You have two ways in which you can run the tutorial locally.

To run the tutorial locally, you should first install conda (or mamba).

It is also suggested that you have a recent version of git. Check out how to install git on your operating system.

Go to the directory on your machine where you want to download the material and clone the repository:

git clone https://github.com/empa-scientific-it/python-tutorial

Alternatively, you can manually download a ZIP archive with the latest version of the material:

Download ZIP archive

Extract the archive in a directory of your choice.

Enter the tutorial folder with

cd /path/to/python-tutorial

If you have installed Anaconda, then you can use Anaconda Prompt to run the following commands.

You should now create a new environment with conda:

conda env create -f binder/environment.yml

Then activate the environment with

conda activate python-tutorial

You can update the existing environment (that is, downloading the latest version of the packages) with:

conda env update -f binder/environment.yml

Finally, launch JupyterLab with

jupyter lab

1. Install Docker Desktop: First, you need to install Docker Desktop on your Windows machine. You can download it from the official Docker website: https://www.docker.com/products/docker-desktop.

2. Create a folder: Open File Explorer and create a new folder where you want to save the tutorial's materials. For example, you could create a folder called "python-tutorial" on your Desktop.

3. Open PowerShell: Once Docker Desktop is installed, open PowerShell on your Windows machine. You can do this by pressing the "Windows" key and typing "PowerShell" in the search bar.

4. Pull the Docker image: In PowerShell, run the following command to pull the Docker image:

docker pull ghcr.io/empa-scientific-it/python-tutorial:latest

docker pull ghcr.io/empa-scientific-it/python-tutorial:cuda

5. Run the Docker container: Once the image is downloaded, run the following command to start a Docker container from the image:

docker run -p 8888:8888 --name python_tutorial -v /path/to/python-tutorial:/home/jovyan/python-tutorial ghcr.io/empa-scientific-it/python-tutorial:latest jupyter lab --ip 0.0.0.0 --no-browser

Replace /path/to/python-tutorial with the path to the folder you created in step 2, for example C:/Users/yourusername/Desktop/python-tutorial.

6. Access the Jupyter Notebook: Open a web browser and navigate to http://localhost:8888/lab. You should see the Jupyter Notebook interface. Enter the token provided in the PowerShell console to access the notebook. Alternatively, you can directly click on the link that appears in the PowerShell after the container has started.

You can now use the Jupyter in the Docker container to run the python-tutorial. When you're done, you can stop the container by pressing Ctrl+C in the PowerShell console.

If you prefer not to use Conda and/or Docker, you can set up a lightweight development environment using either "uv" (a faster alternative to pip) or traditional "pip". Both methods will install the development dependencies specified in the pyproject.toml file.

Both setups assume that you have already cloned the repository with git clone https://github.com/empa-scientific-it/python-tutorial. The commands below should be run from inside the tutorial directory.

uv is a fast, Python package installer and resolver written in Rust.

1. Install uv via pip or any other method:

   pip install uv

2. Create a virtual environment and install dev dependencies:

   # Create and activate a virtual environment
   uv venv
   # On Windows
   .venv\Scripts\activate
   # On macOS/Linux
   source .venv/bin/activate
   
   # Install dev dependencies
   uv pip install -e ".[dev]"

3. Launch JupyterLab:

   jupyter lab

1. Create a virtual environment and install dev dependencies:

   # Create and activate a virtual environment
   python -m venv .venv
   # On Windows
   .venv\Scripts\activate
   # On macOS/Linux
   source .venv/bin/activate
   
   # Install dev dependencies
   pip install -e ".[dev]"

2. Launch JupyterLab:

   jupyter lab