def modelvlosgal(R,phi,l,beta=0.,vc=220.,vtsun=_VTSUN,vrsun=_VRSUN):
sinl= numpy.sin(l)
cosl= numpy.cos(l)
vlosgal= -vrsun*cosl+vtsun*sinl\
-(vc*(R/8.)**beta\
-asymmetricDriftModel.va(R/8.,31.4/vc,
vc=(R/8.)**beta,hR=3./8.,
hs=33.3)*vc)*numpy.sin(l+phi)
return -vlosgal
def dvlosgal(data,beta=0.,vc=220.,vtsun=_VTSUN,vrsun=_VRSUN,
hR=3./8.,hs=33.3):
l= data['GLON']*_DEGTORAD
sinl= numpy.sin(data['GLON']*_DEGTORAD)
cosl= numpy.cos(data['GLON']*_DEGTORAD)
sinb= numpy.sin(data['GLAT']*_DEGTORAD)
cosb= numpy.cos(data['GLAT']*_DEGTORAD)
vlosgal= data['VHELIO_AVG']/cosb-vrsun*cosl+vtsun*sinl\
+_VZSUN*sinb/cosb\
-(vc*(data['RC_GALR']/8.)**beta\
-asymmetricDriftModel.va(data['RC_GALR']/8.,31.4/vc,
vc=(data['RC_GALR']/8.)**beta,hR=hR,
hs=hs)*vc)*numpy.sin(l+data['RC_GALPHI'])
return vlosgal
def plot_asymmetricdrift(outdir='../tex'):
#First plot the fiducial model for vcva/sr2
Ro = 8.
hR = 3. / Ro
hs = 8. / Ro
vc = 1.
#Zero-ish
kinematicsFile = '../data/axi_va-kinematics_0.0.sav'
kinematicsFile = open(kinematicsFile, 'rb')
vas = pickle.load(kinematicsFile) #vas has va/sigmaR^2(R_0)
xs = pickle.load(kinematicsFile)
kinematicsFile.close()
vas = vas[15] # dispersion = small
nrs = 101
rs = numpy.linspace(0.1, 2.2, nrs)
vaInterp = interpolate.InterpolatedUnivariateSpline(rs, vas)
#Plot
rs = numpy.linspace(4., 20., 1001)
so = 0.2
#Hot
sigmaR_hot = 0.2 * numpy.exp(-(rs / Ro - 1.) / hs)
vas_fid = va(rs / Ro, 0.2, hR=hR, hs=hs)
#Cold
dlnnusR2dlnR_correct = dlnnusR2dlnR(hR_fid, hs_fid) - dlnnusR2dlnR(hR, hs)
vas_cold = vaInterp(rs / Ro)
sigmaR_cold = 0.1 * numpy.exp(-(rs / Ro - 1.) / hs)
vas_cold = 0.1**2. / vc * (
vas_cold + 0.5 * dlnnusR2dlnR_correct * numpy.exp(-2. *
(rs / Ro - 1.)))
bovy_plot.bovy_print(fig_height=3.)
lines = []
lines.append(
bovy_plot.bovy_plot(rs,
vas_fid / sigmaR_hot**2.,
'k-',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$V_c\,V_a / \sigma_R^2$',
xrange=[0., 25.],
yrange=[0., 6.5]))
lines.append(
bovy_plot.bovy_plot(rs,
vas_cold / sigmaR_cold**2.,
'--',
color='0.4',
overplot=True))
bovy_plot.bovy_text(r'$h_R = 3\, \mathrm{kpc}$' + '\n' +
r'$h_\sigma = 8\, \mathrm{kpc}$',
top_left=True)
labels = [
r'$\sigma_R(R_0) = 0.2\,V_c(R_0)$', r'$\sigma_R(R_0) = 0.1\,V_c(R_0)$'
]
l1 = pyplot.legend(lines, labels, loc=4, frameon=False, numpoints=1)
bovy_plot.bovy_end_print(os.path.join(outdir, 'vaR.ps'))
#Now plot differences from fiducial
vas_fid = va(rs / Ro, 0.14, hR=hR, hs=hs) #different fiducial
bovy_plot.bovy_print(fig_height=3.)
bovy_plot.bovy_plot([rs[0], rs[-1]], [0., 0.],
'k-',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$(V_a - V_a^{\mathrm{fid}}) / V_c(R_0)$',
xrange=[0., 25.],
yrange=[-0.075, 0.075])
#hR=2 kpc
bovy_plot.bovy_plot(rs,
va(rs / Ro, 0.14, hR=2. / Ro, hs=hs) - vas_fid,
overplot=True,
ls='-.',
color='k')
bovy_plot.bovy_text(0.5, 0.005, r'$h_R = 2\, \mathrm{kpc}$')
bovy_plot.bovy_text(r'$\sigma_R(R_0) = 0.14\,V_c(R_0)$', bottom_right=True)
bovy_plot.bovy_plot(rs,
va(rs / Ro, 0.14, hR=4. / Ro, hs=hs) - vas_fid,
overplot=True,
ls='--',
color='k')
bovy_plot.bovy_text(0.5, -0.02, r'$h_R = 4\, \mathrm{kpc}$')
#hs=6 kpc
bovy_plot.bovy_plot(rs,
va(rs / Ro, 0.14, hR=hR, hs=6. / Ro) - vas_fid,
overplot=True,
ls=':',
color='k')
bovy_plot.bovy_text(.5, 0.035, r'$h_\sigma = 6\, \mathrm{kpc}$')
#hs=6 kpc
bovy_plot.bovy_plot(rs,
va(rs / Ro, 0.14, hR=hR, hs=242. / Ro) - vas_fid,
overplot=True,
ls='-',
color='0.65')
bovy_plot.bovy_text(17.5, 0.055, r'$R_0/h_\sigma = 0.034$')
bovy_plot.bovy_end_print(os.path.join(outdir, 'vaR_diffs.ps'))
#Now plot the actual model used in the bestfit
bovy_plot.bovy_print(fig_height=3.)
bovy_plot.bovy_plot(rs,
va(rs / Ro, 0.14, hR=hR, hs=242. / Ro),
'k-',
xlabel=r'$R\ [\mathrm{kpc}]$',
ylabel=r'$V_a / V_c(R_0)$',
xrange=[0., 25.],
yrange=[0., 0.08])
bovy_plot.bovy_plot(rs,
va(rs / Ro, 0.14, hR=hR, hs=1.),
'k--',
overplot=True)
bovy_plot.bovy_text(r'$h_R = 3\, \mathrm{kpc}$' + '\n' +
r'$R_0/h_\sigma = 0.034$',
top_right=True)
bovy_plot.bovy_text(r'$h_R = 3\, \mathrm{kpc}$' + '\n' +
r'$R_0/h_\sigma = 1$',
top_left=True)
bovy_plot.bovy_text(7., 0.07125, r'$\sigma_R(R_0) = 0.14\,V_c(R_0)$')
bovy_plot.bovy_end_print(os.path.join(outdir, 'vaR_used.ps'))
