docs: render __Contents__ blocks as Markdown lists

scripts/chapter_1_introduction/tutorial_0_visualization.py

@@ -7,12 +7,12 @@
 
 __Contents__
 
-**Directories:** **PyAutoLens assumes** the working directory is `autolens_workspace` on your hard-disk.
-**Dataset:** Load and plot the strong lens dataset.
-**Subplots:** In addition to plotting individual figures, **PyAutoLens** can plot `subplots` which show multiple.
-**Plot Customization:** Does the figure display correctly on your computer screen?
-**Overlays:** Overlays such as critical curves and image positions are added using the `lines=` and `positions=`.
-**Wrap Up:** Summary of the script and next steps.
+- **Directories:** **PyAutoLens assumes** the working directory is `autolens_workspace` on your hard-disk.
+- **Dataset:** Load and plot the strong lens dataset.
+- **Subplots:** In addition to plotting individual figures, **PyAutoLens** can plot `subplots` which show multiple.
+- **Plot Customization:** Does the figure display correctly on your computer screen?
+- **Overlays:** Overlays such as critical curves and image positions are added using the `lines=` and `positions=`.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py

@@ -51,10 +51,10 @@
 
 __Contents__
 
-**Grids:** A `Grid2D` is a set of two-dimensional $(y,x)$ coordinates that represent points in space where we.
-**Geometry:** The above grid is centered on the origin (0.0", 0.0").
-**Light Profiles:** Galaxies are collections of stars, gas, dust, and other astronomical objects that emit light.
-**One Dimension Projection:** We often want to calculative 1D quantities of a light profile, for example to plot how its light.
+- **Grids:** A `Grid2D` is a set of two-dimensional $(y,x)$ coordinates that represent points in space where we.
+- **Geometry:** The above grid is centered on the origin (0.0", 0.0").
+- **Light Profiles:** Galaxies are collections of stars, gas, dust, and other astronomical objects that emit light.
+- **One Dimension Projection:** We often want to calculative 1D quantities of a light profile, for example to plot how its light.
 
 """
 

scripts/chapter_1_introduction/tutorial_2_ray_tracing.py

@@ -58,8 +58,8 @@
 
 __Contents__
 
-**Grid:** In the previous tutorial, we created 2D grids of (y,x) coordinates and showed how shifting and.
-**Mass Profiles:** To perform lensing calculations, we use mass profiles available in the `mass_profile` module.
+- **Grid:** In the previous tutorial, we created 2D grids of (y,x) coordinates and showed how shifting and.
+- **Mass Profiles:** To perform lensing calculations, we use mass profiles available in the `mass_profile` module.
 
 """
 

scripts/chapter_1_introduction/tutorial_3_more_ray_tracing.py

@@ -25,14 +25,14 @@
 
 __Contents__
 
-**Initial Setup:** To begin, lets setup the grid we'll ray-trace using.
-**Concise Code:** Lets set up the tracer used in the previous tutorial.
-**Critical Curves:** To end, we can finally explain what the black lines that have appeared on many of the plots.
-**Caustics:** In the previous tutorial, we plotted the critical curves of the mass profile on the image-plane.
-**Units:** Lets plot the lensing quantities again.
-**More Complexity:** We now make a lens with some attributes we didn`t in the last tutorial.
-**Multi Galaxy Ray Tracing:** Now lets pass our 4 galaxies to a `Tracer`, which means the following will occur.
-**Wrap Up:** Summary of the script and next steps.
+- **Initial Setup:** To begin, lets setup the grid we'll ray-trace using.
+- **Concise Code:** Lets set up the tracer used in the previous tutorial.
+- **Critical Curves:** To end, we can finally explain what the black lines that have appeared on many of the plots.
+- **Caustics:** In the previous tutorial, we plotted the critical curves of the mass profile on the image-plane.
+- **Units:** Lets plot the lensing quantities again.
+- **More Complexity:** We now make a lens with some attributes we didn`t in the last tutorial.
+- **Multi Galaxy Ray Tracing:** Now lets pass our 4 galaxies to a `Tracer`, which means the following will occur.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_1_introduction/tutorial_4_point_sources.py

@@ -11,7 +11,7 @@
 
 __Contents__
 
-**Wrap Up:** Summary of the script and next steps.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_1_introduction/tutorial_5_lensing_formalism.py

@@ -12,7 +12,7 @@
 
 __Contents__
 
-**Wrap Up:** Summary of the script and next steps.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_1_introduction/tutorial_6_data.py

@@ -28,13 +28,13 @@
 
 __Contents__
 
-**Initial Setup:** To create our simulated strong lens image, we first need a 2D grid.
-**Optics Blurring:** All images captured using CCDs (like those on the Hubble Space Telescope or Euclid) experience some.
-**Poisson Noise:** In addition to the blurring caused by telescope optics, we also need to consider Poisson noise when.
-**Background Sky:** The final effect we will consider when simulating imaging data is the background sky.
-**Simulator:** The `SimulatorImaging` object lets us create simulated imaging data while including the effects of.
-**Output:** We will now save these simulated data to `.fits` files, the standard format used by astronomers for.
-**Wrap Up:** Summary of the script and next steps.
+- **Initial Setup:** To create our simulated strong lens image, we first need a 2D grid.
+- **Optics Blurring:** All images captured using CCDs (like those on the Hubble Space Telescope or Euclid) experience some.
+- **Poisson Noise:** In addition to the blurring caused by telescope optics, we also need to consider Poisson noise when.
+- **Background Sky:** The final effect we will consider when simulating imaging data is the background sky.
+- **Simulator:** The `SimulatorImaging` object lets us create simulated imaging data while including the effects of.
+- **Output:** We will now save these simulated data to `.fits` files, the standard format used by astronomers for.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_1_introduction/tutorial_7_fitting.py

@@ -33,12 +33,12 @@
 
 __Contents__
 
-**Dataset & Mask:** Standard set up of the dataset and mask that is fitted.
-**Masked Grid:** In tutorials 1 and 2, we emphasized that the `Grid2D` object is crucial for evaluating a lens's.
-**Fitting:** Fit the lens model to the dataset and inspect the results.
-**Incorrect Fit:** In the previous section, we successfully created and fitted a lens model to the image data.
-**Model Fitting:** In the previous sections, we used the true model to fit the data, which resulted in a high log.
-**Wrap Up:** Summary of the script and next steps.
+- **Dataset & Mask:** Standard set up of the dataset and mask that is fitted.
+- **Masked Grid:** In tutorials 1 and 2, we emphasized that the `Grid2D` object is crucial for evaluating a lens's.
+- **Fitting:** Fit the lens model to the dataset and inspect the results.
+- **Incorrect Fit:** In the previous section, we successfully created and fitted a lens model to the image data.
+- **Model Fitting:** In the previous sections, we used the true model to fit the data, which resulted in a high log.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_1_introduction/tutorial_8_summary.py

@@ -18,12 +18,12 @@
 
 __Contents__
 
-**Start:** Below, we do all the steps we have learned this chapter, making profiles, galaxies, a tracer, etc.
-**Object Composition:** Lets now consider how all of the objects we've covered throughout this chapter (`LightProfile`'s.
-**Visualization:** Furthermore, using the `MatPLot2D` and `lines=`/`positions=` overlays objects we can visualize any.
-**Code Design:** To end, I want to quickly talk about the **PyAutoLens** code-design and structure, which was really.
-**Source Code:** If you do enjoy code, variables, functions, and parameters, you may want to dig deeper into the.
-**Wrap Up:** Summary of the script and next steps.
+- **Start:** Below, we do all the steps we have learned this chapter, making profiles, galaxies, a tracer, etc.
+- **Object Composition:** Lets now consider how all of the objects we've covered throughout this chapter (`LightProfile`'s.
+- **Visualization:** Furthermore, using the `MatPLot2D` and `lines=`/`positions=` overlays objects we can visualize any.
+- **Code Design:** To end, I want to quickly talk about the **PyAutoLens** code-design and structure, which was really.
+- **Source Code:** If you do enjoy code, variables, functions, and parameters, you may want to dig deeper into the.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_2_lens_modeling/tutorial_1_non_linear_search.py

@@ -44,19 +44,19 @@
 
 __Contents__
 
-**Overview:** In this tutorial, we will use a non-linear search to fit a lens model to simulated imaging of.
-**Parameter Space:** In mathematics, a function is defined by its parameters, which map inputs to outputs.
-**Search Types:** There are different types of non-linear searches, each of which explores parameter space in a.
-**Deeper Background:** **The descriptions of how searches work in this example are simplfied and phoenomenological and do.
-**PyAutoFit:** Modeling uses the probabilistic programming language.
-**Initial Setup:** Let's first load the `Imaging` dataset, which we will use to fit a model with a non-linear search.
-**Mask:** Define the 2D mask applied to the dataset for the model-fit.
-**Model:** Compose the lens model fitted to the data.
-**Priors:** When we examine the `.info` of our model, we notice that each parameter (like `centre`.
-**Analysis:** Create the Analysis object that defines how the model is fitted to the data.
-**Searches:** To perform a non-linear search, we create an instance of a `NonLinearSearch` object.
-**Nested Sampling:** **Nested Sampling** is an advanced method for model-fitting that excels in handling complex models.
-**Wrap Up:** Summary of the script and next steps.
+- **Overview:** In this tutorial, we will use a non-linear search to fit a lens model to simulated imaging of.
+- **Parameter Space:** In mathematics, a function is defined by its parameters, which map inputs to outputs.
+- **Search Types:** There are different types of non-linear searches, each of which explores parameter space in a.
+- **Deeper Background:** **The descriptions of how searches work in this example are simplfied and phoenomenological and do.
+- **PyAutoFit:** Modeling uses the probabilistic programming language.
+- **Initial Setup:** Let's first load the `Imaging` dataset, which we will use to fit a model with a non-linear search.
+- **Mask:** Define the 2D mask applied to the dataset for the model-fit.
+- **Model:** Compose the lens model fitted to the data.
+- **Priors:** When we examine the `.info` of our model, we notice that each parameter (like `centre`.
+- **Analysis:** Create the Analysis object that defines how the model is fitted to the data.
+- **Searches:** To perform a non-linear search, we create an instance of a `NonLinearSearch` object.
+- **Nested Sampling:** **Nested Sampling** is an advanced method for model-fitting that excels in handling complex models.
+- **Wrap Up:** Summary of the script and next steps.
 
 __Parameter Space__
 

scripts/chapter_2_lens_modeling/tutorial_2_practicalities.py

@@ -17,23 +17,23 @@
 
 __Contents__
 
-**PyAutoFit:** Modeling uses the probabilistic programming language.
-**Initial Setup:** Lets first load the `Imaging` dataset we'll fit a model with using a non-linear search.
-**Mask:** Define the 2D mask applied to the dataset for the model-fit.
-**Model:** Compose the lens model fitted to the data.
-**Search:** Configure the non-linear search used to fit the model.
-**Search Settings:** Nautilus samples parameter space by placing "live points" representing different galaxy models.
-**Iterations Per Update:** Every N iterations, the non-linear search outputs the current results to the folder.
-**Analysis:** Create the Analysis object that defines how the model is fitted to the data.
-**VRAM Use:** When running AutoLens with JAX on a GPU, the analysis must fit within the GPU’s available VRAM.
-**Run Times:** Profiling the expected run time of the model-fit.
-**Result Info:** A concise readable summary of the results is given by printing its `info` attribute.
-**Output Folder:** Now checkout the `autolens_workspace/output` folder.
-**Unique Identifier:** In the output folder, you will note that results are in a folder which is a collection of random.
-**Output Folder Contents:** Now this is running you should checkout the `autolens_workspace/output` folder.
-**Result:** Overview of the results of the model-fit.
-**Other Practicalities:** The following are examples of other practicalities which I will document fully in this example.
-**Wrap Up:** Summary of the script and next steps.
+- **PyAutoFit:** Modeling uses the probabilistic programming language.
+- **Initial Setup:** Lets first load the `Imaging` dataset we'll fit a model with using a non-linear search.
+- **Mask:** Define the 2D mask applied to the dataset for the model-fit.
+- **Model:** Compose the lens model fitted to the data.
+- **Search:** Configure the non-linear search used to fit the model.
+- **Search Settings:** Nautilus samples parameter space by placing "live points" representing different galaxy models.
+- **Iterations Per Update:** Every N iterations, the non-linear search outputs the current results to the folder.
+- **Analysis:** Create the Analysis object that defines how the model is fitted to the data.
+- **VRAM Use:** When running AutoLens with JAX on a GPU, the analysis must fit within the GPU’s available VRAM.
+- **Run Times:** Profiling the expected run time of the model-fit.
+- **Result Info:** A concise readable summary of the results is given by printing its `info` attribute.
+- **Output Folder:** Now checkout the `autolens_workspace/output` folder.
+- **Unique Identifier:** In the output folder, you will note that results are in a folder which is a collection of random.
+- **Output Folder Contents:** Now this is running you should checkout the `autolens_workspace/output` folder.
+- **Result:** Overview of the results of the model-fit.
+- **Other Practicalities:** The following are examples of other practicalities which I will document fully in this example.
+- **Wrap Up:** Summary of the script and next steps.
 
 __Search Settings__
 

scripts/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.py

@@ -24,13 +24,13 @@
 
 __Contents__
 
-**Initial Setup:** we'll use new strong lensing data, where.
-**Mask:** Define the 2D mask applied to the dataset for the model-fit.
-**Model:** Compose the lens model fitted to the data.
-**Run Time:** Profiling the expected run time of the model-fit.
-**Result:** Overview of the results of the model-fit.
-**Global and Local Maxima:** Up to now, all our non-linear searches have successfully found lens models that provide visibly.
-**Wrap Up:** Summary of the script and next steps.
+- **Initial Setup:** we'll use new strong lensing data, where.
+- **Mask:** Define the 2D mask applied to the dataset for the model-fit.
+- **Model:** Compose the lens model fitted to the data.
+- **Run Time:** Profiling the expected run time of the model-fit.
+- **Result:** Overview of the results of the model-fit.
+- **Global and Local Maxima:** Up to now, all our non-linear searches have successfully found lens models that provide visibly.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.py

@@ -20,12 +20,12 @@
 
 __Contents__
 
-**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where.
-**Mask:** Define the 2D mask applied to the dataset for the model-fit.
-**Prior Tuning:** First, we will try to assist our non-linear search by tuning our priors.
-**Run Time:** Profiling the expected run time of the model-fit.
-**Result:** Overview of the results of the model-fit.
-**Discussion:** By tuning our priors to the specific lens model we are fitting, we increase the chances of finding.
+- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where.
+- **Mask:** Define the 2D mask applied to the dataset for the model-fit.
+- **Prior Tuning:** First, we will try to assist our non-linear search by tuning our priors.
+- **Run Time:** Profiling the expected run time of the model-fit.
+- **Result:** Overview of the results of the model-fit.
+- **Discussion:** By tuning our priors to the specific lens model we are fitting, we increase the chances of finding.
 
 """
 

scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py

@@ -25,21 +25,21 @@
 
 __Contents__
 
-**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where.
-**Mask:** Define the 2D mask applied to the dataset for the model-fit.
-**Linear Light Profiles:** We use a variant of a light profile discussed called a "linear light profile", which is accessed.
-**Run Time:** Profiling the expected run time of the model-fit.
-**Result:** Overview of the results of the model-fit.
-**Intensities:** The intensities of linear light profiles are not a part of the model parameterization and therefore.
-**Visualization:** Linear light profiles and objects containing them (e.g.
-**Basis:** We can use many linear light profiles to build a `Basis`.
-**Model Fit:** Perform the model-fit using the search and analysis.
-**Source MGE:** We now compose a second `Basis` of 15 Gaussians to represent the source galaxy.
-**Multi Gaussian Expansion Benefits:** Fitting a galaxy's light with a superposition of Gaussians is called a Multi-Gaussian Expansion.
-**Disadvantage of Basis Functions:** For many science cases, the MGE can also be a less intuitive model to interpret than a Sersic.
-**Positive Only Solver:** Ensuring positive-only solutions for linear light profile intensities.
-**Other Basis Functions:** In addition to the Gaussians used in this example, there is another basis function implemented in.
-**Wrap Up:** Summary of the script and next steps.
+- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where.
+- **Mask:** Define the 2D mask applied to the dataset for the model-fit.
+- **Linear Light Profiles:** We use a variant of a light profile discussed called a "linear light profile", which is accessed.
+- **Run Time:** Profiling the expected run time of the model-fit.
+- **Result:** Overview of the results of the model-fit.
+- **Intensities:** The intensities of linear light profiles are not a part of the model parameterization and therefore.
+- **Visualization:** Linear light profiles and objects containing them (e.g.
+- **Basis:** We can use many linear light profiles to build a `Basis`.
+- **Model Fit:** Perform the model-fit using the search and analysis.
+- **Source MGE:** We now compose a second `Basis` of 15 Gaussians to represent the source galaxy.
+- **Multi Gaussian Expansion Benefits:** Fitting a galaxy's light with a superposition of Gaussians is called a Multi-Gaussian Expansion.
+- **Disadvantage of Basis Functions:** For many science cases, the MGE can also be a less intuitive model to interpret than a Sersic.
+- **Positive Only Solver:** Ensuring positive-only solutions for linear light profile intensities.
+- **Other Basis Functions:** In addition to the Gaussians used in this example, there is another basis function implemented in.
+- **Wrap Up:** Summary of the script and next steps.
 
 """
 

scripts/chapter_2_lens_modeling/tutorial_6_masking_and_positions.py

@@ -8,13 +8,13 @@
 
 __Contents__
 
-**Initial Setup:** we'll use the same strong lensing data as tutorials 1 & 2, where.
-**Mask:** Define the 2D mask applied to the dataset for the model-fit.
-**Run Time:** Profiling the expected run time of the model-fit.
-**Search:** Configure the non-linear search used to fit the model.
-**Discussion:** So, we can choose the mask we use in a model-fit.
-**Positions Thresholding:** We can manually specify a set of image-plane (y,x) coordinates corresponding to the multiple images.
-**Wrap Up:** Summary of the script and next steps.
+- **Initial Setup:** we'll use the same strong lensing data as tutorials 1 & 2, where.
+- **Mask:** Define the 2D mask applied to the dataset for the model-fit.
+- **Run Time:** Profiling the expected run time of the model-fit.
+- **Search:** Configure the non-linear search used to fit the model.
+- **Discussion:** So, we can choose the mask we use in a model-fit.
+- **Positions Thresholding:** We can manually specify a set of image-plane (y,x) coordinates corresponding to the multiple images.
+- **Wrap Up:** Summary of the script and next steps.
 
 """