removed sklog likelihood as deprecated and not used

hierarc/Likelihood/LensLikelihood/lens_likelihood.py

@@ -1,7 +1,6 @@
 __author__ = 'sibirrer'
 
 from lenstronomy.Cosmo.kde_likelihood import KDELikelihood
-from hierarc.Util import likelihood_util
 import numpy as np
 from scipy import interpolate
 
@@ -34,10 +33,6 @@ def __init__(self, z_lens, z_source, likelihood_type, name='name', **kwargs_like
             self._lens_type = KinLikelihoodGaussian(z_lens, z_source, **kwargs_likelihood)
         elif likelihood_type == 'TDLogNorm':
             self._lens_type = TDLikelihoodLogNorm(z_lens, z_source, **kwargs_likelihood)
-        elif likelihood_type == 'TDKinSkewLogNorm':
-            self._lens_type = TDKinLikelihoodSklogn(z_lens, z_source, **kwargs_likelihood)
-        elif likelihood_type == 'TDSkewLogNorm':
-            self._lens_type = TDLikelihoodSklogn(z_lens, z_source, **kwargs_likelihood)
         elif likelihood_type == 'IFUKinCov':
             from hierarc.Likelihood.LensLikelihood.kin_likelihood import KinLikelihood
             self._lens_type = KinLikelihood(z_lens, z_source, **kwargs_likelihood)
@@ -163,78 +158,6 @@ def log_likelihood(self, ddt, dd, aniso_scaling=None):
         return self._kde_likelihood.logLikelihood(dd_, ddt)[0]
 
 
-class TDKinLikelihoodSklogn(object):
-    """
-    class for evaluating the 2-d posterior of Ddt vs Dd coming from a lens with time delays and kinematics measurement
-    with a provided skewed log normal function for both Ddt and Dd
-    The two distributions are asssumed independant and can be combined.
-    """
-    def __init__(self, z_lens, z_source, mu_ddt, lam_ddt, sigma_ddt, mu_dd, lam_dd, sigma_dd, explim=100):
-        """
-
-        :param z_lens: lens redshift
-        :param z_source: source redshift
-        :param mu_ddt:
-        :param lam_ddt:
-        :param sigma_ddt:
-        :param mu_dd:
-        :param lam_dd:
-        :param sigma_dd:
-        :param explim: exponent limit for evaluating the likelihood
-        """
-        self._mu_ddt, self._lam_ddt, self._sigma_ddt = mu_ddt, lam_ddt, sigma_ddt
-        self._mu_dd, self._lam_dd, self._sigma_dd = mu_dd, lam_dd, sigma_dd
-        self._explim = explim
-        self.num_data = 2
-
-    def log_likelihood(self, ddt, dd, aniso_scaling=None):
-        """
-
-        :param ddt: time-delay distance
-        :param dd: angular diameter distance to the deflector
-        :param aniso_scaling: numpy array of anisotropy scaling on prediction of Ds/Dds
-        :return: log likelihood given the single lens analysis
-        """
-        if aniso_scaling is not None:
-            dd_ = dd * aniso_scaling[0]
-        else:
-            dd_ = dd
-        logl_ddt = likelihood_util.sklogn_likelihood(ddt, self._mu_ddt, self._lam_ddt, self._sigma_ddt, explim=self._explim)
-        logl_dd = likelihood_util.sklogn_likelihood(dd_, self._mu_dd, self._lam_dd, self._sigma_dd, explim=self._explim)
-        return logl_ddt + logl_dd
-
-
-class TDLikelihoodSklogn(object):
-    """
-    class for evaluating Ddt from a lens with time delays without kinematics measurement
-    with a provided skewed log normal function for Ddt.
-    """
-
-    def __init__(self, z_lens, z_source, mu_ddt, lam_ddt, sigma_ddt, explim=100):
-        """
-
-        :param z_lens: lens redshift
-        :param z_source: source redshift
-        :param mu_ddt:
-        :param lam_ddt:
-        :param sigma_ddt:
-        :param explim: exponent limit for evaluating the likelihood
-        """
-        self._mu_ddt, self._lam_ddt, self._sigma_ddt = mu_ddt, lam_ddt, sigma_ddt
-        self._explim = explim
-        self.num_data = 1
-
-    def log_likelihood(self, ddt, dd=None, aniso_scaling=None):
-        """
-
-        :param ddt: time-delay distance
-        :param dd: angular diameter distance to the deflector
-        :param aniso_scaling: numpy array of anisotropy scaling on prediction of Ds/Dds
-        :return: log likelihood given the single lens analysis
-        """
-        return likelihood_util.sklogn_likelihood(ddt, self._mu_ddt, self._lam_ddt, self._sigma_ddt, explim=self._explim)
-
-
 class TDLikelihoodGaussian(object):
     """
     class to handle cosmographic likelihood coming from modeling lenses with imaging and kinematic data but no time delays.

hierarc/Util/likelihood_util.py

@@ -41,37 +41,3 @@ def get_truncated_normal(mean=0, sd=1, low=0, upp=10, size=1):
     """
     return truncnorm(
         (low - mean) / sd, (upp - mean) / sd, loc=mean, scale=sd).rvs(size)
-
-
-def sklogn_likelihood(x, mu, lam, sigma, explim=100):
-    """
-
-    :param x: value to evaluate log(p(x))
-    :param mu:
-    :param lam:
-    :param sigma:
-    :param explim: exponent limit for evaluating the likelihood
-    :return: log likelihood
-    """
-    if (x <= lam) or ((-mu + math.log(x - lam)) ** 2 / (2. * sigma ** 2) > explim):
-        return -np.inf
-    else:
-        llh = sklogn(x, mu, lam, sigma)
-
-        if np.isnan(llh):
-            return -np.inf
-        else:
-            return np.log(llh)
-
-
-def sklogn(x, mu, lam, sigma):
-    """
-    skewed log normal distribution
-
-    :param x: value to evaluate p(x)
-    :param mu:
-    :param lam:
-    :param sigma:
-    :return:
-    """
-    return math.exp(-((-mu + math.log(x - lam)) ** 2 / (2. * sigma ** 2))) / (math.sqrt(2 * math.pi) * (x - lam) * sigma)