Documentation

.pre-commit-config.yaml

@@ -17,11 +17,18 @@ repos:
     hooks:
       - id: poetry-check
   - repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 23.7.0
     hooks:
       - id: black-jupyter
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.275
+    rev: v0.0.280
     hooks:
       - id: ruff
-        args: [--fix, --exit-non-zero-on-fix]
+        args: [ --fix, --exit-non-zero-on-fix ]
+  - repo: https://github.com/adamchainz/blacken-docs
+    rev: 1.15.0
+    hooks:
+      - id: blacken-docs
+        entry: bash -c "blacken-docs -l 90 $(find doc/ -name '*.md')"
+        args: [ -l 90 ]
+        additional_dependencies: [ black==23.7.0 ]

README.md

@@ -7,7 +7,7 @@
 *Roseau Load Flow* is a highly capable three-phase load flow solver. This project is compatible with Python 3.9 and
 above.
 
-Please take a look at our documentation to see how to install and use `roseau_load_flow`.
+Please take a look at our documentation to see how to install and use `roseau-load-flow`.
 
 * [Installation](https://roseautechnologies.github.io/Roseau_Load_Flow/Installation.html)
 * [Usage](https://roseautechnologies.github.io/Roseau_Load_Flow/notebooks/Getting_Started.html)

doc/Bibliography.bib

@@ -0,0 +1,12 @@
+@article{Klimek_2020,
+    doi = {10.21468/scipostphys.9.4.053},
+    url = {https://doi.org/10.21468%2Fscipostphys.9.4.053},
+    year = 2020,
+    month = {oct},
+    publisher = {Stichting {SciPost}},
+    volume = {9},
+    number = {4},
+    author = {Matthew Klimek and Maxim Perelstein},
+    title = {Neural network-based approach to phase space integration},
+    journal = {{SciPost} Physics}
+}

doc/Changelog.md

@@ -1,12 +1,20 @@
 # Changelog
 
-## Version 0.4.1
+## Version 0.5.0
 
 **In development**
 
-* [PR92](https://github.com/RoseauTechnologies/Roseau_Load_Flow/pull/92) Add the changelog in the documentation
-* [PR92](https://github.com/RoseauTechnologies/Roseau_Load_Flow/pull/92) Use NodeJs 20 in the Dockerfile
-* [PR92](https://github.com/RoseauTechnologies/Roseau_Load_Flow/pull/92) Correction of a dead link in the README
+* [GH100](https://github.com/RoseauTechnologies/Roseau_Load_Flow/issues/100) Fix the `Yz` transformers
+* [PR97](https://github.com/RoseauTechnologies/Roseau_Load_Flow/pull/97) Add the model section to the documentation
+* [PR96](https://github.com/RoseauTechnologies/Roseau_Load_Flow/pull/96)
+  * Add single-phase transformer
+  * Add center-tapped transformer
+  * Remove the `TransformerType` enumeration
+* [PR93](https://github.com/RoseauTechnologies/Roseau_Load_Flow/pull/93) Add short circuit computation
+* [PR92](https://github.com/RoseauTechnologies/Roseau_Load_Flow/pull/92)
+  * Add the changelog in the documentation
+  *  Use NodeJs 20 in the Dockerfile
+  *  Correction of a dead link in the README
 
 ## Version 0.4.0
 

doc/Installation.md

@@ -1,6 +1,8 @@
-# Using `pip` #
+# Installation
 
-`roseau_load_flow` is available on [PyPI](https://pypi.org/project/roseau-load-flow/). It can be
+## Using `pip`
+
+`roseau-load-flow` is available on [PyPI](https://pypi.org/project/roseau-load-flow/). It can be
 installed using pip with:
 ```console
 $ python -m pip install roseau-load-flow
@@ -46,9 +48,9 @@ To upgrade to the latest version (recommended), use:
 $ python -m pip install --upgrade roseau-load-flow
 ```
 
-# Using `conda` #
+## Using `conda`
 
-`roseau_load_flow` is also available on [conda-forge](https://anaconda.org/conda-forge/roseau-load-flow).
+`roseau-load-flow` is also available on [conda-forge](https://anaconda.org/conda-forge/roseau-load-flow).
 It can be installed using conda with:
 ```console
 $ conda install -c conda-forge roseau-load-flow
@@ -59,9 +61,9 @@ If you use *conda* to manage your project, it is recommended to use the `conda`
 instead of `pip`.
 ```
 
-# Using `docker` #
+## Using `docker`
 
-`roseau_load_flow` provides a *docker image* with all required dependencies pre-installed,
+`roseau-load-flow` provides a *docker image* with all required dependencies pre-installed,
 including *Python*. The image runs a *Jupyter* session that you can access in your browser. To
 install and run your docker environment, follow the steps corresponding to your operating system
 below.
@@ -113,7 +115,7 @@ below.
 
 8. The container should start in a few seconds. Open a web browser and navigate to
    [http://localhost:8080](http://localhost:8080) to find the JupyterLab page of the container. A
-   basic python environment is set up with the `roseau_load_flow` package already installed.
+   basic python environment is set up with the `roseau-load-flow` package already installed.
 
 ````
 
@@ -139,7 +141,7 @@ below.
 
 5. Open a web browser and navigate to [http://localhost:8080](http://localhost:8080) to find the
    JupyterLab page of the container. A basic python environment is set up with the
-   `roseau_load_flow` package already installed.
+   `roseau-load-flow` package already installed.
 
 ````
 

doc/Makefile

@@ -15,6 +15,9 @@ help:
 images:
 	@cd images && $(MAKE)
 
+images-%:
+	@cd images && $(MAKE) $*
+
 .PHONY: help Makefile images
 
 # Catch-all target: route all unknown targets to Sphinx using the new

doc/Solvers.md

@@ -24,7 +24,7 @@ Computationally, this translates to finding a solution $x$ such that:
 With $\varepsilon$ being a small *tolerance*.
 In code, $\varepsilon$ can be set with `en.solve_load_flow(tolerance=...)` (by default `1e-6`).
 
-There are several solvers to solve this kind of problems. In Roseau Load Flow, the following
+There are several solvers to solve this kind of problems. In *Roseau Load Flow*, the following
 solvers are available:
 
 
@@ -84,7 +84,7 @@ q(t) &:= g(x_k + t d(x_k))
 A search is made to find $t$ such that:
 ```{math}
 :label: goldstein_and_price
-m_2q'(0) \leq \frac{q(t) - q(0)}{t} \leq m_1q'(0)
+m_2q'(0) \leqslant \frac{q(t) - q(0)}{t} \leqslant m_1q'(0)
 ```
 
 ```{image} /_static/Goldstein_And_Price.svg