def plotMass(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=True)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=True)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
else:
tightbinned= binned
#Savefile
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
mass= pickle.load(savefile)
ii= pickle.load(savefile)
jj= pickle.load(savefile)
savefile.close()
else:
mass= []
ii, jj= 0, 0
#parameters
if os.path.exists(args[1]):#Load initial
savefile= open(args[1],'rb')
fits= pickle.load(savefile)
savefile.close()
else:
print "Error: must provide parameters of best fits"
print "Returning ..."
return None
#Mass uncertainties are in savefile3
if len(args) > 2 and os.path.exists(args[2]):
savefile= open(args[2],'rb')
masssamples= pickle.load(savefile)
savefile.close()
masserrors= True
else:
masssamples= None
masserrors= False
if len(args) > 3 and os.path.exists(args[3]): #Load savefile
savefile= open(args[3],'rb')
denssamples= pickle.load(savefile)
savefile.close()
denserrors= True
else:
denssamples= None
denserrors= False
if len(args) > 4 and os.path.exists(args[4]):#Load savefile
savefile= open(args[4],'rb')
velfits= pickle.load(savefile)
savefile.close()
velfitsLoaded= True
else:
velfitsLoaded= False
if len(args) > 5 and os.path.exists(args[5]):
savefile= open(args[5],'rb')
velsamples= pickle.load(savefile)
savefile.close()
velerrors= True
else:
velsamples= None
velerrors= False
#Now plot
#Run through the pixels and gather
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
plotthis= []
else:
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))
if denserrors: errors= []
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
if afeindx+fehindx*binned.npixafe() >= len(fits):
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
thismass= mass[afeindx+fehindx*binned.npixafe()]
if masserrors:
thismasssamples= masssamples[afeindx+fehindx*binned.npixafe()]
else:
thismasssamples= None
thisfit= fits[afeindx+fehindx*binned.npixafe()]
if denserrors:
thisdenssamples= denssamples[afeindx+fehindx*binned.npixafe()]
else:
thisdenssamples= None
if velfitsLoaded:
thisvelfit= velfits[afeindx+fehindx*binned.npixafe()]
if velerrors:
thisvelsamples= velsamples[afeindx+fehindx*binned.npixafe()]
if thisfit is None:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
if len(data) < options.minndata:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
if options.type == 'mass':
if not options.distzmin is None or not options.distzmax is None:
if options.distzmin is None and not options.distzmax is None:
thismass*= (1.-numpy.exp(-options.distzmax/numpy.exp(thisfit[0])/1000.))
elif not options.distzmin is None and options.distzmax is None:
thismass*= numpy.exp(-options.distzmin/numpy.exp(thisfit[0])/1000.)
else:
thismass*= (numpy.exp(-options.distzmin/numpy.exp(thisfit[0])/1000.)-numpy.exp(-options.distzmax/numpy.exp(thisfit[0])/1000.))
if options.logmass:
plotthis[ii,jj]= numpy.log10(thismass/10**6.)
else:
plotthis[ii,jj]= thismass/10**6.
elif options.type == 'nstars':
if options.logmass:
plotthis[ii,jj]= numpy.log10(len(data))
else:
plotthis[ii,jj]= len(data)
elif options.model.lower() == 'hwr':
if options.type == 'hz':
plotthis[ii,jj]= numpy.exp(thisfit[0])*1000.
elif options.type == 'hr':
plotthis[ii,jj]= numpy.exp(thisfit[1])
elif options.type.lower() == 'afe' \
or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
plotthis.append([tightbinned.feh(ii),
tightbinned.afe(jj),
numpy.exp(thisfit[0])*1000.,
numpy.exp(thisfit[1]),
len(data),
thismass/10.**6.,
thismasssamples])
if denserrors:
theseerrors= []
thesesamples= denssamples[afeindx+fehindx*binned.npixafe()]
if options.model.lower() == 'hwr':
for kk in [0,1]:
xs= numpy.array([s[kk] for s in thesesamples])
theseerrors.append(0.5*(-numpy.exp(numpy.mean(xs)-numpy.std(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
errors.append(theseerrors)
if velfitsLoaded:
if options.velmodel.lower() == 'hwr':
plotthis[-1].extend([numpy.exp(thisvelfit[1]),
numpy.exp(thisvelfit[4]),
thisvelfit[2],
thisvelfit[3]])
if velerrors:
theseerrors= []
thesesamples= velsamples[afeindx+fehindx*binned.npixafe()]
for kk in [1,4]:
xs= numpy.array([s[kk] for s in thesesamples])
theseerrors.append(0.5*(numpy.exp(numpy.mean(xs))-numpy.exp(numpy.mean(xs)-numpy.std(xs))-numpy.exp(numpy.mean(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
xs= numpy.array([s[4] for s in thesesamples])
theseerrors.append(0.5*(numpy.exp(-numpy.mean(xs))-numpy.exp(numpy.mean(-xs)-numpy.std(-xs))-numpy.exp(numpy.mean(-xs))+numpy.exp(numpy.mean(-xs)+numpy.std(-xs))))
errors[-1].extend(theseerrors)
#Set up plot
#print numpy.nanmin(plotthis), numpy.nanmax(plotthis)
if options.type == 'mass':
if options.logmass:
vmin, vmax= numpy.log10(0.01), numpy.log10(2.)
zlabel=r'$\log_{10} \Sigma(R_0)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
else:
vmin, vmax= 0.,1.
zlabel=r'$\Sigma(R_0)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
if not options.distzmin is None or not options.distzmax is None:
vmin, vmax= None, None
title=r'$\mathrm{mass\ weighted}$'
elif options.type == 'nstars':
if options.logmass:
vmin, vmax= 2., 3.
zlabel=r'$\log_{10} \mathrm{raw\ number\ of\ G}$-$\mathrm{type\ dwarfs}$'
else:
vmin, vmax= 100.,1000.
zlabel=r'$\mathrm{raw\ number\ of\ G}$-$\mathrm{type\ dwarfs}$'
title= r'$\mathrm{raw\ sample\ counts}$'
elif options.type == 'afe':
vmin, vmax= 0.0,.5
zlabel=r'$[\alpha/\mathrm{Fe}]$'
elif options.type == 'feh':
vmin, vmax= -1.5,0.
zlabel=r'$[\mathrm{Fe/H}]$'
elif options.type == 'fehafe':
vmin, vmax= -.7,.7
zlabel=r'$[\mathrm{Fe/H}]-[\mathrm{Fe/H}]_{1/2}([\alpha/\mathrm{Fe}])$'
elif options.type == 'afefeh':
vmin, vmax= -.15,.15
zlabel=r'$[\alpha/\mathrm{Fe}]-[\alpha/\mathrm{Fe}]_{1/2}([\mathrm{Fe/H}])$'
if options.tighten:
xrange=[-1.6,0.5]
yrange=[-0.05,0.55]
else:
xrange=[-2.,0.6]
yrange=[-0.1,0.6]
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
bovy_plot.bovy_print(fig_height=3.87,fig_width=5.)
#Gather hR and hz
sz_err, sz, hz_err, hr_err, mass_err, mass, hz, hr,afe, feh, ndata= [], [], [], [], [], [], [], [], [], [], []
for ii in range(len(plotthis)):
if denserrors:
hz_err.append(errors[ii][0]*1000.)
hr_err.append(errors[ii][1])
mass.append(plotthis[ii][5])
hz.append(plotthis[ii][2])
hr.append(plotthis[ii][3])
afe.append(plotthis[ii][1])
feh.append(plotthis[ii][0])
ndata.append(plotthis[ii][4])
if velfitsLoaded:
sz.append(plotthis[ii][7])
if velerrors:
sz_err.append(errors[ii][2])
if masserrors:
mass_err.append(numpy.std(numpy.array(plotthis[ii][6])/10.**6.))
"""
if options.logmass:
mass_err.append(numpy.std(numpy.log10(numpy.array(plotthis[ii][6])/10.**6.)))
else:
mass_err.append(numpy.std(numpy.array(plotthis[ii][6])/10.**6.))
"""
if denserrors:
hz_err= numpy.array(hz_err)
hr_err= numpy.array(hr_err)
if velfitsLoaded:
sz= numpy.array(sz)
if velerrors:
sz_err= numpy.array(sz_err)
mass= numpy.array(mass)
if masserrors:
mass_err= numpy.array(mass_err)
hz= numpy.array(hz)
hr= numpy.array(hr)
afe= numpy.array(afe)
feh= numpy.array(feh)
ndata= numpy.array(ndata)
#Process ndata
ndata= ndata**.5
ndata= ndata/numpy.median(ndata)*35.
#ndata= numpy.log(ndata)/numpy.log(numpy.median(ndata))
#ndata= (ndata-numpy.amin(ndata))/(numpy.amax(ndata)-numpy.amin(ndata))*25+12.
if options.type.lower() == 'afe':
plotc= afe
elif options.type.lower() == 'feh':
plotc= feh
elif options.type.lower() == 'afefeh':
#Go through the bins to determine whether feh is high or low for this alpha
plotc= numpy.zeros(len(afe))
for ii in range(tightbinned.npixfeh()):
fehbin= ii
data= tightbinned.data[(tightbinned.data.feh > tightbinned.fehedges[fehbin])\
*(tightbinned.data.feh <= tightbinned.fehedges[fehbin+1])]
medianafe= numpy.median(data.afe)
for jj in range(len(afe)):
if feh[jj] == tightbinned.feh(ii):
plotc[jj]= afe[jj]-medianafe
else:
#Go through the bins to determine whether feh is high or low for this alpha
plotc= numpy.zeros(len(feh))
for ii in range(tightbinned.npixafe()):
afebin= ii
data= tightbinned.data[(tightbinned.data.afe > tightbinned.afeedges[afebin])\
*(tightbinned.data.afe <= tightbinned.afeedges[afebin+1])]
medianfeh= numpy.median(data.feh)
for jj in range(len(feh)):
if afe[jj] == tightbinned.afe(ii):
plotc[jj]= feh[jj]-medianfeh
xrange= [150,1200]
if options.cumul:
#Print total surface mass and uncertainty
totmass= numpy.sum(mass)
if options.ploterrors:
toterr= numpy.sqrt(numpy.sum(mass_err**2.))
else:
toterr= 0.
print "Total surface-mass density: %4.1f +/- %4.2f" %(totmass,toterr)
ids= numpy.argsort(hz)
plotc= plotc[ids]
ndata= ndata[ids]
mass= mass[ids]
mass= numpy.cumsum(mass)
hz.sort()
ylabel=r'$\mathrm{cumulative}\ \Sigma(R_0)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
if options.logmass:
yrange= [0.01,30.]
else:
yrange= [-0.1,30.]
else:
if options.logmass:
yrange= [0.005*0.13/.07,10.*.13/.07]
else:
yrange= [-0.1,10.*.13/.07]
ylabel=r'$\Sigma_{R_0}(h_z)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
if not options.vstructure and not options.hzhr:
if options.hr:
ploth= hr
plotherr= hr_err
xlabel=r'$\mathrm{radial\ scale\ length\ [kpc]}$'
xrange= [1.2,4.]
bins= 11
elif options.sz:
ploth= sz**2.
plotherr= 2.*sz_err*sz
xlabel=r'$\sigma_z^2\ \mathrm{[km}^2\ \mathrm{s}^{-2}]$'
xrange= [12.**2.,50.**2.]
ylabel=r'$\Sigma_{R_0}(\sigma^2_z)\ [M_{\odot}\ \mathrm{pc}^{-2}]$'
bins= 9
else:
ploth= hz
plotherr= hz_err
xlabel=r'$\mathrm{vertical\ scale\ height}\ h_z\ \mathrm{[pc]}$'
xrange= [165,1200]
bins= 12
bovy_plot.bovy_plot(ploth,mass*.13/0.07,
s=ndata,c=plotc,
cmap='jet',
xlabel=xlabel,
ylabel=ylabel,
clabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
scatter=True,edgecolors='none',
colorbar=True,zorder=2,
semilogy=options.logmass)
if not options.cumul and masserrors and options.ploterrors:
colormap = cm.jet
for ii in range(len(hz)):
pyplot.errorbar(ploth[ii],mass[ii]*.13/0.07,
yerr=mass_err[ii]*.13/0.07,
color=colormap(_squeeze(plotc[ii],
numpy.amax([vmin,
numpy.amin(plotc)]),
numpy.amin([vmax,
numpy.amax(plotc)]))),
elinewidth=1.,capsize=3,zorder=0)
if not options.cumul and denserrors and options.ploterrors:
colormap = cm.jet
for ii in range(len(hz)):
pyplot.errorbar(ploth[ii],mass[ii]*.13/0.07,xerr=plotherr[ii],
color=colormap(_squeeze(plotc[ii],
numpy.amax([vmin,
numpy.amin(plotc)]),
numpy.amin([vmax,
numpy.amax(plotc)]))),
elinewidth=1.,capsize=3,zorder=0)
#Add binsize label
bovy_plot.bovy_text(r'$\mathrm{points\ use}\ \Delta [\mathrm{Fe/H}] = 0.1,$'+'\n'+r'$\Delta [\alpha/\mathrm{Fe}] = 0.05\ \mathrm{bins}$',
bottom_left=True)
#Overplot histogram
#ax2 = pyplot.twinx()
pyplot.hist(ploth,range=xrange,weights=mass*0.13/0.07,color='k',histtype='step',
bins=bins,lw=3.,zorder=10)
#Also XD?
if options.xd:
#Set up data
ydata= numpy.zeros((len(hz),1))
if options.sz:
ydata[:,0]= numpy.log(sz)
else:
ydata[:,0]= numpy.log(hz)
ycovar= numpy.zeros((len(hz),1))
if options.sz:
ycovar[:,0]= sz_err**2./sz**2.
else:
ycovar[:,0]= hz_err**2./hz**2.
#Set up initial conditions
xamp= numpy.ones(options.k)/float(options.k)
xmean= numpy.zeros((options.k,1))
for kk in range(options.k):
xmean[kk,:]= numpy.mean(ydata,axis=0)\
+numpy.random.normal()*numpy.std(ydata,axis=0)
xcovar= numpy.zeros((options.k,1,1))
for kk in range(options.k):
xcovar[kk,:,:]= numpy.cov(ydata.T)
#Run XD
print extreme_deconvolution(ydata,ycovar,xamp,xmean,xcovar,
weight=mass)*len(hz)
print xamp, xmean, xcovar
#Plot
xs= numpy.linspace(xrange[0],xrange[1],1001)
xdys= numpy.zeros(len(xs))
for kk in range(options.k):
xdys+= xamp[kk]/numpy.sqrt(2.*numpy.pi*xcovar[kk,0,0])\
*numpy.exp(-0.5*(numpy.log(xs)-xmean[kk,0])**2./xcovar[kk,0,0])
xdys/= xs
bovy_plot.bovy_plot(xs,xdys,'-',color='0.5',overplot=True)
# ax2.set_yscale('log')
# ax2.set_yticklabels('')
# if options.hr:
# pyplot.ylim(10**-2.,10.**0.)
# if options.sz:
# pyplot.ylim(10**-5.,10.**-2.5)
# else:
# pyplot.ylim(10**-5.5,10.**-1.5)
# pyplot.xlim(xrange[0],xrange[1])
ax= pyplot.gca()
if options.sz:
def my_formatter(x, pos):
"""s^2"""
xs= int(round(math.sqrt(x)))
return r'$%i^2$' % xs
major_formatter = FuncFormatter(my_formatter)
ax.xaxis.set_major_formatter(major_formatter)
xstep= ax.xaxis.get_majorticklocs()
xstep= xstep[1]-xstep[0]
ax.xaxis.set_minor_locator(MultipleLocator(xstep/5.))
elif options.hzhr:
#Make density plot in hR and hz
bovy_plot.scatterplot(hr,hz,'k,',
levels=[1.01],#HACK such that outliers aren't plotted
cmap='gist_yarg',
bins=11,
xrange=[1.,7.],
yrange=[150.,1200.],
ylabel=r'$\mathrm{vertical\ scale\ height\ [pc]}$',
xlabel=r'$\mathrm{radial\ scale\ length\ [kpc]}$',
onedhists=False,
weights=mass)
else:
#Make an illustrative plot of the vertical structure
nzs= 1001
zs= numpy.linspace(200.,3000.,nzs)
total= numpy.zeros(nzs)
for ii in range(len(hz)):
total+= mass[ii]/2./hz[ii]*numpy.exp(-zs/hz[ii])
bovy_plot.bovy_plot(zs,total,color='k',ls='-',lw=3.,
semilogy=True,
xrange=[0.,3200.],
yrange=[0.000001,0.02],
xlabel=r'$\mathrm{vertical\ height}\ Z$',
ylabel=r'$\rho_*(R=R_0,Z)\ [\mathrm{M}_\odot\ \mathrm{pc}^{-3}]$',
zorder=10)
if options.vbinned:
#Bin
mhist, edges= numpy.histogram(hz,range=xrange,
weights=mass,bins=10)
stotal= numpy.zeros(nzs)
for ii in range(len(mhist)):
hz= (edges[ii+1]+edges[ii])/2.
if options.vcumul:
if ii == 0.:
pstotal= numpy.zeros(nzs)+0.0000001
else:
pstotal= copy.copy(stotal)
stotal+= mhist[ii]/2./hz*numpy.exp(-zs/hz)
pyplot.fill_between(zs,stotal,pstotal,
color='%.6f' % (0.25+0.5/(len(mhist)-1)*ii))
else:
bovy_plot.bovy_plot([zs[0],zs[-1]],
1.*numpy.array([mhist[ii]/2./hz*numpy.exp(-zs[0]/hz),
mhist[ii]/2./hz*numpy.exp(-zs[-1]/hz)]),
color='0.5',ls='-',overplot=True,
zorder=0)
else:
colormap = cm.jet
for ii in range(len(hz)):
bovy_plot.bovy_plot([zs[0],zs[-1]],
100.*numpy.array([mass[ii]/2./hz[ii]*numpy.exp(-zs[0]/hz[ii]),
mass[ii]/2./hz[ii]*numpy.exp(-zs[-1]/hz[ii])]),
ls='-',overplot=True,alpha=0.5,
zorder=0,
color=colormap(_squeeze(plotc[ii],
numpy.amax([vmin,
numpy.amin(plotc)]),
numpy.amin([vmax,
numpy.amax(plotc)]))))
else:
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='gist_yarg',
interpolation='nearest',
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
zlabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
onedhists=True,
contours=False)
bovy_plot.bovy_text(title,top_right=True,fontsize=16)
if not options.distzmin is None or not options.distzmax is None:
if options.distzmin is None:
distlabel= r'$|Z| < %i\ \mathrm{pc}$' % int(options.distzmax)
elif options.distzmax is None:
distlabel= r'$|Z| > %i\ \mathrm{pc}$' % int(options.distzmin)
else:
distlabel= r'$%i < |Z| < %i\ \mathrm{pc}$' % (int(options.distzmin),int(options.distzmax))
bovy_plot.bovy_text(distlabel,bottom_left=True,fontsize=16)
bovy_plot.bovy_end_print(options.plotfile)
return None
def plot2d(options,args):
"""Make a plot of a quantity's best-fit vs. FeH and aFe"""
if options.sample.lower() == 'g':
npops= 62
elif options.sample.lower() == 'k':
npops= 54
if options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
savefile.close()
#First calculate the derivative properties
if not options.multi is None:
derivProps= multi.parallel_map((lambda x: calcAllDerivProps(x,options,args)),
range(npops),
numcores=numpy.amin([options.multi,
npops,
multiprocessing.cpu_count()]))
else:
derivProps= []
for ii in range(npops):
derivProps.append(calcAllDerivProps(ii,options,args))
xprop= options.subtype.split(',')[0]
yprop= options.subtype.split(',')[1]
if xprop == 'fracfaint' or yprop == 'fracfaint':
#Read the data
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=0.1,dafe=0.05)
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and (ii == 50 or ii == 57)) \
or (options.sample.lower() == 'k' and ii < 7):
continue
data= binned(fehs[ii],afes[ii])
indx= (data.dered_r > 17.8)
derivProps[ii]['fracfaint']= numpy.sum(indx)/float(len(indx))
derivProps[ii]['fracfaint_err']= 0.
if xprop == 'nfaint' or yprop == 'nfaint':
#Read the data
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=0.1,dafe=0.05)
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
data= binned(fehs[ii],afes[ii])
indx= (data.dered_r > 17.8)
derivProps[ii]['nfaint']= numpy.sum(indx)
derivProps[ii]['nfaint_err']= 0.
if xprop == 'hz' or yprop == 'hz':
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
hz, hzerr= monoAbundanceMW.hz(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k'),
err=True)
derivProps[ii]['hz']= hz
derivProps[ii]['hz_err']= hzerr
if xprop == 'hr' or yprop == 'hr':
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
hr, hrerr= monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k'),
err=True)
derivProps[ii]['hr']= hr
derivProps[ii]['hr_err']= hrerr
#Load into plotthis
plotthis_x= numpy.zeros(npops)+numpy.nan
plotthis_y= numpy.zeros(npops)+numpy.nan
plotthis_x_err= numpy.zeros(npops)+numpy.nan
plotthis_y_err= numpy.zeros(npops)+numpy.nan
for ii in range(npops):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k')) /8.) > -0.5 \
or (options.sample.lower() == 'g' and ii < 6) \
or (options.sample.lower() == 'k' and ii < 7):
continue
plotthis_x[ii]= derivProps[ii][xprop]
plotthis_y[ii]= derivProps[ii][yprop]
plotthis_x_err[ii]= derivProps[ii][xprop+'_err']
plotthis_y_err[ii]= derivProps[ii][yprop+'_err']
#Now plot
bovy_plot.bovy_print(fig_width=6.)
bovy_plot.bovy_plot(plotthis_x,plotthis_y,
s=25.,c=afes,
cmap='jet',
xlabel=labels[xprop],ylabel=labels[yprop],
clabel=r'$[\alpha/\mathrm{Fe}]$',
xrange=ranges[xprop],yrange=ranges[yprop],
vmin=0.,vmax=0.5,
scatter=True,edgecolors='none',
colorbar=True)
colormap = cm.jet
for ii in range(npops):
if numpy.isnan(plotthis_x[ii]): continue
pyplot.errorbar(plotthis_x[ii],
plotthis_y[ii],
xerr=plotthis_x_err[ii],
yerr=plotthis_y_err[ii],
color=colormap(_squeeze(afes[ii],
numpy.amax([numpy.amin(afes)]),
numpy.amin([numpy.amax(afes)]))),
elinewidth=1.,capsize=3,zorder=0)
bovy_plot.bovy_end_print(options.outfilename)
return None
def plotOneDiskVsTwoDisks(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=options.sn)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=options.sn)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=options.sn)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=options.sn)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-2.,fehmax=0.3,afemin=0.,afemax=0.45)
else:
tightbinned= binned
#Savefile1
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
onefits= pickle.load(savefile)
savefile.close()
if os.path.exists(args[1]):#Load savefile
savefile= open(args[1],'rb')
twofits= pickle.load(savefile)
savefile.close()
#Uncertainties are in savefile3 and 4
if len(args) > 3 and os.path.exists(args[3]):
savefile= open(args[3],'rb')
twosamples= pickle.load(savefile)
savefile.close()
twoerrors= True
else:
twosamples= None
twoerrors= False
if len(args) > 2 and os.path.exists(args[2]):
savefile= open(args[2],'rb')
onesamples= pickle.load(savefile)
savefile.close()
oneerrors= True
else:
onesamples= None
oneerrors= False
#If --mass is set to a filename, load the masses from that file
#and use those for the symbol size
if not options.mass is None and os.path.exists(options.mass):
savefile= open(options.mass,'rb')
mass= pickle.load(savefile)
savefile.close()
ndata= mass
masses= True
else:
masses= False
#Run through the pixels and gather
plotthis= []
errors= []
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
if afeindx+fehindx*binned.npixafe() >= len(onefits):
continue
thisonefit= onefits[afeindx+fehindx*binned.npixafe()]
thistwofit= twofits[afeindx+fehindx*binned.npixafe()]
if thisonefit is None:
continue
if len(data) < options.minndata:
continue
#print tightbinned.feh(ii), tightbinned.afe(jj), numpy.exp(thisonefit), numpy.exp(thistwofit)
#Which is the dominant two-exp component?
if thistwofit[4] > 0.5: twoIndx= 1
else: twoIndx= 0
if options.type == 'hz':
if masses:
plotthis.append([numpy.exp(thisonefit[0])*1000.,
numpy.exp(thistwofit[twoIndx])*1000.,
mass[afeindx+fehindx*binned.npixafe()]])
else:
plotthis.append([numpy.exp(thisonefit[0])*1000.,
numpy.exp(thistwofit[twoIndx])*1000.,
len(data)])
#Discrepant point
if plotthis[-1][1] < 250. and plotthis[-1][0] > 500.:
print "strange point: ", tightbinned.feh(ii),tightbinned.afe(jj)
elif options.type == 'hr':
plotthis.append([numpy.exp(thisonefit[1]),numpy.exp(thistwofit[twoIndx+2]),len(data)])
theseerrors= []
if oneerrors:
theseonesamples= onesamples[afeindx+fehindx*binned.npixafe()]
if options.type == 'hz':
xs= numpy.array([s[0] for s in theseonesamples])
theseerrors.append(500.*(-numpy.exp(numpy.mean(xs)-numpy.std(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
elif options.type == 'hr':
xs= numpy.array([s[1] for s in theseonesamples])
theseerrors.append(0.5*(-numpy.exp(numpy.mean(xs)-numpy.std(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
if twoerrors:
thesetwosamples= twosamples[afeindx+fehindx*binned.npixafe()]
if options.type == 'hz':
xs= numpy.array([s[twoIndx] for s in thesetwosamples])
theseerrors.append(500.*(-numpy.exp(numpy.mean(xs)-numpy.std(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
elif options.type == 'hr':
xs= numpy.array([s[2+twoIndx] for s in thesetwosamples])
theseerrors.append(0.5*(-numpy.exp(numpy.mean(xs)-numpy.std(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
errors.append(theseerrors)
x, y, ndata= [], [], []
if oneerrors: x_err= []
if twoerrors: y_err= []
for ii in range(len(plotthis)):
x.append(plotthis[ii][0])
y.append(plotthis[ii][1])
ndata.append(plotthis[ii][2])
if oneerrors: x_err.append(errors[ii][0])
if twoerrors: y_err.append(errors[ii][1])
x= numpy.array(x)
y= numpy.array(y)
if oneerrors: x_err= numpy.array(x_err)
if twoerrors: y_err= numpy.array(y_err)
ndata= numpy.array(ndata)
#Process ndata
if not masses:
ndata= ndata**.5
ndata= ndata/numpy.median(ndata)*35.
ndata= 20
else:
ndata= _squeeze(ndata,numpy.amin(ndata),numpy.amax(ndata))
ndata= ndata*200.+10.
#Now plot
if options.type == 'hz':
xrange= [150,1200]
xlabel=r'$\mathrm{single-exponential\ scale\ height}$'
ylabel=r'$\mathrm{two-exponentials\ scale\ height}$'
elif options.type == 'hr':
xrange= [1.2,5.]
xlabel=r'$\mathrm{single-exponential\ scale\ length}$'
ylabel=r'$\mathrm{two-exponentials\ scale\ length}$'
yrange=xrange
bovy_plot.bovy_print()
bovy_plot.bovy_plot(x,y,color='k',
s=ndata,
ylabel=ylabel,
xlabel=xlabel,
xrange=xrange,yrange=yrange,
scatter=True,edgecolors='none',
colorbar=False,zorder=2)
bovy_plot.bovy_plot([xrange[0],xrange[1]],[xrange[0],xrange[1]],color='0.5',ls='--',overplot=True)
if oneerrors:
#Overplot errors
for ii in range(len(x)):
# if (options.type == 'hr' and x[ii] < 5.) or options.type == 'hz':
pyplot.errorbar(x[ii],y[ii],xerr=x_err[ii],color='k',
elinewidth=1.,capsize=3,zorder=0)
if twoerrors:
#Overplot errors
for ii in range(len(x)):
# if (options.type == 'hr' and x[ii] < 5.) or options.type == 'hz':
pyplot.errorbar(x[ii],y[ii],yerr=y_err[ii],color='k',
elinewidth=1.,capsize=3,zorder=0)
bovy_plot.bovy_end_print(options.plotfile)
return None
def plot_DFRotcurves(options,args):
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
else:
tightbinned= binned
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
counter= 0
abindx= numpy.zeros((len(binned.fehedges)-1,len(binned.afeedges)-1),
dtype='int')
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))
abindx[ii,jj]= counter
counter+= 1
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
#Load each solutions
sols= []
savename= args[0]
initname= options.init
for ii in range(nabundancebins):
spl= savename.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
savefilename= newname
#Read savefile
try:
savefile= open(savefilename,'rb')
except IOError:
print "WARNING: MISSING ABUNDANCE BIN"
sols.append(None)
else:
sols.append(pickle.load(savefile))
savefile.close()
#Load samples as well
if options.mcsample:
#Do the same for init
spl= initname.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
options.init= newname
mapfehs= monoAbundanceMW.fehs()
mapafes= monoAbundanceMW.afes()
#Now plot
#Run through the pixels and plot rotation curves
if options.type == 'afe':
vmin, vmax= 0.0,.5
zlabel=r'$[\alpha/\mathrm{Fe}]$'
elif options.type == 'feh':
vmin, vmax= -1.6,0.4
zlabel=r'$[\mathrm{Fe/H}]$'
overplot= False
if options.subtype is None or options.subtype.lower() != 'median':
bovy_plot.bovy_print(fig_height=3.87,fig_width=5.)
medianvc= []
medianvc_disk= []
medianvc_halo= []
medianvc_bulge= []
medianrs= numpy.linspace(0.001,2.,1001)
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
if len(data) < options.minndata:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
#Find abundance indx
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
solindx= abindx[fehindx,afeindx]
monoabindx= numpy.argmin((tightbinned.feh(ii)-mapfehs)**2./0.01 \
+(tightbinned.afe(jj)-mapafes)**2./0.0025)
if sols[solindx] is None:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
s= get_potparams(sols[solindx],options,1)
#Setup potential
pot= setup_potential(sols[solindx],options,1,returnrawpot=True)
vo= get_vo(sols[solindx],options,1)
ro= get_ro(sols[solindx],options)
if options.type.lower() == 'afe':
plotc= tightbinned.afe(jj)
elif options.type.lower() == 'feh':
plotc= tightbinned.feh(jj)
colormap = cm.jet
if options.subtype is None or options.subtype.lower() == 'full':
potential.plotRotcurve(pot,Rrange=[0.001,2.],
overplot=overplot,ls='-',
color=colormap(_squeeze(plotc,vmin,vmax)),
yrange=[0.,1.29],
ylabel= r"$V_c(R)/V_c(R_0)$",
zorder=int(numpy.random.uniform()*100))
elif options.subtype.lower() == 'disk':
if 'mwpotential' in options.potential.lower():
diskpot= pot[0]
elif 'wgas' in options.potential.lower():
diskpot= [pot[0],pot[-1]]
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
diskpot= pot[0]
elif 'dpdisk' in options.potential.lower():
diskpot= pot[0]
potential.plotRotcurve(diskpot,Rrange=[0.001,2.],
yrange=[0.,1.29],
overplot=overplot,ls='-',
color=colormap(_squeeze(plotc,vmin,vmax)),
ylabel= r"$V_c(R)/V_c(R_0)$",
zorder=int(numpy.random.uniform()*100))
elif options.subtype.lower() == 'halo':
halopot= pot[1]
potential.plotRotcurve(halopot,Rrange=[0.001,2.],
overplot=overplot,ls='-',
yrange=[0.,1.29],
ylabel= r"$V_c(R)/V_c(R_0)$",
color=colormap(_squeeze(plotc,vmin,vmax)),
zorder=int(numpy.random.uniform()*100))
elif options.subtype.lower() == 'bulge':
bulgepot= pot[2]
potential.plotRotcurve(bulgepot,Rrange=[0.001,2.],
overplot=overplot,ls='-',
ylabel= r"$V_c(R)/V_c(R_0)$",
yrange=[0.,1.29],
color=colormap(_squeeze(plotc,vmin,vmax)),
zorder=int(numpy.random.uniform()*100))
elif options.subtype.lower() == 'median':
if 'mwpotential' in options.potential.lower():
diskpot= pot[0]
elif 'wgas' in options.potential.lower():
diskpot= [pot[0],pot[-1]]
elif options.potential.lower() == 'mpdiskplhalofixbulgeflat':
diskpot= pot[0]
elif 'dpdisk' in options.potential.lower():
diskpot= pot[0]
halopot= pot[1]
bulgepot= pot[2]
vo= get_vo(sols[solindx],options,1)
medianvc.append(vo*potential.calcRotcurve(pot,medianrs))
medianvc_disk.append(vo*potential.calcRotcurve(diskpot,medianrs))
medianvc_halo.append(vo*potential.calcRotcurve(halopot,medianrs))
medianvc_bulge.append(vo*potential.calcRotcurve(bulgepot,medianrs))
overplot=True
if options.subtype is None or options.subtype.lower() != 'median':
#Add colorbar
m = cm.ScalarMappable(cmap=cm.jet)
m.set_array(plotc)
m.set_clim(vmin=vmin,vmax=vmax)
cbar= pyplot.colorbar(m,fraction=0.15)
cbar.set_clim((vmin,vmax))
cbar.set_label(zlabel)
if options.subtype is None:
pass
elif options.subtype.lower() == 'disk':
bovy_plot.bovy_text(r'$\mathrm{Disk}$',bottom_right=True,size=_legendsize)
elif options.subtype.lower() == 'halo':
bovy_plot.bovy_text(r'$\mathrm{Halo}$',bottom_right=True,size=_legendsize)
elif options.subtype.lower() == 'bulge':
bovy_plot.bovy_text(r'$\mathrm{Bulge}$',bottom_right=True,size=_legendsize)
else:
#Calc medians
nbins= len(medianvc)
vc= numpy.empty((len(medianrs),nbins))
vc_disk= numpy.empty((len(medianrs),nbins))
vc_bulge= numpy.empty((len(medianrs),nbins))
vc_halo= numpy.empty((len(medianrs),nbins))
for ii in range(nbins):
vc[:,ii]= medianvc[ii]
vc_disk[:,ii]= medianvc_disk[ii]
vc_halo[:,ii]= medianvc_halo[ii]
vc_bulge[:,ii]= medianvc_bulge[ii]
vc= numpy.median(vc,axis=1)*_REFV0
#vcro= vc[numpy.argmin(numpy.fabs(medianrs-1.))]
#vc/= vcro
vc_disk= numpy.median(vc_disk,axis=1)*_REFV0
vc_halo= numpy.median(vc_halo,axis=1)*_REFV0
vc_bulge= numpy.median(vc_bulge,axis=1)*_REFV0
bovy_plot.bovy_print(fig_height=3.87,fig_width=5.)
bovy_plot.bovy_plot(medianrs,vc,'k-',
xlabel=r"$R/R_0$",
ylabel= r"$V_c(R)\ [\mathrm{km\,s}^{-1}]$",
yrange=[0.,1.29*_REFV0],
xrange=[0.,2.])
linedisk= bovy_plot.bovy_plot(medianrs,vc_disk,'k--',
overplot=True)
linedisk[0].set_dashes([5,5])
bovy_plot.bovy_plot(medianrs,vc_halo,'k:',
overplot=True)
linebulge= bovy_plot.bovy_plot(medianrs,vc_bulge,'k--',
overplot=True)
linebulge[0].set_dashes([10,4])
bovy_plot.bovy_text(1.95,120.,r'$\mathrm{Disk}$',size=_legendsize,
horizontalalignment='right')
bovy_plot.bovy_text(1.95,193.,r'$\mathrm{Halo}$',size=_legendsize,
horizontalalignment='right')
bovy_plot.bovy_text(1.95,28.,r'$\mathrm{Bulge}$',size=_legendsize,
horizontalalignment='right')
bovy_plot.bovy_end_print(options.outfilename)
def plotTilt(options,args):
if options.sample.lower() == 'g':
if options.select.lower() == 'program':
raw= read_gdwarfs(_GDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_gdwarfs(logg=True,ebv=True,sn=True)
elif options.sample.lower() == 'k':
if options.select.lower() == 'program':
raw= read_kdwarfs(_KDWARFFILE,logg=True,ebv=True,sn=True)
else:
raw= read_kdwarfs(logg=True,ebv=True,sn=True)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
if options.tighten:
tightbinned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
else:
tightbinned= binned
#Savefile1
if os.path.exists(args[0]):#Load savefile
savefile= open(args[0],'rb')
velfits= pickle.load(savefile)
savefile.close()
#Uncertainties are in savefile2
if len(args) > 2 and os.path.exists(args[2]):
savefile= open(args[2],'rb')
velsamples= pickle.load(savefile)
savefile.close()
velerrors= True
else:
velsamples= None
velerrors= False
#Now plot
#Run through the pixels and gather
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'zfunc' \
or options.type.lower() == 'afefeh':
plotthis= []
errors= []
else:
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))
for ii in range(tightbinned.npixfeh()):
for jj in range(tightbinned.npixafe()):
data= binned(tightbinned.feh(ii),tightbinned.afe(jj))
fehindx= binned.fehindx(tightbinned.feh(ii))#Map onto regular binning
afeindx= binned.afeindx(tightbinned.afe(jj))
if afeindx+fehindx*binned.npixafe() >= len(velfits) \
or afeindx+fehindx*binned.npixafe() >= len(velfits):
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'zfunc' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
thisvelfit= velfits[afeindx+fehindx*binned.npixafe()]
if thisvelfit is None:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'zfunc' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
if len(data) < options.minndata:
if options.type.lower() == 'afe' or options.type.lower() == 'feh' or options.type.lower() == 'fehafe' \
or options.type.lower() == 'zfunc' \
or options.type.lower() == 'afefeh':
continue
else:
plotthis[ii,jj]= numpy.nan
continue
if options.type == 'tilt':
plotthis[ii,jj]= numpy.arctan(thisvelfit[9])/_DEGTORAD
elif options.type == 'tiltslope':
plotthis[ii,jj]= thisvelfit[10]
elif options.type == 'srz':
plotthis[ii,jj]= (numpy.exp(2.*thisvelfit[5])-numpy.exp(2.*thisvelfit[1]))*thisvelfit[9]/(1.-thisvelfit[9]**2.)
elif options.type.lower() == 'afe' \
or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'zfunc' \
or options.type.lower() == 'afefeh':
thisplot=[tightbinned.feh(ii),
tightbinned.afe(jj),
len(data)]
thisplot.extend(thisvelfit)
#Als find min and max z for this data bin, and median
if options.subtype.lower() == 'rfunc':
zsorted= sorted(numpy.sqrt((8.-data.xc)**2.+data.yc**2.))
else:
zsorted= sorted(numpy.fabs(data.zc+_ZSUN))
zmin= zsorted[int(numpy.ceil(0.16*len(zsorted)))]
zmax= zsorted[int(numpy.floor(0.84*len(zsorted)))]
zmin= zsorted[int(numpy.ceil(0.025*len(zsorted)))]
zmax= zsorted[int(numpy.floor(0.975*len(zsorted)))]
if options.pivotmean:
thisplot.extend([zmin,zmax,numpy.mean(numpy.fabs(data.zc+_ZSUN))])
else:
thisplot.extend([zmin,zmax,numpy.median(numpy.fabs(data.zc+_ZSUN))])
plotthis.append(thisplot)
#Errors
if velerrors:
theseerrors= []
thesesamples= velsamples[afeindx+fehindx*binned.npixafe()]
for kk in [1,4,5,8]:
xs= numpy.array([s[kk] for s in thesesamples])
theseerrors.append(0.5*(numpy.exp(numpy.mean(xs))-numpy.exp(numpy.mean(xs)-numpy.std(xs))-numpy.exp(numpy.mean(xs))+numpy.exp(numpy.mean(xs)+numpy.std(xs))))
for kk in [0,2,3,6,7,9,10]: #,11]:
xs= numpy.array([s[kk] for s in thesesamples])
theseerrors.append(numpy.std(xs))
errors.append(theseerrors)
#Set up plot
#print numpy.nanmin(plotthis), numpy.nanmax(plotthis)
if options.type == 'tilt':
print numpy.nanmin(plotthis), numpy.nanmax(plotthis)
vmin, vmax= -20.,20.
zlabel= r'$\mathrm{tilt\ at}\ Z = 0\ [\mathrm{degree}]$'
elif options.type == 'tiltslope':
print numpy.nanmin(plotthis), numpy.nanmax(plotthis)
vmin, vmax= -2.,2.
zlabel= r'$\frac{\mathrm{d}\tan \mathrm{tilt}}{\mathrm{d} (Z/R)}$'
elif options.type == 'srz':
vmin, vmax= -100.,100.
zlabel= r'$\sigma^2_{RZ}\ [\mathrm{km}^2\, \mathrm{s}^{-2}]$'
elif options.type == 'afe':
vmin, vmax= 0.0,.5
zlabel=r'$[\alpha/\mathrm{Fe}]$'
elif options.type == 'feh':
vmin, vmax= -1.6,0.4
zlabel=r'$[\mathrm{Fe/H}]$'
if options.tighten:
xrange=[-1.6,0.5]
yrange=[-0.05,0.55]
else:
xrange=[-2.,0.6]
yrange=[-0.1,0.6]
if options.type.lower() == 'afe' or options.type.lower() == 'feh' \
or options.type.lower() == 'fehafe' \
or options.type.lower() == 'afefeh':
bovy_plot.bovy_print(fig_height=3.87,fig_width=5.)
#Gather everything
zmin, zmax, pivot, tilt, tiltp1, tiltp2, afe, feh, ndata= [], [], [], [], [], [], [], [], []
tilt_err, tiltp1_err, tiltp2_err= [], [], []
for ii in range(len(plotthis)):
if velerrors:
tilt_err.append(errors[ii][9])
tiltp1_err.append(errors[ii][10])
# tiltp2_err.append(errors[ii][11])
tilt.append(plotthis[ii][11])
tiltp1.append(plotthis[ii][12])
# tiltp2.append(plotthis[ii][13])
afe.append(plotthis[ii][1])
feh.append(plotthis[ii][0])
ndata.append(plotthis[ii][4])
zmin.append(plotthis[ii][13])
zmax.append(plotthis[ii][14])
pivot.append(plotthis[ii][15])
indxarray= numpy.array([True for ii in range(len(plotthis))],dtype='bool')
tilt= numpy.array(tilt)[indxarray]
tiltp1= numpy.array(tiltp1)[indxarray]
# tiltp2= numpy.array(tiltp2)[indxarray]
pivot= numpy.array(pivot)[indxarray]
zmin= numpy.array(zmin)[indxarray]
zmax= numpy.array(zmax)[indxarray]
if velerrors:
tilt_err= numpy.array(tilt_err)[indxarray]
tiltp1_err= numpy.array(tiltp1_err)[indxarray]
# tiltp2_err= numpy.array(tiltp2_err)[indxarray]
afe= numpy.array(afe)[indxarray]
feh= numpy.array(feh)[indxarray]
ndata= numpy.array(ndata)[indxarray]
#Process ndata
ndata= ndata**.5
ndata= ndata/numpy.median(ndata)*35.
#ndata= numpy.log(ndata)/numpy.log(numpy.median(ndata))
#ndata= (ndata-numpy.amin(ndata))/(numpy.amax(ndata)-numpy.amin(ndata))*25+12.
if options.type.lower() == 'afe':
plotc= afe
elif options.type.lower() == 'feh':
plotc= feh
if options.subtype.lower() == 'zfunc':
from selectFigs import _squeeze
colormap = cm.jet
#Set up plot
yrange= [-30.,30.]
ylabel=r'$\mathrm{tilt}(Z|R=8\,\mathrm{kpc})\ [\mathrm{degree}]$'
bovy_plot.bovy_plot([-100.,-100.],[100.,100.],'k,',
xrange=[0,2700],yrange=yrange,
xlabel=r'$|z|\ [\mathrm{pc}]$',
ylabel=ylabel)
#Calculate and plot all zfuncs
for ii in numpy.random.permutation(len(afe)):
if velerrors: #Don't plot if errors > 30%
if tilt_err[ii]/tilt[ii] > .2: continue
ds= numpy.linspace(zmin[ii]*1000.,zmax[ii]*1000.,1001)/8000.
thiszfunc= numpy.arctan(tilt[ii]+tiltp1[ii]*ds)/_DEGTORAD #+tiltp2[ii]*ds**2.
pyplot.plot(numpy.linspace(zmin[ii]*1000.,1000*zmax[ii],1001),
thiszfunc,'-',
color=colormap(_squeeze(plotc[ii],vmin,vmax)),
lw=ndata[ii]/15.)
if not options.nofatdots:
#Also plot pivot
pyplot.plot(1000.*pivot[ii],
numpy.arctan(tilt[ii]+tiltp1[ii]*pivot[ii]/8.\
+tiltp2[ii]*(pivot[ii]/8.)**2.)/_DEGTORAD,
'o',ms=8.,mec='none',
color=colormap(_squeeze(plotc[ii],vmin,vmax)))
#Add colorbar
m = cm.ScalarMappable(cmap=cm.jet)
m.set_array(plotc)
m.set_clim(vmin=vmin,vmax=vmax)
cbar= pyplot.colorbar(m,fraction=0.15)
cbar.set_clim((vmin,vmax))
cbar.set_label(zlabel)
else:
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
xlabel=r'$[\mathrm{Fe/H}]$',
ylabel=r'$[\alpha/\mathrm{Fe}]$',
zlabel=zlabel,
xrange=xrange,yrange=yrange,
vmin=vmin,vmax=vmax,
contours=False,
colorbar=True,shrink=0.78)
bovy_plot.bovy_text(r'$\mathrm{median} = %.2f \pm %.2f$' % (numpy.median(plotthis[numpy.isfinite(plotthis)]),
1.4826*numpy.median(numpy.fabs(plotthis[numpy.isfinite(plotthis)]-numpy.median(plotthis[numpy.isfinite(plotthis)])))),
bottom_left=True,size=14.)
bovy_plot.bovy_end_print(options.plotfile)
return None
