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Jammy2211 merged 2 commits into from
Jul 30, 2026
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docs: fix README drift #60

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docs: fix README drift
Jul 29, 2026
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chore: regenerate catalogue with the repo's declared generator project
Jul 29, 2026
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7 changes: 4 additions & 3 deletions config/README.md
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Expand Up @@ -2,13 +2,14 @@ The `config` folder contains configuration files which customize default **PyAut

# Folders

- `grids`: Configs for default behaviour of grids when used for ray-tracing.
- `non-linear`: Configs for default non-linear search (e.g. MCMC, nested sampling) settings.
- `notation`: Configs defining labels and formatting of model parameters when used for visualization.
- `non_linear`: Configs for default non-linear search (e.g. MCMC, nested sampling) settings.
- `priors`: Configs defining default priors assumed on every model component and set of parameters.
- `visualize`: Configs defining what images are output by a model fit.
- `build`: Configs used by the automated build and test system (not relevant to normal use).

# Files

- `general.yaml`: Customizes general **PyAutoLens** settings.
- `logging.yaml`: Customizes the logging behaviour of **PyAutoLens**.
- `notation.yaml`: Defines labels and formatting of model parameters when used for visualization.
- `output.yaml`: Customizes what a model-fit writes to the output folder.
11 changes: 6 additions & 5 deletions config/non_linear/README.md
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@@ -1,8 +1,9 @@
The `non_linear` folder contains configuration files which customize the default behaviour of non-linear searches in
**PyAutoLens**.
The `non_linear` folder contains configuration files which customize the default behaviour of non-linear
searches in **PyAutoLens**.

Defaults for individual searches (e.g. Nautilus, Emcee, LBFGS) ship with **PyAutoFit** itself; this folder
holds only the workspace-level overrides.

# Files

- `mcmc.yaml`: Settings default behaviour of MCMC non-linear searches (e.g. Emcee).
- `nest.yaml`: Settings default behaviour of nested sampler non-linear searches (e.g. Dynesty).
- `mle.yaml`: Settings default behaviour of maximum likelihood estimator (mle) searches (e.g. LBFGS).
- `GridSearch.yaml`: Settings for the non-linear search grid search.
6 changes: 1 addition & 5 deletions config/visualize/README.md
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@@ -1,11 +1,7 @@
The `config` folder contains configuration files which customize default **PyAutoLens**.
The `visualize` folder contains configuration files which customize what a model-fit outputs.

# Files

- `general.yaml`: Customizes general visualization settings (e.g. the matplotlib backend).
- `include.yaml`: Customize features that appears on plotted images by default (e.g. a mask, a grid).
- `plots.yaml`: Customize which figures are output during a model-fit.
- `plots_search.yaml`: Customize which non-linear search figures are output during a model-fit.
- `mat_wrap.yaml`: Specify the default matplotlib settings when figures and subplots are plotted.
- `mat_wrap_1d.yaml`: Specify the default matplotlib settings when 1D figures and subplots are plotted.
- `mat_wrap_2d.yaml`: Specify the default matplotlib settings when 2D figures and subplots are plotted.
1 change: 0 additions & 1 deletion notebooks/README.md
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Expand Up @@ -7,7 +7,6 @@ a strong lens.
- `chapter_2_lens_modeling`: How to model strong lenses, including a primer on Bayesian non-linear analysis.
- `chapter_3_search_chaining`: How to fit complex lens models using non-linear search chaining.
- `chapter_4_pixelizations`: How to perform pixelized reconstructions of the source-galaxy.
- `chapter_5_hyper_mode`: How to use **PyAutoLens** advanced modeling features that adapt the model to the strong lens being analysed.
- `chapter_optional`: Optional tutorials.

# Full Explanation
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1 change: 0 additions & 1 deletion scripts/README.md
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Expand Up @@ -7,7 +7,6 @@ a strong lens.
- `chapter_2_lens_modeling`: How to model strong lenses, including a primer on Bayesian non-linear analysis.
- `chapter_3_search_chaining`: How to fit complex lens models using non-linear search chaining.
- `chapter_4_pixelizations`: How to perform pixelized reconstructions of the source-galaxy.
- `chapter_5_hyper_mode`: How to use **PyAutoLens** advanced modeling features that adapt the model to the strong lens being analysed.
- `chapter_optional`: Optional tutorials.

# Full Explanation
Expand Down
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