def carmapack_to_params(self, pc):
p = self.to_params(np.zeros(self.nparams))
mu = pc[2]
nu = np.sqrt(pc[1])
sigma = pc[0]
p['logit_mu'] = par.bounded_params(mu,
low=self.mu_min,
high=self.mu_max)
p['logit_sigma'] = par.bounded_params(sigma,
low=self.sigma_min,
high=self.sigma_max)
p['logit_nu'] = par.bounded_params(nu,
low=self.nu_min,
high=self.nu_max)
p['ar_roots_p'] = par.stable_polynomial_params(
self._quad_to_roots(pc[3:3 + self.p]))
if self.q >= 1:
p['ma_roots_p'] = par.stable_polynomial_params(
self._quad_to_roots(pc[3 + self.p:]))
return p
def guess_planet_params(P0, K, T):
K = K*(1.0 + 1e-3*np.random.randn()) # part in 10^3
P = P0*(1.0 + 1e-3*np.random.randn()) # part in 10^3
e = np.random.uniform(low=0, high=0.1)
omega = np.random.uniform(low=0, high=2*np.pi)
chi = np.random.uniform(low=0, high=1)
return np.array([np.log(K),
par.bounded_params(P, low=0, high=T),
par.bounded_params(e, low=0, high=1),
par.bounded_params(omega, low=0, high=2*np.pi),
par.bounded_params(chi, low=0, high=1)])
def guess_planet_params(P0, K, T):
K = K * (1.0 + 1e-3 * np.random.randn()) # part in 10^3
P = P0 * (1.0 + 1e-3 * np.random.randn()) # part in 10^3
e = np.random.uniform(low=0, high=0.1)
omega = np.random.uniform(low=0, high=2 * np.pi)
chi = np.random.uniform(low=0, high=1)
return np.array([
np.log(K),
par.bounded_params(P, low=0, high=T),
par.bounded_params(e, low=0, high=1),
par.bounded_params(omega, low=0, high=2 * np.pi),
par.bounded_params(chi, low=0, high=1)
])
def kep_fixed_deparameterize(self, kps):
kps_ext = np.zeros((kps.shape[0], 5))
kps_ext[:, 0] = kps[:, 0]
kps_ext[:, 1] = par.bounded_params(self.Pfixed, low=0, high=self.T)
kps_ext[:, 2:] = kps[:, 1:]
return self.kep_deparameterize(kps_ext)
def kep_fixed_deparameterize(self, kps):
kps_ext = np.zeros((kps.shape[0], 5))
kps_ext[:,0] = kps[:,0]
kps_ext[:,1] = par.bounded_params(self.Pfixed, low=0, high=self.T)
kps_ext[:,2:] = kps[:,1:]
return self.kep_deparameterize(kps_ext)
def draw_prior(self):
mu = np.random.uniform(low=self.mu_min, high=self.mu_max)
sigma = np.exp(
np.random.uniform(low=np.log(self.sigma_min),
high=np.log(self.sigma_max)))
nu = np.exp(
np.random.uniform(low=np.log(self.nu_min),
high=np.log(self.nu_max)))
ar_quad = self._roots_to_quad(self.draw_roots(self.p))
ma_quad = self._roots_to_quad(self.draw_roots(self.q))
mup = par.bounded_params(mu, low=self.mu_min, high=self.mu_max)
sigmap = par.bounded_params(sigma,
low=self.sigma_min,
high=self.sigma_max)
nup = par.bounded_params(nu, low=self.nu_min, high=self.nu_max)
return np.concatenate((mup, sigmap, nup, ar_quad, ma_quad))
def parameterize(self, mu, sigma, nu, ar_roots, ma_roots=None):
p = self.to_params(np.zeros(self.nparams))
p['logit_mu'] = par.bounded_params(mu,
low=self.mu_min,
high=self.mu_max)
p['logit_sigma'] = par.bounded_params(sigma,
low=self.sigma_min,
high=self.sigma_max)
p['logit_nu'] = par.bounded_params(nu,
low=self.nu_min,
high=self.nu_max)
p['ar_roots_p'] = par.stable_polynomial_params(ar_roots, self.root_min,
self.root_max)
if ma_roots is not None:
p['ma_roots_p'] = par.stable_polynomial_params(
ma_roots, self.root_min, self.root_max)
return p
