale = mwdust.Sale14(sf10=True)
zero = mwdust.Zero(sf10=True)
#sfd = mwdust.SFD(sf10=True)
#combined = mwdust.Combined15(sf10=True)
#D = np.array([0.25,0.5,1.,2.,3.,4.,5.,6.])
Ndist = 1000
D = np.linspace(0.05,10.,Ndist)
L = 205.09 # 54.7
B = -0.93 #0.08
f = open("Green15.dat", "w")
for i in range(Ndist):
f.write("%.15E %.15E\n"%(D[i],green(L,B,D)[i]))
f.close()
