def meanExtinction(l, b, ds, filter='Ks', radius=1.5, mar=None):
"""
NAME:
meanExtinction
PURPOSE:
calculate the mean extinction as a function of distance over a field
centered at l and b
INPUT:
l,b- Galactic longitude and latitude (degree)
ds- distances to consider (array)
filter= ('J', 'H', 'Ks') filter to return the extinction in
radius= (1.5) radius of the field (degree)
mar= (None) mwdust.Marshall06 instance to be used for the Marshall map
OUTPUT:
mean extinction over the field as a function of d
HISTORY:
2013-12-18 - Written - Bovy (IAS)
"""
if (l <= 100. or l >= 260.) and numpy.fabs(b) <= 10.:
if mar is None:
mar = mwdust.Marshall06(filter='2MASS ' + filter)
#Find all of the (l,b) pairs in the Marshall map that fall within the field
cosdist=\
mwdust.util.tools.cos_sphere_dist(\
numpy.sin(mar._marshalldata['GLON'].data*_DEGTORAD),
numpy.cos(mar._marshalldata['GLON'].data*_DEGTORAD),
numpy.sin(mar._marshalldata['GLAT'].data*_DEGTORAD),
numpy.cos(mar._marshalldata['GLAT'].data*_DEGTORAD),
numpy.sin(l*_DEGTORAD),
numpy.cos(l*_DEGTORAD),
numpy.sin(b*_DEGTORAD),
numpy.cos(b*_DEGTORAD))
indx = cosdist >= numpy.cos(radius * _DEGTORAD)
return None
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 plot_dustinfield(plotname, field, threshold=False):
savefile = _SAVEFILE_MARSHALL
savefile = savefile.replace('FIELD', '%i' % field)
# Grid
ls = numpy.linspace(-1.75, 1.75, _NL) + field
bs = numpy.linspace(-1.75, 1.75, _NB)
if not os.path.exists(savefile):
# Setup dust map
dmap = mwdust.Marshall06(filter='2MASS H')
plotthis = numpy.empty((_NL, _NB))
for jj in range(_NB):
print jj
for ii in range(_NL):
plotthis[ii, jj] = dmap(ls[ii], bs[jj], 7.)
save_pickles(savefile, plotthis)
else:
with open(savefile, 'rb') as f:
plotthis = pickle.load(f)
# Remove outside the field
tls = numpy.tile(ls, (_NB, 1)).T
tbs = numpy.tile(bs, (_NL, 1))
plotthis[(tls - field)**2. + tbs**2. > 1.5**2.] = numpy.nan
if threshold:
plotthis[plotthis > 1.4] = numpy.nan
# Now plot
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(
plotthis[::-1].T,
origin='lower',
cmap=cm.coolwarm,
interpolation='nearest',
vmin=0.,
vmax=2.,
aspect=1.,
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'$(l,b) = (%i,0)$' % field,
top_left=True,
color='k',
size=16.)
bovy_plot.bovy_end_print(plotname)
return None
def select_targets(outfile, location):
numpy.random.seed(location)
datafilename = '../target_info/SecQuanObj_%03i+00.fits' % location
data = fitsio.read(datafilename)
# De-redden
ak = data['AK']
aj = ak * 2.5
j0 = data['J_M'] - aj
k0 = data['K_M'] - ak
# Cut on jk0,h
indx = ((j0 - k0) > 0.8) * (data['H_M'] > 12.) * (data['H_M'] < 13.)
data = data[indx]
# setup dust map
dmap = mwdust.Marshall06(filter='2MASS H')
# Calculate A_H(7 kpc) according to Marshall et al. (2006)
ahdmap = numpy.zeros(len(data))
lb = bovy_coords.radec_to_lb(data['RA'], data['DEC'], degree=True)
for ii in range(len(data)):
ahdmap[ii] = dmap(lb[ii, 0], lb[ii, 1], 7.)
# Cut on AH <= 1.4
indx = ahdmap <= 1.4
data = data[indx]
# Match against already drilled targets
data = esutil.numpy_util.add_fields(data, [('FLAG_DONT_OBSERVE', int)])
data['FLAG_DONT_OBSERVE'] = 0
yannyfiles = glob.glob('../target_info/plateHolesSorted-*.par')
for yannyfile in yannyfiles:
drillFile = yanny.yanny(filename=yannyfile, np=True)
drilled = drillFile['STRUCT1']
# spherematch
h = esutil.htm.HTM()
m1, m2, d12 = h.match(data['RA'],
data['DEC'],
drilled['target_ra'],
drilled['target_dec'],
2. / 3600.,
maxmatch=1)
data['FLAG_DONT_OBSERVE'][m1] = 1
# Write to file
fitsio.write(outfile,
data[numpy.random.permutation(len(data))],
clobber=True)
return None
def combine_dustmap(picklename):
if os.path.exists(picklename): return None
ndists = len(_GREEN15DISTS)
# First fill in with NSIDE = 512 for Marshall
marshallmap = mwdust.Marshall06()
nside_mar = 512
mar_pix = numpy.arange(healpy.pixelfunc.nside2npix(nside_mar))
mar_val = numpy.zeros((len(mar_pix), ndists)) + healpy.UNSEEN
theta, phi= \
healpy.pixelfunc.pix2ang(nside_mar,mar_pix,nest=True)
bb = (numpy.pi / 2. - theta) / numpy.pi * 180.
ll = phi / numpy.pi * 180.
subIndx= (numpy.fabs(bb) < 10.125)\
*((ll < 100.125)+(ll > 259.875))
mar_pix = mar_pix[subIndx]
mar_val = mar_val[subIndx]
ll = ll[subIndx]
ll[ll > 180.] -= 360.
bb = bb[subIndx]
for pp, dpix in enumerate(mar_pix):
sys.stdout.write('\r' + "Working on pixel %i, %i remaining ...\r" %
(pp + 1, len(mar_pix) - pp))
sys.stdout.flush()
if _DRYISHRUN and pp > 100: break
mar_val[pp] = marshallmap(ll[pp], bb[pp], _GREEN15DISTS)
sys.stdout.write('\r' + _ERASESTR + '\r')
sys.stdout.flush()
# Now load Green15 and remove those pixels that fall within the Marshall map
with h5py.File(os.path.join(_greendir, 'dust-map-3d.h5'),
'r') as greendata:
pix_info = greendata['/pixel_info'][:]
best_fit = greendata['/best_fit'][:]
# Check whether any of these fall within the Marshall map
theta, phi = healpy.pixelfunc.pix2ang(
pix_info['nside'].astype('int32'),
pix_info['healpix_index'].astype('int64'),
nest=True)
inMar= ((phi < 100.125*_DEGTORAD)+(phi > 259.875*_DEGTORAD))\
*(numpy.fabs(numpy.pi/2.-theta) < 10.125*_DEGTORAD)
best_fit[inMar] = healpy.UNSEEN
nside_min = numpy.min(pix_info['nside'])
nside_max = numpy.max(pix_info['nside'])
# Fill in remaining gaps with Drimmel at NSIDE=256
pix_drim = []
pix_drim_nside = []
val_drim = []
drimmelmap = mwdust.Drimmel03()
for nside_drim in 2**numpy.arange(8, int(numpy.log2(nside_max)) + 1, 1):
tpix = numpy.arange(healpy.pixelfunc.nside2npix(nside_drim))
rmIndx = numpy.zeros(len(tpix), dtype='bool')
# Remove pixels that already have values at this or a higher level
for nside in 2**numpy.arange(8, int(numpy.log2(nside_max)) + 1, 1):
mult_factor = (nside / nside_drim)**2
tgpix = pix_info['healpix_index'][pix_info['nside'] == nside]
for offset in range(mult_factor):
rmIndx[numpy.in1d(tpix * mult_factor + offset,
tgpix,
assume_unique=True)] = True
# Remove pixels that already have values at a lower level
for nside in 2**numpy.arange(int(numpy.log2(nside_min)),
int(numpy.log2(nside_drim)), 1):
mult_factor = (nside_drim / nside)**2
# in Green 15
tgpix = pix_info['healpix_index'][pix_info['nside'] == nside]
rmIndx[numpy.in1d(tpix // mult_factor, tgpix,
assume_unique=False)] = True
# In the current Drimmel
tdpix = numpy.array(pix_drim)[numpy.array(pix_drim_nside) == nside]
rmIndx[numpy.in1d(tpix // mult_factor, tdpix,
assume_unique=False)] = True
# Also remove pixels that lie within the Marshall area
theta, phi = healpy.pixelfunc.pix2ang(nside_drim, tpix, nest=True)
inMar= ((phi < 100.125*_DEGTORAD)+(phi > 259.875*_DEGTORAD))\
*(numpy.fabs(numpy.pi/2.-theta) < 10.125*_DEGTORAD)
rmIndx[inMar] = True
tpix = tpix[True - rmIndx]
pix_drim.extend(tpix)
pix_drim_nside.extend(nside_drim * numpy.ones(len(tpix)))
ll = phi[True - rmIndx] / _DEGTORAD
bb = (numpy.pi / 2. - theta[True - rmIndx]) / _DEGTORAD
for pp in range(len(tpix)):
sys.stdout.write(
'\r' + "Working on level %i, pixel %i, %i remaining ...\r" %
(nside_drim, pp + 1, len(tpix) - pp))
sys.stdout.flush()
val_drim.append(drimmelmap(ll[pp], bb[pp], _GREEN15DISTS))
if _DRYISHRUN and pp > 1000: break
sys.stdout.write('\r' + _ERASESTR + '\r')
sys.stdout.flush()
# Save
g15Indx = best_fit[:, 0] != healpy.UNSEEN
save_pickles(picklename, mar_pix, mar_val, pix_info['nside'][g15Indx],
pix_info['healpix_index'][g15Indx], best_fit[g15Indx],
pix_drim, pix_drim_nside, val_drim)
return None
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)