def plotMetallicityRZ(options,args):
if options.png: ext= 'png'
else: ext= 'ps'
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal=options.metal,
sample=options.sample)
R= ((8.-XYZ[:,0])**2.+XYZ[:,1]**2.)**0.5
XYZ[:,2]+= _ZSUN
#Set up ranges
#Set up ranges
if options.sample.lower() == 'g':
xrange=[0.46,0.57]
rmin, rmax= 14.5, 20.2
grmin, grmax= 0.48, 0.55
colorrange=[0.48,0.55]
elif options.sample.lower() == 'k':
xrange=[0.51,0.79]
rmin, rmax= 14.5, 19.
grmin, grmax= 0.55,0.75
colorrange=[0.55,0.75]
if options.metal.lower() == 'rich':
yrange=[-0.55,0.5]
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'poor':
yrange=[-1.6,0.3]
fehrange= _ARICHFEHRANGE
Zrange= [0.25,3.25]
Rrange= [5.,12.]
if options.plottype.lower() == 'z':
_NBINS= 7
Zranges= [[Zrange[0]+ii*(Zrange[1]-Zrange[0])/(_NBINS-1),
Zrange[0]+(ii+1)*(Zrange[1]-Zrange[0])/(_NBINS-1)] \
for ii in range(_NBINS-1)]
Zranges.append([Zranges[-1][1],4.])
Rranges= [Rrange for ii in range(_NBINS)]
elif options.plottype.lower() == 'r':
_NBINS= 7
Zranges= [Zrange for ii in range(_NBINS)]
Rranges= [[5.,7.]]
Rranges.extend([[Rrange[0]+ii*(Rrange[1]-Rrange[0])/_NBINS,
Rrange[0]+(ii+1)*(Rrange[1]-Rrange[0])/_NBINS] \
for ii in range(1,_NBINS)])
#First plot all data
bovy_plot.bovy_print()
_plotMRZ_single(XYZ,R,data,options,args,all=True,overplot=False,
xrange=xrange,yrange=yrange,colorrange=colorrange,
fehrange=fehrange,
Zrange=Zrange,Rrange=Rrange)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_'+options.plottype+'_'+options.sample+'_'+options.metal+'.'+ext))
#Then plot them in ranges
for ii in range(_NBINS):
_plotMRZ_single(XYZ,R,data,options,args,all=False,overplot=False,
xrange=xrange,Zrange=Zranges[ii],Rrange=Rranges[ii],
yrange=yrange,colorrange=colorrange,fehrange=fehrange)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_'+options.plottype+'_'+options.sample+'_'+options.metal+'_%i.' % ii + ext))
return None
def afeh(options,args):
"""Plot the [alpha/Fe] vs. [Fe/H] distribution of the sample"""
if options.png: ext= 'png'
else: ext= 'ps'
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal='allall',
sample=options.sample)
bovy_plot.bovy_print()
bovy_plot.scatterplot(data.feh,data.afe,'k,',
xrange=[-2.,0.5],
yrange=[-0.1,0.6],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
onedhists=True)
#Overplot cuts, metal-rich
lw=1.3
bovy_plot.bovy_plot(_APOORFEHRANGE,[_APOORAFERANGE[0],_APOORAFERANGE[0]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot(_APOORFEHRANGE,[_APOORAFERANGE[1],_APOORAFERANGE[1]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_APOORFEHRANGE[0],_APOORFEHRANGE[0]],_APOORAFERANGE,
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_APOORFEHRANGE[1],_APOORFEHRANGE[1]],_APOORAFERANGE,
'k--',overplot=True,lw=lw)
#metal-poor
bovy_plot.bovy_plot(_ARICHFEHRANGE,[_ARICHAFERANGE[0],_ARICHAFERANGE[0]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot(_ARICHFEHRANGE,[_ARICHAFERANGE[1],_ARICHAFERANGE[1]],
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_ARICHFEHRANGE[0],_ARICHFEHRANGE[0]],_ARICHAFERANGE,
'k--',overplot=True,lw=lw)
bovy_plot.bovy_plot([_ARICHFEHRANGE[1],_ARICHFEHRANGE[1]],_ARICHAFERANGE,
'k--',overplot=True,lw=lw)
#metal-richrich
bovy_plot.bovy_plot([-0.6,-0.6],_APOORAFERANGE,'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.3,-0.3],_APOORAFERANGE,'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.6,-0.3],[_APOORAFERANGE[0],_APOORAFERANGE[0]],
'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.6,-0.3],[_APOORAFERANGE[0],_APOORAFERANGE[0]],
'k:',overplot=True,lw=lw)
bovy_plot.bovy_plot([-0.6,-0.3],[_APOORAFERANGE[1],_APOORAFERANGE[1]],
'k:',overplot=True,lw=lw)
#metal-poorpoor
bovy_plot.bovy_plot([-0.7,-0.7],_ARICHAFERANGE,
'k:',overplot=True,lw=lw)
bovy_plot.bovy_end_print(os.path.join(args[0],options.type+'_'
+options.sample+'_'+
options.metal+'.'+ext))
def calcOrbits(parser):
options,args= parser.parse_args()
#Read data
XYZ,vxvyvz,cov_vxvyvz,rawdata= readData(metal='allall',
sample=options.sample,
loggmin=4.2,
snmin=15.,
select=options.select)
#Define potential
if options.logp:
pot= LogarithmicHaloPotential(normalize=1.)
else:
pot= MWPotential
ts= numpy.linspace(0.,_MAXT,10000) #times to integrate
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
orbits= pickle.load(savefile)
_ORBITSLOADED= True
try:
samples= pickle.load(savefile)
except EORError:
_SAMPLESLOADED= False
else:
_SAMPLESLOADED= True
finally:
savefile.close()
else:
_ORBITSLOADED= False
if not _ORBITSLOADED:
#First calculate orbits
es, rmeans, rperis, raps, zmaxs = [], [], [], [], []
densrmeans, vzrmeans= [], []
for ii in range(len(rawdata)):
sys.stdout.write('\r'+"Working on object %i/%i" % (ii,len(rawdata)))
sys.stdout.flush()
#Integrate the orbit
data= rawdata[ii]
o= Orbit([data.ra,data.dec,data.dist,data.pmra,data.pmdec,data.vr],
radec=True,vo=220.,ro=8.,zo=_ZSUN)
o.integrate(ts,pot)
es.append(o.e())
rperis.append(o.rperi())
raps.append(o.rap())
zmaxs.append(o.zmax())
rmeans.append(0.5*(o.rperi()+o.rap()))
Rs= o.R(ts)
vz2= o.vz(ts)**2.
dens= evaluateDensities(Rs,0.*Rs,pot)
densrmeans.append(numpy.sum(dens*Rs)/numpy.sum(dens))
vzrmeans.append(numpy.sum(vz2*Rs)/numpy.sum(vz2))
# print " ", rmeans[-1], densrmeans[-1], vzrmeans[-1]
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
es= numpy.array(es)
rmeans= numpy.array(rmeans)
rperis= numpy.array(rperis)
raps= numpy.array(raps)
zmaxs= numpy.array(zmaxs)
orbits= _append_field_recarray(rawdata,'e',es)
orbits= _append_field_recarray(orbits,'rmean',rmeans)
orbits= _append_field_recarray(orbits,'rperi',rperis)
orbits= _append_field_recarray(orbits,'rap',raps)
orbits= _append_field_recarray(orbits,'zmax',zmaxs)
orbits= _append_field_recarray(orbits,'densrmean',densrmeans)
orbits= _append_field_recarray(orbits,'vzrmean',vzrmeans)
#Pickle
savefile= open(args[0],'wb')
pickle.dump(orbits,savefile)
savefile.close()
return None
def fakeDensData(parser):
numpy.random.seed(1)
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
#Set up density model
if options.model.lower() == 'hwr':
densfunc= _HWRDensity
if options.metal.lower() == 'rich':
params= numpy.array([-1.34316986e+00,1.75402412e+00,5.14667706e-04])
else:
params= numpy.array([-0.3508148171668,0.65752,0.00206572947631])
elif options.model.lower() == 'flare':
densfunc= _FlareDensity
if options.metal.lower() == 'rich':
params= numpy.log(numpy.array([0.3,2.5,2.5]))
else:
params= numpy.log(numpy.array([0.3,2.5,2.5]))
elif options.model.lower() == 'tiedflare':
densfunc= _TiedFlareDensity
if options.metal.lower() == 'rich':
params= numpy.log(numpy.array([0.3,2.5]))
else:
params= numpy.log(numpy.array([0.3,2.5]))
elif options.model.lower() == 'twovertical':
densfunc= _TwoVerticalDensity
if options.metal.lower() == 'rich':
params= numpy.log(numpy.array([0.3,0.8,2.5,1.05]))
else:
params= numpy.log(numpy.array([0.3,0.8,2.5,1.05]))
#Data
XYZ,vxvyvz,cov_vxvyvz,rawdata= readData(metal=options.metal,
sample=options.sample)
if options.metal.lower() == 'rich':
feh= -0.15
fehrange= [-0.4,0.5]
elif options.metal.lower() == 'poor':
feh= -0.65
fehrange= [-1.5,-0.5]
else:
feh= -0.5
#Load model distributions
if options.sample.lower() == 'g':
colorrange=[0.48,0.55]
elif options.sample.lower() == 'k':
colorrange=[0.55,0.75]
#FeH
fehdist= DistSpline(*numpy.histogram(rawdata.feh,bins=11,range=fehrange),
xrange=fehrange)
#Color
colordist= DistSpline(*numpy.histogram(rawdata.dered_g-rawdata.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Load selection function
sf= segueSelect.segueSelect(type_bright=options.sel_bright,
type_faint=options.sel_faint,
sample=options.sample)
platelb= bovy_coords.radec_to_lb(sf.platestr.ra,sf.platestr.dec,
degree=True)
if options.sample.lower() == 'g':
rmin, rmax= 14.5, 20.2
grmin, grmax= 0.48, 0.55
elif options.sample.lower() == 'k':
rmin, rmax= 14.5, 19.
grmin, grmax= 0.55, 0.75
#Calculate the r-distribution for each plate
nrs= 1001
ngr, nfeh= 21, 21
grs= numpy.linspace(grmin,grmax,ngr)
fehs= numpy.linspace(fehrange[0],fehrange[1],nfeh)
#Calcuate FeH and gr distriutions
fehdists= numpy.zeros(nfeh)
for ii in range(nfeh): fehdists[ii]= fehdist(fehs[ii])
fehdists= numpy.cumsum(fehdists)
fehdists/= fehdists[-1]
colordists= numpy.zeros(ngr)
for ii in range(ngr): colordists[ii]= colordist(grs[ii])
colordists= numpy.cumsum(colordists)
colordists/= colordists[-1]
rs= numpy.linspace(rmin,rmax,nrs)
rdists= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
for ii in range(len(sf.plates)):
p= sf.plates[ii]
sys.stdout.write('\r'+"Working on plate %i (%i/%i)" % (p,ii+1,len(sf.plates)))
sys.stdout.flush()
rdists[ii,:,:,:]= _predict_rdist_plate(rs,densfunc,params,rmin,rmax,
platelb[ii,0],platelb[ii,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[ii],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
sys.stdout.write('\r'+segueSelect._ERASESTR+'\r')
sys.stdout.flush()
numbers= numpy.sum(rdists,axis=3)
numbers= numpy.sum(numbers,axis=2)
numbers= numpy.sum(numbers,axis=1)
numbers= numpy.cumsum(numbers)
numbers/= numbers[-1]
rdists= numpy.cumsum(rdists,axis=1)
for ii in range(len(sf.plates)):
for jj in range(ngr):
for kk in range(nfeh):
rdists[ii,:,jj,kk]/= rdists[ii,-1,jj,kk]
#Now sample until we're done
out= []
while len(out) < options.nsamples:
#First sample a plate
ran= numpy.random.uniform()
kk= 0
while numbers[kk] < ran: kk+= 1
#Also sample a Feh and a color
ran= numpy.random.uniform()
ff= 0
while fehdists[ff] < ran: ff+= 1
ran= numpy.random.uniform()
cc= 0
while colordists[cc] < ran: cc+= 1
#plate==kk, feh=ff,color=cc; now sample from the rdist of this plate
ran= numpy.random.uniform()
jj= 0
while rdists[kk,jj,cc,ff] < ran: jj+= 1
#r=jj
out.append([rs[jj],grs[cc],fehs[ff],platelb[kk,0],platelb[kk,1],
sf.plates[kk]])
#Save as pickle
savefile= open(args[0],'wb')
pickle.dump(out,savefile)
savefile.close()
def plotMetallicityColor(options,args):
if options.png: ext= 'png'
else: ext= 'ps'
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal=options.metal,
sample=options.sample)
#Load plates
platestr= segueSelect._load_fits(os.path.join(segueSelect._SEGUESELECTDIR,
'segueplates.fits'))
#Set up ranges
if options.sample.lower() == 'g':
xrange=[0.46,0.57]
rmin, rmax= 14.5, 20.2
grmin, grmax= 0.48, 0.55
rx= 0.47
colorrange=[0.48,0.55]
elif options.sample.lower() == 'k':
xrange=[0.51,0.79]
rmin, rmax= 14.5, 19.
grmin, grmax= 0.55,0.75
rx= 0.01/.11*(xrange[1]-xrange[0])+xrange[0]
colorrange=[0.55,0.75]
if options.metal.lower() == 'rich':
yrange=[-0.55,0.5]
ry= 0.275
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'poor':
yrange=[-1.6,0.3]
ry= yrange[1]-(.5-0.275)*(yrange[1]-yrange[0])
fehrange= _ARICHFEHRANGE
#First plot all data, faint, and bright
bovy_plot.bovy_print()
_plotMC_single(data,options,args,all=True,overplot=False,xrange=xrange,
yrange=yrange,fehrange=fehrange,colorrange=colorrange)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_gr_'+options.sample+'_'+options.metal+'.'+ext))
#Then plot them pixel-by-pixel
platelb= bovy_coords.radec_to_lb(platestr.ra,platestr.dec,
degree=True)
nside= 1
for ii in range(12*nside**2):
#First find all of the plates in this pixel
indx= (platestr.healpix_level1 == ii)
if numpy.sum(indx) == 0: continue #No match
thisplatestr= platestr[indx]
platels= platelb[indx,0]
platebs= platelb[indx,1]
indx= []
for jj in range(len(data.ra)):
if data[jj].plate in list(thisplatestr.plate):
indx.append(True)
else:
indx.append(False)
indx= numpy.array(indx,dtype='bool')
if numpy.sum(indx) == 0: continue #No matches
bovy_plot.bovy_print()
_plotMC_single(data[indx],options,args,all=False,overplot=False,
xrange=xrange,yrange=yrange,platels=platels,
platebs=platebs,
ry=ry,rx=rx,
rmin=rmin,rmax=rmax,grmin=grmin,grmax=grmax)
_overplot_model(data,xrange=xrange,yrange=yrange,fehrange=fehrange,
colorrange=colorrange)
bovy_plot.bovy_end_print(os.path.join(args[0],'FeH_gr_'+options.sample+'_'+options.metal+'_%i.' % ii + ext))
return None
def scatterData(options,args):
if options.png: ext= 'png'
else: ext= 'ps'
#Load sf
sf= segueSelect.segueSelect(sample=options.sample,sn=True,
type_bright='sharprcut',
type_faint='sharprcut')
if options.fake:
fakefile= open(options.fakefile,'rb')
fakedata= pickle.load(fakefile)
fakefile.close()
#Calculate distance
ds, ls, bs, rs, grs, fehs= [], [], [], [], [], []
for ii in range(len(fakedata)):
ds.append(_ivezic_dist(fakedata[ii][1],fakedata[ii][0],fakedata[ii][2]))
ls.append(fakedata[ii][3]+(2*numpy.random.uniform()-1.)\
*1.49)
bs.append(fakedata[ii][4]+(2*numpy.random.uniform()-1.)\
*1.49)
rs.append(fakedata[ii][0])
grs.append(fakedata[ii][1])
fehs.append(fakedata[ii][2])
ds= numpy.array(ds)
ls= numpy.array(ls)
bs= numpy.array(bs)
rs= numpy.array(rs)
grs= numpy.array(grs)
fehs= numpy.array(fehs)
XYZ= bovy_coords.lbd_to_XYZ(ls,bs,ds,degree=True)
else:
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal=options.metal,
select=options.select,
sample=options.sample)
#Cut out bright stars on faint plates and vice versa
indx= []
for ii in range(len(data.feh)):
if sf.platebright[str(data[ii].plate)] and data[ii].dered_r >= 17.8:
indx.append(False)
elif not sf.platebright[str(data[ii].plate)] and data[ii].dered_r < 17.8:
indx.append(False)
else:
indx.append(True)
indx= numpy.array(indx,dtype='bool')
data= data[indx]
XYZ= XYZ[indx,:]
vxvyvz= vxvyvz[indx,:]
cov_vxvyvz= cov_vxvyvz[indx,:]
R= ((8.-XYZ[:,0])**2.+XYZ[:,1]**2.)**0.5
bovy_plot.bovy_print()
if options.type.lower() == 'dataxy':
bovy_plot.bovy_plot(XYZ[:,0],XYZ[:,1],'k,',
xlabel=r'$X\ [\mathrm{kpc}]$',
ylabel=r'$Y\ [\mathrm{kpc}]$',
xrange=[5,-5],yrange=[5,-5],
onedhists=True)
elif options.type.lower() == 'datarz':
bovy_plot.bovy_plot(R,XYZ[:,2],'k,',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$Z\ [\mathrm{kpc}]$',
xrange=[5,14],
yrange=[-4,4],
onedhists=True)
if options.fake:
bovy_plot.bovy_end_print(os.path.join(args[0],options.type+'_'
+'fake_'+
options.sample+'_'+
options.metal+'.'+ext))
else:
bovy_plot.bovy_end_print(os.path.join(args[0],options.type+'_'
+options.sample+'_'+
options.metal+'.'+ext))
def compareGRichRdist(options,args):
if options.png: ext= 'png'
else: ext= 'ps'
#Set up density models and their parameters
model1= _TwoDblExpDensity
model2= _TwoDblExpDensity
model3= _TwoDblExpDensity
params2= None
params3= None
#model2= _HWRDensity
left_legend= None
if options.metal.lower() == 'rich':
params1= numpy.array([-1.36358793,
-0.40929899,
1.29196694,
1.85495651,
0.0123595])
params2= numpy.array([-1.36358793,
-0.40929899,
numpy.log(2.),
1.85495651,
0.0123595])
params3= numpy.array([-1.36358793,
-0.40929899,
numpy.log(3.),
1.85495651,
0.0123595])
left_legend= r'$[\mathrm{Fe/H}] > -0.3$'
elif options.metal.lower() == 'poor':
params1= numpy.array([-0.37728915,
-0.06891146,
0.69623296,
1.0986486,
0.04188442])
params2= numpy.array([-0.37728915,
-0.06891146,
numpy.log(3.),
1.0986486,
0.04188442])
params3= numpy.array([-0.37728915,
-0.06891146,
numpy.log(4.),
1.0986486,
0.04188442])
elif options.metal.lower() == 'poorpoor':
model1= _TwoDblExpDensity
model2= _TwoDblExpDensity
model3= _TwoDblExpDensity
params1= numpy.array([-0.15566038,
-0.14511052,
0.72449094,
0.73929871,
0.06788415])
params2= numpy.array([-0.15566038,
-0.14511052,
numpy.log(3.),
0.73929871,
0.06788415])
params3= numpy.array([-0.15566038,
-0.14511052,
numpy.log(4.),
0.73929871,
0.06788415])
left_legend= r'$[\mathrm{Fe/H}] < -0.70$'
elif options.metal.lower() == 'poorrich':
model1= _TwoDblExpDensity
model2= _TwoDblExpDensity
model3= _TwoDblExpDensity
params1= numpy.array([-0.5387634,
-0.13596311,
0.67857382,
1.37668202,
0.03020303])
params2= numpy.array([-0.5387634,
-0.13596311,
numpy.log(3.),
1.37668202,
0.03020303])
params3= numpy.array([-0.5387634,
-0.13596311,
numpy.log(4.),
1.37668202,
0.03020303])
left_legend= r'$[\mathrm{Fe/H}] \geq -0.70$'
elif options.metal.lower() == 'richpoor':
model1= _TwoDblExpDensity
model2= _TwoDblExpDensity
model3= _TwoDblExpDensity
params1= numpy.array([-1.02044843,
-0.05517342,
3.35487325,
3.69485597,
0.01818825])
params2= numpy.array([-1.02044843,
-0.05517342,
numpy.log(2.),
3.69485597,
0.01818825])
params3= numpy.array([-1.02044843,
-0.05517342,
numpy.log(3.),
3.69485597,
0.01818825])
left_legend= r'$-0.6 < [\mathrm{Fe/H}] < -0.3$'
elif options.metal.lower() == 'richrich':
model1= _TwoDblExpDensity
params1= numpy.array([-1.36358793,
-0.40929899,
1.29196694,
1.85495651,
0.0123595])
params2= numpy.array([-1.36358793,
-0.40929899,
numpy.log(2.),
1.85495651,
0.0123595])
params3= numpy.array([-1.36358793,
-0.40929899,
numpy.log(3.),
1.85495651,
0.0123595])
left_legend= r'$[\mathrm{Fe/H}] \geq -0.25$'
params3= None
elif options.metal.lower() == 'richpoorest':
model1= _TwoDblExpDensity
params1= numpy.array([-0.37302496,
0.35869237,
4.60494691,
0.13423782,
0.02584325])
params2= numpy.array([-0.37302496,
0.35869237,
numpy.log(2.),
0.13423782,
0.02584325])
params3= numpy.array([-0.37302496,
0.35869237,
numpy.log(3.),
0.13423782,
0.02584325])
left_legend= r'$-1.5 < [\mathrm{Fe/H}] < -0.6$'
elif options.metal.lower() == 'apoorpoor':
model1= _TwoDblExpDensity
params1= numpy.array([-1.42966594,
-0.43592629,
1.46092453,
2.31726435,
0.01014193])
params2= None
left_legend= r'$0.00 < [\alpha/\mathrm{Fe}] < 0.15$'
elif options.metal.lower() == 'apoorrich':
model1= _TwoDblExpDensity
params1= numpy.array([-1.05609215,
-0.04159115,
0.85264104,
1.61003202,
0.01804995])
params2= None
left_legend= r'$0.15 \leq [\alpha/\mathrm{Fe}] < 0.25$'
elif options.metal.lower() == 'arichpoor':
model1= _TwoDblExpDensity
params1= numpy.array([-0.46676741,
-0.22070634,
0.80226706,
1.24844397,
0.03106556])
left_legend= r'$0.25 \leq [\alpha/\mathrm{Fe}] < 0.35$'
params2= None
elif options.metal.lower() == 'arichrich':
model1= _TwoDblExpDensity
params1= numpy.array([-0.26808571,
-0.19104086,
0.63738051,
0.71367505,
0.02827808])
params2= None
left_legend= r'$0.35 \leq [\alpha/\mathrm{Fe}] < 0.45$'
#Legend
right_legend= None
if options.all:
if options.type == 'z':
right_legend= r'$h_z = %i\ \mathrm{pc}$' % (1000.*numpy.exp(params1[0]))
else:
right_legend= r'$h_R = %.1f\ \mathrm{kpc}$' % numpy.exp(params1[2])
#Load sf
sf= segueSelect.segueSelect(sample=options.sample,sn=True,
type_bright='tanhrcut',
type_faint='tanhrcut')
if options.metal.lower() == 'rich':
feh= -0.15
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'poor':
feh= -0.65
fehrange= _ARICHFEHRANGE
elif options.metal.lower() == 'richdiag' \
or options.metal.lower() == 'richlowerdiag':
feh= -0.15
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'poorpoor':
feh= -0.7
fehrange= [_ARICHFEHRANGE[0],-0.7]
elif options.metal.lower() == 'poorrich':
feh= -0.7
fehrange= [-0.7,_ARICHFEHRANGE[1]]
elif options.metal.lower() == 'richpoor':
feh= -0.35
fehrange= [-0.6,_APOORFEHRANGE[0]]
elif options.metal.lower() == 'richrich':
feh= -0.2
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'richpoorest':
feh= -0.7
fehrange= [-1.5,-0.6]
elif options.metal.lower() == 'apoorpoor' or options.metal.lower() == 'apoorrich':
feh= -0.15
fehrange= _APOORFEHRANGE
elif options.metal.lower() == 'arichpoor' or options.metal.lower() == 'arichrich':
feh= -0.65
fehrange= _ARICHFEHRANGE
else:
feh= -0.5
fehrange= [-1.5,0.5]
#Load data
XYZ,vxvyvz,cov_vxvyvz,data= readData(metal=options.metal,select=options.select,
sample=options.sample)
#Cut out bright stars on faint plates and vice versa
indx= []
nfaintbright, nbrightfaint= 0, 0
for ii in range(len(data.feh)):
if sf.platebright[str(data[ii].plate)] and data[ii].dered_r >= 17.8:
indx.append(False)
nbrightfaint+= 1
elif not sf.platebright[str(data[ii].plate)] and data[ii].dered_r < 17.8:
indx.append(False)
nfaintbright+= 1
else:
indx.append(True)
print nbrightfaint, nfaintbright
indx= numpy.array(indx,dtype='bool')
data= data[indx]
XYZ= XYZ[indx,:]
vxvyvz= vxvyvz[indx,:]
cov_vxvyvz= cov_vxvyvz[indx,:]
if options.sample.lower() == 'g':
colorrange=[0.48,0.55]
rmax= 19.5 #SF cuts off here
elif options.sample.lower() == 'k':
colorrange=[0.55,0.75]
rmax= 19.
#Load model distributions
#FeH
fehdist= DistSpline(*numpy.histogram(data.feh,bins=11,range=fehrange),
xrange=fehrange)
#Color
cdist= DistSpline(*numpy.histogram(data.dered_g-data.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Ranges
if options.type == 'z':
xrange= [-0.1,5.]
elif options.type == 'R':
xrange= [4.8,14.2]
elif options.type == 'r':
xrange= [14.2,20.1]
#We do bright/faint for 4 directions and all, all bright, all faint
ls= [180,180,45,45]
bs= [0,90,-23,23]
bins= 21
#Set up comparison
if options.type == 'r':
compare_func= compareDataModel.comparerdistPlate
elif options.type == 'z':
compare_func= compareDataModel.comparezdistPlate
elif options.type == 'R':
compare_func= compareDataModel.compareRdistPlate
#all, faint, bright
if options.metal.lower() == 'poor':
bins= [31,31,31]
elif options.metal.lower() == 'rich':
bins= [51,31,31]
else:
bins= [31,31,31]
plates= ['all','bright','faint']
for ii in range(len(plates)):
plate= plates[ii]
if plate == 'all':
thisleft_legend= left_legend
thisright_legend= right_legend
else:
thisleft_legend= None
thisright_legend= None
bovy_plot.bovy_print()
compare_func(model1,params1,sf,cdist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
bins=bins[ii],ls='-',left_legend=thisleft_legend,
right_legend=thisright_legend)
if not params2 is None:
compare_func(model2,params2,sf,cdist,fehdist,
data,plate,color='k',bins=bins[ii],
rmin=14.5,rmax=rmax,
grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None:
compare_func(model3,params3,sf,cdist,fehdist,
data,plate,color='k',bins=bins[ii],
rmin=14.5,rmax=rmax,
grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
overplot=True,ls=':')
if options.type == 'r':
bovy_plot.bovy_end_print(os.path.join(args[0],'model_data_g_'+options.metal+'_'+plate+'.'+ext))
else:
bovy_plot.bovy_end_print(os.path.join(args[0],'model_data_g_'+options.metal+'_'+options.type+'dist_'+plate+'.'+ext))
if options.all: return None
bins= 16
for ii in range(len(ls)):
#Bright
plate= compareDataModel.similarPlatesDirection(ls[ii],bs[ii],20.,
sf,data,
faint=False)
bovy_plot.bovy_print()
compare_func(model1,params1,sf,cdist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
bins=bins,ls='-')
if not params2 is None:
compare_func(model2,params2,sf,cdist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None:
compare_func(model3,params3,sf,cdist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
overplot=True,ls=':')
if options.type == 'r':
bovy_plot.bovy_end_print(os.path.join(args[0],'model_data_g_'+options.metal+'_l%i_b%i_bright.' % (ls[ii],bs[ii]))+ext)
else:
bovy_plot.bovy_end_print(os.path.join(args[0],'model_data_g_'+options.metal+'_'+options.type+'dist_l%i_b%i_bright.' % (ls[ii],bs[ii]))+ext)
#Faint
plate= compareDataModel.similarPlatesDirection(ls[ii],bs[ii],20.,
sf,data,
bright=False)
bovy_plot.bovy_print()
compare_func(model1,params1,sf,cdist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
bins=bins,ls='-')
if not params2 is None:
compare_func(model2,params2,sf,cdist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None:
compare_func(model3,params3,sf,cdist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,grmin=colorrange[0],
grmax=colorrange[1],
fehmin=fehrange[0],fehmax=fehrange[1],feh=feh,
xrange=xrange,
overplot=True,ls=':')
if options.type == 'r':
bovy_plot.bovy_end_print(os.path.join(args[0],'model_data_g_'+options.metal+'_l%i_b%i_faint.' % (ls[ii],bs[ii]))+ext)
else:
bovy_plot.bovy_end_print(os.path.join(args[0],'model_data_g_'+options.metal+'_'+options.type+'dist_l%i_b%i_faint.' % (ls[ii],bs[ii]))+ext)
return None
