Update docs and improve formatting

DEVELOPMENT.md

@@ -27,29 +27,30 @@ This is an example of a workflow that describes the development process.
   ```console
   python -m pip install --upgrade pip
   ```
-- Install easydiffraction from root with `dev` extras for development, 
-  `charts` extras for Jupyter notebooks and `docs` extras for building
-  documentation
+- Install easydiffraction from root with `dev` extras for development, `charts`
+  extras for Jupyter notebooks and `docs` extras for building documentation
   ```console
   pip install '.[dev,charts,docs]'
   ```
 - Make changes in the code
   ```console
   ...
   ```
+- Check the validity of pyproject.toml
+  ```console
+  validate-pyproject pyproject.toml
+  ```
 - Run Ruff - Python linter and code formatter (configuration is in
-  pyproject.toml)<br/>
-  Linting (overwriting files)
+  pyproject.toml)<br/> Linting (overwriting files)
   ```console
   ruff check . --fix
   ```
   Formatting (overwriting files)
   ```console
   ruff format .
   ```
-- Install and run Prettier - code formatter for Markdown, YAML, TOML, 
-  etc. files (configuration in prettierrc.toml)<br/>
-  Formatting (overwriting files)
+- Install and run Prettier - code formatter for Markdown, YAML, TOML, etc. files
+  (configuration in prettierrc.toml)<br/> Formatting (overwriting files)
   ```console
   npm install prettier prettier-plugin-toml --save-dev --save-exact
   npx prettier . --write --config=prettierrc.toml
@@ -58,9 +59,8 @@ This is an example of a workflow that describes the development process.
   ```console
   pytest tests/ --color=yes -n auto
   ```
-- Clear all Jupyter notebooks output (Only those that were changed!). 
-  Replace `examples/*.ipynb` with the path to the notebook(s) you want 
-  to clear
+- Clear all Jupyter notebooks output (Only those that were changed!). Replace
+  `examples/*.ipynb` with the path to the notebook(s) you want to clear
   ```console
   jupyter nbconvert --clear-output --inplace examples/*.ipynb
   ```
@@ -72,12 +72,12 @@ This is an example of a workflow that describes the development process.
   ```console
   pytest --nbmake examples/ --ignore-glob='examples/*emcee*' --nbmake-timeout=300 --color=yes -n=auto
   ```
-- Add extra files to build documentation (from `../assets-docs/` and 
+- Add extra files to build documentation (from `../assets-docs/` and
   `../assets-branding/` directories)
   ```console
   cp -R ../assets-docs/docs/assets/ docs/assets/
   cp -R ../assets-docs/includes/ includes/
-  cp -R ../assets-docs/overrides/ overrides/ 
+  cp -R ../assets-docs/overrides/ overrides/
   mkdir -p docs/assets/images/
   cp ../assets-branding/EasyDiffraction/logos/edl-logo_dark.svg docs/assets/images/logo_dark.svg
   cp ../assets-branding/EasyDiffraction/logos/edl-logo_light.svg docs/assets/images/logo_light.svg
@@ -89,14 +89,14 @@ This is an example of a workflow that describes the development process.
   cp ../assets-docs/mkdocs.yml mkdocs.yml
   echo "" >> mkdocs.yml
   cat docs/mkdocs.yml >> mkdocs.yml
-  ```  
+  ```
 - Build documentation with MkDocs - static site generator
   ```console
   export JUPYTER_PLATFORM_DIRS=1
   mkdocs serve
   ```
-- Test the documentation locally (built in the `site/` directory). E.g.,
-  on macOS, open the site in the default browser via the terminal 
+- Test the documentation locally (built in the `site/` directory). E.g., on
+  macOS, open the site in the default browser via the terminal
   ```console
   open http://127.0.0.1:8000
   ```

docs/analysis.md

@@ -1,14 +1,26 @@
 # Analysis
 
-This section contains information about the analysis of diffraction data in EasyDiffraction.
+This section contains information about the analysis of diffraction data in
+EasyDiffraction.
 
 ### Model-dependent analysis
 
-There are two general approaches to the analysis of data: **model-dependent** and **model-independent**. In the following examples, we are going to focus on the former. However, the latter is worth briefly highlighting.
+There are two general approaches to the analysis of data: **model-dependent**
+and **model-independent**. In the following examples, we are going to focus on
+the former. However, the latter is worth briefly highlighting.
 
-A model-independent approach to analysis is where no assumptions are made about the system that is being studied and conclusions are drawn only from the data that has been observed. However, in many applications, it is desirable to include what we think we know about the system, and so model-dependent analysis is used.
+A model-independent approach to analysis is where no assumptions are made about
+the system that is being studied and conclusions are drawn only from the data
+that has been observed. However, in many applications, it is desirable to
+include what we think we know about the system, and so model-dependent analysis
+is used.
 
-Model-dependent analysis involves the development of a mathematical model that describes the model dataset that would be found for our system. This mathematical model usually has parameters that are linked to the physics and chemistry of our system. These parameters are varied to optimise the model, using an optimisation algorithm, with respect to the experimental data, i.e., to get the best agreement between the model data and the experimental data.
+Model-dependent analysis involves the development of a mathematical model that
+describes the model dataset that would be found for our system. This
+mathematical model usually has parameters that are linked to the physics and
+chemistry of our system. These parameters are varied to optimise the model,
+using an optimisation algorithm, with respect to the experimental data, i.e., to
+get the best agreement between the model data and the experimental data.
 
 Below is a diagram illustrating this process:
 
@@ -26,20 +38,40 @@ flowchart LR
     d-- Threshold<br/>reached -->e
 ```
 
-Model-dependent analysis is popular in the analysis of neutron scattering data, and we will use it in the following examples.
+Model-dependent analysis is popular in the analysis of neutron scattering data,
+and we will use it in the following examples.
 
 ## Calculation engines
 
-EasyDiffraction is designed to be a flexible and extensible tool for calculating diffraction patterns. It can use different calculation engines to perform the calculations.
+EasyDiffraction is designed to be a flexible and extensible tool for calculating
+diffraction patterns. It can use different calculation engines to perform the
+calculations.
 
-We currently rely on [CrysPy](https://www.cryspy.fr) as a calculation engine. CrysPy is a Python library originally developed for analysing polarised neutron diffraction data. It is now evolving into a more general purpose library and covers powders and single crystals, nuclear and (commensurate) magnetic structures, unpolarised neutron and X-ray diffraction.
+We currently rely on [CrysPy](https://www.cryspy.fr) as a calculation engine.
+CrysPy is a Python library originally developed for analysing polarised neutron
+diffraction data. It is now evolving into a more general purpose library and
+covers powders and single crystals, nuclear and (commensurate) magnetic
+structures, unpolarised neutron and X-ray diffraction.
 
-Another calculation engine is [CrysFML](https://code.ill.fr/scientific-software/CrysFML2008). This library is a collection of Fortran modules for crystallographic computations. It is used in the software package [FullProf](https://www.ill.eu/sites/fullprof/), and we are currently working on its integration into EasyDiffraction.
+Another calculation engine is
+[CrysFML](https://code.ill.fr/scientific-software/CrysFML2008). This library is
+a collection of Fortran modules for crystallographic computations. It is used in
+the software package [FullProf](https://www.ill.eu/sites/fullprof/), and we are
+currently working on its integration into EasyDiffraction.
 
 ## Minimisation engines
 
-EasyDiffraction uses different third-party libraries to perform the model-dependent analysis.
+EasyDiffraction uses different third-party libraries to perform the
+model-dependent analysis.
 
-Most of the examples in this section will use the [lmfit](https://lmfit.github.io/lmfit-py/) package, which provides a high-level interface to non-linear optimisation and curve fitting problems for Python. It is one of the tools that can be used to fit models to the experimental data.
+Most of the examples in this section will use the
+[lmfit](https://lmfit.github.io/lmfit-py/) package, which provides a high-level
+interface to non-linear optimisation and curve fitting problems for Python. It
+is one of the tools that can be used to fit models to the experimental data.
 
-Another package that can be used for the same purpose is [bumps](https://bumps.readthedocs.io/en/latest/). In addition to traditional optimizers which search for the best minimum they can find in the search space, bumps provides Bayesian uncertainty analysis which explores all viable minima and finds confidence intervals on the parameters based on uncertainty in the measured values.
+Another package that can be used for the same purpose is
+[bumps](https://bumps.readthedocs.io/en/latest/). In addition to traditional
+optimizers which search for the best minimum they can find in the search space,
+bumps provides Bayesian uncertainty analysis which explores all viable minima
+and finds confidence intervals on the parameters based on uncertainty in the
+measured values.