feature/numba_speed_up

autolens/imaging/model/analysis.py

@@ -62,7 +62,13 @@ def log_likelihood_function(self, instance: af.ModelInstance) -> float:
             xp=self._xp
         )
 
-        return self.fit_from(instance=instance).figure_of_merit - log_likelihood_penalty
+        if self.use_jax:
+            return self.fit_from(instance=instance).figure_of_merit - log_likelihood_penalty
+
+        try:
+            return self.fit_from(instance=instance).log_likelihood - log_likelihood_penalty
+        except Exception as e:
+            raise af.exc.FitException
 
     def fit_from(
         self,

autolens/imaging/simulator.py

@@ -122,7 +122,8 @@ def via_deflections_and_galaxies_from(self, deflections : aa.VectorYX2D, galaxie
             values=grid - deflections,
             mask=grid.mask,
             over_sample_size=1,
-            over_sampled=grid - deflections
+            over_sampled=grid - deflections,
+            over_sampler=grid.over_sampler
         )
 
         image = sum(map(lambda g: g.image_2d_from(grid=deflected_grid), galaxies))

autolens/interferometer/simulator.py

@@ -89,6 +89,7 @@ def via_deflections_and_galaxies_from(
             mask=grid.mask,
             over_sample_size=1,
             over_sampled=grid - deflections,
+            over_sampler=grid.over_sampler
         )
 
         image = sum(map(lambda g: g.image_2d_from(grid=deflected_grid), galaxies))

autolens/lens/tracer.py

@@ -282,6 +282,7 @@ def traced_grid_2d_list_from(
                     mask=grid.mask,
                     over_sampled=grid_2d_over_sampled_list[i],
                     over_sample_size=grid.over_sample_size,
+                    over_sampler=grid.over_sampler
                 )
 
                 grid_2d_new_list.append(grid_2d_new)