docs: render __Contents__ blocks as Markdown lists

scripts/chapter_1_introduction/tutorial_0_visualization.py

@@ -7,12 +7,12 @@
 
 __Contents__
 
-**Directories:** Set the working directory so PyAutoGalaxy can find configs, data and output folders.
-**Dataset:** Load an example imaging dataset of a galaxy.
-**Plot Customization:** Customize matplotlib options like title, figure size and colormap.
-**Subplots:** Plot all components of a dataset simultaneously using subplots.
-**Visuals:** Add visual overlays like masks and grids to figures.
-**Wrap Up:** Summary of visualization in PyAutoGalaxy.
+- **Directories:** Set the working directory so PyAutoGalaxy can find configs, data and output folders.
+- **Dataset:** Load an example imaging dataset of a galaxy.
+- **Plot Customization:** Customize matplotlib options like title, figure size and colormap.
+- **Subplots:** Plot all components of a dataset simultaneously using subplots.
+- **Visuals:** Add visual overlays like masks and grids to figures.
+- **Wrap Up:** Summary of visualization in PyAutoGalaxy.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py

@@ -31,13 +31,13 @@
 
 __Contents__
 
-**Grids:** Create a uniform grid of (y,x) coordinates and show how it can be used to measure the light of a galaxy.
-**Geometry:** How to shift and rotate a grid, and convert it to elliptical coordinates.
-**Light Profiles:** Using light profiles, analytic functions that describe how a galaxy's light is distributed.
-**Galaxies:** Creating galaxies containing light profiles and computing the image of a galaxy.
-**One Dimension Projection:** Create projected 2D radial grids for 1D profile calculations.
-**Unit Conversion:** Converting angular distances to physical distances using cosmology.
-**Wrap Up:** Summary of the key concepts covered in this tutorial.
+- **Grids:** Create a uniform grid of (y,x) coordinates and show how it can be used to measure the light of a galaxy.
+- **Geometry:** How to shift and rotate a grid, and convert it to elliptical coordinates.
+- **Light Profiles:** Using light profiles, analytic functions that describe how a galaxy's light is distributed.
+- **Galaxies:** Creating galaxies containing light profiles and computing the image of a galaxy.
+- **One Dimension Projection:** Create projected 2D radial grids for 1D profile calculations.
+- **Unit Conversion:** Converting angular distances to physical distances using cosmology.
+- **Wrap Up:** Summary of the key concepts covered in this tutorial.
 
 The imports below are required to run the howtogalaxy tutorials in a Jupiter notebook. They also import the
 `autogalaxy` package and the `autogalaxy.plot` module which are used throughout the tutorials.

scripts/chapter_1_introduction/tutorial_2_data.py

@@ -20,13 +20,13 @@
 
 __Contents__
 
-**Initial Setup:** Create a 2D grid and define galaxy light profiles for simulation.
-**Optics Blurring:** Simulate how the telescope optics blur the galaxy's light using PSF convolution.
-**Poisson Noise:** Add Poisson noise to the image, simulating CCD photon-to-electron randomness.
-**Background Sky:** Add background sky light that introduces noise across the entire image.
-**Simulator:** Use the SimulatorImaging object to simulate imaging data with all effects combined.
-**Output:** Save the simulated data to .fits files for use in future tutorials.
-**Wrap Up:** Summary of how CCD imaging data is simulated.
+- **Initial Setup:** Create a 2D grid and define galaxy light profiles for simulation.
+- **Optics Blurring:** Simulate how the telescope optics blur the galaxy's light using PSF convolution.
+- **Poisson Noise:** Add Poisson noise to the image, simulating CCD photon-to-electron randomness.
+- **Background Sky:** Add background sky light that introduces noise across the entire image.
+- **Simulator:** Use the SimulatorImaging object to simulate imaging data with all effects combined.
+- **Output:** Save the simulated data to .fits files for use in future tutorials.
+- **Wrap Up:** Summary of how CCD imaging data is simulated.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_1_introduction/tutorial_3_fitting.py

@@ -21,13 +21,13 @@
 
 __Contents__
 
-**Dataset:** Load the imaging dataset previously simulated, consisting of the image, noise map, and PSF.
-**Mask:** Apply a mask to the data, excluding regions with low signal-to-noise ratios from the analysis.
-**Masked Grid:** Create a masked grid containing only coordinates of unmasked pixels.
-**Fitting:** Fit the data with a galaxy model, computing the model image, residuals, chi-squared, and log likelihood.
-**Incorrect Fit:** Demonstrate how small deviations from true parameters impact fit quality.
-**Model Fitting:** Perform a basic model fit, adjusting parameters to improve the fit.
-**Wrap Up:** Summary of the fitting process and key statistical concepts.
+- **Dataset:** Load the imaging dataset previously simulated, consisting of the image, noise map, and PSF.
+- **Mask:** Apply a mask to the data, excluding regions with low signal-to-noise ratios from the analysis.
+- **Masked Grid:** Create a masked grid containing only coordinates of unmasked pixels.
+- **Fitting:** Fit the data with a galaxy model, computing the model image, residuals, chi-squared, and log likelihood.
+- **Incorrect Fit:** Demonstrate how small deviations from true parameters impact fit quality.
+- **Model Fitting:** Perform a basic model fit, adjusting parameters to improve the fit.
+- **Wrap Up:** Summary of the fitting process and key statistical concepts.
 """
 
 import numpy as np

scripts/chapter_1_introduction/tutorial_5_summary.py

@@ -17,12 +17,12 @@
 
 __Contents__
 
-**Initial Setup:** Create profiles, galaxies and a Galaxies object for illustration.
-**Object Composition:** How Galaxies, Galaxy and Profile objects compose together.
-**Visualization:** Customize and visualize any aspect of galaxies using the plotting API.
-**Code Design:** Discussion of PyAutoGalaxy's object-oriented design philosophy.
-**Source Code:** Links to the source code repositories for PyAutoFit, PyAutoArray and PyAutoGalaxy.
-**Wrap Up:** Summary of chapter 1 and preview of the modeling chapter.
+- **Initial Setup:** Create profiles, galaxies and a Galaxies object for illustration.
+- **Object Composition:** How Galaxies, Galaxy and Profile objects compose together.
+- **Visualization:** Customize and visualize any aspect of galaxies using the plotting API.
+- **Code Design:** Discussion of PyAutoGalaxy's object-oriented design philosophy.
+- **Source Code:** Links to the source code repositories for PyAutoFit, PyAutoArray and PyAutoGalaxy.
+- **Wrap Up:** Summary of chapter 1 and preview of the modeling chapter.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_2_modeling/tutorial_3_realism_and_complexity.py

@@ -19,11 +19,11 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset and apply a mask.
-**Model + Search + Analysis:** Compose a bulge+disk model and fit it using Nautilus.
-**Result:** Inspect the result and compare to the true model.
-**Global and Local Maxima:** Demonstrate how the search can infer incorrect local maxima solutions.
-**Wrap Up:** Summary of the challenges of fitting more complex and realistic models.
+- **Initial Setup:** Load the dataset and apply a mask.
+- **Model + Search + Analysis:** Compose a bulge+disk model and fit it using Nautilus.
+- **Result:** Inspect the result and compare to the true model.
+- **Global and Local Maxima:** Demonstrate how the search can infer incorrect local maxima solutions.
+- **Wrap Up:** Summary of the challenges of fitting more complex and realistic models.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_2_modeling/tutorial_4_dealing_with_failure.py

@@ -20,10 +20,10 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset and apply a mask.
-**Approach 1: Prior Tuning:** Narrow the priors to guide the search to the correct region of parameter space.
-**Approach 2: Reducing Complexity:** Simplify the model to reduce the dimensionality of parameter space.
-**Approach 3: Look Harder:** Increase the thoroughness of the non-linear search sampling.
+- **Initial Setup:** Load the dataset and apply a mask.
+- **Approach 1: Prior Tuning:** Narrow the priors to guide the search to the correct region of parameter space.
+- **Approach 2: Reducing Complexity:** Simplify the model to reduce the dimensionality of parameter space.
+- **Approach 3: Look Harder:** Increase the thoroughness of the non-linear search sampling.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_2_modeling/tutorial_5_linear_profiles.py

@@ -25,19 +25,19 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset and apply a mask.
-**Linear Light Profiles:** Fit a model using linear light profiles that solve for intensity via linear algebra.
-**Run Time:** Estimate the run time of the model-fit before starting.
-**Result:** Inspect the result and the solved intensity values.
-**Intensities:** Access the intensity values of linear light profiles after the fit.
-**Visualization:** Visualize the fit using linear light profiles.
-**Basis:** Combine many linear light profiles into a basis for fitting complex structures.
-**Model Fit:** Fit the basis model to the data.
-**Disk MGE:** Use a Multi-Gaussian Expansion for the disk component.
-**Multi Gaussian Expansion Benefits:** Discussion of the advantages of MGE models.
-**Positive Only Solver:** Ensure linear algebra solutions have positive intensity values.
-**Other Basis Functions:** Overview of other basis functions like shapelets.
-**Wrap Up:** Summary of linear light profiles and basis functions.
+- **Initial Setup:** Load the dataset and apply a mask.
+- **Linear Light Profiles:** Fit a model using linear light profiles that solve for intensity via linear algebra.
+- **Run Time:** Estimate the run time of the model-fit before starting.
+- **Result:** Inspect the result and the solved intensity values.
+- **Intensities:** Access the intensity values of linear light profiles after the fit.
+- **Visualization:** Visualize the fit using linear light profiles.
+- **Basis:** Combine many linear light profiles into a basis for fitting complex structures.
+- **Model Fit:** Fit the basis model to the data.
+- **Disk MGE:** Use a Multi-Gaussian Expansion for the disk component.
+- **Multi Gaussian Expansion Benefits:** Discussion of the advantages of MGE models.
+- **Positive Only Solver:** Ensure linear algebra solutions have positive intensity values.
+- **Other Basis Functions:** Overview of other basis functions like shapelets.
+- **Wrap Up:** Summary of linear light profiles and basis functions.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_2_modeling/tutorial_6_masking.py

@@ -8,11 +8,11 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset and apply a mask.
-**Mask:** Apply a custom mask to focus the fit on specific regions.
-**Model + Search + Analysis:** Fit the model using the custom mask.
-**Discussion:** How the mask affects the fit quality and run time.
-**Wrap Up:** Summary of masking strategies for galaxy modeling.
+- **Initial Setup:** Load the dataset and apply a mask.
+- **Mask:** Apply a custom mask to focus the fit on specific regions.
+- **Model + Search + Analysis:** Fit the model using the custom mask.
+- **Discussion:** How the mask affects the fit quality and run time.
+- **Wrap Up:** Summary of masking strategies for galaxy modeling.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_2_modeling/tutorial_7_results.py

@@ -7,12 +7,12 @@
 
 __Contents__
 
-**Initial Setup:** Perform a model-fit to obtain a Result object.
-**Galaxies & Fit:** Access the maximum likelihood galaxies and fit from the result.
-**Samples:** Inspect the non-linear search samples, including parameter estimates and errors.
-**Workspace:** Pointers to more detailed results examples in the workspace.
-**Database:** Overview of the database functionality for managing large numbers of results.
-**Wrap Up:** Summary of the Result object and its key attributes.
+- **Initial Setup:** Perform a model-fit to obtain a Result object.
+- **Galaxies & Fit:** Access the maximum likelihood galaxies and fit from the result.
+- **Samples:** Inspect the non-linear search samples, including parameter estimates and errors.
+- **Workspace:** Pointers to more detailed results examples in the workspace.
+- **Database:** Overview of the database functionality for managing large numbers of results.
+- **Wrap Up:** Summary of the Result object and its key attributes.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_2_modeling/tutorial_8_need_for_speed.py

@@ -7,10 +7,10 @@
 
 __Contents__
 
-**Searching Non-linear Parameter Space:** How dimensionality, priors and settings drive search run-times.
-**Algorithmic Optimization:** How PyAutoGalaxy uses numba and JAX for fast computation.
-**Data Quantity:** How the number of image pixels affects run-time.
-**Wrap Up:** Summary of strategies for keeping model-fit run-times manageable.
+- **Searching Non-linear Parameter Space:** How dimensionality, priors and settings drive search run-times.
+- **Algorithmic Optimization:** How PyAutoGalaxy uses numba and JAX for fast computation.
+- **Data Quantity:** How the number of image pixels affects run-time.
+- **Wrap Up:** Summary of strategies for keeping model-fit run-times manageable.
 
 For fitting more complex models, the final challenge that we face is keeping the run-time low. One can easily end
 up in a situation where a model-fit takes days, or longer, to fit just one image. For fitting complex models and high

scripts/chapter_3_search_chaining/tutorial_1_search_chaining.py

@@ -37,13 +37,13 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset and apply a mask.
-**Model:** Compose a simplified model for the first search with aligned bulge-disk assumptions.
-**Search + Analysis:** Run the first search with the simplified model.
-**Result:** Inspect the result of the first search.
-**Prior Passing:** Manually pass results from search 1 as priors for search 2.
-**Result:** Inspect the final result of the chained search.
-**Wrap Up:** Summary of how search chaining improves model-fitting reliability.
+- **Initial Setup:** Load the dataset and apply a mask.
+- **Model:** Compose a simplified model for the first search with aligned bulge-disk assumptions.
+- **Search + Analysis:** Run the first search with the simplified model.
+- **Result:** Inspect the result of the first search.
+- **Prior Passing:** Manually pass results from search 1 as priors for search 2.
+- **Result:** Inspect the final result of the chained search.
+- **Wrap Up:** Summary of how search chaining improves model-fitting reliability.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_3_search_chaining/tutorial_2_prior_passing.py

@@ -12,15 +12,15 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset and apply a mask.
-**Model:** Compose the model for the first search.
-**Search:** Run the first search.
-**Result (Search 1):** Inspect the result of the first search.
-**Prior Passing:** Use the prior passing API to pass results to the second search.
-**Result:** Inspect the final result of the chained search.
-**Wrap Up:** Summary of the prior passing API.
-**Detailed Explanation Of Prior Passing:** In-depth explanation of how priors are passed between searches.
-**EXAMPLE:** A worked example of prior passing for a Sersic sersic_index parameter.
+- **Initial Setup:** Load the dataset and apply a mask.
+- **Model:** Compose the model for the first search.
+- **Search:** Run the first search.
+- **Result (Search 1):** Inspect the result of the first search.
+- **Prior Passing:** Use the prior passing API to pass results to the second search.
+- **Result:** Inspect the final result of the chained search.
+- **Wrap Up:** Summary of the prior passing API.
+- **Detailed Explanation Of Prior Passing:** In-depth explanation of how priors are passed between searches.
+- **EXAMPLE:** A worked example of prior passing for a Sersic sersic_index parameter.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_3_search_chaining/tutorial_3_x2_galaxies.py

@@ -17,17 +17,17 @@
 
 __Contents__
 
-**Initial Setup:** Load the double-galaxy dataset and apply a mask.
-**Paths:** Set the output path for chained search results.
-**Search Chaining Approach:** Strategy for fitting two galaxies using a sequence of searches.
-**Model + Search + Analysis + Model-Fit (Search 1):** Fit the first galaxy alone.
-**Result (Search 1):** Inspect the result of fitting the first galaxy.
-**Model (Search 2):** Compose a model for the second galaxy, fixing the first.
-**Search + Analysis + Model-Fit (Search 2):** Fit the second galaxy.
-**Result (Search 2):** Inspect the result of fitting the second galaxy.
-**Model + Search + Analysis + Model-Fit (Search 4):** Fit both galaxies simultaneously.
-**Result (Search 3):** Inspect the final simultaneous fit result.
-**Wrap Up:** Summary of search chaining for multi-galaxy systems.
+- **Initial Setup:** Load the double-galaxy dataset and apply a mask.
+- **Paths:** Set the output path for chained search results.
+- **Search Chaining Approach:** Strategy for fitting two galaxies using a sequence of searches.
+- **Model + Search + Analysis + Model-Fit (Search 1):** Fit the first galaxy alone.
+- **Result (Search 1):** Inspect the result of fitting the first galaxy.
+- **Model (Search 2):** Compose a model for the second galaxy, fixing the first.
+- **Search + Analysis + Model-Fit (Search 2):** Fit the second galaxy.
+- **Result (Search 2):** Inspect the result of fitting the second galaxy.
+- **Model + Search + Analysis + Model-Fit (Search 4):** Fit both galaxies simultaneously.
+- **Result (Search 3):** Inspect the final simultaneous fit result.
+- **Wrap Up:** Summary of search chaining for multi-galaxy systems.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_4_pixelizations/tutorial_1_pixelizations.py

@@ -10,9 +10,9 @@
 
 __Contents__
 
-**Initial Setup:** Create a grid for illustration.
-**Mesh:** Set up a rectangular mesh for the pixelization.
-**Wrap Up:** Summary of pixelization concepts.
+- **Initial Setup:** Create a grid for illustration.
+- **Mesh:** Set up a rectangular mesh for the pixelization.
+- **Wrap Up:** Summary of pixelization concepts.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_4_pixelizations/tutorial_2_mappers.py

@@ -9,10 +9,10 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset for illustration.
-**Mappers:** Understand how mappers map image-plane pixels to pixelization pixels.
-**Mask:** Apply a mask and see how it affects the mapper.
-**Wrap Up:** Summary of mapper concepts.
+- **Initial Setup:** Load the dataset for illustration.
+- **Mappers:** Understand how mappers map image-plane pixels to pixelization pixels.
+- **Mask:** Apply a mask and see how it affects the mapper.
+- **Wrap Up:** Summary of mapper concepts.
 """
 
 # from autoconf import setup_notebook; setup_notebook()

scripts/chapter_4_pixelizations/tutorial_3_inversions.py

@@ -12,11 +12,11 @@
 
 __Contents__
 
-**Initial Setup:** Load the dataset for illustration.
-**Pixelization:** Create a pixelization and perform an inversion to reconstruct the galaxy.
-**Positive Only Solver:** Ensure the reconstruction has only positive intensity values.
-**Wrap Up:** Summary of inversion concepts.
-**Detailed Explanation:** In-depth explanation of the linear algebra behind inversions.
+- **Initial Setup:** Load the dataset for illustration.
+- **Pixelization:** Create a pixelization and perform an inversion to reconstruct the galaxy.
+- **Positive Only Solver:** Ensure the reconstruction has only positive intensity values.
+- **Wrap Up:** Summary of inversion concepts.
+- **Detailed Explanation:** In-depth explanation of the linear algebra behind inversions.
 """
 
 # from autoconf import setup_notebook; setup_notebook()