def ln_likelihood_hernq(p, dt, nsteps, prog_w, star_w, betas, sat_mass):
alpha, log_m, c = p
params = dict()
params['m'] = np.exp(log_m)
params['c'] = c
pot = gp.HernquistPotential(units=galactic, **params)
nstars = star_w.shape[0]
ll = np.zeros((nsteps,nstars), dtype=float) - 9999.
rewinder_likelihood(ll, dt, nsteps, pot.c_instance, pot.G, prog_w[None].copy(), star_w,
sat_mass, 0., alpha, betas, 0., True)
return integrate_tub(ll, dt).sum()
def ln_likelihood_nfw(p, dt, nsteps, prog_w, star_w, betas, sat_mass):
alpha, v_c, log_r_s = p
params = true_params.copy()
params['v_c'] = v_c
params['r_s'] = np.exp(log_r_s)
pot = gp.LeeSutoTriaxialNFWPotential(units=galactic, **params)
nstars = star_w.shape[0]
ll = np.zeros((nsteps,nstars), dtype=float) - 9999.
rewinder_likelihood(ll, dt, nsteps, pot.c_instance, pot.G, prog_w[None].copy(), star_w,
sat_mass, 0., alpha, betas, 0., True)
return integrate_tub(ll, dt).sum()
def ln_likelihood_nfw(p, dt, nsteps, prog_w, star_w, betas, sat_mass):
alpha, v_c, log_r_s = p
params = true_params.copy()
params['v_c'] = v_c
params['r_s'] = np.exp(log_r_s)
pot = gp.LeeSutoTriaxialNFWPotential(units=galactic, **params)
nstars = star_w.shape[0]
ll = np.zeros((nsteps, nstars), dtype=float) - 9999.
rewinder_likelihood(ll, dt, nsteps, pot.c_instance, pot.G,
prog_w[None].copy(), star_w, sat_mass, 0., alpha,
betas, 0., True)
return integrate_tub(ll, dt).sum()
def ln_likelihood_hernq(p, dt, nsteps, prog_w, star_w, betas, sat_mass):
alpha, log_m, c = p
params = dict()
params['m'] = np.exp(log_m)
params['c'] = c
pot = gp.HernquistPotential(units=galactic, **params)
nstars = star_w.shape[0]
ll = np.zeros((nsteps, nstars), dtype=float) - 9999.
rewinder_likelihood(ll, dt, nsteps, pot.c_instance, pot.G,
prog_w[None].copy(), star_w, sat_mass, 0., alpha,
betas, 0., True)
return integrate_tub(ll, dt).sum()
def ln_likelihood_bfe(p, dt, nsteps, prog_w, star_w, betas, sat_mass):
alpha = p[0]
log_m = p[1]
c = p[2]
params = dict()
params['m'] = np.exp(log_m)
params['c'] = c
params['coeffs'] = np.array(p[3:])
pot = gp.SphericalBFEPotential(units=galactic, **params)
nstars = star_w.shape[0]
ll = np.zeros((nsteps,nstars), dtype=float) - 9999.
rewinder_likelihood(ll, dt, nsteps, pot.c_instance, pot.G, prog_w[None].copy(), star_w,
sat_mass, 0., alpha, betas, 0., True)
return integrate_tub(ll, dt).sum()
def ln_likelihood_bfe(p, dt, nsteps, prog_w, star_w, betas, sat_mass):
alpha = p[0]
log_m = p[1]
c = p[2]
params = dict()
params['m'] = np.exp(log_m)
params['c'] = c
params['coeffs'] = np.array(p[3:])
pot = gp.SphericalBFEPotential(units=galactic, **params)
nstars = star_w.shape[0]
ll = np.zeros((nsteps, nstars), dtype=float) - 9999.
rewinder_likelihood(ll, dt, nsteps, pot.c_instance, pot.G,
prog_w[None].copy(), star_w, sat_mass, 0., alpha,
betas, 0., True)
return integrate_tub(ll, dt).sum()