maintenance: raw-string the LaTeX-carrying docstrings

scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py

@@ -1,4 +1,4 @@
-"""
+r"""
 HowToLens: Introduction
 =======================
 
@@ -171,7 +171,7 @@
 """
 aplt.plot_grid(grid=grid_shifted, title="Grid Centered Around (0.3, 0.5)")
 
-"""
+r"""
 Next, we can rotate the grid by an angle `phi` (in degrees). The rotation is counter-clockwise from the positive x-axis.
 
 To rotate the grid:
@@ -203,7 +203,7 @@
 """
 aplt.plot_grid(grid=grid_rotated, title="Grid Rotated 60 Degrees")
 
-"""
+r"""
 Next, we convert the rotated grid to elliptical coordinates using:
 
 $\eta = \sqrt{(x_r)^2 + (y_r)^2/q^2}$
@@ -220,7 +220,7 @@
 axis_ratio = 0.5
 eta = np.sqrt((grid_rotated[:, 0]) ** 2 + (grid_rotated[:, 1]) ** 2 / axis_ratio**2)
 
-"""
+r"""
 Above, the angle $\phi$ (in degrees) was used to rotate the grid, and the axis-ratio $q$ was used to convert the grid 
 to elliptical coordinates.
 
@@ -247,7 +247,7 @@
 print(ell_comps)
 
 
-"""
+r"""
 __Light Profiles__
 
 Galaxies are collections of stars, gas, dust, and other astronomical objects that emit light. Astronomers study this 
@@ -426,7 +426,7 @@
 plt.close()
 
 
-"""
+r"""
 Since galaxy light distributions often cover a wide range of values, they are typically better visualized on a log10 
 scale. This approach helps highlight details in the faint outskirts of a light profile.
 

scripts/chapter_1_introduction/tutorial_2_ray_tracing.py

@@ -124,7 +124,7 @@
 """
 deflections = sis_mass_profile.deflections_yx_2d_from(grid=image_plane_grid)
 
-"""
+r"""
 Like grids and arrays, the deflection angles can be accessed using the `native` and `slim` attributes. These are 
 structured similarly to a `Grid2D` object:
 
@@ -151,7 +151,7 @@
 """
 print(type(deflections))
 
-"""
+r"""
 This structure includes a `grid`, which represents the `Grid2D` of coordinates where the deflection angles are 
 calculated (in this case, the `image_plane_grid` we defined earlier). It also has vector-specific methods, 
 such as `magnitude`, which calculates the magnitude of each deflection vector using \((x^2 + y^2)^{0.5}\).
@@ -229,7 +229,7 @@
     use_log10=True,
 )
 
-"""
+r"""
 __Ray Tracing Grids__
 
 We now have all the tools we need to perform our first ray-tracing calculation.
@@ -367,7 +367,7 @@
 aplt.plot_grid(grid=image_plane_grid_traced, title="Image Plane Grid")
 aplt.plot_grid(grid=source_plane_grid_traced, title="Source Plane Grid")
 
-"""
+r"""
 __Wrap Up__
 
 In this tutorial, you performed your first lensing calculations. Let's summarise what we've learnt:

scripts/chapter_1_introduction/tutorial_4_point_sources.py

@@ -161,7 +161,7 @@
 
 tracer = al.Tracer(galaxies=[lens_galaxy, source_galaxy])
 
-"""
+r"""
 Note that we attached the point source to its galaxy with the name `point_0`. This name is a label that PyAutoLens
 uses when fitting real data to pair each point source in the model with the dataset containing its observed image
 positions. With one source the name is a formality, but group- and cluster-scale lenses can contain many point
@@ -270,7 +270,7 @@
     title="Multiple Images and Critical Curve",
 )
 
-"""
+r"""
 __Magnifications__
 
 Lensing does not just relocate a point source's light — it magnifies it. Each multiple image has its own
@@ -340,7 +340,7 @@
 print("Time Delays relative to first image (days):")
 print(time_delays - time_delays[0])
 
-"""
+r"""
 __Extended Versus Point Computations__
 
 We can now state precisely why point-source lensing works so differently from everything in tutorials 2 and 3, on

scripts/chapter_1_introduction/tutorial_7_fitting.py

@@ -328,7 +328,7 @@
 aplt.plot_array(array=fit.data, title="Data")
 aplt.plot_array(array=fit.model_data, title="Model Image")
 
-"""
+r"""
 The `residual_map` is the different between the observed image and model image, showing where in the image the fit is
 good (e.g. low residuals) and where it is bad (e.g. high residuals).
 
@@ -348,7 +348,7 @@
 
 aplt.plot_array(array=fit.residual_map, title="Residual Map")
 
-"""
+r"""
 Are these residuals indicative of a good fit to the data? Without considering the noise in the data, it's difficult 
 to ascertain. That is, its hard to ascenrtain if a residual value is large or small because this depends on the
 amount of noise in that pixel.
@@ -372,7 +372,7 @@
 
 aplt.plot_array(array=fit.normalized_residual_map, title="Normalized Residual Map")
 
-"""
+r"""
 Next, we define the `chi_squared_map`, which is obtained by squaring the `normalized_residual_map` and serves as a 
 measure of goodness of fit.
 
@@ -395,7 +395,7 @@
 
 aplt.plot_array(array=fit.chi_squared_map, title="Chi Squared Map")
 
-"""
+r"""
 Now, we consolidate all the information in our `chi_squared_map` into a single measure of goodness-of-fit 
 called `chi_squared`. 
 
@@ -428,7 +428,7 @@
 reduced_chi_squared = chi_squared / dataset.mask.pixels_in_mask
 print("Reduced Chi-squared = ", reduced_chi_squared)
 
-"""
+r"""
 Another quantity that contributes to our final assessment of the goodness-of-fit is the `noise_normalization`.
 
 The `noise_normalization` is computed by summing, over every pixel, the logarithm of 2 pi times the squared noise value:
@@ -448,7 +448,7 @@
 print("Noise Normalization = ", noise_normalization)
 print("Noise Normalization via fit = ", fit.noise_normalization)
 
-"""
+r"""
 From the `chi_squared` and `noise_normalization`, we can define a final goodness-of-fit measure known as 
 the `log_likelihood`. 
 
@@ -464,7 +464,7 @@
 print("Log Likelihood = ", log_likelihood)
 print("Log Likelihood via fit = ", fit.log_likelihood)
 
-"""
+r"""
 In the previous discussion, we noted that a lower \(\chi^2\) value indicates a better fit of the model to the 
 observed data. 
 

scripts/chapter_2_lens_modeling/tutorial_1_non_linear_search.py

@@ -1,4 +1,4 @@
-"""
+r"""
 Tutorial 1: Non-linear Search
 =============================
 

scripts/chapter_3_pixelizations/tutorial_5_bayesian_formalism.py

@@ -175,7 +175,7 @@
 
 tracer = al.Tracer(galaxies=[lens_galaxy, source_galaxy])
 
-"""
+r"""
 __Ray Tracing__
 
 Every 2D (y,x) image-plane coordinate $\theta$ is ray-traced to its source-plane coordinate $\beta$ by subtracting
@@ -331,7 +331,7 @@
 plt.show()
 plt.close()
 
-"""
+r"""
 __Data Vector (D)__
 
 We now pose the reconstruction as a linear inversion, converting the blurred mapping matrix, data and noise-map
@@ -363,7 +363,7 @@
 plt.show()
 plt.close()
 
-"""
+r"""
 __Curvature Matrix (F)__
 
 The curvature matrix has dimensions `(total_source_pixels, total_source_pixels)` and is given by (WD03):
@@ -386,7 +386,7 @@
 plt.show()
 plt.close()
 
-"""
+r"""
 __Unregularized Solve__
 
 The inversion seeks the source-pixel fluxes $s$ (a vector with one entry per source pixel) that minimize the
@@ -410,7 +410,7 @@
 
 print(reconstruction)
 
-"""
+r"""
 The reconstructed source-pixel fluxes are a noisy, unsmooth mess -- exactly the over-fitting we saw in tutorial 4
 when we set the regularization coefficient to zero. The linear inversion is fitting the noise in the data, because
 this system of equations is ill-posed: we need a smoothness prior.
@@ -486,7 +486,7 @@
 
 aplt.plot_array(array=mapped_reconstructed_data, title="Reconstructed Image")
 
-"""
+r"""
 __Likelihood Function__
 
 We now quantify the goodness-of-fit of the source reconstruction, computing the quantity tutorial 4 called the
@@ -521,7 +521,7 @@
 
 print(chi_squared)
 
-"""
+r"""
 __Regularization Term__
 
 The second term, $s^{T} H s$, is the $\lambda \, G_{L}$ regularization penalty evaluated at the solution: the summed
@@ -536,7 +536,7 @@
 
 print(regularization_term)
 
-"""
+r"""
 __Complexity Terms__
 
 Up to this point, nothing has justified our choice of `regularization_coefficient=1.0`. We cannot choose it using

scripts/chapter_4_scaling_up_lensing/tutorial_3_scaling_relation.py

@@ -1,4 +1,4 @@
-"""
+r"""
 Tutorial 3: Scaling Relations
 =============================
 
@@ -541,7 +541,7 @@
         f"Tied galaxy {i}: relation predicts         = {einstein_radius_predicted:.3f}"
     )
 
-"""
+r"""
 The prediction lands within a few percent of the truth: this simulated pair happens to sit almost exactly on the
 Faber-Jackson relation. That is why the tied fit succeeds. Real galaxies are not always so obliging.
 

scripts/chapter_4_scaling_up_lensing/tutorial_5_cluster_scale.py

@@ -1,4 +1,4 @@
-"""
+r"""
 Tutorial 5: Cluster Scale
 =========================