Feature/graphical analysis output

autogalaxy/aggregator/agg_util.py

@@ -11,6 +11,52 @@
 from autogalaxy.analysis.adapt_images.adapt_images import AdaptImages
 
 
+def instance_list_from(
+    fit: af.Fit, instance: Optional[af.ModelInstance] = None
+) -> List[af.ModelInstance]:
+    """
+    Returns the list of instances of the maximum likelihood model, depending on the model composition and whether
+    multiple `Analysis` objects were fitted simultaneously.
+
+    This if loop accounts for 4 scenarios:
+
+    - A single `Analysis` object was fitted, in which case the instance is a single object and converted to a list.
+
+    - Multiple `Analysis` objects were fitted via a `FactorGraphModel`, in which case the instance is a list of
+    objects and all but the last object (which is the overall `FactorGraphModel` are returned.
+
+    - A single instance is manually input, in which case it is converted to a list.
+
+    - Multiple `Analysis` objects were fitted via a `FactorGraphModel`, in which case the instance is a list of
+    objects and all but the last object (which is the overall `FactorGraphModel` are returned.
+
+    Parameters
+    ----------
+    fit
+        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
+        an output directory or from an sqlite database.
+    instance
+        An optional instance that overwrites the max log likelihood instance in fit (e.g. for drawing the instance
+        randomly from the PDF).
+
+    Returns
+    -------
+    The list of instances of the maximum likelihood model.
+    """
+
+    if instance is None:
+        if len(fit.children) == 0:
+            return [fit.instance]
+        return fit.instance[
+            0:-1
+        ]  # [0:-1] excludes the last instance, which is the `FactorGraphModel` object itself.
+
+    if isinstance(list(instance.child_items.values())[-1], af.FactorGraphModel):
+        return list(instance.child_items.values())[0:-1]
+
+    return [instance]
+
+
 def mask_header_from(fit, name="dataset"):
     """
     Returns the mask, header and pixel scales of the `PyAutoFit` `Fit` object.

autogalaxy/aggregator/dataset_model.py

@@ -5,6 +5,8 @@
 import autofit as af
 import autoarray as aa
 
+from autogalaxy.aggregator import agg_util
+
 logger = logging.getLogger(__name__)
 
 
@@ -38,28 +40,14 @@ def _dataset_model_from(
         randomly from the PDF).
     """
 
-    if instance is not None:
-        try:
-            dataset_model = instance.dataset_model
-        except AttributeError:
-            dataset_model = None
-    else:
-        try:
-            dataset_model = fit.instance.dataset_model
-        except AttributeError:
-            dataset_model = None
-
-    if fit.children is not None:
-        if len(fit.children) > 0:
-            logger.info(
-                """
-                Using database for a fit with multiple summed Analysis objects.
-
-                DatasetModel objects do not fully support this yet (e.g. variables across Analysis objects may not be correct)
-                so proceed with caution!
-                """
-            )
-
-            return [dataset_model] * len(fit.children)
-
-    return [dataset_model]
+    instance_list = agg_util.instance_list_from(fit=fit, instance=instance)
+
+    dataset_model_list = []
+
+    for instance in instance_list:
+
+        dataset_model = instance.dataset_model
+
+        dataset_model_list.append(dataset_model)
+
+    return dataset_model_list

autogalaxy/aggregator/galaxies.py

@@ -7,6 +7,7 @@
 
 import autofit as af
 
+from autogalaxy.aggregator import agg_util
 
 logger = logging.getLogger(__name__)
 
@@ -40,34 +41,21 @@ def _galaxies_from(fit: af.Fit, instance: af.ModelInstance) -> List[Galaxy]:
         randomly from the PDF).
     """
 
-    if instance is not None:
+    instance_list = agg_util.instance_list_from(fit=fit, instance=instance)
+
+    galaxies_list = []
+
+    for instance in instance_list:
+
         galaxies = instance.galaxies
 
         if hasattr(instance, "extra_galaxies"):
-            if fit.instance.extra_galaxies is not None:
-                galaxies = galaxies + fit.instance.extra_galaxies
-
-    else:
-        galaxies = fit.instance.galaxies
-
-        if hasattr(fit.instance, "extra_galaxies"):
-            if fit.instance.extra_galaxies is not None:
-                galaxies = galaxies + fit.instance.extra_galaxies
-
-    if fit.children is not None:
-        if len(fit.children) > 0:
-            logger.info(
-                """
-                Using database for a fit with multiple summed Analysis objects.
-
-                Galaxy objects do not fully support this yet (e.g. variables across Analysis objects may not be correct)
-                so proceed with caution!
-                """
-            )
-
-            return [galaxies] * len(fit.children)
-
-    return [galaxies]
+            if instance.extra_galaxies is not None:
+                galaxies = galaxies + instance.extra_galaxies
+
+        galaxies_list.append(galaxies)
+
+    return galaxies_list
 
 
 class GalaxiesAgg(af.AggBase):

autogalaxy/aggregator/imaging/imaging.py

@@ -58,9 +58,10 @@ def values_from(hdu: int, cls):
         data = values_from(hdu=1, cls=aa.Array2D)
         noise_map = values_from(hdu=2, cls=aa.Array2D)
 
-        psf = values_from(hdu=3, cls=aa.Kernel2D)
-
-        print(psf)
+        try:
+            psf = values_from(hdu=3, cls=aa.Kernel2D)
+        except (TypeError, IndexError):
+            psf = None
 
         dataset = aa.Imaging(
             data=data,

test_autogalaxy/aggregator/imaging/test_aggregator_fit_imaging.py

@@ -39,58 +39,122 @@ def make_agg_7x7(samples, model, analysis_imaging_7x7):
     return agg
 
 
-def test__fit_imaging_randomly_drawn_via_pdf_gen_from__analysis_has_single_dataset(
+def test__fit_imaging__max_log_likelihood__analysis_has_single_dataset(
     agg_7x7,
 ):
     fit_agg = ag.agg.FitImagingAgg(aggregator=agg_7x7)
-    fit_pdf_gen = fit_agg.randomly_drawn_via_pdf_gen_from(total_samples=2)
-
-    i = 0
-
-    for fit_gen in fit_pdf_gen:
-        for fit_list in fit_gen:
-            i += 1
+    fit_max_lh_gen = fit_agg.max_log_likelihood_gen_from()
 
-            assert fit_list[0].galaxies[0].redshift == 0.5
-            assert fit_list[0].galaxies[0].light.centre == (10.0, 10.0)
+    for (
+        fit_list
+    ) in (
+        fit_max_lh_gen
+    ):  # Only Max LH sample so fit_list contains 1 lists of a single fit.
 
-            assert fit_list[0].dataset_model.background_sky_level == 10.0
+        assert fit_list[0].galaxies[0].redshift == 0.5
+        assert fit_list[0].galaxies[0].light.centre == (10.0, 10.0)
 
-    assert i == 2
+        assert fit_list[0].dataset_model.background_sky_level == 10.0
 
     clean(database_file=database_file)
 
 
-def test__fit_imaging_randomly_drawn_via_pdf_gen_from__analysis_multi(
-    analysis_imaging_7x7, samples, model
+def test__fit_imaging__randomly_drawn_via_pdf_gen_from__analysis_has_single_dataset(
+    agg_7x7,
 ):
-    agg = aggregator_from(
-        database_file=database_file,
-        analysis=analysis_imaging_7x7 + analysis_imaging_7x7,
-        model=model,
-        samples=samples,
-    )
-
-    fit_agg = ag.agg.FitImagingAgg(aggregator=agg)
-    fit_pdf_gen = fit_agg.randomly_drawn_via_pdf_gen_from(total_samples=2)
+    fit_agg = ag.agg.FitImagingAgg(aggregator=agg_7x7)
+    fit_pdf_gen = fit_agg.randomly_drawn_via_pdf_gen_from(total_samples=3)
 
     i = 0
 
-    for fit_gen in fit_pdf_gen:
-        for fit_list in fit_gen:
+    for fit_list_gen in fit_pdf_gen:  # 1 Dataset so just one fit
+        for (
+            fit_list
+        ) in (
+            fit_list_gen
+        ):  # Iterate over each total_samples=3, each with two fits for each analysis.
+
             i += 1
 
+            # Check fit for each `Analysis` so take first and only dataset.
             assert fit_list[0].galaxies[0].redshift == 0.5
             assert fit_list[0].galaxies[0].light.centre == (10.0, 10.0)
+            assert fit_list[0].dataset_model.background_sky_level == 10.0
 
-            assert fit_list[1].galaxies[0].redshift == 0.5
-            assert fit_list[1].galaxies[0].light.centre == (10.0, 10.0)
-
-    assert i == 2
+    assert i == 3
 
     clean(database_file=database_file)
 
 
+# TODO : These need to use FactorGraphModel
+
+
+# def test__fit_imaging__max_log_likelihood_gen_from__analysis_multi(
+#     analysis_imaging_7x7, samples, model
+# ):
+#
+#     analysis_factor_list = []
+#
+#     for i, analysis in enumerate([analysis_imaging_7x7, analysis_imaging_7x7]):
+#
+#         model_analysis = model.copy()
+#         analysis_factor = af.AnalysisFactor(prior_model=model_analysis, analysis=analysis)
+#
+#         analysis_factor_list.append(analysis_factor)
+#
+#     factor_graph = af.FactorGraphModel(*analysis_factor_list)
+#
+#     agg = aggregator_from(
+#         database_file=database_file,
+#         analysis=factor_graph,
+#         model=factor_graph.global_prior_model,
+#         samples=samples,
+#     )
+#
+#     fit_agg = ag.agg.FitImagingAgg(aggregator=agg)
+#
+#     fit_max_lh_gen = fit_agg.max_log_likelihood_gen_from()
+#
+#     for fit_list in fit_max_lh_gen: # Only Max LH sample so fit_list contains 1 lists of the 2 fit (one for each analysis).
+#
+#         assert fit_list[0].galaxies[0].redshift == 0.5
+#         assert fit_list[0].galaxies[0].light.centre == (10.0, 10.0)
+#
+#         assert fit_list[1].galaxies[0].redshift == 0.5
+#         assert fit_list[1].galaxies[0].light.centre == (10.0, 10.0)
+#
+#     clean(database_file=database_file)
+#
+# def test__fit_imaging__randomly_drawn_via_pdf_gen_from__analysis_multi(
+#     analysis_imaging_7x7, samples, model
+# ):
+#     agg = aggregator_from(
+#         database_file=database_file,
+#         analysis=analysis_imaging_7x7 + analysis_imaging_7x7,
+#         model=model,
+#         samples=samples,
+#     )
+#
+#     fit_agg = ag.agg.FitImagingAgg(aggregator=agg)
+#     fit_pdf_gen = fit_agg.randomly_drawn_via_pdf_gen_from(total_samples=3)
+#
+#     i = 0
+#
+#     for fit_gen in fit_pdf_gen: # 1 Dataset so just one fit
+#         for fit_list in fit_gen: # Iterate over each total_samples=3, each with two fits for each analysis.
+#             i += 1
+#
+#             assert fit_list[0].galaxies[0].redshift == 0.5
+#             assert fit_list[0].galaxies[0].light.centre == (10.0, 10.0)
+#
+#             assert fit_list[1].galaxies[0].redshift == 0.5
+#             assert fit_list[1].galaxies[0].light.centre == (10.0, 10.0)
+#
+#     assert i == 3
+#
+#     clean(database_file=database_file)
+
+
 def test__fit_imaging_all_above_weight_gen(agg_7x7):
     fit_agg = ag.agg.FitImagingAgg(aggregator=agg_7x7)
     fit_pdf_gen = fit_agg.all_above_weight_gen_from(minimum_weight=-1.0)

test_autogalaxy/aggregator/interferometer/test_aggregator_fit_interferometer.py

@@ -40,6 +40,8 @@ def test__fit_interferometer_randomly_drawn_via_pdf_gen_from(
     clean(database_file=database_file)
 
 
+# TODO : These need to use FactorGraphModel
+
 # def test__fit_interferometer_randomly_drawn_via_pdf_gen_from__analysis_multi(
 #     analysis_interferometer_7, samples, model
 # ):