remove make_result

Jammy2211 · Mar 29, 2024 · 3220778 · 3220778
1 parent e04a962
commit 3220778
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Showing 3 changed files with 8 additions and 90 deletions.
diff --git a/autolens/imaging/model/analysis.py b/autolens/imaging/model/analysis.py
@@ -24,6 +24,8 @@
 
 class AnalysisImaging(AnalysisDataset):
 
+    Result = ResultImaging
+
     def modify_before_fit(self, paths: af.DirectoryPaths, model: af.Collection):
         """
         This function is called immediately before the non-linear search begins and performs final tasks and checks 
@@ -281,40 +283,6 @@ def visualize(
                     inversion=fit.inversion, during_analysis=during_analysis
                 )
 
-    def make_result(
-        self,
-        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:
-
-        - The samples of the non-linear search (E.g. MCMC chains, nested sampling samples) which are used to compute
-          the maximum likelihood model, posteriors and other properties.
-
-        - The model used to fit the data, which uses the samples to create specific instances of the model (e.g.
-          an instance of the maximum log likelihood model).
-
-        - The non-linear search used to perform the model fit.
-
-        The `ResultImaging` object contains a number of methods which use the above objects to create the max
-        log likelihood `Tracer`, `FitImaging`, adapt-galaxy images,etc.
-
-        Parameters
-        ----------
-        samples
-            A PyAutoFit object which contains the samples of the non-linear search, for example the chains of an MCMC
-            run of samples of the nested sampler.
-
-        Returns
-        -------
-        ResultImaging
-            The result of fitting the model to the imaging dataset, via a non-linear search.
-        """
-        return ResultImaging(samples_summary=samples_summary, paths=paths, samples=samples, search_internal=search_internal, analysis=self)
-
     def save_attributes(self, paths: af.DirectoryPaths):
         """
         Before the non-linear search begins, this routine saves attributes of the `Analysis` object to the `pickles`

diff --git a/autolens/interferometer/model/analysis.py b/autolens/interferometer/model/analysis.py
@@ -14,7 +14,6 @@
 from autolens.analysis.preloads import Preloads
 from autolens.analysis.positions import PositionsLHResample
 from autolens.analysis.positions import PositionsLHPenalty
-from autolens.lens.tracer import Tracer
 from autolens.interferometer.model.result import ResultInterferometer
 from autolens.interferometer.model.visualizer import VisualizerInterferometer
 from autolens.interferometer.fit_interferometer import FitInterferometer
@@ -27,6 +26,9 @@
 
 
 class AnalysisInterferometer(AnalysisDataset):
+
+    Result = ResultInterferometer
+
     def __init__(
         self,
         dataset,
@@ -328,46 +330,6 @@ def visualize(self, paths: af.DirectoryPaths, instance, during_analysis):
             except IndexError:
                 pass
 
-    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:
-
-        - The samples of the non-linear search (E.g. MCMC chains, nested sampling samples) which are used to compute
-          the maximum likelihood model, posteriors and other properties.
-
-        - The model used to fit the data, which uses the samples to create specific instances of the model (e.g.
-          an instance of the maximum log likelihood model).
-
-        - The non-linear search used to perform the model fit.
-
-        The `ResultInterferometer` object contains a number of methods which use the above objects to create the max
-        log likelihood `Plane`, `FitInterferometer`, adapt-galaxy images,etc.
-
-        Parameters
-        ----------
-        samples
-            A PyAutoFit object which contains the samples of the non-linear search, for example the chains of an MCMC
-            run of samples of the nested sampler.
-
-        Returns
-        -------
-        ResultImaging
-            The result of fitting the model to the imaging dataset, via a non-linear search.
-        """
-        return ResultInterferometer(
-            samples_summary=samples_summary,
-            paths=paths,
-            samples=samples,
-            analysis=self,
-            search_internal=search_internal,
-        )
-
     def save_attributes(self, paths: af.DirectoryPaths):
         """
          Before the non-linear search begins, this routine saves attributes of the `Analysis` object to the `pickles`

diff --git a/autolens/point/model/analysis.py b/autolens/point/model/analysis.py
@@ -24,6 +24,9 @@
 
 
 class AnalysisPoint(AgAnalysis, AnalysisLens):
+
+    Result = ResultPoint
+
     def __init__(
         self,
         point_dict: PointDict,
@@ -98,21 +101,6 @@ def visualize(self, paths, instance, during_analysis):
 
         visualizer = Visualizer(visualize_path=paths.image_path)
 
-    def make_result(
-        self,
-        samples_summary: af.SamplesSummary,
-        paths: af.AbstractPaths,
-        samples: Optional[af.SamplesPDF] = None,
-        search_internal: Optional[object] = None,
-    ):
-        return ResultPoint(
-            samples_summary=samples_summary,
-            paths=paths,
-            samples=samples,
-            search_internal=search_internal,
-            analysis=self,
-        )
-
     def save_attributes(self, paths: af.DirectoryPaths):
         self.point_dict.output_to_json(
             file_path=paths._files_path / "point_dict.json", overwrite=True