def plotDensComparisonDFMulti(options, args):
# Read data etc.
print "Reading the data ..."
raw = read_rawdata(options)
# Bin the data
binned = pixelAfeFeh(raw, dfeh=options.dfeh, dafe=options.dafe)
# Map the bins with ndata > minndata in 1D
fehs, afes = [], []
for ii in range(len(binned.fehedges) - 1):
for jj in range(len(binned.afeedges) - 1):
data = binned(binned.feh(ii), binned.afe(jj))
if len(data) < options.minndata:
continue
# print binned.feh(ii), binned.afe(jj), len(data)
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins = len(fehs)
fehs = numpy.array(fehs)
afes = numpy.array(afes)
gafes, gfehs, left_legend = getMultiComparisonBins(options)
if options.usemedianpotential:
potparams = get_median_potential(options, nabundancebins)
print "Median potential parameters: ", potparams
# Setup everything for the selection function
print "Setting up stuff for the normalization integral ..."
normintstuff = setup_normintstuff(options, raw, binned, fehs, afes)
M = len(gfehs)
# Check whether fits exist, if not, pop
removeBins = numpy.ones(M, dtype="bool")
for jj in range(M):
# Find pop corresponding to this bin
pop = numpy.argmin((gfehs[jj] - fehs) ** 2.0 / 0.1 + (gafes[jj] - afes) ** 2.0 / 0.0025)
# Load savefile
if not options.init is None:
# Load initial parameters from file
savename = options.init
spl = savename.split(".")
newname = ""
for ll in range(len(spl) - 1):
newname += spl[ll]
if not ll == len(spl) - 2:
newname += "."
newname += "_%i." % pop
newname += spl[-1]
if not os.path.exists(newname):
removeBins[jj] = False
else:
raise IOError("base filename not specified ...")
if numpy.sum(removeBins) == 0:
raise IOError("None of the group bins have been fit ...")
elif numpy.sum(removeBins) < M:
# Some bins have not been fit yet, and have to be removed
gfehs = list((numpy.array(gfehs))[removeBins])
gafes = list((numpy.array(gafes))[removeBins])
print "Only using %i bins out of %i ..." % (numpy.sum(removeBins), M)
M = len(gfehs)
model1s = []
model2s = []
model3s = []
params1 = []
params2 = []
params3 = []
data = []
colordists = []
fehdists = []
fehmins = []
fehmaxs = []
cfehs = []
for jj in range(M):
print "Working on group %i / %i ..." % (jj + 1, M)
# Find pop corresponding to this bin
pop = numpy.argmin((gfehs[jj] - fehs) ** 2.0 / 0.1 + (gafes[jj] - afes) ** 2.0 / 0.0025)
# Load savefile
if not options.init is None:
# Load initial parameters from file
savename = options.init
spl = savename.split(".")
newname = ""
for ll in range(len(spl) - 1):
newname += spl[ll]
if not ll == len(spl) - 2:
newname += "."
newname += "_%i." % pop
newname += spl[-1]
savefile = open(newname, "rb")
tparams = pickle.load(savefile)
savefile.close()
else:
raise IOError("base filename not specified ...")
if options.usemedianpotential:
tparams = set_potparams(potparams, tparams, options, 1)
print tparams
# Set up density models and their parameters
model1s.append(interpDens)
paramsInterp, surfz = calc_model(tparams, options, 0, _retsurfz=True)
params1.append(paramsInterp)
if True:
# Add outlier and plot sum
if not options.usemedianpotential:
potparams = get_potparams(tparams, options, 1)
logoutfrac = numpy.log(get_outfrac(tparams, 0, options))
logoutfrac += numpy.log(surfz)
outfrac = numpy.exp(logoutfrac)
ro = get_ro(tparams, options)
halodens = ro * outDens(1.0, 0.0, None)
model2s.append(interpDenswoutlier)
params2.append([paramsInterp, outfrac * halodens])
model3s.append(None)
params3.append(None)
elif False:
if not options.usemedianpotential:
potparams = get_potparams(tparams, options, 1)
if options.potential.lower() == "flatlog":
tparams = set_potparams([potparams[0] * 1.05, potparams[1]], tparams, options, 1)
model2s.append(interpDens)
params2.append(calc_model(tparams, options, 0))
tparams = set_potparams([potparams[0] * 0.95, potparams[1]], tparams, options, 1)
model3s.append(interpDens)
params3.append(calc_model(tparams, options, 0))
elif options.potential.lower() == "mwpotentialfixhalo":
tparams = set_potparams(
[numpy.log(2.0 / _REFR0), potparams[1], potparams[2], potparams[3], potparams[4]],
tparams,
options,
1,
)
model2s.append(interpDens)
params2.append(calc_model(tparams, options, 0))
tparams = set_potparams(
[numpy.log(3.0 / 8.0), potparams[1], potparams[2], potparams[3], potparams[4]], tparams, options, 1
)
model3s.append(interpDens)
params3.append(calc_model(tparams, options, 0))
else:
model2s.append(None)
params2.append(None)
model3s.append(None)
params3.append(None)
data.append(binned(fehs[pop], afes[pop]))
# Setup everything for selection function
thisnormintstuff = normintstuff[pop]
if _PRECALCVSAMPLES:
sf, plates, platel, plateb, platebright, platefaint, grmin, grmax, rmin, rmax, fehmin, fehmax, feh, colordist, fehdist, gr, rhogr, rhofeh, mr, dmin, dmax, ds, surfscale, hr, hz, surfnrs, surfnzs, surfRgrid, surfzgrid, surfvrs, surfvts, surfvzs = unpack_normintstuff(
thisnormintstuff, options
)
else:
sf, plates, platel, plateb, platebright, platefaint, grmin, grmax, rmin, rmax, fehmin, fehmax, feh, colordist, fehdist, gr, rhogr, rhofeh, mr, dmin, dmax, ds, surfscale, hr, hz = unpack_normintstuff(
thisnormintstuff, options
)
colordists.append(colordist)
fehdists.append(fehdist)
fehmins.append(fehmin)
fehmaxs.append(fehmax)
cfehs.append(feh)
if True:
# Cut out bright stars on faint plates and vice versa
indx = []
nfaintbright, nbrightfaint = 0, 0
for ii in range(len(data[jj].feh)):
if sf.platebright[str(data[jj][ii].plate)] and data[jj][ii].dered_r >= 17.8:
indx.append(False)
nbrightfaint += 1
elif not sf.platebright[str(data[jj][ii].plate)] and data[jj][ii].dered_r < 17.8:
indx.append(False)
nfaintbright += 1
else:
indx.append(True)
print "nbrightfaint, nfaintbright", nbrightfaint, nfaintbright
indx = numpy.array(indx, dtype="bool")
if numpy.sum(indx) > 0:
data[jj] = data[jj][indx]
# Ranges
if options.type == "z":
xrange = [-0.1, 5.0]
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.comparerdistPlateMulti
elif options.type == "z":
compare_func = compareDataModel.comparezdistPlateMulti
elif options.type == "R":
compare_func = compareDataModel.compareRdistPlateMulti
# all, faint, bright
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
# thisleft_legend= None
thisright_legend = None
else:
thisleft_legend = None
thisright_legend = None
bovy_plot.bovy_print()
compare_func(
model1s,
params1,
sf,
colordists,
fehdists,
data,
plate,
color="k",
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
bins=bins[ii],
ls="-",
left_legend=thisleft_legend,
right_legend=thisright_legend,
)
if not params2[0] is None:
compare_func(
model2s,
params2,
sf,
colordists,
fehdists,
data,
plate,
color="k",
bins=bins[ii],
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
overplot=True,
ls="--",
)
if not params3[0] is None:
compare_func(
model3s,
params3,
sf,
colordists,
fehdists,
data,
plate,
color="k",
bins=bins[ii],
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
overplot=True,
ls=":",
)
if options.type == "r":
bovy_plot.bovy_end_print(args[0] + "model_data_g_" + options.group + "_" + plate + "." + options.ext)
else:
bovy_plot.bovy_end_print(
args[0] + "model_data_g_" + options.group + "_" + options.type + "dist_" + plate + "." + options.ext
)
if options.all:
return None
bins = 16
for ii in range(len(ls)):
# Bright
plate = compareDataModel.similarPlatesDirection(ls[ii], bs[ii], 20.0, sf, data, faint=False)
bovy_plot.bovy_print()
compare_func(
model1s,
params1,
sf,
colordists,
fehdists,
data,
plate,
color="k",
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
bins=bins,
ls="-",
)
if not params2[0] is None:
compare_func(
model2s,
params2,
sf,
colordists,
fehdists,
data,
plate,
color="k",
bins=bins,
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
overplot=True,
ls="--",
)
if not params3[0] is None:
compare_func(
model3s,
params3,
sf,
colordists,
fehdists,
data,
plate,
color="k",
bins=bins,
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
overplot=True,
ls=":",
)
if options.type == "r":
bovy_plot.bovy_end_print(
args[0] + "model_data_g_" + options.group + "_" + "l%i_b%i_bright." % (ls[ii], bs[ii]) + options.ext
)
else:
bovy_plot.bovy_end_print(
args[0]
+ "model_data_g_"
+ options.group
+ "_"
+ options.type
+ "dist_l%i_b%i_bright." % (ls[ii], bs[ii])
+ options.ext
)
# Faint
plate = compareDataModel.similarPlatesDirection(ls[ii], bs[ii], 20.0, sf, data, bright=False)
bovy_plot.bovy_print()
compare_func(
model1s,
params1,
sf,
colordists,
fehdists,
data,
plate,
color="k",
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
bins=bins,
ls="-",
)
if not params2[0] is None:
compare_func(
model2s,
params2,
sf,
colordists,
fehdists,
data,
plate,
color="k",
bins=bins,
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
overplot=True,
ls="--",
)
if not params3[0] is None:
compare_func(
model3s,
params3,
sf,
colordists,
fehdists,
data,
plate,
color="k",
bins=bins,
rmin=14.5,
rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmins,
fehmax=fehmaxs,
feh=cfehs,
xrange=xrange,
overplot=True,
ls=":",
)
if options.type == "r":
bovy_plot.bovy_end_print(
args[0] + "model_data_g_" + options.group + "_" + "l%i_b%i_faint." % (ls[ii], bs[ii]) + options.ext
)
else:
bovy_plot.bovy_end_print(
args[0]
+ "model_data_g_"
+ options.group
+ "_"
+ options.type
+ "dist_l%i_b%i_faint." % (ls[ii], bs[ii])
+ options.ext
)
return None
def plotDensComparisonDF(options,args):
#Read data etc.
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
#print binned.feh(ii), binned.afe(jj), len(data)
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
if not options.singlefeh is None:
if True: #Just to keep indentation the same
#Set up single feh
indx= binned.callIndx(options.singlefeh,options.singleafe)
if numpy.sum(indx) == 0:
raise IOError("Bin corresponding to singlefeh and singleafe is empty ...")
allraw= copy.copy(raw)
raw= copy.copy(binned.data[indx])
#newerrstuff= []
#for ii in range(len(binned.data)):
# if indx[ii]: newerrstuff.append(errstuff[ii])
#errstuff= newerrstuff
print "Using %i data points ..." % (len(data))
#Bin again
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
if options.singles:
run_abundance_singles_plotdens(options,args,fehs,afes)
return None
if options.andistances:
data= binned(fehs[0],afes[0])
distfac= AnDistance.AnDistance(data.dered_g-data.dered_r,
data.feh)
if options.fixdm is None:
options.fixdm= numpy.log10(distfac)*5.
else:
options.fixdm= options.fixdm+numpy.log10(distfac)*5.
options.andistances= False
#Start over
plotDensComparisonDF(options,args)
return None
#Setup everything for the selection function
print "Setting up stuff for the normalization integral ..."
normintstuff= setup_normintstuff(options,raw,binned,fehs,afes,allraw)
##########POTENTIAL PARAMETERS####################
potparams1= numpy.array([numpy.log(2.5/8.),options.fixvc/220.,
numpy.log(400./8000.),0.2,0.])
potparams2= numpy.array([numpy.log(3./8.),options.fixvc/220.,
numpy.log(400./8000.),0.466666666,0.])
potparams3= numpy.array([numpy.log(2.5/8.),options.fixvc/220.,
numpy.log(400./8000.),0.8,0.])
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
#Set up density models and their parameters
pop= 0 #assume first population
#Load savefile
if not options.init is None:
#Load initial parameters from file
savename= options.init
# spl= savename.split('.')
# newname= ''
# for ll in range(len(spl)-1):
# newname+= spl[ll]
# if not ll == len(spl)-2: newname+= '.'
# newname+= '_%i.' % pop
# newname+= spl[-1]
savefile= open(savename,'rb')
try:
if not _NOTDONEYET:
params= pickle.load(savefile)
mlogl= pickle.load(savefile)
logl= pickle.load(savefile)
except:
if savetopickle:
save_pickles(tmpfiles[jj],None)
return None
else:
return None
finally:
savefile.close()
else:
raise IOError("base filename not specified ...")
#First model is best-fit for this particular bin
marglogl= numpy.zeros((8,16))
for ll in range(8):
for kk in range(16):
marglogl[ll,kk]= logsumexp(logl[ll,0,0,kk,:,:,:,0])
indx= numpy.unravel_index(numpy.nanargmax(marglogl),(8,16))
print "Maximum for %i at %i,%i" % (pop,indx[0],indx[1])
rds= numpy.linspace(2.0,3.4,options.nrds)/_REFR0
rds= numpy.log(rds)
fhs= numpy.linspace(0.,1.,options.nfhs)
potparams1[0]= rds[indx[0]]
potparams1[3]= fhs[indx[1]]
#######DF PARAMETER RANGES###########
hrs, srs, szs= setup_dfgrid(fehs,afes,options)
dfindx= numpy.unravel_index(numpy.nanargmax(logl[indx[0],0,0,indx[1],:,:,:,0]),
(8,8,16))
print "Maximum for %i at %i,%i,%i" % (pop,dfindx[0],dfindx[1],dfindx[2])
tparams= initialize(options,fehs,afes)
startindx= 0
if options.fitdvt: startindx+= 1
tparams[startindx]= hrs[dfindx[0]]
tparams[startindx+4]= srs[dfindx[1]]
tparams[startindx+2]= szs[dfindx[2]]
tparams[startindx+5]= 0. #outlier fraction
tparams= set_potparams(potparams1,tparams,options,1)
#Set up density models and their parameters
model1= interpDens#woutlier
print "Working on model 1 ..."
paramsInterp= calc_model(tparams,options,0,_retsurfz=False,
normintstuff=normintstuff)
params1= paramsInterp
if False:
indx0= numpy.argmin((potparams2[0]-rds)**2.)
indx1= numpy.argmin((potparams2[3]-fhs)**2.)
#indx0= indx[0]
#indx1= indx[1]
dfindx= numpy.unravel_index(numpy.argmax(logl[indx0,0,0,indx1,:,:,:,0]),
(8,8,16))
tparams[startindx]= hrs[dfindx[0]]
tparams[startindx+4]= srs[dfindx[1]]
tparams[startindx+2]= szs[dfindx[2]]
#print "BOVY: YOU HAVE MESSED WITH MODEL 2"
tparams= set_potparams(potparams2,tparams,toptions,1)
model2= interpDens
print "Working on model 2 ..."
paramsInterp, surfz= calc_model(tparams,toptions,0,_retsurfz=True)
params2= paramsInterp
indx0= numpy.argmin((potparams3[0]-rds)**2.)
indx1= numpy.argmin((potparams3[3]-fhs)**2.)
dfindx= numpy.unravel_index(numpy.argmax(logl[indx0,0,0,indx1,:,:,:,0]),
(8,8,16))
tparams[startindx]= hrs[dfindx[0]]
tparams[startindx+4]= srs[dfindx[1]]
tparams[startindx+2]= szs[dfindx[2]]
tparams= set_potparams(potparams3,tparams,toptions,1)
model3= interpDens
print "Working on model 3 ..."
paramsInterp, surfz= calc_model(tparams,toptions,0,_retsurfz=True)
params3= paramsInterp
else:
model2= None
params2= None
model3= None
params3= None
data= binned(fehs[pop],afes[pop])
#Setup everything for selection function
thisnormintstuff= normintstuff[pop]
if _PRECALCVSAMPLES:
sf, plates,platel,plateb,platebright,platefaint,grmin,grmax,rmin,rmax,fehmin,fehmax,feh,colordist,fehdist,gr,rhogr,rhofeh,mr,dmin,dmax,ds, surfscale, hr, hz, colorfehfac,normR, normZ,surfnrs, surfnzs, surfRgrid, surfzgrid, surfvrs, surfvts, surfvzs= unpack_normintstuff(thisnormintstuff,options)
else:
sf, plates,platel,plateb,platebright,platefaint,grmin,grmax,rmin,rmax,fehmin,fehmax,feh,colordist,fehdist,gr,rhogr,rhofeh,mr,dmin,dmax,ds, surfscale, hr, hz, colorfehfac, normR, normZ= unpack_normintstuff(thisnormintstuff,options)
if True:
#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", nbrightfaint, nfaintbright
indx= numpy.array(indx,dtype='bool')
data= data[indx]
#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
#First do R ranges for z
if options.type.lower() == 'z' and _RRANGES:
bins=21
Rmins= [None,7.,9.]
Rmaxs= [7.,9.,None]
nameRmins= [4,7,9]
nameRmaxs= [7,9,13]
for ii in range(len(Rmins)):
plate= 'all'
if Rmins[ii] is None:
thisleft_legend= r'$R \leq 7\,\mathrm{kpc\ plates}$'
elif Rmins[ii] == 7.:
thisleft_legend= r'$7\,\mathrm{kpc} < R \leq 9\,\mathrm{kpc\ plates}$'
elif Rmaxs[ii] is None:
thisleft_legend= r'$R \geq 9\,\mathrm{kpc\ plates}$'
thisright_legend= None
bovy_plot.bovy_print()
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
bins=bins,ls='-',left_legend=thisleft_legend,
right_legend=thisright_legend,
Rmin=Rmins[ii],Rmax=Rmaxs[ii])
if not params2 is None:
compare_func(model2,params2,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--',
Rmin=Rmins[ii],Rmax=Rmaxs[ii])
if not params3 is None:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':',
Rmin=Rmins[ii],Rmax=Rmaxs[ii])
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_%iR%i' % (nameRmins[ii],nameRmaxs[ii])+'.'+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_%iR%i' % (nameRmins[ii],nameRmaxs[ii])+'.'+options.ext)
#all, faint, bright
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
thisleft_legend= None
thisright_legend= None
else:
thisleft_legend= None
thisright_legend= None
bovy_plot.bovy_print()
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,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,colordist,fehdist,
data,plate,color='k',bins=bins[ii],
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins[ii],
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':')
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+plate+'.'+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_'+plate+'.'+options.ext)
if options.all: return None
bins= 16
for ii in range(len(ls)):
nodata= False
#Bright
plate= compareDataModel.similarPlatesDirection(ls[ii],bs[ii],20.,
sf,data,
faint=False)
bovy_plot.bovy_print()
try:
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
bins=bins,ls='-')
except IndexError:
#no data
nodata= True
if not params2 is None and not nodata:
compare_func(model2,params2,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None and not nodata:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':')
if not nodata:
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+'l%i_b%i_bright.' % (ls[ii],bs[ii])+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_l%i_b%i_bright.' % (ls[ii],bs[ii])+options.ext)
#Faint
plate= compareDataModel.similarPlatesDirection(ls[ii],bs[ii],20.,
sf,data,
bright=False)
bovy_plot.bovy_print()
try:
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
bins=bins,ls='-')
except IndexError:
#No data
continue
if not params2 is None:
compare_func(model2,params2,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':')
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+'l%i_b%i_faint.' % (ls[ii],bs[ii])+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_l%i_b%i_faint.' % (ls[ii],bs[ii])+options.ext)
return None
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
