def plot_rcdistancecomparison(plotfilename):
# Get the sample
rcdata = define_rcsample.get_rcsample()
# Now plot the differece
bovy_plot.bovy_print()
levels = special.erf(numpy.arange(1, 3) / numpy.sqrt(2.))
bovy_plot.scatterplot(
rcdata['RC_DIST'],
(rcdata['RC_DIST_H'] - rcdata['RC_DIST']) / rcdata['RC_DIST'],
conditional=True,
levels=levels,
linestyle='none',
color='k',
marker=',',
xrange=[0., 7.49],
yrange=[-0.075, 0.075],
xlabel=r'$M_{K_s}\!-\!\mathrm{based\ distance\,(kpc)}$',
ylabel=
r'$\mathrm{Fractional\ difference\ of}\ M_H\ \mathrm{vs.}\ M_{K_s}$',
onedhistx=True,
bins=31)
bovy_plot.bovy_plot([0., 10.], [0., 0.],
'--',
lw=2.,
color='0.75',
overplot=True)
# Plot lowess
lowess = sm.nonparametric.lowess
z = lowess((rcdata['RC_DIST_H'] - rcdata['RC_DIST']) / rcdata['RC_DIST'],
rcdata['RC_DIST'],
frac=.3)
bovy_plot.bovy_plot(z[:, 0], z[:, 1], 'w--', lw=2., overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
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 load_data(sample):
global ldata
global data_highbIndx
global data_outDiskIndx
global data_betwDiskIndx
global data_inDiskIndx
global data_bulgeIndx
global data_brightIndx
global data_mediumIndx
global data_faintIndx
if sample.lower() == 'all':
ldata = define_rcsample.get_rcsample()
elif sample.lower() == 'alllowalpha':
ldata = define_rcsample.get_rcsample()
ldata = ldata[ldata[define_rcsample._AFETAG] < 0.1]
elif sample.lower() == 'lowlow':
ldata = define_rcsample.get_lowlowsample()
elif sample.lower() == 'highfeh':
ldata = define_rcsample.get_highfehsample()
elif sample.lower() == 'highalpha':
ldata = define_rcsample.get_highalphasample()
elif sample.lower() == 'solar':
ldata = define_rcsample.get_solarsample()
# Get the indices of the various subsamples defined above
data_highbIndx = numpy.zeros(len(ldata), dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[highbIndx]:
data_highbIndx[ii] = True
data_outDiskIndx = numpy.zeros(len(ldata), dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[outDiskIndx]:
data_outDiskIndx[ii] = True
data_betwDiskIndx = numpy.zeros(len(ldata), dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[betwDiskIndx]:
data_betwDiskIndx[ii] = True
data_inDiskIndx = numpy.zeros(len(ldata), dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[inDiskIndx]:
data_inDiskIndx[ii] = True
return None
def load_data(sample):
global ldata
global data_highbIndx
global data_outDiskIndx
global data_betwDiskIndx
global data_inDiskIndx
global data_bulgeIndx
global data_brightIndx
global data_mediumIndx
global data_faintIndx
if sample.lower() == 'all':
ldata= define_rcsample.get_rcsample()
elif sample.lower() == 'alllowalpha':
ldata= define_rcsample.get_rcsample()
ldata= ldata[ldata[define_rcsample._AFETAG] < 0.1]
elif sample.lower() == 'lowlow':
ldata= define_rcsample.get_lowlowsample()
elif sample.lower() == 'highfeh':
ldata= define_rcsample.get_highfehsample()
elif sample.lower() == 'highalpha':
ldata= define_rcsample.get_highalphasample()
elif sample.lower() == 'solar':
ldata= define_rcsample.get_solarsample()
# Get the indices of the various subsamples defined above
data_highbIndx= numpy.zeros(len(ldata),dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[highbIndx]:
data_highbIndx[ii]= True
data_outDiskIndx= numpy.zeros(len(ldata),dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[outDiskIndx]:
data_outDiskIndx[ii]= True
data_betwDiskIndx= numpy.zeros(len(ldata),dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[betwDiskIndx]:
data_betwDiskIndx[ii]= True
data_inDiskIndx= numpy.zeros(len(ldata),dtype='bool')
for ii in range(len(ldata)):
if ldata[ii]['LOCATION_ID'] in numpy.array(locations)[inDiskIndx]:
data_inDiskIndx[ii]= True
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 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_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_rcdistancecomparison(plotfilename):
# Get the sample
rcdata= define_rcsample.get_rcsample()
# Now plot the differece
bovy_plot.bovy_print()
levels= special.erf(numpy.arange(1,3)/numpy.sqrt(2.))
bovy_plot.scatterplot(rcdata['RC_DIST'],
(rcdata['RC_DIST_H']-rcdata['RC_DIST'])/rcdata['RC_DIST'],
conditional=True,
levels=levels,
linestyle='none',color='k',marker=',',
xrange=[0.,7.49],yrange=[-0.075,0.075],
xlabel=r'$M_{K_s}\!-\!\mathrm{based\ distance\,(kpc)}$',
ylabel=r'$\mathrm{Fractional\ difference\ of}\ M_H\ \mathrm{vs.}\ M_{K_s}$',
onedhistx=True,bins=31)
bovy_plot.bovy_plot([0.,10.],[0.,0.],'--',lw=2.,color='0.75',overplot=True)
# Plot lowess
lowess= sm.nonparametric.lowess
z= lowess((rcdata['RC_DIST_H']-rcdata['RC_DIST'])/rcdata['RC_DIST'],
rcdata['RC_DIST'],frac=.3)
bovy_plot.bovy_plot(z[:,0],z[:,1],'w--',lw=2.,overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_afefeh(plotfilename):
# Load the data
data = define_rcsample.get_rcsample()
# Plot the data
bovy_plot.bovy_print()
bovy_plot.scatterplot(data[define_rcsample._FEHTAG],
data[define_rcsample._AFETAG],
'k.',
ms=.8,
levels=special.erf(
numpy.arange(1, 2) / numpy.sqrt(2.)),
xrange=[-1., 0.4],
yrange=[-0.15, 0.35],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=define_rcsample._AFELABEL)
# Overplot sub-samples
# low alpha, low feh
lowfeh = define_rcsample._lowlow_lowfeh(0.)
highfeh = define_rcsample._lowlow_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._lowlow_lowafe(highfeh),
define_rcsample._lowlow_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._lowlow_highafe(lowfeh),
define_rcsample._lowlow_highafe(highfeh)
],
'k--',
lw=2.)
# high alpha
lowfeh = define_rcsample._highalpha_lowfeh(0.)
highfeh = define_rcsample._highalpha_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._highalpha_lowafe(highfeh),
define_rcsample._highalpha_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highalpha_highafe(lowfeh),
define_rcsample._highalpha_highafe(highfeh)
],
'k--',
lw=2.)
# solar
lowfeh = define_rcsample._solar_lowfeh(0.)
highfeh = define_rcsample._solar_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._solar_lowafe(highfeh),
define_rcsample._solar_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._solar_highafe(lowfeh),
define_rcsample._solar_highafe(highfeh)
],
'k--',
lw=2.)
# high [Fe/H]
lowfeh = define_rcsample._highfeh_lowfeh(0.)
highfeh = define_rcsample._highfeh_highfeh(0.)
pyplot.plot([lowfeh, lowfeh], [
define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_highafe(lowfeh)
],
'k--',
lw=2.)
pyplot.plot([highfeh, highfeh], [
define_rcsample._highfeh_lowafe(highfeh),
define_rcsample._highfeh_highafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_lowafe(highfeh)
],
'k--',
lw=2.)
pyplot.plot([lowfeh, highfeh], [
define_rcsample._highfeh_highafe(lowfeh),
define_rcsample._highfeh_highafe(highfeh)
],
'k--',
lw=2.)
# Label them
bovy_plot.bovy_text(-0.4,
0.265,
r'$\mathrm{high}\ [\alpha/\mathrm{Fe}]$',
size=15.,
backgroundcolor='w')
bovy_plot.bovy_text(-0.975,
0.05,
r'$\mathrm{low\ [Fe/H]}$',
size=15.,
backgroundcolor='w')
bovy_plot.bovy_text(0.,
-0.125,
r'$\mathrm{high\ [Fe/H]}$',
size=15.,
backgroundcolor='w')
bovy_plot.bovy_text(-0.225,
-0.125,
r'$\mathrm{solar}$',
size=15.,
backgroundcolor='w')
# Loci
if False:
haloc = define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'k-',
lw=2.,
overplot=True)
haloc = define_rcsample.lowalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'k-',
lw=2.,
overplot=True)
bovy_plot.bovy_end_print(plotfilename)
return None
parser.add_option("--H0",dest='H0',default=-1.49,type='float',
help="RC absolute magnitude")
parser.add_option("--nls",dest='nls',default=101,type='int',
help="Number of longitudes to bin each field in")
parser.add_option("--nmock",dest='nmock',default=20000,type='int',
help="Number of mock samples to generate")
# Dust map to use
parser.add_option("--extmap",dest='extmap',default='green15',
help="Dust map to use ('Green15', 'Marshall03', 'Drimmel03', 'Sale14', or 'zero'")
# Multiprocessing?
parser.add_option("-m","--multi",dest='multi',default=1,type='int',
help="number of cpus to use")
return parser
if __name__ == '__main__':
parser= get_options()
options, args= parser.parse_args()
data= define_rcsample.get_rcsample()
locations= list(set(list(data['LOCATION_ID'])))
#locations= [4240,4242]
out= generate(locations,
type=options.type,
sample=options.sample,
extmap=options.extmap,
nls=options.nls,
nmock=options.nmock,
H0=options.H0,
ncpu=options.multi)
fitsio.write(args[0],out[0],clobber=True)
def plot_afe_spectra(savename,plotname):
# Load the data
data= define_rcsample.get_rcsample()
data= data[data['SNR'] > 200.]
fehindx= (data['FE_H'] <= -0.35)*(data['FE_H'] > -0.45)
fehdata= data[fehindx]
# First compute the residuals and do the EM-PCA smoothing
if not os.path.exists(savename):
nspec= len(fehdata)
spec= numpy.zeros((nspec,7214))
specerr= numpy.zeros((nspec,7214))
for ii in range(nspec):
sys.stdout.write('\r'+"Loading spectrum %i / %i ...\r" % (ii+1,nspec))
sys.stdout.flush()
spec[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=1,header=False,aspcapWavegrid=True)
specerr[ii]= apread.aspcapStar(fehdata['LOCATION_ID'][ii],
fehdata['APOGEE_ID'][ii],
ext=2,header=False,
aspcapWavegrid=True)
teffs= fehdata['FPARAM'][:,paramIndx('teff')]
loggs= fehdata['FPARAM'][:,paramIndx('logg')]
metals= fehdata[define_rcsample._FEHTAG]
cf, s, r= apcannon.quadfit(spec,specerr,
teffs-4800.,loggs-2.85,metals+0.3,
return_residuals=True)
pr= numpy.zeros_like(r)
# Deal w/ bad data
_MAXERR= 0.02
npca= 8
pca_input= r
pca_weights= (1./specerr**2.)
pca_weights[pca_weights < 1./_MAXERR**2.]= 0.
nanIndx= numpy.isnan(pca_input) + numpy.isnan(pca_weights)
pca_weights[nanIndx]= 0.
pca_input[nanIndx]= 0.
# Run EM-PCA
m= empca.empca(pca_input,pca_weights,nvec=npca,niter=25)#,silent=False)
for jj in range(nspec):
for kk in range(npca):
pr[jj]+= m.coeff[jj,kk]*m.eigvec[kk]
save_pickles(savename,pr,r,cf)
else:
with open(savename,'rb') as savefile:
pr= pickle.load(savefile)
# Now plot the various elements
colormap= cm.seismic
colorFunc= lambda afe: afe/0.25
widths= [3.5,2.]
yranges= [[-0.05,0.02],[-0.03,0.01]]
for ee, elem in enumerate(['S','Ca1']):
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),elem,)
kwargs= {'markLines':ii==4,
'yrange':yranges[ee],
'ylabel':'',
'cleanZero':False,
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':widths[ee]}
if ii>0: kwargs.pop('fig_width')
splot.windows(*args,**kwargs)
bovy_plot.bovy_end_print(plotname.replace('ELEM',
elem.lower().capitalize()))
# Also do Mg
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[3012,3120,3990],
'endindxs':[3083,3158,4012],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15745.017,15753.189,15770.055,15958.836],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':4.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Mg}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Mg'))
# Also do Si
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[4469, 4624,5171, 7205, 7843],
'endindxs':[4488, 4644,5182, 7243, 7871],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Si'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':6.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Si}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Si2'))
# Also do Oxygen
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startlams':[15558,16242,16536,16720],
'endlams':[15566,16250,16544,16728],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':[15562,16246,16539,16723.5],
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':5.,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{O}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','O'))
# Also do Ti
for ii in range(5):
tindx= (fehdata[define_rcsample._AFETAG] > ii*0.05-0.025)\
*(fehdata[define_rcsample._AFETAG] <= (ii+1)*0.05-0.025)
args= (apstack.median(pr[tindx][:12]),)
kwargs={'startindxs':[1116,2100,2899],
'endindxs':[1146,2124,2922],
'yrange':[-0.05,0.02],
'ylabel':'',
'cleanZero':False,
'_markwav':apwindow.lines('Ti'),
'zorder':int(numpy.floor(numpy.random.uniform()*5)),
'color':colormap(colorFunc(ii*0.05)),
'overplot':ii>0,
'fig_width':3.5,
'markLines':True}
if ii>0: kwargs.pop('fig_width')
if ii != 4:
kwargs.pop('_markwav')
kwargs.pop('markLines')
kwargs['_startendskip']= 0
kwargs['_noxticks']= True
kwargs['_labelwav']= True
splot.waveregions(*args,**kwargs)
bovy_plot.bovy_text(r'$\mathrm{Ti}$',
top_left=True,fontsize=10,backgroundcolor='w')
bovy_plot.bovy_end_print(plotname.replace('ELEM','Ti'))
return None
def plot_afefeh(plotfilename):
# Load the data
data= define_rcsample.get_rcsample()
# Plot the data
bovy_plot.bovy_print()
bovy_plot.scatterplot(data[define_rcsample._FEHTAG],
data[define_rcsample._AFETAG],
'k.',ms=.8,
levels=special.erf(numpy.arange(1,2)/numpy.sqrt(2.)),
xrange=[-1.,0.4],
yrange=[-0.15,0.35],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=define_rcsample._AFELABEL)
# Overplot sub-samples
# low alpha, low feh
lowfeh= define_rcsample._lowlow_lowfeh(0.)
highfeh= define_rcsample._lowlow_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._lowlow_lowafe(highfeh),
define_rcsample._lowlow_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._lowlow_lowafe(lowfeh),
define_rcsample._lowlow_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._lowlow_highafe(lowfeh),
define_rcsample._lowlow_highafe(highfeh)],
'k--',lw=2.)
# high alpha
lowfeh= define_rcsample._highalpha_lowfeh(0.)
highfeh= define_rcsample._highalpha_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._highalpha_lowafe(highfeh),
define_rcsample._highalpha_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highalpha_lowafe(lowfeh),
define_rcsample._highalpha_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highalpha_highafe(lowfeh),
define_rcsample._highalpha_highafe(highfeh)],
'k--',lw=2.)
# solar
lowfeh= define_rcsample._solar_lowfeh(0.)
highfeh= define_rcsample._solar_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._solar_lowafe(highfeh),
define_rcsample._solar_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._solar_lowafe(lowfeh),
define_rcsample._solar_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._solar_highafe(lowfeh),
define_rcsample._solar_highafe(highfeh)],
'k--',lw=2.)
# high [Fe/H]
lowfeh= define_rcsample._highfeh_lowfeh(0.)
highfeh= define_rcsample._highfeh_highfeh(0.)
pyplot.plot([lowfeh,lowfeh],[define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_highafe(lowfeh)],
'k--',lw=2.)
pyplot.plot([highfeh,highfeh],[define_rcsample._highfeh_lowafe(highfeh),
define_rcsample._highfeh_highafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highfeh_lowafe(lowfeh),
define_rcsample._highfeh_lowafe(highfeh)],
'k--',lw=2.)
pyplot.plot([lowfeh,highfeh],[define_rcsample._highfeh_highafe(lowfeh),
define_rcsample._highfeh_highafe(highfeh)],
'k--',lw=2.)
# Label them
bovy_plot.bovy_text(-0.4,0.265,r'$\mathrm{high}\ [\alpha/\mathrm{Fe}]$',
size=15.,backgroundcolor='w')
bovy_plot.bovy_text(-0.975,0.05,r'$\mathrm{low\ [Fe/H]}$',
size=15.,backgroundcolor='w')
bovy_plot.bovy_text(0.,-0.125,r'$\mathrm{high\ [Fe/H]}$',
size=15.,backgroundcolor='w')
bovy_plot.bovy_text(-0.225,-0.125,r'$\mathrm{solar}$',
size=15.,backgroundcolor='w')
# Loci
if False:
haloc= define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:,0],haloc[:,1],'k-',lw=2.,overplot=True)
haloc= define_rcsample.lowalphalocus()
bovy_plot.bovy_plot(haloc[:,0],haloc[:,1],'k-',lw=2.,overplot=True)
bovy_plot.bovy_end_print(plotfilename)
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 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
dest='extmap',
default='green15',
help=
"Dust map to use ('Green15', 'Marshall03', 'Drimmel03', 'Sale14', or 'zero'"
)
# Multiprocessing?
parser.add_option("-m",
"--multi",
dest='multi',
default=1,
type='int',
help="number of cpus to use")
return parser
if __name__ == '__main__':
parser = get_options()
options, args = parser.parse_args()
data = define_rcsample.get_rcsample()
locations = list(set(list(data['LOCATION_ID'])))
#locations= [4240,4242]
out = generate(locations,
type=options.type,
sample=options.sample,
extmap=options.extmap,
nls=options.nls,
nmock=options.nmock,
H0=options.H0,
ncpu=options.multi)
fitsio.write(args[0], out[0], clobber=True)
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_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
