def myloglike(cube, ndim, nparams):
tmp_profs = Profiles(gp.pops, gp.nipol)
off = 0
rho_param = np.array(cube[off:off+gp.nepol])
if gprio.check_nr(rho_param[2:-1]):
print('dn/dr too big!')
return gh.err(0.7)
tmp_rho = phys.rho(gp.xepol, rho_param)
if(gprio.check_rho(tmp_rho)):
print('rho slope error')
return gh.err(1.)
tmp_profs.set_rho(tmp_rho[:gp.nipol])
tmp_profs.set_M(rho_SUM_Mr(gp.xepol, tmp_rho)[:gp.nipol]) # [munit,3D]
# TODO: mass is set at binmax, not rbin!
# implement integration routine working with density function
# based on density parametrization
# to give mass below rbin
# TODO: implement above function as gl_project.rho_INT_Mr()
off += gp.nepol
nuparstore = []
for pop in np.arange(gp.pops)+1:
nu_param = cube[off:off+gp.nepol]
nuparstore.append(nu_param)
tmp_nu = phys.rho(gp.xepol, nu_param) # [1], [pc]
# if gprio.check_nu(tmp_nu):
# print('nu error')
# return err/2.
if gp.bprior and gprio.check_bprior(tmp_rho, tmp_nu):
print('bprior error')
return gh.err(1.5)
tmp_profs.set_nu(pop, tmp_nu[:gp.nipol]) # [munit/pc^3]
off += gp.nepol
beta_param = np.array(cube[off:off+gp.nbeta])
tmp_beta = phys.beta(gp.xipol, beta_param)
if gprio.check_beta(tmp_beta):
print('beta error')
return gh.err(2.)
tmp_profs.set_beta(pop, tmp_beta)
off += gp.nbeta
try:
# beta_param = np.array([0.,0.])
sig, kap = phys.sig_kap_los(gp.xepol, pop, rho_param, nu_param, beta_param)
# sig and kap already are on data radii only, so no extension by 3 bins here
except Exception as detail:
return gh.err(3.)
tmp_profs.set_sig_kap(pop, sig, kap)
# determine log likelihood (*not* reduced chi2)
chi2 = gc.calc_chi2(tmp_profs, nuparstore)
# print('found log likelihood = ', -chi2/2.)
return -chi2/2. # from likelihood L = exp(-\chi^2/2), want log of that
def physical(r0, prof, pop, tmp_rho, tmp_nu, tmp_beta):
if prof == "rho":
tmp_prof = phys.rho(r0, tmp_rho)
elif prof == 'nr':
tmp_prof = tmp_rho[1:]
elif prof == "nu":
tmp_prof = rho_INT_Rho(r0, phys.rho(r0, tmp_nu))
elif prof == "betastar":
tmp_prof = phys.mapping_beta_star_poly(r0, tmp_beta)
elif prof == "beta":
tmp_prof = phys.beta(r0, tmp_beta)
elif prof == "sig":
tmp_sig, tmp_kap = phys.sig_kap_los(r0, pop, tmp_rho, tmp_nu, tmp_beta)
tmp_prof = tmp_sig
elif prof == "kap":
tmp_sig, tmp_kap = phys.sig_kap_los(r0, pop, tmp_rho, tmp_nu, tmp_beta)
tmp_prof = tmp_kap
return tmp_prof
