Merge pull request #37 from PyAutoLabs/feature/gated-readme-drift

docs: fix README drift

Author: Jammy2211

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.
 

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.

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.

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
 

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.
 

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.

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.
 

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.