Feature/samples summary in result

autolens/imaging/model/analysis.py

@@ -283,8 +283,10 @@ def visualize(
 
     def make_result(
         self,
-        samples: af.SamplesPDF,
-        search_internal = None
+        samples_summary: af.SamplesSummary,
+        paths: af.AbstractPaths,
+        samples: Optional[af.SamplesPDF] = None,
+        search_internal: Optional[object] = None,
     ) -> ResultImaging:
         """
         After the non-linear search is complete create its `Result`, which includes:
@@ -311,7 +313,7 @@ def make_result(
         ResultImaging
             The result of fitting the model to the imaging dataset, via a non-linear search.
         """
-        return ResultImaging(samples=samples, analysis=self, search_internal=search_internal)
+        return ResultImaging(samples_summary=samples_summary, paths=paths, samples=samples, search_internal=search_internal, analysis=self)
 
     def save_attributes(self, paths: af.DirectoryPaths):
         """

autolens/interferometer/model/analysis.py

@@ -328,7 +328,13 @@ def visualize(self, paths: af.DirectoryPaths, instance, during_analysis):
             except IndexError:
                 pass
 
-    def make_result(self, samples: af.SamplesPDF, search_internal=None):
+    def make_result(
+        self,
+        samples_summary: af.SamplesSummary,
+        paths: af.AbstractPaths,
+        samples: Optional[af.SamplesPDF] = None,
+        search_internal: Optional[object] = None,
+    ):
         """
         After the non-linear search is complete create its `Result`, which includes:
 
@@ -355,7 +361,11 @@ def make_result(self, samples: af.SamplesPDF, search_internal=None):
             The result of fitting the model to the imaging dataset, via a non-linear search.
         """
         return ResultInterferometer(
-            samples=samples, analysis=self, search_internal=search_internal
+            samples_summary=samples_summary,
+            paths=paths,
+            samples=samples,
+            analysis=self,
+            search_internal=search_internal
         )
 
     def save_attributes(self, paths: af.DirectoryPaths):

autolens/lens/to_inversion.py

@@ -70,7 +70,7 @@ def lp_linear_func_list_galaxy_dict(
             )
 
         for plane_index, galaxies in enumerate(self.planes):
-            plane_to_inversion = ag.GalaxiesToInversion(
+            galaxies_to_inversion = ag.GalaxiesToInversion(
                 galaxies=galaxies,
                 sky=self.sky,
                 dataset=self.dataset,
@@ -82,7 +82,7 @@ def lp_linear_func_list_galaxy_dict(
             )
 
             lp_linear_galaxy_dict_of_plane = (
-                plane_to_inversion.lp_linear_func_list_galaxy_dict
+                galaxies_to_inversion.lp_linear_func_list_galaxy_dict
             )
 
             lp_linear_galaxy_dict_list = {

autolens/point/model/analysis.py

@@ -100,11 +100,17 @@ def visualize(self, paths, instance, during_analysis):
 
     def make_result(
         self,
-        samples: af.SamplesPDF,
-        search_internal=None,
+        samples_summary: af.SamplesSummary,
+        paths: af.AbstractPaths,
+        samples: Optional[af.SamplesPDF] = None,
+        search_internal: Optional[object] = None,
     ):
         return ResultPoint(
-            samples=samples, analysis=self, search_internal=search_internal
+            samples_summary=samples_summary,
+            paths=paths,
+            samples=samples,
+            search_internal=search_internal,
+            analysis=self,
         )
 
     def save_attributes(self, paths: af.DirectoryPaths):

autolens/point/point_solver.py

@@ -232,8 +232,8 @@ def grid_within_distance_of_source_plane_centre(
         )
 
         grid_within_distance_of_centre = grid_within_distance(
-            distances_1d=source_plane_distances,
-            grid_slim=grid,
+            distances_1d=np.asarray(source_plane_distances),
+            grid_slim=np.asarray(grid),
             within_distance=distance,
         )
 

autolens/quantity/model/analysis.py

@@ -1,4 +1,5 @@
 import os
+from typing import Optional
 
 import autofit as af
 import autogalaxy as ag
@@ -126,8 +127,10 @@ def visualize(
 
     def make_result(
         self,
-        samples: af.SamplesPDF,
-        search_internal=None,
+        samples_summary: af.SamplesSummary,
+        paths: af.AbstractPaths,
+        samples: Optional[af.SamplesPDF] = None,
+        search_internal: Optional[object] = None,
     ) -> ResultQuantity:
         """
         After the non-linear search is complete create its `ResultQuantity`, which includes:
@@ -157,5 +160,9 @@ def make_result(
             The result of fitting the model to the imaging dataset, via a non-linear search.
         """
         return ResultQuantity(
-            samples=samples, analysis=self, search_internal=search_internal
+            samples_summary=samples_summary,
+            paths=paths,
+            samples=samples,
+            search_internal=search_internal,
+            analysis=self
         )

docs/api/plot.rst

@@ -56,11 +56,9 @@ by **PyAutoGalaxy**.
    :template: custom-class-template.rst
    :recursive:
 
-   DynestyPlotter
-   UltraNestPlotter
-   EmceePlotter
-   ZeusPlotter
-   PySwarmsPlotter
+   NestPlotter
+   MCMCPlotter
+   OptimizePlotter
 
 Plot Customization [aplt]
 -------------------------

docs/overview/overview_3_modeling.rst

@@ -523,8 +523,8 @@ mass its name ``mass`` defined when making the ``Model`` above is used).
 
 .. code-block:: python
 
-    search_plotter = aplt.DynestyPlotter(samples=result.samples)
-    search_plotter.corner()
+    search_plotter = aplt.NestPlotter(samples=result.samples)
+    search_plotter.corner_cornerpy()
 
 Here is an example of how a PDF estimated for a lens model appears:
 

test_autolens/aggregator/conftest.py

@@ -66,10 +66,6 @@ def make_model():
 
 @pytest.fixture(name="samples")
 def make_samples(model):
-    galaxy_0 = al.Galaxy(redshift=0.5, light=al.lp.Sersic(centre=(0.0, 1.0)))
-    galaxy_1 = al.Galaxy(redshift=1.0, light=al.lp.Sersic())
-
-    tracer = al.Tracer(galaxies=[galaxy_0, galaxy_1])
 
     parameters = [model.prior_count * [1.0], model.prior_count * [10.0]]
 
@@ -84,6 +80,5 @@ def make_samples(model):
     return al.m.MockSamples(
         model=model,
         sample_list=sample_list,
-        max_log_likelihood_instance=tracer,
         prior_means=[1.0] * model.prior_count,
     )

test_autolens/analysis/test_result.py

@@ -23,13 +23,7 @@ def test__max_log_likelihood_tracer(
         )
     )
 
-    samples = al.m.MockSamples(
-        model=model,
-        max_log_likelihood_instance=tracer_x2_plane_7x7,
-        prior_means=[1.0] * model.prior_count,
-    )
-
-    search = al.m.MockSearch(name="test_search_2", samples=samples)
+    search = al.m.MockSearch(name="test_search_2")
 
     result = search.fit(model=model, analysis=analysis_imaging_7x7)
 
@@ -58,9 +52,9 @@ def test__max_log_likelihood_positions_threshold(masked_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis)
 
     assert result.max_log_likelihood_positions_threshold == pytest.approx(
         0.8309561230, 1.0e-4
@@ -76,9 +70,9 @@ def test__source_plane_light_profile_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre.in_list == [(1.0, 2.0)]
 
@@ -94,9 +88,9 @@ def test__source_plane_light_profile_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source_0, source_1])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre.in_list == [(1.0, 2.0)]
 
@@ -110,17 +104,17 @@ def test__source_plane_light_profile_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source_0, source_1])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre.in_list == [(5.0, 6.0)]
 
     tracer = al.Tracer(galaxies=[al.Galaxy(redshift=0.5)])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre == None
 
@@ -137,9 +131,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert (
         result.source_plane_inversion_centre.in_list[0]
@@ -155,9 +149,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_inversion_centre == None
 
@@ -166,9 +160,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_inversion_centre == None
 
@@ -189,9 +183,9 @@ def test__source_plane_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_centre.in_list[0] == pytest.approx(
         (-0.916666, -0.916666), 1.0e-4
@@ -220,9 +214,9 @@ def test__image_plane_multiple_image_positions(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     multiple_images = result.image_plane_multiple_image_positions
 
@@ -243,9 +237,9 @@ def test__image_plane_multiple_image_positions(analysis_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.image_plane_multiple_image_positions.in_list[0][0] == pytest.approx(
         1.0004, 1.0e-2
@@ -268,9 +262,9 @@ def test__positions_threshold_from(analysis_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.positions_threshold_from() == pytest.approx(0.000973519, 1.0e-4)
     assert result.positions_threshold_from(factor=5.0) == pytest.approx(
@@ -297,9 +291,9 @@ def test__positions_likelihood_from(analysis_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     positions_likelihood = result.positions_likelihood_from(
         factor=0.1, minimum_threshold=0.2
@@ -317,21 +311,21 @@ def test__positions_likelihood_from(analysis_imaging_7x7):
 
 
 def test__results_include_mask__available_as_property(
-    analysis_imaging_7x7, masked_imaging_7x7, samples_with_result
+    analysis_imaging_7x7, masked_imaging_7x7, samples_summary_with_result
 ):
     result = res.ResultDataset(
-        samples=samples_with_result,
+        samples_summary=samples_summary_with_result,
         analysis=analysis_imaging_7x7,
     )
 
     assert (result.mask == masked_imaging_7x7.mask).all()
 
 
 def test__results_include_positions__available_as_property(
-    analysis_imaging_7x7, masked_imaging_7x7, samples_with_result
+    analysis_imaging_7x7, masked_imaging_7x7, samples_summary_with_result
 ):
     result = res.ResultDataset(
-        samples=samples_with_result,
+        samples_summary=samples_summary_with_result,
         analysis=analysis_imaging_7x7,
     )
 
@@ -346,7 +340,7 @@ def test__results_include_positions__available_as_property(
     )
 
     result = res.ResultDataset(
-        samples=samples_with_result,
+        samples_summary=samples_summary_with_result,
         analysis=analysis,
     )
 
@@ -362,7 +356,7 @@ def test___image_dict(analysis_imaging_7x7):
     instance.galaxies = galaxies
 
     result = ResultImaging(
-        samples=al.m.MockSamples(max_log_likelihood_instance=instance),
+        samples_summary=al.m.MockSamplesSummary(max_log_likelihood_instance=instance),
         analysis=analysis_imaging_7x7,
     )
 

test_autolens/conftest.py

@@ -391,6 +391,6 @@ def make_include_all():
     return fixtures.make_include_2d_all()
 
 
-@pytest.fixture(name="samples_with_result")
-def make_samples_with_result():
-    return fixtures.make_samples_with_result()
+@pytest.fixture(name="samples_summary_with_result")
+def make_samples_summary_with_result():
+    return fixtures.make_samples_summary_with_result()