added info functionality

hierarc/Likelihood/LensLikelihood/base_lens_likelihood.py

@@ -49,7 +49,7 @@ def __init__(
         :param kwargs_likelihood: keyword arguments specifying the likelihood function,
         see individual classes for their use
         """
-        self._name = name
+        self.name = name
         self.z_lens = z_lens
         self.z_source = z_source
         self.z_source2 = z_source2

hierarc/Likelihood/hierarchy_likelihood.py

@@ -168,6 +168,19 @@ def __init__(
         )
         self._prior = PriorLikelihood(prior_list=prior_list)
 
+    def info(self):
+        """
+        information about the lens
+
+        :return: print statement
+        """
+        gamma_pl_index = self._lens_distribution.gamma_pl_index
+        print("Name: ", self.name)
+        print("likelihood type: ", self.likelihood_type)
+        if gamma_pl_index is not None:
+            print("gamma_pl_index", gamma_pl_index)
+        print("========")
+
     def lens_log_likelihood(
         self,
         cosmo,
@@ -213,7 +226,7 @@ def lens_log_likelihood(
             cosmo=cosmo,
         )
         if verbose:
-            print("log likelihood of lens %s = %s" % (self._name, a))
+            print("log likelihood of lens %s = %s" % (self.name, a))
         return np.nan_to_num(a)
 
     def hyper_param_likelihood(

hierarc/Likelihood/lens_sample_likelihood.py

@@ -88,6 +88,15 @@ def num_data(self):
             num += lens.num_data()
         return num
 
+    def info(self):
+        """
+
+        :return:
+        """
+        print("number of lenses: ", self.num_data())
+        for lens in self._lens_list:
+            lens.info()
+
     @property
     def gamma_pl_num(self):
         """Number of power-law density slope parameters being sampled on individual

hierarc/Sampling/Distributions/lens_distribution.py

@@ -90,10 +90,10 @@ def __init__(
         ), kwargs_max.get("log_m2l", np.inf)
         if gamma_pl_index is not None:
             self._gamma_pl_model = True
-            self._gamma_pl_index = gamma_pl_index
+            self.gamma_pl_index = gamma_pl_index
         else:
             self._gamma_pl_model = False
-            self._gamma_pl_index = None
+            self.gamma_pl_index = None
 
     def draw_lens(
         self,
@@ -224,7 +224,7 @@ def draw_lens(
                 )
             kwargs_return["log_m2l"] = log_m2l_draw
         if self._gamma_pl_model is True:
-            kwargs_return["gamma_pl"] = gamma_pl_list[self._gamma_pl_index]
+            kwargs_return["gamma_pl"] = gamma_pl_list[self.gamma_pl_index]
         elif self._gamma_pl_global_sampling is True:
             if self._gamma_pl_global_dist in ["GAUSSIAN"]:
                 gamma_pl_draw = np.random.normal(gamma_pl_mean, gamma_pl_sigma)

test/test_Likelihood/test_hierarchy_likelihood.py

@@ -341,6 +341,9 @@ def test_lum_dist_likelihood(self):
         log_l = likelihood.lens_log_likelihood(cosmo=cosmo, kwargs_source=kwargs_source)
         npt.assert_almost_equal(log_l, -24, decimal=0)
 
+    def test_info(self):
+        self.likelihood.info()
+
 
 if __name__ == "__main__":
     pytest.main()

test/test_Likelihood/test_lens_sample_likelihood.py

@@ -72,6 +72,9 @@ def test_num_data(self):
         num_data = self.likelihood.num_data()
         assert num_data == 3
 
+    def test_info(self):
+        self.likelihood.info()
+
     def test_double_source_plane(self):
         cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Ob0=0.05)
         zl = 0.5