adapt images removed subfolder

autolens/analysis/result.py

@@ -183,13 +183,6 @@ def positions_likelihood_from(
             return PositionsLHPenalty(positions=positions, threshold=threshold)
         return PositionsLHResample(positions=positions, threshold=threshold)
 
-    @property
-    def path_galaxy_tuples(self) -> [(str, ag.Galaxy)]:
-        """
-        Tuples associating the names of galaxies with instances from the best fit
-        """
-        return self.instance.path_instance_tuples_for_class(cls=ag.Galaxy)
-
 
 class ResultDataset(Result):
     @property

autolens/imaging/model/result.py

@@ -59,7 +59,6 @@ def max_log_likelihood_tracer(self) -> Tracer:
         The `Tracer` is computed from the `max_log_likelihood_fit`, as this ensures that all linear light profiles
         are converted to normal light profiles with their `intensity` values updated.
         """
-
         return (
             self.max_log_likelihood_fit.model_obj_linear_light_profiles_to_light_profiles
         )

test_autolens/analysis/test_result.py

@@ -366,11 +366,11 @@ def test___image_dict(analysis_imaging_7x7):
     )
 
     image_dict = result.image_galaxy_dict
-    assert isinstance(image_dict[("galaxies", "lens")], np.ndarray)
-    assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
+    assert isinstance(image_dict[str(("galaxies", "lens"))], np.ndarray)
+    assert isinstance(image_dict[str(("galaxies", "source"))], np.ndarray)
 
     result.instance.galaxies.lens = al.Galaxy(redshift=0.5)
 
     image_dict = result.image_galaxy_dict
-    assert (image_dict[("galaxies", "lens")].native == np.zeros((7, 7))).all()
-    assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
+    assert (image_dict[str(("galaxies", "lens"))].native == np.zeros((7, 7))).all()
+    assert isinstance(image_dict[str(("galaxies", "source"))], np.ndarray)