Refactored code some more in GaussianLikelihood

tupak/core/likelihood.py

@@ -118,14 +118,14 @@ class GaussianLikelihood(Analytical1DLikelihood):
         # that if sigma is not in parameters the attribute is used which was
         # given at init (i.e. the known sigma as either a float or array).
         sigma = self.parameters.get('sigma', self.sigma)
-        res = self.__calculate_residual()
-        return self.summed_log_likelihood(res, sigma)
+        residual = self.__calculate_residual()
+        return self.__summed_log_likelihood(residual=residual, sigma=sigma)
 
     def __calculate_residual(self):
         return self.y - self.func(self.x, **self.model_parameters)
 
-    def summed_log_likelihood(self, res, sigma):
-        return -0.5 * (np.sum((res / sigma) ** 2)
+    def __summed_log_likelihood(self, residual, sigma):
+        return -0.5 * (np.sum((residual / sigma) ** 2)
                        + self.n * np.log(2 * np.pi * sigma ** 2))