Merge pull request #993 from rhayes777/feature/visualiser

feature/visualiser

autofit/graphical/declarative/factor/analysis.py

@@ -93,7 +93,10 @@ def __setstate__(self, state):
         self.__dict__.update(state)
 
     def __getattr__(self, item):
-        return getattr(self.prior_model, item)
+        try:
+            return super().__getattr__(item)
+        except AttributeError:
+            return getattr(self.prior_model, item)
 
     def name_for_variable(self, variable):
         path = ".".join(self.prior_model.path_for_prior(variable))

autofit/non_linear/analysis/analysis.py

@@ -14,6 +14,7 @@
 from autofit.non_linear.samples.pdf import SamplesPDF
 from autofit.non_linear.result import Result
 from autofit.non_linear.samples.samples import Samples
+from .visualize import Visualizer
 
 logger = logging.getLogger(__name__)
 
@@ -26,6 +27,25 @@ class Analysis(ABC):
     """
 
     Result = Result
+    Visualizer = Visualizer
+
+    def __getattr__(self, item: str):
+        """
+        If a method starts with 'visualize_' then we assume it is associated with
+        the Visualizer and forward the call to the visualizer.
+
+        It may be desirable to remove this behaviour as the visualizer component of
+        the system becomes more sophisticated.
+        """
+        if item.startswith("visualize"):
+            _method = getattr(Visualizer, item)
+        else:
+            raise AttributeError(f"Analysis has no attribute {item}")
+
+        def method(*args, **kwargs):
+            return _method(self, *args, **kwargs)
+
+        return method
 
     def compute_all_latent_variables(
         self, samples: Samples
@@ -136,22 +156,6 @@ def should_visualize(
     def log_likelihood_function(self, instance):
         raise NotImplementedError()
 
-    def visualize_before_fit(self, paths: AbstractPaths, model: AbstractPriorModel):
-        pass
-
-    def visualize(self, paths: AbstractPaths, instance, during_analysis):
-        pass
-
-    def visualize_before_fit_combined(
-        self, analyses, paths: AbstractPaths, model: AbstractPriorModel
-    ):
-        pass
-
-    def visualize_combined(
-        self, analyses, paths: AbstractPaths, instance, during_analysis
-    ):
-        pass
-
     def save_attributes(self, paths: AbstractPaths):
         pass
 
@@ -238,7 +242,7 @@ def make_result(
             paths=paths,
             samples=samples,
             search_internal=search_internal,
-            analysis=None
+            analysis=None,
         )
 
     def profile_log_likelihood_function(self, paths: AbstractPaths, instance):

autofit/non_linear/analysis/visualize.py

@@ -0,0 +1,45 @@
+from autofit.non_linear.paths.abstract import AbstractPaths
+from autofit.mapper.prior_model.abstract import AbstractPriorModel
+
+
+class Visualizer:
+    """
+    Methods associated with visualising analysis, model and data before, during
+    or after an optimisation.
+    """
+
+    @staticmethod
+    def visualize_before_fit(
+        analysis,
+        paths: AbstractPaths,
+        model: AbstractPriorModel,
+    ):
+        pass
+
+    @staticmethod
+    def visualize(
+        analysis,
+        paths: AbstractPaths,
+        instance,
+        during_analysis,
+    ):
+        pass
+
+    @staticmethod
+    def visualize_before_fit_combined(
+        analysis,
+        analyses,
+        paths: AbstractPaths,
+        model: AbstractPriorModel,
+    ):
+        pass
+
+    @staticmethod
+    def visualize_combined(
+        analysis,
+        analyses,
+        paths: AbstractPaths,
+        instance,
+        during_analysis,
+    ):
+        pass

autofit/non_linear/search/abstract_search.py

@@ -676,7 +676,7 @@ def start_resume_fit(self, analysis: Analysis, model: AbstractPriorModel) -> Res
             samples_summary=samples.summary(),
             paths=self.paths,
             samples=samples,
-            search_internal=search_internal
+            search_internal=search_internal,
         )
 
         if self.is_master:
@@ -730,10 +730,7 @@ def result_via_completed_fit(
         model.freeze()
         samples_summary = self.paths.load_samples_summary()
 
-        result = analysis.make_result(
-            samples_summary=samples_summary,
-            paths=self.paths
-        )
+        result = analysis.make_result(samples_summary=samples_summary, paths=self.paths)
 
         if self.is_master:
             self.logger.info(f"Fit Already Completed: skipping non-linear search.")
@@ -912,19 +909,18 @@ def perform_update(
             instance = samples_summary.instance
         except exc.FitException:
             return samples
-
-        if self.is_master:
 
+        if self.is_master:
             self.paths.save_samples_summary(samples_summary=samples_summary)
 
-            samples = samples.samples_above_weight_threshold_from(log_message=not during_analysis)
+            samples = samples.samples_above_weight_threshold_from(
+                log_message=not during_analysis
+            )
             self.paths.save_samples(samples=samples)
 
-            if (
-                    (during_analysis and conf.instance["output"]["latent_during_fit"]) or
-                    (not during_analysis and conf.instance["output"]["latent_after_fit"])
+            if (during_analysis and conf.instance["output"]["latent_during_fit"]) or (
+                not during_analysis and conf.instance["output"]["latent_after_fit"]
             ):
-
                 latent_variables = analysis.compute_all_latent_variables(samples)
 
                 if latent_variables:
@@ -979,7 +975,7 @@ def perform_visualization(
         self,
         model: AbstractPriorModel,
         analysis: AbstractPriorModel,
-        samples_summary : SamplesSummary,
+        samples_summary: SamplesSummary,
         during_analysis: bool,
         search_internal=None,
     ):
@@ -1014,7 +1010,7 @@ def perform_visualization(
             analysis.visualize(
                 paths=self.paths,
                 instance=samples_summary.instance,
-                during_analysis=during_analysis
+                during_analysis=during_analysis,
             )
             analysis.visualize_combined(
                 analyses=None,
@@ -1024,10 +1020,10 @@ def perform_visualization(
             )
 
         if analysis.should_visualize(paths=self.paths, during_analysis=during_analysis):
-
             if not isinstance(self.paths, NullPaths):
-
-                samples = self.samples_from(model=model, search_internal=search_internal)
+                samples = self.samples_from(
+                    model=model, search_internal=search_internal
+                )
 
                 self.plot_results(samples=samples)