def com_shrinkcircle_v_2D(x, y, vlos, pm):
#eps = 1e-6
com_x = 1. * np.sum(x * pm) / np.sum(pm)
com_y = 1. * np.sum(y * pm) / np.sum(pm)
com_vlos = 1. * np.sum(vlos * pm) / np.sum(pm)
bucom_x = com_x
bucom_y = com_y
bucom_vlos = com_vlos
x -= com_x
y -= com_y
vlos -= com_vlos
dr = np.sqrt(com_x**2 + com_y**2)
r0 = np.sqrt(x**2 + y**2)
nit = 0
minlen = len(x) / 2.
while nit < 200 and len(x) > minlen:
nit += 1
print('it ',nit,' with ',len(x),\
' part, COM=', \
gh.pretty(bucom_x), gh.pretty(bucom_y),\
' offset ', gh.pretty(dr))
# shrink sphere:
# 1) calc radius
r0 = np.sqrt(x**2 + y**2)
# 2) sort remaining particles
order = np.argsort(r0)
r0 = np.array(r0)[order]
x = np.array(x)[order]
y = np.array(y)[order]
pm = np.array(pm)[order]
vlos = np.array(vlos)[order]
# 3) cut x,y,z,pm after 1-10%
end = len(r0) * 0.95
r0 = r0[:end]
x = x[:end]
y = y[:end]
vlos = vlos[:end]
pm = pm[:end]
# calculate new COM
com_x = 1. * np.sum(x * pm) / np.sum(pm)
com_y = 1. * np.sum(y * pm) / np.sum(pm)
com_vlos = 1. * np.sum(vlos * pm) / np.sum(pm)
dr = np.sqrt(com_x**2 + com_y**2)
# add to bucom
bucom_x += com_x
bucom_y += com_y
bucom_vlos += com_vlos
# recenter particles
x -= com_x
y -= com_y
vlos -= com_vlos
return bucom_x, bucom_y, bucom_vlos
def com_shrinkcircle(x, y, z, pm):
eps = 1e-6
com_x = 1. * np.sum(x * pm) / np.sum(pm)
com_y = 1. * np.sum(y * pm) / np.sum(pm)
com_z = 1. * np.sum(z * pm) / np.sum(pm)
bucom_x = 0. + com_x
bucom_y = 0. + com_y
bucom_z = 0. + com_z
x -= com_x
y -= com_y
z -= com_z
dr = np.sqrt(com_x**2 + com_y**2 + com_z**2)
r0 = np.sqrt(x**2 + y**2 + z**2)
nit = 0
minlen = len(x) * 0.666666666
while nit < 200 and len(x) > minlen:
nit += 1
print('it ',nit,' with ',len(x), ' part',\
' COM= ', gh.pretty(bucom_x), gh.pretty(bucom_y), gh.pretty(bucom_z),\
' offset ', gh.pretty(dr))
# shrink sphere:
# 1) calc radius
r0 = np.sqrt(x**2 + y**2 + z**2)
# 2) sort remaining particles
order = np.argsort(r0)
r0 = np.array(r0)[order]
x = np.array(x)[order]
y = np.array(y)[order]
z = np.array(z)[order]
pm = np.array(pm)[order]
# 3) cut x,y,z,pm after 1-10%
end = len(r0) * 0.95
r0 = r0[:end]
x = x[:end]
y = y[:end]
z = z[:end]
pm = pm[:end]
# calculate new COM
com_x = 1. * np.sum(x * pm) / np.sum(pm)
com_y = 1. * np.sum(y * pm) / np.sum(pm)
com_z = 1. * np.sum(z * pm) / np.sum(pm)
dr = np.sqrt(com_x**2 + com_y**2 + com_z**2)
# add to bucom
bucom_x += com_x
bucom_y += com_y
bucom_z += com_z
# recenter particles
x -= com_x
y -= com_y
z -= com_z
return bucom_x, bucom_y, bucom_z
def com_shrinkcircle_v(x, y, z, vz, pm):
eps = 1e-6
com_x = 1.*np.sum(x*pm)/np.sum(pm)
com_y = 1.*np.sum(y*pm)/np.sum(pm)
com_z = 1.*np.sum(z*pm)/np.sum(pm)
com_vz = 1.*np.sum(vz*pm)/np.sum(pm)
bucom_x = 0.+com_x; bucom_y = 0.+com_y; bucom_z = 0.+com_z; bucom_vz = 0.+com_vz
x -= com_x; y -= com_y; z -= com_z; vz -= com_vz
dr = np.sqrt(com_x**2+com_y**2+com_z**2)
r0 = np.sqrt(x**2+y**2+z**2)
nit = 0; minlen = len(x)*0.666666666
while nit < 200 and len(x) > minlen:
nit += 1
print('it ',nit,' with ',len(x), ' part',\
' COM= ', \
gh.pretty(bucom_x), gh.pretty(bucom_y), gh.pretty(bucom_z),\
' vel=', gh.pretty(bucom_vz),\
' offset ', gh.pretty(dr))
# shrink sphere:
# 1) calc radius
r0 = np.sqrt(x**2+y**2+z**2)
# 2) sort remaining particles
order = np.argsort(r0)
r0 = np.array(r0)[order]
x = np.array(x)[order]
y = np.array(y)[order]
z = np.array(z)[order]
vz = np.array(vz)[order]
pm = np.array(pm)[order]
# 3) cut x,y,z,pm after 1-10%
end = len(r0)*0.95
r0 = r0[:end]; x = x[:end]; y = y[:end]; z = z[:end]; vz = vz[:end]; pm = pm[:end]
# calculate new COM
pmsum = np.sum(pm)
com_x = 1.*np.sum(x*pm)/pmsum
com_y = 1.*np.sum(y*pm)/pmsum
com_z = 1.*np.sum(z*pm)/pmsum
com_vz = 1.*np.sum(vz*pm)/pmsum
dr = np.sqrt(com_x**2+com_y**2+com_z**2)
# add to bucom
bucom_x += com_x; bucom_y += com_y; bucom_z += com_z; bucom_vz += com_vz
# recenter particles
x -= com_x; y -= com_y; z -= com_z; vz -= com_vz
return bucom_x, bucom_y, bucom_z, com_vz
def com_shrinkcircle_2D(x, y):
com_x = np.mean(x)
com_y = np.mean(y)
bucom_x = 0. + com_x
bucom_y = 0. + com_y
x -= com_x
y -= com_y
dr = np.sqrt(com_x**2 + com_y**2)
R0 = np.sqrt(x**2 + y**2)
nit = 0
minlen = len(x) * 0.666666666
while nit < 200 and len(x) > minlen:
nit += 1
print('it ',nit,' with ',len(x), ' part',\
' COM= ', gh.pretty(bucom_x), gh.pretty(bucom_y),\
' offset ', gh.pretty(dr))
# shrink sphere:
# 1) calc radius
R0 = np.sqrt(x**2 + y**2)
# 2) sort remaining particles
order = np.argsort(R0)
R0 = np.array(R0)[order]
x = np.array(x)[order]
y = np.array(y)[order]
# 3) cut x,y,z,pm after 1-10%
end = len(R0) * 0.95
R0 = R0[:end]
x = x[:end]
y = y[:end]
# calculate new COM
com_x = np.mean(x)
com_y = np.mean(y)
dr = np.sqrt(com_x**2 + com_y**2)
# add to bucom
bucom_x += com_x
bucom_y += com_y
# recenter particles
x -= com_x
y -= com_y
return bucom_x, bucom_y
def com_shrinkcircle_2D(x, y):
com_x = np.mean(x)
com_y = np.mean(y)
bucom_x = 0.+com_x
bucom_y = 0.+com_y
x -= com_x
y -= com_y
dr = np.sqrt(com_x**2+com_y**2)
R0 = np.sqrt(x**2+y**2)
nit = 0; minlen = len(x)*0.666666666
while nit < 200 and len(x) > minlen:
nit += 1
print('it ',nit,' with ',len(x), ' part',\
' COM= ', gh.pretty(bucom_x), gh.pretty(bucom_y),\
' offset ', gh.pretty(dr))
# shrink sphere:
# 1) calc radius
R0 = np.sqrt(x**2+y**2)
# 2) sort remaining particles
order = np.argsort(R0)
R0 = np.array(R0)[order]
x = np.array(x)[order]
y = np.array(y)[order]
# 3) cut x,y,z,pm after 1-10%
end = len(R0)*0.95
R0 = R0[:end]
x = x[:end]
y = y[:end]
# calculate new COM
com_x = np.mean(x)
com_y = np.mean(y)
dr = np.sqrt(com_x**2+com_y**2)
# add to bucom
bucom_x += com_x
bucom_y += com_y
# recenter particles
x -= com_x
y -= com_y
return bucom_x, bucom_y
def com_shrinkcircle_v_2D(x, y, vlos, pm):
#eps = 1e-6
com_x = 1.*np.sum(x*pm)/np.sum(pm); com_y = 1.*np.sum(y*pm)/np.sum(pm);
com_vlos = 1.*np.sum(vlos*pm)/np.sum(pm)
bucom_x = com_x; bucom_y = com_y; bucom_vlos = com_vlos
x -= com_x; y -= com_y; vlos -= com_vlos
dr = np.sqrt(com_x**2+com_y**2)
r0 = np.sqrt(x**2+y**2)
nit = 0; minlen = len(x)/2.
while nit < 200 and len(x) > minlen:
nit += 1
print('it ',nit,' with ',len(x),\
' part, COM=', \
gh.pretty(bucom_x), gh.pretty(bucom_y),\
' offset ', gh.pretty(dr))
# shrink sphere:
# 1) calc radius
r0 = np.sqrt(x**2+y**2)
# 2) sort remaining particles
order = np.argsort(r0)
r0 = np.array(r0)[order]; x = np.array(x)[order]; y = np.array(y)[order]; pm = np.array(pm)[order]
vlos = np.array(vlos)[order]
# 3) cut x,y,z,pm after 1-10%
end = len(r0)*0.95
r0 = r0[:end]; x = x[:end]; y = y[:end]; vlos = vlos[:end]; pm = pm[:end]
# calculate new COM
com_x = 1.*np.sum(x*pm)/np.sum(pm); com_y = 1.*np.sum(y*pm)/np.sum(pm)
com_vlos = 1.*np.sum(vlos*pm)/np.sum(pm)
dr = np.sqrt(com_x**2+com_y**2)
# add to bucom
bucom_x += com_x; bucom_y += com_y; bucom_vlos += com_vlos
# recenter particles
x -= com_x; y -= com_y; vlos -= com_vlos
return bucom_x, bucom_y, bucom_vlos
def geom_loglike(cube, ndim, nparams, gp):
tmp_profs = Profiles(gp.pops, gp.nepol)
off = 0
offstep = gp.nrho
if gp.chi2_Sig_converged <= 0:
rhodmpar = np.array(cube[off : off + offstep])
tmp_rho0 = phys.rho(gp.xepol, rhodmpar, 0, gp)
# for J factor calculation (has been deferred to output routine)
# tmp_rhofine = phys.rho(gp.xfine, rhodmpar, 0, gp)
# tmp_Jfine = gip.Jpar(gp.xfine, tmp_rhofine, gp) #tmp_rhofine
# tck = splrep(gp.xfine[:-3], tmp_Jfine)
# tmp_J = splev(gp.xepol, tck)
# rhodmpar hold [rho(rhalf), nr to be used for integration
# from halflight radius, defined on gp.xepol]
# (only calculate) M, check
tmp_M0 = gip.rho_SUM_Mr(gp.xepol, tmp_rho0)
# store profiles
tmp_profs.set_prof("nr", 1.0 * rhodmpar[1 + 1 : -1], 0, gp)
tmp_profs.set_prof("rho", tmp_rho0, 0, gp)
# tmp_profs.set_prof('J', tmp_J, 0, gp)
tmp_profs.set_prof("M", tmp_M0, 0, gp)
off += offstep # anyhow, even if Sig not yet converged
# get profile for rho*
if gp.investigate == "obs":
offstep = gp.nrho
lbaryonpar = np.array(cube[off : off + offstep])
rhostar = phys.rho(gp.xepol, lbaryonpar, 0, gp)
off += offstep
Signu = gip.rho_param_INT_Sig(gp.xepol, lbaryonpar, 0, gp) # [Munit/pc^2]
MtoL = cube[off]
off += 1
# store these profiles every time
tmp_profs.set_prof("nu", rhostar, 0, gp)
tmp_profs.set_prof("Sig", Signu, 0, gp)
tmp_profs.set_MtoL(MtoL)
else:
lbaryonpar = np.zeros(gp.nrho)
MtoL = 0.0
for pop in np.arange(1, gp.pops + 1): # [1, 2, ..., gp.pops]
offstep = gp.nrho
nupar = np.array(cube[off : off + offstep])
tmp_nrnu = 1.0 * nupar[1 + 1 : -1]
tmp_nu = phys.rho(gp.xepol, nupar, pop, gp)
tmp_Signu = gip.rho_param_INT_Sig(gp.xepol, nupar, pop, gp)
# tmp_nu = pool.apply_async(phys.rho, [gp.xepol, nupar, pop, gp])
# tmp_Signu = pool.apply_async(gip.rho_param_INT_Sig, [gp.xepol, nupar, pop, gp])
off += offstep
offstep = 1
tmp_hyperSig = cube[off : off + offstep]
off += offstep
offstep = 1
tmp_hypersig = cube[off : off + offstep]
off += offstep
offstep = gp.nbeta
if gp.chi2_Sig_converged <= 0:
betapar = np.array(cube[off : off + offstep])
tmp_beta, tmp_betastar = phys.beta(gp.xepol, betapar, gp)
if check_beta(tmp_beta, gp):
gh.LOG(2, "beta error")
tmp_profs.chi2 = gh.err(1.0, gp)
return tmp_profs
try:
# if True:
if gp.checksig and gp.investigate == "hern":
import gi_analytic as ga
anrho = ga.rho(gp.xepol, gp)[0]
rhodmpar_half = np.exp(splev(gp.dat.rhalf[0], splrep(gp.xepol, np.log(anrho))))
nr = -gh.derivipol(np.log(anrho), np.log(gp.xepol))
dlr = np.hstack([nr[0], nr, nr[-1]])
if gp.investigate == "gaia":
dlr[-1] = 4
rhodmpar = np.hstack([rhodmpar_half, dlr])
lbaryonpar = 0.0 * rhodmpar
MtoL = 0.0
betapar = np.array([0, 0, 2, max(gp.xipol) / 2]) # for hern
annu = ga.rho(gp.xepol, gp)[1]
nupar_half = np.exp(splev(gp.dat.rhalf[1], splrep(gp.xepol, np.log(annu))))
nrnu = -gh.derivipol(np.log(annu), np.log(gp.xepol))
dlrnu = np.hstack([nrnu[0], nrnu, nrnu[-1]])
if gp.investigate == "gaia":
dlrnu[-1] = 6
nupar = np.hstack([nupar_half, dlrnu])
elif gp.checkbeta and gp.investigate == "gaia":
# rhodmpar = np.array([ 0.41586608, 0.38655515, 0.60898657, 0.50936769, 0.52601378, 0.54526758, 0.5755599, 0.57900806, 0.60252357, 0.60668445, 0.62252721, 0.63173754, 0.64555439, 0.65777175, 0.67083556, 0.68506606, 0.69139872, 0.66304763, 0.61462276, 0.70916575, 0.53287872])
rhodmpar = np.array(
[
0.18235821,
0.4719348,
0.0,
0.0,
0.10029569,
0.11309553,
0.25637863,
0.31815175,
0.40621336,
0.46247927,
0.53545415,
0.60874961,
0.68978141,
0.79781574,
0.91218048,
1.08482356,
1.36074895,
1.88041885,
2.31792908,
2.62089078,
3.001,
]
)
betapar = np.array([1.23555034e-03, 9.89999994e-01, 2.03722518e00, 5.85640906e00])
nupar = np.array(
[
0.15649498,
6.65618254,
0.10293663,
0.1087109,
0.13849277,
0.24371261,
0.62633345,
1.05913181,
1.43774113,
1.82346043,
2.20091446,
2.60007997,
2.98745825,
3.423104,
3.80766658,
4.2089698,
4.62950843,
4.91166037,
4.97380638,
4.99718073,
5.2277589,
]
)
gp.dat.nrnu = [
np.array(
[
0.15476906,
0.85086798,
0.9342867,
0.88161169,
0.83254241,
0.85086798,
0.99930431,
1.22211638,
1.47184763,
1.78910057,
2.1987677,
2.51961046,
2.80345393,
3.10336133,
3.88504346,
4.52442727,
4.88817769,
5.07880404,
4.83455511,
6.32165657,
4.88817769,
]
),
np.array(
[
0.15476906,
0.85086798,
0.9342867,
0.88161169,
0.83254241,
0.85086798,
0.99930431,
1.22211638,
1.47184763,
1.78910057,
2.1987677,
2.51961046,
2.80345393,
3.10336133,
3.88504346,
4.52442727,
4.88817769,
5.07880404,
4.83455511,
6.32165657,
4.88817769,
]
),
np.array(
[
0.15476906,
0.85086798,
0.9342867,
0.88161169,
0.83254241,
0.85086798,
0.99930431,
1.22211638,
1.47184763,
1.78910057,
2.1987677,
2.51961046,
2.80345393,
3.10336133,
3.88504346,
4.52442727,
4.88817769,
5.07880404,
4.83455511,
6.32165657,
4.88817769,
]
),
np.array(
[
0.15476906,
0.85086798,
0.9342867,
0.88161169,
0.83254241,
0.85086798,
0.99930431,
1.22211638,
1.47184763,
1.78910057,
2.1987677,
2.51961046,
2.80345393,
3.10336133,
3.88504346,
4.52442727,
4.88817769,
5.07880404,
4.83455511,
6.32165657,
4.88817769,
]
),
]
gp.dat.nrnuerr = [
np.array(
[
0.05158969,
12.22044422,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
]
),
np.array(
[
0.05158969,
12.22044422,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
]
),
np.array(
[
0.05158969,
12.22044422,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
]
),
np.array(
[
0.05158969,
12.22044422,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
0.48881777,
2.44408884,
2.44408884,
2.44408884,
2.44408884,
]
),
]
lbaryonpar = 0.0 * rhodmpar
MtoL = 0.0
sig, kap, zetaa, zetab = phys.sig_kap_zet(gp.xepol, rhodmpar, lbaryonpar, MtoL, nupar, betapar, pop, gp)
# fill_between(gp.xipol, gp.dat.sig[1]-gp.dat.sigerr[1], gp.dat.sig[1]+gp.dat.sigerr[1])
# plot(gp.xepol, sig, 'r')
# xscale('log')
# ylim([0, 30])
# xlabel('$r$ [pc]')
# ylabel('$\sigma_{LOS}$ [km/s]')
# savefig('siglos_gaia_2.pdf')
# pdb.set_trace()
except Exception:
gh.LOG(1, "sigma error")
tmp_profs.chi2 = gh.err(2.0, gp)
return tmp_profs
# now store the profiles
gh.sanitize_vector(tmp_beta, len(tmp_profs.x0), -200, 1, gp.debug)
tmp_profs.set_prof("beta", tmp_beta, pop, gp)
gh.sanitize_vector(tmp_betastar, len(tmp_profs.x0), -1, 1, gp.debug)
tmp_profs.set_prof("betastar", tmp_betastar, pop, gp)
tmp_profs.set_prof("sig", sig, pop, gp)
tmp_profs.hypersig = tmp_hypersig
tmp_profs.set_prof("kap", kap, pop, gp)
tmp_profs.set_zeta(zetaa, zetab, pop)
tmp_profs.set_prof("nrnu", tmp_nrnu, pop, gp)
tmp_profs.set_prof("nu", tmp_nu, pop, gp) # pool: tmp_nu.get()
# following profile needs to be stored at all times, to calculate chi
tmp_profs.set_prof("Sig", tmp_Signu, pop, gp)
tmp_profs.hyperSig = tmp_hyperSig
off += offstep # still do this even if gp.chi2_Sig_converged is False
if off != gp.ndim:
gh.LOG(1, "wrong subscripts in gi_loglike")
pdb.set_trace()
# determine log likelihood
chi2 = calc_chi2(tmp_profs, gp)
gh.LOG(-1, gp.investigate + "/" + str(gp.case) + "/" + gp.files.timestamp + ": ln L = ", gh.pretty(-chi2 / 2.0))
# x=gp.dat.rbin
# linedat,=ax.loglog(x, gp.dat.Sig[1], 'b')
# line,=ax.loglog(x, tmp_profs.get_prof("Sig", 1), 'r', alpha=0.1)
# plt.draw()
# plt.show()
tmp_profs.chi2 = chi2
# after some predefined wallclock time and Sig convergence, plot all profiles
# if time.time() - gp.last_plot >= gp.plot_after and gp.chi2_Sig_converged <= 0:
# gp.last_plot = time.time()
# try:
# import plotting.plot_profiles
# plotting.plot_profiles.run(gp.files.timestamp, gp.files.outdir, gp)
# except:
# print('plotting error in gi_loglike!')
# close pool automatically after with clause
return tmp_profs
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)
def geom_loglike(cube, ndim, nparams, gp):
tmp_profs = Profiles(gp.pops, gp.nepol)
off = 0
offstep = gp.nrho
if gp.chi2_Sig_converged <= 0:
rhodmpar = np.array(cube[off:off + offstep])
tmp_rho0 = phys.rho(gp.xepol, rhodmpar, 0, gp)
# for J factor calculation (has been deferred to output routine)
#tmp_rhofine = phys.rho(gp.xfine, rhodmpar, 0, gp)
#tmp_Jfine = gip.Jpar(gp.xfine, tmp_rhofine, gp) #tmp_rhofine
#tck = splrep(gp.xfine[:-3], tmp_Jfine)
#tmp_J = splev(gp.xepol, tck)
# rhodmpar hold [rho(rhalf), nr to be used for integration
# from halflight radius, defined on gp.xepol]
# (only calculate) M, check
tmp_M0 = gip.rho_SUM_Mr(gp.xepol, tmp_rho0)
# store profiles
tmp_profs.set_prof('nr', 1. * rhodmpar[1 + 1:-1], 0, gp)
tmp_profs.set_prof('rho', tmp_rho0, 0, gp)
#tmp_profs.set_prof('J', tmp_J, 0, gp)
tmp_profs.set_prof('M', tmp_M0, 0, gp)
off += offstep # anyhow, even if Sig not yet converged
# get profile for rho*
if gp.investigate == 'obs':
offstep = gp.nrho
lbaryonpar = np.array(cube[off:off + offstep])
rhostar = phys.rho(gp.xepol, lbaryonpar, 0, gp)
off += offstep
Signu = gip.rho_param_INT_Sig(gp.xepol, lbaryonpar, 0,
gp) # [Munit/pc^2]
MtoL = cube[off]
off += 1
# store these profiles every time
tmp_profs.set_prof('nu', rhostar, 0, gp)
tmp_profs.set_prof('Sig', Signu, 0, gp)
tmp_profs.set_MtoL(MtoL)
else:
lbaryonpar = np.zeros(gp.nrho)
MtoL = 0.
for pop in np.arange(1, gp.pops + 1): # [1, 2, ..., gp.pops]
offstep = gp.nrho
nupar = np.array(cube[off:off + offstep])
tmp_nrnu = 1. * nupar[1 + 1:-1]
tmp_nu = phys.rho(gp.xepol, nupar, pop, gp)
tmp_Signu = gip.rho_param_INT_Sig(gp.xepol, nupar, pop, gp)
#tmp_nu = pool.apply_async(phys.rho, [gp.xepol, nupar, pop, gp])
#tmp_Signu = pool.apply_async(gip.rho_param_INT_Sig, [gp.xepol, nupar, pop, gp])
off += offstep
offstep = 1
tmp_hyperSig = cube[off:off + offstep]
off += offstep
offstep = 1
tmp_hypersig = cube[off:off + offstep]
off += offstep
offstep = gp.nbeta
if gp.chi2_Sig_converged <= 0:
betapar = np.array(cube[off:off + offstep])
tmp_beta, tmp_betastar = phys.beta(gp.xepol, betapar, gp)
if check_beta(tmp_beta, gp):
gh.LOG(2, 'beta error')
tmp_profs.chi2 = gh.err(1., gp)
return tmp_profs
try:
#if True:
if gp.checksig and gp.investigate == 'hern':
import gi_analytic as ga
anrho = ga.rho(gp.xepol, gp)[0]
rhodmpar_half = np.exp(
splev(gp.dat.rhalf[0], splrep(gp.xepol,
np.log(anrho))))
nr = -gh.derivipol(np.log(anrho), np.log(gp.xepol))
dlr = np.hstack([nr[0], nr, nr[-1]])
if gp.investigate == 'gaia':
dlr[-1] = 4
rhodmpar = np.hstack([rhodmpar_half, dlr])
lbaryonpar = 0.0 * rhodmpar
MtoL = 0.0
betapar = np.array([0, 0, 2,
max(gp.xipol) / 2]) # for hern
annu = ga.rho(gp.xepol, gp)[1]
nupar_half = np.exp(
splev(gp.dat.rhalf[1], splrep(gp.xepol, np.log(annu))))
nrnu = -gh.derivipol(np.log(annu), np.log(gp.xepol))
dlrnu = np.hstack([nrnu[0], nrnu, nrnu[-1]])
if gp.investigate == 'gaia':
dlrnu[-1] = 6
nupar = np.hstack([nupar_half, dlrnu])
elif gp.checkbeta and gp.investigate == 'gaia':
# rhodmpar = np.array([ 0.41586608, 0.38655515, 0.60898657, 0.50936769, 0.52601378, 0.54526758, 0.5755599, 0.57900806, 0.60252357, 0.60668445, 0.62252721, 0.63173754, 0.64555439, 0.65777175, 0.67083556, 0.68506606, 0.69139872, 0.66304763, 0.61462276, 0.70916575, 0.53287872])
rhodmpar = np.array([
0.18235821, 0.4719348, 0., 0., 0.10029569, 0.11309553,
0.25637863, 0.31815175, 0.40621336, 0.46247927,
0.53545415, 0.60874961, 0.68978141, 0.79781574,
0.91218048, 1.08482356, 1.36074895, 1.88041885,
2.31792908, 2.62089078, 3.001
])
betapar = np.array([
1.23555034e-03, 9.89999994e-01, 2.03722518e+00,
5.85640906e+00
])
nupar = np.array([
0.15649498, 6.65618254, 0.10293663, 0.1087109,
0.13849277, 0.24371261, 0.62633345, 1.05913181,
1.43774113, 1.82346043, 2.20091446, 2.60007997,
2.98745825, 3.423104, 3.80766658, 4.2089698,
4.62950843, 4.91166037, 4.97380638, 4.99718073,
5.2277589
])
gp.dat.nrnu = [
np.array([
0.15476906, 0.85086798, 0.9342867, 0.88161169,
0.83254241, 0.85086798, 0.99930431, 1.22211638,
1.47184763, 1.78910057, 2.1987677, 2.51961046,
2.80345393, 3.10336133, 3.88504346, 4.52442727,
4.88817769, 5.07880404, 4.83455511, 6.32165657,
4.88817769
]),
np.array([
0.15476906, 0.85086798, 0.9342867, 0.88161169,
0.83254241, 0.85086798, 0.99930431, 1.22211638,
1.47184763, 1.78910057, 2.1987677, 2.51961046,
2.80345393, 3.10336133, 3.88504346, 4.52442727,
4.88817769, 5.07880404, 4.83455511, 6.32165657,
4.88817769
]),
np.array([
0.15476906, 0.85086798, 0.9342867, 0.88161169,
0.83254241, 0.85086798, 0.99930431, 1.22211638,
1.47184763, 1.78910057, 2.1987677, 2.51961046,
2.80345393, 3.10336133, 3.88504346, 4.52442727,
4.88817769, 5.07880404, 4.83455511, 6.32165657,
4.88817769
]),
np.array([
0.15476906, 0.85086798, 0.9342867, 0.88161169,
0.83254241, 0.85086798, 0.99930431, 1.22211638,
1.47184763, 1.78910057, 2.1987677, 2.51961046,
2.80345393, 3.10336133, 3.88504346, 4.52442727,
4.88817769, 5.07880404, 4.83455511, 6.32165657,
4.88817769
])
]
gp.dat.nrnuerr = [
np.array([
0.05158969, 12.22044422, 2.44408884, 2.44408884,
2.44408884, 2.44408884, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 2.44408884, 2.44408884, 2.44408884,
2.44408884
]),
np.array([
0.05158969, 12.22044422, 2.44408884, 2.44408884,
2.44408884, 2.44408884, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 2.44408884, 2.44408884, 2.44408884,
2.44408884
]),
np.array([
0.05158969, 12.22044422, 2.44408884, 2.44408884,
2.44408884, 2.44408884, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 2.44408884, 2.44408884, 2.44408884,
2.44408884
]),
np.array([
0.05158969, 12.22044422, 2.44408884, 2.44408884,
2.44408884, 2.44408884, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 0.48881777, 0.48881777, 0.48881777,
0.48881777, 2.44408884, 2.44408884, 2.44408884,
2.44408884
])
]
lbaryonpar = 0.0 * rhodmpar
MtoL = 0.0
sig, kap, zetaa, zetab = phys.sig_kap_zet(
gp.xepol, rhodmpar, lbaryonpar, MtoL, nupar, betapar, pop,
gp)
#fill_between(gp.xipol, gp.dat.sig[1]-gp.dat.sigerr[1], gp.dat.sig[1]+gp.dat.sigerr[1])
#plot(gp.xepol, sig, 'r')
#xscale('log')
#ylim([0, 30])
#xlabel('$r$ [pc]')
#ylabel('$\sigma_{LOS}$ [km/s]')
#savefig('siglos_gaia_2.pdf')
#pdb.set_trace()
except Exception:
gh.LOG(1, 'sigma error')
tmp_profs.chi2 = gh.err(2., gp)
return tmp_profs
# now store the profiles
gh.sanitize_vector(tmp_beta, len(tmp_profs.x0), -200, 1, gp.debug)
tmp_profs.set_prof('beta', tmp_beta, pop, gp)
gh.sanitize_vector(tmp_betastar, len(tmp_profs.x0), -1, 1,
gp.debug)
tmp_profs.set_prof('betastar', tmp_betastar, pop, gp)
tmp_profs.set_prof('sig', sig, pop, gp)
tmp_profs.hypersig = tmp_hypersig
tmp_profs.set_prof('kap', kap, pop, gp)
tmp_profs.set_zeta(zetaa, zetab, pop)
tmp_profs.set_prof('nrnu', tmp_nrnu, pop, gp)
tmp_profs.set_prof('nu', tmp_nu, pop, gp) # pool: tmp_nu.get()
# following profile needs to be stored at all times, to calculate chi
tmp_profs.set_prof('Sig', tmp_Signu, pop, gp)
tmp_profs.hyperSig = tmp_hyperSig
off += offstep # still do this even if gp.chi2_Sig_converged is False
if off != gp.ndim:
gh.LOG(1, 'wrong subscripts in gi_loglike')
pdb.set_trace()
# determine log likelihood
chi2 = calc_chi2(tmp_profs, gp)
gh.LOG(
-1, gp.investigate + '/' + str(gp.case) + '/' + gp.files.timestamp +
': ln L = ', gh.pretty(-chi2 / 2.))
# x=gp.dat.rbin
# linedat,=ax.loglog(x, gp.dat.Sig[1], 'b')
# line,=ax.loglog(x, tmp_profs.get_prof("Sig", 1), 'r', alpha=0.1)
# plt.draw()
# plt.show()
tmp_profs.chi2 = chi2
# after some predefined wallclock time and Sig convergence, plot all profiles
#if time.time() - gp.last_plot >= gp.plot_after and gp.chi2_Sig_converged <= 0:
# gp.last_plot = time.time()
# try:
# import plotting.plot_profiles
# plotting.plot_profiles.run(gp.files.timestamp, gp.files.outdir, gp)
# except:
# print('plotting error in gi_loglike!')
# close pool automatically after with clause
return tmp_profs
