def distanceAreaIntegrand(dist, cosb, Gsamples, rmcenter, onlygreen):
# Calculate the density
densmap = densprofiles.healpixelate(dist,
densprofiles.expdisk,
[1. / 3., 1. / 0.3],
nside=_NSIDE,
nest=False)
# Load the dust map
if onlygreen:
combinedmap = dust.load_green15(dist, nest=False, nside_out=_NSIDE)
combinedmap[combinedmap == healpy.UNSEEN] = 0.
else:
combinedmap = dust.load_combined(dist, nest=False, nside_out=_NSIDE)
# Sample over the distribution of MG
combinedmask = numpy.zeros_like(combinedmap)
G0 = 0.68 + dust.dist2distmod(dist)
if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
print numpy.sum(numpy.isnan(combinedmap))
for jj in range(_NGSAMPLES):
combinedmask+= ((combinedmap > (_GMIN-G0-Gsamples[jj]+0.68))\
*(combinedmap < (_GMAX-G0-Gsamples[jj]+0.68))).astype('float')
combinedmask /= _NGSAMPLES
if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
print numpy.sum(combinedmask)
print dust.dist2distmod(dist), numpy.sum(cosb * densmap * combinedmask)
# If rmcenter, rm the center of the MW
if rmcenter:
theta, phi = healpy.pixelfunc.pix2ang(
_NSIDE,
numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),
nest=False)
combinedmask[((phi < 25.*_DEGTORAD)+(phi > (360.-25.)*_DEGTORAD))\
*(numpy.fabs(numpy.pi/2.-theta) < 25.*_DEGTORAD)]= 0.
# Compute cross correlation
return (cosb * densmap * combinedmask)
def distanceAreaIntegrand(dist,cosb,Gsamples,rmcenter,onlygreen):
# Calculate the density
densmap= densprofiles.healpixelate(dist,densprofiles.expdisk,
[1./3.,1./0.3],nside=_NSIDE,
nest=False)
# Load the dust map
if onlygreen:
combinedmap= dust.load_green15(dist,nest=False,nside_out=_NSIDE)
combinedmap[combinedmap == healpy.UNSEEN]= 0.
else:
combinedmap= dust.load_combined(dist,nest=False,nside_out=_NSIDE)
# Sample over the distribution of MG
combinedmask= numpy.zeros_like(combinedmap)
G0= 0.68+dust.dist2distmod(dist)
if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
print numpy.sum(numpy.isnan(combinedmap))
for jj in range(_NGSAMPLES):
combinedmask+= ((combinedmap > (_GMIN-G0-Gsamples[jj]+0.68))\
*(combinedmap < (_GMAX-G0-Gsamples[jj]+0.68))).astype('float')
combinedmask/= _NGSAMPLES
if dust.dist2distmod(dist) == 9.5 or dust.dist2distmod(dist) == 10.5:
print numpy.sum(combinedmask)
print dust.dist2distmod(dist), numpy.sum(cosb*densmap*combinedmask)
# If rmcenter, rm the center of the MW
if rmcenter:
theta, phi= healpy.pixelfunc.pix2ang(_NSIDE,
numpy.arange(healpy.pixelfunc.nside2npix(_NSIDE)),
nest=False)
combinedmask[((phi < 25.*_DEGTORAD)+(phi > (360.-25.)*_DEGTORAD))\
*(numpy.fabs(numpy.pi/2.-theta) < 25.*_DEGTORAD)]= 0.
# Compute cross correlation
return (cosb*densmap*combinedmask)
def plot_dustwapogee(plotname):
# Load the dust map
green15map= dust.load_green15(5.,nest=True,nside_out=_NSIDE)
green15map[green15map == healpy.UNSEEN]= -1.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,min=0.,max=.8,
format=r'$%g$',
title='',
cmap='gist_yarg',
unit='$A_H\,(\mathrm{mag})$')
# Load the RC data to get the fields
data= define_rcsample.get_rcsample()
loc_ids= numpy.array(list(set(data['LOCATION_ID'])))
# Load the selection function, just to get the field centers
apo= apogee.select.apogeeSelect(_justprocessobslog=True)
theta= numpy.empty(len(loc_ids))
phi= numpy.empty(len(loc_ids))
for ii,loc_id in enumerate(loc_ids):
tl, tb= apo.glonGlat(loc_id)
theta[ii]= (90.-tb)/180.*numpy.pi
phi[ii]= tl/180.*numpy.pi
hib= numpy.fabs((numpy.pi/2.-theta)) > (8./180.*numpy.pi)
healpy.visufunc.projplot(theta[hib],phi[hib],'o',ms=5.,mfc='none',mew=0.8,
mec='k')
lowb= True-hib
healpy.visufunc.projplot(theta[lowb],phi[lowb],'o',ms=5.,mfc='none',
mec='w',mew=0.8)
bovy_plot.bovy_end_print(plotname)
def plot_dust(dist,plotname):
# Load the dust map
green15map= dust.load_green15(dist,nest=True,nside_out=_NSIDE)
dm= dust.dist2distmod(dist)
green15map[green15map == healpy.UNSEEN]= -1.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,min=0.,
max=round(10.*(12.8-dm+1.49))/10.,
format=r'$%g$',
title=r'$\mathrm{Extinction\ to\ %.1f\,kpc}$' % dist,
cmap='gist_yarg',
unit='$A_H\,(\mathrm{mag})$')
bovy_plot.bovy_end_print(plotname)
def plot_dustwapogee(plotname):
# Load the dust map
green15map = dust.load_green15(5., nest=True, nside_out=_NSIDE)
green15map[green15map == healpy.UNSEEN] = -1.
# plot it
healpy.visufunc.mollview(green15map,
nest=True,
xsize=4000,
min=0.,
max=.8,
format=r'$%g$',
title='',
cmap='gist_yarg',
unit='$A_H\,(\mathrm{mag})$')
# Load the RC data to get the fields
data = define_rcsample.get_rcsample()
loc_ids = numpy.array(list(set(data['LOCATION_ID'])))
# Load the selection function, just to get the field centers
apo = apogee.select.apogeeSelect(_justprocessobslog=True)
theta = numpy.empty(len(loc_ids))
phi = numpy.empty(len(loc_ids))
for ii, loc_id in enumerate(loc_ids):
tl, tb = apo.glonGlat(loc_id)
theta[ii] = (90. - tb) / 180. * numpy.pi
phi[ii] = tl / 180. * numpy.pi
hib = numpy.fabs((numpy.pi / 2. - theta)) > (8. / 180. * numpy.pi)
healpy.visufunc.projplot(theta[hib],
phi[hib],
'o',
ms=5.,
mfc='none',
mew=0.8,
mec='k')
lowb = True - hib
healpy.visufunc.projplot(theta[lowb],
phi[lowb],
'o',
ms=5.,
mfc='none',
mec='w',
mew=0.8)
bovy_plot.bovy_end_print(plotname)
