def resampleMags(raw,comps,options,args):
#For each data point's line-of-sight and color and feh
#calculate the "thin" and "thick" distributions
model_thick= _DblExpDensity
model_thin= _DblExpDensity
params_thick= numpy.array([numpy.log(options.hz_thick/1000.),
-numpy.log(options.hr_thick)])
params_thin= numpy.array([numpy.log(options.hz_thin/1000.),
-numpy.log(options.hr_thin)])
if options.allthin:
params_thick= params_thin
elif options.allthick:
params_thin= params_thick
#Load sf
sf= segueSelect(sample=options.sample,sn=True,
type_bright='tanhrcut',
type_faint='tanhrcut')
platelb= bovy_coords.radec_to_lb(sf.platestr.ra,sf.platestr.dec,
degree=True)
#Cut out bright stars on faint plates and vice versa
cutbright= False
if cutbright:
indx= []
for ii in range(len(raw.feh)):
if sf.platebright[str(raw[ii].plate)] and raw[ii].dered_r >= 17.8:
indx.append(False)
elif not sf.platebright[str(raw[ii].plate)] and raw[ii].dered_r < 17.8:
indx.append(False)
else:
indx.append(True)
indx= numpy.array(indx,dtype='bool')
raw= raw[indx]
comps= comps[indx]
#Loadthe data into the pixelAfeFeh structure
raw= _append_field_recarray(raw,'comps',comps)
binned= pixelAfeFeh(raw,dfeh=0.1,dafe=0.05,fehmin=-1.6,
fehmax=0.5,afemin=-0.05,afemax=0.55)
#Color
if options.sample.lower() == 'g':
colorrange=[0.48,0.55]
rmax= 20.2
elif options.sample.lower() == 'k':
colorrange=[0.55,0.75]
rmax= 19.
if options.sample.lower() == 'g':
grmin, grmax= 0.48, 0.55
rmin,rmax= 14.5, 20.2
ngr, nfeh, nrs= 2, 2, 201
grs= numpy.linspace(grmin,grmax,ngr)
rs= numpy.linspace(rmin,rmax,nrs)
#Run through the bins
ii, jj= 0, 0
while ii < len(binned.fehedges)-1:
while jj < len(binned.afeedges)-1:
data= binned(binned.feh(ii),binned.afe(jj))
rawIndx= binned.callIndx(binned.feh(ii),binned.afe(jj))
if len(data) < 1:
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
break
continue
#Set up feh and color
feh= binned.feh(ii)
fehrange= [binned.fehedges[ii],binned.fehedges[ii+1]]
#FeH
fehdist= DistSpline(*numpy.histogram(data.feh,bins=5,
range=fehrange),
xrange=fehrange,dontcuttorange=False)
#Color
colordist= DistSpline(*numpy.histogram(data.dered_g\
-data.dered_r,
bins=9,range=colorrange),
xrange=colorrange)
#Predict the r-distribution for all plates
#Thick or thin?
thick_amp= numpy.mean(data.comps)
rdists_thin= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
rdists_thick= numpy.zeros((len(sf.plates),nrs,ngr,nfeh))
for pp in range(len(sf.plates)):
sys.stdout.write('\r'+"Working on bin %i / %i: plate %i / %i" \
% (ii*(len(binned.afeedges)-1)+jj+1,
(len(binned.afeedges)-1)*(len(binned.fehedges)-1),pp+1,len(sf.plates))+'\r')
sys.stdout.flush()
rdists_thin[pp,:,:,:]= _predict_rdist_plate(rs,model_thin,
params_thin,
rmin,rmax,
platelb[pp,0],platelb[pp,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[pp],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
rdists_thick[pp,:,:,:]= _predict_rdist_plate(rs,model_thick,
params_thick,
rmin,rmax,
platelb[pp,0],platelb[pp,1],
grmin,grmax,
fehrange[0],fehrange[1],feh,
colordist,
fehdist,sf,sf.plates[pp],
dontmarginalizecolorfeh=True,
ngr=ngr,nfeh=nfeh)
rdists= thick_amp*rdists_thick+(1.-thick_amp)*rdists_thin
rdists[numpy.isnan(rdists)]= 0.
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 ww in range(len(sf.plates)):
for ll in range(ngr):
for kk in range(nfeh):
if rdists[ww,-1,ll,kk] != 0.:
rdists[ww,:,ll,kk]/= rdists[ww,-1,ll,kk]
#Now sample
nout= 0
while nout < len(data):
#First sample a plate
ran= numpy.random.uniform()
kk= 0
while numbers[kk] < ran: kk+= 1
#plate==kk; now sample from the rdist of this plate
ran= numpy.random.uniform()
ll= 0
#Find cc and ff for this data point
cc= int(numpy.floor((data[nout].dered_g-data[nout].dered_r-colorrange[0])/(colorrange[1]-colorrange[0])*ngr))
ff= int(numpy.floor((data[nout].feh-fehrange[0])/(fehrange[1]-fehrange[0])*nfeh))
while rdists[kk,ll,cc,ff] < ran and ll < nrs-1: ll+= 1
#r=ll
oldgr= data.dered_g[nout]-data.dered_r[nout]
oldr= data.dered_r[nout]
data.dered_r[nout]= rs[ll]
data.dered_g[nout]= oldgr+data.dered_r[nout]
#Also change plate and l and b
data.plate[nout]= sf.plates[kk]
data.ra[nout]= sf.platestr.ra[kk]
data.dec[nout]= sf.platestr.dec[kk]
data.l[nout]= platelb[kk,0]
data.b[nout]= platelb[kk,1]
nout+= 1
raw.dered_r[rawIndx]= data.dered_r
raw.dered_g[rawIndx]= data.dered_g
raw.plate[rawIndx]= data.plate
raw.ra[rawIndx]= data.ra
raw.dec[rawIndx]= data.dec
raw.l[rawIndx]= data.l
raw.b[rawIndx]= data.b
jj+= 1
if jj == len(binned.afeedges)-1:
jj= 0
ii+= 1
sys.stdout.write('\r'+_ERASESTR+'\r')
sys.stdout.flush()
#Dump raw
fitsio.write(args[0],raw,clobber=True)
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 plotComps(options,args,comps):
raw= readRealData(options,args)
raw= _append_field_recarray(raw,'comps',comps)
pix= pixelAfeFeh(raw,dfeh=0.1,dafe=0.05,fehmin=-1.6,
fehmax=0.5,afemin=-0.05,afemax=0.55)
plotthis= numpy.zeros((pix.npixfeh(),pix.npixafe()))
for ii in range(pix.npixfeh()):
for jj in range(pix.npixafe()):
data= pix(pix.feh(ii),pix.afe(jj))
if len(data) < 100:
plotthis[ii,jj]= numpy.nan
continue
plotthis[ii,jj]= numpy.mean(data.comps)
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='gist_yarg',
interpolation='nearest',
xrange=[-1.6,0.5],
yrange=[-0.05,0.55],
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
zlabel=r'$\mathrm{fraction\ of\ stars\ in\ thick\ component}$',
# vmin=vmin,vmax=vmax,
colorbar=True,shrink=0.78,
contours=False)
#Overplot contours of the underlying components
#Restore deconvolution
outfile= open(options.xdfile,'rb')
xamp= pickle.load(outfile)
xmean= pickle.load(outfile)
xcovar= pickle.load(outfile)
outfile.close()
from matplotlib import pyplot
from matplotlib.patches import Ellipse
eigs1= numpy.linalg.eig(xcovar[0,:,:])
eigs2= numpy.linalg.eig(xcovar[1,:,:])
angle1= math.atan(-eigs1[1][0,1]/eigs1[1][1,1])/numpy.pi*180.
angle2= math.atan(-eigs2[1][0,1]/eigs2[1][1,1])/numpy.pi*180.
thisellipse= Ellipse(xmean[0,:],2.*numpy.sqrt(eigs1[0][0]),
2*numpy.sqrt(eigs1[0][1]),angle1)
ells= [thisellipse]
ells.append(Ellipse(xmean[0,:],4.*numpy.sqrt(eigs1[0][0]),
4*numpy.sqrt(eigs1[0][1]),angle1))
ells.append(Ellipse(xmean[0,:],6.*numpy.sqrt(eigs1[0][0]),
6*numpy.sqrt(eigs1[0][1]),angle1))
ells.append(Ellipse(xmean[1,:],2.*numpy.sqrt(eigs2[0][0]),
2*numpy.sqrt(eigs2[0][1]),angle2))
ells.append(Ellipse(xmean[1,:],4.*numpy.sqrt(eigs2[0][0]),
4*numpy.sqrt(eigs2[0][1]),angle2))
ells.append(Ellipse(xmean[1,:],6.*numpy.sqrt(eigs2[0][0]),
6*numpy.sqrt(eigs2[0][1]),angle2))
ax= pyplot.gca()
xlims= ax.get_xlim()
ylims= ax.get_ylim()
for e in ells:
ax.add_artist(e)
e.set_facecolor('none')
e.set_edgecolor('0.85')
e.set_linestyle('dashed')
ax.set_xlim(xlims)
ax.set_ylim(ylims)
bovy_plot.bovy_end_print(options.compsplotfile)
