def run(gp, pop):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall = np.loadtxt(gp.files.get_com_file(0), skiprows=1, \
usecols=(0,1), unpack=True)
# 2*[Rscale0]
R = np.sqrt(xall**2+yall**2) # [Rscale0]
# set number and size of (linearly spaced) bins
Rmin = 0. #[Rscale0]
Rmax = max(R) if gp.maxR < 0 else 1.0*gp.maxR # [Rscale0]
R = R[(R<Rmax)] # [Rscale0]
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack([minr/8., minr/4., minr/2., Rbin, 2*maxr, 4*maxr, 8*maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
Rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)) # [pc]
print('####### working on component ',pop)
print('input: ', gp.files.get_com_file(pop))
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop))<2:
return
# only read in data if needed: pops = 1: reuse data from pop=0 part
x,y = np.loadtxt(gp.files.get_com_file(pop), skiprows=1, usecols=(0,1), unpack = True)
# [Rscalei], [Rscalei]
# calculate 2D radius on the skyplane
R = np.sqrt(x**2+y**2) #[Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set maximum radius (if gp.maxR is set)
Rmax = max(R) if gp.maxR<0 else 1.0*gp.maxR # [Rscale0]
print('Rmax [Rscale0] = ', Rmax)
sel = (R * Rscalei <= Rmax * Rscale0)
x = x[sel] # [Rscalei]
y = y[sel] # [Rscalei]
R = R[sel] # [Rscalei]
totmass_tracers = float(len(x)) # [Munit], Munit = 1/star
Rs = R # + possible starting offset, [Rscalei]
tr = open(gp.files.get_ntracer_file(pop),'w')
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
Sig_phot = np.zeros((gp.nipol, gpr.n))
# particle selections, shared by density, siglos, kappa and zeta calculations
tpb = np.zeros((gp.nipol,gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi * Rscalei >= Binmin[i] * Rscale0, \
Rsi * Rscalei < Binmax[i] * Rscale0)).flatten() # [1]
tpb[i][k] = float(len(ind1)) #[1]
Sig_phot[i][k] = float(len(ind1))*totmass_tracers/Vol[i] # [Munit/rscale^2]
# do the following for all populations
Sig0 = np.sum(Sig_phot[0])/float(gpr.n) # [Munit/Rscale^2]
Sig0pc = Sig0/Rscale0**2 # [munis/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Sig0pc, totmass_tracers)
# calculate density and mass profile, store it
# ----------------------------------------------------------------------
P_dens = np.zeros(gp.nipol)
P_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
Sig = np.sum(Sig_phot[b])/(1.*gpr.n) # [Munit/Rscale^2]
tpbb = np.sum(tpb[b])/float(gpr.n) # [1], mean number of tracers in bin
Sigerr = Sig/np.sqrt(tpbb) # [Munit/Rscale^2], Poissonian error
# compare data and analytic profile <=> get stellar
# density or mass ratio from Matt Walker
if(np.isnan(Sigerr)):
P_dens[b] = P_dens[b-1] # [1]
P_edens[b]= P_edens[b-1] # [1]
else:
P_dens[b] = Sig/Sig0 # [1]
P_edens[b]= Sigerr/Sig0 # [1]
print(Rbin[b], Binmin[b], Binmax[b], P_dens[b], P_edens[b], file=f_Sig)
# 3*[rscale], [dens0], [dens0]
indr = (R<Binmax[b])
Menclosed = float(np.sum(indr))/totmass_tracers # for normalization to 1#[totmass_tracers]
Merr = Menclosed/np.sqrt(tpbb) # or artificial Menclosed/10 #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], Menclosed, Merr, file=f_mass) # [Rscale0], 2* [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*P_dens, gp)
#numin = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens-P_edens), gp)
numax = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens+P_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax-numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b],\
numedi[b]/nu0pc, nuerr[b]/nu0pc, \
file = f_nu)
f_nu.close()
# write dummy sig scale, not to be used later on
maxsiglos = -1. #[km/s]
fpars = open(gp.files.get_scale_file(pop),'a')
print(maxsiglos, file=fpars) #[km/s]
fpars.close()
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 run(gp):
pop = 0
import gr_params
gpr = gr_params.grParams(gp)
xall, yall = np.loadtxt(gp.files.get_com_file(0),
skiprows=1,
usecols=(0, 1),
unpack=True)
# 2*[Rscale0]
R = np.sqrt(xall**2 + yall**2) # [Rscale0]
# set number and size of (linearly spaced) bins
Rmin = 0. # [Rscale0]
Rmax = max(R) if gp.maxR < 0 else 1.0 * gp.maxR # [Rscale0]
R = R[(R < Rmax)] # [Rscale0]
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack(
[minr / 8., minr / 4., minr / 2., Rbin, 2 * maxr, 4 * maxr,
8 * maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
Rscale0 = float(gf.read_Xscale(gp.files.get_scale_file(0))) # [pc]
print('####### working on component ', pop)
print('input: ', gp.files.get_com_file(pop))
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop)) < 2:
return
# only read in data if needed: pops = 1: reuse data from pop=0 part
x, y = np.loadtxt(gp.files.get_com_file(pop),
skiprows=1,
usecols=(0, 1),
unpack=True)
# [Rscalei], [Rscalei]
# calculate 2D radius on the skyplane
R = np.sqrt(x**2 + y**2) #[Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set maximum radius (if gp.maxR is set)
Rmax = max(R) if gp.maxR < 0 else 1.0 * gp.maxR # [Rscale0]
print('Rmax [Rscale0] = ', Rmax)
sel = (R * Rscalei <= Rmax * Rscale0)
x = x[sel] # [Rscalei]
y = y[sel] # [Rscalei]
R = R[sel] # [Rscalei]
totmass_tracers = float(len(x)) # [Munit], Munit = 1/star
Rs = R # + possible starting offset, [Rscalei]
tr = open(gp.files.get_ntracer_file(pop), 'w')
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
Sig_phot = np.zeros((gp.nipol, gpr.n))
# particle selections, shared by density, siglos, kappa and zeta calculations
tpb = np.zeros((gp.nipol, gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi * Rscalei >= Binmin[i] * Rscale0, \
Rsi * Rscalei < Binmax[i] * Rscale0)).flatten() # [1]
tpb[i][k] = float(len(ind1)) #[1]
Sig_phot[i][k] = float(
len(ind1)) * totmass_tracers / Vol[i] # [Munit/rscale^2]
# do the following for all populations
Sig0 = np.sum(Sig_phot[0]) / float(gpr.n) # [Munit/Rscale^2]
Sig0pc = Sig0 / Rscale0**2 # [munis/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Sig0pc, totmass_tracers)
# calculate density and mass profile, store it
# ----------------------------------------------------------------------
P_dens = np.zeros(gp.nipol)
P_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
Sig = np.sum(Sig_phot[b]) / (1. * gpr.n) # [Munit/Rscale^2]
tpbb = np.sum(tpb[b]) / float(
gpr.n) # [1], mean number of tracers in bin
Sigerr = Sig / np.sqrt(tpbb) # [Munit/Rscale^2], Poissonian error
# compare data and analytic profile <=> get stellar
# density or mass ratio from Matt Walker
if (np.isnan(Sigerr)):
P_dens[b] = P_dens[b - 1] # [1]
P_edens[b] = P_edens[b - 1] # [1]
else:
P_dens[b] = Sig / Sig0 # [1]
P_edens[b] = Sigerr / Sig0 # [1]
print(Rbin[b], Binmin[b], Binmax[b], P_dens[b], P_edens[b], file=f_Sig)
# 3*[rscale], [dens0], [dens0]
indr = (R < Binmax[b])
Menclosed = float(
np.sum(indr)
) / totmass_tracers # for normalization to 1#[totmass_tracers]
Merr = Menclosed / np.sqrt(
tpbb) # or artificial Menclosed/10 #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], Menclosed, Merr,
file=f_mass) # [Rscale0], 2* [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin * Rscalei, Sig0pc * P_dens, gp)
#numin = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens-P_edens), gp)
numax = gip.Sig_INT_rho(Rbin * Rscalei, Sig0pc * (P_dens + P_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax - numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b],\
numedi[b]/nu0pc, nuerr[b]/nu0pc, \
file = f_nu)
f_nu.close()
# write dummy sig scale, not to be used later on
maxsiglos = -1. #[km/s]
fpars = open(gp.files.get_scale_file(pop), 'a')
print(maxsiglos, file=fpars) #[km/s]
fpars.close()
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print("scalefile: ", gp.files.get_scale_file(0))
Rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)) # [pc]
print("input: ", gp.files.get_com_file(0))
# start from data centered on COM already:
x, y, v = np.loadtxt(
gp.files.get_com_file(0), skiprows=1, usecols=(0, 1, 2), unpack=True
) # [Rscalei], [Rscalei], [km/s]
for pop in range(2):
# calculate 2D radius on the skyplane
R = np.sqrt(x ** 2 + y ** 2) # [Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set number and size of bins
Rmin = 0.0 # [rscale]
Rmax = max(R) if gp.maxR < 0 else float(gp.maxR) # [Rscale0]
sel = R * Rscalei < Rmax * Rscale0
x = x[sel]
y = y[sel]
v = v[sel] # [rscale]
totmass_tracers = 1.0 * len(x) # [munit], munit = 1/star
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack([minr / 8.0, minr / 4.0, minr / 2.0, Rbin, 2 * maxr, 4 * maxr, 8 * maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
# rs = gpr.Rerr*np.random.randn(len(r))+r
Rs = R # [Rscale] # if no initial offset is whished
tr = open(gp.files.get_ntracer_file(pop), "w")
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
# 30 iterations for getting random picked radius values
Density = np.zeros((gp.nipol, gpr.n))
tpb = np.zeros((gp.nipol, gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
for j in range(gp.nipol):
ind1 = np.argwhere(
np.logical_and(Rsi * Rscalei >= Binmin[j] * Rscale0, Rsi * Rscalei < Binmax[j] * Rscale0)
).flatten() # [1]
Density[j][k] = float(len(ind1)) / Vol[j] * totmass_tracers # [munit/Rscale0^2]
tpb[j][k] = float(len(ind1)) # [1]
Dens0 = np.sum(Density[0]) / float(gpr.n) # [Munit/Rscale0^2]
Dens0pc = Dens0 / Rscale0 ** 2 # [Munit/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Dens0pc, totmass_tracers)
tpbb0 = np.sum(tpb[0]) / float(gpr.n) # [1]
Denserr0 = Dens0 / np.sqrt(tpbb0) # [Munit/rscale^2]
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^2]
tpbb = np.sum(tpb[b]) / float(gpr.n) # [1]
Denserr = Dens / np.sqrt(tpbb) # [Munit/rscale^2]
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
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], p_dens[b], p_edens[b], file=f_Sig)
# [rscale], [dens0], [dens0]
indr = R < Binmax[b]
menclosed = float(np.sum(indr)) / totmass_tracers
# /totmass_tracers for normalization to 1 at last bin #[totmass_tracers]
merr = menclosed / np.sqrt(tpbb) # artificial menclosed/10 gives good approximation #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], menclosed, merr, file=f_mass)
# [rscale], [totmass_tracers], [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin * Rscalei, Dens0pc * p_dens, gp)
numin = gip.Sig_INT_rho(Rbin * Rscalei, Dens0pc * (p_dens - p_edens), gp)
numax = gip.Sig_INT_rho(Rbin * Rscalei, Dens0pc * (p_dens + p_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax - numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], numedi[b] / nu0pc, nuerr[b] / nu0pc, file=f_nu)
f_nu.close()
if gpr.showplots:
gpr.show_plots_dens_2D(Rbin * Rscalei, p_dens, p_edens, Dens0pc)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
xall,yall = np.loadtxt(gp.files.get_com_file(0), skiprows=1, usecols=(0,1), unpack=True)
# 2*[Rscale0]
R = np.sqrt(xall**2+yall**2) # [Rscale0]
# set number and size of (linearly spaced) bins
Rmin = 0. #[Rscale0]
Rmax = max(R) if gp.maxR < 0 else 1.0*gp.maxR # [Rscale0]
R = R[(R<Rmax)] # [Rscale0]
Binmin, Binmax, Rbin = gh.determine_radius(R, Rmin, Rmax, gp) # [Rscale0]
gp.xipol = Rbin
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
gp.xepol = np.hstack([minr/8., minr/4., minr/2., Rbin, 2*maxr, 4*maxr, 8*maxr]) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [Rscale0^2]
Rscale0 = gf.read_Xscale(gp.files.get_scale_file(0)) # [pc]
for pop in range(gp.pops+1):
print('####### working on component ',pop)
print('input: ', gp.files.get_com_file(pop))
# exclude second condition if self-consistent approach wished
if gp.investigate == "obs" and gp.case==1 and pop==0:
# for Fornax, overwrite first Sigma with deBoer data
import gr_MCMCbin_for
gr_MCMCbin_for.run(gp)
continue
# start from data centered on COM already:
if gf.bufcount(gp.files.get_com_file(pop))<2:
continue
# only read in data if needed: pops = 1: reuse data from pop=0 part
if (gp.pops == 1 and pop < 1 or gp.pops == 2) or gp.investigate == 'obs':
x,y,v = np.loadtxt(gp.files.get_com_file(pop), skiprows=1,usecols=(0,1,2),unpack=True)
# [Rscalei], [Rscalei], [km/s]
# calculate 2D radius on the skyplane
R = np.sqrt(x**2+y**2) #[Rscalei]
Rscalei = gf.read_Xscale(gp.files.get_scale_file(pop)) # [pc]
# set maximum radius (if gp.maxR is set)
Rmax = max(R) if gp.maxR<0 else 1.0*gp.maxR # [Rscale0]
print('Rmax [Rscale0] = ', Rmax)
#pdb.set_trace()
#from pylab import clf, hist, axvline, xlim
#clf()
#hist(np.log10(R*Rscalei), 40)
#for i in range(len(Rbin)):
# axvline(np.log10(Rbin[i]*Rscale0))
#xlim([np.log10(min(gp.xepol*Rscale0)), np.log10(max(gp.xepol*Rscale0))])
sel = (R * Rscalei <= Rmax * Rscale0)
x = x[sel]
y = y[sel]
v = v[sel]
R = R[sel] # [Rscalei]
totmass_tracers = float(len(x)) # [Munit], Munit = 1/star
Rs = R # + possible starting offset, [Rscalei]
vlos = v # + possible starting offset, [km/s]
tr = open(gp.files.get_ntracer_file(pop),'w')
print(totmass_tracers, file=tr)
tr.close()
f_Sig, f_nu, f_mass, f_sig, f_kap, f_zeta = gf.write_headers_2D(gp, pop)
if (gp.pops == 1 and pop < 1) or gp.pops == 2 or gp.investigate == 'obs':
Sig_kin = np.zeros((gp.nipol, gpr.n))
siglos = np.zeros((gp.nipol, gpr.n))
if gp.usekappa:
kappa = np.zeros((gp.nipol, gpr.n))
if gp.usezeta:
v2 = np.zeros((gp.nipol, gpr.n))
v4 = np.zeros((gp.nipol, gpr.n))
Ntot = np.zeros(gpr.n)
zetaa = np.zeros(gpr.n)
zetab = np.zeros(gpr.n)
# particle selections, shared by density, siglos, kappa and zeta calculations
tpb = np.zeros((gp.nipol,gpr.n))
for k in range(gpr.n):
Rsi = gh.add_errors(Rs, gpr.Rerr) # [Rscalei]
vlosi = gh.add_errors(vlos, gpr.vrerr) # [km/s]
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi * Rscalei >= Binmin[i] * Rscale0, Rsi * Rscalei < Binmax[i] * Rscale0)).flatten() # [1]
tpb[i][k] = float(len(ind1)) # [1]
Sig_kin[i][k] = float(len(ind1))*totmass_tracers/Vol[i] # [Munit/rscale**2]
if(len(ind1)<=1):
siglos[i][k] = siglos[i-1][k]
print('### using last value, missing data')
if gp.usekappa:
kappa[i][k] = kappa[i-1][k]
# attention! should be 0, uses last value
if gp.usezeta:
v2[i][k] = v2[i-1][k]
v4[i][k] = v4[i-1][k]
else:
siglos[i][k] = meanbiweight(vlosi[ind1], ci_perc=68.4, \
ci_mean=True, ci_std=True)[1]
# [km/s], see BiWeight.py
if gp.usekappa:
kappa[i][k] = kurtosis(vlosi[ind1], axis=0, \
fisher=False, bias=False) # [1]
if gp.usezeta:
ave, adev, sdev, var, skew, curt = gh.moments(vlosi[ind1])
v2[i][k] = var
v4[i][k] = (curt+3)*var**2
Sigma = Sig_kin[:,k]
if gp.usezeta:
pdb.set_trace()
Ntot[k] = gh.Ntot(Rbin, Sigma, gp)
zetaa[k] = gh.starred(Rbin, v4[:,k], Sigma, Ntot[k], gp)
v2denom = (gh.starred(Rbin, v2[:,k], Sigma, Ntot[k], gp))**2
zetaa[k] /= v2denom
zetab[k] = gh.starred(Rbin, v4[:,k]*Rbin**2, Sigma, Ntot[k], gp)
zetab[k] /= v2denom
zetab[k] /= (gh.starred(Rbin, Rbin, Sigma, Ntot[k], gp))**2
if gp.investigate == 'obs' and gp.case < 5:
Sig_phot = obs_Sig_phot(Binmin, Binmax, Rscale0, Sig_kin, gp, gpr)
else:
Sig_phot = Sig_kin
# do the following for all populations
Sig0 = np.sum(Sig_phot[0])/float(gpr.n) # [Munit/Rscale^2]
Sig0pc = Sig0/Rscale0**2 # [munis/pc^2]
gf.write_Sig_scale(gp.files.get_scale_file(pop), Sig0pc, totmass_tracers)
# calculate density and mass profile, store it
# ----------------------------------------------------------------------
#tpb0 = np.sum(tpb[0])/float(gpr.n) # [1]
#Sigerr0 = Sig0/np.sqrt(tpb0) # [Munit/Rscale^2]
P_dens = np.zeros(gp.nipol)
P_edens = np.zeros(gp.nipol)
for b in range(gp.nipol):
Sig = np.sum(Sig_kin[b])/(1.*gpr.n) # [Munit/Rscale^2]
tpbb = np.sum(tpb[b])/float(gpr.n) # [1], mean number of tracers in bin
Sigerr = Sig/np.sqrt(tpbb) # [Munit/Rscale^2], Poissonian error
# compare data and analytic profile <=> get stellar
# density or mass ratio from Matt Walker
if(np.isnan(Sigerr)):
P_dens[b] = P_dens[b-1] # [1]
P_edens[b]= P_edens[b-1] # [1]
else:
P_dens[b] = Sig/Sig0 # [1]
P_edens[b]= Sigerr/Sig0 # [1]
print(Rbin[b], Binmin[b], Binmax[b], P_dens[b], P_edens[b], file=f_Sig)
# 3*[rscale], [dens0], [dens0]
indr = (R<Binmax[b])
Menclosed = float(np.sum(indr))/totmass_tracers # for normalization to 1#[totmass_tracers]
Merr = Menclosed/np.sqrt(tpbb) # or artificial Menclosed/10 #[totmass_tracers]
print(Rbin[b], Binmin[b], Binmax[b], Menclosed, Merr, file=f_mass) # [Rscale0], 2* [totmass_tracers]
f_Sig.close()
f_mass.close()
# deproject Sig to get nu
numedi = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*P_dens, gp)
#numin = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens-P_edens), gp)
numax = gip.Sig_INT_rho(Rbin*Rscalei, Sig0pc*(P_dens+P_edens), gp)
nu0pc = numedi[0]
gf.write_nu_scale(gp.files.get_scale_file(pop), nu0pc)
nuerr = numax-numedi
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], numedi[b]/nu0pc, nuerr[b]/nu0pc, file = f_nu)
f_nu.close()
# calculate and output siglos
# --------------------------------------------
p_dvlos = np.zeros(gp.nipol)
p_edvlos = np.zeros(gp.nipol)
for b in range(gp.nipol):
sig = np.sum(siglos[b])/gpr.n #[km/s]
tpbb = np.sum(tpb[b])/float(gpr.n) #[1]
if tpbb == 0:
sigerr = p_edvlos[b-1] #[km/s]
# attention! uses last error
else:
# Poisson error with measurement errors
#sigerr = sig/np.sqrt(tpbb)
#sigerr = np.sqrt(sigerr**2+2**2) # 2km/s
# standard deviation
#sigerr = stddevbiweight(siglos[b])
# Poisson error, first guess
sigerr = sig/np.sqrt(tpbb) #[km/s]
p_dvlos[b] = sig #[km/s]
p_edvlos[b]= sigerr #[km/s]
maxsiglos = max(p_dvlos) #[km/s]
print('maxsiglos = ', maxsiglos, '[km/s]')
fpars = open(gp.files.get_scale_file(pop),'a')
print(maxsiglos, file=fpars) #[km/s]
fpars.close()
for b in range(gp.nipol):
print(Rbin[b], Binmin[b], Binmax[b], np.abs(p_dvlos[b]/maxsiglos),\
np.abs(p_edvlos[b]/maxsiglos), file=f_sig)
# 3*[rscale], 2*[maxsiglos]
f_sig.close()
# calculate and output kurtosis kappa
# --------------------------------------------
if gp.usekappa:
p_kappa = np.zeros(gp.nipol) # needed for plotting later
p_ekappa = np.zeros(gp.nipol)
for b in range(gp.nipol):
kappavel = np.sum(kappa[b])/gpr.n #[1]
tpbb = np.sum(tpb[b])/float(gpr.n) #[1]
if tpbb == 0:
kappavelerr = p_edvlos[b-1] #[1]
# attention! uses last error
else:
kappavelerr = np.abs(kappavel/np.sqrt(tpbb)) #[1]
p_kappa[b] = kappavel
p_ekappa[b] = kappavelerr
print(Rbin[b], Binmin[b], Binmax[b], \
kappavel, kappavelerr, file=f_kap)
# [rscale], 2*[1]
f_kap.close()
# output zetas
# -------------------------------------------------------------
if gp.usezeta:
print(np.median(zetaa), np.median(zetab), file=f_zeta)
f_zeta.close()
if gpr.showplots:
gpr.show_plots_dens_2D(Rbin*Rscalei, P_dens, P_edens, Sig0pc)
gpr.show_plots_sigma(Rbin*Rscalei, p_dvlos, p_edvlos)
if gp.usekappa:
gpr.show_plots_kappa(Rbin*Rscalei, p_kappa, p_ekappa)
# overwrite Sig profile if photometric data is used
if gp.investigate == 'obs' and gp.case==1 and pop==1 and not gp.selfconsistentnu:
import os
os.system('cp '+gp.files.get_scale_file(0)+' '+gp.files.get_scale_file(1))
# replace last line with actual maxsiglos from tracer particles
os.system("sed -i '$s/^.*/"+str(maxsiglos)+"/' "+gp.files.get_scale_file(1))
os.system('cp '+gp.files.Sigfiles[0]+' '+gp.files.Sigfiles[1])
continue
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()