def plot_jr(plotfilename):
lp= potential.LogarithmicHaloPotential(q=0.9,normalize=1.)
aAI= actionAngleIsochroneApprox(b=0.8,pot=lp,tintJ=200,ntintJ=20000)
obs= numpy.array([1.56148083,0.35081535,-1.15481504,0.88719443,
-0.47713334,0.12019596])
bovy_plot.bovy_print(fig_width=6.)
aAI.plot(*obs,type='jr',downsample=True)
bovy_plot.bovy_end_print(plotfilename)
def plot_pdfs_l(plotfilename):
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,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
#Calculate the density as a function of l, p(l)
#Sample from sdf
llbd= sdf.sample(n=40000,lb=True)
tlbd= sdft.sample(n=50000,lb=True)
b,e= numpy.histogram(llbd[0],bins=101,normed=True)
t= ((numpy.roll(e,1)-e)/2.+e)[1:]
lspl= interpolate.UnivariateSpline(t,numpy.log(b),k=3,s=1.)
lls= numpy.linspace(t[0],t[-1],_NLS)
lps= numpy.exp(lspl(lls))
lps/= numpy.sum(lps)*(lls[1]-lls[0])*2.
b,e= numpy.histogram(tlbd[0],bins=101,normed=True)
t= ((numpy.roll(e,1)-e)/2.+e)[1:]
tspl= interpolate.UnivariateSpline(t,numpy.log(b),k=3,s=0.5)
tls= numpy.linspace(t[0],t[-1],_NLS)
tps= numpy.exp(tspl(tls))
tps/= numpy.sum(tps)*(tls[1]-tls[0])*2.
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
bovy_plot.bovy_plot(lls,lps,'k-',lw=1.5,
xlabel=r'$\mathrm{Galactic\ longitude}\,(\mathrm{deg})$',
ylabel=r'$p(l)$',
xrange=[65.,250.],
yrange=[0.,1.2*numpy.nanmax(numpy.hstack((lps,tps)))])
bovy_plot.bovy_plot(tls,tps,'k-',lw=1.5,overplot=True)
#Also plot the stream histogram
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
#Transform to (l,b)
XYZ= bovy_coords.galcenrect_to_XYZ(data[:,1],data[:,3],data[:,2],Xsun=8.)
lbd= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=True)
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
lbd= lbd[indx,:]
bovy_plot.bovy_hist(lbd[:,0],bins=40,range=[65.,250.],
histtype='step',normed=True,
overplot=True,
lw=1.5,color='k')
bovy_plot.bovy_end_print(plotfilename)
def plot_pdfs_x(plotfilename):
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])
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
#Calculate the density as a function of l, p(l)
txs= numpy.linspace(3.,12.4,_NLS)
tlogps= multi.parallel_map((lambda x: sdft.callMarg([txs[x]/8.,None,None,None,None,None],
interp=True,ngl=_NGL,
nsigma=3)),
range(_NLS),
numcores=numpy.amin([_NLS,
multiprocessing.cpu_count()]))
tlogps= numpy.array(tlogps)
tlogps[numpy.isnan(tlogps)]= -100000000000000000.
tps= numpy.exp(tlogps-logsumexp(tlogps))
tps/= numpy.nansum(tps)*(txs[1]-txs[0])
bovy_plot.bovy_print()
bovy_plot.bovy_plot(txs,tps,'k-',lw=1.5,
xlabel=r'$X\,(\mathrm{kpc})$',
ylabel=r'$p(X)$',
xrange=[3.,12.4],
yrange=[0.,1.2*numpy.nanmax(tps)])
bovy_plot.bovy_plot(txs,tps,'k-',lw=1.5,overplot=True)
#Also plot the stream histogram
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= thetar-numpy.pi < -(5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
data= data[indx,:]
bovy_plot.bovy_hist(data[:,1],bins=20,range=[3.,12.4],
histtype='step',normed=True,
overplot=True,
lw=1.5,color='k')
bovy_plot.bovy_end_print(plotfilename)
def calc_actions(snapfile=None):
#Directories
snapdir= 'snaps/'
basefilename= snapfile.split('.')[0]
nsnap= len(glob.glob(os.path.join(snapdir,basefilename+'_*.dat')))
print "Processing %i snapshots ..." % nsnap
#Setup potential
lp= potential.LogarithmicHaloPotential(normalize=1.,q=0.9)
if False:
aA= actionAngleStaeckel(pot=lp,delta=1.20,c=True)
snapaadir= 'snaps_aas/'
else:
aA= actionAngleIsochroneApprox(pot=lp,b=0.8)
snapaadir= 'snaps_aai/'
#Run each snapshot
if True:
calcThese= []
for ii in range(nsnap):
csvfilename= os.path.join(snapaadir,basefilename+'_aa_%s.dat' % str(ii).zfill(5))
if os.path.exists(csvfilename):
#Don't recalculate those that have already been calculated
nstart= int(subprocess.check_output(['wc','-l',csvfilename]).split(' ')[0])
if nstart < 10000:
calcThese.append(ii)
else:
calcThese.append(ii)
nsnap= len(calcThese)
if len(calcThese) == 0:
print "All done with everything ..."
return None
args= (aA,snapdir,basefilename,snapaadir)
print "Using %i cpus ..." % (numpy.amin([64,nsnap,
multiprocessing.cpu_count()]))
dummy= multi.parallel_map((lambda x: indiv_calc_actions(x,
*args)),
calcThese,
# range(nsnap),
numcores=numpy.amin([64,nsnap,
multiprocessing.cpu_count()]))
return None
def calc_progenitor_actions(savefilename):
# Setup potential
lp = potential.LogarithmicHaloPotential(normalize=1.0, q=0.9)
# Setup orbit
x, z, y, vx, vz, vy = -11.63337239, -10.631736273934635, -20.76235661, -128.8281653, 79.172383882274971, 42.88727925
R, phi, z = bovy_coords.rect_to_cyl(x, y, z)
vR, vT, vz = bovy_coords.rect_to_cyl_vec(vx, vy, vz, R, phi, z, cyl=True)
R /= 8.0
z /= 8.0
vR /= 220.0
vT /= 220.0
vz /= 220.0
o = Orbit([R, vR, vT, z, vz, phi])
ts = numpy.linspace(0.0, 5.125 * 220.0 / 8.0, 1313) # times of the snapshots
o.integrate(ts, lp, method="dopr54_c")
if "aas" in savefilename:
aA = actionAngleStaeckel(pot=lp, delta=1.20, c=True)
else:
aA = actionAngleIsochroneApprox(pot=lp, b=0.8)
# Now calculate actions, frequencies, and angles for all positions
Rs = o.R(ts)
vRs = o.vR(ts)
vTs = o.vT(ts)
zs = o.z(ts)
vzs = o.vz(ts)
phis = o.phi(ts)
csvfile = open(savefilename, "wb")
writer = csv.writer(csvfile, delimiter=",")
for ii in range(len(ts)):
acfs = aA.actionsFreqsAngles(Rs[ii], vRs[ii], vTs[ii], zs[ii], vzs[ii], phis[ii])
writer.writerow(
[acfs[0][0], acfs[1][0], acfs[2][0], acfs[3][0], acfs[4][0], acfs[5][0], acfs[6][0], acfs[7][0], acfs[8][0]]
)
csvfile.flush()
csvfile.close()
return None
def plot_stream_xz(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_00800.dat'),
delimiter=',')
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_00800.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
if 'sim' in plotfilename:
sindx= numpy.fabs(thetar-numpy.pi) > (4.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
else:
sindx= numpy.fabs(thetar-numpy.pi) > (1.5*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
includeorbit= True
if includeorbit:
npts= 201
pot= potential.LogarithmicHaloPotential(normalize=1.,q=0.9)
pts= numpy.linspace(0.,4.,npts)
#Calculate progenitor orbit around this point
pox= numpy.median(data[:,1])
poy= numpy.median(data[:,3])
poz= numpy.median(data[:,2])
povx= numpy.median(data[:,4])
povy= numpy.median(data[:,6])
povz= numpy.median(data[:,5])
pR,pphi,pZ= bovy_coords.rect_to_cyl(pox,poy,poz)
pvR,pvT,pvZ= bovy_coords.rect_to_cyl_vec(povx,povy,povz,pR,
pphi,pZ,cyl=True)
ppo= Orbit([pR/8.,pvR/220.,pvT/220.,pZ/8.,pvZ/220.,pphi])
pno= Orbit([pR/8.,-pvR/220.,-pvT/220.,pZ/8.,-pvZ/220.,pphi])
ppo.integrate(pts,pot)
pno.integrate(pts,pot)
pvec= numpy.zeros((3,npts*2-1))
pvec[0,:npts-1]= pno.x(pts)[::-1][:-1]
pvec[1,:npts-1]= pno.z(pts)[::-1][:-1]
pvec[2,:npts-1]= pno.y(pts)[::-1][:-1]
pvec[0,npts-1:]= ppo.x(pts)
pvec[1,npts-1:]= ppo.z(pts)
pvec[2,npts-1:]= ppo.y(pts)
pvec*= 8.
includetrack= True
if includetrack:
#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])
#Obs is at time 1312, need to go back 2 Gyr to time 800
obs[1]*= -1.
obs[2]*= -1.
obs[4]*= -1.
o= Orbit(obs)
ts= numpy.linspace(0.,2.*977.7922212082034/1000./bovy_conversion.time_in_Gyr(220.,8.),1001)
o.integrate(ts,lp)
obs= o(ts[-1])._orb.vxvv
obs[1]*= -1.
obs[2]*= -1.
obs[4]*= -1.
tdisrupt= 4.5-2.*977.7922212082034/1000.
sdf= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=True,nTrackChunks=_NTRACKCHUNKS,
tdisrupt=tdisrupt/bovy_conversion.time_in_Gyr(220.,8.),
deltaAngleTrack=1.5*3./5.,multi=_NTRACKCHUNKS)
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
tdisrupt=tdisrupt/bovy_conversion.time_in_Gyr(220.,8.),
deltaAngleTrack=1.5*3./5.,multi=_NTRACKCHUNKS)
if 'sim' in plotfilename:
#Replace data with simulated data
forwardXY= sdf.sample(int(round(numpy.sum(sindx)/2.)),
xy=True)
backwardXY= sdft.sample(int(round(numpy.sum(sindx)/2.)),
xy=True)
data= numpy.empty((forwardXY.shape[1]+backwardXY.shape[1],7))
data[:forwardXY.shape[1],1]= forwardXY[0]*8.
data[:forwardXY.shape[1],2]= forwardXY[2]*8.
data[:forwardXY.shape[1],3]= forwardXY[1]*8.
data[:forwardXY.shape[1],4]= forwardXY[3]*220.
data[:forwardXY.shape[1],5]= forwardXY[5]*220.
data[:forwardXY.shape[1],6]= forwardXY[4]*220.
data[forwardXY.shape[1]:,1]= backwardXY[0]*8.
data[forwardXY.shape[1]:,2]= backwardXY[2]*8.
data[forwardXY.shape[1]:,3]= backwardXY[1]*8.
data[forwardXY.shape[1]:,4]= backwardXY[3]*220.
data[forwardXY.shape[1]:,5]= backwardXY[5]*220.
data[forwardXY.shape[1]:,6]= backwardXY[4]*220.
sindx= numpy.ones(data.shape[0],dtype='bool')
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data[sindx,1],data[sindx,2],'k,',ms=2.,
xlabel=r'$X\,(\mathrm{kpc})$',
ylabel=r'$Z\,(\mathrm{kpc})$',
xrange=[-12.5,-3.],
yrange=[-12.5,-7.])
if numpy.sum(True-sindx) > 0:
#Also plot progenitor
pindx= copy.copy(True-sindx)
pindx[0:9900]= False #subsample
bovy_plot.bovy_plot(data[pindx,1],data[pindx,2],
'k,',overplot=True)
if includeorbit:
bovy_plot.bovy_plot(pox,poz,'o',color='0.5',mec='none',overplot=True,ms=8)
bovy_plot.bovy_plot(pvec[0,:],pvec[1,:],'k--',overplot=True,lw=1.)
if 'sim' in plotfilename:
bovy_plot.bovy_text(r'$\mathrm{mock\ stream}$',
bottom_left=True,size=16.)
else:
bovy_plot.bovy_text(r'$N\!\!-\!\!\mathrm{body\ stream}$',
bottom_left=True,size=16.)
if includetrack:
d1= 'x'
d2= 'z'
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True)
#Also create inset
pyplot.plot([-9.,-9.],[-11.75,-10.3],'k-')
pyplot.plot([-6.,-6.],[-11.75,-10.3],'k-')
pyplot.plot([-9.,-6.],[-11.75,-11.75],'k-')
pyplot.plot([-9.,-6.],[-10.3,-10.3],'k-')
pyplot.plot([-6.,-3.4],[-10.3,-10.],'k:')
pyplot.plot([-9.,-8.85],[-10.3,-10.],'k:')
insetAxes= pyplot.axes([0.42,0.47,0.45,0.42])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(data[sindx,1],data[sindx,2],'k.',ms=2.,zorder=0.,
overplot=True)
if numpy.sum(True-sindx) > 0:
pindx= copy.copy(True-sindx)
pindx[0:9700]= False #subsample
bovy_plot.bovy_plot(data[pindx,1],data[pindx,2],'k,',
zorder=0.,
overplot=True)
bovy_plot.bovy_plot(pvec[0,:],pvec[1,:],'k--',overplot=True,lw=1.,
zorder=1)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True,zorder=2)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(-9.,-6.)
insetAxes.set_ylim(-11.75,-10.3)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
bovy_plot.bovy_end_print(plotfilename)
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_stream_aa(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
includeorbit= True
includetrack= True
fmt= 'k,'
if includeorbit:
#Read progenitor actions
progfile= '../sim/gd1_evol_hitres_progaai.dat'
progaa= numpy.loadtxt(progfile,delimiter=',')
if includetrack:
#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,nosetup=True,
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.))
if 'araz' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,6]
ploty= data[indx,8]
plotx= (numpy.pi+(plotx-numpy.median(data[:,6]))) % (2.*numpy.pi)
ploty= (numpy.pi+(ploty-numpy.median(data[:,8]))) % (2.*numpy.pi)
xrange=[numpy.pi-1.,numpy.pi+1.]
yrange=[numpy.pi-1.,numpy.pi+1.]
xlabel=r'$\theta_R$'
ylabel=r'$\theta_Z$'
elif 'arap' in plotfilename and not 'aparaperp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,6]
ploty= data[indx,7]
plotx= (numpy.pi+(plotx-numpy.median(data[:,6]))) % (2.*numpy.pi)
ploty= (numpy.pi+(ploty-numpy.median(data[:,7]))) % (2.*numpy.pi)
xrange=[numpy.pi-1.,numpy.pi+1.]
yrange=[numpy.pi-1.,numpy.pi+1.]
xlabel=r'$\theta_R$'
ylabel=r'$\theta_\phi$'
elif 'oroz' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,3]*bovy_conversion.freq_in_Gyr(220.,8.)
ploty= data[indx,5]*bovy_conversion.freq_in_Gyr(220.,8.)
xrange=[15.45,15.95]
yrange=[11.7,12.05]
xlabel=r'$\Omega_R\,(\mathrm{Gyr}^{-1})$'
ylabel=r'$\Omega_Z\,(\mathrm{Gyr}^{-1})$'
elif 'orop' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,3]*bovy_conversion.freq_in_Gyr(220.,8.)
ploty= data[indx,4]*bovy_conversion.freq_in_Gyr(220.,8.)
xrange=[15.45,15.95]
yrange=[-10.98,-10.65]
xlabel=r'$\Omega_R\,(\mathrm{Gyr}^{-1})$'
ylabel=r'$\Omega_\phi\,(\mathrm{Gyr}^{-1})$'
elif 'jrjz' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,0]*8.
ploty= data[indx,2]*8.
xrange=[1.2,1.42]
yrange=[3.98,4.18]
xlabel=r'$J_R\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
ylabel=r'$J_Z\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
elif 'jrjp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
plotx= data[indx,0]*8.
ploty= data[indx,1]*8.
xrange=[1.2,1.42]
yrange=[-14.64,-14.23]
xlabel=r'$J_R\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
ylabel=r'$L_Z\,(220\,\mathrm{km\,s}^{-1}\,\mathrm{kpc})$'
elif 'dohist' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
dO1d= numpy.dot(dOdir,dO)
print "Misalignment:", numpy.arccos(numpy.sum(dOdir*progaa[-1,3:6])/numpy.sqrt(numpy.sum(dOdir**2.)*numpy.sum(progaa[-1,3:6]**2.)))/numpy.pi*180.-180.
dO1d[dO1d < 0.]*= -1.
bovy_plot.bovy_print()
bovy_plot.bovy_hist(dO1d,range=[0.,0.4],bins=61,
normed=True,
xlabel=r'$\Large|\Delta \mathbf{\Omega}_\parallel\Large|\,(\mathrm{Gyr}^{-1})$',
histtype='step',color='k',zorder=10)
#Overplot best-fit Gaussian
xs= numpy.linspace(0.,0.4,1001)
print numpy.mean(dO1d), numpy.std(dO1d)
bovy_plot.bovy_plot(xs,1./numpy.sqrt(2.*numpy.pi)/numpy.std(dO1d)\
*numpy.exp(-(xs-numpy.mean(dO1d))**2./2./numpy.var(dO1d)),
'--',color='k',overplot=True,lw=2.,zorder=2)
bestfit= optimize.fmin_powell(gausstimesvalue,
numpy.array([numpy.log(numpy.mean(dO1d)*2.),
numpy.log(numpy.std(dO1d))]),
args=(dO1d,))
print numpy.exp(bestfit)
bovy_plot.bovy_plot(xs,gausstimesvalue(bestfit,xs,nologsum=True),
'-',color='0.4',overplot=True,lw=2.,zorder=1)
bovy_plot.bovy_end_print(plotfilename)
return None
elif 'dahist' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Gram-Schmidt to get the perpendicular directions
v2= numpy.array([1.,0.,0.])
v3= numpy.array([0.,1.,0.])
u2= v2-numpy.sum(dOdir*v2)*dOdir
u2/= numpy.sqrt(numpy.sum(u2**2.))
u3= v3-numpy.sum(dOdir*v3)*dOdir-numpy.sum(u2*v3)*u2
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
dts= numpy.sum(dO*dangle,axis=0)/numpy.sum(dO**2.,axis=0)
#Rewind angles
dangle-= dO*dts
newdangle= numpy.empty_like(dangle)
newdangle[0,:]= numpy.dot(dOdir,dangle)
newdangle[1,:]= numpy.dot(u2,dangle)
newdangle[2,:]= numpy.dot(u3,dangle)
bovy_plot.bovy_print()
xmin= -0.015
bovy_plot.bovy_hist(newdangle[2,:].flatten(),range=[xmin,-xmin],bins=61,
xlabel=r'$|\Delta \mathbf{\theta}|$',
normed=True,lw=2.,
color='k',zorder=10,
histtype='step')
#Overplot best-fit Gaussian
xs= numpy.linspace(xmin,-xmin,1001)
bovy_plot.bovy_plot(xs,1./numpy.sqrt(2.*numpy.pi)/numpy.std(newdangle[1:,:])\
*numpy.exp(-(xs-numpy.mean(newdangle[1:,:]))**2./2./numpy.var(newdangle[1:,:])),
'--',color='k',overplot=True,lw=2.,zorder=0)
print "along", numpy.mean(newdangle[0,:]), numpy.std(newdangle[0,:])
print "perpendicular 1", numpy.mean(newdangle[1,:]), numpy.std(newdangle[1,:])
print "perpendicular 2", numpy.mean(newdangle[2,:]), numpy.std(newdangle[2,:])
bovy_plot.bovy_end_print(plotfilename)
return None
elif 'aparopar' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= numpy.fabs(numpy.dot(dO.T,dOdir))
xrange=[0.,1.3]
yrange=[0.1,0.3]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$\Large|\Delta \mathbf{\Omega}_\parallel\Large|\,(\mathrm{Gyr}^{-1})$'
fmt= 'k.'
elif 'aparoperp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= numpy.sqrt(numpy.sum(dO**2.,axis=0)\
-(numpy.dot(dO.T,dOdir))**2.)
print numpy.std(ploty)
xrange=[0.,1.3]
yrange=[0.,0.005]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$\Large|\Delta \mathbf{\Omega}_\perp\Large|\,(\mathrm{Gyr}^{-1})$'
fmt= 'k.'
elif 'aparaperp' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= numpy.sqrt(numpy.sum(dangle**2.,axis=0)\
-(numpy.dot(dangle.T,dOdir))**2.)
xrange=[0.,1.3]
yrange=[0.,0.03]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$\Large|\Delta \mathbf{\theta}_\perp\Large|$'
fmt= 'k.'
elif 'apartime' in plotfilename:
thetar= data[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
indx= numpy.fabs(thetar-numpy.pi) > (5.*numpy.median(numpy.fabs(thetar-numpy.median(thetar))))
thetar= thetar[indx]
thetap= data[:,7]
thetap= (numpy.pi+(thetap-numpy.median(thetap))) % (2.*numpy.pi)
thetap= thetap[indx]
thetaz= data[:,8]
thetaz= (numpy.pi+(thetaz-numpy.median(thetaz))) % (2.*numpy.pi)
thetaz= thetaz[indx]
#center around 0 (instead of pi)
thetar-= numpy.pi
thetap-= numpy.pi
thetaz-= numpy.pi
#Frequencies
Or= data[:,3]
Op= data[:,4]
Oz= data[:,5]
dOr= Or[indx]-numpy.median(Or)
dOp= Op[indx]-numpy.median(Op)
dOz= Oz[indx]-numpy.median(Oz)
dO= numpy.vstack((dOr,dOp,dOz))*bovy_conversion.freq_in_Gyr(220.,8.)
#Direction in which the stream spreads
dO4dir= copy.copy(dO)
dO4dir[:,dO4dir[:,0] < 0.]*= -1.
dOdir= numpy.median(dO4dir,axis=1)
dOdir/= numpy.sqrt(numpy.sum(dOdir**2.))
#Times
dangle= numpy.vstack((thetar,thetap,thetaz))
dts= numpy.sum(dO*dangle,axis=0)/numpy.sum(dO**2.,axis=0)
plotx= numpy.fabs(numpy.dot(dangle.T,dOdir))
ploty= dts
xrange=[0.,1.3]
yrange=[0.,5.]
xlabel= r'$\Large|\Delta \mathbf{\theta}_\parallel\Large|$'
ylabel= r'$t_s\,(\mathrm{Gyr})$'
fmt= 'k.'
bovy_plot.bovy_print()
bovy_plot.bovy_plot(plotx,ploty,fmt,
xlabel=xlabel,
ylabel=ylabel,
xrange=xrange,
yrange=yrange,zorder=5)
if includeorbit and 'araz' in plotfilename:
#plot frequency line
xs= numpy.array(xrange)
ys= (xs-numpy.pi)*progaa[-1,5]/progaa[-1,3]+numpy.pi
bovy_plot.bovy_plot(xs,ys,'k--',overplot=True,
zorder=0)
elif includeorbit and 'arap' in plotfilename:
#plot frequency line
xs= numpy.array(xrange)
ys= (xs-numpy.pi)*progaa[-1,4]/progaa[-1,3]+numpy.pi
bovy_plot.bovy_plot(xs,ys,'k--',overplot=True,
zorder=0)
elif includeorbit and 'oroz' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,3]*bovy_conversion.freq_in_Gyr(220.,8.),
progaa[-1,5]*bovy_conversion.freq_in_Gyr(220.,8.),
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
elif includeorbit and 'orop' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,3]*bovy_conversion.freq_in_Gyr(220.,8.),
progaa[-1,4]*bovy_conversion.freq_in_Gyr(220.,8.),
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
elif includeorbit and 'jrjz' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,0]*8.,
progaa[-1,2]*8.,
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
elif includeorbit and 'jrjp' in plotfilename:
bovy_plot.bovy_plot(progaa[-1,0]*8.,
progaa[-1,1]*8.,
'o',overplot=True,color='0.5',
mec='none',ms=8.,
zorder=0)
if includetrack and 'aparopar' in plotfilename:
#Calculate mean and std of Omegapar as a function of anglepar
das= numpy.linspace(0.,1.3,1001)
dOs= numpy.array([sdf.meanOmega(da,oned=True) for da in das])
sOs= numpy.array([sdf.sigOmega(da) for da in das])
bovy_plot.bovy_plot(das,dOs*bovy_conversion.freq_in_Gyr(220.,8),
'-',color='0.75',lw=2.,overplot=True,zorder=10)
pyplot.fill_between(das,(dOs+sOs)*bovy_conversion.freq_in_Gyr(220.,8),
(dOs-sOs)*bovy_conversion.freq_in_Gyr(220.,8),
color='0.6',zorder=1)
pyplot.fill_between(das,(dOs+2*sOs)*bovy_conversion.freq_in_Gyr(220.,8),
(dOs-2*sOs)*bovy_conversion.freq_in_Gyr(220.,8),
color='0.8',zorder=0)
#Also plot the apar at which t_d becomes important
pyplot.plot([sdf.meanOmega(0.01,oned=True)*sdf._tdisrupt,
sdf.meanOmega(0.01,oned=True)*sdf._tdisrupt],
[0.,0.3],
'k--')
elif includetrack and 'apartime' in plotfilename:
das= numpy.linspace(0.01,1.3,101)
mts= numpy.array([sdf.meantdAngle(da) for da in das])
sts= numpy.array([sdf.sigtdAngle(da) for da in das])
bovy_plot.bovy_plot(das,mts*bovy_conversion.time_in_Gyr(220.,8),
'-',color='0.75',lw=2.,overplot=True,zorder=10)
pyplot.fill_between(das,(mts+2*sts)*bovy_conversion.time_in_Gyr(220.,8),
(mts-2*sts)*bovy_conversion.time_in_Gyr(220.,8),
color='0.8',zorder=0)
pyplot.fill_between(das,(mts+sts)*bovy_conversion.time_in_Gyr(220.,8),
(mts-sts)*bovy_conversion.time_in_Gyr(220.,8),
color='0.6',zorder=1)
elif includetrack and 'aparaperp' in plotfilename:
das= numpy.linspace(0.01,1.3,101)
sas= numpy.array([sdf.sigangledAngle(da) for da in das])*numpy.sqrt(2.)
sass= numpy.array([sdf.sigangledAngle(da,simple=True) for da in das])*numpy.sqrt(2.)
pyplot.fill_between(das,0.,
(2*sas),
color='0.8',zorder=0)
pyplot.fill_between(das,0.,
(sas),
color='0.6',zorder=1)
pyplot.plot(das,sass,'--',color='w',zorder=7,lw=2.)
#ax= pyplot.gca()
#ax.set_rasterized(True)
bovy_plot.bovy_end_print(plotfilename)
def plot_stream_xz(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
includeorbit= True
if includeorbit:
npts= 201
pot= potential.LogarithmicHaloPotential(normalize=1.,q=0.9)
pts= numpy.linspace(0.,4.,npts)
#Calculate progenitor orbit around this point
pox= numpy.median(data[:,1])
poy= numpy.median(data[:,3])
poz= numpy.median(data[:,2])
povx= numpy.median(data[:,4])
povy= numpy.median(data[:,6])
povz= numpy.median(data[:,5])
pR,pphi,pZ= bovy_coords.rect_to_cyl(pox,poy,poz)
pvR,pvT,pvZ= bovy_coords.rect_to_cyl_vec(povx,povy,povz,pR,
pphi,pZ,cyl=True)
ppo= Orbit([pR/8.,pvR/220.,pvT/220.,pZ/8.,pvZ/220.,pphi])
pno= Orbit([pR/8.,-pvR/220.,-pvT/220.,pZ/8.,-pvZ/220.,pphi])
ppo.integrate(pts,pot)
pno.integrate(pts,pot)
pvec= numpy.zeros((3,npts*2-1))
pvec[0,:npts-1]= pno.x(pts)[::-1][:-1]
pvec[1,:npts-1]= pno.z(pts)[::-1][:-1]
pvec[2,:npts-1]= pno.y(pts)[::-1][:-1]
pvec[0,npts-1:]= ppo.x(pts)
pvec[1,npts-1:]= ppo.z(pts)
pvec[2,npts-1:]= ppo.y(pts)
pvec*= 8.
includetrack= True
if includetrack:
#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.))
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.))
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data[:,1],data[:,2],'k,',
xlabel=r'$X\,(\mathrm{kpc})$',
ylabel=r'$Z\,(\mathrm{kpc})$',
xrange=[0.,16.],
yrange=[-0.5,11.])
if includeorbit:
bovy_plot.bovy_plot(pox,poz,'o',color='0.5',mec='none',overplot=True,ms=8)
bovy_plot.bovy_plot(pvec[0,:],pvec[1,:],'k--',overplot=True,lw=1.)
if includetrack:
d1= 'x'
d2= 'z'
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True)
#Also create inset
pyplot.plot([12.,12.],[0.5,7.5],'k-')
pyplot.plot([14.5,14.5],[0.5,7.5],'k-')
pyplot.plot([12.,14.5],[0.5,0.5],'k-')
pyplot.plot([12.,14.5],[7.5,7.5],'k-')
pyplot.plot([12.,8.8],[7.5,7.69],'k:')
pyplot.plot([12.,8.8],[0.5,-0.21],'k:')
insetAxes= pyplot.axes([0.15,0.12,0.4,0.55])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(data[:,1],data[:,2],'k,',
overplot=True)
bovy_plot.bovy_plot(pvec[0,:],pvec[1,:],'k--',overplot=True,lw=1.)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(12.,14.5)
insetAxes.set_ylim(.5,7.5)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
bovy_plot.bovy_end_print(plotfilename)
def plot_stream_lb(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1_evol_hitres_01312.dat'),
delimiter=',')
aadata= numpy.loadtxt(os.path.join(_STREAMSNAPAADIR,
'gd1_evol_hitres_aa_01312.dat'),
delimiter=',')
thetar= aadata[:,6]
thetar= (numpy.pi+(thetar-numpy.median(thetar))) % (2.*numpy.pi)
sindx= numpy.fabs(thetar-numpy.pi) > (1.5*numpy.median(numpy.fabs(thetar-numpy.median(thetar)))) #stars in the stream
#Transform to (l,b)
XYZ= bovy_coords.galcenrect_to_XYZ(data[:,1],data[:,3],data[:,2],Xsun=8.)
lbd= bovy_coords.XYZ_to_lbd(XYZ[0],XYZ[1],XYZ[2],degree=True)
vXYZ= bovy_coords.galcenrect_to_vxvyvz(data[:,4],data[:,6],data[:,5],
vsun=[0.,30.24*8.,0.])
vlbd= bovy_coords.vxvyvz_to_vrpmllpmbb(vXYZ[0],vXYZ[1],vXYZ[2],
lbd[:,0],lbd[:,1],lbd[:,2],
degree=True)
includeorbit= True
if includeorbit:
npts= 201
pot= potential.LogarithmicHaloPotential(normalize=1.,q=0.9)
pts= numpy.linspace(0.,4.,npts)
#Calculate progenitor orbit around this point
pox= numpy.median(data[:,1])
poy= numpy.median(data[:,3])
poz= numpy.median(data[:,2])
povx= numpy.median(data[:,4])
povy= numpy.median(data[:,6])
povz= numpy.median(data[:,5])
pR,pphi,pZ= bovy_coords.rect_to_cyl(pox,poy,poz)
pvR,pvT,pvZ= bovy_coords.rect_to_cyl_vec(povx,povy,povz,pR,
pphi,pZ,cyl=True)
ppo= Orbit([pR/8.,pvR/220.,pvT/220.,pZ/8.,pvZ/220.,pphi])
pno= Orbit([pR/8.,-pvR/220.,-pvT/220.,pZ/8.,-pvZ/220.,pphi])
ppo.integrate(pts,pot)
pno.integrate(pts,pot)
pvec= numpy.zeros((6,npts*2-1))
pvec[0,:npts-1]= pno.x(pts)[::-1][:-1]
pvec[1,:npts-1]= pno.z(pts)[::-1][:-1]
pvec[2,:npts-1]= pno.y(pts)[::-1][:-1]
pvec[0,npts-1:]= ppo.x(pts)
pvec[1,npts-1:]= ppo.z(pts)
pvec[2,npts-1:]= ppo.y(pts)
pvec[3,:npts-1]= -pno.vx(pts)[::-1][:-1]
pvec[4,:npts-1]= -pno.vz(pts)[::-1][:-1]
pvec[5,:npts-1]= -pno.vy(pts)[::-1][:-1]
pvec[3,npts-1:]= ppo.vx(pts)
pvec[4,npts-1:]= ppo.vz(pts)
pvec[5,npts-1:]= ppo.vy(pts)
pvec[:3,:]*= 8.
pvec[3:,:]*= 220.
pXYZ= bovy_coords.galcenrect_to_XYZ(pvec[0,:],pvec[2,:],pvec[1,:],
Xsun=8.)
plbd= bovy_coords.XYZ_to_lbd(pXYZ[0],pXYZ[1],pXYZ[2],degree=True)
pvXYZ= bovy_coords.galcenrect_to_vxvyvz(pvec[3,:],pvec[5,:],pvec[4,:],
vsun=[0.,30.24*8.,0.])
pvlbd= bovy_coords.vxvyvz_to_vrpmllpmbb(pvXYZ[0],pvXYZ[1],pvXYZ[2],
plbd[:,0],plbd[:,1],plbd[:,2],
degree=True)
includetrack= True
if includetrack:
#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,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
vsun=[0.,30.24*8.,0.],
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
multi=_NTRACKCHUNKS)
#Plot
bovy_plot.bovy_print(fig_width=8.25,fig_height=3.5)
if 'ld' in plotfilename:
lbindx= 2
ylabel=r'$\mathrm{Distance}\,(\mathrm{kpc})$'
yrange=[0.,30.]
elif 'lvlos' in plotfilename:
lbindx= 0
ylabel=r'$V_\mathrm{los}\,(\mathrm{km\,s}^{-1})$'
yrange=[-500.,500.]
elif 'lpmll' in plotfilename:
lbindx= 1
ylabel=r'$\mu_{l}\cos b\,(\mathrm{mas\,yr}^{-1})$'
yrange=[-2.,13.5]
elif 'lpmbb' in plotfilename:
lbindx= 2
ylabel=r'$\mu_{b}\,(\mathrm{mas\,yr}^{-1})$'
yrange=[-8.,7.]
else:
lbindx= 1
yrange=[-10.,60.]
ylabel=r'$\mathrm{Galactic\ latitude}\,(\mathrm{deg})$'
if 'vlos' in plotfilename or 'pm' in plotfilename:
#Stream
bovy_plot.bovy_plot(lbd[sindx,0],vlbd[sindx,lbindx],'k,',
xlabel=r'$\mathrm{Galactic\ longitude}\,(\mathrm{deg})$',
ylabel=ylabel,
xrange=[0.,290.],
yrange=yrange)
#Progenitor
pindx= copy.copy(True-sindx)
pindx[0:9900]= False
bovy_plot.bovy_plot(lbd[pindx,0],vlbd[pindx,lbindx],'k,',overplot=True)
else:
bovy_plot.bovy_plot(lbd[sindx,0],lbd[sindx,lbindx],'k,',
xlabel=r'$\mathrm{Galactic\ longitude}\,(\mathrm{deg})$',
ylabel=ylabel,
xrange=[0.,290.],
yrange=yrange)
#Progenitor
pindx= copy.copy(True-sindx)
pindx[0:9900]= False
bovy_plot.bovy_plot(lbd[pindx,0],lbd[pindx,lbindx],'k,',overplot=True)
if includeorbit:
if 'vlos' in plotfilename or 'pm' in plotfilename:
bovy_plot.bovy_plot(plbd[npts,0],pvlbd[npts,lbindx],
'o',color='0.5',mec='none',overplot=True,ms=8)
bovy_plot.bovy_plot(plbd[:,0],pvlbd[:,lbindx],'k--',overplot=True)
else:
bovy_plot.bovy_plot(plbd[npts,0],plbd[npts,lbindx],
'o',color='0.5',mec='none',overplot=True,ms=8)
bovy_plot.bovy_plot(plbd[:,0],plbd[:,lbindx],'k--',overplot=True)
if includetrack:
d1= 'll'
if 'vlos' in plotfilename:
d2= 'vlos'
elif 'pmll' in plotfilename:
d2= 'pmll'
elif 'pmbb' in plotfilename:
d2= 'pmbb'
elif 'ld' in plotfilename:
d2= 'dist'
else:
d2= 'bb'
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Insets
if 'vlos' in plotfilename:
xmin, xmax= 220., 250.
ymin, ymax= 230., 390.
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,152.],[ymin,-100.],'k:')
pyplot.plot([xmin,152.],[ymax,460.],'k:')
insetAxes= pyplot.axes([0.15,0.42,0.38,0.45])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],vlbd[:,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([240.,240.],[250.,275.],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(241.,255.,r'$25\,\mathrm{km\,s}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
elif 'pmll' in plotfilename:
xmin, xmax= 158.,205.
ymin, ymax= 10.5, 13.
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,113.],[ymin,6.1],'k:')
pyplot.plot([xmax,227.],[ymin,6.1],'k:')
insetAxes= pyplot.axes([0.43,0.12,0.3,0.4])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[sindx,0],vlbd[sindx,lbindx],'k,',
overplot=True)
bovy_plot.bovy_plot(lbd[pindx,0],vlbd[pindx,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([168.5,168.5],[10.75,11.25],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(169.8,10.875,r'$0.5\,\mathrm{mas\,yr}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
elif 'pmbb' in plotfilename:
xmin, xmax= 185., 230.
ymin, ymax= -7.4, -4.7
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,159.],[ymax,1.],'k:')
pyplot.plot([xmax,287.],[ymax,1.],'k:')
#2nd inset
xmin2, xmax2= 80., 125.
ymin2, ymax2= 4.2, 5.8
pyplot.plot([xmin2,xmin2],[ymin2,ymax2],'k-')
pyplot.plot([xmax2,xmax2],[ymin2,ymax2],'k-')
pyplot.plot([xmin2,xmax2],[ymin2,ymin2],'k-')
pyplot.plot([xmin2,xmax2],[ymax2,ymax2],'k-')
pyplot.plot([xmin2,8.],[ymin2,-1.],'k:')
pyplot.plot([xmax2,155.],[ymin2,-1.],'k:')
insetAxes= pyplot.axes([0.55,0.57,0.34,0.3])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],vlbd[:,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([200.,200.],[-5.75,-5.25],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(201.25,-5.675,r'$0.5\,\mathrm{mas\,yr}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
#Also make second inset
insetAxes= pyplot.axes([0.14,0.12,0.4,0.35])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],vlbd[:,lbindx],'k,',
overplot=True)
sdft.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([103.,103.],[4.35,4.85],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(104.,4.5,r'$0.5\,\mathrm{mas\,yr}^{-1}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin2,xmax2)
insetAxes.set_ylim(ymin2,ymax2)
elif 'ld' in plotfilename:
xmin, xmax= 158., 227.
ymin, ymax= 7.7,9.5
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,70.],[ymax,18.5],'k:')
pyplot.plot([xmax,248.],[ymax,18.5],'k:')
#2nd inset
xmin2, xmax2= 72.,100.
ymin2, ymax2= 11.5, 16.1
pyplot.plot([xmin2,xmin2],[ymin2,ymax2],'k-')
pyplot.plot([xmax2,xmax2],[ymin2,ymax2],'k-')
pyplot.plot([xmin2,xmax2],[ymin2,ymin2],'k-')
pyplot.plot([xmin2,xmax2],[ymax2,ymax2],'k-')
pyplot.plot([xmin2,66.5],[ymax2,15.85],'k:')
pyplot.plot([xmin2,66.5],[ymin2,0.5],'k:')
insetAxes= pyplot.axes([0.31,0.6,0.48,0.27])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[sindx,0],lbd[sindx,lbindx],'k,',
overplot=True)
bovy_plot.bovy_plot(lbd[pindx,0],lbd[pindx,lbindx],'k,',
overplot=True)
sdf.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([168.,168.],[8.7,9.2],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(169.7,8.8,r'$0.5\,\mathrm{kpc}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
#Also make second inset
insetAxes= pyplot.axes([0.13,0.12,0.17,0.4])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[:,0],lbd[:,lbindx],'k,',
overplot=True)
sdft.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([74.,74.],[11.95,12.45],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(76.,12.01,r'$0.5\,\mathrm{kpc}$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin2,xmax2)
insetAxes.set_ylim(ymin2,ymax2)
else:
xmin, xmax= 90., 165.
ymin, ymax= 47., 59.
pyplot.plot([xmin,xmin],[ymin,ymax],'k-')
pyplot.plot([xmax,xmax],[ymin,ymax],'k-')
pyplot.plot([xmin,xmax],[ymin,ymin],'k-')
pyplot.plot([xmin,xmax],[ymax,ymax],'k-')
pyplot.plot([xmin,70.],[ymin,31.],'k:')
pyplot.plot([xmax,213.],[ymin,31.],'k:')
insetAxes= pyplot.axes([0.31,0.12,0.38,0.45])
pyplot.sca(insetAxes)
bovy_plot.bovy_plot(lbd[sindx,0],lbd[sindx,lbindx],'k,',
overplot=True)
bovy_plot.bovy_plot(lbd[pindx,0],lbd[pindx,lbindx],'k,',
overplot=True)
sdft.plotProgenitor(d1=d1,d2=d2,color='k',ls='--',
overplot=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.)
#Plot approximate scale
bovy_plot.bovy_plot([115.,115.],[48.5,49.5],'k-',lw=2.,
overplot=True)
bovy_plot.bovy_text(117.2,48.5,r'$1^\circ$',
size=16.)
nullfmt = NullFormatter() # no labels
insetAxes.xaxis.set_major_formatter(nullfmt)
insetAxes.yaxis.set_major_formatter(nullfmt)
insetAxes.set_xlim(xmin,xmax)
insetAxes.set_ylim(ymin,ymax)
pyplot.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off', # ticks along the bottom edge are off
right='off') # ticks along the top edge are off
bovy_plot.bovy_end_print(plotfilename)
def make_action_movie(aa=False):
skip= 1
if aa:
savedir= '../movies/aa/'
savedirpng= '../movies/aa/pngs/'
moviefilename= os.path.join(savedir,'aa_orbit_skip%i.mpg' % skip)
basefilename= 'aa_'
xrange=[-0.7,7.]
yrange=[-0.7,7.]
xlabel=r'$\theta_R$'
ylabel=r'$\theta_Z$'
else:
savedir= '../movies/actions/'
savedirpng= '../movies/actions/pngs/'
basefilename= 'real_'
moviefilename= os.path.join(savedir,'real_orbit_skip%i.mpg' % skip)
xrange=[0.,7.]
yrange=[-4.25,4.25]
xlabel=r'$R$'
ylabel=r'$Z$'
pot= potential.MWPotential
o= Orbit([1.,0.8,1.5,0.8,0.5,0.])
ts= numpy.linspace(0.,300.,1000)
o.integrate(ts,pot)
Rs= numpy.zeros(len(ts)+23)
zs= numpy.zeros(len(ts)+23)
Rs[:23]= numpy.nan
zs[:23]= numpy.nan
if aa:
aAIA= actionAngleIsochroneApprox(b=5.,pot=pot)
acfs= aAIA.actionsFreqsAngles(o,maxn=3)
Rs[23:]= (acfs[6]+acfs[3]*ts) % (2.*numpy.pi)
zs[23:]= (acfs[8]+acfs[5]*ts) % (2.*numpy.pi)
else:
Rs[23:]= o.R(ts)
zs[23:]= o.z(ts)
print numpy.nanmax(Rs)
print numpy.nanmax(numpy.fabs(zs))
if True:
for ii in range(len(ts)-1):
bovy_plot.bovy_print()
bovy_plot.bovy_plot(Rs[ii:ii+24:skip],zs[ii:ii+24:skip],
scatter=True,color='k',
s=numpy.arange(24/skip)*skip,
xlabel=xlabel,
ylabel=ylabel,
xrange=xrange,
yrange=yrange)
bovy_plot.bovy_end_print(os.path.join(savedirpng,basefilename+'%s.png' % str(ii).zfill(5)))
#Turn into movie
framerate= 25
bitrate= 1000000
try:
subprocess.check_call(['ffmpeg',
'-i',
os.path.join(savedirpng,basefilename+'%05d.png'),
'-y',
'-r',str(framerate),
'-b', str(bitrate),
moviefilename])
except subprocess.CalledProcessError:
print "'ffmpeg' failed"
return None
def plot_stream_xz(plotfilename):
#Read stream
data= numpy.loadtxt(os.path.join(_STREAMSNAPDIR,'gd1-hisigv_evol_00041.dat'),
delimiter=',')
includeorbit= True
if includeorbit:
npts= 201
pot= potential.LogarithmicHaloPotential(normalize=1.,q=0.9)
pts= numpy.linspace(0.,17.,npts)
#Calculate progenitor orbit around this point
pox= numpy.median(data[:,1])
poy= numpy.median(data[:,3])
poz= numpy.median(data[:,2])
povx= numpy.median(data[:,4])
povy= numpy.median(data[:,6])
povz= numpy.median(data[:,5])
pR,pphi,pZ= bovy_coords.rect_to_cyl(pox,poy,poz)
pvR,pvT,pvZ= bovy_coords.rect_to_cyl_vec(povx,povy,povz,pR,
pphi,pZ,cyl=True)
ppo= Orbit([pR/8.,pvR/220.,pvT/220.,pZ/8.,pvZ/220.,pphi])
pno= Orbit([pR/8.,-pvR/220.,-pvT/220.,pZ/8.,-pvZ/220.,pphi])
ppo.integrate(pts,pot)
pno.integrate(pts,pot)
pvec= numpy.zeros((3,npts*2-1))
pvec[0,:npts-1]= pno.x(pts)[::-1][:-1]
pvec[1,:npts-1]= pno.z(pts)[::-1][:-1]
pvec[2,:npts-1]= pno.y(pts)[::-1][:-1]
pvec[0,npts-1:]= ppo.x(pts)
pvec[1,npts-1:]= ppo.z(pts)
pvec[2,npts-1:]= ppo.y(pts)
pvec*= 8.
includetrack= True
if includetrack:
#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.),
deltaAngleTrack=13.5,multi=_NTRACKCHUNKS)
sdft= streamdf(_SIGV/220.,progenitor=Orbit(obs),pot=lp,aA=aAI,
leading=False,nTrackChunks=_NTRACKCHUNKS,
tdisrupt=4.5/bovy_conversion.time_in_Gyr(220.,8.),
deltaAngleTrack=13.5,multi=_NTRACKCHUNKS)
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot(data[:,1],data[:,2],'k,',
xlabel=r'$X\,(\mathrm{kpc})$',
ylabel=r'$Z\,(\mathrm{kpc})$',
xrange=[-30.,30.],
yrange=[-20.,20])
if includeorbit:
bovy_plot.bovy_plot(pox,poz,'o',color='0.5',mec='none',overplot=True,ms=8)
bovy_plot.bovy_plot(pvec[0,:],pvec[1,:],'k--',overplot=True,lw=1.)
if includetrack:
d1= 'x'
d2= 'z'
sdf.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True)
sdft.plotTrack(d1=d1,d2=d2,interp=True,color='k',spread=0,
overplot=True,lw=1.,scaleToPhysical=True)
bovy_plot.bovy_text(r'$M^p = 2\times 10^7\,M_\odot$'+'\n'+
r'$\sigma_v^p = 14\,\mathrm{km\,s}^{-1}$',
top_left=True,size=16.)
bovy_plot.bovy_end_print(plotfilename)