Feature/ellipse for sam

autogalaxy/config/visualize/plots.yaml

@@ -49,7 +49,9 @@ fit_interferometer:                        # Settings for plots of fits to inter
   fits_dirty_images: false                 # output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?
 
 fit_ellipse:                               # Settings for plots of ellipse fitting fits (e.g. FitEllipse)
+  subplot_fit_ellipse : true               # Plot subplot of all fit quantities for ellipse fits (e.g. the model data, residual-map, etc.)?
   data : true                              # Plot the data of the ellipse fit?
   data_no_ellipse: true                    # Plot the data without the black data ellipses, which obscure noisy data?
+  ellipse_residuals: true                  # Plot the residuals of the ellipse fit?
 
 fit_quantity: {}                           # Settings for plots of fit quantities (e.g. FitQuantityPlotter).

autogalaxy/ellipse/ellipse/ellipse_multipole.py

@@ -38,7 +38,7 @@ class representing the multipole of an ellispe with, which is used to perform el
         self.multipole_comps = multipole_comps
 
     def points_perturbed_from(
-        self, pixel_scale, points, ellipse: Ellipse
+        self, pixel_scale, points, ellipse: Ellipse, n_i: int = 0
     ) -> np.ndarray:
         """
         Returns the (y,x) coordinates of the input points, which are perturbed by the multipole of the ellipse.
@@ -57,7 +57,7 @@ def points_perturbed_from(
         The (y,x) coordinates of the input points, which are perturbed by the multipole.
         """
 
-        angles = ellipse.angles_from_x0_from(pixel_scale=pixel_scale)
+        angles = ellipse.angles_from_x0_from(pixel_scale=pixel_scale, n_i=n_i)
 
         radial = np.add(
             self.multipole_comps[1] * np.cos(self.m * (angles - ellipse.angle_radians)),

autogalaxy/ellipse/fit_ellipse.py

@@ -56,6 +56,14 @@ def points_from_major_axis_from(self) -> np.ndarray:
             pixel_scale=self.dataset.pixel_scales[0],
         )
 
+        if self.multipole_list is not None:
+            for multipole in self.multipole_list:
+                points = multipole.points_perturbed_from(
+                    pixel_scale=self.dataset.pixel_scales[0],
+                    points=points,
+                    ellipse=self.ellipse,
+                )
+
         if self.interp.mask_interp is not None:
 
             i_total = 300
@@ -74,6 +82,16 @@ def points_from_major_axis_from(self) -> np.ndarray:
                     pixel_scale=self.dataset.pixel_scales[0], n_i=i
                 )
 
+                if self.multipole_list is not None:
+                    for multipole in self.multipole_list:
+
+                        points = multipole.points_perturbed_from(
+                            pixel_scale=self.dataset.pixel_scales[0],
+                            points=points,
+                            ellipse=self.ellipse,
+                            n_i=i
+                        )
+
                 if i == i_total:
 
                     raise ValueError(
@@ -88,14 +106,6 @@ def points_from_major_axis_from(self) -> np.ndarray:
 
             points = points[self.interp.mask_interp(points) == 0]
 
-        if self.multipole_list is not None:
-            for multipole in self.multipole_list:
-                points = multipole.points_perturbed_from(
-                    pixel_scale=self.dataset.pixel_scales[0],
-                    points=points,
-                    ellipse=self.ellipse,
-                )
-
         return points
 
     @cached_property
@@ -279,4 +289,4 @@ def figure_of_merit(self) -> float:
         -------
         The figure of merit of the fit.
         """
-        return self.log_likelihood
+        return -0.5* (self.chi_squared + self.noise_normalization)

autogalaxy/ellipse/model/plotter_interface.py

@@ -93,18 +93,22 @@ def should_plot(name):
             fit_list=fit_list, mat_plot_2d=mat_plot_2d, include_2d=self.include_2d
         )
 
-        try:
-            fit_plotter.figures_2d(data=should_plot("data"))
-        except ValueError:
-            print(fit_plotter.fit_list[0].ellipse.major_axis)
-            print(fit_plotter.fit_list[0].ellipse.ell_comps)
+        fit_plotter.figures_2d(
+            data=should_plot("data"),
+            ellipse_residuals=should_plot("ellipse_residuals"),
+        )
 
         if should_plot("data_no_ellipse"):
             fit_plotter.figures_2d(
                 data=True,
                 disable_data_contours=True,
+
             )
 
+        if should_plot("subplot_fit_ellipse"):
+
+            fit_plotter.subplot_fit_ellipse()
+
         fit_plotter.mat_plot_2d.use_log10 = True
 
         fit_plotter.figures_2d(data=should_plot("data"))
@@ -113,4 +117,4 @@ def should_plot(name):
             fit_plotter.figures_2d(
                 data=True,
                 disable_data_contours=True,
-            )
+            )