def sig_rz(z, zpars, tpars):
# Mirror prior
tparsu = abs(tpars)
# dz = zpars[2]-zpars[1]
# sig_Rz = np.zeros(gp.nipol)
# sig_Rz[0] = tparsu[0] * dz / 2.
# for i in range(1,gp.nipol):
# sig_Rz[i] = sig_Rz[i-1] + tparsu[i] * dz
# f = gp.ipol(zpars,sig_Rz,z)
# Alternative here --> don't assume monotonic!
f = gh.ipol(zpars, tparsu, z)
return f
def sig_rz(z, zpars, tpars):
# Mirror prior
tparsu = abs(tpars)
# dz = zpars[2]-zpars[1]
# sig_Rz = np.zeros(gp.nipol)
# sig_Rz[0] = tparsu[0] * dz / 2.
# for i in range(1,gp.nipol):
# sig_Rz[i] = sig_Rz[i-1] + tparsu[i] * dz
# f = gp.ipol(zpars,sig_Rz,z)
# Alternative here --> don't assume monotonic!
f = gh.ipol(zpars, tparsu, z)
return f
def disc_sim(gp):
gp.zpmin = -1
gp.zpmax = -1
#import all data from files
if gp.importdata:
z_nu1_raw,nu1_dat_raw,nu1_dat_err_raw = gh.readcoln(gp.files.Sigfiles[0])
z_sig1_raw,sig1_dat_raw,sig1_dat_err_raw = gh.readcoln(gp.files.sigfiles[0])
#z_surf_raw,surftot_dat_raw,surftot_dat_err_raw = gh.readcoln(gp.files.surfdenfiles[0])
z_surf_raw,surfbar_dat_raw,surfbar_dat_err_raw = gh.readcoln(gp.files.surfdenfiles[0])
z_surf_raw,surfdm_dat_raw,surfdm_dat_err_raw = gh.readcoln(gp.files.surfdenfiles[1])
selnu1 = (z_nu1_raw > 0)
selsig1 = (z_sig1_raw > 0)
selsurf = (z_surf_raw > 0)
if gp.pops == 2:
z_nu2_raw,nu2_dat_raw,nu2_dat_err_raw = gh.readcoln(gp.files.Sigfiles[1])
z_sig2_raw,sig2_dat_raw,sig2_dat_err_raw = gh.readcoln(gp.files.sigfiles[2])
selnu2 = (z_nu2_raw > 0)
selsig2 = (z_sig2_raw > 0)
# baryonic surface density
gp.dat.Mx = z_surf_raw[selsurf]*1000. # [pc]
gp.dat.Mrdat = surfbar_dat_raw[selsurf] # [Munit/pc^2]
gp.dat.Mrerr = surfbar_dat_err_raw[selsurf] # [Munit/pc^2]
# total surface density
gp.Mmodel = surftot_dat_raw[selsusrf] # [Munit/pc^2]
Kz_zstar = -gp.Mmodel * (2.*np.pi*gu.G1__pcMsun_1km2s_2)
# should be kappa data (not sure whether this is necessary)
gp.dat.densx = z_surf_raw[selsusrf]*1000. # [pc]
gp.dat.densdat = phys.kappa(gp.dat.densx, Kz_zstar) #should be the total kappa, not sure about x array though
gp.dat.denserr = gp.dat.densdat #not necessary for a certainty
gp.dat.nux1 = z_nu1_raw[selnu1]*1000. # [pc]
gp.dat.nu1 = nu1_dat_raw[selnu1]
gp.dat.nuerr1 = nu1_dat_err_raw[selnu1]
gp.dat.sigx1 = z_sig1_raw[selsig1]*1000.
gp.dat.sig1 = sig1_dat_raw[selsig1]
gp.dat.sigerr1 = sig1_dat_err_raw[selsig1]
if gp.pops == 2:
gp.dat.nux2 = z_nu2_raw[selnu2]*1000.
gp.dat.nu2 = nu2_dat_raw[selnu2]
gp.dat.nuerr2 = nu2_dat_err_raw[selnu2]
gp.dat.sigx2 = z_sig2_raw[selsig2]*1000.
gp.dat.sig2 = z_sig2_raw[selsig2]
gp.dat.sigerr2 = z_sig2_raw[selsig2]
gp.dat.output()
return gp.dat
else:
# import simulation datapoints and calculate nu, sig
zmin = 100. ; zmax = 1300. # [pc]
zbinbndry = np.linspace(zmin, zmax, gp.nipol+1) # [pc] assuming linear spacing of bins
zbinmin = zbinbndry[:-1] # [pc]
zbinmax = zbinbndry[1:] # [pc]
gp.xipol= zbinmin + (zbinmax-zbinmin)/2. # [pc]
# Read in the data:
mass, x_dat,y_dat,z_dat, vx_dat,vy_dat,vz_dat, pot_dat = gh.readcoln(gp.files.posvelfiles[0])
# assume units: Munit, 3*kpc, 3*km/s, [pot] <= last one not needed
# [Dave] v is in units [100 km/s] <= not possible?!
if max(mass) != min(mass):
print('**** Multimass data not yet supported ****')
exit(1)
# change to [pc]
x_dat *= 1000.; y_dat *= 1000.; z_dat *= 1000. # [pc]
z_mean = np.sum(mass*z_dat)/np.sum(mass) # [pc]
vz_mean = np.sum(mass*vz_dat)/np.sum(mass) # [km/s]
# center on coordinate, if also negative z values read in
if min(z_dat)<0:
z_dat = z_dat - z_mean # [pc]
vz_dat = vz_dat - vz_mean # [km/s]
# Add errors:
if gp.adderrors:
# Assume normal errors for now:
xerrfac = 10.0; yerrfac = 10.0; zerrfac = 10.0
vxerrfac = 10.0; vyerrfac = 10.0; vzerrfac = 10.0
x_dat_err = abs(x_dat/xerrfac) # [pc]
y_dat_err = abs(y_dat/yerrfac) # [pc]
z_dat_err = abs(z_dat/zerrfac) # [pc]
vx_dat_err = abs(vx_dat/vxerrfac) # [km/s]
vy_dat_err = abs(vy_dat/vyerrfac) # [km/s]
vz_dat_err = abs(vz_dat/vzerrfac) # [km/s]
x_dat = x_dat + npr.normal(-1.,1.,len(z_dat)) * x_dat_err # [pc]
y_dat = y_dat + npr.normal(-1.,1.,len(z_dat)) * y_dat_err # [pc]
z_dat = z_dat + npr.normal(-1.,1.,len(z_dat)) * z_dat_err # [pc]
vx_dat = vx_dat + npr.normal(-1.,1.,len(z_dat)) * vx_dat_err # [km/s]
vy_dat = vy_dat + npr.normal(-1.,1.,len(z_dat)) * vy_dat_err # [km/s]
vz_dat = vz_dat + npr.normal(-1.,1.,len(z_dat)) * vz_dat_err # [km/s]
# Cut on zmax, cut zero velocities
sel = (z_dat < zmax) * (abs(vz_dat) >= 0.) # [bool]
z_dat = z_dat[sel] # [pc]
vz_dat = vz_dat[sel] # [km/s]
# determine sigma_v
sig_dat_bin = np.zeros(gp.nipol)
sig_dat_err_bin = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat > zbinmin[i])*(z_dat < zbinmax[i]) # select bin
vtemp = np.array(vz_dat[sel]) # [km/s]
sig_dat_bin[i] = np.sqrt(np.mean(vtemp**2) - np.mean(vtemp)**2) # [km/s]
sig_dat_err_bin[i] = sig_dat_bin[i]/(1.*np.sum(sel)) # [km/s]
nu_dat_bin = np.zeros(gp.nipol)
nu_dat_err_bin = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat > zbinmin[i])*(z_dat < zbinmax[i]) # select bin
nu_dat_bin[i] = 1.*np.sum(sel)/(1.*(zbinmax[i]-zbinmin[i])) # [1/tot. area/pc]
nu_dat_err_bin[i] = nu_dat_bin[i] / np.sqrt(np.sum(sel)) # [1/tot. area/pc], Poisson distributed
renorm = 1.*max(nu_dat_bin) # [1/tot.area/pc]
nu_dat_bin = nu_dat_bin / renorm # [1]
nu_dat_err_bin = nu_dat_err_bin / renorm # [1]
if gp.pops == 2:
mass2, x_dat2,y_dat2,z_dat2, vx_dat2,vy_dat2,vz_dat2, pot_dat2 = gh.readcoln(gp.files.posvelfiles[1])
if max(mass2) > min(mass2):
print('**** Multimass data not yet supported ****')
exit(1)
# change to [pc]
x_dat2 *= 1000.; y_dat2 *= 1000.; z_dat2 *= 1000. # [pc]
z_mean2 = np.sum(mass2*z_dat2)/np.sum(mass2) # [pc]
vz_mean2 = np.sum(mass2*vz_dat2)/np.sum(mass2) # [km/s]
# center on coordinate, if also negative z values read in
if min(z_dat2)<0:
z_dat2 = z_dat2 - z_mean2 # [pc]
vz_dat2 = vz_dat2 - vz_mean2 # [km/s]
# Add errors:
if gp.adderrors:
# Assume normal errors for now:
x_dat_err2 = abs(x_dat2/xerrfac) # [pc]
y_dat_err2 = abs(y_dat2/yerrfac) # [pc]
z_dat_err2 = abs(z_dat2/zerrfac) # [pc]
vx_dat_err2 = abs(vx_dat2/vxerrfac) # [km/s]
vy_dat_err2 = abs(vy_dat2/vyerrfac) # [km/s]
vz_dat_err2 = abs(vz_dat2/vzerrfac) # [km/s]
x_dat2 = x_dat2 + npr.normal(-1.,1.,len(z_dat2)) * x_dat_err2 # [pc]
y_dat2 = y_dat2 + npr.normal(-1.,1.,len(z_dat2)) * y_dat_err2 # [pc]
z_dat2 = z_dat2 + npr.normal(-1.,1.,len(z_dat2)) * z_dat_err2 # [pc]
vx_dat2 = vx_dat2 + npr.normal(-1.,1.,len(z_dat2)) * vx_dat_err2 # [km/s]
vy_dat2 = vy_dat2 + npr.normal(-1.,1.,len(z_dat2)) * vy_dat_err2 # [km/s]
vz_dat2 = vz_dat2 + npr.normal(-1.,1.,len(z_dat2)) * vz_dat_err2 # [km/s]
# Cut on zmax, cut zero velocities
sel = (z_dat2 < zmax) * (abs(vz_dat2) >= 0.) # [bool]
z_dat2 = z_dat2[sel] # [pc]
vz_dat2 = vz_dat2[sel] # [km/s]
# determine sigma_v
sig_dat_bin2 = np.zeros(gp.nipol)
sig_dat_err_bin2 = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat2 > zbinmin[i])*(z_dat2 < zbinmax[i]) # select bin
vtemp = np.array(vz_dat2[sel]) # [km/s]
sig_dat_bin2[i] = np.sqrt(np.mean(vtemp**2) - np.mean(vtemp)**2) # [km/s]
sig_dat_err_bin2[i] = sig_dat_bin2[i]/(1.*np.sum(sel)) # [km/s]
nu_dat_bin2 = np.zeros(gp.nipol)
nu_dat_err_bin2 = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat2 > zbinmin[i])*(z_dat2 < zbinmax[i]) # select bin
nu_dat_bin2[i] = 1.*np.sum(sel)/(1.*(zbinmax[i]-zbinmin[i])) # [1/tot. area/pc]
nu_dat_err_bin2[i] = nu_dat_bin2[i] / np.sqrt(np.sum(sel)) # [1/tot. area/pc], Poisson distributed
renorm = 1.*max(nu_dat_bin2) # [1/tot.area/pc]
nu_dat_bin2 /= renorm # [1]
nu_dat_err_bin2 /= renorm # [1]
# if gp.bprior:
# Load the baryonic model:
if gp.baryonmodel == 'silvia':
zvis,sigexpvis,sigexpviserr,sigsecvis,sigsecviserr = gh.readcoln('/home/ast/user/jread/Data/Local_dm/Vis/Sigma_MM.txt')
# [kpc, Munit/pc^2, Msun/pc^2, Msun/pc^2, Msun/pc^2]
sigusevis = sigsecvis # [Munit/pc^2]
siguseviserr = sigsecviserr # [Munit/pc^2]
elif gp.baryonmodel == 'sim':
zvis, sigusevis, siguseviserr = gh.readcol3(gp.files.surfdenfiles[0])
# [kpc, Munit/pc^2, Munit/pc^2]
zvis *= 1000. # [pc]
sigusevis = gh.ipol(zvis, sigusevis, gp.xipol) # interpolate to xipol radius array
siguseviserr = gh.ipol(zvis, siguseviserr, gp.xipol)
zvis = gp.xipol # [pc]
# read in DM surface density
zdm, sigusedm, sigusedmerr = gh.readcol3(gp.files.surfdenfiles[1])
# [kpc, Munit/pc^2, Munit/pc^2]
zdm *= 1000. # [pc]
sigusedm = gh.ipol(zdm, sigusedm, gp.xipol) # interpolate to xipol radius array
sigusedmerr = gh.ipol(zdm, sigusedmerr, gp.xipol)
zdm = gp.xipol # [pc]
elif gp.baryonmodel == 'simple':
zvis = gp.xipol # [pc]
D = 250. # [pc]
K = 1.65
sigusevis = K*zvis/sqrt(zvis**2.+D**2.) / (2.0*np.pi*G1)
siguseviserr = sigusevis*0.01
# baryonic surface density, really a Sig
gp.dat.Mx = gp.xipol # [pc]
gp.dat.Mrdat = sigusevis # [Munit/pc^2]
gp.dat.Mrerr = siguseviserr # [Munit/pc^2]
# total surface density (same z array as baryonic)
gp.Mmodel = sigusevis + sigusedm # [Munit/pc^2]
Kz_zstar = -gp.Mmodel * (2.*np.pi*gu.G1__pcMsun_1km2s_2) # [1000/pc (km/s)^2]
# should be kappa data (not sure whether this is necessary)
gp.dat.densx = gp.xipol # [pc]
gp.dat.densdat = phys.kappa(gp.dat.densx, Kz_zstar)
gp.dat.denserr = gp.dat.densdat/np.sqrt(len(Kz_zstar))
gp.dat.nux1 = gp.xipol # [pc]
gp.dat.nu1 = nu_dat_bin # [Munit/pc^3]
gp.dat.nuerr1 = nu_dat_err_bin # [Munit/pc^3]
gp.dat.sigx1 = gp.xipol # [pc]
gp.dat.sig1 = sig_dat_bin # [km/s]
gp.dat.sigerr1 = sig_dat_err_bin # [km/s]
if gp.pops == 2:
gp.dat.nux2 = gp.xipol # [pc]
gp.dat.nu2 = nu_dat_bin2 # [Munit/pc^3]
gp.dat.nuerr2 = nu_dat_err_bin2 # [Munit/pc^3]
gp.dat.sigx2 = gp.xipol # [pc]
gp.dat.sig2 = sig_dat_bin2 # [km/s]
gp.dat.sigerr2 = sig_dat_err_bin2 # [km/s]
gp.dat.output()
return gp.dat
def disc_sim(gp):
gp.zpmin = -1
gp.zpmax = -1
#import all data from files
if gp.importdata:
z_nu1_raw, nu1_dat_raw, nu1_dat_err_raw = gh.readcoln(
gp.files.Sigfiles[0])
z_sig1_raw, sig1_dat_raw, sig1_dat_err_raw = gh.readcoln(
gp.files.sigfiles[0])
#z_surf_raw,surftot_dat_raw,surftot_dat_err_raw = gh.readcoln(gp.files.surfdenfiles[0])
z_surf_raw, surfbar_dat_raw, surfbar_dat_err_raw = gh.readcoln(
gp.files.surfdenfiles[0])
z_surf_raw, surfdm_dat_raw, surfdm_dat_err_raw = gh.readcoln(
gp.files.surfdenfiles[1])
selnu1 = (z_nu1_raw > 0)
selsig1 = (z_sig1_raw > 0)
selsurf = (z_surf_raw > 0)
if gp.pops == 2:
z_nu2_raw, nu2_dat_raw, nu2_dat_err_raw = gh.readcoln(
gp.files.Sigfiles[1])
z_sig2_raw, sig2_dat_raw, sig2_dat_err_raw = gh.readcoln(
gp.files.sigfiles[2])
selnu2 = (z_nu2_raw > 0)
selsig2 = (z_sig2_raw > 0)
# baryonic surface density
gp.dat.Mx = z_surf_raw[selsurf] * 1000. # [pc]
gp.dat.Mrdat = surfbar_dat_raw[selsurf] # [Munit/pc^2]
gp.dat.Mrerr = surfbar_dat_err_raw[selsurf] # [Munit/pc^2]
# total surface density
gp.Mmodel = surftot_dat_raw[selsusrf] # [Munit/pc^2]
Kz_zstar = -gp.Mmodel * (2. * np.pi * gu.G1__pcMsun_1km2s_2)
# should be kappa data (not sure whether this is necessary)
gp.dat.densx = z_surf_raw[selsusrf] * 1000. # [pc]
gp.dat.densdat = phys.kappa(
gp.dat.densx, Kz_zstar
) #should be the total kappa, not sure about x array though
gp.dat.denserr = gp.dat.densdat #not necessary for a certainty
gp.dat.nux1 = z_nu1_raw[selnu1] * 1000. # [pc]
gp.dat.nu1 = nu1_dat_raw[selnu1]
gp.dat.nuerr1 = nu1_dat_err_raw[selnu1]
gp.dat.sigx1 = z_sig1_raw[selsig1] * 1000.
gp.dat.sig1 = sig1_dat_raw[selsig1]
gp.dat.sigerr1 = sig1_dat_err_raw[selsig1]
if gp.pops == 2:
gp.dat.nux2 = z_nu2_raw[selnu2] * 1000.
gp.dat.nu2 = nu2_dat_raw[selnu2]
gp.dat.nuerr2 = nu2_dat_err_raw[selnu2]
gp.dat.sigx2 = z_sig2_raw[selsig2] * 1000.
gp.dat.sig2 = z_sig2_raw[selsig2]
gp.dat.sigerr2 = z_sig2_raw[selsig2]
gp.dat.output()
return gp.dat
else:
# import simulation datapoints and calculate nu, sig
zmin = 100.
zmax = 1300. # [pc]
zbinbndry = np.linspace(zmin, zmax, gp.nipol +
1) # [pc] assuming linear spacing of bins
zbinmin = zbinbndry[:-1] # [pc]
zbinmax = zbinbndry[1:] # [pc]
gp.xipol = zbinmin + (zbinmax - zbinmin) / 2. # [pc]
# Read in the data:
mass, x_dat, y_dat, z_dat, vx_dat, vy_dat, vz_dat, pot_dat = gh.readcoln(
gp.files.posvelfiles[0])
# assume units: Munit, 3*kpc, 3*km/s, [pot] <= last one not needed
# [Dave] v is in units [100 km/s] <= not possible?!
if max(mass) != min(mass):
print('**** Multimass data not yet supported ****')
exit(1)
# change to [pc]
x_dat *= 1000.
y_dat *= 1000.
z_dat *= 1000. # [pc]
z_mean = np.sum(mass * z_dat) / np.sum(mass) # [pc]
vz_mean = np.sum(mass * vz_dat) / np.sum(mass) # [km/s]
# center on coordinate, if also negative z values read in
if min(z_dat) < 0:
z_dat = z_dat - z_mean # [pc]
vz_dat = vz_dat - vz_mean # [km/s]
# Add errors:
if gp.adderrors:
# Assume normal errors for now:
xerrfac = 10.0
yerrfac = 10.0
zerrfac = 10.0
vxerrfac = 10.0
vyerrfac = 10.0
vzerrfac = 10.0
x_dat_err = abs(x_dat / xerrfac) # [pc]
y_dat_err = abs(y_dat / yerrfac) # [pc]
z_dat_err = abs(z_dat / zerrfac) # [pc]
vx_dat_err = abs(vx_dat / vxerrfac) # [km/s]
vy_dat_err = abs(vy_dat / vyerrfac) # [km/s]
vz_dat_err = abs(vz_dat / vzerrfac) # [km/s]
x_dat = x_dat + npr.normal(-1., 1., len(z_dat)) * x_dat_err # [pc]
y_dat = y_dat + npr.normal(-1., 1., len(z_dat)) * y_dat_err # [pc]
z_dat = z_dat + npr.normal(-1., 1., len(z_dat)) * z_dat_err # [pc]
vx_dat = vx_dat + npr.normal(-1., 1.,
len(z_dat)) * vx_dat_err # [km/s]
vy_dat = vy_dat + npr.normal(-1., 1.,
len(z_dat)) * vy_dat_err # [km/s]
vz_dat = vz_dat + npr.normal(-1., 1.,
len(z_dat)) * vz_dat_err # [km/s]
# Cut on zmax, cut zero velocities
sel = (z_dat < zmax) * (abs(vz_dat) >= 0.) # [bool]
z_dat = z_dat[sel] # [pc]
vz_dat = vz_dat[sel] # [km/s]
# determine sigma_v
sig_dat_bin = np.zeros(gp.nipol)
sig_dat_err_bin = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat > zbinmin[i]) * (z_dat < zbinmax[i]) # select bin
vtemp = np.array(vz_dat[sel]) # [km/s]
sig_dat_bin[i] = np.sqrt(np.mean(vtemp**2) -
np.mean(vtemp)**2) # [km/s]
sig_dat_err_bin[i] = sig_dat_bin[i] / (1. * np.sum(sel)) # [km/s]
nu_dat_bin = np.zeros(gp.nipol)
nu_dat_err_bin = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat > zbinmin[i]) * (z_dat < zbinmax[i]) # select bin
nu_dat_bin[i] = 1. * np.sum(sel) / (1. * (zbinmax[i] - zbinmin[i])
) # [1/tot. area/pc]
nu_dat_err_bin[i] = nu_dat_bin[i] / np.sqrt(
np.sum(sel)) # [1/tot. area/pc], Poisson distributed
renorm = 1. * max(nu_dat_bin) # [1/tot.area/pc]
nu_dat_bin = nu_dat_bin / renorm # [1]
nu_dat_err_bin = nu_dat_err_bin / renorm # [1]
if gp.pops == 2:
mass2, x_dat2, y_dat2, z_dat2, vx_dat2, vy_dat2, vz_dat2, pot_dat2 = gh.readcoln(
gp.files.posvelfiles[1])
if max(mass2) > min(mass2):
print('**** Multimass data not yet supported ****')
exit(1)
# change to [pc]
x_dat2 *= 1000.
y_dat2 *= 1000.
z_dat2 *= 1000. # [pc]
z_mean2 = np.sum(mass2 * z_dat2) / np.sum(mass2) # [pc]
vz_mean2 = np.sum(mass2 * vz_dat2) / np.sum(mass2) # [km/s]
# center on coordinate, if also negative z values read in
if min(z_dat2) < 0:
z_dat2 = z_dat2 - z_mean2 # [pc]
vz_dat2 = vz_dat2 - vz_mean2 # [km/s]
# Add errors:
if gp.adderrors:
# Assume normal errors for now:
x_dat_err2 = abs(x_dat2 / xerrfac) # [pc]
y_dat_err2 = abs(y_dat2 / yerrfac) # [pc]
z_dat_err2 = abs(z_dat2 / zerrfac) # [pc]
vx_dat_err2 = abs(vx_dat2 / vxerrfac) # [km/s]
vy_dat_err2 = abs(vy_dat2 / vyerrfac) # [km/s]
vz_dat_err2 = abs(vz_dat2 / vzerrfac) # [km/s]
x_dat2 = x_dat2 + npr.normal(-1., 1.,
len(z_dat2)) * x_dat_err2 # [pc]
y_dat2 = y_dat2 + npr.normal(-1., 1.,
len(z_dat2)) * y_dat_err2 # [pc]
z_dat2 = z_dat2 + npr.normal(-1., 1.,
len(z_dat2)) * z_dat_err2 # [pc]
vx_dat2 = vx_dat2 + npr.normal(
-1., 1., len(z_dat2)) * vx_dat_err2 # [km/s]
vy_dat2 = vy_dat2 + npr.normal(
-1., 1., len(z_dat2)) * vy_dat_err2 # [km/s]
vz_dat2 = vz_dat2 + npr.normal(
-1., 1., len(z_dat2)) * vz_dat_err2 # [km/s]
# Cut on zmax, cut zero velocities
sel = (z_dat2 < zmax) * (abs(vz_dat2) >= 0.) # [bool]
z_dat2 = z_dat2[sel] # [pc]
vz_dat2 = vz_dat2[sel] # [km/s]
# determine sigma_v
sig_dat_bin2 = np.zeros(gp.nipol)
sig_dat_err_bin2 = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat2 > zbinmin[i]) * (z_dat2 < zbinmax[i]
) # select bin
vtemp = np.array(vz_dat2[sel]) # [km/s]
sig_dat_bin2[i] = np.sqrt(
np.mean(vtemp**2) - np.mean(vtemp)**2) # [km/s]
sig_dat_err_bin2[i] = sig_dat_bin2[i] / (1. * np.sum(sel)
) # [km/s]
nu_dat_bin2 = np.zeros(gp.nipol)
nu_dat_err_bin2 = np.zeros(gp.nipol)
for i in range(gp.nipol):
sel = (z_dat2 > zbinmin[i]) * (z_dat2 < zbinmax[i]
) # select bin
nu_dat_bin2[i] = 1. * np.sum(sel) / (
1. * (zbinmax[i] - zbinmin[i])) # [1/tot. area/pc]
nu_dat_err_bin2[i] = nu_dat_bin2[i] / np.sqrt(
np.sum(sel)) # [1/tot. area/pc], Poisson distributed
renorm = 1. * max(nu_dat_bin2) # [1/tot.area/pc]
nu_dat_bin2 /= renorm # [1]
nu_dat_err_bin2 /= renorm # [1]
# if gp.bprior:
# Load the baryonic model:
if gp.baryonmodel == 'silvia':
zvis, sigexpvis, sigexpviserr, sigsecvis, sigsecviserr = gh.readcoln(
'/home/ast/user/jread/Data/Local_dm/Vis/Sigma_MM.txt')
# [kpc, Munit/pc^2, Msun/pc^2, Msun/pc^2, Msun/pc^2]
sigusevis = sigsecvis # [Munit/pc^2]
siguseviserr = sigsecviserr # [Munit/pc^2]
elif gp.baryonmodel == 'sim':
zvis, sigusevis, siguseviserr = gh.readcol3(
gp.files.surfdenfiles[0])
# [kpc, Munit/pc^2, Munit/pc^2]
zvis *= 1000. # [pc]
sigusevis = gh.ipol(zvis, sigusevis,
gp.xipol) # interpolate to xipol radius array
siguseviserr = gh.ipol(zvis, siguseviserr, gp.xipol)
zvis = gp.xipol # [pc]
# read in DM surface density
zdm, sigusedm, sigusedmerr = gh.readcol3(gp.files.surfdenfiles[1])
# [kpc, Munit/pc^2, Munit/pc^2]
zdm *= 1000. # [pc]
sigusedm = gh.ipol(zdm, sigusedm,
gp.xipol) # interpolate to xipol radius array
sigusedmerr = gh.ipol(zdm, sigusedmerr, gp.xipol)
zdm = gp.xipol # [pc]
elif gp.baryonmodel == 'simple':
zvis = gp.xipol # [pc]
D = 250. # [pc]
K = 1.65
sigusevis = K * zvis / sqrt(zvis**2. + D**2.) / (2.0 * np.pi * G1)
siguseviserr = sigusevis * 0.01
# baryonic surface density, really a Sig
gp.dat.Mx = gp.xipol # [pc]
gp.dat.Mrdat = sigusevis # [Munit/pc^2]
gp.dat.Mrerr = siguseviserr # [Munit/pc^2]
# total surface density (same z array as baryonic)
gp.Mmodel = sigusevis + sigusedm # [Munit/pc^2]
Kz_zstar = -gp.Mmodel * (2. * np.pi * gu.G1__pcMsun_1km2s_2
) # [1000/pc (km/s)^2]
# should be kappa data (not sure whether this is necessary)
gp.dat.densx = gp.xipol # [pc]
gp.dat.densdat = phys.kappa(gp.dat.densx, Kz_zstar)
gp.dat.denserr = gp.dat.densdat / np.sqrt(len(Kz_zstar))
gp.dat.nux1 = gp.xipol # [pc]
gp.dat.nu1 = nu_dat_bin # [Munit/pc^3]
gp.dat.nuerr1 = nu_dat_err_bin # [Munit/pc^3]
gp.dat.sigx1 = gp.xipol # [pc]
gp.dat.sig1 = sig_dat_bin # [km/s]
gp.dat.sigerr1 = sig_dat_err_bin # [km/s]
if gp.pops == 2:
gp.dat.nux2 = gp.xipol # [pc]
gp.dat.nu2 = nu_dat_bin2 # [Munit/pc^3]
gp.dat.nuerr2 = nu_dat_err_bin2 # [Munit/pc^3]
gp.dat.sigx2 = gp.xipol # [pc]
gp.dat.sig2 = sig_dat_bin2 # [km/s]
gp.dat.sigerr2 = sig_dat_err_bin2 # [km/s]
gp.dat.output()
return gp.dat
def disc_mock(gp):
global K,C,D,F, zth, zp_kz, zmin, zmax, z0, z02
# Set up simple population here using analytic formulae:
zmin = 100. # [pc], first bin center
zmax = 1300. # [pc], last bin center
# get Stuetzpunkte for theoretical profiles (not yet stars, finer spacing in real space)
nth = 3*gp.nipol
zth = 1.* np.arange(nth) * (zmax-zmin)/(nth-1.) + zmin
z0 = 240. # [pc], scaleheight of first population
z02 = 200. # [pc], scaleheight of second population
D = 250. # [pc], scaleheight of all stellar tracers
K = 1.65
F = 1.65e-4
C = 17.**2. # [km/s] integration constant in sig
# Draw mock data:
nu_zth = np.exp(-zth/z0) # [1]
Kz_zth = -(K*zth/np.sqrt(zth**2.+D**2.) + 2.0 * F * zth)
if gp.adddarkdisc:
DD = 600 # [pc]
KD = 0.15 * 1.650
Kz_zth = Kz_zth - KD*zth/np.sqrt(zth**2. + DD**2.)
# calculate sig_z^2
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i]*nu_zth[:i], zth[:i])
sigzth = np.sqrt((inti + C) / nu_zth)
# project back to positions of stars
ran = npr.uniform(size=int(gp.ntracer[1-1])) # [1]
zstar = -z0 * np.log(1.0 - ran) # [pc] stellar positions
sigzstar = gh.ipol(zth, sigzth, zstar)
# > 0 ((IDL, Justin)) stellar velocity dispersion
# assign [0,1] * maxsig
ran2 = npr.normal(size=int(gp.ntracer[1-1])) # [1]
vzstar = ran2 * sigzstar # [km/s]
# Add second population [thick-disc like]:
if gp.pops == 2:
nu_zth2 = gp.ntracer[2-1]/gp.ntracer[1-1]*np.exp(-zth/z02)
# no normalization to 1
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i]*nu_zth2[:i], zth[:i])
sigzth2 = np.sqrt((inti + C) / nu_zth2) # same integration constant
ran = npr.uniform(-1., 1., gp.ntracer[2-1]) # [1]
zstar2 = -z02 * np.log(1.0 - ran) # [pc]
zstarobs = np.hstack([zstar, zstar2]) # concat pop1, pop2 for all stars
sigzstar2 = gh.ipol(zth, sigzth2, zstar2)
ran2 = npr.normal(-1., 1, gp.ntracer[2-1]) # [1]
vzstar2 = ran2 * sigzstar2 # [(km/2)^2]
# enforce observational cut on zmax:
sel = (zstar < zmax)
print('fraction of z<zmax selected elements: ', 1.*sum(sel)/(1.*len(sel)))
z_dat = zstar[sel]
vz_dat = vzstar[sel]
# throw away velocities of value zero (unstable?):
sel = (abs(vz_dat) > 0)
print('fraction of vz_dat>0 selected elements: ', 1.*sum(sel)/(1.*len(sel)))
z_dat = z_dat[sel]
vz_dat = vz_dat[sel]
# Calulate binned data (for plots/binned anal.). old way, linear spacings, no const #particles/bin
binmin, binmax, z_dat_bin, sig_dat_bin, count_bin = gh.binsmooth(z_dat, vz_dat, \
zmin, zmax, gp.nipol, 0.)
sig_dat_err_bin = sig_dat_bin / np.sqrt(count_bin)
nu_dat_bin, count_bin = gh.bincount(z_dat, binmax)
nu_dat_err_bin = nu_dat_bin / np.sqrt(count_bin)
renorm = max(nu_dat_bin)
nu_dat_bin = nu_dat_bin / renorm
nu_dat_err_bin = nu_dat_err_bin / renorm
# if only 1 pop, use 0 for all components
binmin0 = binmin; binmax0 = binmax; z_dat_bin0 = z_dat_bin
sig_dat_bin0 = sig_dat_bin; sig_dat_err_bin0 = sig_dat_err_bin
nu_dat_bin0 = nu_dat_bin; nu_dat_err_bin0 = nu_dat_err_bin
if gp.pops == 2:
# enforce observational constraint on z<z_max
sel = (zstar2 < zmax)
z_dat2 = zstar2[sel]
vz_dat2 = vzstar2[sel]
# cut zero velocities:
sel = (abs(vz_dat2) > 0)
z_dat2 = z_dat2[sel]
vz_dat2 = vz_dat2[sel]
# Calulate binned data (for plots/binned analysis):
binmin2, binmax2, z_dat_bin2, sig_dat_bin2, count_bin2 = gh.binsmooth(z_dat2, vz_dat2, \
zmin, zmax, gp.nipol, 0.)
sig_dat_err_bin2 = sig_dat_bin2 / np.sqrt(count_bin2)
nu_dat_bin2, count_bin2 = gh.bincount(z_dat2, binmax2)
nu_dat_err_bin2 = nu_dat_bin2 / np.sqrt(count_bin2)
renorm2 = max(nu_dat_bin2) # normalize by max density of first bin, rather
nu_dat_bin2 = nu_dat_bin2 / renorm2
nu_dat_err_bin2 = nu_dat_err_bin2 / renorm2
# CALCULATE PROPERTIES FOR ALL POP TOGETHER
z_dat0 = np.hstack([z_dat, z_dat2])
vz_dat0 = np.hstack([vz_dat, vz_dat2])
# Calulate binned data (for plots/binned anal.). old way, linear spacings, no const #particles/bin
binmin0, binmax0, z_dat_bin0, sig_dat_bin0, count_bin0 = gh.binsmooth(z_dat0, vz_dat0, \
zmin, zmax, gp.nipol, 0.)
sig_dat_err_bin0 = sig_dat_bin0 / np.sqrt(count_bin0)
# binmin, binmax, z_dat_bin = gh.bin_r_const_tracers(z_dat, gp.nipol)
nu_dat_bin0, count_bin0 = gh.bincount(z_dat0, binmax0)
nu_dat_err_bin0 = nu_dat_bin0 / np.sqrt(count_bin0)
renorm0 = max(nu_dat_bin0)
nu_dat_bin0 = nu_dat_bin0 / renorm0
nu_dat_err_bin0 = nu_dat_err_bin0 / renorm0
xip = np.copy(z_dat_bin0) # [pc]
gp.dat.binmin = binmin0
gp.dat.rbin = xip
gp.xipol = gp.dat.rbin
gp.Rscale.append(D) # [pc]
gp.Rscale.append(z0) # [pc]
maxr = max(gp.dat.rbin)
gp.xepol = np.hstack([gp.dat.rbin, 2*maxr, 4*maxr, 8*maxr])
gp.dat.binmax = binmax0
gp.dat.Mrdat = K*xip/np.sqrt(xip**2.+D**2.) / (2.0*np.pi*gu.G1__pcMsun_1km2s_2)
gp.dat.Mrerr = gp.dat.Mrdat*nu_dat_err_bin/nu_dat_bin
gp.dat.nu.append(nu_dat_bin0) # [Msun/pc^3], normalized to 1 at center
gp.dat.nuerr.append(nu_dat_err_bin0) # [Msun/pc^3], normalized
gp.dat.sig.append(sig_dat_bin0) # [km/s]
gp.dat.sigerr.append(sig_dat_err_bin0)# [km/s]
gp.dat.nu.append(nu_dat_bin) # [Msun/pc^3], normalized to 1
gp.dat.nuerr.append(nu_dat_err_bin) # [Msun/pc^3], normalized
gp.dat.sig.append(sig_dat_bin) # [km/s]
gp.dat.sigerr.append(sig_dat_err_bin) # [km/s]
if gp.pops == 2:
gp.Rscale.append(z02) # [pc]
gp.dat.nu.append(nu_dat_bin2) # [Msun/pc^3], normalized to 1
gp.dat.nuerr.append(nu_dat_err_bin2) # [Msun/pc^3], normalized
gp.dat.sig.append(sig_dat_bin2) # [km/s]
gp.dat.sigerr.append(sig_dat_err_bin2)# [km/s]
return gp.dat
def get_prof(prof, pop, gp):
zmin = 100. # [pc], first bin center
zmax = 1300. # [pc], last bin center
# get Stuetzpunkte for theoretical profiles (not yet stars, finer spacing in real space)
nth = gp.nipol # [1] number of bins
zth = 1.* np.arange(nth) * (zmax-zmin)/(nth-1.) + zmin # [pc] bin centers
z0 = 240. # [pc], scaleheight of first population
z02 = 200. # [pc], scaleheight of second population
D = 250. # [pc], scaleheight of all stellar tracers
K = 1.65
F = 1.65e-4
C = 17.**2. # [km/s] integration constant in sig
# Draw mock data from exponential disk:
nu_zth = np.exp(-zth/z0) # [nu0] = [Msun/A/pc] 3D tracer density
if prof == 'nu' and pop==1:
return zth, nu_zth
Kz_zth = -(K*zth/np.sqrt(zth**2.+D**2.) + 2.0 * F * zth)
if gp.adddarkdisc:
DD = 600 # [pc] scaleheight of dark disc
KD = 0.15 * 1.650
Kz_zth = Kz_zth - KD*zth/np.sqrt(zth**2. + DD**2.)
# calculate sig_z^2
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i]*nu_zth[:i], zth[:i])
sigzth = np.sqrt((inti + C) / nu_zth)
if prof == 'sig' and pop == 1:
return zth, sigzth
# project back to positions of stars
ran = npr.uniform(size=int(gp.ntracer[1-1])) # [1]
zstar = -z0 * np.log(1.0 - ran) # [pc] stellar positions, exponential falloff
sigzstar = gh.ipol(zth, sigzth, zstar)
# > 0 ((IDL, Justin)) stellar velocity dispersion
# assign [0,1] * maxsig
ran2 = npr.normal(size=int(gp.ntracer[2-1])) # [1]
vzstar = ran2 * sigzstar # [km/s]
# Add second population [thick-disc like]:
if gp.pops == 2:
nu_zth2 = gp.ntracer[2-1]/gp.ntracer[1-1]*np.exp(-zth/z02)
if prof == 'nu' and pop == 2:
return zth, nu_zth2
# [nu0,2] = [Msun/A/pc], 3D tracer density, exponentially falling
# no normalization to 1 done here
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i]*nu_zth2[:i], zth[:i])
sigzth2 = np.sqrt((inti + C) / nu_zth2) # same integration constant
if prof == 'sig' and pop == 2:
return zth, sigzth2
ran = npr.uniform(-1., 1., gp.ntracer[2-1]) # [1]
zstar2 = -z02 * np.log(1.0 - ran) # [pc]
#zstarobs = np.hstack([zstar, zstar2]) # concat pop1, pop2 for all stars
sigzstar2 = gh.ipol(zth, sigzth2, zstar2)
ran2 = npr.normal(-1., 1, gp.ntracer[2-1]) # [1]
vzstar2 = ran2 * sigzstar2 # [(km/2)^2]
def run(gp):
global K, C, D, F, zth, zp_kz, zmin, zmax, z0, z02
# Set up simple population here using analytic formulae:
zmin = 100.0 # [pc], first bin center
zmax = 1300.0 # [pc], last bin center
# get Stuetzpunkte for theoretical profiles (not yet stars, finer spacing in real space)
nth = gp.nipol # [1] number of bins
zth = 1.0 * np.arange(nth) * (zmax - zmin) / (nth - 1.0) + zmin # [pc] bin centers
z0 = 240.0 # [pc], scaleheight of first population
z02 = 200.0 # [pc], scaleheight of second population
D = 250.0 # [pc], scaleheight of all stellar tracers
K = 1.65
F = 1.65e-4
C = 17.0 ** 2.0 # [km/s] integration constant in sig
# Draw mock data from exponential disk:
nu_zth = np.exp(-zth / z0) # [nu0] = [Msun/A/pc] 3D tracer density
Kz_zth = -(K * zth / np.sqrt(zth ** 2.0 + D ** 2.0) + 2.0 * F * zth)
if gp.adddarkdisc:
DD = 600 # [pc] scaleheight of dark disc
KD = 0.15 * 1.650
Kz_zth = Kz_zth - KD * zth / np.sqrt(zth ** 2.0 + DD ** 2.0)
# calculate sig_z^2
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i] * nu_zth[:i], zth[:i])
sigzth = np.sqrt((inti + C) / nu_zth)
# project back to positions of stars
ran = npr.uniform(size=int(gp.ntracer[1 - 1])) # [1]
zstar = -z0 * np.log(1.0 - ran) # [pc] stellar positions, exponential falloff
sigzstar = gh.ipol(zth, sigzth, zstar)
# > 0 ((IDL, Justin)) stellar velocity dispersion
# assign [0,1] * maxsig
ran2 = npr.normal(size=int(gp.ntracer[1 - 1])) # [1]
vzstar = ran2 * sigzstar # [km/s]
# Add second population [thick-disc like]:
if gp.pops == 2:
nu_zth2 = gp.ntracer[2 - 1] / gp.ntracer[1 - 1] * np.exp(-zth / z02)
# [nu0,2] = [Msun/A/pc], 3D tracer density, exponentially falling
# no normalization to 1 done here
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i] * nu_zth2[:i], zth[:i])
sigzth2 = np.sqrt((inti + C) / nu_zth2) # same integration constant
ran = npr.uniform(-1.0, 1.0, gp.ntracer[2 - 1]) # [1]
zstar2 = -z02 * np.log(1.0 - ran) # [pc]
# zstarobs = np.hstack([zstar, zstar2]) # concat pop1, pop2 for all stars
sigzstar2 = gh.ipol(zth, sigzth2, zstar2)
ran2 = npr.normal(-1.0, 1, gp.ntracer[2 - 1]) # [1]
vzstar2 = ran2 * sigzstar2 # [(km/2)^2]
# enforce observational cut on zmax:
sel = zstar < zmax
print("fraction of z<zmax selected elements: ", 1.0 * sum(sel) / (1.0 * len(sel)))
z_dat1 = zstar[sel]
vz_dat1 = vzstar[sel]
# throw away velocities of value zero (unstable?):
sel = abs(vz_dat1) > 0
print("fraction of vz_dat>0 selected elements: ", 1.0 * sum(sel) / (1.0 * len(sel)))
z_dat1 = z_dat1[sel]
vz_dat1 = vz_dat1[sel]
# Calulate binned data (for plots/binned anal.). old way, linear spacings, no const #particles/bin
binmin1, binmax1, z_dat_bin1, sig_dat_bin1, count_bin1 = gh.binsmooth(z_dat1, vz_dat1, zmin, zmax, gp.nipol, 0.0)
sig_dat_err_bin1 = np.sqrt(sig_dat_bin1) # Poisson errors
nu_dat_bin1, nu_dat_err_bin1 = gh.bincount(z_dat1, binmax1)
nu_dat_bin1 /= binmax1 - binmin1
nu_dat_err_bin1 /= binmax1 - binmin1
import gr_params
gpr = gr_params.grParams(gp)
if gpr.showplots:
nuscaleb = nu_zth[np.argmin(np.abs(zth - z0))]
plt.loglog(zth, nu_zth / nuscaleb, "b.-")
nuscaler = nu_dat_bin1[np.argmin(np.abs(zth - z0))]
plt.loglog(zth, nu_dat_bin1 / nuscaler, "r.-")
# pdb.set_trace()
Sig_dat_bin1 = np.cumsum(nu_dat_bin1)
Sig_dat_err_bin1 = np.sqrt(Sig_dat_bin1)
Mrdat1 = np.cumsum(Sig_dat_bin1)
Mrerr1 = Mrdat1 * Sig_dat_err_bin1 / Sig_dat_bin1
scales = [[], [], []]
scales[1].append(z0) # [pc]
scales[1].append(Sig_dat_bin1[0])
scales[1].append(Mrdat1[-1])
scales[1].append(nu_dat_bin1[0])
scales[1].append(max(sig_dat_bin1))
# start analysis of "all stars" with only component 1,
# append to it later if more populations required
z_dat0 = z_dat1 # [pc]
vz_dat0 = vz_dat1 # [km/s]
if gp.pops == 2:
# enforce observational constraints on z<z_max
sel = zstar2 < zmax
z_dat2 = zstar2[sel]
vz_dat2 = vzstar2[sel]
# cut zero velocities:
sel = abs(vz_dat2) > 0
z_dat2 = z_dat2[sel]
vz_dat2 = vz_dat2[sel]
# Calulate binned data (for plots/binned analysis):
binmin2, binmax2, z_dat_bin2, sig_dat_bin2, count_bin2 = gh.binsmooth(
z_dat2, vz_dat2, zmin, zmax, gp.nipol, 0.0
)
sig_dat_err_bin2 = np.sqrt(sig_dat_bin2) # Poissonian errors
nu_dat_bin2, nu_dat_err_bin2 = gh.bincount(z_dat2, binmax2)
nu_dat_bin2 /= binmax2 - binmin2
nu_dat_err_bin2 /= binmax2 - binmin2
Sig_dat_bin2 = np.cumsum(nu_dat_bin2)
Sig_dat_err_bin2 = np.sqrt(Sig_dat_bin2)
Mrdat2 = np.cumsum(nu_dat_bin2)
Mrerr2 = np.sqrt(Mrdat2)
scales[2].append(z02) # [pc]
scales[2].append(Sig_dat_bin2[0])
scales[2].append(Mrdat2[-1])
scales[2].append(nu_dat_bin2[0]) # normalize by max density of first bin, rather
scales[2].append(max(sig_dat_bin2))
# calculate properties for all pop together with stacked values
z_dat0 = np.hstack([z_dat1, z_dat2])
vz_dat0 = np.hstack([vz_dat1, vz_dat2])
# Calulate binned data (for plots/binned anal.). old way, linear spacings, no const #particles/bin
binmin0, binmax0, z_dat_bin0, sig_dat_bin0, count_bin0 = gh.binsmooth(z_dat0, vz_dat0, zmin, zmax, gp.nipol, 0.0)
sig_dat_err_bin0 = np.sqrt(sig_dat_bin0)
# binmin, binmax, z_dat_bin = gh.bin_r_const_tracers(z_dat, gp.nipol)
nu_dat_bin0, nu_dat_err_bin0 = gh.bincount(z_dat0, binmax0)
nu_dat_bin0 /= binmax0 - binmin0
nu_dat_err_bin0 /= binmax0 - binmin0
Sig_dat_bin0 = np.cumsum(nu_dat_bin0)
Sig_dat_err_bin0 = np.sqrt(Sig_dat_bin0)
# renorm0 = max(nu_dat_bin0)
xip = np.copy(z_dat_bin0) # [pc]
Mrdat0 = K * xip / np.sqrt(xip ** 2.0 + D ** 2.0) / (2.0 * np.pi * gu.G1__pcMsun_1km2s_2)
Mrerr0 = Mrdat0 * nu_dat_err_bin0 / nu_dat_bin0
scales[0].append(D) # [pc]
scales[0].append(Sig_dat_bin0[0])
scales[0].append(Mrdat0[-1])
scales[0].append(nu_dat_bin0[0])
scales[0].append(max(sig_dat_bin0))
rmin = binmin0 / scales[0][0] # [pc]
rbin = xip / scales[0][0] # [pc]
rmax = binmax0 / scales[0][0] # [pc]
# store parameters for output
# normalized by scale values
nudat = []
nudat.append(nu_dat_bin0 / scales[0][3]) # [Msun/pc^3]
nudat.append(nu_dat_bin1 / scales[1][3])
if gp.pops == 2:
nudat.append(nu_dat_bin2 / scales[2][3])
nuerr = []
nuerr.append(nu_dat_err_bin0 / scales[0][3]) # [Msun/pc^3]
nuerr.append(nu_dat_err_bin1 / scales[1][3])
if gp.pops == 2:
nuerr.append(nu_dat_err_bin2 / scales[2][3])
Mrdat = []
Mrdat.append(Mrdat0 / scales[0][2]) # [Msun]
Mrdat.append(Mrdat1 / scales[1][2])
if gp.pops == 2:
Mrdat.append(Mrdat2 / scales[2][2])
Mrerr = []
Mrerr.append(Mrerr0 / scales[0][2]) # [Msun]
Mrerr.append(Mrerr1 / scales[1][2])
if gp.pops == 2:
Mrerr.append(Mrerr2 / scales[2][2])
Sigdat = []
Sigdat.append(Sig_dat_bin0 / scales[0][1])
Sigdat.append(Sig_dat_bin1 / scales[1][1])
if gp.pops == 2:
Sigdat.append(Sig_dat_bin2 / scales[2][1])
Sigerr = []
Sigerr.append(Sig_dat_err_bin0 / scales[0][1])
Sigerr.append(Sig_dat_err_bin1 / scales[1][1])
if gp.pops == 2:
Sigerr.append(Sig_dat_err_bin2 / scales[2][1])
sigdat = []
sigdat.append(sig_dat_bin0 / scales[0][4]) # [km/s]
sigdat.append(sig_dat_bin1 / scales[1][4])
if gp.pops == 2:
sigdat.append(sig_dat_bin2 / scales[2][4])
sigerr = []
sigerr.append(sig_dat_err_bin0 / scales[0][4]) # [km/s]
sigerr.append(sig_dat_err_bin1 / scales[1][4])
if gp.pops == 2:
sigerr.append(sig_dat_err_bin2 / scales[2][4])
write_disc_output_files(rbin, rmin, rmax, nudat, nuerr, Sigdat, Sigerr, Mrdat, Mrerr, sigdat, sigerr, scales, gp)
return gp.dat
def get_prof(prof, pop, gp):
zmin = 100. # [pc], first bin center
zmax = 1300. # [pc], last bin center
# get Stuetzpunkte for theoretical profiles (not yet stars, finer spacing in real space)
nth = gp.nipol # [1] number of bins
zth = 1. * np.arange(nth) * (zmax - zmin) / (nth -
1.) + zmin # [pc] bin centers
z0 = 240. # [pc], scaleheight of first population
z02 = 200. # [pc], scaleheight of second population
D = 250. # [pc], scaleheight of all stellar tracers
K = 1.65
F = 1.65e-4
C = 17.**2. # [km/s] integration constant in sig
# Draw mock data from exponential disk:
nu_zth = np.exp(-zth / z0) # [nu0] = [Msun/A/pc] 3D tracer density
if prof == 'nu' and pop == 1:
return zth, nu_zth
Kz_zth = -(K * zth / np.sqrt(zth**2. + D**2.) + 2.0 * F * zth)
if gp.adddarkdisc:
DD = 600 # [pc] scaleheight of dark disc
KD = 0.15 * 1.650
Kz_zth = Kz_zth - KD * zth / np.sqrt(zth**2. + DD**2.)
# calculate sig_z^2
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i] * nu_zth[:i], zth[:i])
sigzth = np.sqrt((inti + C) / nu_zth)
if prof == 'sig' and pop == 1:
return zth, sigzth
# project back to positions of stars
ran = npr.uniform(size=int(gp.ntracer[1 - 1])) # [1]
zstar = -z0 * np.log(
1.0 - ran) # [pc] stellar positions, exponential falloff
sigzstar = gh.ipol(zth, sigzth, zstar)
# > 0 ((IDL, Justin)) stellar velocity dispersion
# assign [0,1] * maxsig
ran2 = npr.normal(size=int(gp.ntracer[2 - 1])) # [1]
vzstar = ran2 * sigzstar # [km/s]
# Add second population [thick-disc like]:
if gp.pops == 2:
nu_zth2 = gp.ntracer[2 - 1] / gp.ntracer[1 - 1] * np.exp(-zth / z02)
if prof == 'nu' and pop == 2:
return zth, nu_zth2
# [nu0,2] = [Msun/A/pc], 3D tracer density, exponentially falling
# no normalization to 1 done here
inti = np.zeros(nth)
for i in range(1, nth):
inti[i] = simps(Kz_zth[:i] * nu_zth2[:i], zth[:i])
sigzth2 = np.sqrt((inti + C) / nu_zth2) # same integration constant
if prof == 'sig' and pop == 2:
return zth, sigzth2
ran = npr.uniform(-1., 1., gp.ntracer[2 - 1]) # [1]
zstar2 = -z02 * np.log(1.0 - ran) # [pc]
#zstarobs = np.hstack([zstar, zstar2]) # concat pop1, pop2 for all stars
sigzstar2 = gh.ipol(zth, sigzth2, zstar2)
ran2 = npr.normal(-1., 1, gp.ntracer[2 - 1]) # [1]
vzstar2 = ran2 * sigzstar2 # [(km/2)^2]