This is the repository for a Jupyter Book website with tutorial material for Xarray, an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!
The website is hosted at https://tutorial.xarray.dev
Tutorials are written as interactive Jupyter Notebooks with executable code examples that you can easily run and modify:
All notebooks can be run via the Mybinder.org 'Launch Binder' badge at the top of this page. This will load a pre-configured JupyterLab interface with all tutorial notebooks for you to run. You have minimal computing resources and any changes you make will not be saved.
This tutorial is available to run within Github Codespaces - "a development environment that's hosted in the cloud" - with the conda environment specification in the conda-lock.yml file.
☝️ Click the button above to go to options window to launch a Github codespace.
A codespace is a development environment that's hosted in the cloud. GitHub currently gives every user 120 vCPU hours per month for free, beyond that you must pay. So be sure to explicitly stop or shut down your codespace when you are done by going to this page (https://github.com/codespaces).
Once your codespace is launched, the following happens:
- Visual Studio Code Interface will open up within your browser.
- A built in terminal will open and it will execute
jupyter labautomatically. - Once you see a url to click within the terminal, simply
cmd + clickthe given url. - This will open up another tab in your browser, leading to a Jupyter Lab Interface.
You can also run these notebooks on your own computer! We recommend using micromamba or conda-lock to ensure a fully reproducible Python environment:
git clone https://github.com/xarray-contrib/xarray-tutorial.git
cd xarray-tutorial
conda-lock install conda/conda-lock.yml --name xarray-tutorial
# Or `micromamba create -n xarray-tutorial -f conda-lock.yml`
# Or latest package versions: `mamba env create -f conda/environment-unpinned.yml`
conda activate xarray-tutorial
jupyter labContributions are welcome and greatly appreciated! See our CONTRIBUTING.md document.
Thanks to our contributors so far!
This website is the result of many contributions from the Xarray community! We're very grateful for everyone's volunteered effort as well as sponsored development. Funding for SciPy 2022, SciPy 2023 tutorial material development specifically was supported by NASA's Open Source Tools, Frameworks, and Libraries Program (award 80NSSC22K0345).