improve sensitivity plot

autolens/analysis/result.py

@@ -169,6 +169,36 @@ def positions_likelihood_from(
         use_resample=False,
         positions: Optional[aa.Grid2DIrregular] = None,
     ) -> Union[PositionsLHPenalty, PositionsLHResample]:
+        """
+        Returns a `PositionsLH` object from the result of a lens model-fit, where the maximum log likelihood mass
+        and source models are used to determine the multiple image positions in the image-plane and source-plane
+        and ray-trace them to the source-plane to determine the threshold.
+
+        In chained fits, for example the SLaM pipelines, this means that a simple initial fit (e.g. SIE mass model,
+        parametric source) can be used to determine the multiple image positions and threshold for a more complex
+        subsequent fit (e.g. power-law mass model, pixelized source).
+
+        Parameters
+        ----------
+        factor
+            The value the computed threshold is multiplied by to make the position threshold larger or smaller than the
+            maximum log likelihood model's threshold.
+        minimum_threshold
+            The output threshold is rounded up to this value if it is below it, to avoid extremely small threshold
+            values.
+        use_resample
+            If `False` the `PositionsLH` object is created using the `PositionsLHPenalty` class, which uses the
+            threshold to apply a penalty term to the likelihood. If `True` the `PositionsLH` object is created using
+            the `PositionsLHResample` class, which resamples the positions to the threshold.
+        positions
+            If input, these positions are used instead of the computed multiple image positions from the lens mass
+            model.
+
+        Returns
+        -------
+        The `PositionsLH` object used to apply a likelihood penalty or resample the positions.
+        """
+
         if os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
             return None
 

autolens/lens/sensitivity.py

@@ -375,17 +375,22 @@ def subplot_sensitivity(self):
         except TypeError:
             log_evidences = np.zeros_like(log_likelihoods)
 
-        self.open_subplot_figure(number_subplots=4, subplot_shape=(1,4))
+        self.open_subplot_figure(number_subplots=8, subplot_shape=(2,4))
 
         plotter = aplt.Array2DPlotter(
             array=self.data_subtracted,
             mat_plot_2d=self.mat_plot_2d,
         )
 
         max_value = np.round(np.nanmax(log_likelihoods), 2)
-        plotter.set_title(label=f"Sensitivity Map {max_value}")
         plotter.figure_2d()
 
+        self.mat_plot_2d.plot_array(
+            array=log_evidences,
+            visuals_2d=self.visuals_2d,
+            auto_labels=AutoLabels(title="Increase in Log Evidence"),
+        )
+
         self.mat_plot_2d.plot_array(
             array=log_likelihoods,
             visuals_2d=self.visuals_2d,
@@ -398,7 +403,6 @@ def subplot_sensitivity(self):
             values=above_threshold,
             mask=log_likelihoods.mask
         )
-        plotter.set_title(label=None)
 
         self.mat_plot_2d.plot_array(
             array=above_threshold,
@@ -407,9 +411,27 @@ def subplot_sensitivity(self):
         )
 
         self.mat_plot_2d.plot_array(
-            array=log_evidences,
+            array=self.result._array_2d_from(self.result.log_evidences_base),
             visuals_2d=self.visuals_2d,
-            auto_labels=AutoLabels(title="Increase in Log Evidence"),
+            auto_labels=AutoLabels(title="Log Evidence Base"),
+        )
+
+        self.mat_plot_2d.plot_array(
+            array=self.result._array_2d_from(self.result.log_evidences_perturbed),
+            visuals_2d=self.visuals_2d,
+            auto_labels=AutoLabels(title="Log Evidence Perturb"),
+        )
+
+        self.mat_plot_2d.plot_array(
+            array=self.result._array_2d_from(self.result.log_likelihoods_base),
+            visuals_2d=self.visuals_2d,
+            auto_labels=AutoLabels(title="Log Likelihood Base"),
+        )
+
+        self.mat_plot_2d.plot_array(
+            array=self.result._array_2d_from(self.result.log_likelihoods_perturbed),
+            visuals_2d=self.visuals_2d,
+            auto_labels=AutoLabels(title="Log Likelihood Perturb"),
         )
 
         self.mat_plot_2d.output.subplot_to_figure(auto_filename="subplot_sensitivity")