A set of self-contained examples showing various features of xBOUT.

The quickest way to get started is to run the notebook in your browser, on mybinder.org, by clicking this badge:

Alternatively, to run these examples on your own machine, you will require xBOUT and Jupyter. You can install these and all their requirements using conda (after setting up anaconda or miniconda) by running

$ conda install -c conda-forge xbout jupyter

If you prefer to use pip run

$ pip install xbout jupyter

We suggest getting a 'shallow clone' of this repo, to minimise the download size. Clone with

$ git clone --depth 1 https://github.com/boutproject/xBOUT-examples.git

Finally, navigate into the xBOUT-examples/ directory and run

$ jupyter notebook

A Jupyter session will open in a web browser. Click on the subdirectory for the example you are interested in, and then click on the Jupyter notebook (file ending .ipynb) to open the example (if you are new to xarray and xBOUT, we suggest starting with 'tutorial'), and follow the instructions there.

In order to make the examples easy to use, we keep them in a single repo and do not use git-lfs. That means we need to stop the repo from getting too big. We also want the examples to be self-contained, so people do not need to have BOUT set up and running before they can run these examples.

Some guidelines for making examples:

• Ideally the code and inputs to create datasets should be publically available and linked from the example.

• Even if the code is available, the dataset needed for each example should be archived on Zenodo so that the example can be run independently.

  • Please provide a setup call to download the data from Zenodo if it is not present.

  • Add a line or lines to the postBuild file to download your data for mybinder.org.

• We will use squash-merging of pull requests to minimise the number of data objects (e.g. plot output in Jupyter notebooks) present in the history.

  • We should also try to make PRs with significant changes as complete as possible before merging so that we minimise the number of PRs that make changes to data objects.

  • Small PRs fixing typos, etc., should be fine as long as they do not modify data.