def veldist_1d_rolr(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_rolr
PURPOSE:
make a plot showing the influence of the bar R_OLR
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-09-11 - Written - Bovy (NYU)
"""
rolrs= [0.85,0.9,0.95]
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
basesavefilename= os.path.join(saveDir,'rolr_')
for rolr in rolrs:
thissavefilename= basesavefilename+'%.3f.sav' % rolr
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at R_OLR %.2f" % rolr
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at R_OLR %.2f" % rolr
potparams= (rolr,0.01,25.*_degtorad,.8,None)
vlosd= predictVlos(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=potparams)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
savefile.close()
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[1],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.amax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[0],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[2],ls='-',color='0.5',
overplot=True,zorder=2,lw=1.5)
plot.bovy_text(r'$\mathrm{bar}\ R_{\mathrm{OLR}}$',title=True)
plot.bovy_text(0.36,.75,r'$R_{\mathrm{OLR}} = 0.95\ R_0$'+'\n'+r'$R_{\mathrm{OLR}} = 0.90\ R_0$'+ '\n'+r'$R_{\mathrm{OLR}} = 0.85\ R_0$')
plot.bovy_end_print(plotfilename)
def veldist_1d_Rphi(plotfilename,nx=100,ny=20,dx=_XWIDTH/20.,dy=_YWIDTH/20.,
ngrid=201,rrange=[0.7,1.3],
phirange=[-m.pi/2.,m.pi/2.],
saveDir='../bar/1dLarge/',normalize=True,
row=None):
"""
NAME:
veldist_1d_Rphi
PURPOSE:
plot how the los-velocity distribution changes as a function of
R and phi
INPUT:
nx - number of plots in the x-direction
ny - number of plots in the y direction
dx - x-spacing
dy - y-spacing
ngrid - number of gridpoints to evaluate the density on
rrange - range of Galactocentric radii to consider
phirange - range of Galactic azimuths to consider
saveDir - directory to save the pickles in
normalize - if True (default), normalize the los-vd to integrate to one
row - if set to a row number, calculate the los-velocity distributions
for this row only, and do not plot anything (just save for later)
OUTPUT:
plot!
HISTORY:
2010-04-21 - Written - Bovy (NYU)
"""
if row == None:
rowStart= 0
rowEnd= nx
calcOnly= False
else:
rowStart= row
rowEnd= rowStart+1
calcOnly= True
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
picklebasename= '1d_%i_%i_%i_%.1f_%.1f_%.1f_%.1f' % (nx,ny,ngrid,rrange[0],rrange[1],phirange[0],phirange[1])
if not os.path.exists(saveDir):
os.mkdir(saveDir)
left, bottom = 0.1, 0.1
width= (nx*_XWIDTH+(nx-1)*dx)/(1.-2.*left)
height= (ny*_YWIDTH+(ny-1)*dy)/(1.-2.*bottom)
if not calcOnly:
plot.bovy_print(fig_width=width,fig_height=height,
xtick_major_size=2.,ytick_major_size=2.,
xtick_minor_size=0.,ytick_minor_size=0.)
fig= pyplot.figure()
#Make theta-R axes
fudge= 8.0
thisax= fig.add_axes([left-_XWIDTH/width/fudge,
bottom-_XWIDTH/height/fudge,
1.+2*_XWIDTH/width/fudge-2*left,
1.+2*_XWIDTH/height/fudge-2*bottom])
xrange= sc.array(phirange)*_radtodeg
yrange=rrange
thisax.xaxis.set_label_text(r'$\mathrm{Galactocentric\ azimuth}\ [\mathrm{deg}]$')
thisax.set_xlim(-90.01,90.01)
thisax.yaxis.set_label_text(r'$\mathrm{Galactocentric\ radius}\ / R_0$')
thisax.set_ylim(yrange[0],yrange[1])
thisax.xaxis.set_major_locator(ticker.MultipleLocator(10.))
for ii in range(rowStart,rowEnd):
for jj in range(ny):
if not calcOnly:
thisax= fig.add_axes([left+ii*(_XWIDTH+dx)/width,
bottom+jj*(_YWIDTH+dy)/height,
_XWIDTH/width,_YWIDTH/height])
thisR= (rrange[0]+(rrange[1]-rrange[0])/
(ny*_YWIDTH+(ny-1)*dy)*(jj*(_YWIDTH+dy)+_YWIDTH/2.))
thisphi= (phirange[0]+(phirange[1]-phirange[0])/
(nx*_XWIDTH+(nx-1)*dx)*(ii*(_XWIDTH+dx)+_XWIDTH/2.))
thissavefilename= os.path.join(saveDir,picklebasename+'_%i_%i.sav' %(ii,jj))
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at %.1f, %.1f ..." %(thisR,thisphi)
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
axivlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at %.1f, %.1f ..." %(thisR,thisphi)
vlosd= predictVlos(vloslinspace,
l=thisphi,
d=thisR,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=(0.9,0.01,25.*_degtorad,.8,None))
axivlosd= predictVlos(vloslinspace,
l=thisphi,
d=thisR,
distCoord='GCGC',
pot='bar',beta=0.,t=-0.00001,
potparams=(0.9,0.0,25.*_degtorad,.8,None))
if normalize:
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
axivlosd= axivlosd/(sc.nansum(axivlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
pickle.dump(axivlosd,savefile)
savefile.close()
if calcOnly:
continue
fig.sca(thisax)
plot.bovy_plot(vloss,vlosd,'k',
overplot=True,zorder=3)
plot.bovy_plot(vloss,axivlosd,ls='-',color='0.5',
overplot=True,zorder=2)
thisax.set_xlim(vloslinspace[0],vloslinspace[1])
thisax.set_ylim(0.,sc.amax(sc.concatenate((axivlosd,vlosd)))*1.1)
thisax.xaxis.set_ticklabels('')
thisax.yaxis.set_ticklabels('')
if not calcOnly:
plot.bovy_end_print(plotfilename)
def apogee_figures(
plotfilename,
savefilename=None,
bar_angle=25.0,
dt=None,
l=None,
rolr=None,
bar_strength=None,
slope=None,
vlosgrid=201,
dgrid=101,
conditional=False,
dmax=10.0 / 8.0,
):
"""
NAME:
apogee_figures
PURPOSE:
make a vlos-d plot for APOGEE
INPUT:
plotfilename - name of the file the figure will be saved to
savefilename - name of the file the velocity distributions will
be saved to
bar_angle= angle between the GC-Sun lin and the bar major axis
dt= - time to integrate for (in bar-periods)
l= - Galactic longitude
rolr= radius of outer lindblad radius
bar_strength= strength of the bar
slop= slope of the rotation curve (power-law index)
conditional= if True, normalize each velocity distribution independently
OUTPUT:
saves velocity distributions to pickle save file and produces plot
HISTORY:
2011-03-19 - Written - Bovy (NYU)
"""
# Grid
vlosgrid = _VLOSGRID
dgrid = _DGRID
vloslinspace = (-0.9, 0.9, vlosgrid)
vloss = sc.linspace(*vloslinspace)
dlinspace = (0.0001, dmax, dgrid)
ds = sc.linspace(*dlinspace)
# Set up parameters
potparams = (rolr, bar_strength, bar_angle * _degtorad, 0.8, None)
if os.path.exists(savefilename):
savefile = open(savefilename, "rb")
vlosds = pickle.load(savefile)
dd = pickle.load(savefile)
savefile.close()
else:
vlosds = []
dd = 0
while dd < dgrid:
print "Working on %i / %i ..." % (dd + 1, dgrid)
# Calculate vlos for this distance
if dt is None:
vlosd = predictVlos(
vloslinspace, l=(l * _degtorad), d=ds[dd], distCoord="Sun", pot="bar", beta=slope, potparams=potparams
)
else:
vlosd = predictVlos(
vloslinspace,
l=(l * _degtorad),
d=ds[dd],
distCoord="Sun",
pot="bar",
beta=slope,
potparams=potparams,
t=dt,
)
vlosds.append(vlosd)
dd += 1
# Save
savefile = open(savefilename, "wb")
pickle.dump(vlosds, savefile)
pickle.dump(dd, savefile)
savefile.close()
# Plot
if conditional:
newvlosds = []
for vlosd in vlosds:
newvlosds.append(vlosd / (sc.nansum(vlosd) * (vloss[1] - vloss[0])))
vlosds = newvlosds
bovy_plot.bovy_print()
bovy_plot.bovy_dens2d(
sc.array(vlosds).T,
origin="lower",
cmap="gist_yarg",
aspect=0.66,
xlabel=r"$d\ /\ R_0$",
ylabel=r"$(v_{\mathrm{los}} - \vec{v}_c \cdot \vec{d})\ /\ v_0$",
yrange=sc.array([vloslinspace[0], vloslinspace[1]]),
xrange=sc.array([dlinspace[0], dlinspace[1]]),
contours=True,
cntrmass=True,
cntrcolors=["w", "w", "w", "w", "w", "k", "k", "k", "k", "k"],
)
if bar_strength == 0.0:
bovy_plot.bovy_text(r"$l = %i^\circ$" % int(l), top_right=True)
else:
bovy_plot.bovy_text(
r"$l = %i^\circ$" % int(l)
+ "\n"
+ r"$R_{\mathrm{OLR}} = %3.1f$" % rolr
+ "\n"
+ r"$\alpha = %5.3f$" % bar_strength
+ "\n"
+ r"$\phi_{\mathrm{bar}} = %i^\circ$" % int(bar_angle),
top_right=True,
)
bovy_plot.bovy_end_print(plotfilename)
def veldist_1d_apogee(plotfilename,l=250./180.*m.pi,d=0.25,
ngrid=201,saveDir='../bar/apogee/'):
"""
NAME:
veldist_1d_apogee
PURPOSE:
make a plot showing a 1d velocity distribution in an apogee los
INPUT:
plotfilename - filename for figure
l - Galactic longitude
d - distance from the Sun
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-05-28 - Written - Bovy (NYU)
"""
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
basesavefilename= os.path.join(saveDir,'apogee_')
thissavefilename= basesavefilename+'%.2f_%.2f.sav' % (d,l)
if os.path.exists(thissavefilename):
print "Restoring apogee los-velocity distribution at d,l = %.2f,%.2f" % (d,l)
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
axivlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating apogee los-velocity distribution at d,l = %.2f,%.2f" % (d,l)
potparams= (0.9,0.01,25.*_degtorad,.8,None)
vlosd= predictVlos(vloslinspace,
l=l,
d=d,
distCoord='sun',
pot='bar',beta=0.,
potparams=potparams)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
potparams= (0.9,0.00,25.*_degtorad,.8,None)
axivlosd= predictVlos(vloslinspace,
l=l,
d=d,
t=0.01,
distCoord='sun',
pot='bar',beta=0.,
potparams=potparams)
axivlosd= axivlosd/(sc.nansum(axivlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
pickle.dump(axivlosd,savefile)
savefile.close()
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosd,'k',
xlabel=r'$v_{\mathrm{los}} / v_0$',zorder=3)
plot.bovy_plot(vloss,axivlosd,ls='-',color='0.5',
overplot=True,zorder=2)
thisax= pyplot.gca()
thisax.set_xlim(vloslinspace[0],vloslinspace[1])
thisax.set_ylim(0.,sc.amax(sc.concatenate((axivlosd,vlosd)))*1.1)
plot.bovy_text(r'$d = %.2f R_0$' % d + '\n'+r'$l = %.0f^\circ$' %(l/m.pi*180.),
top_right=True)
plot.bovy_end_print(plotfilename)
def veldist_1d_slope(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_slope
PURPOSE:
make a plot showing the influence of the shape of the rotation curve
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-05-15 - Written - Bovy (NYU)
"""
slopes= [-0.2,-0.1,0.,0.1,0.2]
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
basesavefilename= os.path.join(saveDir,'slope_')
for slope in slopes:
thissavefilename= basesavefilename+'%.1f.sav' % slope
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at slope %.1f" % slope
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at slope %.1f" % slope
potparams= (0.9,0.01,25.*_degtorad,.8,None)
vlosd= predictVlos(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=slope,
potparams=potparams)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
savefile.close()
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[2],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.nanmax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[0],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[1],ls='-',color='0.60',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[3],ls='-',color='0.45',
overplot=True,zorder=2,lw=1.5)
plot.bovy_plot(vloss,vlosds[4],ls='-',color='0.3',
overplot=True,zorder=2,lw=1.5)
plot.bovy_text(r'$\mathrm{shape\ of\ the\ rotation\ curve}$',title=True)
plot.bovy_text(0.5,.5,r'$\beta = -0.2$'+'\n'+r'$\beta = -0.1$'+ '\n'+
r'$\beta = \phantom{-}0.0$'+'\n'+
r'$\beta= \phantom{-}0.1$'+'\n'+
r'$\beta= \phantom{-}0.2$')
plot.bovy_end_print(plotfilename)
def veldist_1d_df(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_df
PURPOSE:
make a plot showing the influence of the DF
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-05-15 - Written - Bovy (NYU)
"""
dftypes= ['dehnen','dehnen','dehnen','dehnen','shu']
scalelengths= [1./3.,1./3.,1./4.,4./10.,1./3]
sigscales= [1.,2./3.,3./4.,12./10.,1.]
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
basesavefilename= os.path.join(saveDir,'df_')
ndfs= len(dftypes)
for ii in range(ndfs):
thissavefilename= basesavefilename+dftypes[ii]+'_%.3f_%.3f.sav' % (scalelengths[ii],sigscales[ii])
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at df: "+dftypes[ii]+' %.3f and %.3f' % (scalelengths[ii],sigscales[ii])
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at df: "+dftypes[ii]+' %.3f and %.3f' % (scalelengths[ii],sigscales[ii])
potparams= (0.9,0.01,25.*_degtorad,.8,None)
dftype= dftypes[ii]
dfparams= (scalelengths[ii],sigscales[ii],0.2)
vlosd= predictVlos(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=potparams,
dftype=dftype,dfparams=dfparams)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
savefile.close()
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[0],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.amax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[1],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[2],ls='-',color='0.60',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[3],ls='-',color='0.45',
overplot=True,zorder=2,lw=1.5)
plot.bovy_plot(vloss,vlosds[4],ls='-',color='0.3',
overplot=True,zorder=2,lw=1.5)
plot.bovy_text(r'$\mathrm{distribution\ function}$',title=True)
plot.bovy_text(0.53,.3,r'$R_s = 0.25 R_0$'+'\n'
+r'$R_{\sigma} = 2 R_s$'+'\n'
+r'$\mathrm{fiducial}$'+'\n'
+r'$\mathrm{Shu\ DF}$'+'\n'
+r'$R_s = 0.4 R_0$',size=10.)
plot.bovy_end_print(plotfilename)
def veldist_1d_convolve(plotfilename,phi=_DEFAULTPHI,R=_DEFAULTR,
ngrid=201,saveDir='../bar/1dvar/'):
"""
NAME:
veldist_1d_convolve
PURPOSE:
make a plot showing the influence of the distance uncertainties
INPUT:
plotfilename - filename for figure
phi - Galactocentric azimuth
R - Galactocentric radius
ngrid - number of grid-points to calculate the los velocity distribution
on
saveDir - save pickles here
OUTPUT:
Figure in plotfilename
HISTORY:
2010-05-15 - Written - Bovy (NYU)
"""
convolves= [0.,0.2,0.3]
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
vlosds= []
basesavefilename= os.path.join(saveDir,'convolve_')
for distsig in convolves:
thissavefilename= basesavefilename+'%.1f.sav' % distsig
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at distance uncertainties %.1f" % distsig
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
savefile.close()
else:
print "Calculating los-velocity distribution at distance uncertainties %.1f" % distsig
potparams= (0.9,0.01,25.*_degtorad,.8,None)
if distsig == 0.:
vlosd= predictVlos(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=potparams)
else:
vlosd= predictVlosConvolve(vloslinspace,
l=phi,
d=R,
distCoord='GCGC',
pot='bar',beta=0.,
potparams=potparams,
convolve=distsig)
vlosd= vlosd/(sc.nansum(vlosd)*(vloss[1]-vloss[0]))
savefile= open(thissavefilename,'w')
pickle.dump(vlosd,savefile)
savefile.close()
vlosds.append(vlosd)
#Plot
plot.bovy_print()
plot.bovy_plot(vloss,vlosds[0],'k-',zorder=3,
xrange=[vloslinspace[0],vloslinspace[1]],
yrange=[0.,sc.nanmax(sc.array(vlosds).flatten())*1.1],
xlabel=r'$v_{\mathrm{los}} / v_0$')
plot.bovy_plot(vloss,vlosds[1],ls='-',color='0.75',
overplot=True,zorder=2,lw=2.)
plot.bovy_plot(vloss,vlosds[2],ls='-',color='0.6',
overplot=True,zorder=2,lw=2.)
#plot.bovy_plot(vloss,vlosds[3],ls='-',color='0.45',
# overplot=True,zorder=2,lw=2.)
plot.bovy_text(r'$\mathrm{distance\ uncertainties}$',title=True)
plot.bovy_text(0.5,.65,r'$\sigma_d = 0$'+'\n'+r'$\sigma_d = 20 \%$'+'\n'+r'$\sigma_d = 30 \%$')
plot.bovy_end_print(plotfilename)
