llms-full.txt

AUTO-GENERATED by PyAutoHands — do not edit by hand; regenerate with generate.py.

# HowToGalaxy Lectures — Full Catalogue

> Complete, generated listing of every script (and its matching notebook) in this
> workspace, grouped by top-level `scripts/` folder. This is the expanded companion to
> the curated `llms.txt` routing layer. Each entry links the script's title to its path
> and gives the first line of its docstring; `Contents:` lists the sections within.

## chapter_1_introduction

- [Tutorial 0: Visualization](scripts/chapter_1_introduction/tutorial_0_visualization.py): In this tutorial, we quickly cover visualization in **PyAutoGalaxy** and make sure images display clearly in your Jupyter notebook and on your computer screen.
  - Contents: Directories, Dataset, Plot Customization, Subplots, Visuals, Wrap Up
- [HowToGalaxy: Introduction](scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py): Nearly a century ago, Edwin Hubble famously classified galaxies into three distinct groups: ellipticals, spirals and irregulars. He produced a diagram of these galaxies, called the Hubble Tuning Fork, which is shown below and still discussed by astronomers in the modern day:
  - Contents: Grids, Geometry, Light Profiles, One Dimension Projection, Galaxies, Unit Conversion, Wrap Up
- [Tutorial 2: Data](scripts/chapter_1_introduction/tutorial_2_data.py): In the previous tutorial, we used light profiles to create images of galaxies. However, those images don't accurately represent what we would observe through a telescope.
  - Contents: Initial Setup, Optics Blurring, Poisson Noise, Background Sky, Simulator, Output, Interferometer Data, Wrap Up
- [Tutorial 3: Fitting](scripts/chapter_1_introduction/tutorial_3_fitting.py): In previous tutorials, we used light profiles to create simulated images of galaxies and visualized how these images would appear when captured by a CCD detector on a telescope like the Hubble Space Telescope.
  - Contents: Dataset, Mask, Masked Grid, Fitting, Incorrect Fit, Model Fitting, Wrap Up
- [Tutorial 4: Methods](scripts/chapter_1_introduction/tutorial_4_methods.py): This tutorial is not written yet, but will explain in more detail the different methods used to fit and analyse galaxies.
  - Contents: Wrap Up
- [Tutorial 5: Summary](scripts/chapter_1_introduction/tutorial_5_summary.py): In this chapter, we have learnt that:
  - Contents: Initial Setup, Object Composition, Visualization, Code Design, Source Code, Wrap Up

## chapter_2_modeling

- [Tutorial 10: Prior Passing](scripts/chapter_2_modeling/tutorial_10_prior_passing.py): In the previous tutorial, we used non-linear search chaining to break the model-fitting procedure down into two non-linear searches. This used an initial search to fit a simple model, whose results were used to tune and initialize the priors of a more complex model that was fitted by the second search.
  - Contents: Initial Setup, Dataset Auto-Simulation, Model, Search, Result (Search 1), Prior Passing, Result, Wrap Up, Detailed Explanation Of Prior Passing, EXAMPLE
- [Tutorial 1: Non-linear Search](scripts/chapter_2_modeling/tutorial_1_non_linear_search.py): The starting point for most scientific analysis conducted by an Astronomer is that they have observations of a galaxy using a telescope like the Hubble Space Telescope, and seek to learn about the galaxy and the Universe from these observations. With **PyAutoGalaxy**, we seek to learn about the galaxy's structure and morphology, asking questions like how big is the galaxy, is it disky or bulgy, and how is its light distributed?
  - Contents: Parameter Space, Non-Linear Search, Search Types, Deeper Background, Data, Model, Priors, Analysis, Searches, Maximum Likelihood Estimation (MLE), Markov Chain Monte Carlo (MCMC), Nested Sampling, Result, Samples, Customizing Searches, Wrap Up
- [Tutorial 2: Practicalities](scripts/chapter_2_modeling/tutorial_2_practicalities.py): In the last tutorial, we introduced foundational statistical concepts essential for model-fitting, such as parameter spaces, likelihoods, priors, and non-linear searches. Understanding these statistical concepts is crucial for performing model fits effectively.
- [Tutorial 3: Realism and Complexity](scripts/chapter_2_modeling/tutorial_3_realism_and_complexity.py): In the previous two tutorials, we fitted a fairly basic model: the galaxy's light was a single bulge component. In real observations we know that galaxies are observed to have multiple different morphological structures.
  - Contents: Initial Setup, Model + Search + Analysis, Result, Global and Local Maxima, Wrap Up
- [Tutorial 4: Dealing With Failure](scripts/chapter_2_modeling/tutorial_4_dealing_with_failure.py): In the previous tutorial we intentionally made our non-linear search infer a local maxima solution and therefore return a physically incorrect model. In this tutorial, we will pretend that we have modeled our galaxy and inferred a local maxima. We introduce three approaches one can take that changes how we fit the model, all of which have the aim of ensuring we infer the global maxima:
  - Contents: Initial Setup, Approach 1: Prior Tuning, Approach 2: Reducing Complexity, Approach 3: Look Harder
- [Tutorial 5: Linear Profiles](scripts/chapter_2_modeling/tutorial_5_linear_profiles.py): In the previous tutorial we learned how to balance model complexity with our non-linear search in order to infer accurate model solutions and avoid failure. We saw how in order to fit a model accurately one may have to parameterize and fit a simpler model with fewer non-linear parameters, at the expense of fitting the data less accurately.
  - Contents: Initial Setup, Linear Light Profiles, Run Time, Result, Intensities, Visualization, Basis, Model Fit, Disk MGE, Multi Gaussian Expansion Benefits, Positive Only Solver, Other Basis Functions, Wrap Up
- [Tutorial 6: Masking](scripts/chapter_2_modeling/tutorial_6_masking.py): We have learnt everything we need to know about non-linear searches to model a galaxy and infer a good model solution. Now, lets consider masking in more detail, something we have not given much consideration previously. We'll also learn a neat trick to improve the speed and accuracy of a non-linear search.
  - Contents: Initial Setup, Mask, Model + Search + Analysis, Discussion, Wrap Up
- [Tutorial 7: Results](scripts/chapter_2_modeling/tutorial_7_results.py): In the previous tutorials, each search returned a `Result` object, which we used to plot the maximum log likelihood fit of each model-fit. In this tutorial, we'll take a look at the result object in a little more detail.
  - Contents: Initial Setup, Galaxies & Fit, Samples, Workspace, Database, Wrap Up
- [Tutorial 8: Need For Speed](scripts/chapter_2_modeling/tutorial_8_need_for_speed.py): In this chapter, we have learnt how to model galaxies and how to balance complexity and realism to ensure that we infer a good model.
  - Contents: Searching Non-linear Parameter Space, Cost Per Evaluation, JAX, Run Time Estimation, Data Quantity, Wrap Up
- [Tutorial 9: Search Chaining](scripts/chapter_2_modeling/tutorial_9_search_chaining.py): Throughout this chapter, we have fitted the data using just one non-linear search. The previous tutorial discussed the run-time cost of doing this: with a single search, the dimensionality of the model, the priors on its parameters and the search settings must all be juggled at once, leaving us little flexibility to trade them off against one another.
  - Contents: Initial Setup, Dataset Auto-Simulation, Model, Search + Analysis, Result, Prior Passing, Result, Wrap Up

## chapter_3_pixelizations

- [Tutorial 1: Pixelizations](scripts/chapter_3_pixelizations/tutorial_1_pixelizations.py): In the previous chapters, we used light profiles to model the light of a galaxy, where the light profile was an analytic description of how the luminosity varies as a function of radius.
  - Contents: Initial Setup, Mesh, Wrap Up
- [Tutorial 2: Mappers](scripts/chapter_3_pixelizations/tutorial_2_mappers.py): In the previous tutorial, we used a pixelization to create a `Mapper`. However, it was not clear what a `Mapper` does, why it was called a mapper and whether it was mapping anything at all!
  - Contents: Initial Setup, Dataset Auto-Simulation, Mappers, Mask, Wrap Up
- [Tutorial 3: Inversions](scripts/chapter_3_pixelizations/tutorial_3_inversions.py): In the previous two tutorials, we introduced:
  - Contents: Initial Setup, Dataset Auto-Simulation, Pixelization, Positive Only Solver, Detailed Explanation, Wrap Up
- [Tutorial 4: Bayesian Regularization](scripts/chapter_3_pixelizations/tutorial_4_bayesian_regularization.py): So far, we have:
  - Contents: Initial Setup, Dataset Auto-Simulation, Convenience Function, Pixelization, Regularization, Bayesian Evidence, Non-Linear and Linear, Detailed Description, Wrap Up
- [Tutorial 5: Bayesian Formalism](scripts/chapter_3_pixelizations/tutorial_5_bayesian_formalism.py): In tutorials 1 to 4, we built an intuition for how pixelized galaxy reconstruction works: pixelizations place a pixel-grid over the galaxy's image, mappers pair pixelization pixels with image-pixels, inversions solve for the pixel fluxes that best fit the data, and regularization smooths the solution within a Bayesian framework.
  - Contents: Initial Setup, Dataset Auto-Simulation, Mask, Over Sampling, Mesh Shape, Galaxy, Image Grid, Mesh Pixel Centres, Interpolation, Mapper, Mapping Matrix, Blurred Mapping Matrix, Data Vector (D), Curvature Matrix (F), Unregularized Solve, Regularization Matrix (H), Galaxy Reconstruction (s), Image Reconstruction, Likelihood Function, Chi Squared, Regularization Term, Complexity Terms, Noise Normalization Term, Log Evidence, Fit, Wrap Up
- [Tutorial 6: Model Fit](scripts/chapter_3_pixelizations/tutorial_6_model_fit.py): In the previous tutorials we used an inversion to reconstruct a complex galaxy. However, from the perspective of a scientific analysis, it is not clear how useful this was. When we fit a galaxy with light profiles, we learn about its brightness (`intensity`), size (`effective_radius`), compactness (`sersic_index`), etc.
  - Contents: Initial Setup, Dataset Auto-Simulation, Model + Search + Analysis + Model-Fit (Search 1), Mesh Shape, Model + Search + Analysis + Model-Fit (Search 2), Model + Search (Search 3), Wrap Up

## chapter_4_scaling_up_galaxies

- [Tutorial 1: Extra Galaxies](scripts/chapter_4_scaling_up_galaxies/tutorial_1_extra_galaxies.py): Welcome to chapter 4 of **HowToGalaxy**, where we scale up galaxy modeling beyond a single galaxy.
  - Contents: Initial Setup, Dataset Auto-Simulation, The Decision, Mask, Approach 1 Noise Scaling, Noise Scaling Fit, Approach 2 Extra Galaxies Model, Extra Galaxy Centres, Extra Galaxies Model Composition, Extra Galaxies Fit, Which Approach When, Wrap Up
- [Tutorial 2: Multi-Galaxy Blends](scripts/chapter_4_scaling_up_galaxies/tutorial_2_multi_galaxy.py): In the previous tutorial, we learned how to deal with extra galaxies near the galaxy we care about — nuisance objects whose light contaminates the data but which are not themselves the subject of our study. We removed their emission from the analysis, or gave them a heavily restricted model, and the single galaxy we were studying remained the star of the show.
  - Contents: Initial Setup, Dataset Auto-Simulation, Mask, Over Sampling, Why Not Fit Them Separately?, Model, Fixing the Centres, Model Fit, Result, Light Decomposition Degeneracy, Wrap Up
- [Tutorial 3: Cluster](scripts/chapter_4_scaling_up_galaxies/tutorial_3_cluster.py): Throughout this chapter we have been scaling up: from a single galaxy with extra galaxies nearby, to blended multi-galaxy systems where every galaxy received its own free light model.
  - Contents: The Scaling Problem, Dataset, Dataset Auto-Simulation, Member Catalogue, Masking, Model, Search + Analysis, Model Fit, Result, Per-Member Results, Refinements, Wrap Up

## chapter_optional

- [Tutorial: Alternative Searches](scripts/chapter_optional/tutorial_searches.py): Up to now, we've always used the non-linear search Nautilus and not considered the input parameters that control its sampling. In this tutorial, we'll consider how we can change these setting to balance finding the global maxima solution with fast run time.
  - Contents: Nested Sampling, Optimizers, MCMC

## simulators

- [Simulator: Cluster](scripts/simulators/cluster.py): This script simulates `Imaging` of a cluster field: a brightest cluster galaxy (BCG) surrounded by ten lower-luminosity member galaxies. It is used in chapter 4 of the **HowToGalaxy** lectures, where the member population is modeled via a **catalogue**: a CSV of member centres and luminosities whose photometry pins the faint galaxies while only a shared normalization stays free.
  - Contents: Dataset Paths, Grid, Galaxies, Output, Member Catalogue CSV, Visualize, Plane Output
- [Simulator: Extra Galaxy](scripts/simulators/extra_galaxy.py): This script simulates `Imaging` of a galaxy using light profiles where:
  - Contents: Dataset Paths, Grid, Galaxies, Output, Visualize, Mask Extra Galaxies, Plane Output, Extra Galaxies Centres
- [Simulator: Interferometer](scripts/simulators/interferometer.py): This script simulates `Interferometer` data of a galaxy, as would be observed by a radio or sub-mm interferometer like ALMA or the JVLA, where:
  - Contents: Dataset Paths, Simulate, Galaxies, Output, Visualize, Plane Output
- [Simulator: Sersic](scripts/simulators/sersic.py): This script simulates `Imaging` of a galaxy using light profiles where:
  - Contents: Dataset Paths, Grid, Galaxies, Output, Visualize, Plane Output
- [Simulator: Sersic x2](scripts/simulators/sersic_x2.py): This script simulates `Imaging` of two galaxies where:
  - Contents: Dataset Paths, Grid, Galaxies, Output, Visualize, Plane Output
- [Simulator: Sersic + Exp](scripts/simulators/simple.py): This script simulates `Imaging` of a galaxy using light profiles where:
  - Contents: Dataset Paths, Grid, Over Sampling, Galaxies, Output, Visualize, Plane Output