Upgrade Python version from 3.9 to 3.12

.github/workflows/build.yml

@@ -18,15 +18,15 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
-      - name: Setup Python 3.9
-        uses: actions/setup-python@v4
+      - name: Setup Python 3.12
+        uses: actions/setup-python@v6
         with:
-          python-version: '3.9'
+          python-version: '3.12'
           architecture: 'x64'
       - name: Install dependencies
         run: |
           python -m pip install --progress-bar off --upgrade pip setuptools wheel
           python -m pip install --progress-bar off -r requirements.txt
       - name: Build Sphinx
         run: |
-          sphinx-build -b html source build -W --keep-going
+          sphinx-build -b html source build -W --keep-going

.github/workflows/dependabot.yaml

@@ -0,0 +1,6 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"

.github/workflows/pr_preview.yml

@@ -17,10 +17,10 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
-      - name: Setup Python 3.9
-        uses: actions/setup-python@v4
+      - name: Setup Python 3.13
+        uses: actions/setup-python@v6
         with:
-          python-version: '3.9'
+          python-version: '3.12'
           architecture: 'x64'
       - name: Install dependencies
         run: |
@@ -30,6 +30,6 @@ jobs:
         run: |
           sphinx-build -b html source build
       - name: Deploy preview
-        uses: rossjrw/pr-preview-action@v1.4.4
+        uses: rossjrw/pr-preview-action@v1.8
         with:
           source-dir: ./build

.github/workflows/publish.yml

@@ -15,7 +15,7 @@ jobs:
         requirements_path: './requirements.txt'
         publish: false
     - name: Deploy 🚀
-      uses: JamesIves/github-pages-deploy-action@v4
+      uses: JamesIves/github-pages-deploy-action@v5
       with:
         folder: ${{ steps.build_and_commit.outputs.artifact }}
         clean-exclude: pr-preview

.github/workflows/spelling.yml

@@ -18,10 +18,10 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
-      - name: Setup Python 3.9
-        uses: actions/setup-python@v4
+      - name: Setup Python 3.12
+        uses: actions/setup-python@v6
         with:
-          python-version: '3.9'
+          python-version: '3.12'
           architecture: 'x64'
       - name: Install dependencies
         run: |

source/spelling_wordlist.txt

@@ -0,0 +1,22 @@
+neuroimaging
+microstates
+fNIRS
+neurofeedback
+
+conda
+Miniconda
+Spyder
+Cartool
+Freesurfer
+VScode
+Github
+Codespaces
+Cheatsheet
+
+Pygame
+scikit
+MNE
+Pycartool
+Pycrostates
+Nilearn
+Jupyter

source/wiki/basics/00_about.md

@@ -29,7 +29,7 @@ Python's simplicity and readability make it an ideal programming language for ed
 
 Python's lack of licensing requirements makes it a convenient choice for working in remote environments. This includes:
 
-- High-Performance Computing (HPC): HPC are remote computing environment usedto run complexe scripts that usually requires a lot of computational resources.
+- High-Performance Computing (HPC): HPC are remote computing environment used to run complex scripts that usually requires a lot of computational resources.
 - Workers (such as GitHub Workers) Workers are background processes that handle tasks like sending notifications, executing long-running scripts
 
 ### For Researchers
@@ -47,7 +47,7 @@ These libraries are maintained by a variety of entities. Some are maintained by
 
 This ecosystem of libraries greatly extends the capabilities of Python, allowing it to be used for an almost infinite range of tasks. It also means that Python can be as lightweight or as feature-rich as you need it to be, depending on which libraries you choose to use.
 
-### Most important python librairies for data sciences
+### Most important python libraries for data sciences
 
 Although there are thousands of libraries, some are used much more than others. For data and time series analysis, which are key in neuroscience, it is important to familiarize yourself with the main ones:
 
@@ -146,7 +146,7 @@ M/EEG - Intra - fNIRS
 :::{card} fMRIprep
 :link: https://fmriprep.org/en/stable/index.html
 +++
-fMRI Preprocessing
+fMRI Pre-processing
 :::
 
 :::{card} dabest

source/wiki/basics/01_installation.md

@@ -55,8 +55,8 @@ conda activate {environment_name}
 
 After activating an environment, your terminal will typically display the name of the current environment at the beginning of the line, usually in parentheses. This helps you keep track of which environment you're currently working in.
 
-Once an environment is activated, all subsequent commands you run will use the Python executables and libraries installed in that environment. This isolation is what allows you to have different environments with different versions of the same package installed.
+Once an environment is activated, all subsequent commands you run will use the Python runtimes and libraries installed in that environment. This isolation is what allows you to have different environments with different versions of the same package installed.
 
 ```{note}
-Conda isn't just for Python - it can also be used to manage environments for the R programming language. You can learn more about this in the [Conda documentation](https://docs.anaconda.com/free/working-with-conda/packages/using-r-language/).
+Conda is not just for Python - it can also be used to manage environments for the R programming language. You can learn more about this in the [Conda documentation](https://docs.anaconda.com/free/working-with-conda/packages/using-r-language/).
 ```

source/wiki/basics/02_IDE.md

@@ -12,7 +12,7 @@ Then, you can use Python to execute it:
 python hello.py
 ```
 
-While this workflow is entirely functional, developers have created tools to facilitate script development and data exploration. These tools are known as **Integrated Development Environments (IDEs)**. IDEs are applications that provide comprehensive facilities for coding. They often include features like syntax highlighting, autocompletion, and debugging tools, which can significantly enhance productivity and code quality. Moreover, many modern IDEs allow you to run your code interactively within the IDE itself, further enhancing the development and data exploration experience.
+While this workflow is entirely functional, developers have created tools to facilitate script development and data exploration. These tools are known as **Integrated Development Environments (IDEs)**. IDEs are applications that provide comprehensive facilities for coding. They often include features like syntax highlighting, auto completion, and debugging tools, which can significantly enhance productivity and code quality. Moreover, many modern IDEs allow you to run your code interactively within the IDE itself, further enhancing the development and data exploration experience.
 
 
 ## Spyder
@@ -81,12 +81,12 @@ Because Jupyter Notebook and JupyterLab are server-client applications, they can
 - **Google Colab**: An online Jupyter Notebook environment hosted by Google, which allows you to write and execute Python code in your browser with zero configuration required.
   [Google Colab](https://colab.research.google.com/)
 
-- **Binder**: A service that lets you create sharable, interactive computing environments from GitHub repositories. It enables you to run Jupyter Notebooks without needing to install any software locally.
+- **Binder**: A service that lets you create shareable, interactive computing environments from GitHub repositories. It enables you to run Jupyter Notebooks without needing to install any software locally.
   [Binder](https://mybinder.org/)
 
 This feature can also be used to run code on another computer within your network and/or a virtual machine on your own computer (docker/ Windows subsystem for Linux (WSL))
 
-Additionally, you can access powerful computational resources by starting a Jupyter server on the High-Performance Computing (HPC) cluster. This is particularly useful for running resource-intensive analyses that wouldn't run efficiently on your personal computer. For more details on how to use Jupyter servers on HPC, check this link:
+Additionally, you can access powerful computational resources by starting a Jupyter server on the High-Performance Computing (HPC) cluster. This is particularly useful for running resource-intensive analyses that would not run efficiently on your personal computer. For more details on how to use Jupyter servers on HPC, check this link:
 [Using Jupyter on HPC](https://doc.eresearch.unige.ch/hpc/how_to_use_openondemand)
 ```