class TestSkewGaussian(object):
def setup(self):
self.skewGassian = SkewGaussian()
np.random.seed(seed=42)
def test_pdf(self):
x = 1
y = self.skewGassian.pdf(x, e=0., w=1., a=0.)
assert y == 0.24197072451914337
x = np.array([0, 1])
y = self.skewGassian.pdf(x, e=0., w=1., a=0.)
assert y[0] == 0.3989422804014327
assert y[1] == 0.24197072451914337
def test_pdf_skew(self):
x = 1
y = self.skewGassian.pdf_skew(x, mu=1, sigma=1, skw=0.5)
assert y == 0.39834240320473779
y = self.skewGassian.pdf_skew(x, mu=1, sigma=1, skw=-0.5)
assert y == 0.39834240320473779
def __init__(self,
z_d,
z_s,
alpha,
beta,
sigma_D,
skw=0,
sampling_option="H0_only",
omega_m_fixed=0.3,
omega_mh2_fixed=0.14157,
skewness=True,
kappa_d=0):
"""
initializes all the classes needed for the chain (i.e. redshifts of lens and source)
"""
self.z_d = z_d
self.z_s = z_s
self.cosmoProp = FlatLCDM(z_lens=z_d, z_source=z_s)
self.alpha = alpha
self.beta = beta
self.sigma_D = sigma_D
self.source_prior = False
self.kappa_d = kappa_d
if skw != 0 and skewness is True:
if skw < -1 or skw > 1:
raise ValueError(
"skewness parameter %s out of allowed range [-1,1]" % skw)
self.skw = skw
self.skewGaussian = SkewGaussian()
self.e_skw, self.w_skw, self.a_skw = self.skewGaussian.map_mu_sigma_skw(
0, sigma_D, skw)
self.skewness = True
else:
self.skewness = False
self.sampling_option = sampling_option
self.omega_m_fixed = omega_m_fixed
self.omega_mh2_fixed = omega_mh2_fixed
def setup(self):
self.skewGassian = SkewGaussian()
np.random.seed(seed=42)
def test_raise(self):
with self.assertRaises(ValueError):
skewGassian = SkewGaussian()
skewGassian.pdf_skew(x=1, mu=1, sigma=1, skw=-1)
with self.assertRaises(ValueError):
prob_density.compute_lower_upper_errors(sample=None, num_sigma=4)
class CosmoLikelihood(object):
"""
this class contains the likelihood function of the Strong lensing analysis
"""
def __init__(self,
z_d,
z_s,
alpha,
beta,
sigma_D,
skw=0,
sampling_option="H0_only",
omega_m_fixed=0.3,
omega_mh2_fixed=0.14157,
skewness=True,
kappa_d=0):
"""
initializes all the classes needed for the chain (i.e. redshifts of lens and source)
"""
self.z_d = z_d
self.z_s = z_s
self.cosmoProp = FlatLCDM(z_lens=z_d, z_source=z_s)
self.alpha = alpha
self.beta = beta
self.sigma_D = sigma_D
self.source_prior = False
self.kappa_d = kappa_d
if skw != 0 and skewness is True:
if skw < -1 or skw > 1:
raise ValueError(
"skewness parameter %s out of allowed range [-1,1]" % skw)
self.skw = skw
self.skewGaussian = SkewGaussian()
self.e_skw, self.w_skw, self.a_skw = self.skewGaussian.map_mu_sigma_skw(
0, sigma_D, skw)
self.skewness = True
else:
self.skewness = False
self.sampling_option = sampling_option
self.omega_m_fixed = omega_m_fixed
self.omega_mh2_fixed = omega_mh2_fixed
def X2_chain_H0(self, args):
"""
routine to compute X2 given variable parameters for a MCMC/PSO chainF
"""
#extract parameters
H0 = args
omega_m = self.omega_m_fixed
logL, bool = self.prior_H0(H0)
if bool is True:
logL += self.LCDM_lensLikelihood(H0, omega_m, self.alpha,
self.beta, self.sigma_D)
return logL, None
else:
pass
return logL, None
def X2_chain_omega_mh2(self, args):
"""
routine to compute the log likelihood of given a omega_m h**2 prior fixed
:param args:
:return:
"""
H0 = args
h = H0 / 100.
omega_m = self.omega_mh2_fixed / h**2
logL, bool = self.prior_omega_mh2(h, omega_m)
if bool is True:
logL += self.LCDM_lensLikelihood(H0, omega_m, self.alpha,
self.beta, self.sigma_D)
return logL, None
else:
pass
return logL, None
def X2_chain_H0_omgega_m(self, args):
"""
routine to compute X^2 value of lens light profile
:param args:
:return:
"""
#extract parameters
[H0, omega_m] = args
logL_H0, bool_H0 = self.prior_H0(H0)
logL_omega_m, bool_omega_m = self.prior_omega_m(omega_m)
if bool_H0 is True and bool_omega_m is True:
logL = self.LCDM_lensLikelihood(H0, omega_m, self.alpha, self.beta,
self.sigma_D)
return logL + logL_H0 + logL_omega_m, None
else:
pass
return logL_H0 + logL_omega_m, None
def LCDM_lensLikelihood(self, H0, omega_m, alpha, beta, sigma_D):
Dd = self.cosmoProp.D_d(H0, omega_m) * (1 - self.kappa_d)
Ds = self.cosmoProp.D_s(H0, omega_m)
Dds = self.cosmoProp.D_ds(H0, omega_m)
return self.lensLikelihood(Dd, Ds, Dds, alpha, beta, sigma_D)
def lensLikelihood(self, Dd, Ds, Dds, alpha, beta, sigma_D):
"""
:param alpha:
:param beta:
:param sigma_D:
:return:
"""
delta_y = Dds / (Dd * Ds) - (alpha * np.log(Dd) + beta)
if self.skewness is True:
logL = np.log(
self.skewGaussian.pdf(x=delta_y,
e=self.e_skw,
w=self.w_skw,
a=self.a_skw))
else:
logL = -delta_y**2 / (2 * sigma_D**2)
if self.source_prior:
logL += self.prior_beta(Dds / (Dd * Ds))
return logL
def prior_H0(self, H0, H0_min=0, H0_max=200):
"""
checks whether the parameter vector has left its bound, if so, adds a big number
"""
if H0 < H0_min or H0 > H0_max:
penalty = -10**15
return penalty, False
else:
return 0, True
def init_source_prior(self, beta0, alpha_beta, alpha_luminosity):
"""
initializes the prior description for the source scale
:param beta0: intercept of ln Dd vs beta relation
:param alpha_beta: slope of ln Dd vs beta relation
:param alpha_luminosity: power-law slope of the luminosity function
:return: initialized parameter in self
"""
self.beta0 = beta0
self.alpha_beta = alpha_beta
self.alpha_luminosity = alpha_luminosity
self.source_prior = True
def prior_beta(self, Dds_DdDs):
beta = self.beta0 + self.alpha_beta * Dds_DdDs
beta = max(0.01, beta)
p_beta = beta**(2 * self.alpha_luminosity + 1)
return np.log(p_beta)
def prior_omega_m(self, omega_m, omega_m_min=0, omega_m_max=1):
"""
checks whether the parameter omega_m is within the given bounds
:param omega_m:
:param omega_m_min:
:param omega_m_max:
:return:
"""
if omega_m < omega_m_min or omega_m > omega_m_max:
penalty = -10**15
return penalty, False
else:
return 0, True
def prior_omega_mh2(self, h, omega_m, h_max=2):
"""
"""
if omega_m > 1 or h > h_max:
penalty = -10**15
return penalty, False
else:
prior = np.log(np.sqrt(1 + 4 * self.omega_mh2_fixed**2 / h**6))
return prior, True
def __call__(self, a):
if self.sampling_option == 'H0_only':
return self.X2_chain_H0(a)
elif self.sampling_option == 'H0_omega_m':
return self.X2_chain_H0_omgega_m(a)
elif self.sampling_option == "fix_omega_mh2":
return self.X2_chain_omega_mh2(a)
else:
raise ValueError("wrong sampling option specified")
def likelihood(self, a):
if self.sampling_option == 'H0_only':
return self.X2_chain_H0(a)
elif self.sampling_option == 'H0_omega_m':
return self.X2_chain_H0_omgega_m(a)
elif self.sampling_option == "fix_omega_mh2":
return self.X2_chain_omega_mh2(a)
else:
raise ValueError("wrong sampling option specified")
def computeLikelihood(self, ctx):
if self.sampling_option == 'H0_only':
likelihood, _ = self.X2_chain_H0(ctx.getParams())
elif self.sampling_option == 'H0_omega_m':
likelihood, _ = self.X2_chain_H0_omgega_m(ctx.getParams())
elif self.sampling_option == "fix_omega_mh2":
likelihood, _ = self.X2_chain_omega_mh2(ctx.getParams())
else:
raise ValueError("wrong sampling option specified")
return likelihood
def setup(self):
pass