def plot_rovo(filename, plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile = open(filename, 'rb')
params = pickle.load(savefile)
savefile.close()
if _ANALYTIC: #Calculate by fixing everything except for Ro anv vo
options = plot_pdfs.set_options(None)
nros = 15
noos = 15
ros = numpy.linspace(7., 13., nros)
oos = numpy.linspace(20., 30., noos)
ll = numpy.zeros((noos, nros))
for ii in range(noos):
if not _MULTI is None:
theseparamss = []
for jj in range(nros):
theseparams = copy.copy(params)
theseparams[0] = oos[ii] * ros[jj] / _REFV0
theseparams[1] = ros[jj] / _REFR0
theseparamss.append(theseparams)
thisll = multi.parallel_map(
(lambda x: numpy.sum(
logl.logl(init=theseparamss[x], options=options))),
range(nros),
numcores=numpy.amin(
[nros, _MULTI,
multiprocessing.cpu_count()]))
ll[ii, :] = thisll
else:
for jj in range(nros):
theseparams = copy.copy(params)
theseparams[0] = oos[ii] * ros[jj] / _REFV0
theseparams[1] = ros[jj] / _REFR0
ll[ii, jj] = numpy.sum(
logl.logl(init=theseparams, options=options))
#Normalize
ll -= logsumexp(ll)
ll = numpy.exp(ll)
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
bovy_plot.bovy_dens2d(
ll.T,
origin='lower',
levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
xrange=[20., 35.],
yrange=[7., 13.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
else:
vos = numpy.array([s[0] for s in params]) * _REFV0
ros = numpy.array([s[1] for s in params]) * _REFR0
bovy_plot.bovy_print()
levels = list(special.erf(0.5 * numpy.arange(1, 4)))
levels.append(1.01) #HACK to not plot outliers
bovy_plot.scatterplot(
vos / ros,
ros,
'k,',
levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
bins=31,
xrange=[200. / 8., 250. / 8.],
yrange=[7., 9.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
bovy_plot.bovy_end_print(plotfilename)
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 mockTable(parser):
(options, args) = parser.parse_args()
cmdline = '%python mockTable.py ' + args[0]
if len(args) == 0:
parser.print_help()
return
#Open savefile for flat fit and samples
savefile = open(_FLATFIT, 'rb')
flatparams = pickle.load(savefile)
savefile.close()
savefile = open(_FLATSAMPLES, 'rb')
flatsamples = pickle.load(savefile)
savefile.close()
#Open savefile for all mocks' fit and samples
mockparams = []
mocksamples = []
for ii in range(_NMOCKS):
savefile = open(_MOCKFIT[ii], 'rb')
mockparams.append(pickle.load(savefile))
savefile.close()
savefile = open(_MOCKSAMPLES[ii], 'rb')
mocksamples.append(pickle.load(savefile))
savefile.close()
#Set up sections
names = [
'$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$', '$R_0\ [\mathrm{kpc}]$',
'$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$',
'$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
'$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$', '$R_0/h_\sigma$',
'$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$',
'$\Delta\chi^2/\mathrm{dof}$'
]
scale = [_REFV0, _REFR0, _VRSUN, 1., _REFV0, 1., 1., 1.]
flatbestfits = []
flatxs = []
mockbestfits = []
mockxs = []
#V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
#R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
#VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
#Omega_sun
flatbestfits.append(flatparams[7] * _PMSGRA)
flatxs.append(numpy.array([s[7] * _PMSGRA for s in flatsamples]))
#\sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
#R_0/h_sigma
flatbestfits.append(flatparams[1] * flatparams[9])
flatxs.append(numpy.array([s[1] * s[9] for s in flatsamples]))
#X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
#chi2
options = set_options(None)
data = readVclosData(validfeh=True)
options.isofile = '../fits/isos.dat'
thislogl = logl.logl(init=flatparams, options=options, data=data)
flatbestfits.append(-2. * numpy.sum(thislogl) /
(len(data) - len(flatparams)))
if _INDIVCHI2:
locs = numpy.array(sorted(list(set(data['LOCATION']))))
nlocs = len(locs)
platel = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locs[ii])
platel[ii] = numpy.mean(data['GLON'][indx])
sortindx = numpy.argsort(platel)
locs = locs[sortindx]
platel = platel[sortindx]
for jj in range(len(locs)):
names.append(
'$\Delta\chi^2/\mathrm{dof}\ \mathrm{w/o}\ l=%i^\circ$' %
int(round(platel[jj])))
scale.append(1.)
indx = (data['LOCATION'] != locs[jj])
flatbestfits.append(-2. * numpy.sum(thislogl[indx]) /
(len(data[indx]) - len(flatparams)))
else:
locs = []
##Same for all mocks
for ii in range(_NMOCKS):
thisbestfits = []
thisxs = []
#V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
#R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
#VRsun
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
#Omega_sun
thisbestfits.append(mockparams[ii][7] * _PMSGRA)
thisxs.append(numpy.array([s[7] * _PMSGRA for s in mocksamples[ii]]))
#\sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
#R_0/h_sigma
thisbestfits.append(mockparams[ii][1] * mockparams[ii][9])
thisxs.append(numpy.array([s[1] * s[9] for s in mocksamples[ii]]))
#X^2
thisbestfits.append(mockparams[ii][8])
thisxs.append(numpy.array([s[8] for s in mocksamples[ii]]))
#chi2
#Load mock data
data = readVclosData(datafilename=_MOCKDATA[ii])
thislogl = logl.logl(init=mockparams[ii], data=data, options=options)
thisbestfits.append(-2. * numpy.sum(thislogl) /
(len(data) - len(mockparams[ii])))
if _INDIVCHI2:
for jj in range(len(locs)):
indx = (data['LOCATION'] != locs[jj])
thisbestfits.append(-2. * numpy.sum(thislogl[indx]) /
(len(data[indx]) - len(mockparams)))
#Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
#Make table
quantile = 0.68
outfile = open(args[0], 'w')
for ii in range(len(names)):
#Set up line
printline = names[ii]
#Flat
printline += ' & '
if ii >= len(names) - (len(locs) + 1): #chi2
printline += ' \ldots & '
else:
bestfit = flatbestfits[ii] * scale[ii]
xs = flatxs[ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
print names[ii], maxerr
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
#Mocks
for jj in range(_NMOCKS):
printline += ' & '
if ii >= len(names) - (len(locs) + 1): #chi2
bestfit = mockbestfits[jj][ii] * scale[ii] - flatbestfits[
ii] * scale[ii]
printline += '$%.2f$ & ' % bestfit
if jj == _NMOCKS - 1:
if not ii == (len(names) - 1):
printline += '\\\\'
outfile.write(printline + '\n')
continue
bestfit = mockbestfits[jj][ii] * scale[ii]
xs = mockxs[jj][ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit,
xs,
quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
#Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.:
value = '$%.0f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.:
err = '$\pm$%.0f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.0f}_{-%.0f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -1.:
value = '$%.1f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = '$\pm$%.1f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.1f}_{-%.1f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -2.:
value = '$%.2f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = '$\pm$%.2f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.2f}_{-%.2f}$' % (dhigh, dlow)
elif math.log10(maxerr) >= -3.:
value = '$%.3f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = '$\pm$%.3f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.3f}_{-%.3f}$' % (dhigh, dlow)
else:
value = '$%.4f$' % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = '$\pm$%.4f' % ((dlow + dhigh) / 2.)
else:
err = '$^{+%.4f}_{-%.4f}$' % (dhigh, dlow)
printline += value + '&' + err
if not ii == (len(names) - 1):
printline += '\\\\'
if ii == (len(names) - (len(locs) + 2)):
printline += '\\\\'
if ii < (len(names) - (len(locs) + 1)):
outfile.write(printline + '\n')
outfile.write('\\enddata\n')
outfile.write(cmdline + '\n')
outfile.close()
def plot_rovo(filename,plotfilename):
if not os.path.exists(filename):
raise IOError("given filename does not exist")
savefile= open(filename,'rb')
params= pickle.load(savefile)
savefile.close()
if _ANALYTIC: #Calculate by fixing everything except for Ro anv vo
options= plot_pdfs.set_options(None)
nros= 15
noos= 15
ros= numpy.linspace(7.,13.,nros)
oos= numpy.linspace(20.,30.,noos)
ll= numpy.zeros((noos,nros))
for ii in range(noos):
if not _MULTI is None:
theseparamss= []
for jj in range(nros):
theseparams= copy.copy(params)
theseparams[0]= oos[ii]*ros[jj]/_REFV0
theseparams[1]= ros[jj]/_REFR0
theseparamss.append(theseparams)
thisll= multi.parallel_map((lambda x: numpy.sum(logl.logl(init=theseparamss[x],options=options))),
range(nros),
numcores=numpy.amin([nros,_MULTI,multiprocessing.cpu_count()]))
ll[ii,:]= thisll
else:
for jj in range(nros):
theseparams= copy.copy(params)
theseparams[0]= oos[ii]*ros[jj]/_REFV0
theseparams[1]= ros[jj]/_REFR0
ll[ii,jj]= numpy.sum(logl.logl(init=theseparams,
options=options))
#Normalize
ll-= logsumexp(ll)
ll= numpy.exp(ll)
levels= list(special.erf(0.5*numpy.arange(1,4)))
bovy_plot.bovy_dens2d(ll.T,origin='lower',levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
xrange=[20.,35.],
yrange=[7.,13.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
else:
vos= numpy.array([s[0] for s in params])*_REFV0
ros= numpy.array([s[1] for s in params])*_REFR0
bovy_plot.bovy_print()
levels= list(special.erf(0.5*numpy.arange(1,4)))
levels.append(1.01) #HACK to not plot outliers
bovy_plot.scatterplot(vos/ros,ros,'k,',levels=levels,
xlabel=r'$\Omega_0\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$',
ylabel=r'$R_0\ [\mathrm{kpc}]$',
bins=31,
xrange=[200./8.,250./8.],
yrange=[7.,9.],
contours=True,
cntrcolors='k',
onedhists=True,
cmap='gist_yarg')
bovy_plot.bovy_end_print(plotfilename)
def mockTable(parser):
(options, args) = parser.parse_args()
cmdline = "%python mockTable.py " + args[0]
if len(args) == 0:
parser.print_help()
return
# Open savefile for flat fit and samples
savefile = open(_FLATFIT, "rb")
flatparams = pickle.load(savefile)
savefile.close()
savefile = open(_FLATSAMPLES, "rb")
flatsamples = pickle.load(savefile)
savefile.close()
# Open savefile for all mocks' fit and samples
mockparams = []
mocksamples = []
for ii in range(_NMOCKS):
savefile = open(_MOCKFIT[ii], "rb")
mockparams.append(pickle.load(savefile))
savefile.close()
savefile = open(_MOCKSAMPLES[ii], "rb")
mocksamples.append(pickle.load(savefile))
savefile.close()
# Set up sections
names = [
"$V_c(R_0)\ [\mathrm{km\ s}^{-1}]$",
"$R_0\ [\mathrm{kpc}]$",
"$V_{R,\odot}\ [\mathrm{km\ s}^{-1}]$",
"$\Omega_{\odot}\ [\mathrm{km\ s}^{-1}\ \mathrm{kpc}^{-1}]$",
"$\\sigma_R(R_0)\ [\mathrm{km\ s}^{-1}]$",
"$R_0/h_\sigma$",
"$X^2 \equiv \sigma_\phi^2 / \sigma_R^2$",
"$\Delta\chi^2/\mathrm{dof}$",
]
scale = [_REFV0, _REFR0, _VRSUN, 1.0, _REFV0, 1.0, 1.0, 1.0]
flatbestfits = []
flatxs = []
mockbestfits = []
mockxs = []
# V_c
flatbestfits.append(flatparams[0])
flatxs.append(numpy.array([s[0] for s in flatsamples]))
# R_0
flatbestfits.append(flatparams[1])
flatxs.append(numpy.array([s[1] for s in flatsamples]))
# VRsun
flatbestfits.append(flatparams[6])
flatxs.append(numpy.array([s[6] for s in flatsamples]))
# Omega_sun
flatbestfits.append(flatparams[7] * _PMSGRA)
flatxs.append(numpy.array([s[7] * _PMSGRA for s in flatsamples]))
# \sigma_R(R_0)
flatbestfits.append(numpy.exp(flatparams[2]))
flatxs.append(numpy.exp(numpy.array([s[2] for s in flatsamples])))
# R_0/h_sigma
flatbestfits.append(flatparams[1] * flatparams[9])
flatxs.append(numpy.array([s[1] * s[9] for s in flatsamples]))
# X^2
flatbestfits.append(flatparams[8])
flatxs.append(numpy.array([s[8] for s in flatsamples]))
# chi2
options = set_options(None)
data = readVclosData(validfeh=True)
options.isofile = "../fits/isos.dat"
thislogl = logl.logl(init=flatparams, options=options, data=data)
flatbestfits.append(-2.0 * numpy.sum(thislogl) / (len(data) - len(flatparams)))
if _INDIVCHI2:
locs = numpy.array(sorted(list(set(data["LOCATION"]))))
nlocs = len(locs)
platel = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = data["LOCATION"] == locs[ii]
platel[ii] = numpy.mean(data["GLON"][indx])
sortindx = numpy.argsort(platel)
locs = locs[sortindx]
platel = platel[sortindx]
for jj in range(len(locs)):
names.append("$\Delta\chi^2/\mathrm{dof}\ \mathrm{w/o}\ l=%i^\circ$" % int(round(platel[jj])))
scale.append(1.0)
indx = data["LOCATION"] != locs[jj]
flatbestfits.append(-2.0 * numpy.sum(thislogl[indx]) / (len(data[indx]) - len(flatparams)))
else:
locs = []
##Same for all mocks
for ii in range(_NMOCKS):
thisbestfits = []
thisxs = []
# V_c
thisbestfits.append(mockparams[ii][0])
thisxs.append(numpy.array([s[0] for s in mocksamples[ii]]))
# R_0
thisbestfits.append(mockparams[ii][1])
thisxs.append(numpy.array([s[1] for s in mocksamples[ii]]))
# VRsun
thisbestfits.append(mockparams[ii][6])
thisxs.append(numpy.array([s[6] for s in mocksamples[ii]]))
# Omega_sun
thisbestfits.append(mockparams[ii][7] * _PMSGRA)
thisxs.append(numpy.array([s[7] * _PMSGRA for s in mocksamples[ii]]))
# \sigma_R(R_0)
thisbestfits.append(numpy.exp(mockparams[ii][2]))
thisxs.append(numpy.exp(numpy.array([s[2] for s in mocksamples[ii]])))
# R_0/h_sigma
thisbestfits.append(mockparams[ii][1] * mockparams[ii][9])
thisxs.append(numpy.array([s[1] * s[9] for s in mocksamples[ii]]))
# X^2
thisbestfits.append(mockparams[ii][8])
thisxs.append(numpy.array([s[8] for s in mocksamples[ii]]))
# chi2
# Load mock data
data = readVclosData(datafilename=_MOCKDATA[ii])
thislogl = logl.logl(init=mockparams[ii], data=data, options=options)
thisbestfits.append(-2.0 * numpy.sum(thislogl) / (len(data) - len(mockparams[ii])))
if _INDIVCHI2:
for jj in range(len(locs)):
indx = data["LOCATION"] != locs[jj]
thisbestfits.append(-2.0 * numpy.sum(thislogl[indx]) / (len(data[indx]) - len(mockparams)))
# Append all
mockbestfits.append(thisbestfits)
mockxs.append(thisxs)
# Make table
quantile = 0.68
outfile = open(args[0], "w")
for ii in range(len(names)):
# Set up line
printline = names[ii]
# Flat
printline += " & "
if ii >= len(names) - (len(locs) + 1): # chi2
printline += " \ldots & "
else:
bestfit = flatbestfits[ii] * scale[ii]
xs = flatxs[ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit, xs, quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
# Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
print names[ii], maxerr
if math.log10(maxerr) >= 0.0:
value = "$%.0f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.0:
err = "$\pm$%.0f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.0f}_{-%.0f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -1.0:
value = "$%.1f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = "$\pm$%.1f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.1f}_{-%.1f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -2.0:
value = "$%.2f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = "$\pm$%.2f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.2f}_{-%.2f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -3.0:
value = "$%.3f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = "$\pm$%.3f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.3f}_{-%.3f}$" % (dhigh, dlow)
else:
value = "$%.4f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = "$\pm$%.4f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.4f}_{-%.4f}$" % (dhigh, dlow)
printline += value + "&" + err
# Mocks
for jj in range(_NMOCKS):
printline += " & "
if ii >= len(names) - (len(locs) + 1): # chi2
bestfit = mockbestfits[jj][ii] * scale[ii] - flatbestfits[ii] * scale[ii]
printline += "$%.2f$ & " % bestfit
if jj == _NMOCKS - 1:
if not ii == (len(names) - 1):
printline += "\\\\"
outfile.write(printline + "\n")
continue
bestfit = mockbestfits[jj][ii] * scale[ii]
xs = mockxs[jj][ii] * scale[ii]
lowval, highval = sixtyeigthinterval(bestfit, xs, quantile=quantile)
dlow, dhigh = bestfit - lowval, highval - bestfit
# Prepare
maxerr = numpy.amin(numpy.fabs([dlow, dhigh]))
if math.log10(maxerr) >= 0.0:
value = "$%.0f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 1.0:
err = "$\pm$%.0f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.0f}_{-%.0f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -1.0:
value = "$%.1f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.1:
err = "$\pm$%.1f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.1f}_{-%.1f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -2.0:
value = "$%.2f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.01:
err = "$\pm$%.2f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.2f}_{-%.2f}$" % (dhigh, dlow)
elif math.log10(maxerr) >= -3.0:
value = "$%.3f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.001:
err = "$\pm$%.3f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.3f}_{-%.3f}$" % (dhigh, dlow)
else:
value = "$%.4f$" % (bestfit)
if numpy.fabs((dlow - dhigh)) < 0.0001:
err = "$\pm$%.4f" % ((dlow + dhigh) / 2.0)
else:
err = "$^{+%.4f}_{-%.4f}$" % (dhigh, dlow)
printline += value + "&" + err
if not ii == (len(names) - 1):
printline += "\\\\"
if ii == (len(names) - (len(locs) + 2)):
printline += "\\\\"
if ii < (len(names) - (len(locs) + 1)):
outfile.write(printline + "\n")
outfile.write("\\enddata\n")
outfile.write(cmdline + "\n")
outfile.close()
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_externalcomparison(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)
#data= data[0:20]
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
#Cut outliers
#data= data[(data['VHELIO'] < 200.)*(data['VHELIO'] > -200.)]
#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)
elif 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
#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)
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))
#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()
#First calculate fiducial model
if not options.dwarf:
logpisodwarf= None
fid_logl= numpy.sum(logl.logl(init=params,data=data,options=copy.copy(options))) #Copy options, since logl sets multi=1
avg_plate_model_fid= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#Plot everything
bovy_plot.bovy_print(fig_height=6.,fig_width=7.)
dx= 0.8/5.
left, bottom, width, height= 0.1, 0.9-dx, 0.8, dx
axTop= pyplot.axes([left,bottom,width,height])
allaxes= [axTop]
fig= pyplot.gcf()
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_fid,
'ko',overplot=True)
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{fiducial}$',top_right=True,size=14.)
thisax= pyplot.gca()
thisax.set_ylim(-19.5,19.5)
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()
#Second is powerlaw with beta=0.15
#fid_slope= params[5-options.nooutliermean+options.dwarf]
#params[5-options.nooutliermean+options.dwarf]= 0.
fid_params= copy.copy(params)
if options.rotcurve.lower() == 'flat':
params= list(params)
params.insert(6,0.15)
params= numpy.array(params)
fid_rotcurve= options.rotcurve
options.rotcurve= 'powerlaw'
elif options.rotcurve.lower() == 'powerlaw':
params[6]= 0.15
fid_rotcurve= options.rotcurve
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#params[5-options.nooutliermean+options.dwarf]= fid_slope
#Undo changes
params= fid_params
options.rotcurve= fid_rotcurve
left, bottom, width, height= 0.1, 0.9-2.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{power\!\!-\!\!law\ rotation\ curve}, \beta = 0.15$',
top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Third is powerlaw with beta=-0.15
#fid_slope= params[5-options.nooutliermean+options.dwarf]
#params[5-options.nooutliermean+options.dwarf]= 0.
fid_params= copy.copy(params)
if options.rotcurve.lower() == 'flat':
params= list(params)
params.insert(6,-0.15)
params= numpy.array(params)
fid_rotcurve= options.rotcurve
options.rotcurve= 'powerlaw'
elif options.rotcurve.lower() == 'powerlaw':
params[6]= -0.15
fid_rotcurve= options.rotcurve
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#params[5-options.nooutliermean+options.dwarf]= fid_slope
#Undo changes
params= fid_params
options.rotcurve= fid_rotcurve
left, bottom, width, height= 0.1, 0.9-3.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{power\!\!-\!\!law\ rotation\ curve}, \beta = -0.15$',
top_right=True,size=14.)
pyplot.ylabel(r'$\bar{V}_{\mathrm{data}}-\bar{V}_{\mathrm{model}}$')
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Second is vc=250
#Load initial parameters from file
savefile= open(options.vo250file,'rb')
params= pickle.load(savefile)
savefile.close()
options.fixvo= 250.
options.fixro= 8.4
vo250_logl= numpy.sum(logl.logl(init=params,data=data,options=copy.copy(options)))
avg_plate_modelr84= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
#Load initial parameters from file
savefile= open(options.vo250r8file,'rb')
paramsr8= pickle.load(savefile)
savefile.close()
options.fixvo= 250.
options.fixro= 8.
vo250r8_logl= numpy.sum(logl.logl(init=paramsr8,data=data,options=copy.copy(options)))
avg_plate_modelr8= calc_model(paramsr8,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
options.fixro= None
options.fixvo= None
left, bottom, width, height= 0.1, 0.9-4.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,
zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_modelr84,
'x',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_modelr84,
yerr=siga_plate,marker='x',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_modelr8,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_modelr8,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
#top_right=True,size=14.)
bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ V_c(R_0) = 250\ \mathrm{km\ s}^{-1}, R_0 = %.1f\,\mathrm{kpc}, \Delta \chi^2 = %.0f\ (%.1f\sigma)$' % (8.,2.*(fid_logl-vo250r8_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-vo250r8_logl),10.))*numpy.sqrt(2.)),
top_right=True,size=13.)
bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ V_c(R_0) = 250\ \mathrm{km\ s}^{-1}, R_0 = %.1f\,\mathrm{kpc}, \Delta \chi^2 = %.0f\ (%.1f\sigma)$' % (8.4,2.*(fid_logl-vo250_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-vo250_logl),10.))*numpy.sqrt(2.)),
bottom_right=True,size=13.)
bovy_plot.bovy_plot([40.],[-15.],'kx',overplot=True)
bovy_plot.bovy_plot([40.],[12.],'ko',overplot=True)
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
thisax.xaxis.set_major_formatter(nullfmt)
#pyplot.xlabel(r'$\mathrm{Galactic\ longitude}\ [\mathrm{deg}]$')
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#Fourth = vpec=SBD10
#Load sbd fit
#Load initial parameters from file
savefile= open(options.sbdfile,'rb')
params= pickle.load(savefile)
savefile.close()
options.sbdvpec= True
options.fitvpec= False
sbd_logl= numpy.nansum(logl.logl(init=params,data=data,options=copy.copy(options)))
avg_plate_model= calc_model(params,options,data,
logpiso,logpisodwarf,
df,nlocs,locations,iso)
left, bottom, width, height= 0.1, 0.9-5.*dx, 0.8, dx
thisax= pyplot.axes([left,bottom,width,height])
allaxes.append(thisax)
fig.sca(thisax)
bovy_plot.bovy_plot([0.,360.],[0.,0.],'-',color='0.5',overplot=True,zorder=-1)
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model_fid,
'o',overplot=True,color='0.6')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model_fid,
yerr=siga_plate,marker='o',color='0.6',
linestyle='none',elinestyle='-')
bovy_plot.bovy_plot(l_plate,
avg_plate-avg_plate_model,
'o',overplot=True,color='k')
pyplot.errorbar(l_plate,avg_plate-avg_plate_model,
yerr=siga_plate,marker='o',color='k',
linestyle='none',elinestyle='-')
bovy_plot.bovy_text(r'$\mathrm{best\!\!-\!\!fit}\ \vec{V}_\odot = \vec{V}_c(R_0) + \mathrm{SBD10}, \Delta \chi^2 = %.0f$' % (2.*(fid_logl-sbd_logl)),#,special.erfinv(stats.chi2.cdf(2.*(fid_logl-sbd_logl),10.))*numpy.sqrt(2.)),
top_right=True,size=14.)
#pyplot.ylabel(r'$\langle V_{\mathrm{los}}\rangle_{\mathrm{data}}-\langle V_{\mathrm{los}}\rangle_{\mathrm{model}}$')
thisax.set_ylim(-19.5,19.5)
pyplot.xlim(0.,360.)
bovy_plot._add_ticks()
#thisax.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)
print fid_logl, sbd_logl, 2.*(fid_logl-sbd_logl),special.erfinv(stats.chi2.cdf(2.*(fid_logl-sbd_logl),10.))*numpy.sqrt(2.)
return None
