bug fix for scaling of gamma_pl pre-processing

hierarc/Diagnostics/goodness_of_fit.py

@@ -1,25 +1,31 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from hierarc.Likelihood.lens_sample_likelihood import LensSampleLikelihood
+from hierarc.Likelihood.cosmo_likelihood import CosmoLikelihood
 
 
 class GoodnessOfFit(object):
     """Class to manage goodness of fit diagnostics."""
 
-    def __init__(self, kwargs_likelihood_list):
+    def __init__(self, kwargs_likelihood_list, kwargs_model):
         """
 
         :param kwargs_likelihood_list: list of likelihood kwargs of individual lenses consistent with the
          LensLikelihood module
+        :param kwargs_model: model settings for ParamManager() class
+        :type kwargs_model: dict
         """
         self._kwargs_likelihood_list = kwargs_likelihood_list
         self._sample_likelihood = LensSampleLikelihood(
-            kwargs_likelihood_list, normalized=False
+            kwargs_likelihood_list,
+            normalized=False,
+            kwargs_global_model=kwargs_model,
         )
 
     def reduced_chi2(self, cosmo, kwargs_lens, kwargs_kin):
         """Reduced chi^2 of fit."""
-        logL = self._sample_likelihood.log_likelihood(cosmo, kwargs_lens, kwargs_kin)
+        logL = self._sample_likelihood.log_likelihood(cosmo=cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin,
+                                                      verbose=False)
         num_data = self._sample_likelihood.num_data()
         return -logL * 2 / num_data
 

hierarc/LensPosterior/imaging_constraints.py

@@ -27,7 +27,9 @@ def draw_lens(self, gamma_pl=None, no_error=False):
         :return: theta_E, gamma, r_eff, delta_r_eff
         """
         if no_error is True:
-            return self._theta_E, self._gamma, self._r_eff, 1
+            if gamma_pl is None:
+                gamma_pl = self._gamma
+            return self._theta_E, gamma_pl, self._r_eff, 1
         theta_E_draw = np.maximum(
             np.random.normal(loc=self._theta_E, scale=self._theta_E_error), 0
         )

hierarc/Likelihood/cosmo_likelihood.py

@@ -47,7 +47,7 @@ def __init__(
          log likelihood value (e.g. prior)
         :param interpolate_cosmo: bool, if True, uses interpolated comoving distance in the calculation for speed-up
         :param num_redshift_interp: int, number of redshift interpolation steps
-        :param cosmo_fixed: astropy.cosmology instance to be used and held fixed throughout the sampling
+        :param cosmo_fixed: ~astropy.cosmology instance to be used and held fixed throughout the sampling
         :param normalized: bool, if True, returns the normalized likelihood, if False, separates the constant prefactor
          (in case of a Gaussian 1/(sigma sqrt(2 pi)) ) to compute the reduced chi2 statistics
         """

hierarc/Likelihood/kin_scaling.py

@@ -84,7 +84,7 @@ def __init__(self, param_grid_axes, j_kin_scaling_grid):
 
             if self._dim_scaling == 1:
                 self._f_ani = interp1d(
-                    param_grid_axes[0], j_kin_scaling_grid, kind="linear"
+                    param_grid_axes[0], j_kin_scaling_grid, kind="linear", fill_value="extrapolate"
                 )
             elif self._dim_scaling == 2:
                 self._f_ani = interp2d(
@@ -103,7 +103,6 @@ def j_scaling(self, param_array):
         :param param_array: sorted list of parameters for the interpolation function
         :return: scaling J(a_ani) for single slit
         """
-
         if self._evalute_scaling is not True or len(param_array) == 0:
             return 1
         if self._dim_scaling == 1:

test/test_Diagnostics/test_goodness_of_fit.py

@@ -100,7 +100,7 @@ def setup_method(self):
             self.kwargs_likelihood_list[i]["likelihood_type"] = likelihood_type
 
         self.goodnessofFit = GoodnessOfFit(
-            kwargs_likelihood_list=self.kwargs_likelihood_list
+            kwargs_likelihood_list=self.kwargs_likelihood_list, kwargs_model={}
         )
 
     def test_plot_ddt_fit(self):
@@ -153,7 +153,7 @@ def test_raise(self):
                 }
             ]
             goodness_of_fit = GoodnessOfFit(
-                kwargs_likelihood_list=kwargs_likelihood_list
+                kwargs_likelihood_list=kwargs_likelihood_list, kwargs_model={}
             )
             f, ax = plt.subplots(1, 1, figsize=(4, 4))
             kwargs_lens = {"lambda_mst": 1}