def query_dustmodel_coords(ra,dec,dust):
if dust == 'allsky':
reddenMap = mwdust.Combined19()
ext = extinction('green19')
if dust == 'green19':
reddenMap = mwdust.Green19()
ext = extinction('green19')
if dust == 'zero':
reddenMap = mwdust.Zero()
ext = extinction('cardelli')
if dust == 'none':
reddenMap = 0
ext = extinction('cardelli')
print('Fitting for reddening.')
return reddenMap,ext
sightLines = SkyCoord(ra*units.deg,dec*units.deg,frame='galactic')
distanceSamples = np.loadtxt(f"{PACKAGEDIR}/data/distance-samples-green19.txt",delimiter=',')*1000.
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,ext
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 __init__(self, tgasSel, MJ=1.8, JK=0.25, dmap3d=None, maxd=None):
"""
NAME:
__init__
PURPOSE:
Initialize the effective TGAS selection function for a population of stars
INPUT:
tgasSel - a tgasSelect object with the TGAS selection function
MJ= (1.8) absolute magnitude in J or an array of samples of absolute magnitudes in J for the tracer population
JK= (0.25) J-Ks color or an array of samples of the J-Ks color
dmap3d= if given, a mwdust.Dustmap3D object that returns the J-band extinction in 3D; if not set use no extinction
maxd= (None) if given, only consider distances up to this maximum distance (in kpc)
OUTPUT:
TGAS-effective-selection-function object
HISTORY:
2017-01-18 - Started - Bovy (UofT/CCA)
"""
self._tgasSel = tgasSel
self._maxd = maxd
# Parse MJ
if isinstance(MJ, (int, float)):
self._MJ = numpy.array([MJ])
elif isinstance(MJ, list):
self._MJ = numpy.array(MJ)
else:
self._MJ = MJ
# Parse JK
if isinstance(JK, (int, float)):
self._JK = numpy.array([JK])
elif isinstance(JK, list):
self._JK = numpy.array(JK)
else:
self._JK = JK
# Parse dust map
if dmap3d is None:
if not _MWDUSTLOADED:
raise ImportError(
"mwdust module not installed, required for extinction tools; download and install from http://github.com/jobovy/mwdust"
)
dmap3d = mwdust.Zero(filter='2MASS J')
self._dmap3d = dmap3d
return None
def generate_evfs(mj_tight,
jk_tight,
spt,
zspace,
nintt_step=None,
filename="default_evfs.txt",
save=True):
zWidth = np.mean((np.roll(zspace, -1) - zspace)[:-1])
evfs = np.array([])
tesf = gaia_tools.select.gaiaEffectiveSelect(tsf,
dmap3d=mwdust.Zero(),
MJ=mj_tight,
JK=jk_tight,
maxd=max_dist)
if nintt_step == None:
nintt_step = (2501 * ('A' in spt) + 1001 * (True - ('A' in spt)))
for i, z_i in enumerate(zspace):
zmin = z_i - zWidth / 2.
zmax = z_i + zWidth / 2.
evfs = np.append(
evfs,
tesf.volume(lambda x, y, z: cyl_vol_func(
x, y, z, xymax=r_cyl_cut, zmin=zmin, zmax=zmax),
ndists=nintt_step,
xyz=True,
relative=True))
if save == True:
np.savetxt(filename,
np.transpose(np.array([zspace, evfs])),
delimiter=',',
header="z_Bin_center, rel_effective_vol",
fmt='%10.5f')
return (zspace, evfs)
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
#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()
f = open("Drimmel03.dat", "w")
for i in range(Ndist):