def plotGraph():
fig = plt.figure()
xlabel(r'$r\quad[\mathrm{pc}]$')
if prof == 'M':
ylabel(r'$M\quad[\mathrm{M}_{\odot}]$') #[10^5 M_{\odot}]')
elif prof == 'rho':
ylabel(r'$\rho\quad[\mathrm{M}_{\odot}/\mathrm{pc}^3]$') #[10^5 M_{\odot}]')
elif prof=='beta':
ylabel(r'$\beta_'+str(pop)+'$') #[10^5 M_{\odot}]')
elif prof == 'nu':
ylabel(r'$\nu_'+str(pop)+'$')
elif prof == 'sig':
ylabel(r'$\sigma_'+str(pop)+'$')
fill_between(radsc, M95lo*Msc, M95hi*Msc, color='black',alpha=0.2,lw=1)
fill_between(radsc, M68lo*Msc, M68hi*Msc, color='black',alpha=0.4,lw=1)
plot(radsc,Mmedi*Msc,'r',lw=1)
r1, r2 = halflightradii(gp.case)
if prof == 'dens' or prof == 'M' or prof=='delta1' or prof=='nu1' or prof=='sig1':
axvline(x=r1, visible=True)
if prof == 'dens' or prof == 'M' or prof=='delta2':
axvline(x=r2, visible=True)
# theoretical model
if prof == 'M':
plot(radsc,Msc*Mwalkertot(radsc),'--',color='black',lw=1)
elif prof == 'rho':
plot(radsc,Msc*rhowalktot_3D(radsc),'--',color='black',lw=1) # 7 co
# plot lowest profile as well
# plot(radsc,Msc*profs[0],'.',color='orange',lw=1)
elif prof == 'beta':
plot(radsc,betawalker(radsc)[pop],color='black')
elif prof == 'sig':
rad, dummy, dummy, sig1, sigerr1 = gh.readcol5(gp.files.sigfiles[pop])
rad *= gp.Rscale[pop]
sigdat *= gp.maxvlos[pop]
sigerr *= gp.maxvlos[pop]
# plot(rad, sigdat, '--', color='b', lw=2)
# plot(rad, sigdat-sigerr, '--', color='b', lw=1)
# plot(rad, sigdat+sigerr, '--', color='b', lw=1)
fill_between(rad, sigdat-sigerr, sigdat+sigerr, color='blue', alpha=0.3,lw=0)
elif prof == 'nu':
rad, dummy, dummy, nudat, nuerr = gh.readcol5(gp.files.nufiles[pop])
rad *= gp.Rscale[pop]
nudat *=gp.Nu0pc[pop]
nuerr *=gp.Nu0pc[pop]
fill_between(rad, nudat-nuerr, nudat+nuerr, color='blue', alpha=0.3,lw=0)
if prof!='beta' and prof != 'sig':
yscale('log')
if prof == 'nu':
yscale('log')
if prof == "M" or prof == 'rho' or prof == 'nr':
xscale('log')
xlim([100.,1200.]); ylim([0.005,1.5])
if prof == 'beta':
xlim([100.,1200.]); ylim([-0.15,0.9])
return fig
def plotGraph():
fig = figure()
xlabel(r'$r\quad[\mathrm{pc}]$')
if prof == 'M':
ylabel(r'$M\quad[\mathrm{M}_{\odot}]$') #[10^5 M_{\odot}]')
elif prof == 'rho':
ylabel(r'$\rho\quad[\mathrm{M}_{\odot}/\mathrm{pc}^3]$') #[10^5 M_{\odot}]')
elif prof=='delta1' or prof == 'delta2':
ylabel(r'$\beta$') #[10^5 M_{\odot}]')
elif prof == 'nu1':
ylabel(r'$\nu_1$')
elif prof == 'sig1':
ylabel(r'$\sigma_1$')
fill_between(radsc, M95lo*Msc, M95hi*Msc, color='black',alpha=0.2,lw=1)
fill_between(radsc, M68lo*Msc, M68hi*Msc, color='black',alpha=0.4,lw=1)
plot(radsc,Mmedi*Msc,'r',lw=1)
r1 = halflightradius(gp.case)
if prof == 'rho' or prof == 'M' or prof=='beta1' or prof=='nu1' or prof=='sig1':
axvline(x=r1, visible=True)
# theoretical model
if prof == 'M':
plot(radsc,Msc*Mwalkertot(radsc),'--',color='black',lw=1)
elif prof == 'rho':
plot(radsc,Msc*rhogaiatot_3D(radsc),'--',color='black',lw=1) # 7 co
# plot lowest profile as well
# plot(radsc,Msc*profs[0],'.',color='orange',lw=1)
elif prof == 'beta1':
plot(radsc, betagaia(radsc), color='black')
elif prof == 'sig1':
rad, dummy, dummy, sig1, sigerr1 = gh.readcol5(gp.files.sigfiles[1])
rad *= gp.Rscale[1]
sig1*=gp.maxvlos[1]
sigerr1*=gp.maxvlos[1]
# plot(rad, sig1, '--', color='b', lw=2)
# plot(rad, sig1-sigerr1, '--', color='b', lw=1)
# plot(rad, sig1+sigerr1, '--', color='b', lw=1)
fill_between(rad, sig1-sigerr1, sig1+sigerr1, color='blue', alpha=0.3,lw=0)
elif prof == 'nu1':
rad, dummy, dummy, nu1, nuerr1 = gh.readcol5(gp.files.nufiles[1])
rad *= gp.Rscale[1]
# TODO: missing factor 10 somewhere..
pdb.set_trace()
nu1 *= gp.Nu0pc[1]; nuerr1 *= gp.Nu0pc[1]
fill_between(rad, nu1-nuerr1, nu1+nuerr1, color='blue', alpha=0.3,lw=0)
if prof!='delta1' and prof!='delta2' and prof != 'sig1' and prof != 'nu1':
# xscale('log')
yscale('log')
if prof == 'nu1':
yscale('log')
if prof == "M" or prof == 'rho':
xscale('log')
#xlim([100.,1200.]); ylim([0.005,1.5])
# if prof == 'delta1':
# xlim([100.,1200.]); ylim([-0.15,0.9])
return fig
def read_nu(self):
for pop in np.arange(gp.pops+1):
Nux, binmin, binmax, Nudat, Nuerr = \
gh.readcol5(gp.files.nufiles[pop]) # component 1
# 3*[rscale], [Nu0], [Nu0]
Nuerr *= gp.nuerrcorr # [rho0]
# switch to Munit (msun) and pc here
Nux = Nux[:] * gp.Rscale[pop] # [pc]
Nudat = Nudat[:] * gp.Nu0pc[pop] # [Msun/pc^2]
Nuerr = Nuerr[:] * gp.Nu0pc[pop] # [Msun/pc^2]
self.rbin = Nux # [pc]
self.binmin = binmin; self.binmax = binmax # 2*[pc]
gp.xipol = self.rbin # [pc]
maxr = max(self.rbin)
gp.xepol = np.hstack([self.rbin, 2*maxr, 4*maxr, 8*maxr])
# deproject, # takes [pc], 2* [munit/pc^2], gives [pc], 2* [munit/pc^3],
# already normalized to same total mass
if gp.geom=='sphere':
dummy, nudat, nuerr = Rho_NORM_rho(self.rbin, Nudat, Nuerr)
else:
Nudat, Nuerr = Nudat, Nuerr
self.Nudat.append(Nudat) # [Msun/pc^2]
self.Nuerr.append(Nuerr) # [Msun/pc^2]
self.nudat.append(nudat) # [Msun/pc^3]
self.nuerr.append(nuerr) # [Msun/pc^3]
return
def read_kappa(self):
for pop in np.arange(gp.pops+1):
Dummy, Dummy, Dummy, kapdat, kaperr = gh.readcol5(gp.files.kappafiles[pop])
# 3*[Rscale], [1], [1]
kaperr /= gp.kaperrcorr # [1]
self.kapdat.append(kapdat) # [1]
self.kaperr.append(kaperr) # [1]
return
def read_sigma(self):
for pop in np.arange(gp.pops+1):
# print(gp.files.sigfiles[pop])
Dummy, Dummy, Dummy, sigdat, sigerr = gh.readcol5(gp.files.sigfiles[pop])
# 3*[Rscale], [maxvlos], [maxvlos]
sigerr /= gp.sigerrcorr # [maxvlos]
# change to km/s here
self.sigdat.append(sigdat[:] * gp.maxvlos[pop]) # [km/s]
self.sigerr.append(sigerr[:] * gp.maxvlos[pop]) # [km/s]
return
def run(basename, prof):
read_scale()
# read in half light radius, Nu0[munit/Rscale^2], Nu0[munit/pc^2], munit
print(gp.files.get_scale_file(0))
A = np.loadtxt(gp.files.get_scale_file(0), unpack=False, skiprows=1)
gp.rstarhalf = A[0] # halflight radius (all tracer part.s), stored first ("0")
# read in radial bins
radii, binmin, binmax, nudat1, nuerr1 = gh.readcol5(gp.files.nufiles[0])
gp.xipol = radii*gp.rstarhalf # [pc]
maxr = max(radii)
radii = np.hstack([radii, 2*maxr, 4*maxr, 8*maxr])
gp.xepol = radii*gp.rstarhalf
models = read_models()
# use physical representation for profiles
profs = []
for i in range(len(models)):
M = models[i]
tmp_rho = M[0:gp.nepol]
off = gp.nipol
tmp_nu = M[off:off+gp.nepol]
off += gp.nipol
tmp_beta = M[off:off+gp.nbeta]
# TODO: include handling of N populations
try:
tmp_prof = physical(gp.xepol, prof, pop, tmp_rho, tmp_nu, tmp_beta)
profs.append(tmp_prof)
except Exception as detail:
print('handling error in profile', detail)
continue
sortedprofs = gh.sort_profiles_binwise(np.array(profs).transpose())
Mmin, M95lo, M68lo, Mmedi, \
M68hi, M95hi, Mmax = gh.get_median_1_2_sig(sortedprofs)
# plot ranges
if prof=='rho' or prof=='nu':
gpl.startlog(); gpl.xscale('log')
else:
gpl.start()
plot_labels(prof, pop)
if prof=='nr':
gpl.xscale('log')
# gpl.xlim([0.,2.*gp.xipol[-1]])
gp.xipol = np.hstack([gp.xipol[0]/2., gp.xipol, gp.rinfty*gp.xipol[-1]])
if prof=='nu' or prof=='sig':
plot_data(gp.xipol, prof, pop)
plot_analytic(gp.xepol, prof, pop)
radfill = gp.xepol
if prof == 'sig':
radfill = gp.xipol
fill_nice(radfill, M95lo, M68lo, Mmedi, M68hi, M95hi)
gpl.savefig(basename+'/prof_'+prof+'_'+str(pop)+'.png')
gpl.ion(); gpl.show(); gpl.ioff()
return
