How to use the autogalaxy.plot.inversion_plots function in autogalaxy

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github Jammy2211 / PyAutoLens / autolens / pipeline / visualizer.py View on Github external
fit_interferometer_plots.individuals(
                    fit=fit,
                    plot_visibilities=True,
                    plot_noise_map=True,
                    plot_signal_to_noise_map=True,
                    plot_model_visibilities=True,
                    plot_residual_map=True,
                    plot_normalized_residual_map=True,
                    plot_chi_squared_map=True,
                    include=self.include,
                    plotter=fits_plotter,
                )

                if fit.inversion is not None:
                    inversion_plots.individuals(
                        inversion=fit.inversion,
                        image_positions=self.include.positions_from_fit(fit=fit),
                        source_positions=self.include.positions_of_plane_from_fit_and_plane_index(
                            fit=fit, plane_index=-1
                        ),
                        grid=self.include.inversion_image_pixelization_grid_from_fit(
                            fit=fit
                        ),
                        light_profile_centres=self.include.light_profile_centres_from_obj(
                            obj=fit.tracer.image_plane
                        ),
                        mass_profile_centres=self.include.mass_profile_centres_from_obj(
                            obj=fit.tracer.image_plane
                        ),
                        critical_curves=self.include.critical_curves_from_obj(
                            obj=fit.tracer
github Jammy2211 / PyAutoLens / autolens / pipeline / visualizer.py View on Github external
plot_signal_to_noise_map=self.plot_fit_signal_to_noise_map,
            plot_model_image=self.plot_fit_model_data,
            plot_residual_map=self.plot_fit_residual_map,
            plot_chi_squared_map=self.plot_fit_chi_squared_map,
            plot_normalized_residual_map=self.plot_fit_normalized_residual_map,
            plot_subtracted_images_of_planes=self.plot_fit_subtracted_images_of_planes,
            plot_model_images_of_planes=self.plot_fit_model_images_of_planes,
            plot_plane_images_of_planes=self.plot_fit_plane_images_of_planes,
            include=self.include,
            plotter=plotter,
        )

        if fit.inversion is not None:

            if self.plot_subplot_inversion:
                inversion_plots.subplot_inversion(
                    inversion=fit.inversion,
                    image_positions=self.include.positions_from_fit(fit=fit),
                    source_positions=self.include.positions_of_plane_from_fit_and_plane_index(
                        fit=fit, plane_index=-1
                    ),
                    grid=self.include.inversion_image_pixelization_grid_from_fit(
                        fit=fit
                    ),
                    light_profile_centres=self.include.light_profile_centres_from_obj(
                        obj=fit.tracer.image_plane
                    ),
                    mass_profile_centres=self.include.mass_profile_centres_from_obj(
                        obj=fit.tracer.image_plane
                    ),
                    critical_curves=self.include.critical_curves_from_obj(
                        obj=fit.tracer
github Jammy2211 / PyAutoLens / autolens / pipeline / visualizer.py View on Github external
plot_normalized_residual_map=True,
            plot_chi_squared_map=True,
            plot_subtracted_images_of_planes=True,
            plot_model_images_of_planes=True,
            plot_plane_images_of_planes=True,
            include=self.include,
            plotter=fits_plotter,
        )

        if fit.inversion is not None:

            fits_plotter = self.plotter.plotter_with_new_output(
                path=self.plotter.output.path + "inversion/fits/", format="fits"
            )

            inversion_plots.individuals(
                inversion=fit.inversion,
                plot_reconstructed_image=True,
                plot_interpolated_reconstruction=True,
                plot_interpolated_errors=True,
                include=self.include,
                plotter=fits_plotter,
            )