Installation
Notebook run / script run
As it provides a Jupyter widget, pylifemap is primarily designed to be used in a notebook environment such as Jupyter or VSCode.
But it is also possible to use it in a Python script or in the Python REPL. In this case you can use the save() method to export the widget to an HTML file that can be opened later. You can also use the show() method, ins this case the widget is exported to a temporary file and opened in the user’s browser.
pylifemap visualizations can also be embedded in Quarto documents in HTML format.
Install locally
For the moment only the development version can be installed from Github.
You can add the package to a script or project with uv:
uv add git+https://github.com/Lifemap-ToL/pylifemap.gitYou can also install it (preferably in a virtual environment) with pip:
pip install git+https://github.com/Lifemap-ToL/pylifemap.gitRun in Google Colab
You can try the package (without installing anything) in Google Colab.
The easiest way is by using our introduction notebook directly in Colab: 
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Run with juv
juv is a toolkit for reproducible Jupyter notebooks, powered by uv.
You can easily create and run a notebook with pylifemap with:
juv init notebook.ipynb
juv add notebook.ipynb git+https://github.com/Lifemap-ToL/pylifemap.git
juv run notebook.ipynbRun with Docker
Run in Jupyter
Another way to use the package without having to install it is to use our Docker image.
To use pylifemap in a Jupyter environment, you have to install Docker, open a terminal in the directory containing your data and notebook files, and run:
docker run -it -p 8899:8899 -v $PWD:/local/ ghcr.io/lifemap-tol/pylifemap:latestOpen the following URL in your browser: http://127.0.0.1:8899/lab, and you will have access to a Jupyter notebook environment with pylifemap and its dependencies preinstalled.
Run a script
If you just want to run a Python script generating a pylifemap visualization, you can open a terminal in the script directory and run the following command:
docker run -v $PWD:/local/ ghcr.io/lifemap-tol/pylifemap:latest myscript.pyIn this case, if you use show() in your script the result will not be opened in your browser but will instead be saved in a lifemap.html file in your working directory.