maintenance: raw-string the LaTeX-carrying docstrings

scripts/group/features/linear_light_profiles/likelihood_function.py

@@ -245,7 +245,7 @@
 
 aplt.subplot_fit_imaging(fit=fit)
 
-"""
+r"""
 __Likelihood Function__
 
 The likelihood function for linear light profiles includes the same terms as the standard parametric case:

scripts/group/features/multi_gaussian_expansion/likelihood_function.py

@@ -261,7 +261,7 @@
 
 aplt.plot_array(array=convolved_image_2d, title="Convolved Image")
 
-"""
+r"""
 __Likelihood Function__
 
 We now quantify the goodness-of-fit of our group-scale lens model.

scripts/group/likelihood_function.py

@@ -154,7 +154,7 @@
     f"(y,x) coordinates of first ten unmasked image-pixels {masked_dataset.grid[0:9]}"
 )
 
-"""
+r"""
 __Lens Galaxy Light (Setup)__
 
 To perform a likelihood evaluation we now compose our lens model.
@@ -388,7 +388,7 @@
 
 aplt.plot_array(array=convolved_image_2d, title="Convolved Image")
 
-"""
+r"""
 __Likelihood Function__
 
 We now quantify the goodness-of-fit of our group-scale lens model.

scripts/guides/galaxies.py

@@ -197,7 +197,7 @@
 """
 aplt.plot_array(array=tracer.image_2d_from(grid=grid), title="Image")
 
-"""
+r"""
 __Log10__
 
 The light distributions of galaxies are closer to a log10 distribution than a linear one. 

scripts/guides/results/aggregator/data_fitting.py

@@ -147,7 +147,7 @@
 
     aplt.subplot_fit_imaging(fit=fit)
 
-"""
+r"""
 __Visualization Customization__
 
 The benefit of inspecting fits using the aggregator, rather than the files outputs to the hard-disk, is that we can 

scripts/guides/tracer.py

@@ -214,7 +214,7 @@
     array=tracer.image_2d_from(grid=grid), title="Image of Strong Lens System"
 )
 
-"""
+r"""
 __Log10__
 
 The light and masss distributions of galaxies are closer to a log10 distribution than a linear one. 

scripts/imaging/features/advanced/potential_correction/likelihood_function.py

@@ -1,4 +1,4 @@
-"""
+r"""
 __Log Likelihood Function: Potential Correction (Gravitational Imaging)__
 
 This script provides a step-by-step guide of the **PyAutoLens** potential-correction `log_likelihood_function`
@@ -70,7 +70,7 @@
 import autolens as al
 import autolens.plot as aplt
 
-"""
+r"""
 __Dataset__
 
 We simulate the dataset in-memory (seeded, so this script is fully reproducible): an `IsothermalSph` lens whose
@@ -104,7 +104,7 @@
     tracer=al.Tracer(galaxies=[lens_true, source_true]), grid=grid
 )
 
-"""
+r"""
 __Arc Mask__
 
 The corrections respond to the data only through the source's brightness gradients, which vanish away from the
@@ -124,7 +124,7 @@
 n_data = int(np.count_nonzero(~mask_array))
 print(f"unmasked data pixels: n_data = {n_data}")
 
-"""
+r"""
 __Smooth Starting Model__
 
 Potential corrections perturb a smooth starting model — in a real analysis, the maximum-likelihood result of a
@@ -136,7 +136,7 @@
 lens_smooth = al.Galaxy(redshift=0.2, mass=lens_true.mass)
 source_start = al.pc.AnalyticSrcFactory(source_galaxy=source_true)
 
-"""
+r"""
 __Dpsi Mesh__
 
 The corrections are defined on a rectangular mesh a factor coarser than the data grid (here factor 2).
@@ -160,7 +160,7 @@
     f"itp_mat shape = {pair.itp_mat.shape}, row sums all 1: {np.allclose(pair.itp_mat.sum(axis=1), 1.0)}"
 )
 
-"""
+r"""
 __Dpsi Gradient Operator__
 
 The correction $\delta\psi$ deflects rays by its gradient: $\delta\alpha = \nabla \delta\psi$. The sparse operator
@@ -175,7 +175,7 @@
 )
 print(f"D_psi shape = {dpsi_gradient_matrix.shape}")
 
-"""
+r"""
 __Source Gradients__
 
 A small extra deflection $\delta\alpha$ at an image pixel re-samples the source at a position shifted
@@ -207,7 +207,7 @@
 )
 print(f"B shape = {psf_matrix.shape}")
 
-"""
+r"""
 __Dpsi Mapping Matrix__
 
 Combining the three operators gives the linear response of the observed image to the mesh corrections
@@ -222,7 +222,7 @@
 )
 print(f"dpsi mapping matrix shape = {dpsi_mapping_matrix.shape}")
 
-"""
+r"""
 __Source Inversion Blocks__
 
 The joint inversion reconstructs the source simultaneously. Its source blocks come from the standard pixelized
@@ -248,7 +248,7 @@
 n_src = src_regularization_matrix.shape[0]
 print(f"F_src shape = {src_mapping_matrix.shape}, n_src = {n_src}")
 
-"""
+r"""
 __Joint System__
 
 The joint linear system stacks the two blocks:
@@ -274,7 +274,7 @@
 )
 print(f"joint mapping matrix shape = {mapping_matrix.shape}")
 
-"""
+r"""
 __Solve__
 
 With diagonal noise covariance $C^{-1} = \rm{diag}(1/\sigma_i^2)$, the maximum-evidence solution of the joint
@@ -298,7 +298,7 @@
 dpsi_solution = solution[n_src:]
 model_image = mapping_matrix @ solution
 
-"""
+r"""
 __Dkappa Map__
 
 The corrections' physical meaning is clearest as a convergence correction,
@@ -325,7 +325,7 @@
 plt.show()
 plt.close()
 
-"""
+r"""
 __Evidence Terms__
 
 The Bayesian evidence of the joint inversion has five terms (Suyu et al. 2006 eq. 19; Cao et al. 2025):

scripts/imaging/features/advanced/potential_correction/start_here.py

@@ -1,4 +1,4 @@
-"""
+r"""
 Potential Correction: Start Here
 ================================
 
@@ -68,7 +68,7 @@
 import autolens as al
 import autolens.plot as aplt
 
-"""
+r"""
 __Simulate__
 
 We simulate imaging of a strong lens whose mass model is an `Isothermal` plus a $10^{10} \, M_\odot$ NFW dark
@@ -198,7 +198,7 @@
 
 print(f"joint source + dpsi log evidence = {fit.log_evidence:.4e}")
 
-"""
+r"""
 __Dkappa Map__
 
 The reconstructed corrections live on the coarse dpsi mesh (`fit.best_fit_dpsi`). Their physical meaning is clearest
@@ -224,7 +224,7 @@
     fit, output=path.join("output", "potential_correction_joint_fit.png")
 )
 
-"""
+r"""
 __Iterative Fit__
 
 The joint fit above linearizes the corrections around the smooth model once. The iterative engine
@@ -261,7 +261,7 @@
     f"{iter_fit.pair_dpsi_data_obj.xgrid_dpsi_1d[peak]:.2f})"
 )
 
-"""
+r"""
 __Evidence Sampling__
 
 In a real analysis the regularization hyper-parameters (the coefficients and scales above) are not known. They are

scripts/imaging/features/linear_light_profiles/likelihood_function.py

@@ -1,4 +1,4 @@
-"""
+r"""
 __Log Likelihood Function: Linear Light Profile__
 
 This script provides a step-by-step guide of the `log_likelihood_function` which is used to fit `Imaging` data with
@@ -389,7 +389,7 @@
     f"Mapping between image pixel 0 and linear light profile pixel 1 = {mapping_matrix[0, 1]}"
 )
 
-"""
+r"""
 __Data Vector (D)__
 
 To solve for the linear light profile intensities we now pose the problem as a linear inversion.
@@ -443,7 +443,7 @@
 print(data_vector)
 print(data_vector.shape)
 
-"""
+r"""
 __Curvature Matrix (F)__
 
 The `curvature_matrix` $F$ is the second matrix and it has 
@@ -475,7 +475,7 @@
 plt.close()
 
 
-"""
+r"""
 __Reconstruction (Positive-Negative)__
 
 The following chi-squared is minimized when we perform the inversion and reconstruct the galaxy:
@@ -559,7 +559,7 @@
 aplt.plot_array(array=mapped_reconstructed_operated_data, title="")
 
 
-"""
+r"""
 __Likelihood Function__
 
 We now quantify the goodness-of-fit of our galaxy model.

scripts/imaging/features/multi_gaussian_expansion/likelihood_function.py

@@ -1,4 +1,4 @@
-"""
+r"""
 __Log Likelihood Function: Multi Gaussian Expansion__
 
 This script provides a step-by-step guide of the `log_likelihood_function` which is used to fit `Imaging` data with
@@ -472,7 +472,7 @@
     f"Mapping between image pixel 0 and Gaussian linear light profile pixel 1 = {mapping_matrix[0, 1]}"
 )
 
-"""
+r"""
 __Data Vector (D)__
 
 To solve for the linear light profile intensities we now pose the problem as a linear inversion.
@@ -526,7 +526,7 @@
 print(data_vector)
 print(data_vector.shape)
 
-"""
+r"""
 __Curvature Matrix (F)__
 
 The `curvature_matrix` $F$ is the second matrix and it has 
@@ -558,7 +558,7 @@
 plt.close()
 
 
-"""
+r"""
 __Reconstruction (Positive-Negative)__
 
 The following chi-squared is minimized when we perform the inversion and reconstruct the galaxy:
@@ -683,7 +683,7 @@
 aplt.plot_array(array=mapped_reconstructed_operated_data, title="")
 
 
-"""
+r"""
 __Likelihood Function__
 
 We now quantify the goodness-of-fit of our galaxy model.

scripts/imaging/features/pixelization/likelihood_function.py

@@ -1,4 +1,4 @@
-"""
+r"""
 __Log Likelihood Function: Pixelization__
 
 This script provides a step-by-step guide of the **PyAutoLens** `log_likelihood_function` which is used to fit
@@ -267,7 +267,7 @@
 
 aplt.plot_array(array=lens_subtracted_image, title="")
 
-"""
+r"""
 __Ray Tracing__
 
 To perform lensing calculations we ray-trace every 2d (y,x) coordinate $\theta$ from the image-plane to its (y,x) 
@@ -578,7 +578,7 @@
 """
 print(f"Mapping between image pixel 0 and source pixel 2 = {mapping_matrix[0, 2]}")
 
-"""
+r"""
 __Data Vector (D)__
 
 To solve for the source pixel fluxes we now pose the problem as a linear inversion.
@@ -622,7 +622,7 @@
 plt.show()
 plt.close()
 
-"""
+r"""
 __Curvature Matrix (F)__
 
 The `curvature_matrix` $F$ is the second matrix and it has dimensions `(total_source_pixels, total_source_pixels)`.
@@ -672,7 +672,7 @@
 
 aplt.plot_array(array=array_2d, title="")
 
-"""
+r"""
 The following chi-squared is minimized when we perform the inversion and reconstruct the source:
 
 $\chi^2 = \sum_{\rm  j=1}^{J} \bigg[ \frac{(\sum_{\rm  i=1}^{I} s_{i} f_{ij}) + b_{j} - d_{j}}{\sigma_{j}} \bigg]$
@@ -706,7 +706,7 @@
 
 aplt.plot_grid(grid=mapper.source_plane_mesh_grid, title="Source-Plane Mesh Grid")
 
-"""
+r"""
 __Regularization Matrix (H)__
 
 Regularization adds a linear regularization term $G_{\rm L}$ to the $\chi^2$ we solve for giving us a new merit 
@@ -802,7 +802,7 @@
 
 aplt.plot_array(array=mapped_reconstructed_operated_data, title="")
 
-"""
+r"""
 __Likelihood Function__
 
 We now quantify the goodness-of-fit of our lens model and source reconstruction. 
@@ -851,7 +851,7 @@
 aplt.plot_array(array=chi_squared_map, title="")
 
 
-"""
+r"""
 __Regularization Term__
 
 The second term, $s^{T} H s$, corresponds to the $\lambda $G_{\rm L}$ regularization term we added to our merit 
@@ -870,7 +870,7 @@
 
 print(regularization_term)
 
-"""
+r"""
 __Complexity Terms__
 
 Up to this point, it is unclear why we chose a value of `regularization_coefficient=1.0`.