maintenance: raw-string the LaTeX-carrying docstrings

scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py

@@ -1,4 +1,4 @@
-"""
+r"""
 HowToGalaxy: Introduction
 =========================
 
@@ -147,7 +147,7 @@
 """
 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:
@@ -179,7 +179,7 @@
 """
 aplt.plot_grid(grid=grid_rotated, title="Grid Rotated 60 Degrees")
 
-"""
+r"""
 Next, we convert the rotated grid to elliptical coordinates using:
 
 $\eta = \sqrt{(x_r)^2 + (y_r)^2/q^2}$
@@ -199,7 +199,7 @@
 print("First Ten Elliptical Coordinates:")
 print(eta[:10])
 
-"""
+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.
 
@@ -226,7 +226,7 @@
 print(ell_comps)
 
 
-"""
+r"""
 __Light Profiles__
 
 Galaxies are collections of stars, gas, dust, and other astronomical objects that emit light. Astronomers study this 

scripts/chapter_1_introduction/tutorial_3_fitting.py

@@ -297,7 +297,7 @@
 aplt.plot_array(array=dataset.data, title="Data")
 aplt.plot_array(array=fit.model_data, title="Model Image")
 
-"""
+r"""
 The `residual_map` is the difference 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).
 
@@ -317,7 +317,7 @@
 
 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 ascertain if a residual value is large or small because this depends on the
 amount of noise in that pixel.
@@ -341,7 +341,7 @@
 
 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.
 
@@ -364,7 +364,7 @@
 
 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`. 
 
@@ -397,7 +397,7 @@
 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 as the logarithm of the sum of squared noise values in our data: 
@@ -417,7 +417,7 @@
 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`. 
 
@@ -433,7 +433,7 @@
 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_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

@@ -302,7 +302,7 @@
 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
@@ -335,7 +335,7 @@
 plt.show()
 plt.close()
 
-"""
+r"""
 __Curvature Matrix (F)__
 
 The curvature matrix has dimensions `(total_mesh_pixels, total_mesh_pixels)` and is given by (WD03):
@@ -359,7 +359,7 @@
 plt.show()
 plt.close()
 
-"""
+r"""
 __Unregularized Solve__
 
 The inversion seeks the mesh-pixel fluxes $s$ (a vector with one entry per mesh pixel) that minimize the
@@ -383,7 +383,7 @@
 
 plot_mapper(mapper=mapper, solution_vector=reconstruction)
 
-"""
+r"""
 The reconstructed mesh-pixel fluxes are a noisy, unsmooth mess -- exactly the over-fitting we saw in tutorial 4
 when we lowered the regularization coefficient towards zero. The linear inversion is fitting the noise in the
 data, because this system of equations is ill-posed: we need a smoothness prior.
@@ -459,7 +459,7 @@
 
 aplt.plot_array(array=mapped_reconstructed_data, title="Reconstructed Image")
 
-"""
+r"""
 __Likelihood Function__
 
 We now quantify the goodness-of-fit of the galaxy reconstruction, computing the quantity tutorial 4 called the
@@ -494,7 +494,7 @@
 
 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
@@ -509,7 +509,7 @@
 
 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