maintenance: raw-string the LaTeX-carrying docstrings

notebooks/chapter_1_introduction/tutorial_4_why_modeling_is_hard.ipynb

@@ -623,7 +623,7 @@
     "plt.plot(xvalues, normalized_residual_map, color=\"k\")\n",
     "plt.title(f\"Normalized Residuals (log likelihood = {result.log_likelihood})\")\n",
     "plt.xlabel(\"x values of profile\")\n",
-    "plt.ylabel(\"Normalized Residuals ($\\sigma$)\")\n",
+    "plt.ylabel(r\"Normalized Residuals ($\\sigma$)\")\n",
     "plt.show()\n",
     "plt.clf()\n",
     "plt.close()"
@@ -834,7 +834,7 @@
     "plt.plot(xvalues, normalized_residual_map, color=\"k\")\n",
     "plt.title(f\"Normalized Residuals (log likelihood = {result.log_likelihood})\")\n",
     "plt.xlabel(\"x values of profile\")\n",
-    "plt.ylabel(\"Normalized Residuals ($\\sigma$)\")\n",
+    "plt.ylabel(r\"Normalized Residuals ($\\sigma$)\")\n",
     "plt.show()\n",
     "plt.clf()\n",
     "plt.close()"
@@ -986,7 +986,7 @@
     "plt.plot(xvalues, normalized_residual_map, color=\"k\")\n",
     "plt.title(f\"Normalized Residuals (log likelihood = {result.log_likelihood})\")\n",
     "plt.xlabel(\"x values of profile\")\n",
-    "plt.ylabel(\"Normalized Residuals ($\\sigma$)\")\n",
+    "plt.ylabel(r\"Normalized Residuals ($\\sigma$)\")\n",
     "plt.show()\n",
     "plt.clf()\n",
     "plt.close()"
@@ -1148,7 +1148,7 @@
     "plt.plot(xvalues, normalized_residual_map, color=\"k\")\n",
     "plt.title(f\"Normalized Residuals (log likelihood = {result.log_likelihood})\")\n",
     "plt.xlabel(\"x values of profile\")\n",
-    "plt.ylabel(\"Normalized Residuals ($\\sigma$)\")\n",
+    "plt.ylabel(r\"Normalized Residuals ($\\sigma$)\")\n",
     "plt.show()\n",
     "plt.clf()\n",
     "plt.close()"

scripts/chapter_1_introduction/tutorial_1_models.py

@@ -76,7 +76,7 @@
 
 import autofit as af
 
-"""
+r"""
 __Paths__
 
 PyAutoFit assumes the current working directory is /path/to/HowToFit/ on your hard-disk (or in Colab). 
@@ -288,7 +288,7 @@ def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
 plt.show()
 plt.clf()
 
-"""
+r"""
 __Complex Models__
 
 The code above may seem like a lot of steps just to create an instance of the `Gaussian` class. Couldn't we have 

scripts/chapter_1_introduction/tutorial_2_fitting_data.py

@@ -286,7 +286,7 @@ def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
 plt.show()
 plt.clf()
 
-"""
+r"""
 __Normalized Residuals__
 
 Another method to quantify and visualize the quality of the fit is using the normalized residual map, also known as 
@@ -312,7 +312,7 @@ def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
 plt.show()
 plt.clf()
 
-"""
+r"""
 __Chi Squared__
 
 Next, we define the `chi_squared_map`, which is obtained by squaring the `normalized_residual_map` and serves as a 
@@ -335,7 +335,7 @@ def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
 plt.show()
 plt.clf()
 
-"""
+r"""
 Now, we consolidate all the information in our `chi_squared_map` into a single measure of goodness-of-fit 
 called `chi_squared`. 
 
@@ -350,7 +350,7 @@ def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
 chi_squared = np.sum(chi_squared_map)
 print("Chi-squared = ", chi_squared)
 
-"""
+r"""
 
 The lower the `chi_squared`, the fewer residuals exist between the model's fit and the data, indicating a better 
 overall fit!
@@ -375,7 +375,7 @@ def model_data_from(self, xvalues: np.ndarray) -> np.ndarray:
 """
 noise_normalization = np.sum(np.log(2 * np.pi * noise_map**2.0))
 
-"""
+r"""
 __Likelihood__
 
 From the `chi_squared` and `noise_normalization`, we can define a final goodness-of-fit measure known as 

scripts/chapter_1_introduction/tutorial_3_non_linear_search.py

@@ -1,4 +1,4 @@
-"""
+r"""
 Tutorial 3: Non Linear Search
 =============================
 

scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py

@@ -434,7 +434,7 @@ def model_data_from_instance(self, instance):
 plt.plot(xvalues, normalized_residual_map, color="k")
 plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})")
 plt.xlabel("x values of profile")
-plt.ylabel("Normalized Residuals ($\sigma$)")
+plt.ylabel(r"Normalized Residuals ($\sigma$)")
 plt.show()
 plt.clf()
 plt.close()
@@ -601,7 +601,7 @@ def model_data_from_instance(self, instance):
 plt.plot(xvalues, normalized_residual_map, color="k")
 plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})")
 plt.xlabel("x values of profile")
-plt.ylabel("Normalized Residuals ($\sigma$)")
+plt.ylabel(r"Normalized Residuals ($\sigma$)")
 plt.show()
 plt.clf()
 plt.close()
@@ -709,7 +709,7 @@ def model_data_from_instance(self, instance):
 plt.plot(xvalues, normalized_residual_map, color="k")
 plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})")
 plt.xlabel("x values of profile")
-plt.ylabel("Normalized Residuals ($\sigma$)")
+plt.ylabel(r"Normalized Residuals ($\sigma$)")
 plt.show()
 plt.clf()
 plt.close()
@@ -817,12 +817,12 @@ def model_data_from_instance(self, instance):
 plt.plot(xvalues, normalized_residual_map, color="k")
 plt.title(f"Normalized Residuals (log likelihood = {result.log_likelihood})")
 plt.xlabel("x values of profile")
-plt.ylabel("Normalized Residuals ($\sigma$)")
+plt.ylabel(r"Normalized Residuals ($\sigma$)")
 plt.show()
 plt.clf()
 plt.close()
 
-"""
+r"""
 If you repeat the fit multiple times, you will find that the model-fit is more likely to produce a good fit than
 previously.