def set_analytic(self, x0, gp):
r0 = x0 # [pc], spherical case
self.analytic.x0 = r0
anbeta = []; annu = []; anSig = []
if gp.investigate == 'gaia':
anrho = ga.rho_gaia(r0, gp)[0]
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_gaia(r0, gp)
tmp_annu = ga.rho_gaia(r0, gp)[1]
annu.append( tmp_annu )
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp) )
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_gaia(r0, gp)[pop]
anbeta.append(beta)
nu = ga.rho_gaia(r0,gp)[pop]
annu.append(nu)
anSig.append(gip.rho_INT_Sig(r0, nu, gp))
elif gp.investigate == 'walk':
anrho = ga.rho_walk(r0, gp)[0]
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_deriv_walk(r0, gp) # TODO too high in case of core
tmp_annu = ga.rho_walk(r0, gp)[1]
annu.append( tmp_annu )
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp) )
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_walk(r0, gp)[pop]
anbeta.append(beta)
nu = ga.rho_walk(r0, gp)[pop]
dum,dum,dum,nudat,nuerr = np.transpose(np.loadtxt(gp.files.nufiles[pop], unpack=False, skiprows=1))
locrhalf = np.argmin(abs(gp.xipol-gp.Xscale[pop]))
nuhalf = nudat[locrhalf]*gp.nu0pc[pop]
annuhalf = nu[np.argmin(abs(r0-locrhalf))]
annu.append(nu*nuhalf/annuhalf)
dum,dum,dum,Sigdat,Sigerr = np.transpose(np.loadtxt(gp.files.Sigfiles[pop], unpack=False, skiprows=1))
locrhalf = np.argmin(abs(gp.xipol-gp.Xscale[pop]))
Sighalf = Sigdat[locrhalf]*gp.Sig0pc[pop]
Sig = gip.rho_INT_Sig(r0, nu, gp)
anSighalf = Sig[np.argmin(abs(r0-locrhalf))]
anSig.append(Sig*Sighalf/anSighalf)
elif gp.investigate == 'triax':
anrho = ga.rho_triax(r0, gp) # one and only
anM = gip.rho_SUM_Mr(r0, anrho)
annr = ga.nr3Dtot_deriv_triax(r0, gp)
tmp_annu = ga.rho_triax(r0, gp) # TODO, M/L=1 assumed here, wrong
annu.append(tmp_annu)
anSig.append( gip.rho_INT_Sig(r0, tmp_annu, gp))
for pop in np.arange(1, gp.pops+1):
beta = ga.beta_triax(r0)
anbeta.append(beta)
nu = ga.rho_triax(r0, gp) # TODO, assumes M/L=1
annu.append(nu)
anSig.append( gip.rho_INT_Sig(r0, nu, gp))
self.analytic.set_prof('rho', anrho, 0, gp)
self.analytic.set_prof('M', anM, 0, gp)
self.analytic.set_prof('nr', annr, 0, gp)
self.analytic.set_prof('nu', annu[0], 0, gp)
self.analytic.set_prof('nrnu', -gh.derivipol(np.log(annu[0]), np.log(r0)), 0, gp)
self.analytic.set_prof('Sig', anSig[0], 0, gp)
for pop in np.arange(1, gp.pops+1):
self.analytic.set_prof('beta', anbeta[pop-1], pop, gp)
self.analytic.set_prof('betastar', anbeta[pop-1]/(2.-anbeta[pop-1]), pop, gp)
self.analytic.set_prof('nu', annu[pop], pop, gp)
nrnu = -gh.derivipol(np.log(annu[pop]), np.log(r0))
self.analytic.set_prof('nrnu', nrnu, pop, gp)
self.analytic.set_prof('Sig', anSig[pop] , pop, gp)#/ Signorm, pop, gp)
self.analytic.set_prof('sig', -np.ones(len(r0)), pop, gp)
return
def 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 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