def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
print('input: ', gpr.fil)
x0,y0,vlos = np.genfromtxt(gpr.fil, skiprows=0, unpack = True,
usecols = (0,1,5))
# use only 3000 random particles:
ind = np.arange(len(x0))
np.random.shuffle(ind)
ind = ind[:3000]
x0 = x0[ind]; y0 = y0[ind]; vlos = vlos[ind]
x0 *= 1000. # [pc]
y0 *= 1000. # [pc]
# shrinking sphere method
pm = np.ones(len(x0))
com_x, com_y, com_vz = gc.com_shrinkcircle_v_2D(x0, y0, vlos, pm)
x0 -= com_x # [pc]
y0 -= com_y # [pc]
vlos -= com_vz #[km/s]
import gi_file as gf
for pop in range(2):
Rc = np.sqrt(x0**2+y0**2) # [pc]
Rhalf = np.median(Rc) # [pc]
Rscale = Rhalf # or gpr.r_DM # [pc]
gp.Xscale.append(Rscale) # [pc]
print('Rscale = ', Rscale,' pc')
print('max(R) = ', max(Rc),' pc')
print('total number of stars: ', len(Rc))
R0 = np.sqrt(x0**2+y0**2)/Rscale
sel = (R0 < gp.maxR)
x = x0[sel]/Rscale; y = y0[sel]/Rscale # [Rscale]
vz = vlos[sel] # [km/s]
m = np.ones(len(x))
R = np.sqrt(x*x+y*y)*Rscale # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), np.median(R))
c = open(gp.files.get_com_file(pop), 'w')
print('# x [Xscale],','y [Xscale],','vLOS [km/s],','Xscale = ', \
Rscale, ' pc', file=c)
for k in range(len(x)):
print(x[k], y[k], vz[k], file=c) #[rscale], [rscale], [km/s]
c.close()
if gpr.showplots:
gpr.show_part_pos(x, y, np.ones(len(x)), Rscale)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gpr.fil = gpr.dir+"/data/tracers.dat"
A = np.loadtxt(gpr.fil, skiprows=25)
RAh,RAm,RAs,DEd,DEm,DEs,Vlos,e_Vlos,Teff,e_Teff,logg,e_logg,Fe,e_Fe,Nobs = A.T
# only use stars which have Mg measurements
pm = (Nobs>0) # (PM>=0.95)*
print("f_members = ", gh.pretty(1.*sum(pm)/len(pm)))
RAh=RAh[pm]
RAm=RAm[pm]
RAs=RAs[pm]
DEd=DEd[pm]
DEm=DEm[pm]
DEs=DEs[pm]
Vlos=Vlos[pm]
e_Vlos=e_Vlos[pm]
Teff=Teff[pm]
e_Teff=e_Teff[pm]
logg=logg[pm]
e_logg=e_logg[pm]
Fe=Fe[pm]
e_Fe=e_Fe[pm]
Nobs = Nobs[pm]
sig = abs(RAh[0])/RAh[0]
#print('RAh: signum = ',gh.pretty(sig))
RAh = RAh/sig
xs = 15*(RAh*3600+RAm*60+RAs)*sig # [arcsec/15]
sig = abs(DEd[0])/DEd[0]
#print('DEd: signum = ', gh.pretty(sig))
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]
x0 = np.copy(xs)
y0 = np.copy(ys) # [pc]
vz0 = np.copy(Vlos) # [km/s]
Fe0 = np.copy(Fe)
# only use stars which are members of the dwarf: exclude pop3 by construction
#pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination, outliers
#x0, y0, vz0, Mg0, PM0 = select_pm(x0, y0, vz0, Mg0, PM0, pm)
# assign population
if gp.pops == 2:
# drawing of populations based on metallicity
# get parameters from function in pymcmetal.py
#[p, mu1, sig1, mu2, sig2] = np.loadtxt(gp.files.dir+'metalsplit.dat')
#[pm1, pm2] = np.loadtxt(gp.files.dir+'metalsplit_assignment.dat')
popass = np.loadtxt(gp.files.dir+'popass')
pm1 = (popass==1)
pm2 = (popass==2)
elif gp.pops == 1:
pm1 = (Teff >= 0)
pm2 = (Teff < 0) # assign none, but of same length as xs
x1, y1, vz1, Fe1, PM1 = select_pm(x0, y0, vz0, Fe, pm, pm1)
x2, y2, vz2, Fe2, PM2 = select_pm(x0, y0, vz0, Fe, pm, pm2)
# cutting pm_i to a maximum of ntracers_i particles each:
ind1 = np.arange(len(x1))
np.random.shuffle(ind1) # random.shuffle already changes ind
ind1 = ind1[:gp.ntracer[1-1]]
ind2 = np.arange(len(x2))
np.random.shuffle(ind2) # random.shuffle already changes ind
ind2 = ind2[:gp.ntracer[2-1]]
x1, y1, vz1, Fe1, PMS1 = select_pm(x1, y1, vz1, Fe1, PM1, ind1)
x2, y2, vz2, Fe2, PMS2 = select_pm(x2, y2, vz2, Fe2, PM2, ind2)
x0, y0, vz0, pm1, pm2, pm = concat_pops(x1, x2, y1, y2, vz1, vz2, gp)
# optimum: get 3D center of mass with means
# com_x, com_y, com_z = com_mean(x0,y0,z0,PM0) # 3*[pc], z component included if available
com_x, com_y, com_vz = com_shrinkcircle_v_2D(x0, y0, vz0, pm) # [pc], [km/s]
# from now on, work with 2D data only; z0 was only used to get center in (x,y) better
# x0 -= com_x; y0 -= com_y # [pc]
# vz0 -= com_vz #[km/s]
R0 = np.sqrt(x0**2+y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] overall
pop = -1
for pmn in [pm, pm1, pm2]:
pop = pop+1
pmr = (R0<(gp.maxR*Rscale)) # read max extension for data
# (rprior*Rscale) from gi_params
pmn = pmn*pmr # [1]
print("fraction of members = ", 1.0*sum(pmn)/len(pmn))
x, y, vz, Fe, PMN = select_pm(x0, y0, vz0, Fe0, pm, pmn)
R = np.sqrt(x*x+y*y) # [pc]
Rscalei = np.median(R) # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei) # [pc]
gf.write_data_output(gp.files.get_com_file(pop), x/Rscalei, y/Rscalei, vz, Rscalei) # [pc]
if gpr.showplots:
gpr.show_part_pos(x, y, pmn, Rscale)
def run(gp):
import gr_params
gpr = gr_params.grParams(gp)
gpr.fil = gpr.dir + "/data/tracers.dat"
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)
RAh, RAm, RAs, DEd, DEm, DEs, Vmag, VI, VHel, e_VHel, SigFe, e_SigFe, SigMg, e_SigMg, PM = np.genfromtxt(
gpr.fil,
skiprows=29,
unpack=True,
usecols=tuple(range(2, 17)),
delimiter=delim,
filling_values=-1)
# only use stars which have Mg measurements
pm = (SigMg > -1) * (PM >= 0.95)
print("f_members = ", gh.pretty(1. * sum(pm) / len(pm)))
ID = ID[1][pm]
RAh = RAh[pm]
RAm = RAm[pm]
RAs = RAs[pm]
DEd = DEd[pm]
DEm = DEm[pm]
DEs = DEs[pm]
Vmag = Vmag[pm]
VI = VI[pm]
VHel = VHel[pm]
e_VHel = e_VHel[pm]
SigFe = SigFe[pm]
e_SigFe = e_SigFe[pm]
SigMg = SigMg[pm]
e_SigMg = e_SigMg[pm]
PM = PM[pm]
Mg0 = SigMg
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]
PM0 = np.copy(PM)
x0 = np.copy(xs)
y0 = np.copy(ys) # [pc]
vz0 = np.copy(VHel) # [km/s]
# only use stars which are members of the dwarf: exclude pop3 by construction
#pm = (PM0 >= gpr.pmsplit) # exclude foreground contamination, outliers
#x0, y0, vz0, Mg0, PM0 = select_pm(x0, y0, vz0, Mg0, PM0, pm)
# assign population
if gp.pops == 2:
# drawing of populations based on metallicity
# get parameters from function in pymcmetal.py
#[p, mu1, sig1, mu2, sig2] = np.loadtxt(gp.files.dir+'metalsplit.dat')
#[pm1, pm2] = np.loadtxt(gp.files.dir+'metalsplit_assignment.dat')
popass = np.loadtxt(gp.files.dir + 'popass')
pm1 = (popass == 1)
pm2 = (popass == 2)
elif gp.pops == 1:
pm1 = (PM >= 0)
pm2 = (PM < 0) # assign none, but of same length as xs
x1, y1, vz1, Mg1, PM1 = select_pm(x0, y0, vz0, Mg0, PM0, pm1)
x2, y2, vz2, Mg2, PM2 = select_pm(x0, y0, vz0, Mg0, PM0, pm2)
# cutting pm_i to a maximum of ntracers_i particles each:
ind1 = np.arange(len(x1))
np.random.shuffle(ind1) # random.shuffle already changes ind
ind1 = ind1[:gp.ntracer[1 - 1]]
ind2 = np.arange(len(x2))
np.random.shuffle(ind2) # random.shuffle already changes ind
ind2 = ind2[:gp.ntracer[2 - 1]]
x1, y1, vz1, Mg1, PMS1 = select_pm(x1, y1, vz1, Mg1, PM1, ind1)
x2, y2, vz2, Mg2, PMS2 = select_pm(x2, y2, vz2, Mg2, PM2, ind2)
x0, y0, vz0, pm1, pm2, pm = concat_pops(x1, x2, y1, y2, vz1, vz2, gp)
# optimum: get 3D center of mass with means
# com_x, com_y, com_z = com_mean(x0,y0,z0,PM0) # 3*[pc], z component included if available
com_x, com_y, com_vz = com_shrinkcircle_v_2D(x0, y0, vz0,
pm) # [pc], [km/s]
# from now on, work with 2D data only; z0 was only used to get center in (x,y) better
# x0 -= com_x; y0 -= com_y # [pc]
# vz0 -= com_vz #[km/s]
R0 = np.sqrt(x0**2 + y0**2) # [pc]
Rhalf = np.median(R0) # [pc]
Rscale = Rhalf # [pc] overall
pop = -1
for pmn in [pm, pm1, pm2]:
pop = pop + 1
pmr = (R0 < (gp.maxR * Rscale)) # read max extension for data
# (rprior*Rscale) from gi_params
pmn = pmn * pmr # [1]
print("fraction of members = ", 1.0 * sum(pmn) / len(pmn))
x, y, vz, Mg, PMN = select_pm(x0, y0, vz0, Mg0, PM0, pmn)
R = np.sqrt(x * x + y * y) # [pc]
Rscalei = np.median(R) # [pc]
gf.write_Xscale(gp.files.get_scale_file(pop), Rscalei) # [pc]
gf.write_data_output(gp.files.get_com_file(pop), x / Rscalei,
y / Rscalei, vz, Rscalei) # [pc]
if gpr.showplots:
gpr.show_part_pos(x, y, pmn, Rscale)
# instead of using other datasets for individual dSph, # determine Heliocentric-Rest-Frame Line-Of-Sight velocity of dwarf Vd # and position of dwarf (ad, dd) # from probability-of-membership weighted means directly Vd = np.sum(Vhel * PM) / np.sum(PM) # [km/s] ad = np.sum(alpha_s * PM) / np.sum(PM) # [arcsec] dd = np.sum(delta_s * PM) / np.sum(PM) # [arcsec] # determine distance to dwarf xs = alpha_s * DL # [pc] ys = delta_s * DL # [pc] PM0 = 1. * np.copy(PM) x0 = 1. * np.copy(xs) y0 = 1. * np.copy(ys) # [pc] # remove center displacement, already change x0 com_x, com_y, com_vz = com_shrinkcircle_v_2D(x0, y0, Vhel, PM) # [pc], [km/s] R0 = np.sqrt(x0**2 + y0**2) # sort by R0 so integral makes sense later order = np.argsort(R0) R0 = R0[order] PM0 = PM0[order] x0 = x0[order] y0 = y0[order] xs = xs[order] ys = ys[order] alpha_s = alpha_s[order] delta_s = delta_s[order] Vhel = Vhel[order] Vhel_err = Vhel_err[order] Fe = Fe[order] Fe_err = Fe_err[order]
