def calc_model(params,options,data,logpiso,logpisodwarf,df,nlocs,locations,iso):
avg_plate_model= numpy.zeros(nlocs)
for ii in range(nlocs):
#Calculate vlos | los
indx= (data['LOCATION'] == locations[ii])
thesedata= data[indx]
thislogpiso= logpiso[indx,:]
if options.dwarf:
thislogpisodwarf= logpisodwarf[indx,:]
else:
thislogpisodwarf= None
vlos= numpy.linspace(-200.,200.,options.nvlos)
pvlos= numpy.zeros(options.nvlos)
if not options.multi is None:
pvlos= multi.parallel_map((lambda x: pvlosplate(params,vlos[x],
thesedata,
df,options,
thislogpiso,
thislogpisodwarf,iso)),
range(options.nvlos),
numcores=numpy.amin([len(vlos),multiprocessing.cpu_count(),options.multi]))
else:
for jj in range(options.nvlos):
print jj
pvlos[jj]= pvlosplate(params,vlos[jj],thesedata,df,options,
thislogpiso,thislogpisodwarf)
pvlos-= logsumexp(pvlos)
pvlos= numpy.exp(pvlos)
#Calculate mean and velocity dispersion
avg_plate_model[ii]= numpy.sum(vlos*pvlos)
return avg_plate_model
def plot_chi2(parser):
(options,args)= parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
#Read the data
print "Reading the data ..."
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
#Cut inner disk locations
#data= data[(data['GLON'] > 75.)]
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
print "Using %i data points ..." % len(data)
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile= open(options.isofile,'rb')
iso= pickle.load(isofile)
if options.indivfeh:
zs= pickle.load(isofile)
if options.varfeh:
locl= pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso= []
zs= numpy.arange(0.0005,0.03005,0.0005)
for ii in range(len(zs)):
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs= list(set(data['LOCATION']))
iso= []
for ii in range(len(locs)):
indx= (data['LOCATION'] == locs[ii])
locl= numpy.mean(data['GLON'][indx]*_DEGTORAD)
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso= [iso,
isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z,
dwarf=True,expsfh=options.expsfh)]
else:
iso= [iso]
if not options.isofile is None:
isofile= open(options.isofile,'wb')
pickle.dump(iso,isofile)
if options.indivfeh:
pickle.dump(zs,isofile)
elif options.varfeh:
pickle.dump(locl,isofile)
isofile.close()
df= None
print "Pre-calculating isochrone distance prior ..."
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
#Load initial parameters from file
savefile= open(args[0],'rb')
params= pickle.load(savefile)
if not options.index is None:
params= params[options.index]
savefile.close()
#params[0]= 245./235.
#params[1]= 8.5/8.
#Calculate data means etc.
#Calculate means
locations= list(set(data['LOCATION']))
nlocs= len(locations)
l_plate= numpy.zeros(nlocs)
avg_plate= numpy.zeros(nlocs)
sig_plate= numpy.zeros(nlocs)
siga_plate= numpy.zeros(nlocs)
sigerr_plate= numpy.zeros(nlocs)
fidlogl= logl.logl(init=params,data=data,options=options)
logl_plate= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locations[ii])
l_plate[ii]= numpy.mean(data['GLON'][indx])
avg_plate[ii]= numpy.mean(data['VHELIO'][indx])
sig_plate[ii]= numpy.std(data['VHELIO'][indx])
siga_plate[ii]= numpy.std(data['VHELIO'][indx])/numpy.sqrt(numpy.sum(indx))
sigerr_plate[ii]= bootstrap_sigerr(data['VHELIO'][indx])
#Logl
logl_plate[ii]= -2.*(numpy.sum(fidlogl[indx])-numpy.sum(fidlogl)/len(indx)*numpy.sum(indx))
#Calculate plate means and variances from the model
avg_plate_model= numpy.zeros(nlocs)
sig_plate_model= numpy.zeros(nlocs)
for ii in range(nlocs):
#Calculate vlos | los
indx= (data['LOCATION'] == locations[ii])
thesedata= data[indx]
thislogpiso= logpiso[indx,:]
if options.dwarf:
thislogpisodwarf= logpisodwarf[indx,:]
else:
thislogpisodwarf= None
vlos= numpy.linspace(-200.,200.,options.nvlos)
pvlos= numpy.zeros(options.nvlos)
if not options.multi is None:
pvlos= multi.parallel_map((lambda x: pvlosplate(params,vlos[x],
thesedata,
df,options,
thislogpiso,
thislogpisodwarf,iso)),
range(options.nvlos),
numcores=numpy.amin([len(vlos),multiprocessing.cpu_count(),options.multi]))
else:
for jj in range(options.nvlos):
print jj
pvlos[jj]= pvlosplate(params,vlos[jj],thesedata,df,options,
thislogpiso,thislogpisodwarf,iso)
pvlos-= logsumexp(pvlos)
pvlos= numpy.exp(pvlos)
#Calculate mean and velocity dispersion
avg_plate_model[ii]= numpy.sum(vlos*pvlos)
sig_plate_model[ii]= numpy.sqrt(numpy.sum(vlos**2.*pvlos)\
-avg_plate_model[ii]**2.)
#Plot everything
left, bottom, width, height= 0.1, 0.4, 0.8, 0.3
axTop= pyplot.axes([left,bottom,width,height])
left, bottom, width, height= 0.1, 0.1, 0.8, 0.3
axChi2= pyplot.axes([left,bottom,width,height])
#left, bottom, width, height= 0.1, 0.1, 0.8, 0.2
#axSig= pyplot.axes([left,bottom,width,height])
fig= pyplot.gcf()
#Plot the difference
fig.sca(axTop)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'ko',overplot=True)
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',linestyle='none',elinestyle='-')
pyplot.ylabel(r'$\langle v_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle v_{\mathrm{los}}\rangle_{\mathrm{model}}$')
pyplot.ylim(-14.5,14.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Plot the chi2
fig.sca(axChi2)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True)
bovy_plot.bovy_plot(l_plate,
logl_plate,
'ko',overplot=True)
pyplot.ylabel(r'$\Delta \chi^2$')
#pyplot.ylim(numpy.amin(logl_plate),numpy.amax(logl_plate))
pyplot.ylim(-150.,150.)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
def plot_bestfit(parser):
(options, args) = parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
# Read the data
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, # if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata,
)
# HACK
indx = data["J0MAG"] - data["K0MAG"] < 0.5
data["J0MAG"][indx] = 0.5 + data["K0MAG"][indx]
# Cut inner disk locations
# data= data[(data['GLON'] > 75.)]
# Cut outliers
# data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
print "Using %i data points ..." % len(data)
# Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, "rb")
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
if options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
# Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, Z=zs[ii]))
elif options.varfeh:
locs = list(set(data["LOCATION"]))
iso = []
for ii in range(len(locs)):
indx = data["LOCATION"] == locs[ii]
locl = numpy.mean(data["GLON"][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel, expsfh=options.expsfh, marginalizefeh=True, glon=locl)
)
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, expsfh=options.expsfh)
if options.dwarf:
iso = [iso, isomodel.isomodel(imfmodel=options.imfmodel, Z=options.Z, dwarf=True, expsfh=options.expsfh)]
else:
iso = [iso]
if not options.isofile is None:
isofile = open(options.isofile, "wb")
pickle.dump(iso, isofile)
if options.indivfeh:
pickle.dump(zs, isofile)
elif options.varfeh:
pickle.dump(locl, isofile)
isofile.close()
df = None
print "Pre-calculating isochrone distance prior ..."
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
dm = _dm(ds)
for ii in range(len(data)):
mh = data["H0MAG"][ii] - dm
if options.indivfeh:
# Find closest Z
thisZ = isodist.FEH2Z(data[ii]["FEH"])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh)
elif options.varfeh:
# Find correct iso
indx = locl == data[ii]["LOCATION"]
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh)
else:
logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh)
if options.dwarf:
logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS))
dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF, _BINTEGRATENBINS)
dm = _dm(dwarfds)
for ii in range(len(data)):
mh = data["H0MAG"][ii] - dm
logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) + (data["J0MAG"] - data["K0MAG"])[ii], mh)
else:
logpisodwarf = None
# Calculate data means etc.
# Calculate means
locations = list(set(data["LOCATION"]))
nlocs = len(locations)
l_plate = numpy.zeros(nlocs)
avg_plate = numpy.zeros(nlocs)
sig_plate = numpy.zeros(nlocs)
siga_plate = numpy.zeros(nlocs)
sigerr_plate = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = data["LOCATION"] == locations[ii]
l_plate[ii] = numpy.mean(data["GLON"][indx])
avg_plate[ii] = numpy.mean(data["VHELIO"][indx])
sig_plate[ii] = numpy.std(data["VHELIO"][indx])
siga_plate[ii] = numpy.std(data["VHELIO"][indx]) / numpy.sqrt(numpy.sum(indx))
sigerr_plate[ii] = bootstrap_sigerr(data["VHELIO"][indx])
# Calculate plate means and variances from the model
# Load initial parameters from file
savefile = open(args[0], "rb")
params = pickle.load(savefile)
if not options.index is None:
params = params[options.index]
savefile.close()
# params[0]= 245./235.
# params[1]= 8.5/8.
avg_plate_model = numpy.zeros(nlocs)
sig_plate_model = numpy.zeros(nlocs)
for ii in range(nlocs):
# Calculate vlos | los
indx = data["LOCATION"] == locations[ii]
thesedata = data[indx]
thislogpiso = logpiso[indx, :]
if options.dwarf:
thislogpisodwarf = logpisodwarf[indx, :]
else:
thislogpisodwarf = None
vlos = numpy.linspace(-200.0, 200.0, options.nvlos)
pvlos = numpy.zeros(options.nvlos)
if not options.multi is None:
pvlos = multi.parallel_map(
(lambda x: pvlosplate(params, vlos[x], thesedata, df, options, thislogpiso, thislogpisodwarf, iso)),
range(options.nvlos),
numcores=numpy.amin([len(vlos), multiprocessing.cpu_count(), options.multi]),
)
else:
for jj in range(options.nvlos):
print jj
pvlos[jj] = pvlosplate(params, vlos[jj], thesedata, df, options, thislogpiso, thislogpisodwarf, iso)
pvlos -= logsumexp(pvlos)
pvlos = numpy.exp(pvlos)
# Calculate mean and velocity dispersion
avg_plate_model[ii] = numpy.sum(vlos * pvlos)
sig_plate_model[ii] = numpy.sqrt(numpy.sum(vlos ** 2.0 * pvlos) - avg_plate_model[ii] ** 2.0)
# Plot everything
left, bottom, width, height = 0.1, 0.4, 0.8, 0.5
axTop = pyplot.axes([left, bottom, width, height])
left, bottom, width, height = 0.1, 0.1, 0.8, 0.3
axMean = pyplot.axes([left, bottom, width, height])
# left, bottom, width, height= 0.1, 0.1, 0.8, 0.2
# axSig= pyplot.axes([left,bottom,width,height])
fig = pyplot.gcf()
fig.sca(axTop)
pyplot.ylabel(r"$\mathrm{Heliocentric\ velocity}\ [\mathrm{km\ s}^{-1}]$")
pyplot.xlabel(r"$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$")
pyplot.xlim(0.0, 360.0)
pyplot.ylim(-200.0, 200.0)
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_plot(data["GLON"], data["VHELIO"], "k,", yrange=[-200.0, 200.0], xrange=[0.0, 360.0], overplot=True)
ndata_t = int(math.floor(len(data) / 1000.0))
ndata_h = len(data) - ndata_t * 1000
bovy_plot.bovy_plot(l_plate, avg_plate, "o", overplot=True, mfc="0.5", mec="none")
bovy_plot.bovy_plot(l_plate, avg_plate_model, "x", overplot=True, ms=10.0, mew=1.5, color="0.7")
# Legend
bovy_plot.bovy_plot([260.0], [150.0], "k,", overplot=True)
bovy_plot.bovy_plot([260.0], [120.0], "o", mfc="0.5", mec="none", overplot=True)
bovy_plot.bovy_plot([260.0], [90.0], "x", ms=10.0, mew=1.5, color="0.7", overplot=True)
bovy_plot.bovy_text(270.0, 145.0, r"$\mathrm{data}$")
bovy_plot.bovy_text(270.0, 115.0, r"$\mathrm{data\ mean}$")
bovy_plot.bovy_text(270.0, 85.0, r"$\mathrm{model\ mean}$")
bovy_plot._add_ticks()
# Now plot the difference
fig.sca(axMean)
bovy_plot.bovy_plot([0.0, 360.0], [0.0, 0.0], "-", color="0.5", overplot=True)
bovy_plot.bovy_plot(l_plate, avg_plate - avg_plate_model, "ko", overplot=True)
pyplot.errorbar(
l_plate, avg_plate - avg_plate_model, yerr=siga_plate, marker="o", color="k", linestyle="none", elinestyle="-"
)
pyplot.ylabel(r"$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$")
pyplot.ylim(-14.5, 14.5)
pyplot.xlim(0.0, 360.0)
bovy_plot._add_ticks()
# axMean.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r"$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$")
pyplot.xlim(0.0, 360.0)
bovy_plot._add_ticks()
# Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
# Sigma
fig.sca(axSig)
pyplot.plot([0.0, 360.0], [1.0, 1.0], "-", color="0.5")
bovy_plot.bovy_plot(l_plate, sig_plate / sig_plate_model, "ko", overplot=True)
pyplot.errorbar(
l_plate,
sig_plate / sig_plate_model,
yerr=sigerr_plate / sig_plate_model,
marker="o",
color="k",
linestyle="none",
elinestyle="-",
)
pyplot.ylabel(r"$\sigma_{\mathrm{los}}^{\mathrm{data}}/ \sigma_{\mathrm{los}}^{\mathrm{model}}$")
pyplot.ylim(0.5, 1.5)
def plot_bestfit(parser):
(options, args) = parser.parse_args()
if len(args) == 0 or options.plotfilename is None:
parser.print_help()
return
#Read the data
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
#HACK
indx = (data['J0MAG'] - data['K0MAG'] < 0.5)
data['J0MAG'][indx] = 0.5 + data['K0MAG'][indx]
#Cut inner disk locations
#data= data[(data['GLON'] > 75.)]
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
print "Using %i data points ..." % len(data)
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, 'rb')
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
if options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs = list(set(data['LOCATION']))
iso = []
for ii in range(len(locs)):
indx = (data['LOCATION'] == locs[ii])
locl = numpy.mean(data['GLON'][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
expsfh=options.expsfh)
if options.dwarf:
iso = [
iso,
isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
dwarf=True,
expsfh=options.expsfh)
]
else:
iso = [iso]
if not options.isofile is None:
isofile = open(options.isofile, 'wb')
pickle.dump(iso, isofile)
if options.indivfeh:
pickle.dump(zs, isofile)
elif options.varfeh:
pickle.dump(locl, isofile)
isofile.close()
df = None
print "Pre-calculating isochrone distance prior ..."
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
dm = _dm(ds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpiso[ii, :] = iso[0](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii], mh)
if options.dwarf:
logpisodwarf = numpy.zeros((len(data), _BINTEGRATENBINS))
dwarfds = numpy.linspace(_BINTEGRATEDMIN_DWARF, _BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm = _dm(dwarfds)
for ii in range(len(data)):
mh = data['H0MAG'][ii] - dm
logpisodwarf[ii, :] = iso[1](numpy.zeros(_BINTEGRATENBINS) +
(data['J0MAG'] - data['K0MAG'])[ii],
mh)
else:
logpisodwarf = None
#Calculate data means etc.
#Calculate means
locations = list(set(data['LOCATION']))
nlocs = len(locations)
l_plate = numpy.zeros(nlocs)
avg_plate = numpy.zeros(nlocs)
sig_plate = numpy.zeros(nlocs)
siga_plate = numpy.zeros(nlocs)
sigerr_plate = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locations[ii])
l_plate[ii] = numpy.mean(data['GLON'][indx])
avg_plate[ii] = numpy.mean(data['VHELIO'][indx])
sig_plate[ii] = numpy.std(data['VHELIO'][indx])
siga_plate[ii] = numpy.std(data['VHELIO'][indx]) / numpy.sqrt(
numpy.sum(indx))
sigerr_plate[ii] = bootstrap_sigerr(data['VHELIO'][indx])
#Calculate plate means and variances from the model
#Load initial parameters from file
savefile = open(args[0], 'rb')
params = pickle.load(savefile)
if not options.index is None:
params = params[options.index]
savefile.close()
#params[0]= 245./235.
#params[1]= 8.5/8.
avg_plate_model = numpy.zeros(nlocs)
sig_plate_model = numpy.zeros(nlocs)
for ii in range(nlocs):
#Calculate vlos | los
indx = (data['LOCATION'] == locations[ii])
thesedata = data[indx]
thislogpiso = logpiso[indx, :]
if options.dwarf:
thislogpisodwarf = logpisodwarf[indx, :]
else:
thislogpisodwarf = None
vlos = numpy.linspace(-200., 200., options.nvlos)
pvlos = numpy.zeros(options.nvlos)
if not options.multi is None:
pvlos = multi.parallel_map(
(lambda x: pvlosplate(params, vlos[x], thesedata, df, options,
thislogpiso, thislogpisodwarf, iso)),
range(options.nvlos),
numcores=numpy.amin(
[len(vlos),
multiprocessing.cpu_count(), options.multi]))
else:
for jj in range(options.nvlos):
print jj
pvlos[jj] = pvlosplate(params, vlos[jj], thesedata, df,
options, thislogpiso, thislogpisodwarf,
iso)
pvlos -= logsumexp(pvlos)
pvlos = numpy.exp(pvlos)
#Calculate mean and velocity dispersion
avg_plate_model[ii] = numpy.sum(vlos * pvlos)
sig_plate_model[ii]= numpy.sqrt(numpy.sum(vlos**2.*pvlos)\
-avg_plate_model[ii]**2.)
#Plot everything
left, bottom, width, height = 0.1, 0.4, 0.8, 0.5
axTop = pyplot.axes([left, bottom, width, height])
left, bottom, width, height = 0.1, 0.1, 0.8, 0.3
axMean = pyplot.axes([left, bottom, width, height])
#left, bottom, width, height= 0.1, 0.1, 0.8, 0.2
#axSig= pyplot.axes([left,bottom,width,height])
fig = pyplot.gcf()
fig.sca(axTop)
pyplot.ylabel(r'$\mathrm{Heliocentric\ velocity}\ [\mathrm{km\ s}^{-1}]$')
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
pyplot.ylim(-200., 200.)
nullfmt = NullFormatter() # no labels
axTop.xaxis.set_major_formatter(nullfmt)
bovy_plot.bovy_plot(data['GLON'],
data['VHELIO'],
'k,',
yrange=[-200., 200.],
xrange=[0., 360.],
overplot=True)
ndata_t = int(math.floor(len(data) / 1000.))
ndata_h = len(data) - ndata_t * 1000
bovy_plot.bovy_plot(l_plate,
avg_plate,
'o',
overplot=True,
mfc='0.5',
mec='none')
bovy_plot.bovy_plot(l_plate,
avg_plate_model,
'x',
overplot=True,
ms=10.,
mew=1.5,
color='0.7')
#Legend
bovy_plot.bovy_plot([260.], [150.], 'k,', overplot=True)
bovy_plot.bovy_plot([260.], [120.],
'o',
mfc='0.5',
mec='none',
overplot=True)
bovy_plot.bovy_plot([260.], [90.],
'x',
ms=10.,
mew=1.5,
color='0.7',
overplot=True)
bovy_plot.bovy_text(270., 145., r'$\mathrm{data}$')
bovy_plot.bovy_text(270., 115., r'$\mathrm{data\ mean}$')
bovy_plot.bovy_text(270., 85., r'$\mathrm{model\ mean}$')
bovy_plot._add_ticks()
#Now plot the difference
fig.sca(axMean)
bovy_plot.bovy_plot([0., 360.], [0., 0.], '-', color='0.5', overplot=True)
bovy_plot.bovy_plot(l_plate,
avg_plate - avg_plate_model,
'ko',
overplot=True)
pyplot.errorbar(l_plate,
avg_plate - avg_plate_model,
yerr=siga_plate,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
pyplot.ylabel(r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$')
pyplot.ylim(-14.5, 14.5)
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#axMean.xaxis.set_major_formatter(nullfmt)
pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0., 360.)
bovy_plot._add_ticks()
#Save
bovy_plot.bovy_end_print(options.plotfilename)
return None
#Sigma
fig.sca(axSig)
pyplot.plot([0., 360.], [1., 1.], '-', color='0.5')
bovy_plot.bovy_plot(l_plate,
sig_plate / sig_plate_model,
'ko',
overplot=True)
pyplot.errorbar(l_plate,
sig_plate / sig_plate_model,
yerr=sigerr_plate / sig_plate_model,
marker='o',
color='k',
linestyle='none',
elinestyle='-')
pyplot.ylabel(
r'$\sigma_{\mathrm{los}}^{\mathrm{data}}/ \sigma_{\mathrm{los}}^{\mathrm{model}}$'
)
pyplot.ylim(0.5, 1.5)
