def read(Rdiff, gp):
if Rdiff != 'median' and Rdiff != 'min1s' and Rdiff != 'max1s':
print(
'run grd_metalsplit.py to get the split by metallicity done before reading it in for GravImage'
)
exit(1)
import gr_params
gpr = gr_params.grParams(gp)
global Nsample, split, e_split, PM, split_min, split_max
gpr.fil = gpr.dir + "data/tracers.dat"
# number of measured tracer stars
Nsample = bufcount(gpr.fil)
delim = [0, 22, 3, 3, 6, 4, 3, 5, 6, 6, 7, 5, 6, 5, 6, 5, 6]
#ID = np.genfromtxt(gpr.fil,skiprows=29,unpack=True,usecols=(0,1),delimiter=delim)
if gp.case == 5:
RAh, RAm, RAs, DEd, DEm, DEs, VHel, e_VHel, Teff, e_Teff, logg, e_logg, Fe, e_Fe, N = np.loadtxt(
gpr.fil, skiprows=25, unpack=True)
PM = np.ones(len(RAh))
split = logg
e_split = e_logg
sel = (N > 0)
else:
RAh, RAm, RAs, DEd, DEm, DEs, Vmag, VI, VHel, e_VHel, SigFe, e_SigFe, Mg, Mg_err, PM = np.genfromtxt(
gpr.fil,
skiprows=29,
unpack=True,
usecols=tuple(range(2, 17)),
delimiter=delim,
filling_values=-1)
split = Mg
e_split = Mg_err
sel = (Mg > -1) # exclude missing data on Mg
RAh = RAh[sel]
RAm = RAm[sel]
RAs = RAs[sel]
DEd = DEd[sel]
DEm = DEm[sel]
DEs = DEs[sel]
#Vmag = Vmag[sel]
#VI = VI[sel]
VHel = VHel[sel]
e_VHel = e_VHel[sel]
if gp.case < 5:
Mg = Mg[sel]
Mg_err = Mg_err[sel]
elif gp.case == 5:
Teff = Teff[sel]
e_Teff = e_Teff[sel]
logg = logg[sel]
e_logg = e_logg[sel]
Fe = Fe[sel]
e_Fe = e_Fe[sel]
N = N[sel]
split = split[sel]
e_split = e_split[sel]
PM = PM[sel]
split_min = min(split) # -3, 3 if according to WalkerPenarrubia2011
split_max = max(split)
# but: it's not as easy as that
# we have datapoints with errors and probability of membership weighting
# thus, we need to smear the values out using a Gaussian of width = split_err
# and add them up afterwards after scaling with probability PM
x = np.array(np.linspace(split_min, split_max, 100))
splitdf = np.zeros(100)
for i in range(len(split)):
splitdf += PM[i] * gh.gauss(x, split[i], e_split[i])
splitdf /= sum(PM)
sig = abs(RAh[0]) / RAh[0]
RAh = RAh / sig
xs = 15 * (RAh * 3600 + RAm * 60 + RAs) * sig # [arcsec/15]
sig = abs(DEd[0]) / DEd[0]
DEd = DEd / sig
ys = (DEd * 3600 + DEm * 60 + DEs) * sig # [arcsec]
arcsec = 2. * np.pi / (360. * 60. * 60) # [pc]
kpc = 1000 # [pc]
DL = {
1: lambda x: x * (138), #+/- 8 for Fornax
2: lambda x: x * (101), #+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec * DL) # [pc]
ys *= (arcsec * DL) # [pc]
# alternative: get center of photometric measurements by deBoer
# for Fornax, we have
if gp.case == 1:
com_x = 96203.736358393697
com_y = -83114.080684733024
xs = xs - com_x
ys = ys - com_y
else:
# determine com_x, com_y from shrinking sphere
import gi_centering as grc
com_x, com_y = grc.com_shrinkcircle_2D(xs, ys)
popass = np.loadtxt(gpr.dir + 'data/popass_' + Rdiff)
sel1 = (popass == 1)
sel2 = (popass == 2)
# radii of all stellar tracers from pop 1 and 2
R1 = np.sqrt((xs[sel1])**2 + (ys[sel1])**2)
R2 = np.sqrt((xs[sel2])**2 + (ys[sel2])**2)
R1.sort()
R2.sort()
R0 = np.hstack([R1, R2])
R0.sort()
for pop in np.arange(2) + 1:
if pop == 1:
Rhalf = R1[len(R1) / 2]
co = 'blue'
else:
Rhalf = R2[len(R2) / 2]
co = 'red'
Rmin = min(R0) # [pc]
Rmax = max(R0) # [pc]
Binmin, Binmax, Rbin = gh.determine_radius(R0, Rmin, Rmax, gp) # [pc]
gp.xipol = Rbin # [pc]
minr = min(Rbin) # [pc]
maxr = max(Rbin) # [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [pc^2]
totmass_tracers = float(len(x))
Rsi = gh.add_errors(R0, gpr.Rerr) # [pc], gpr.Rerr was in
tpb = np.zeros(gp.nipol)
Sig_phot = np.zeros(gp.nipol)
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi >= Binmin[i],
Rsi < Binmax[i])).flatten() # [1]
tpb[i] = float(len(ind1)) # [1]
Sig_phot[i] = float(
len(ind1)) * totmass_tracers / Vol[i] # [Munit/pc^2]
#loglog(gp.xipol, Sig_phot, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\Sigma(R)$')
#pdb.set_trace()
# deproject to get 3D nu profiles
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]
gp.xfine = introduce_points_in_between(gp.xepol, gp)
#pdb.set_trace()
#Sigdatnu, Sigerrnu = gh.complete_nu(Rbin, Sig_phot, Sig_phot/10., gp.xfine)
#dummyx,nudatnu,nuerrnu,Mrnu = gip.Sig_NORM_rho(gp.xfine,Sigdatnu,Sigerrnu,gp)
#nudat = gh.linipollog(gp.xfine, nudatnu, gp.xipol)
#nuerr = gh.linipollog(gp.xfine, nuerrnu, gp.xipol)
#loglog(gp.xipol, nudat, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\nu(R)$')
#plum = 100*gh.plummer(gp.xipol, Rhalf, len(R0))
#loglog(gp.xipol, plum, color=co, linestyle='--')
#ylim([min(plum), max(plum)])
#pdb.set_trace()
return
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):
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 read(Rdiff, gp):
if Rdiff != 'median' and Rdiff != 'min1s' and Rdiff != 'max1s':
print('run grd_metalsplit.py to get the split by metallicity done before reading it in for GravImage')
exit(1)
import gr_params
gpr = gr_params.grParams(gp)
global Nsample, split, e_split, PM, split_min, split_max
gpr.fil = gpr.dir+"data/tracers.dat"
# number of measured tracer stars
Nsample = bufcount(gpr.fil)
delim = [0,22,3,3,6,4,3,5,6,6,7,5,6,5,6,5,6]
#ID = np.genfromtxt(gpr.fil,skiprows=29,unpack=True,usecols=(0,1),delimiter=delim)
if gp.case==5:
RAh,RAm,RAs,DEd,DEm,DEs,VHel,e_VHel,Teff,e_Teff,logg,e_logg,Fe,e_Fe,N=np.loadtxt(gpr.fil, skiprows=25, unpack=True)
PM = np.ones(len(RAh))
split = logg
e_split = e_logg
sel = (N>0)
else:
RAh,RAm,RAs,DEd,DEm,DEs,Vmag,VI,VHel,e_VHel,SigFe,e_SigFe, Mg,Mg_err,PM = np.genfromtxt(gpr.fil, skiprows=29, unpack=True, usecols=tuple(range(2,17)), delimiter=delim, filling_values=-1)
split = Mg
e_split = Mg_err
sel = (Mg>-1) # exclude missing data on Mg
RAh = RAh[sel]
RAm = RAm[sel]
RAs = RAs[sel]
DEd = DEd[sel]
DEm = DEm[sel]
DEs = DEs[sel]
#Vmag = Vmag[sel]
#VI = VI[sel]
VHel = VHel[sel]
e_VHel = e_VHel[sel]
if gp.case < 5:
Mg = Mg[sel]
Mg_err = Mg_err[sel]
elif gp.case == 5:
Teff = Teff[sel]
e_Teff = e_Teff[sel]
logg = logg[sel]
e_logg = e_logg[sel]
Fe = Fe[sel]
e_Fe = e_Fe[sel]
N = N[sel]
split = split[sel]
e_split = e_split[sel]
PM = PM[sel]
split_min = min(split) # -3, 3 if according to WalkerPenarrubia2011
split_max = max(split)
# but: it's not as easy as that
# we have datapoints with errors and probability of membership weighting
# thus, we need to smear the values out using a Gaussian of width = split_err
# and add them up afterwards after scaling with probability PM
x = np.array(np.linspace(split_min, split_max, 100))
splitdf = np.zeros(100)
for i in range(len(split)):
splitdf += PM[i]*gh.gauss(x, split[i], e_split[i])
splitdf /= sum(PM)
sig = abs(RAh[0])/RAh[0]
RAh = RAh/sig
xs = 15*(RAh*3600+RAm*60+RAs)*sig # [arcsec/15]
sig = abs(DEd[0])/DEd[0]
DEd = DEd/sig
ys = (DEd*3600+DEm*60+DEs)*sig # [arcsec]
arcsec = 2.*np.pi/(360.*60.*60) # [pc]
kpc = 1000 # [pc]
DL = {1: lambda x: x * (138),#+/- 8 for Fornax
2: lambda x: x * (101),#+/- 5 for Carina
3: lambda x: x * (79), #+/- 4 for Sculptor
4: lambda x: x * (86), #+/- 4 for Sextans
5: lambda x: x * (80) #+/- 10 for Draco
}[gp.case](kpc)
xs *= (arcsec*DL) # [pc]
ys *= (arcsec*DL) # [pc]
# alternative: get center of photometric measurements by deBoer
# for Fornax, we have
if gp.case == 1:
com_x = 96203.736358393697
com_y = -83114.080684733024
xs = xs-com_x
ys = ys-com_y
else:
# determine com_x, com_y from shrinking sphere
import gi_centering as grc
com_x, com_y = grc.com_shrinkcircle_2D(xs, ys)
popass = np.loadtxt(gpr.dir+'data/popass_'+Rdiff)
sel1 = (popass==1)
sel2 = (popass==2)
# radii of all stellar tracers from pop 1 and 2
R1 = np.sqrt((xs[sel1])**2 + (ys[sel1])**2)
R2 = np.sqrt((xs[sel2])**2 + (ys[sel2])**2)
R1.sort()
R2.sort()
R0 = np.hstack([R1, R2])
R0.sort()
for pop in np.arange(2)+1:
if pop == 1:
Rhalf = R1[len(R1)/2]
co = 'blue'
else:
Rhalf = R2[len(R2)/2]
co = 'red'
Rmin = min(R0) # [pc]
Rmax = max(R0) # [pc]
Binmin, Binmax, Rbin = gh.determine_radius(R0, Rmin, Rmax, gp) # [pc]
gp.xipol = Rbin # [pc]
minr = min(Rbin)# [pc]
maxr = max(Rbin)# [pc]
Vol = gh.volume_circular_ring(Binmin, Binmax, gp) # [pc^2]
totmass_tracers = float(len(x))
Rsi = gh.add_errors(R0, gpr.Rerr) # [pc], gpr.Rerr was in
tpb = np.zeros(gp.nipol)
Sig_phot = np.zeros(gp.nipol)
for i in range(gp.nipol):
ind1 = np.argwhere(np.logical_and(Rsi >= Binmin[i], Rsi < Binmax[i])).flatten() # [1]
tpb[i] = float(len(ind1)) # [1]
Sig_phot[i] = float(len(ind1))*totmass_tracers/Vol[i] # [Munit/pc^2]
#loglog(gp.xipol, Sig_phot, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\Sigma(R)$')
#pdb.set_trace()
# deproject to get 3D nu profiles
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]
gp.xfine = introduce_points_in_between(gp.xepol, gp)
#pdb.set_trace()
#Sigdatnu, Sigerrnu = gh.complete_nu(Rbin, Sig_phot, Sig_phot/10., gp.xfine)
#dummyx,nudatnu,nuerrnu,Mrnu = gip.Sig_NORM_rho(gp.xfine,Sigdatnu,Sigerrnu,gp)
#nudat = gh.linipollog(gp.xfine, nudatnu, gp.xipol)
#nuerr = gh.linipollog(gp.xfine, nuerrnu, gp.xipol)
#loglog(gp.xipol, nudat, co)
#axvline(Rhalf, color=co)
#xlim([min(gp.xipol), max(gp.xipol)])
#xlabel(r'$R$')
#ylabel(r'$\nu(R)$')
#plum = 100*gh.plummer(gp.xipol, Rhalf, len(R0))
#loglog(gp.xipol, plum, color=co, linestyle='--')
#ylim([min(plum), max(plum)])
#pdb.set_trace()
return
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