diff --git a/README.md b/README.md index fd388ec..6372bec 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ they are concise, API-focused, and assume the concepts taught in **HowToFit** as interpret the results of a fit. - `chapter_2_scientific_workflow` — Reserved for future material on building a full scientific model-fitting workflow. Currently a stub; the corresponding overview lives in - `autofit_workspace/scripts/overview/overview_2_science_workflow.py`. + `autofit_workspace/scripts/overview/overview_2_scientific_workflow.py`. - `chapter_3_graphical_models` — Fitting many datasets simultaneously with graphical models, hierarchical models, and Expectation Propagation. diff --git a/config/README.md b/config/README.md index 115a631..c27f43f 100644 --- a/config/README.md +++ b/config/README.md @@ -1,13 +1,15 @@ -The `config` folder contains configuration files which customize default **PyAutoLens**. +The `config` folder contains configuration files which customize default **PyAutoFit**. # Folders +- `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. -- `non-linear.yaml`: Configs for default non-linear search (e.g. MCMC, nested sampling) settings. -- `logging.yaml`: Customizes the logging behaviour of **PyAutoLens**. -- `visualize.yaml`: Configs defining what images are output by a lens model fit. -- `notation.yaml`: Configs defining labels and formatting of model parameters when used for visualization. +- `general.yaml`: Customizes general **PyAutoFit** settings. +- `logging.yaml`: Customizes the logging behaviour of **PyAutoFit**. +- `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. diff --git a/config/non_linear/README.md b/config/non_linear/README.md index f69c3b3..e1075f3 100644 --- a/config/non_linear/README.md +++ b/config/non_linear/README.md @@ -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 **PyAutoFit**. + +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. diff --git a/config/visualize/README.md b/config/visualize/README.md index f5c6146..d7c7ef2 100644 --- a/config/visualize/README.md +++ b/config/visualize/README.md @@ -1,4 +1,4 @@ -The `config` folder contains configuration files which customize default **PyAutoLens**. +The `visualize` folder contains configuration files which customize what a model-fit outputs. # Files diff --git a/notebooks/README.md b/notebooks/README.md index 30b0d45..b5e3ca9 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -3,7 +3,7 @@ The `scripts` folder contains **HowToFit** lectures, which teach a new user how # Folders - `chapter_1_introduction`: Introduction lectures describing how to compose and fit models in **PyAutoFit**. -- `chapter_2_scientific_workflow`: Reserved stub for future material on building a full scientific model-fitting workflow. The equivalent overview currently lives in `autofit_workspace/scripts/overview/overview_2_science_workflow.py`. +- `chapter_2_scientific_workflow`: Reserved stub for future material on building a full scientific model-fitting workflow. The equivalent overview currently lives in `autofit_workspace/scripts/overview/overview_2_scientific_workflow.py`. - `chapter_3_graphical_models`: How to compose and fit graphical models which fit many datasets simultaneously, including hierarchical models and Expectation Propagation. - `simulators`: Simulator scripts that generate the tutorial 1D datasets at runtime. diff --git a/notebooks/chapter_2_scientific_workflow/README.md b/notebooks/chapter_2_scientific_workflow/README.md index 4d6ea83..a8c2d78 100644 --- a/notebooks/chapter_2_scientific_workflow/README.md +++ b/notebooks/chapter_2_scientific_workflow/README.md @@ -4,7 +4,7 @@ how to build a scientific workflow. However, the chapter is not written yet. The functionality required to develop a scientific workflow is fully supported in autofit and described in the following overview example: -`autofit_workspace/*/overview/overview_2_science_workflow.py` +`autofit_workspace/*/overview/overview_2_scientific_workflow.py` If you are a confident model-fitting practitioner, you should be able to follow this example to perform the tasks required to build a scientific workflow. diff --git a/scripts/README.md b/scripts/README.md index 30b0d45..b5e3ca9 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -3,7 +3,7 @@ The `scripts` folder contains **HowToFit** lectures, which teach a new user how # Folders - `chapter_1_introduction`: Introduction lectures describing how to compose and fit models in **PyAutoFit**. -- `chapter_2_scientific_workflow`: Reserved stub for future material on building a full scientific model-fitting workflow. The equivalent overview currently lives in `autofit_workspace/scripts/overview/overview_2_science_workflow.py`. +- `chapter_2_scientific_workflow`: Reserved stub for future material on building a full scientific model-fitting workflow. The equivalent overview currently lives in `autofit_workspace/scripts/overview/overview_2_scientific_workflow.py`. - `chapter_3_graphical_models`: How to compose and fit graphical models which fit many datasets simultaneously, including hierarchical models and Expectation Propagation. - `simulators`: Simulator scripts that generate the tutorial 1D datasets at runtime. diff --git a/scripts/chapter_2_scientific_workflow/README.md b/scripts/chapter_2_scientific_workflow/README.md index 4d6ea83..a8c2d78 100644 --- a/scripts/chapter_2_scientific_workflow/README.md +++ b/scripts/chapter_2_scientific_workflow/README.md @@ -4,7 +4,7 @@ how to build a scientific workflow. However, the chapter is not written yet. The functionality required to develop a scientific workflow is fully supported in autofit and described in the following overview example: -`autofit_workspace/*/overview/overview_2_science_workflow.py` +`autofit_workspace/*/overview/overview_2_scientific_workflow.py` If you are a confident model-fitting practitioner, you should be able to follow this example to perform the tasks required to build a scientific workflow.