docs: fix README drift

README.md

@@ -46,8 +46,7 @@ pitched at undergraduate level and above.
 The workspace includes the following main directories:
 
 - `notebooks`: **PyAutoFit** examples written as Jupyter notebooks.
-- `scipts`: **PyAutoFit** examples written as Python scripts.
-- `projects`: Example projects which use **PyAutoFit**, which serve as a illustration of model-fitting problems and the **PyAutoFit** API.
+- `scripts`: **PyAutoFit** examples written as Python scripts.
 - `config`: Configuration files which customize **PyAutoFit**'s behaviour.
 - `dataset`: Where data is stored, including example datasets distributed with **PyAutoFit**.
 - `output`: Where the **PyAutoFit** analysis and visualization are output.

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/cookbooks/README.md

@@ -7,7 +7,6 @@ The `cookbooks` folder contains cookbooks concisely explaining how to use differ
 - `search.py`: Custom settings of non-linear searches and a list of all searches available (e.g. MCM, nested sampling).
 - `result.py`: Using results from a non-linear search.
 - `samples.py`: Analysing the samples of a non linear search (e.g. maximum likelihood model, parameter errors).
-- `config.py`: Defining configuration files associated with your user define model (e.g. automatic prior setup and parameter labels).
+- `configs.py`: Defining configuration files associated with your user define model (e.g. automatic prior setup and parameter labels).
 - `multiple_datasets.py`: Fitting multiple datasets simultaneously via `Analysis` class summing.
-- `database.py`: Using an sqlite3 database to store results of non-linear searches for fits to large datasets.
 - `multi_level_model.py`: Composing multi-level models from hierarchicies of Python classes.

scripts/cookbooks/README.md

@@ -7,7 +7,6 @@ The `cookbooks` folder contains cookbooks concisely explaining how to use differ
 - `search.py`: Custom settings of non-linear searches and a list of all searches available (e.g. MCM, nested sampling).
 - `result.py`: Using results from a non-linear search.
 - `samples.py`: Analysing the samples of a non linear search (e.g. maximum likelihood model, parameter errors).
-- `config.py`: Defining configuration files associated with your user define model (e.g. automatic prior setup and parameter labels).
+- `configs.py`: Defining configuration files associated with your user define model (e.g. automatic prior setup and parameter labels).
 - `multiple_datasets.py`: Fitting multiple datasets simultaneously via `Analysis` class summing.
-- `database.py`: Using an sqlite3 database to store results of non-linear searches for fits to large datasets.
 - `multi_level_model.py`: Composing multi-level models from hierarchicies of Python classes.