llms-full.txt

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

# HowToLens 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 **PyAutoLens** and make sure images display clearly in your Jupyter notebook and on your computer screen.
  - Contents: Directories, Dataset, Dataset Auto-Simulation, Subplots, Plot Customization, Overlays, Wrap Up
- [HowToLens: Introduction](scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py): A strong gravitational lens is a system where two (or more) galaxies align perfectly down our line of sight from Earth such that the foreground galaxy's mass curves space-time in on itself, such that the light of a background source galaxy is deflected and magnified. This means we can see the background source galaxy multiple times, as multiple arcs or rings, because multiple paths through the foreground galaxy's mass are taken by the source's light.
  - Contents: Grids, Geometry, Light Profiles, One Dimension Projection, Galaxies, Units, Wrap Up, Advanced Topics, Other Unit Conversion, Over Sampling
- [Tutorial 2: Ray Tracing](scripts/chapter_1_introduction/tutorial_2_ray_tracing.py): Strong gravitational lensing occurs when the mass of a foreground galaxy (or galaxies) curves space-time around it, causing light rays from a background source to appear deflected.
  - Contents: Grid, Mass Profiles, Ray Tracing Grids, Ray Tracing Images, Galaxies, Tracer, Mappings, Wrap Up
- [Tutorial 3: More Ray Tracing](scripts/chapter_1_introduction/tutorial_3_more_ray_tracing.py): We'll now reinforce the ideas that we learnt about ray-tracing in the previous tutorial and introduce the following new concepts:
  - Contents: Initial Setup, Concise Code, Critical Curves, Caustics, Units, More Complexity, Multi Galaxy Ray Tracing, Wrap Up
- [Tutorial 4: Point Sources](scripts/chapter_1_introduction/tutorial_4_point_sources.py): In the previous tutorials, the background sources we lensed were galaxies: extended objects whose light spreads over many thousands of light years. When lensed, their light is warped into the arcs and Einstein rings we produced with the `Tracer`, spread across many pixels of the image.
  - Contents: Initial Setup, Point Sources, Point Source Tracer, The Lens Equation, Point Solver, Multiple Images and Critical Curves, Magnifications, Time Delays, Extended Versus Point Computations, Wrap Up
- [Tutorial 5: Lensing Formalism](scripts/chapter_1_introduction/tutorial_5_lensing_formalism.py): This tutorial is the equations lecture of **HowToLens**.
  - Contents: Initial Setup, Cosmological Distances, The Lens Equation, Convergence, Deflection Angles, The Lensing Potential, Shear and Magnification, Critical Curves and Caustics, Einstein Radius, Time Delays, Wrap Up
- [Tutorial 6: Data](scripts/chapter_1_introduction/tutorial_6_data.py): In the last tutorials, we use tracers to create images of strong lenses. 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, Weak Lensing Data, Wrap Up
- [Tutorial 7: Fitting](scripts/chapter_1_introduction/tutorial_7_fitting.py): In previous tutorials, we used light profiles to create simulated images of a tracer and visualized how these images would appear when captured by a CCD detector on a telescope like the Hubble Space Telescope.
  - Contents: Dataset, Dataset Auto-Simulation, Mask, Masked Grid, Fitting, Incorrect Fit, Model Fitting, Wrap Up
- [Tutorial 8: Summary](scripts/chapter_1_introduction/tutorial_8_summary.py): In this chapter, we have learnt that:
  - Contents: Start, Object Composition, Visualization, Code Design, Source Code, Wrap Up

## chapter_2_lens_modeling

- [Tutorial 10: Prior Passing](scripts/chapter_2_lens_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 lens model, whose results were used to tune and initialize the priors of a more complex lens 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 11: SLaM](scripts/chapter_2_lens_modeling/tutorial_11_slam.py): In the previous two tutorials, we learnt how search chaining breaks a lens model-fit into a sequence of simpler non-linear searches, and how prior passing carries the results of each search into the next. Together, they give us the flexibility to juggle the dimensionality, priors and settings of each search — the three drivers of run-time we met in tutorial 8 — whilst still fitting a complex and realistic lens model at the end.
  - Contents: Search Chaining In The Workspace, SLaM (Source, Light and Mass), Wrap Up
- [Tutorial 1: Non-linear Search](scripts/chapter_2_lens_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 strong lens using a telescope like the Hubble Space Telescope, and seek to learn about the lens galaxy, source galaxy and the Universe from these observations. With **PyAutoLens**, we seek to learn about the lens's mass and ray-tracing, asking questions like how big is the lens galaxy and what does the unlensed source galaxy look like?
  - Contents: Overview, Parameter Space, Non-Linear Search, Search Types, Deeper Background, PyAutoFit, Initial Setup, Dataset Auto-Simulation, Mask, Model, Priors, Analysis, Searches, Maximum Likelihood Estimation (MLE), Markov Chain Monte Carlo (MCMC), Nested Sampling, What is The Best Search To Use?, Wrap Up
- [Tutorial 2: Practicalities](scripts/chapter_2_lens_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.
  - Contents: PyAutoFit, Initial Setup, Dataset Auto-Simulation, Mask, Model, Search, Search Settings, Iterations Per Update, Analysis, VRAM Use, Run Times, Model-Fit, Result Info, Output Folder, Unique Identifier, Output Folder Contents, Result, Other Practicalities, Wrap Up
- [Tutorial 3: Realism and Complexity](scripts/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.py): In the previous two tutorials, we fitted a fairly crude and unrealistic model: the lens's mass was spherical, as was the source's light. Given most lens galaxies are literally called 'elliptical galaxies' we should probably model their mass as elliptical! Furthermore, we have completely omitted the lens galaxy's light, which in real observations outshines the source's light and therefore must be included in the lens model.
  - Contents: Initial Setup, Dataset Auto-Simulation, Mask, Model, Search + Analysis, Run Time, Result, Global and Local Maxima, Wrap Up
- [Tutorial 4: Dealing With Failure](scripts/chapter_2_lens_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 lens model. In this tutorial, we will pretend that we have modeled our lens 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, Dataset Auto-Simulation, Mask, Prior Tuning, Run Time, Result, Discussion, Approach 2: Reducing Complexity, Approach 3: Look Harder, Wrap Up
- [Tutorial 5: Linear Profiles](scripts/chapter_2_lens_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 lens 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, Dataset Auto-Simulation, Mask, Linear Light Profiles, Run Time, Result, Intensities, Visualization, Basis, Model Fit, Source MGE, Multi Gaussian Expansion Benefits, Disadvantage of Basis Functions, Positive Only Solver, Other Basis Functions, Wrap Up
- [Tutorial 6: Masking and Positions](scripts/chapter_2_lens_modeling/tutorial_6_masking_and_positions.py): We have learnt everything we need to know about non-linear searches to model a strong lens and infer a good lens 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, Dataset Auto-Simulation, Mask, Model + Analysis, Run Time, Search, Discussion, Positions Thresholding, Wrap Up
- [Tutorial 7: Results](scripts/chapter_2_lens_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 each model-fit. In this tutorial, we'll take a look at the result object in a little more detail.
  - Contents: Initial Setup, Dataset Auto-Simulation, Tracer & Fit, Samples, Workspace, Database, Wrap Up
- [Tutorial 8: Need For Speed](scripts/chapter_2_lens_modeling/tutorial_8_need_for_speed.py): In this chapter, we have learnt how to model strong lenses and how to balance complexity and realism to ensure that we infer a good lens 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_lens_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, Run Time, Model Fit, Wrap Up

## chapter_3_pixelizations

- [Tutorial 10: Fit Problems](scripts/chapter_3_pixelizations/tutorial_10_fit_problems.py): To begin, make sure you have read tutorials 4 and 5 carefully, as a clear understanding of how the Bayesian evidence works is key to understanding the adaption tutorials that close this chapter!
  - Contents: Initial Setup, Mask, Simulator, Fitting, Fit Problems, Discussion, Wrap Up
- [Tutorial 11: Brightness Adaption](scripts/chapter_3_pixelizations/tutorial_11_brightness_adaption.py): In the previous tutorial we motivated our need to adapt the pixelization to the source's morphology, such that source pixels congregates in the source's brightest regions regardless of where the source is located in the source-plane.
  - Contents: Initial Setup, Adapt Image, Adaption, Hilbert, Weight Map, Wrap Up
- [Tutorial 12: Adaptive Regularization](scripts/chapter_3_pixelizations/tutorial_12_adaptive_regularization.py): In tutorial 10, we discussed why the `Constant` regularization scheme was sub-optimal. Different regions of the source demand different levels of regularization, motivating a regularization scheme which adapts to the reconstructed source's surface brightness.
  - Contents: Initial Setup, Convenience Function, Adaptive Regularization, Wrap Up
- [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 strong lens's source galaxy, where the light profile was an analytic description of how the luminosity varies as a function of radius. In this chapter, we are instead going to reconstruct the source's light on a pixel-grid, and in this tutorial we will learn how to create a source-plane pixelization.
  - 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, 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, Pixelization, Positive Only Solver, Wrap Up, Detailed Explanation
- [Tutorial 4: Bayesian Regularization](scripts/chapter_3_pixelizations/tutorial_4_bayesian_regularization.py): So far, we have:
  - Contents: Initial Setup, Convenience Function, Pixelization, Regularization, Bayesian Evidence, Detailed Description
- [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 source reconstruction works: pixelizations place a pixel-grid in the source-plane, mappers pair source-pixels with image-pixels, inversions solve for the source-pixel fluxes that best fit the data, and regularization smooths the solution within a Bayesian framework.
  - Contents: Initial Setup, Mesh Shape, Ray Tracing, Border Relocation, Source Pixel Centres, Interpolation, Mapper, Mapping Matrix, Blurred Mapping Matrix, Data Vector, Curvature Matrix, Unregularized Solve, Regularization Matrix, Source Reconstruction, Image Reconstruction, Likelihood Function, Chi Squared, Regularization Term, Complexity Terms, Noise Normalization Term, Log Evidence, Fit, Wrap Up
- [Tutorial 6: Borders](scripts/chapter_3_pixelizations/tutorial_6_borders.py): In the previous tutorials, the source-plane pixel grid perfectly mapped over the traced image-pixel $(y,x)$ coordinates in the source plane. If these pixels mapped to a larger area in the source plane, its pixel-grid would automatically increase its size so as to cover every source-plane coordinate.
  - Contents: Initial Setup, Borders, Wrap Up
- [Tutorial 7: Lens Modeling](scripts/chapter_3_pixelizations/tutorial_7_lens_modeling.py): When modeling complex sources with parametric profiles, we quickly entered a regime where our non-linear search was faced with a parameter space of dimensionality N=20+ parameters. This made the model-fitting inefficient and likely to infer a local maxima.
  - Contents: Initial Setup, Unphysical Solutions, Brief Description, Light Profiles, Wrap Up
- [Tutorial 8: Adaptive Pixelization](scripts/chapter_3_pixelizations/tutorial_8_adaptive_pixelization.py): In this tutorial we will introduce a new `Pixelization` object, which uses an `Overlay` image-mesh and a `Delaunay` mesh.
  - Contents: Initial Setup, Advantages and Disadvantages, Image Mesh, Regularization, Wrap Up
- [Tutorial 9: Model-Fit](scripts/chapter_3_pixelizations/tutorial_9_model_fit.py): You should now perform lens modeling using a pixelization, which is described fully in the example:

## chapter_4_scaling_up_lensing

- [Tutorial 1: Extra Galaxies](scripts/chapter_4_scaling_up_lensing/tutorial_1_extra_galaxies.py): Welcome to chapter 4 of **HowToLens**, where we scale up lens modeling beyond a single lens galaxy.
  - Contents: Initial Setup, Light Versus Mass, Mask, Approach 1 Noise Scaling, Noise Scaling Fit, Approach 2 Extra Galaxies Model, Extra Galaxy Centres, Extra Galaxies Fit, Which Approach When, Wrap Up
- [Tutorial 2: Multi-Galaxy Lenses](scripts/chapter_4_scaling_up_lensing/tutorial_2_multi_galaxy.py): In the previous tutorial, we learned how to deal with extra galaxies near a strong lens — nuisance objects whose light contaminates the data but which play no meaningful role in the lensing itself. We removed their emission from the analysis, or gave them a heavily restricted model, and the single dominant lens galaxy remained the star of the show.
  - Contents: Initial Setup, Mask, Over Sampling, Model, Fixing the Mass Centres, Model Fit, Result, Mass Degeneracies, No Shared Halo, Three Lens Galaxies, Wrap Up
- [Tutorial 3: Scaling Relations](scripts/chapter_4_scaling_up_lensing/tutorial_3_scaling_relation.py): The previous tutorial ended on a warning: every deflector we add to a lens model brings its own free parameters. Two galaxies were manageable, but each one cost us a mass profile's worth of dimensions, and the arithmetic only gets worse. A group-scale lens may have ten member galaxies, a cluster hundreds. If every member keeps its own free mass, the parameter space explodes — a 100-galaxy cluster with 5 free mass parameters per galaxy is a 500-dimensional model, which no non-linear search can sample and no dataset can constrain anyway.
  - Contents: Mass Follows Light, Initial Setup, Mask, Measured Luminosities, The Anchor, Over Sampling, Light Via MGE, Scaling Relation, Model, Parameter Counts, Scaling To Many Galaxies, Model Fit, Results, Limitations, Lens Environments, Wrap Up
- [Tutorial 4: Group Scale](scripts/chapter_4_scaling_up_lensing/tutorial_4_group_scale.py): In the previous tutorials we took our first steps beyond the single lens galaxy: we included extra galaxies near the lens in the model, we modeled systems where two or more galaxies of comparable mass share the lensing, and we introduced scaling relations, which tie the mass of a galaxy to its light so that adding more galaxies to a model does not mean adding more free parameters.
  - Contents: Initial Setup, Mask, Galaxy Centres, The dPIE Profile, Fitting a Group, A Group Halo?, Model Fit, Scaling Relation Members, The Group Scale Sweet Spot, Wrap Up
- [Tutorial 5: Cluster Scale](scripts/chapter_4_scaling_up_lensing/tutorial_5_cluster_scale.py): Throughout this chapter we have been climbing a ladder of scale: from a single lens galaxy with an extra galaxy nearby, to multi-galaxy lenses, to galaxy groups whose members share a common dark matter halo.
  - Contents: Multi-Plane Ray Tracing, Dataset, Point Source Modeling, Point Datasets, The CSV Interface, Point Solver, Model, Analysis + Factor Graph, Search, Model Fit, Results, Customization, Wrap Up
- [Tutorial 6: Weak Lensing](scripts/chapter_4_scaling_up_lensing/tutorial_6_weak_lensing.py): Every tutorial in this series so far — indeed, every fit performed in all four chapters of **HowToLens** — has been a *strong* lensing analysis. Strong lensing occurs when a background galaxy lies so close (in projection) to a foreground mass that its light is bent into multiple images, arcs or a complete Einstein ring. These dramatic features are what we simulated, fitted and modeled, from the single galaxy-scale lenses of chapters 1 and 2 up to the group-scale and cluster-scale systems earlier in this chapter.
  - Contents: Shear Catalogues, Mass Scales, Ray Tracing, Source Galaxy Positions, Simulate, Visualize, Mass Map, Fitting, Model Fit, Result, Joint Strong and Weak Lensing, 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

## simulator

- [Simulator: Cluster](scripts/simulator/cluster.py): This script simulates a strong lens on the 'cluster' scale: 2 main lens galaxies (a brightest cluster galaxy and a satellite), 10 lower-mass cluster member galaxies on a luminosity-mass scaling relation, a cluster-scale dark matter halo not tied to any individual galaxy, and 2 multiply-imaged background source galaxies at *different* redshifts (z = 1.0 and z = 2.0) — a genuine multi-plane lens.
  - Contents: Dataset Paths, Redshifts, Galaxy Centres, Grids, Main Lens Galaxies, Scaling Member Galaxies, Host Dark Matter Halo, Source Galaxies, Ray Tracing, Point Solver, Point Datasets, Combined CSV, Scaling Galaxies CSV, Model CSVs, Tracer json, Imaging, Visualize
- [Simulator: Group Scale Lens](scripts/simulator/group.py): This script simulates `Imaging` of a 'group-scale' strong lens, which is used in chapter 4 of the **HowToLens** lectures to illustrate lens modeling at the group scale.
  - Contents: Dataset Paths, Grid, Galaxy Centres, Over Sampling, PSF / Simulator, Main Lens Galaxy, Member Galaxies, Source Galaxy, Ray Tracing, Output, Visualize, Tracer json, Centre JSON Files, Positions
- [Simulator: Interferometer](scripts/simulator/interferometer.py): This script simulates `Interferometer` data of a 'galaxy-scale' strong lens, as would be observed by a radio or sub-mm interferometer like ALMA or the JVLA.
  - Contents: Dataset Paths, Simulate, Ray Tracing, Output, Visualize, Tracer json
- [Simulator: Lens With Extra Galaxy](scripts/simulator/lens_extra_galaxy.py): This script simulates `Imaging` of a 'galaxy-scale' strong lens which is identical to the `lens_sersic` dataset simulated for chapter 2 (lens light + mass + lensed source), but with one extra galaxy located a few arc-seconds from the lens galaxy.
  - Contents: Model, Dataset Paths, Simulate, Ray Tracing, Extra Galaxy, Output, Mask Extra Galaxy, Visualize, Tracer json, Extra Galaxy Centre
- [Simulator: Simple Sersic Lens](scripts/simulator/lens_sersic.py): This script simulates `Imaging` of a 'galaxy-scale' which is identical to the `simple` simulated in the `start_here.py` script, but where the lens galaxy's light is an `Sersic` profile.
  - Contents: Model, Dataset Paths, Simulate, Ray Tracing, Output, Visualize, Tracer json
- [Simulator: Lens x2](scripts/simulator/lens_x2.py): This script simulates `Imaging` of a 'galaxy-scale' lens where there are two lens galaxies, each with their own light and mass profiles.
  - Contents: Model, Dataset Paths, Simulate, Ray Tracing, Output, Visualize, Tracer json
- [Simulator: Lens x3](scripts/simulator/lens_x3.py): This script simulates `Imaging` of a 'galaxy-scale' lens where there are three lens galaxies, each with their own light and mass profiles, which all contribute significantly to the lensing of a single background source.
  - Contents: Model, Dataset Paths, Simulate, Ray Tracing, Output, Visualize, Tracer json
- [Simulator: No Lens Light](scripts/simulator/no_lens_light.py): This script simulates `Imaging` of a 'galaxy-scale' which is identical to the `simple` simulated in the `start_here.py` script, but where the lens galaxy's light is omitted.
  - Contents: Model, Dataset Paths, Simulate, Ray Tracing, Output, Visualize, Mask Extra Galaxies, Tracer json
- [Simulator: SIS](scripts/simulator/no_lens_light__mass_sis.py): This script simulates `Imaging` of a 'galaxy-scale' which is identical to the `simple` simulated in the `start_here.py` script, but where the lens galaxy's light is omitted and the lens's mass distribution is a Singular Isothermal Sphere.
  - Contents: Model, Dataset Paths, Simulate, Ray Tracing, Output, Visualize, Tracer json
- [Simulator: Source Complex](scripts/simulator/source_complex.py): This script simulates `Imaging` of a 'galaxy-scale' strong lens where the source galaxy's light is more complex than other examples, being composed of 4 Sersics.
  - Contents: Model, Dataset Paths, Simulate, Ray Tracing, Output, Visualize, Tracer json
- [Simulator: Weak Lensing](scripts/simulator/weak_lensing.py): This script simulates a weak gravitational lensing shear catalogue. Unlike the imaging simulators (which produce a 2D image of the lensed source) the weak-lensing simulator produces a *catalogue* of (gamma_2, gamma_1) shear measurements at the (y, x) positions of a population of background source galaxies.
  - Contents: Dataset Paths, Ray Tracing, Source Positions, Simulator, Output, Visualize