def plot_Xscale_3D(self, ax, gp):
rmin = np.log10(min(gp.xipol))
rmax = np.log10(max(gp.xipol))
gp.xfine = np.logspace(rmin, rmax, gp.nfine)
if gp.investigate == 'walk':
if gp.pops == 1:
rhodm, nu1 = ga.rho_walk(gp.xepol, gp)
else:
rhodm, nu1, nu2 = ga.rho_walk(gp.xepol, gp)
elif gp.investigate == 'gaia':
rhodm, nu1 = ga.rho_gaia(gp.xepol, gp)
for pop in range(gp.pops):
# use our models
nuprof = self.Mmedi.get_prof('nu', pop+1)
tck = splrep(gp.xepol, nuprof)
nuproffine = splev(gp.xfine, tck)
if gp.investigate == 'walk' or gp.investigate == 'gaia':
# or rather use analytic values, where available
if pop == 0:
nuprof = nu1
elif pop == 1:
nuprof = nu2
if gp.geom == 'sphere':
Mprof = gip.rho_SUM_Mr(gp.xfine, nuproffine)
Mmax = max(Mprof) # Mprof[-1]
ihalf = -1
for kk in range(len(Mprof)):
# half-light radius (3D) is where mass is more than half
# ihalf gives the iindex of where this happens
if Mprof[kk] >= Mmax/2 and ihalf < 0:
xx = (gp.xfine[kk-1]+gp.xfine[kk])/2
print('rhalf = ', xx, ' pc')
ax.axvline(xx, color='green', lw=0.5, alpha=0.7)
ihalf = kk
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall,zall = np.loadtxt(gp.files.get_com_file(0),skiprows=1,\
usecols=(0,1,2),unpack=True) # 2*[rscale0]
rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)+'_3D')
xall *= rscale0
yall *= rscale0
zall *= rscale0
# calculate 3D
r = np.sqrt(xall**2+yall**2+zall**2) #[pc]
# set number and size of (linearly spaced) bins
rmin = 0. # [pc]
rmax = max(r) if gp.maxR < 0 else 1.0*gp.maxR # [pc]
print('rmax [rscale] = ', rmax)
r = r[(r<rmax)] # [pc]
binmin, binmax, rbin = gh.determine_radius(r, rmin, rmax, gp) # [pc]
vol = volume_spherical_shell(binmin, binmax, gp) # [pc^3]
for pop in range(gp.pops+1):
print('####### working on component ',pop)
print('input: ',gp.files.get_com_file(pop)+'_3D')
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop)+'_3D')<2: continue
x,y,z,v = np.loadtxt(gp.files.get_com_file(pop)+'_3D',\
skiprows=1,usecols=(0,1,2,3),unpack=True)
# 3*[rscale], [km/s]
rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
x *= rscalei
y *= rscalei
z *= rscalei
# calculate 2D radius on the skyplane
r = np.sqrt(x**2+y**2+z**2) # [pc]
# set maximum radius (if gp.maxR is set)
rmax = max(r) if gp.maxR<0 else 1.0*gp.maxR # [pc]
print('rmax [pc] = ', rmax)
sel = (r<=rmax)
x = x[sel]; y = y[sel]; z = z[sel]; v = v[sel]; r = r[sel] # [rscale]
totmass_tracers = 1.*len(x) # [Munit], Munit = 1/star
rs = r # + possible starting offset, [rscale]
vlos = v # + possible starting offset, [km/s]
gf.write_tracer_file(gp.files.get_ntracer_file(pop)+'_3D', totmass_tracers)
de, em = gf.write_headers_3D(gp, pop)
# gpr.n=30 iterations for getting random picked radius values
density = np.zeros((gp.nipol,gpr.n))
a = np.zeros((gp.nipol,gpr.n)) # shared by density, siglos, kappa calcs
for k in range(gpr.n):
rsi = gpr.Rerr * np.random.randn(len(rs)) + rs # [pc]
vlosi = gpr.vrerr*np.random.randn(len(vlos)) + vlos # [km/s]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(rsi>=binmin[i], rsi<binmax[i])).flatten() # [1]
density[i][k] = (1.*len(ind1))/vol[i]*totmass_tracers # [Munit/rscale^2]
vlos1 = vlosi[ind1] # [km/s]
a[i][k] = 1.*len(ind1) # [1]
dens0 = np.sum(density[0])/(1.*gpr.n) # [Munit/rscale^3]
print('dens0 = ',dens0,' [Munit/rscale^3]')
dens0pc = dens0/rscale0**3
gf.write_Sig_scale(gp.files.get_scale_file(pop)+'_3D', dens0pc, totmass_tracers)
tpb0 = np.sum(a[0])/float(gpr.n) # [1] tracers per bin
denserr0 = dens0/np.sqrt(tpb0) # [Munit/rscale^3]
p_dens = np.zeros(gp.nipol)
p_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
dens = np.sum(density[b])/float(gpr.n) # [Munit/rscale^3]
tpb = np.sum(a[b])/float(gpr.n) # [1]
denserr = dens/np.sqrt(tpb) # [Munit/rscale^3]
if(np.isnan(denserr)):
p_dens[b] = p_dens[b-1] # [1]
p_edens[b]= p_edens[b-1] # [1]
else:
p_dens[b] = dens/dens0 # [1]
p_edens[b]= denserr/dens0 # [1] #100/rbin would be artificial guess
print(rbin[b], binmin[b], binmax[b], p_dens[b], p_edens[b], file=de)
# [rscale], 2*[dens0]
indr = (r<binmax[b])
menclosed = float(np.sum(indr))/totmass_tracers # for normalization to 1
# [totmass_tracers]
merr = menclosed/np.sqrt(tpb) # artificial menclosed/10 # [totmass_tracers]
print(rbin[b], binmin[b], binmax[b], menclosed, merr, file=em)
# [rscale], 2*[totmass_tracers]
de.close()
em.close()
if gpr.showplots:
print('plotting for pop ', pop)
#show_plots_dens(rbin, p_dens, p_edens, gp)
mf1 = 0.02 #1/totmass_tracers
mf2 = 0.02
rho_dm, rho_star1, rho_star2 = ga.rho_walk(rbin*rscale0, gp, mf1, mf2)
if pop == 0:
loglog(rbin*rscale0, rho_star1+rho_star2, 'k.-', lw=0.5)
elif pop == 1:
loglog(rbin*rscale0, rho_star1, 'b.-', lw = 0.5)
elif pop == 2:
loglog(rbin*rscale0, rho_star2, 'g.-', lw = 0.5)
loglog(rbin*rscale0, dens0pc*p_dens, 'r.-')
pdb.set_trace()
clf()
def set_analytic(self, x0, gp):
r0 = x0 # [pc], spherical case
self.analytic.x0 = r0
anbeta = []; annu = []; anSig = []
if gp.investigate == 'gaia':
anrho = ga.rho_gaia(r0, gp)[0]
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_gaia(r0, gp)
tmp_annu = ga.rho_gaia(r0, gp)[1]
annu.append( tmp_annu )
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp) )
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_gaia(r0, gp)[pop]
anbeta.append(beta)
nu = ga.rho_gaia(r0,gp)[pop]
annu.append(nu)
anSig.append(gip.rho_INT_Sig(r0, nu, gp))
elif gp.investigate == 'walk':
anrho = ga.rho_walk(r0, gp)[0]
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_deriv_walk(r0, gp) # TODO too high in case of core
tmp_annu = ga.rho_walk(r0, gp)[1]
annu.append( tmp_annu )
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp) )
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_walk(r0, gp)[pop]
anbeta.append(beta)
nu = ga.rho_walk(r0, gp)[pop]
dum,dum,dum,nudat,nuerr = np.transpose(np.loadtxt(gp.files.nufiles[pop], unpack=False, skiprows=1))
locrhalf = np.argmin(abs(gp.xipol-gp.Xscale[pop]))
nuhalf = nudat[locrhalf]*gp.nu0pc[pop]
annuhalf = nu[np.argmin(abs(r0-locrhalf))]
annu.append(nu*nuhalf/annuhalf)
dum,dum,dum,Sigdat,Sigerr = np.transpose(np.loadtxt(gp.files.Sigfiles[pop], unpack=False, skiprows=1))
locrhalf = np.argmin(abs(gp.xipol-gp.Xscale[pop]))
Sighalf = Sigdat[locrhalf]*gp.Sig0pc[pop]
Sig = gip.rho_INT_Sig(r0, nu, gp)
anSighalf = Sig[np.argmin(abs(r0-locrhalf))]
anSig.append(Sig*Sighalf/anSighalf)
elif gp.investigate == 'triax':
anrho = ga.rho_triax(r0, gp) # one and only
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_deriv_triax(r0, gp)
tmp_annu = ga.rho_triax(r0, gp) # TODO, M/L=1 assumed here, wrong
annu.append(tmp_annu)
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp))
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_triax(r0)
anbeta.append(beta)
nu = ga.rho_triax(r0, gp) # TODO, assumes M/L=1
annu.append(nu)
anSig.append( gip.rho_INT_Sig(r0, nu, gp))
self.analytic.set_prof('rho', anrho, 0, gp)
self.analytic.set_prof('M', anM, 0, gp)
self.analytic.set_prof('nr', annr, 0, gp)
self.analytic.set_prof('nu', annu[0], 0, gp)
self.analytic.set_prof('nrnu', -gh.derivipol(np.log(annu[0]), np.log(r0)), 0, gp)
self.analytic.set_prof('Sig', anSig[0], 0, gp)
for pop in np.arange(1, gp.pops+1):
self.analytic.set_prof('beta', anbeta[pop-1], pop, gp)
self.analytic.set_prof('betastar', anbeta[pop-1]/(2.-anbeta[pop-1]), pop, gp)
self.analytic.set_prof('nu', annu[pop], pop, gp)
nrnu = -gh.derivipol(np.log(annu[pop]), np.log(r0))
self.analytic.set_prof('nrnu', nrnu, pop, gp)
self.analytic.set_prof('Sig', anSig[pop] , pop, gp)#/ Signorm, pop, gp)
self.analytic.set_prof('sig', -np.ones(len(r0)), pop, gp)
return
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall,zall = np.loadtxt(gp.files.get_com_file(0),skiprows=1,\
usecols=(0,1,2),unpack=True) # 2*[rscale0]
rscale0 = gf.read_Xscale(gp.files.get_scale_file(0) + '_3D')
xall *= rscale0
yall *= rscale0
zall *= rscale0
# calculate 3D
r = np.sqrt(xall**2 + yall**2 + zall**2) #[pc]
# set number and size of (linearly spaced) bins
rmin = 0. # [pc]
rmax = max(r) if gp.maxR < 0 else 1.0 * gp.maxR # [pc]
print('rmax [rscale] = ', rmax)
r = r[(r < rmax)] # [pc]
binmin, binmax, rbin = gh.determine_radius(r, rmin, rmax, gp) # [pc]
vol = volume_spherical_shell(binmin, binmax, gp) # [pc^3]
for pop in range(gp.pops + 1):
print('####### working on component ', pop)
print('input: ', gp.files.get_com_file(pop) + '_3D')
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop) + '_3D') < 2: continue
x,y,z,v = np.loadtxt(gp.files.get_com_file(pop)+'_3D',\
skiprows=1,usecols=(0,1,2,3),unpack=True)
# 3*[rscale], [km/s]
rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
x *= rscalei
y *= rscalei
z *= rscalei
# calculate 2D radius on the skyplane
r = np.sqrt(x**2 + y**2 + z**2) # [pc]
# set maximum radius (if gp.maxR is set)
rmax = max(r) if gp.maxR < 0 else 1.0 * gp.maxR # [pc]
print('rmax [pc] = ', rmax)
sel = (r <= rmax)
x = x[sel]
y = y[sel]
z = z[sel]
v = v[sel]
r = r[sel] # [rscale]
totmass_tracers = 1. * len(x) # [Munit], Munit = 1/star
rs = r # + possible starting offset, [rscale]
vlos = v # + possible starting offset, [km/s]
gf.write_tracer_file(
gp.files.get_ntracer_file(pop) + '_3D', totmass_tracers)
de, em = gf.write_headers_3D(gp, pop)
# gpr.n=30 iterations for getting random picked radius values
density = np.zeros((gp.nipol, gpr.n))
a = np.zeros(
(gp.nipol, gpr.n)) # shared by density, siglos, kappa calcs
for k in range(gpr.n):
rsi = gpr.Rerr * np.random.randn(len(rs)) + rs # [pc]
vlosi = gpr.vrerr * np.random.randn(len(vlos)) + vlos # [km/s]
for i in range(gp.nipol):
ind1 = np.argwhere(
np.logical_and(rsi >= binmin[i],
rsi < binmax[i])).flatten() # [1]
density[i][k] = (
1. *
len(ind1)) / vol[i] * totmass_tracers # [Munit/rscale^2]
vlos1 = vlosi[ind1] # [km/s]
a[i][k] = 1. * len(ind1) # [1]
dens0 = np.sum(density[0]) / (1. * gpr.n) # [Munit/rscale^3]
print('dens0 = ', dens0, ' [Munit/rscale^3]')
dens0pc = dens0 / rscale0**3
gf.write_Sig_scale(
gp.files.get_scale_file(pop) + '_3D', dens0pc, totmass_tracers)
tpb0 = np.sum(a[0]) / float(gpr.n) # [1] tracers per bin
denserr0 = dens0 / np.sqrt(tpb0) # [Munit/rscale^3]
p_dens = np.zeros(gp.nipol)
p_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
dens = np.sum(density[b]) / float(gpr.n) # [Munit/rscale^3]
tpb = np.sum(a[b]) / float(gpr.n) # [1]
denserr = dens / np.sqrt(tpb) # [Munit/rscale^3]
if (np.isnan(denserr)):
p_dens[b] = p_dens[b - 1] # [1]
p_edens[b] = p_edens[b - 1] # [1]
else:
p_dens[b] = dens / dens0 # [1]
p_edens[
b] = denserr / dens0 # [1] #100/rbin would be artificial guess
print(rbin[b],
binmin[b],
binmax[b],
p_dens[b],
p_edens[b],
file=de)
# [rscale], 2*[dens0]
indr = (r < binmax[b])
menclosed = float(
np.sum(indr)) / totmass_tracers # for normalization to 1
# [totmass_tracers]
merr = menclosed / np.sqrt(
tpb) # artificial menclosed/10 # [totmass_tracers]
print(rbin[b], binmin[b], binmax[b], menclosed, merr, file=em)
# [rscale], 2*[totmass_tracers]
de.close()
em.close()
if gpr.showplots:
print('plotting for pop ', pop)
#show_plots_dens(rbin, p_dens, p_edens, gp)
mf1 = 0.02 #1/totmass_tracers
mf2 = 0.02
rho_dm, rho_star1, rho_star2 = ga.rho_walk(rbin * rscale0, gp, mf1,
mf2)
if pop == 0:
loglog(rbin * rscale0, rho_star1 + rho_star2, 'k.-', lw=0.5)
elif pop == 1:
loglog(rbin * rscale0, rho_star1, 'b.-', lw=0.5)
elif pop == 2:
loglog(rbin * rscale0, rho_star2, 'g.-', lw=0.5)
loglog(rbin * rscale0, dens0pc * p_dens, 'r.-')
pdb.set_trace()
clf()
