def test_log_likelihood(self):
for i, likelihood_type in enumerate(self.likelihood_type_list):
likelihood = LensLikelihoodBase(z_lens=self.z_lens,
z_source=self.z_source,
likelihood_type=likelihood_type,
**self.kwargs_likelihood_list[i])
print(likelihood_type)
logl = likelihood.log_likelihood(ddt=1,
dd=1,
aniso_scaling=None,
sigma_v_sys_error=1,
mu_intrinsic=1)
print(logl)
assert logl > -np.inf
def __init__(self, z_lens, z_source, name='name', likelihood_type='TDKin', anisotropy_model='NONE',
ani_param_array=None, ani_scaling_array_list=None, ani_scaling_array=None,
num_distribution_draws=50, kappa_ext_bias=False, kappa_pdf=None, kappa_bin_edges=None, mst_ifu=False,
lambda_scaling_property=0, **kwargs_likelihood):
"""
:param z_lens: lens redshift
:param z_source: source redshift
:param name: string (optional) to name the specific lens
:param likelihood_type: string to specify the likelihood type
:param ani_param_array: array of anisotropy parameter values for which the kinematics are predicted
:param ani_scaling_array: velocity dispersion sigma**2 scaling (also J scaling) of anisotropy parameter relative
to default prediction. The scaling corresponds to the ani_param_array parameter spacing
(to generate an interpolation function). A value =1 in ani_scaling_array results in the value stored in the
provided J() predictions.
:param ani_scaling_array_list: list of array with the scalings of J() for each IFU
:param num_distribution_draws: int, number of distribution draws from the likelihood that are being averaged over
:param kappa_ext_bias: bool, if True incorporates the global external selection function into the likelihood.
If False, the likelihood needs to incorporate the individual selection function with sufficient accuracy.
:param kappa_pdf: array of probability density function of the external convergence distribution
binned according to kappa_bin_edges
:param kappa_bin_edges: array of length (len(kappa_pdf)+1), bin edges of the kappa PDF
:param mst_ifu: bool, if True replaces the lambda_mst parameter by the lambda_ifu parameter (and distribution)
in sampling this lens.
:param lambda_scaling_property: float (optional), scaling of lambda_mst = lambda_mst_global + alpha * lambda_scaling_property
:param kwargs_likelihood: keyword arguments specifying the likelihood function,
see individual classes for their use
"""
TransformedCosmography.__init__(self, z_lens=z_lens, z_source=z_source)
if ani_scaling_array_list is None and ani_scaling_array is not None:
ani_scaling_array_list = [ani_scaling_array]
AnisotropyScalingIFU.__init__(self, anisotropy_model=anisotropy_model, ani_param_array=ani_param_array,
ani_scaling_array_list=ani_scaling_array_list)
LensLikelihoodBase.__init__(self, z_lens=z_lens, z_source=z_source, likelihood_type=likelihood_type, name=name,
**kwargs_likelihood)
self._num_distribution_draws = int(num_distribution_draws)
self._kappa_ext_bias = kappa_ext_bias
self._mst_ifu = mst_ifu
if kappa_pdf is not None and kappa_bin_edges is not None:
self._kappa_dist = PDFSampling(bin_edges=kappa_bin_edges, pdf_array=kappa_pdf)
self._draw_kappa = True
else:
self._draw_kappa = False
self._lambda_scaling_property = lambda_scaling_property
def test_predictions_measurements(self):
for i, likelihood_type in enumerate(self.likelihood_type_list):
likelihood = LensLikelihoodBase(z_lens=self.z_lens,
z_source=self.z_source,
likelihood_type=likelihood_type,
**self.kwargs_likelihood_list[i])
ddt_measurement = likelihood.ddt_measurement()
likelihood.sigma_v_measurement(sigma_v_sys_error=0)
likelihood.sigma_v_prediction(ddt=1, dd=1, aniso_scaling=1)
assert len(ddt_measurement) == 2
def test_raise(self):
with self.assertRaises(ValueError):
LensLikelihoodBase(z_lens=0.5, z_source=2, likelihood_type='BAD')
with self.assertRaises(ValueError):
likelihood = LensLikelihoodBase(z_lens=0.5,
z_source=2,
likelihood_type='DdtGaussian',
**{
'ddt_mean': 1,
'ddt_sigma': 0.1
})
likelihood.likelihood_type = 'BAD'
likelihood.log_likelihood(ddt=1, dd=1)