def plot_vr(plotfilename):
#Read the APOGEE-RC data and pixelate it
#APOGEE-RC
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Get velocity field
xmin, xmax= 5.5, 13.5
dx= 1.
pix= pixelize_sample.pixelXY(data,
xmin=xmin,xmax=xmax,
ymin=-dx/2.,ymax=dx/2.,
dx=dx,dy=dx)
# dx=_RCDX,dy=_RCDX)
vr= pix.plot(lambda x: dvlosgal(x),
returnz=True,justcalc=True)
vrunc= pix.plot(lambda x: dvlosgal(x),
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x)))/numpy.sqrt(len(x)),
returnz=True,justcalc=True)
sr= pix.plot(lambda x: dvlosgal(x),
func=lambda x: 1.4826*numpy.median(numpy.fabs(x-numpy.median(x))),
returnz=True,justcalc=True)
srunc= pix.plot(lambda x: dvlosgal(x),
func=disperror,
returnz=True,justcalc=True)
#print numpy.median(vr.flatten()[numpy.array([True,True,False,True,True,True,True,True,True],dtype='bool')])
print vr.flatten()
print vrunc.flatten()
print sr.flatten()
print srunc.flatten()
rs= numpy.arange(xmin+dx/2.,xmax-dx/2.+0.00001,dx)
print rs
bovy_plot.bovy_print()
srAxes= pyplot.axes([0.1,0.5,0.8,0.4])
vrAxes= pyplot.axes([0.1,0.1,0.8,0.4])
pyplot.sca(srAxes)
pyplot.errorbar(rs,sr.flatten(),yerr=srunc.flatten(),
marker='o',ls='none',ms=6.,color='k')
pyplot.xlim(0.,15.)
pyplot.ylim(9.5,49.)
#srAxes.set_yscale('log')
bovy_plot._add_ticks(yticks=False)
bovy_plot._add_axislabels(r'$ $',
r'$\sigma_R\,(\mathrm{km\,s}^{-1})$')
nullfmt = NullFormatter() # no labels
srAxes.xaxis.set_major_formatter(nullfmt)
pyplot.sca(vrAxes)
pyplot.errorbar(rs,vr.flatten(),yerr=vrunc.flatten(),
marker='o',ls='none',ms=6.,color='k')
pyplot.plot([0.,20.],numpy.median(vr.flatten())*numpy.ones(2),'k--')
bovy_plot._add_ticks()
pyplot.xlim(0.,15.)
pyplot.ylim(-14.,14.)
bovy_plot._add_axislabels(r'$R\,(\mathrm{kpc})$',
r'$\langle V_R\rangle\,(\mathrm{km\,s}^{-1})$')
bovy_plot.bovy_end_print(plotfilename)
return None
def illustrate_track(plotfilename1,plotfilename2,plotfilename3):
#Setup stream model
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,
0.88719443,-0.47713334,0.12019596])
sdf= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=True,nTrackChunks=_NTRACKCHUNKS,
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.))
#First calculate meanOmega and sigOmega
mOs= numpy.array([sdf.meanOmega(t,oned=True) for t in sdf._thetasTrack])
sOs= numpy.array([sdf.sigOmega(t) for t in sdf._thetasTrack])
mOs-= sdf._progenitor_Omega_along_dOmega
mOs*= -bovy_conversion.freq_in_Gyr(220.,8.)
sOs*= bovy_conversion.freq_in_Gyr(220.,8.)
progAngle= numpy.dot(sdf._progenitor_angle,sdf._dsigomeanProgDirection)
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,mOs,'ko',ms=8.,
xlabel=r'$\theta_\parallel$',
ylabel=r'$\Omega_\parallel\,(\mathrm{Gyr}^{-1})$',
xrange=[-0.2-1.14,1.6-1.14],
yrange=[22.05,22.55])
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,mOs,'k-',lw=1.5,overplot=True)
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,
mOs[0]*numpy.ones(len(sdf._thetasTrack))+0.03,
'ko',ls='--',dashes=(20,10),lw=1.5,overplot=True,
ms=6.)
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,mOs+2*sOs,'ko',ms=6.,mfc='none',
zorder=1,overplot=True)
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,mOs-2*sOs,'ko',ms=6.,mfc='none',
zorder=1,overplot=True)
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,mOs+2*sOs,'k-.',lw=1.5,
zorder=0,overplot=True)
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,mOs-2*sOs,'k-.',lw=1.5,
zorder=0,overplot=True)
bovy_plot.bovy_plot(sdf._thetasTrack+progAngle,sdf._progenitor_Omega_along_dOmega*bovy_conversion.freq_in_Gyr(220.,8.)*numpy.ones(len(sdf._thetasTrack)),
'k--',lw=1.5,overplot=True)
bovy_plot.bovy_plot((sdf._thetasTrack+progAngle)[0],(sdf._progenitor_Omega_along_dOmega*bovy_conversion.freq_in_Gyr(220.,8.)*numpy.ones(len(sdf._thetasTrack)))[0],
'ko',ms=6.,overplot=True)
bovy_plot.bovy_text(1.05+progAngle,22.475,r'$\mathrm{progenitor\ orbit}$',size=16.)
bovy_plot.bovy_text(progAngle+0.05,22.50,r'$\mathrm{current\ progenitor\ position}$',size=16.)
bovy_plot.bovy_plot([progAngle+0.05,progAngle],[22.50,sdf._progenitor_Omega_along_dOmega*bovy_conversion.freq_in_Gyr(220.,8.)],'k:',overplot=True)
bovy_plot.bovy_text(-1.2,22.35,r"$\mathrm{At\ the\ progenitor's}\ \theta_{\parallel}, \mathrm{we\ calculate\ an\ auxiliary\ orbit\ through}$"+'\n'+r"$(\mathbf{x}_a,\mathbf{v}_a) = (\mathbf{\Omega}_p+\Delta \mathbf{\Omega}^m,\boldsymbol{\theta}_p)\ \mathrm{using\ a\ linearized}\ (\mathbf{\Omega},\boldsymbol{\theta})\ \mathrm{to}\ (\mathbf{x},\mathbf{v}).$",size=16.)
yarcs= numpy.linspace(22.30,22.39,101)
bovy_plot.bovy_plot(sdf._thetasTrack[0]+progAngle-0.1*numpy.sqrt(1.-(yarcs-22.35)**2./0.05**2.),yarcs,'k:',
overplot=True)
bovy_plot.bovy_text(-1.3,22.07,r'$\mathrm{At\ a\ small\ number\ of\ points, we\ calculate}$'+'\n'+r'$\partial(\mathbf{\Omega},\boldsymbol{\theta})/\partial (\mathbf{x},\mathbf{v}), \mathrm{the\ mean\ stream\ track\ in}\ (\mathbf{\Omega},\boldsymbol{\theta})^\dagger,$'+'\n'+r'$\mathrm{and\ estimate\ the\ spread\ around\ the\ track}.$',size=16.)
bovy_plot.bovy_plot([-0.9,sdf._thetasTrack[1]+progAngle],
[22.185,mOs[1]+0.03],
'k:',overplot=True)
bovy_plot.bovy_plot([-0.9,progAngle+sdf._thetasTrack[1]],
[22.185,mOs[1]],
'k:',overplot=True)
bovy_plot.bovy_text(-0.18,22.265,r'$\mathrm{stream\ track\ +\ spread}$',
size=16.,
rotation=-20.)
bovy_plot.bovy_end_print(plotfilename1)
#Now plot Z,X
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
pyplot.figure()
sdf.plotTrack(d1='z',d2='x',interp=True,
color='k',spread=2,overplot=True,lw=1.5,
scaleToPhysical=True)
sdf.plotTrack(d1='z',d2='x',interp=False,marker='o',ms=8.,color='k',
overplot=True,ls='none',
scaleToPhysical=True)
sdf.plotProgenitor(d1='z',d2='x',color='k',
overplot=True,ls='--',lw=1.5,dashes=(20,10),
scaleToPhysical=True)
pyplot.plot(sdf._progenitor.z(sdf._trackts)*8.,
sdf._progenitor.x(sdf._trackts)*8.,marker='o',ms=6.,
ls='none',
color='k')
pyplot.xlim(8.,-3.)
pyplot.ylim(12.,15.5)
bovy_plot._add_ticks()
bovy_plot._add_axislabels(r'$Z\,(\mathrm{kpc})$',r'$X\,(\mathrm{kpc})$')
bovy_plot.bovy_text(0.,14.25,r'$\mathrm{auxiliary\ orbit}$',
size=16.,rotation=-20.)
bovy_plot.bovy_text(1.,13.78,r'$\mathrm{stream\ track\ +\ spread}$',
size=16.,rotation=-25.)
bovy_plot.bovy_text(7.5,14.2,r"$\mathrm{At\ these\ points, we\ calculate\ the\ stream\ position\ in}\ (\mathbf{x},\mathbf{v})\ \mathrm{from}$"+
'\n'+r"$\mathrm{the\ auxiliary's}\ (\mathbf{x}_a,\mathbf{v}_a) = (\mathbf{\Omega}_a,\boldsymbol{\theta}_a), \mathrm{the\ mean\ offset} (\Delta \mathbf{\Omega},\Delta \boldsymbol{\theta}),$"+'\n'+
r"$\mathrm{and}\ \left(\frac{\partial(\mathbf{\Omega},\boldsymbol{\theta})}{\partial (\mathbf{x},\mathbf{v})}\right)^{-1 \, \dagger}.$",
size=16.)
bovy_plot.bovy_plot([sdf._progenitor.z(sdf._trackts[1])*8.,4.5],
[sdf._progenitor.x(sdf._trackts[1])*8.,14.8],
'k:',overplot=True)
bovy_plot.bovy_text(5.6,12.4,r"$\mathrm{We\ interpolate\ the\ track\ between\ the}$"+'\n'+r"$\mathrm{calculated\ points\ and\ use\ slerp\ to}$"+'\n'+r"$\mathrm{interpolate\ the\ estimated\ 6D\ spread.}$",
size=16.)
bovy_plot.bovy_plot([3.,sdf._interpolatedObsTrackXY[500,2]*8.],
[13.3,sdf._interpolatedObsTrackXY[500,0]*8.],
'k:',overplot=True)
bovy_plot.bovy_end_print(plotfilename2)
#Finally plot l vs. d
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
pyplot.figure()
sdf.plotTrack(d1='ll',d2='dist',interp=True,
color='k',spread=2,overplot=True,lw=1.5)
sdf.plotTrack(d1='ll',d2='dist',interp=False,marker='o',ms=8.,color='k',
overplot=True,ls='none')
sdf.plotProgenitor(d1='ll',d2='dist',color='k',dashes=(20,10),
overplot=True,ls='--',lw=1.5)
pyplot.plot(sdf._progenitor.ll(sdf._trackts,
obs=[sdf._R0,0.,sdf._Zsun],ro=sdf._Rnorm),
sdf._progenitor.dist(sdf._trackts,
obs=[sdf._R0,0.,sdf._Zsun],ro=sdf._Rnorm),
marker='o',ms=6.,
ls='none',
color='k')
pyplot.xlim(157.,260.)
pyplot.ylim(7.4,15.5)
bovy_plot._add_ticks()
bovy_plot._add_axislabels(r'$\mathrm{Galactic\ longitude\, (deg)}$',
r'$\mathrm{distance\, (kpc)}$')
bovy_plot.bovy_text(165.,13.5,r"$\mathrm{Finally, the\ interpolated\ track\ in}\ (\mathbf{x},\mathbf{v})\ \mathrm{is}$"+'\n'+r"$\mathrm{converted\ to\ observable\ quantities\ (here}, l\ \mathrm{and}\ D).$",
size=16.)
bovy_plot.bovy_plot([230.,sdf._interpolatedObsTrackLB[850,0]],
[13.25,sdf._interpolatedObsTrackLB[850,2]],
'k:',overplot=True)
bovy_plot.bovy_text(170.,9.4,r"$\mathrm{The\ estimated\ spread\ is\ propagated}$"+'\n'+r"$\mathrm{at\ the\ points\ directly\ from}\ (\mathbf{\Omega},\boldsymbol{\theta})\ \mathrm{to}$"+'\n'+r"$(l,b,D,\ldots)\ \mathrm{and\ interpolated}$"+'\n'+r"$\mathrm{using\ slerp}.$",
size=16.)
bovy_plot.bovy_plot([195.,sdf._ObsTrackLB[1,0]],
[9.7,sdf._ObsTrackLB[1,2]],
'k:',overplot=True)
bovy_plot.bovy_end_print(plotfilename3)
return None
def plot_rckinematics(plotfilename,subsun=False):
#Set up 3 axes
bovy_plot.bovy_print(fig_width=8.,axes_labelsize=14)
axdx= 1./3.
#APOGEE-RC observations
tdy= (_RCYMAX-_RCYMIN+4.5)/(_RCXMAX-_RCXMIN+4.5)*axdx
obsAxes= pyplot.axes([0.1,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(obsAxes)
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Get velocity field
pixrc= pixelize_sample.pixelXY(data,
xmin=_RCXMIN-2.25,xmax=_RCXMAX+2.25,
ymin=_RCYMIN-2.25,ymax=_RCYMAX+2.25,
dx=_RCDX,dy=_RCDX)
if subsun:
vmin, vmax= 0., 250.
pixrc.plot(lambda x: vlosgal(x),
func=lambda x: numpy.fabs(numpy.median(x)),
zlabel=r'$|\mathrm{median}\ V^{\mathrm{GC}}_{\mathrm{los}}|\,(\mathrm{km\,s}^{-1})$',
vmin=vmin,vmax=vmax)
else:
vmin, vmax= -75., 75.
img= pixrc.plot('VHELIO_AVG',
vmin=vmin,vmax=vmax,overplot=True,
colorbar=False)
resv= pixrc.plot('VHELIO_AVG',
justcalc=True,returnz=True) #for later
bovy_plot.bovy_text(r'$\mathrm{typical\ uncertainty\!:}\ 3\,\mathrm{km\,s}^{-1}$',
bottom_left=True,size=8.25)
bovy_plot.bovy_text(r'$|Z| < 250\,\mathrm{pc}$',top_right=True,size=10.)
pyplot.annotate(r'$\mathrm{APOGEE\!-\!RC\ data}$',
(0.5,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.)
pyplot.axis([pixrc.xmin,pixrc.xmax,pixrc.ymin,pixrc.ymax])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$',
r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$')
#Colorbar
cbaxes = pyplot.axes([0.1625,(1.-tdy)/2.+tdy+0.065,2.*axdx-0.195,0.02])
CB1= pyplot.colorbar(img,orientation='horizontal',
cax=cbaxes)#,ticks=[-16.,-8.,0.,8.,16.])
CB1.set_label(r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',labelpad=-35,fontsize=14.)
#Now calculate the expected field
xgrid= numpy.arange(_RCXMIN-2.25+_RCDX/2.,_RCXMAX+2.25+_RCDX/2.,_RCDX)
ygrid= numpy.arange(_RCYMIN-2.25+_RCDX/2.,_RCYMAX+2.25+_RCDX/2.,_RCDX)
xv,yv= numpy.meshgrid(xgrid,ygrid,indexing='ij')
rs= numpy.sqrt(xv**2.+yv**2.)
phis= numpy.arctan2(yv,xv)
d,l= bovy_coords.rphi_to_dl_2d(rs/8.,phis)
expec_vlos= numpy.empty((len(xgrid),len(ygrid)))
for ii in range(len(xgrid)):
for jj in range(len(ygrid)):
expec_vlos[ii,jj]= modelvlosgal(rs[ii,jj],phis[ii,jj],l[ii,jj],
vc=218.,vtsun=242.)
modelAxes= pyplot.axes([0.03+axdx,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(modelAxes)
xlabel=r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$'
ylabel=r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$'
indx= True-numpy.isnan(resv)
plotthis= copy.copy(expec_vlos)
plotthis[numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
xlabel=xlabel,ylabel=ylabel,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,zorder=3)
if True:
#Now plot the pixels outside the APOGEE data set
plotthis= copy.copy(expec_vlos)
plotthis[True-numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
alpha=0.3,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,
zorder=0)
pyplot.annotate(r'$\mathrm{Bovy\ et.\ al\ (2012)\ model}$',
(1.02,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.,zorder=3)
pyplot.axis([_RCXMIN-2.25,_RCXMAX+2.25,_RCYMIN-2.25,_RCYMAX+2.25])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(xlabel,r'$ $')
#Finally, add a polar plot of the whole disk
res= 51
rmin, rmax= 0.2, 2.4
xgrid= numpy.linspace(0.,2.*numpy.pi*(1.-1./res/2.),
2.*res)
ygrid= numpy.linspace(rmin,rmax,res)
nx= len(xgrid)
ny= len(ygrid)
savefile= 'expec_vlos.sav'
if os.path.exists(savefile):
savefile= open(savefile,'rb')
expec_vlos= pickle.load(savefile)
savefile.close()
else:
expec_vlos= numpy.zeros((nx,ny))
for ii in range(nx):
for jj in range(ny):
R, phi= ygrid[jj], xgrid[ii]
d,l= bovy_coords.rphi_to_dl_2d(R,phi)
expec_vlos[ii,jj]= modelvlosgal(R*8.,phi,l,
vc=218.,vtsun=242.)
save_pickles(savefile,expec_vlos)
plotxgrid= numpy.linspace(xgrid[0]-(xgrid[1]-xgrid[0])/2.,
xgrid[-1]+(xgrid[1]-xgrid[0])/2.,
len(xgrid)+1)
plotygrid= numpy.linspace(ygrid[0]-(ygrid[1]-ygrid[0])/2.,
ygrid[-1]+(ygrid[1]-ygrid[0])/2.,
len(ygrid)+1)
fullmodelAxes= pyplot.axes([-0.05+2.*axdx,(1.-tdy)/2.,axdx,tdy],polar=True)
ax= fullmodelAxes
pyplot.sca(fullmodelAxes)
vmin, vmax= -150., 150.
zlabel= r'$\mathrm{line\!-\!of\!-\!sight\ velocity}\ (\mathrm{km\,s}^{-1})$'
out= ax.pcolor(plotxgrid,plotygrid,expec_vlos.T,cmap='jet',
vmin=vmin,vmax=vmax,clip_on=False)
shrink= 0.8
if False:
CB1= pyplot.colorbar(out,shrink=shrink)
bbox = CB1.ax.get_position().get_points()
CB1.ax.set_position(transforms.Bbox.from_extents(bbox[0,0]+0.025,
bbox[0,1],
bbox[1,0],
bbox[1,1]))
CB1.set_label(zlabel)
from matplotlib.patches import FancyArrowPatch
arr= FancyArrowPatch(posA=(numpy.pi+0.1,1.8),
posB=(3*numpy.pi/2.+0.1,1.8),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (numpy.pi/8.-0.05),
shrinkA=2.0, shrinkB=2.0, mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax.add_patch(arr)
bovy_plot.bovy_text(numpy.pi+0.17,1.7,r'$\mathrm{Galactic\ rotation}$',
rotation=-30.,size=9.)
radii= numpy.array([0.5,1.,1.5,2.,2.5])
labels= []
for r in radii:
ax.plot(numpy.linspace(0.,2.*numpy.pi,501,),
numpy.zeros(501)+r,ls='-',color='0.65',zorder=1,lw=0.5)
labels.append(r'$%i$' % int(r*8.))
pyplot.rgrids(radii,labels=labels,angle=147.5)
thetaticks = numpy.arange(0,360,45)
# set ticklabels location at x times the axes' radius
ax.set_thetagrids(thetaticks,frac=1.16,backgroundcolor='w',zorder=3)
bovy_plot.bovy_text(3.*numpy.pi/4.+0.06,2.095,r'$\mathrm{kpc}$',size=10.)
pyplot.ylim(0.,2.8)
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the connectors on the modelAxes
xlow=-4.*8.
ylow= 2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMAX+2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
xlow=-4.*8.
ylow= -2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMIN-2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
#Colorbar
cbaxes = pyplot.axes([0.01+2.*axdx,(1.-tdy)/2.+tdy+0.065,axdx-0.125,0.02])
CB1= pyplot.colorbar(out,orientation='horizontal',
cax=cbaxes,ticks=[-150.,-75.,0.,75.,150.])
#CB1.set_label(r'$\mathrm{median}\ V_{\mathrm{los}}\,(\mathrm{km\,s}^{-1})$',labelpad=-35,fontsize=14.)
bovy_plot.bovy_end_print(plotfilename,dpi=300)
return None
def plot_rckinematics(plotfilename,subsun=False):
#Set up 3 axes
bovy_plot.bovy_print(fig_width=8.,axes_labelsize=14)
axdx= 1./3.
#APOGEE-RC observations
tdy= (_RCYMAX-_RCYMIN+4.5)/(_RCXMAX-_RCXMIN+4.5)*axdx
obsAxes= pyplot.axes([0.1,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(obsAxes)
data= apread.rcsample()
if _ADDLLOGGCUT:
data= data[data['ADDL_LOGG_CUT'] == 1]
#Cut
indx= (numpy.fabs(data['RC_GALZ']) < 0.25)*(data['METALS'] > -1000.)
data= data[indx]
#Get velocity field
pixrc= pixelize_sample.pixelXY(data,
xmin=_RCXMIN-2.25,xmax=_RCXMAX+2.25,
ymin=_RCYMIN-2.25,ymax=_RCYMAX+2.25,
dx=_RCDX,dy=_RCDX)
vmin, vmax= -16.,16.
img= pixrc.plot(lambda x: dvlosgal(x,vtsun=220.+24.),
vmin=vmin,vmax=vmax,overplot=True,
colorbar=False)
resv= pixrc.plot(lambda x: dvlosgal(x,vtsun=220.+24.),
justcalc=True,returnz=True) #for later
pyplot.annotate(r'$\mathrm{APOGEE\!-\!RC\ data}$',
(0.5,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.)
pyplot.axis([pixrc.xmin,pixrc.xmax,pixrc.ymin,pixrc.ymax])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$',
r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$')
#Colorbar
cbaxes = pyplot.axes([0.1+axdx/2.,(1.-tdy)/2.+tdy+0.065,2.*axdx-0.195,0.02])
CB1= pyplot.colorbar(img,orientation='horizontal',
cax=cbaxes)#,ticks=[-16.,-8.,0.,8.,16.])
CB1.set_label(r'$\mathrm{median}\ \Delta V_{\mathrm{los,rot}}\,(\mathrm{km\,s}^{-1})$',labelpad=-35,fontsize=14.)
#Now calculate the expected field
expec_vlos= galpy_simulations.vlos_altrect('../sim/bar_altrect_alpha0.015_hivres.sav')*220.
modelAxes= pyplot.axes([0.03+axdx,(1.-tdy)/2.,axdx,tdy])
pyplot.sca(modelAxes)
xlabel=r'$X_{\mathrm{GC}}\,(\mathrm{kpc})$'
ylabel=r'$Y_{\mathrm{GC}}\,(\mathrm{kpc})$'
indx= True-numpy.isnan(resv)
plotthis= copy.copy(expec_vlos)
plotthis[numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
xlabel=xlabel,ylabel=ylabel,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,zorder=3)
if True:
#Now plot the pixels outside the APOGEE data set
plotthis= copy.copy(expec_vlos)
plotthis[True-numpy.isnan(resv)]= numpy.nan #turn these off
bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
interpolation='nearest',
alpha=0.3,
xrange=[_RCXMIN-2.25,_RCXMAX+2.25],
yrange=[_RCYMIN-2.25,_RCYMAX+2.25],
contours=False,
vmin=vmin,vmax=vmax,overplot=True,
zorder=0)
pyplot.annotate(r'$\mathrm{Favored\ bar\ model}$',
(1.02,1.09),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',size=10.,zorder=3)
pyplot.axis([_RCXMIN-2.25,_RCXMAX+2.25,_RCYMIN-2.25,_RCYMAX+2.25])
bovy_plot._add_ticks()
bovy_plot._add_axislabels(xlabel,r'$ $')
#Finally, add a polar plot of the whole disk
res= 51
rmin, rmax= 0.2, 2.4
xgrid= numpy.linspace(0.,2.*numpy.pi*(1.-1./res/2.),
2.*res)
ygrid= numpy.linspace(rmin,rmax,res)
expec_vlos= galpy_simulations.vlos_polar('../sim/bar_polar_alpha0.015_hivres.sav')*220.
plotxgrid= numpy.linspace(xgrid[0]-(xgrid[1]-xgrid[0])/2.,
xgrid[-1]+(xgrid[1]-xgrid[0])/2.,
len(xgrid)+1)
plotygrid= numpy.linspace(ygrid[0]-(ygrid[1]-ygrid[0])/2.,
ygrid[-1]+(ygrid[1]-ygrid[0])/2.,
len(ygrid)+1)
fullmodelAxes= pyplot.axes([-0.05+2.*axdx,(1.-tdy)/2.,axdx,tdy],polar=True)
ax= fullmodelAxes
pyplot.sca(fullmodelAxes)
out= ax.pcolor(plotxgrid,plotygrid,expec_vlos.T,cmap='jet',
vmin=vmin,vmax=vmax,clip_on=False)
from matplotlib.patches import FancyArrowPatch
arr= FancyArrowPatch(posA=(numpy.pi+0.1,1.8),
posB=(3*numpy.pi/2.+0.1,1.8),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (numpy.pi/8.-0.05),
shrinkA=2.0, shrinkB=2.0, mutation_scale=20.0,
mutation_aspect=None,fc='k',zorder=10)
ax.add_patch(arr)
bovy_plot.bovy_text(numpy.pi+0.17,1.7,r'$\mathrm{Galactic\ rotation}$',
rotation=-30.,size=9.)
radii= numpy.array([0.5,1.,1.5,2.,2.5])
labels= []
for r in radii:
ax.plot(numpy.linspace(0.,2.*numpy.pi,501,),
numpy.zeros(501)+r,ls='-',color='0.65',zorder=1,lw=0.5)
labels.append(r'$%i$' % int(r*8.))
pyplot.rgrids(radii,labels=labels,angle=147.5)
thetaticks = numpy.arange(0,360,45)
# set ticklabels location at x times the axes' radius
ax.set_thetagrids(thetaticks,frac=1.16,backgroundcolor='w',zorder=3)
bovy_plot.bovy_text(3.*numpy.pi/4.+0.06,2.095,r'$\mathrm{kpc}$',size=10.)
pyplot.ylim(0.,2.8)
# Plot the bar position
ets= numpy.linspace(0.,2.*numpy.pi,501,)
a= 0.421766
b= a*0.4
dtr= numpy.pi/180.
ax.plot(ets,
a*b/numpy.sqrt((b*numpy.cos(ets-25.*dtr))**2.
+(a*numpy.sin(ets-25.*dtr))**2.),
zorder=1,lw=1.5,color='w')
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
xs= numpy.linspace(_RCXMIN-2.25,_RCXMAX+2.25,101)
ys= numpy.ones(101)*(_RCYMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMIN-2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the box
ys= numpy.linspace(_RCYMIN-2.25,_RCYMAX+2.25,101)
xs= numpy.ones(101)*(_RCXMAX+2.25)
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
ax.plot(phis,rs,'--',lw=1.25,color='k')
#Plot the connectors on the modelAxes
xlow=-4.*8.
ylow= 2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMAX+2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
xlow=-4.*8.
ylow= -2.77*8.
xs= numpy.linspace(xlow,(_RCXMAX+2.25),101)
ys= (ylow-(_RCYMIN-2.25))/(xlow-(_RCXMAX+2.25))*(xs-xlow)+ylow
rs= numpy.sqrt(xs**2.+ys**2.)/8.
phis= numpy.arctan2(ys,xs)
line= ax.plot(phis,rs,':',lw=1.,color='k',zorder=2)
line[0].set_clip_on(False)
bovy_plot.bovy_end_print(plotfilename,dpi=300)
return None
def astro_sampling(parser):
options, args = parser.parse_args()
if options.basti:
zs = numpy.array([0.004, 0.008, 0.01, 0.0198, 0.03, 0.04])
elif options.parsec:
zs = numpy.arange(0.0005, 0.06005, 0.0005)
# zs= numpy.arange(0.0005,0.06005,0.005)
else:
zs = numpy.arange(0.0005, 0.03005, 0.0005)
# zs= numpy.arange(0.0005,0.03005,0.005)
if os.path.exists(args[0]):
savefile = open(args[0], "rb")
plotthis = pickle.load(savefile)
zs = pickle.load(savefile)
lages = pickle.load(savefile)
savefile.close()
dlages = lages[1] - lages[0]
if options.type == "massperrc":
savefile = open(args[1], "rb")
plotthis /= pickle.load(savefile)
savefile.close()
if options.type == "mass" and len(args) == 3:
# Also load mass_coarseage and omega
savefile = open(args[1], "rb")
masscoarse = pickle.load(savefile)
savefile.close()
savefile = open(args[2], "rb")
omega = pickle.load(savefile)
savefile.close()
else:
nages = 31
if options.type == "omega" or options.type == "numfrac" or options.coarseage:
nages = 16
lages = numpy.linspace(-1.0, 1.0, nages)
dlages = lages[1] - lages[0]
plotthis = numpy.zeros((len(zs), nages))
multOut = multi.parallel_map(
lambda x: _calc_one(zs[x], options, nages, lages, dlages), range(len(zs)), numcores=32
)
for ii in range(len(zs)):
plotthis[ii, :] = multOut[ii]
# Save
savefile = open(args[0], "wb")
pickle.dump(plotthis, savefile)
pickle.dump(zs, savefile)
pickle.dump(lages, savefile)
savefile.close()
# Plot
# Fist cut out youngest ages, since they are irrelevant
if _CUTLOWAGE:
aindx = lages > numpy.log10(0.8)
lages = lages[aindx]
plotthis = plotthis[:, aindx]
if options.type == "mass":
vmin, vmax = 0.5, 2.3
vmin2, vmax2 = 0.5, 2.0
zlabel = r"$\langle M_{\mathrm{RC}} \rangle \,(M_\odot)$"
# cmap= 'gist_yarg'
cmap = "jet"
elif options.type == "omega":
vmin, vmax = 0.0, 0.03
vmin2, vmax2 = 0.0, 0.015
if options.allapogee:
vmin, vmax = 0.0, 0.035
zlabel = r"$\mathrm{Mass\ fraction\ in}\ (J-K_s)_0 > 0.5\ \mathrm{giants\ (\%)}$"
elif options.redapogee:
vmin, vmax = 0.0, 0.005
vmin2, vmax2 = 0.0, 0.003
zlabel = r"$\mathrm{Mass\ fraction\ in}\ (J-K_s)_0 > 0.8\ \mathrm{giants\ (\%)}$"
else:
zlabel = r"$\mathrm{Mass\ fraction\ in\ RC\ stars\ (\%)}$"
# cmap= 'gist_yarg'
cmap = "jet"
plotthis *= 100.0
elif options.type == "numfrac":
vmin, vmax = 0.0, 0.005
vmin2, vmax2 = 0.0, 0.004
zlabel = r"$\mathrm{Number\ fraction\ in\ RC\ stars\ (\%)}$"
# cmap= 'gist_yarg'
cmap = "jet"
plotthis *= 100.0
elif options.type == "massperrc":
vmin, vmax = 0.0, 50000.0
vmin2, vmax2 = 0.0, 25000.0
zlabel = r"$\mathrm{Stellar\ population\ mass\ per\ RC\ star}\,(M_\odot)$"
# cmap= 'gist_yarg'
cmap = "jet"
if options.redapogee:
vmin, vmax = 0.0, 100000.0
vmin2, vmax2 = 0.0, 200000.0
zlabel = r"$\mathrm{Mass\ fraction\ in}\ (J-K_s)_0 > 0.8\ \mathrm{giants\ (\%)}$"
print numpy.nanmin(plotthis), numpy.nanmax(plotthis)
if options.basti: # Remap the Zs
zs = numpy.array([0.004, 0.008, 0.01, 0.0198, 0.03, 0.04])
regularzs = numpy.arange(0.0005, 0.03005, 0.0005) / 0.019 * 0.0198
regularplotthis = numpy.zeros((nages, len(regularzs)))
for jj in range(len(regularzs)):
# Find z
thisindx = numpy.argmin(numpy.fabs(regularzs[jj] - zs))
for ii in range(nages):
regularplotthis[ii, jj] = plotthis[ii, thisindx]
zs = regularzs
plotthis = regularplotthis
if options.remapz:
zs = zs[:-1]
plotthis = plotthis[:-1, :]
fehs = numpy.linspace(-1.05, isodist.Z2FEH(zs[-1], zsolar=0.017), len(zs))
fehzs = isodist.FEH2Z(fehs, zsolar=0.017)
new_plotthis = numpy.empty_like(plotthis)
for ii in range(plotthis.shape[1]):
goodz = True - numpy.isnan(plotthis[:, ii])
tip = interpolate.InterpolatedUnivariateSpline(zs[goodz], plotthis[goodz, ii], k=3)
new_plotthis[:, ii] = tip(fehzs)
try:
new_plotthis[fehs < numpy.nanmax(isodist.Z2FEH(zs[True - goodz], zsolar=0.017)), ii] = numpy.nan
except ValueError:
continue
plotthis = new_plotthis
xlabel = r"$[\mathrm{Fe/H}]\,(\mathrm{dex})$"
else:
xlabel = r"$Z$"
bovy_plot.bovy_print(fig_height=7.0, fig_width=6.0)
fig = pyplot.gcf()
left, bottom, width, height = 0.1, 0.1, 0.8, 0.6
axBottom = pyplot.axes([left, bottom, width, height])
fig.sca(axBottom)
if options.remapz:
xlimits = [fehs[0], fehs[-1]]
else:
xlimits = [zs[0], zs[-1]]
ylimits = [lages[0] - dlages, lages[-1] + dlages]
bovy_plot.bovy_dens2d(
plotthis.T,
origin="lower",
cmap=cmap,
xrange=xlimits,
yrange=ylimits,
vmin=vmin,
vmax=vmax,
interpolation="nearest",
colorbar=True,
shrink=0.9,
zlabel=zlabel,
overplot=True,
)
extent = xlimits + ylimits
pyplot.axis(extent)
bovy_plot._add_axislabels(xlabel, r"$\log_{10}\,\mathrm{Age} / 1\,\mathrm{Gyr}$")
bovy_plot._add_ticks()
left, bottom, width, height = 0.1, 0.68, 0.64, 0.2
axTop = pyplot.axes([left, bottom, width, height])
fig.sca(axTop)
# Plot the average over SFH
lages = numpy.linspace(-1.0, 1.0, 16)
if _CUTLOWAGE:
aindx = lages > numpy.log10(0.8)
lages = lages[aindx]
if options.type == "mass":
omega = omega[:, aindx]
masscoarse = masscoarse[:, aindx]
mtrend = numpy.zeros(len(zs))
exppage = 10.0 ** lages * numpy.exp((10.0 ** (lages + 2.0)) / 800.0) # e.g., Binney (2010)
exexppage = 10.0 ** lages * numpy.exp((10.0 ** (lages + 2.0)) / 100.0) # e.g., Binney (2010)
page = 10.0 ** lages
if options.type == "massperrc":
mtrend = 1.0 / (numpy.sum(page * 1.0 / plotthis, axis=1) / numpy.sum(page))
expmtrend = 1.0 / (numpy.sum(exppage * 1.0 / plotthis, axis=1) / numpy.sum(exppage))
exexpmtrend = 1.0 / (numpy.sum(exexppage * 1.0 / plotthis, axis=1) / numpy.sum(exexppage))
elif options.type == "mass" and len(args) == 3:
if options.remapz:
omega = omega[:-1, :]
masscoarse = masscoarse[:-1, :]
mtrend = numpy.nansum(page * omega, axis=1) / numpy.nansum(page * omega / masscoarse, axis=1)
expmtrend = numpy.nansum(exppage * omega, axis=1) / numpy.nansum(exppage * omega / masscoarse, axis=1)
exexpmtrend = numpy.nansum(exexppage * omega, axis=1) / numpy.nansum(exexppage * omega / masscoarse, axis=1)
else:
mtrend = numpy.sum(page * plotthis, axis=1) / numpy.sum(page)
expmtrend = numpy.sum(exppage * plotthis, axis=1) / numpy.sum(exppage)
exexpmtrend = numpy.sum(exexppage * plotthis, axis=1) / numpy.sum(exexppage)
if options.remapz:
zs = fehs
pyplot.plot(zs, mtrend, "k-")
pyplot.plot(zs, expmtrend, "k--")
pyplot.plot(zs, exexpmtrend, "k-.")
pyplot.ylim(vmin2, vmax2)
pyplot.xlim(xlimits[0], xlimits[1])
nullfmt = NullFormatter() # no labels
thisax = pyplot.gca()
thisax.xaxis.set_major_formatter(nullfmt)
bovy_plot._add_ticks()
if options.type == "mass":
pyplot.ylabel(zlabel)
if options.basti:
pyplot.annotate(
r"$\mathrm{BaSTI}$",
(0.5, 1.08),
xycoords="axes fraction",
horizontalalignment="center",
verticalalignment="top",
size=16.0,
)
elif options.imfmodel == "kroupa2003":
pyplot.annotate(
r"$\mathrm{Padova, Kroupa\ (2003)\ IMF}$",
(0.5, 1.08),
xycoords="axes fraction",
horizontalalignment="center",
verticalalignment="top",
size=16.0,
)
elif "expsfh" in args[0]:
pyplot.annotate(
r"$\mathrm{Padova, p(\mathrm{Age}) \propto e^{\mathrm{Age}/(8\,\mathrm{Gyr})}}$",
(0.5, 1.08),
xycoords="axes fraction",
horizontalalignment="center",
verticalalignment="top",
size=16.0,
)
elif options.parsec:
pass
# pyplot.annotate(r'$\mathrm{PARSEC}$',
# (0.5,1.08),xycoords='axes fraction',
# horizontalalignment='center',
# verticalalignment='top',size=16.)
else:
pyplot.annotate(
r"$\mathrm{Padova}$",
(0.5, 1.08),
xycoords="axes fraction",
horizontalalignment="center",
verticalalignment="top",
size=16.0,
)
bovy_plot.bovy_end_print(options.outfilename)
return None
def plot_popmass(self):
"""
NAME:
plot_popmass
PURPOSE:
plot the stellar-population mass for each RC star
INPUT:
bovy_plot.bovy_plot **kwargs
OUTPUT:
bovy_plot.bovy_plot output
HISTORY:
2014-02-28 - Written in this form - Bovy (IAS)
"""
if not _BOVY_PLOT_LOADED:
raise ImportError("galpy.util.bovy_plot could not be imported")
fehs = numpy.linspace(-1., 0.5, 101)
lages = self._coarselages
plotthis = numpy.empty((len(fehs), len(lages)))
for ii in range(len(fehs)):
for jj in range(len(lages)):
plotthis[ii, jj] = self.popmass(fehs[ii], lages[jj])
fig = pyplot.gcf()
left, bottom, width, height = 0.1, 0.1, 0.8, 0.6
axBottom = pyplot.axes([left, bottom, width, height])
fig.sca(axBottom)
xlimits = [fehs[0], fehs[-1]]
dlages = (lages[1] - lages[0])
ylimits = [lages[0] - dlages, lages[-1] + dlages]
vmin, vmax = 0., 50000.
vmin2, vmax2 = 0., 25000.
zlabel = r'$\mathrm{Stellar\ population\ mass\ per\ RC\ star}\,(M_\odot)$'
xlabel = r'$[\mathrm{Fe/H}]\,(\mathrm{dex})$'
out = bovy_plot.bovy_dens2d(plotthis.T,
origin='lower',
cmap='jet',
xrange=xlimits,
yrange=ylimits,
vmin=vmin,
vmax=vmax,
interpolation='nearest',
colorbar=True,
shrink=.9,
zlabel=zlabel,
overplot=True)
extent = xlimits + ylimits
pyplot.axis(extent)
bovy_plot._add_axislabels(
xlabel, r'$\log_{10}\,\mathrm{Age} / 1\,\mathrm{Gyr}$')
bovy_plot._add_ticks()
left, bottom, width, height = 0.1, 0.68, 0.64, 0.2
axTop = pyplot.axes([left, bottom, width, height])
fig.sca(axTop)
#Plot the average over SFH
lages = numpy.linspace(-1., 1., 16)
mtrend = numpy.zeros(len(self._zs))
exppage = 10.**self._coarselages * numpy.exp(
(10.**(self._coarselages + 2.)) / 800.) #e.g., Binney (2010)
exexppage = 10.**self._coarselages * numpy.exp(
(10.**(self._coarselages + 2.)) / 100.) #e.g., Binney (2010)
page = 10.**self._coarselages
plotthis = self._coarsemass[:-1, :] / self._omega[:-1, :]
mtrend = 1. / (numpy.sum(page * 1. / plotthis, axis=1) /
numpy.sum(page))
expmtrend = 1. / (numpy.sum(exppage * 1. / plotthis, axis=1) /
numpy.sum(exppage))
exexpmtrend = 1. / (numpy.sum(exexppage * 1. / plotthis, axis=1) /
numpy.sum(exexppage))
fehs = isodist.Z2FEH(self._zs[:-1], zsolar=zsolar())
pyplot.plot(fehs, mtrend, 'k-')
pyplot.plot(fehs, expmtrend, 'k--')
pyplot.plot(fehs, exexpmtrend, 'k-.')
pyplot.ylim(vmin2, vmax2)
pyplot.xlim(xlimits[0], xlimits[1])
nullfmt = NullFormatter() # no labels
thisax = pyplot.gca()
thisax.xaxis.set_major_formatter(nullfmt)
bovy_plot._add_ticks()
return out
def plot_popmass(self):
"""
NAME:
plot_popmass
PURPOSE:
plot the stellar-population mass for each RC star
INPUT:
bovy_plot.bovy_plot **kwargs
OUTPUT:
bovy_plot.bovy_plot output
HISTORY:
2014-02-28 - Written in this form - Bovy (IAS)
"""
if not _BOVY_PLOT_LOADED:
raise ImportError("galpy.util.bovy_plot could not be imported")
fehs= numpy.linspace(-1.,0.5,101)
lages= self._coarselages
plotthis= numpy.empty((len(fehs),len(lages)))
for ii in range(len(fehs)):
for jj in range(len(lages)):
plotthis[ii,jj]= self.popmass(fehs[ii],lages[jj])
fig= pyplot.gcf()
left, bottom, width, height= 0.1, 0.1, 0.8, 0.6
axBottom= pyplot.axes([left,bottom,width,height])
fig.sca(axBottom)
xlimits= [fehs[0],fehs[-1]]
dlages= (lages[1]-lages[0])
ylimits= [lages[0]-dlages,lages[-1]+dlages]
vmin, vmax= 0.,50000.
vmin2, vmax2= 0.,25000.
zlabel= r'$\mathrm{Stellar\ population\ mass\ per\ RC\ star}\,(M_\odot)$'
xlabel= r'$[\mathrm{Fe/H}]\,(\mathrm{dex})$'
out= bovy_plot.bovy_dens2d(plotthis.T,origin='lower',cmap='jet',
xrange=xlimits,
yrange=ylimits,
vmin=vmin,vmax=vmax,
interpolation='nearest',
colorbar=True,
shrink=.9,
zlabel=zlabel,
overplot=True)
extent= xlimits+ylimits
pyplot.axis(extent)
bovy_plot._add_axislabels(xlabel,
r'$\log_{10}\,\mathrm{Age} / 1\,\mathrm{Gyr}$')
bovy_plot._add_ticks()
left, bottom, width, height= 0.1, 0.68, 0.64, 0.2
axTop= pyplot.axes([left,bottom,width,height])
fig.sca(axTop)
#Plot the average over SFH
lages= numpy.linspace(-1.,1.,16)
mtrend= numpy.zeros(len(self._zs))
exppage= 10.**self._coarselages*numpy.exp((10.**(self._coarselages+2.))/800.) #e.g., Binney (2010)
exexppage= 10.**self._coarselages*numpy.exp((10.**(self._coarselages+2.))/100.) #e.g., Binney (2010)
page= 10.**self._coarselages
plotthis= self._coarsemass[:-1,:]/self._omega[:-1,:]
mtrend= 1./(numpy.sum(page*1./plotthis,axis=1)/numpy.sum(page))
expmtrend= 1./(numpy.sum(exppage*1./plotthis,axis=1)/numpy.sum(exppage))
exexpmtrend= 1./(numpy.sum(exexppage*1./plotthis,axis=1)/numpy.sum(exexppage))
fehs= isodist.Z2FEH(self._zs[:-1],zsolar=zsolar())
pyplot.plot(fehs,mtrend,'k-')
pyplot.plot(fehs,expmtrend,'k--')
pyplot.plot(fehs,exexpmtrend,'k-.')
pyplot.ylim(vmin2,vmax2)
pyplot.xlim(xlimits[0],xlimits[1])
nullfmt = NullFormatter() # no labels
thisax= pyplot.gca()
thisax.xaxis.set_major_formatter(nullfmt)
bovy_plot._add_ticks()
return out
def plotDFResults1D(options,args):
#First load the results
#Load g and k fits
options.sample= 'g'
options.select= 'all'
gfits= calcDFResults(options,[_GFIT])
options.sample= 'k'
options.select= 'program'
kfits= calcDFResults(options,[_KFIT])
#Setup plane
tightbinned= pixelAfeFeh(None,dfeh=options.dfeh,dafe=options.dafe,
fehmin=-1.6,fehmax=0.5,afemin=-0.05,
afemax=0.55)
#Setup plotting
bovy_plot.bovy_print(fig_height=8.,fig_width=8.,
axes_labelsize=11,xtick_labelsize=7,
ytick_labelsize=7,
xtick_major_size=2,
xtick_minor_size=1,
ytick_major_size=2,
ytick_minor_size=1)
nullfmt = NullFormatter() # no labels
if options.subtype.lower() == 'regular':
npanels= 5
keys= ['rd','zh','dlnvcdlnr','plhalo','vc']
scale= [_REFR0,_REFR0*1000.,1.,1.,1.]
ranges=[[1.5,4.5],
[100.,800.],
[-0.3,0.1],
[0.,3.],
[180.,260.]]
labels=[r'$R_d^{\mathrm{MN}}\ [\mathrm{kpc}]$',
r'$z_h^{\mathrm{MN}}\ [\mathrm{pc}]$',
r'$\mathrm{d}\ln V_c / \mathrm{d}\ln R\, (R_0)$',
r'$\alpha\ \mathrm{in}\ \rho_{\mathrm{halo}} \propto 1/r^\alpha$',
r'$V_c\ [\mathrm{km\,s}^{-1}]$']
ticks= [[2.,3.,4.],
[100.,300.,500.,700.],
[-0.3,-0.1,0.1],
[0.,1.5,3.],
[180.,220.,260.]]
elif options.subtype.lower() == 'valueadded':
npanels= 4
keys= ['rdexp','zhexp','surfzdisk','rhodm']
scale= [_REFR0,_REFR0*1000.,1.,1.]
ranges=[[1.5,4.5],
[100.,800.],
[40.,80.],
[0.,0.02]]
ticks= [[2.,3.,4.],
[100.,300.,500.,700.],
[40.,60.,80.],
[0.,0.01,0.02]]
labels=[r'$R_d^{\mathrm{Exp}}\ [\mathrm{kpc}]$',
r'$z_h^{\mathrm{Exp}}\ [\mathrm{pc}]$',
r'$\Sigma_{\mathrm{disk}}(R_0)\ [M_{\odot}\,\mathrm{pc}^{-2}]$',
r'$\rho_{\mathrm{DM}}\,(R_0,0)\ [M_{\odot}\,\mathrm{pc}^{-3}]$']
nrows= 2
xrange=[-1.6,0.5]
yrange=[-0.05,0.55]
dx= 0.8/npanels
dy= dx#*(yrange[1]-yrange[0])/(xrange[1]-xrange[0])
allaxes= []
for jj in range(nrows):
for ii in range(npanels):
if jj == 0: thesefits= gfits
else: thesefits= kfits
#Gather results
plotthis= numpy.zeros((tightbinned.npixfeh(),tightbinned.npixafe()))
key= keys[ii]
for tt in range(tightbinned.npixfeh()):
for rr in range(tightbinned.npixafe()):
tfeh= tightbinned.feh(tt)
tafe= tightbinned.afe(rr)
if numpy.amin((thesefits['feh']-tfeh)**2./options.dfeh**2.+(thesefits['afe']-tafe)**2./options.dafe**2.) > 0.:
plotthis[tt,rr]= numpy.nan
continue
tindx= numpy.argmin((thesefits['feh']-tfeh)**2./options.dfeh**2.+(thesefits['afe']-tafe)**2./options.dafe**2.)
plotthis[tt,rr]= thesefits[key][tindx]
left, bottom, width, height= 0.1+ii*dx, 0.1+dy*(1-jj), dx, dy
ax= pyplot.axes([left,bottom,width,height])
allaxes.append(ax)
fig= pyplot.gcf()
fig.sca(ax)
out= bovy_plot.bovy_dens2d(plotthis.T*scale[ii],
origin='lower',cmap='jet',
interpolation='nearest',
xrange=xrange,yrange=yrange,
vmin=ranges[ii][0],vmax=ranges[ii][1],
contours=False,overplot=True,
colorbar=False,aspect='auto')
if jj == 1:
bovy_plot._add_axislabels(r'$[\mathrm{Fe/H}]$',None)
if ii == 0:
bovy_plot._add_axislabels(None,r'$[\alpha/\mathrm{Fe}]$')
if jj == 0:
ax.xaxis.set_major_formatter(nullfmt)
if ii > 0:
ax.yaxis.set_major_formatter(nullfmt)
if jj == 0:
#Add colorbar
left, bottom, width, height= 0.1+ii*dx, 0.1+2.*dy*(1-jj), dx, dy
ax= pyplot.axes([left,bottom,width,height],frameon=False)
ax.xaxis.set_major_formatter(nullfmt)
ax.yaxis.set_major_formatter(nullfmt)
ax.yaxis.set_tick_params(size=0)
ax.xaxis.set_tick_params(size=0)
allaxes.append(ax)
fig.sca(ax)
CB1= pyplot.colorbar(out,shrink=0.85,orientation='horizontal',
fraction=0.25,ticks=ticks[ii])
CB1.set_label(labels[ii],labelpad=-30)
bovy_plot.bovy_end_print(options.outfilename)
return None
