def plotPriorSurf(plotfilename):
#Calculate the surface density profile for each trial potential, then plot the range
if '.png' in plotfilename:
savefilename= plotfilename.replace('.png','.sav')
elif '.ps' in plotfilename:
savefilename= plotfilename.replace('.ps','.sav')
if not os.path.exists(savefilename):
options= setup_options(None)
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
options.fitdvt= False
rs= numpy.linspace(4.2,9.8,101)
rds= numpy.linspace(2.,3.4,8)
fhs= numpy.linspace(0.,1.,16)
surfz= numpy.zeros((len(rs),len(rds)*len(fhs)))+numpy.nan
ro= 1.
vo= 230./220.
dlnvcdlnr= 0.
zh= 400.
for jj in range(len(rds)):
for kk in range(len(fhs)):
#Setup potential to calculate stuff
potparams= numpy.array([numpy.log(rds[jj]/8.),vo,numpy.log(zh/8000.),fhs[kk],dlnvcdlnr])
try:
pot= setup_potential(potparams,options,0,returnrawpot=True)
except RuntimeError:
continue
for ii in range(len(rs)):
surfz[ii,jj*len(fhs)+kk]= 2.*integrate.quad((lambda zz: potential.evaluateDensities(rs[ii]/8.,zz,pot)),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
#Find minimum and maximum curves
minsurfz= numpy.nanmin(surfz,axis=1)
maxsurfz= numpy.nanmax(surfz,axis=1)
#Save
save_pickles(savefilename,rs,minsurfz,maxsurfz)
else:
savefile= open(savefilename,'rb')
rs= pickle.load(savefile)
minsurfz= pickle.load(savefile)
maxsurfz= pickle.load(savefile)
savefile.close()
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot([numpy.nan],[numpy.nan],'ko',
xlabel=r'$R\ (\mathrm{kpc})$',
ylabel=r'$\Sigma(R,|Z| \leq 1.1\,\mathrm{kpc})\ (M_\odot\,\mathrm{pc}^{-2})$',
xrange=[4.,10.],
yrange=[10,1050.],
semilogy=True)
pyplot.fill_between(rs,minsurfz,maxsurfz,
color='0.50')
bovy_plot.bovy_text(8.,68.,r'$\odot$',size=16.,verticalalignment='center',
horizontalalignment='center')
bovy_plot.bovy_end_print(plotfilename)
return None
def plot_forces():
potparams= numpy.array([numpy.log(2.15/_REFR0),
1.,
numpy.log(0.3/_REFR0),
0.307169914244,
-1.79107550983])
options= setup_options(None)
options.potential= 'dpdiskplhalofixcutbulgeflatwgasalt'
pot= setup_potential(potparams,options,0,returnrawpot=True)
bovy_plot.bovy_print(fig_width=8.,fig_height=5.)
potential.plotDensities(pot,rmin=-15./8.,rmax=15./8.,nrs=100,
zmin=-10./8,zmax=10./8.,nzs=101,ncontours=10,
aspect=5./5.,log=True)
bovy_plot.bovy_end_print('/home/bovy/Desktop/test.png')
pass
def plotDistanceSystematics(plotfilename):
#hard-code these paths
savefilename= '../pdfs_margvrvt_gl_hsz/realDFFit_dfeh0.1_dafe0.05_dpdiskplhalofixbulgeflatwgasalt_gridall_fixvc230_an_margvrvt_staeckel_singles_bestr_rvssurf.sav'
savefilename_iv= '../pdfs_margvrvt_gl_hsz/realDFFit_dfeh0.1_dafe0.05_dpdiskplhalofixbulgeflatwgasalt_gridall_fixvc230_margvrvt_staeckel_singles_bestr_rvssurf.sav'
#Read surface densities
if os.path.exists(savefilename):
surffile= open(savefilename,'rb')
surfrs= pickle.load(surffile)
surfs= pickle.load(surffile)
surferrs= pickle.load(surffile)
kzs= pickle.load(surffile)
kzerrs= pickle.load(surffile)
surffile.close()
else:
raise IOError("savefilename with surface-densities has to exist")
#Read surface densities
if os.path.exists(savefilename_iv):
surffile= open(savefilename_iv,'rb')
surfrs_iv= pickle.load(surffile)
surfs_iv= pickle.load(surffile)
surferrs_iv= pickle.load(surffile)
kzs_iv= pickle.load(surffile)
kzerrs_iv= pickle.load(surffile)
altsurfrs_iv= pickle.load(surffile)
altsurfs_iv= pickle.load(surffile)
altsurferrs_iv= pickle.load(surffile)
altkzs_iv= pickle.load(surffile)
altkzerrs_iv= pickle.load(surffile)
surffile.close()
else:
raise IOError("savefilename with surface-densities has to exist")
if True:#options.sample.lower() == 'g':
savefile= open('binmapping_g.sav','rb')
elif False:#options.sample.lower() == 'k':
savefile= open('binmapping_k.sav','rb')
fehs= pickle.load(savefile)
afes= pickle.load(savefile)
indx= numpy.isnan(surfrs)
indx[50]= True
indx[57]= True
indx= True - indx
surfrs= surfrs[indx]
surfs= surfs[indx]
surferrs= surferrs[indx]
kzs= kzs[indx]
kzerrs= kzerrs[indx]
#vo250
surfrs_iv= surfrs_iv[indx]
surfs_iv= surfs_iv[indx]
surferrs_iv= surferrs_iv[indx]
kzs_iv= kzs_iv[indx]
kzerrs_iv= kzerrs_iv[indx]
altsurfrs_iv= altsurfrs_iv[indx]
altsurfs_iv= altsurfs_iv[indx]
altsurferrs_iv= altsurferrs_iv[indx]
altkzs_iv= altkzs_iv[indx]
altkzerrs_iv= altkzerrs_iv[indx]
fehs= fehs[indx]
afes= afes[indx]
#Plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot(surfrs,numpy.log(altsurfs_iv/surfs),'ko',
xlabel=r'$R\ (\mathrm{kpc})$',
ylabel=r'$\ln \Sigma_{1.1}^{\mathrm{Ivezic}} / \Sigma_{1.1}^{\mathrm{An}}$',
xrange=[4.,10.],
yrange=[-0.29,0.29],zorder=10)
pyplot.errorbar(surfrs,numpy.log(altsurfs_iv/surfs),
yerr=surferrs/surfs,
elinewidth=1.,capsize=3,
linestyle='none',zorder=5,
color='k')
#Get distance factors
options= setup_options(None)
raw= read_rawdata(options)
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Get fehs from monoAb
tfehs= monoAbundanceMW.fehs(k=(options.sample.lower() == 'k'))
tafes= monoAbundanceMW.afes(k=(options.sample.lower() == 'k'))
plotthis= numpy.zeros_like(tfehs)
for ii in range(len(tfehs)):
#Get the relevant data
data= binned(tfehs[ii],tafes[ii])
plotthis[ii]= AnDistance.AnDistance(data.dered_g-data.dered_r,
data.feh)
plotthis= plotthis[indx]
#Spline interpolate
distfunc= interpolate.UnivariateSpline(surfrs,numpy.log(plotthis))
plotrs= numpy.linspace(4.5,9.,1001)
pyplot.plot(plotrs,distfunc(plotrs),'--',color='0.5',lw=2.)
pyplot.plot(plotrs,-distfunc(plotrs),'--',color='0.5',lw=2.)
bovy_plot.bovy_end_print(plotfilename)
def plot_hrhrvshr(options,args):
"""Plot hr^out/hr^in as a function of hr for various sr"""
if len(args) == 0.:
print "Must provide a savefilename ..."
print "Returning ..."
return None
if os.path.exists(args[0]):
#Load
savefile= open(args[0],'rb')
plotthis= pickle.load(savefile)
hrs= pickle.load(savefile)
srs= pickle.load(savefile)
savefile.close()
else:
#Grid of models to test
hrs= numpy.linspace(options.hrmin,options.hrmax,options.nhr)
srs= numpy.linspace(options.srmin,options.srmax,options.nsr)
#Tile
hrs= numpy.tile(hrs,(options.nsr,1)).T
srs= numpy.tile(srs,(options.nhr,1))
plotthis= numpy.zeros((options.nhr,options.nsr))
#Setup potential and aA
poptions= setup_options(None)
#poptions.potential= 'dpdiskplhalofixbulgeflatwgasalt'
#params= [0.,0.,0.,0.,0.,0.,-1.16315,1.,-3.,0.4,0.]
poptions.potential= 'btii'
params= None
#pot= MWPotential
pot= setup_potential(params,poptions,1)
if options.aAmethod.lower() == 'staeckel':
aA= actionAngleStaeckel(pot=pot,delta=0.45,c=True)
else:
aA=actionAngleAdiabaticGrid(pot=pot,nR=16,nEz=16,nEr=31,nLz=31,
zmax=1.,Rmax=5.)
for ii in range(options.nhr):
for jj in range(options.nsr):
qdf= quasiisothermaldf(hrs[ii,jj]/8.,srs[ii,jj]/220.,
srs[ii,jj]/numpy.sqrt(3.)/220.,
7./8.,1.,
pot=pot,aA=aA)
plotthis[ii,jj]= qdf.estimate_hr(1.,z=0.8/8.,
dR=0.33,
gl=True)/hrs[ii,jj]*8.
print ii*options.nsr+jj+1, options.nsr*options.nhr, \
hrs[ii,jj], srs[ii,jj], plotthis[ii,jj]
#Save
save_pickles(args[0],plotthis,hrs,srs)
#Now plot
bovy_plot.bovy_print(fig_width=6.,
text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
indx= 0
lines= []
colors= [cm.jet(ii/float(options.nsr-1.)*1.+0.) for ii in range(options.nsr)]
lss= ['-' for ii in range(options.nsr)]#,'--','-.','..']
labels= []
lines.append(bovy_plot.bovy_plot(hrs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
xrange=[0.5,5.5],
yrange=[0.,2.],
xlabel=r'$h^{\mathrm{in}}_R\ \mathrm{at}\ 8\,\mathrm{kpc}$',
ylabel=r'$h^{\mathrm{out}}_R / h^{\mathrm{in}}_R$'))
labels.append(r'$\sigma_R = %.0f \,\mathrm{km\,s}^{-1}$' % srs[0,indx])
for indx in range(1,options.nsr):
lines.append(bovy_plot.bovy_plot(hrs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
overplot=True))
labels.append(r'$\sigma_R = %.0f \,\mathrm{km\,s}^{-1}$' % srs[0,indx])
"""
#Legend
pyplot.legend(lines,#(line1[0],line2[0],line3[0],line4[0]),
labels,#(r'$v_{bc} = 0$',
# r'$v_{bc} = 1\,\sigma_{bc}$',
# r'$v_{bc} = 2\,\sigma_{bc}$',
# r'$v_{bc} = 3\,\sigma_{bc}$'),
loc='lower right',#bbox_to_anchor=(.91,.375),
numpoints=2,
prop={'size':14},
frameon=False)
"""
#Add colorbar
map = cm.ScalarMappable(cmap=cm.jet)
map.set_array(srs[0,:])
map.set_clim(vmin=numpy.amin(srs[0,:]),vmax=numpy.amax(srs[0,:]))
cbar= pyplot.colorbar(map,fraction=0.15)
cbar.set_clim(numpy.amin(srs[0,:]),numpy.amax(srs[0,:]))
cbar.set_label(r'$\sigma_R \,(\mathrm{km\,s}^{-1})$')
bovy_plot.bovy_end_print(options.plotfilename)
def plot_szszvssz(options,args):
"""Plot sz^out/sz^in as a function of sz for various hr"""
if len(args) == 0.:
print "Must provide a savefilename ..."
print "Returning ..."
return None
if os.path.exists(args[0]):
#Load
savefile= open(args[0],'rb')
plotthis= pickle.load(savefile)
szs= pickle.load(savefile)
hrs= pickle.load(savefile)
savefile.close()
else:
#Grid of models to test
if options.subtype.lower() == 'sr':
szs= numpy.linspace(options.srmin,options.srmax,options.nsz)
else:
szs= numpy.linspace(options.szmin,options.szmax,options.nsz)
hrs= numpy.linspace(options.hrmin,options.hrmax,options.nhr)
#Tile
szs= numpy.tile(szs,(options.nhr,1)).T
hrs= numpy.tile(hrs,(options.nsz,1))
plotthis= numpy.zeros((options.nsz,options.nhr))
#Setup potential and aA
poptions= setup_options(None)
#poptions.potential= 'dpdiskplhalofixbulgeflatwgasalt'
#params= [0.,0.,0.,0.,0.,0.,-1.16315,1.,-3.,0.4,0.]
poptions.potential= 'btii'
params= None
#pot= MWPotential
pot= setup_potential(params,poptions,1)
if options.aAmethod.lower() == 'staeckel':
aA= actionAngleStaeckel(pot=pot,delta=0.45,c=True)
else:
aA=actionAngleAdiabaticGrid(pot=pot,nR=16,nEz=16,nEr=31,nLz=31,
zmax=1.,Rmax=5.)
for ii in range(options.nsz):
for jj in range(options.nhr):
if options.subtype.lower() == 'sr':
qdf= quasiisothermaldf(hrs[ii,jj]/8.,
szs[ii,jj]/220.,
szs[ii,jj]/220./numpy.sqrt(3.),
7./8.,1.,
pot=pot,aA=aA)
else:
qdf= quasiisothermaldf(hrs[ii,jj]/8.,
szs[ii,jj]/220.*numpy.sqrt(3.),
szs[ii,jj]/220.,
7./8.,1.,
pot=pot,aA=aA)
if options.subtype.lower() == 'sr':
plotthis[ii,jj]= numpy.sqrt(qdf.sigmaR2(1.,0.8/8.,
gl=True))/szs[ii,jj]*220.
else:
plotthis[ii,jj]= numpy.sqrt(qdf.sigmaz2(1.,0.8/8.,
gl=True))/szs[ii,jj]*220.
print ii*options.nhr+jj+1, options.nhr*options.nsz, \
szs[ii,jj], hrs[ii,jj], plotthis[ii,jj]
#Save
save_pickles(args[0],plotthis,szs,hrs)
#Now plot
bovy_plot.bovy_print(fig_width=6.,
text_fontsize=20.,
legend_fontsize=24.,
xtick_labelsize=18.,
ytick_labelsize=18.,
axes_labelsize=24.)
indx= 0
lines= []
colors= [cm.jet(ii/float(options.nhr-1.)*1.+0.) for ii in range(options.nhr)]
lss= ['-' for ii in range(options.nhr)]#,'--','-.','..']
labels= []
if options.subtype.lower() == 'sr':
lines.append(bovy_plot.bovy_plot(szs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
xrange=[10.,85.],
yrange=[0.8,1.5],
xlabel=r'$\sigma^{\mathrm{in}}_R\ \mathrm{at}\ R = 8\,\mathrm{kpc}$',
ylabel=r'$\sigma^{\mathrm{out}}_R / \sigma^{\mathrm{in}}_R$'))
else:
lines.append(bovy_plot.bovy_plot(szs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
xrange=[10.,115.],
yrange=[0.5,1.2],
xlabel=r'$\sigma^{\mathrm{in}}_Z\ \mathrm{at}\ R = 8\,\mathrm{kpc}$',
ylabel=r'$\sigma^{\mathrm{out}}_Z / \sigma^{\mathrm{in}}_Z$'))
for indx in range(1,options.nhr):
lines.append(bovy_plot.bovy_plot(szs[:,indx],plotthis[:,indx],
color=colors[indx],ls=lss[indx],
overplot=True))
#Add colorbar
map = cm.ScalarMappable(cmap=cm.jet)
map.set_array(hrs[0,:])
map.set_clim(vmin=numpy.amin(hrs[0,:]),vmax=numpy.amax(hrs[0,:]))
cbar= pyplot.colorbar(map,fraction=0.15)
cbar.set_clim(numpy.amin(hrs[0,:]),numpy.amax(hrs[0,:]))
cbar.set_label(r'$h_R\, (\mathrm{kpc})$')
bovy_plot.bovy_end_print(options.plotfilename)
def plotSurfRdfh(plotfilename):
#Calculate the surface density profile for each trial potential, then plot in 2D
if '.png' in plotfilename:
savefilename= plotfilename.replace('.png','.sav')
elif '.ps' in plotfilename:
savefilename= plotfilename.replace('.ps','.sav')
if not os.path.exists(savefilename):
options= setup_options(None)
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
options.fitdvt= False
rs= numpy.array([5.,8.,11.])
rds= numpy.linspace(2.,3.4,_NRDS)
fhs= numpy.linspace(0.,1.,_NFHS)
surfz= numpy.zeros((len(rs),len(rds),len(fhs)))+numpy.nan
ro= 1.
vo= _VC/ _REFV0
dlnvcdlnr= _DLNVCDLNR
zh= _ZH
for jj in range(len(rds)):
for kk in range(len(fhs)):
#Setup potential to calculate stuff
potparams= numpy.array([numpy.log(rds[jj]/8.),vo,numpy.log(zh/8000.),fhs[kk],dlnvcdlnr])
try:
pot= setup_potential(potparams,options,0,returnrawpot=True)
except RuntimeError:
continue
for ii in range(len(rs)):
if False:
surfz[ii,jj,kk]= -potential.evaluatezforces(rs[ii]/_REFR0,options.height/_REFR0/ro,pot)*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro/2./numpy.pi
else:
surfz[ii,jj,kk]= 2.*integrate.quad((lambda zz: potential.evaluateDensities(rs[ii]/8.,zz,pot)),0.,options.height/_REFR0/ro)[0]*_REFV0**2.*vo**2./_REFR0**2./ro**2./4.302*_REFR0*ro
#Save
save_pickles(savefilename,rs,rds,fhs,surfz)
else:
savefile= open(savefilename,'rb')
rs= pickle.load(savefile)
rds= pickle.load(savefile)
fhs= pickle.load(savefile)
surfz= pickle.load(savefile)
savefile.close()
#Now plot
bovy_plot.bovy_print()
data = numpy.ma.masked_invalid(surfz[1,:,:])
bovy_plot.bovy_dens2d(data.filled(data.mean()).T,
origin='lower',
cmap='jet',
colorbar=True,
shrink=0.775,
xlabel=r'$\mathrm{disk\ scale\ length}\,(\mathrm{kpc})$',
ylabel=r'$\mathrm{relative\ halo\ contribution\ to}\ V^2_c(R_0)$',
zlabel=r'$\Sigma(R_0,|Z| \leq 1.1\,\mathrm{kpc})\, (M_\odot\,\mathrm{pc}^{-2})$',
xrange=[rds[0]-(rds[1]-rds[0])/2.,
rds[-1]+(rds[1]-rds[0])/2.],
yrange=[fhs[0]-(fhs[1]-fhs[0])/2.,
fhs[-1]+(fhs[1]-fhs[0])/2.])
#Fix bad data
bad_data = numpy.ma.masked_where(~data.mask, data.mask)
bovy_plot.bovy_dens2d(bad_data.T,
origin='lower',
interpolation='nearest',
cmap=cm.gray_r,
overplot=True,
xrange=[rds[0]-(rds[1]-rds[0])/2.,
rds[-1]+(rds[1]-rds[0])/2.],
yrange=[fhs[0]-(fhs[1]-fhs[0])/2.,
fhs[-1]+(fhs[1]-fhs[0])/2.])
#Overlay contours of sigma at other R
bovy_plot.bovy_dens2d(surfz[0,:,:].T,origin='lower',
xrange=[rds[0]-(rds[1]-rds[0])/2.,
rds[-1]+(rds[1]-rds[0])/2.],
yrange=[fhs[0]-(fhs[1]-fhs[0])/2.,
fhs[-1]+(fhs[1]-fhs[0])/2.],
overplot=True,
justcontours=True,
contours=True,
cntrcolors='k',
cntrls='-')
bovy_plot.bovy_dens2d(surfz[2,:,:].T,origin='lower',
xrange=[rds[0]-(rds[1]-rds[0])/2.,
rds[-1]+(rds[1]-rds[0])/2.],
yrange=[fhs[0]-(fhs[1]-fhs[0])/2.,
fhs[-1]+(fhs[1]-fhs[0])/2.],
overplot=True,
justcontours=True,
contours=True,
cntrcolors='w',
# cntrlabel=True,
cntrls='--')
#Add labels
bovy_plot.bovy_text(r'$\Sigma(R=5\,\mathrm{kpc})$'
+'\n'
+r'$\Sigma(R=11\,\mathrm{kpc})$',
bottom_left=True,size=14.)
bovy_plot.bovy_plot([2.575,2.8],[0.15,0.31],'-',color='0.5',overplot=True)
bovy_plot.bovy_plot([2.625,2.95],[0.06,0.1525],'-',color='0.5',overplot=True)
#overplot actual gridpoints
gridrds= numpy.linspace(2.,3.4,8)
gridfhs= numpy.linspace(0.,1.,16)
for ii in range(len(gridrds)):
bovy_plot.bovy_plot(gridrds[ii]+numpy.zeros(len(gridfhs)),
gridfhs,
's',
color='w',ms=3.,overplot=True,
markeredgecolor='none')
bovy_plot.bovy_end_print(plotfilename)
return None
def plotMAPMassScaleLength(plotfilename):
#First calculate the mass-weighted scale length
fehs= monoAbundanceMW.fehs()
afes= monoAbundanceMW.afes()
#Load the samples of masses and scale lengths
#Savefile
denssamplesfile= '../fits/pixelFitG_DblExp_BigPix0.1_1000samples.sav'
masssamplesfile= '../fits/pixelFitG_Mass_DblExp_BigPix0.1_simpleage_1000samples.sav'
if os.path.exists(denssamplesfile):
savefile= open(denssamplesfile,'rb')
denssamples= pickle.load(savefile)
savefile.close()
denserrors= True
else:
raise IOError("%s file with density samples does not exist" % denssamplesfile)
if os.path.exists(masssamplesfile):
savefile= open(masssamplesfile,'rb')
masssamples= pickle.load(savefile)
savefile.close()
else:
raise IOError("%s file with mass samples does not exist" % masssamplesfile)
#Load the relative distance factors
options= setup_options(None)
raw= read_rawdata(options)
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Get fehs from monoAb
distfac= numpy.zeros_like(fehs)
for ii in range(len(fehs)):
#Get the relevant data
data= binned(fehs[ii],afes[ii])
distfac[ii]= AnDistance.AnDistance(data.dered_g-data.dered_r,
data.feh)
nsamples= 100
rs= numpy.linspace(2.,11.5,101)
rds= numpy.zeros((101,nsamples))
rndindx= numpy.random.permutation(len(masssamples[108]))[0:nsamples]
for jj in range(nsamples):
hrs= numpy.zeros_like(fehs)
mass= numpy.zeros_like(fehs)
kk, ii= 0, 0
while kk < len(denssamples):
if denssamples[kk] is None:
kk+= 1
continue
hrs[ii]= numpy.exp(denssamples[kk][rndindx[jj]][0])*distfac[ii]*8.
mass[ii]= masssamples[kk][rndindx[jj]]
ii+= 1
kk+= 1
#hrs[hrs > 3.5]= 3.5
#Effective density profile
tdens= numpy.zeros_like(rs)
mass/= numpy.sum(mass)
for ii in range(len(rs)):
tdens[ii]= numpy.sum(mass*numpy.exp(-(rs[ii]-8.)/hrs))
tdens= numpy.log(tdens)
rds[:,jj]= -(rs[1]-rs[0])/(numpy.roll(tdens,-1)-tdens)
#for ii in range(len(rs)):
# rds[ii,jj]= numpy.exp(numpy.sum(numpy.log(hrs)*mass*numpy.exp(-(rs[ii]-8.)/hrs))/numpy.sum(mass*numpy.exp(-(rs[ii]-8.)/hrs)))
rds= rds[:-1,:]
rs= rs[:-1]
#Now plot
bovy_plot.bovy_print()
bovy_plot.bovy_plot(rs,numpy.median(rds,axis=1),'k-',
xrange=[0.,12.],
yrange=[0.,4.],
xlabel=r'$R\ (\mathrm{kpc})$',
ylabel=r'$\mathrm{effective\ disk\ scale\ length\,(kpc)}$',
zorder=10)
#Find 68 range at each r
nsigs= 1
rcsigs= numpy.zeros((len(rs),2*nsigs))
fs= rds
for ii in range(nsigs):
for jj in range(len(rs)):
thisf= sorted(fs[jj,:])
thiscut= 0.5*special.erfc((ii+1.)/math.sqrt(2.))
rcsigs[jj,2*ii]= thisf[int(math.floor(thiscut*nsamples))]
thiscut= 1.-thiscut
rcsigs[jj,2*ii+1]= thisf[int(math.floor(thiscut*nsamples))]
colord, cc= (1.-0.75)/(nsigs+1.), 1
nsigma= nsigs
pyplot.fill_between(rs,rcsigs[:,0],rcsigs[:,1],color='0.75')
while nsigma > 1:
pyplot.fill_between(rs,rcsigs[:,cc+1],rcsigs[:,cc-1],
color='%f' % (.75+colord*cc))
pyplot.fill_between(rs,rcsigs[:,cc],rcsigs[:,cc+2],
color='%f' % (.75+colord*cc))
cc+= 2
nsigma-= 1
#pyplot.fill_between(rs,numpy.amin(rds,axis=1),numpy.amax(rds,axis=1),
# color='0.50')
pyplot.errorbar([7.],[2.15],
xerr=[2.],
yerr=[0.14],
elinewidth=1.,capsize=3,
linestyle='none',zorder=15,
marker='o',color='k')
bovy_plot.bovy_text(4.5,0.25,r'$\mathrm{Prediction\ from\ star\ counts}$'
+'\n'
+r'$\mathrm{Dynamical\ measurement}$',
size=14.,
ha='left')
bovy_plot.bovy_plot([2.82,4.0],[0.53,0.53],'k-',overplot=True)
pyplot.fill_between(numpy.array([2.8,4.0]),
numpy.array([0.45,0.45]),
numpy.array([0.61,0.61]),
color='0.75')
pyplot.errorbar([3.5],[0.3],yerr=[0.1],xerr=[0.4],
color='k',marker='o',ls='none')
bovy_plot.bovy_end_print(plotfilename)
def dfResultsTable(args):
cmdline = "%python dfResultsTable.py " + args
# Load g, k, and combined fits
options = setup_options(None)
options.sample = "g"
options.select = "all"
gfits = calcDFResults(options, [_GFIT])
options.sample = "k"
options.select = "program"
kfits = calcDFResults(options, [_KFIT])
options.sample = "gk"
gkfits = calcDFResults(options, [_GFIT, _KFIT])
# Set up sections
names = [
"$R_d\ (\mathrm{kpc})$",
"$z_h\ (\mathrm{pc})$",
"$V_{c,\mathrm{disk}}/V_c\,(R_0)$",
"$V_{c,\mathrm{disk}}/V_c\,(2.2\,R_d)$",
"$\\Sigma_{\mathrm{disk}}(R_0)\ (M_{\odot}\,\mathrm{pc}^{-2})$",
"$M_{\mathrm{disk}}\ (10^{10}\,M_{\odot})$",
"$\\alpha_{h}$",
"$\\rho_{\mathrm{DM}}\,(R_0,0)\ (M_{\odot}\,\mathrm{pc}^{-3})$",
"$V_c(R_0)\ (\mathrm{km\ s}^{-1})$",
"$\\frac{\mathrm{d}\ln V_c}{\mathrm{d}\ln R}\,(R_0)$",
"$\\rho_{\mathrm{total}}\,(R_0,0)\ (M_{\odot}\,\mathrm{pc}^{-3})$",
"$\\Sigma(R_0,|Z|\leq 0.8\,\mathrm{kpc})\ (M_{\odot}\,\mathrm{pc}^{-2})$",
"$\\Sigma(R_0,|Z|\leq 1.1\,\mathrm{kpc})\ (M_{\odot}\,\mathrm{pc}^{-2})$",
]
skip = [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0] # 1 if line skip after this parameter
scale = [_REFR0, _REFR0 * 1000.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
key = [
"rdexp",
"zhexp",
"vcdvcro",
"vcdvc",
"surfzdisk",
"massdisk",
"plhalo",
"rhodm",
"vc",
"dlnvcdlnr",
"rhooalt",
"surfz800",
"surfz",
]
# Make table
outfile = open(args, "w")
for ii in range(len(names)):
# Set up line
printline = names[ii]
# G
printline += " & "
bestfit = gfits[key[ii] + "_m"] * scale[ii]
err = gfits[key[ii] + "_merr"] * scale[ii]
# Prepare
if math.log10(err) >= 0.0:
value = "$%.0f$" % bestfit
err = "$\pm$%.0f" % err
elif math.log10(err) >= -1.0:
value = "$%.1f$" % bestfit
err = "$\pm$%.1f" % err
elif math.log10(err) >= -2.0:
value = "$%.2f$" % bestfit
err = "$\pm$%.2f" % err
elif math.log10(err) >= -3.0:
value = "$%.3f$" % bestfit
err = "$\pm$%.3f" % err
else:
value = "$%.4f$" % bestfit
err = "$\pm$%.4f" % err
printline += value + "&" + err
# K
printline += " & "
bestfit = kfits[key[ii] + "_m"] * scale[ii]
err = kfits[key[ii] + "_merr"] * scale[ii]
# Prepare
if math.log10(err) >= 0.0:
value = "$%.0f$" % bestfit
err = "$\pm$%.0f" % err
elif math.log10(err) >= -1.0:
value = "$%.1f$" % bestfit
err = "$\pm$%.1f" % err
elif math.log10(err) >= -2.0:
value = "$%.2f$" % bestfit
err = "$\pm$%.2f" % err
elif math.log10(err) >= -3.0:
value = "$%.3f$" % bestfit
err = "$\pm$%.3f" % err
else:
value = "$%.4f$" % bestfit
err = "$\pm$%.4f" % err
printline += value + "&" + err
# combined
printline += " & "
bestfit = gkfits[key[ii] + "_m"] * scale[ii]
err = gkfits[key[ii] + "_merr"] * scale[ii]
# Prepare
if math.log10(err) >= 0.0:
value = "$%.0f$" % bestfit
err = "$\pm$%.0f" % err
elif math.log10(err) >= -1.0:
value = "$%.1f$" % bestfit
err = "$\pm$%.1f" % err
elif math.log10(err) >= -2.0:
value = "$%.2f$" % bestfit
err = "$\pm$%.2f" % err
elif math.log10(err) >= -3.0:
value = "$%.3f$" % bestfit
err = "$\pm$%.3f" % err
else:
value = "$%.4f$" % bestfit
err = "$\pm$%.4f" % err
printline += value + "&" + err
if not ii == (len(names) - 1):
printline += "\\\\"
if skip[ii]:
printline += "\\\\"
# Write the line
outfile.write(printline + "\n")
outfile.write("\\enddata\n")
outfile.write(cmdline + "\n")
outfile.close()
