Reporting issues

If your issue (feature request or bug) has not already been filed in the MuMoT GitHub repository (list of all open issues) then please file a new Issue against the MuMoT GitHub repository.

Contribution workflow

If you want to contribute a feature or fix a bug then:

  1. Fork the MuMoT GitHub repository so you have your own personal MuMoT repository on GitHub.

  2. Create a feature branch off the master branch within your personal MuMoT repository. See the branch and versioning policy below for more information about how branches, tags and versions are managed in this project.

  3. Make commits to that branch:

    • Style: write code using standard Python naming conventions.

    • Testing: if you add new features, fix bug(s) or change existing functionality:

      • Add (lower-level) unit tests to the Python source files in the tests/ directory.
      • Add (higher-level) acceptance/regression tests to TestNotebooks/MuMoTtest.ipynb (or to/as Notebooks in the TestNotebooks/MiscTests/ directory).
    • Documentation: include Python docstrings documentation in the numpydoc format for all modules, functions, classes, methods and (if applicable) attributes.

    • Do not commit an updated User Manual Notebook or test notebooks containing output cells; all output cells should be stripped first using:

      jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace docs/MuMoTuserManual.ipynb

      Note that the test suite checks this automatically.

    • Use comments containing @todo in Python code for reminders.

  4. When you are ready to merge your feature branch into the master branch of the ‘upstream’ repository:

    1. Run all tests using tox (see Testing) first.
    2. Create a Pull Request to request that your feature branch be merged into the master branch of the upstream repository. Automated tests will then run against your branch (using GitHub Actions <gh_actions_intro>) and your code will be reviewed by the core development team, after which your changes will be merged into the main MuMoT repository or you will be asked to make further changes to your branch.

Setting up a local development environment

Follow the install instructions but ensure you run:

python3 -m pip install path/to/clone/of/MuMoT/repository[test,docs]

instead of just python3 -m pip install path/to/clone/of/MuMoT/repository. The ‘[test,docs]’ bit ensures that the optional dependencies required to run tests and build the documentation are installed.

If you make local changes to the MuMoT Python package and want to use the updated package you should:

  1. Re-install the package within your conda environment or virtualenv (without upgrading MuMoT’s dependencies):

    python3 -m pip install --upgrade --upgrade-strategy only-if-needed path/to/clone/of/MuMoT/repository[test,docs]
  2. Restart any running IPython kernels within which you have imported the MuMoT package.


Test suite

Testing of MuMoT is currently very basic; the test suite only checks that certain Jupyter Notebooks run without failing i.e. there are no checks for correctness of results. However, there is a framework in place to allow more tests to be written:

  • Unit tests: run by pointing pytest at the tests/ directory; also generates a code coverage data using pytest-cov; only a few tests implemented so far.

  • Basic integration tests: Ensure that certain Jupyter Notebooks can be run without raising Python exceptions/errors:

    • TestNotebooks/MuMoTtest.ipynb
    • docs/MuMoTuserManual.ipynb

    Plus several other Notebooks (see tox.ini).

    These tests are performed by running the Notebooks using the nbval plug-in for pytest, with nbval being run in lax mode. Code coverage data is also captured at this stage when running TestNotebooks/MuMoTtest.ipynb and appended to that captured during the unit testing.

  • Regression tests: not yet implemented. However could be performed by running the Notebook using the nbval plug-in for pytest, with nbval being run in normal (not lax) mode, to ensure that the TestNotebooks/MuMoTtest.ipynb integration test Notebook generates sufficiently similar output cells to those saved in that file when re-run in a clean environment;

  • Notebook formatting/content: Check that the User Manual Notebook does not contain output cells (as they could confuse new users).

  • Documentation: Check that Sphinx can build HTML documentation for the package (more info in Building and Serving Documentation section).

Local testing

To locally run the MuMoT test suite in an isolated Python environment (containing just the necessary dependencies):

  1. Install the tox testing automation tool.

  2. Run:

    cd path/to/clone/of/MuMoT/repository

    This parses the tox.ini file then:

    1. Creates a new virtualenv (Python virtual environment) containing just

      • MuMoT’s dependencies (see install_requires in
      • the packages needed for testing and building the documentation (see extras_require in

      This environment is hidden in a .tox directory to discourage developers from manually tweaking it.

    2. Runs the test suite described above. If nbval encounters any failures/errors then a Jupyter tab is opened in the default web browser showing the location of the failure/error.

Note: attempts to measure code coverage using a Notebook will fail if you call the parseModel function in a Notebook by passing it a reference to an input cell that uses the %%model cell magic; you need to instead call parseModel by passing it a model defined as a simple string (e.g. as is done in TestNotebooks/MuMoTtest.ipynb).

Automated testing using a GitHub Actions workflow

Each Pull Request against the MuMoT GitHub repository and each push to the master branch in that repository trigger a GitHub Actions <gh_actions_intro> continuous integration and continuous delivery (CI/CD) workflow.

Each invocation of the workflow runs a set of user-defined tasks in an isolated execution environment, logs output from those tasks, quits early if an error is encountered and reports the exit status on completion of the job.


  • Tests are run automatically without needing to be manually triggered and the results inspected by developers;
  • If pull requests are made from feature branches against the master branch then you will be notified that tests fail before you merge any changes into master.
  • You can concentrate on other things whilst the CI/CD service is running tests on your behalf.
  • Packages can be automatically be pushed to the Python Package Index (PyPI) if certain conditions are met (the CD part of CI/CD).

The GitHub Actions CI configuration is in the file .github/workflows/test-and-release.yml. In short, this:

  • Calls tox to run tests for all supported Python versions;
  • Uploads code coverage;
  • Checks that source and binary distributions can be built for the package;
  • If the workflow was triggered by a tagged push to master then upload those distributions to PyPI.

The GitHub Actions dashboard for the project shows job exit statuses and logs: <gh_action_dashboard>. From the dashboard you can restart one or more workflow jobs via Re-run jobs, which might be useful if a GH Actions workflow job times out after 6h <gh_actions_timeouts>, a entire GH Actions workflow times out after 72h <gh_actions_timeouts>, a job fails as it has not produced any output for several minutes or you suspect that job failures are otherwise non-deterministic.

Building and serving documentation

This MuMoT documentation is built using the Sphinx tool using/from:

  • The docs/source/ Sphinx config file;
  • A number of anthropogenic pages written in reStructuredText format (see docs/source/*.rst);
  • A number of pages of API documentation that were auto-generated from module/class/method/function docstrings in the MuMoT source code. (These docstrings need to be written in the numpydoc format and are extracted/processed by the autodoc and autosummary Sphinx extensions).

The Sphinx documentation is / can be built under several different circumstances:

Building the docs locally

  1. Ensure the optional docs dependencies of mumot have been installed within your local development environment (a conda environment or virtualenv; see also the MumoT install guide:

    python3 -m pip install path/to/clone/of/MuMoT/repository[docs]
  2. Move into the docs subdirectory within your MuMoT git repository:

    cd path/to/clone/of/MuMoT/repository
    cd docs
  3. Install Sphinx:

    python3 -m pip install sphinx
  4. Use Sphinx to build HTML documentation:

    make html

    This writes output to the _build/html directory, which is ignored by git.

  5. (Optional) view the generated documentation:

    firefox _build/html/index.html

Running the User Manual Notebook on

The User Manual Notebook can be run online without the need for any local installation and configuration.

This is facilitated by, a public instance of the BinderHub service. BinderHub is allows many users to start Binder sessions: within a session, BinderHub creates a per-session software environment on demand on remote hardware (using repo2docker) then starts a Jupyter service within that environment.

As an end user, all you need to start a BinderHub session is

  • The URL of an accessible Git repository that contains a software environment definition (e.g. a Python requirements.txt file, Conda environment.yml or a Docker Dockerfile);
  • The branch, tag or commit that you’d like to access within that repository;
  • (Optional) a relative path within that directory to a Notebook you’d like to run.

These parameters can be supplied via a web form or as URL parameters (allowing someone to just follow a link to start a Binder session).


Behind the scenes uses repo2docker to build an Ubuntu Docker image for running the MuMoT User Manual Notebook in, and pushes this to its Docker image registry. The build process has three steps:

  1. Install several Ubuntu packages (inc. GraphViz and a LaTeX distribution); see the apt.txt file in this repo;
  2. Create a Python virtualenv containing just the MuMoT Python package and its dependencies;
  3. Perform some post-install steps (install the TOC2 (table of contents) Jupyter extension and generate the Matplotlib font cache); see the postBuild file in this repo;

After an image has been created and pushed to the image registry it remains cached there until:

  • a timeout is reached or;
  • a user requests an image for a commit for which an image has not yet been cached (e.g. if the user wants to work with the tip of master and new commits have recently been pushed to that repository.

The repo2docker build process takes ~15 minutes for MuMoT; therefore note that any pushes to the master branch will invalidate any cached image for the tip of the master branch, which will increase startup times from seconds to ~15 minutes.

Button: A session for the User Manual as of the latest stable release of MuMoT can be started by following the link in the instructions for getting started online.

Branch, tag and version policy, inc. how to create a new release

The project uses semantic versioning e.g. compared to version 0.8.0:

  • 0.8.1 is a patch version increase - backwards-compatible bugfixes only
  • 0.9.0 is minor version increase - new functionality added in backwards-compatible manner
  • 1.0.0 is a major version increase - introduces incompatible API changes

In this project the use of branches and git tags is as follows:

  • The master branch is the only long-lived active branch
  • New features are developed by creating feature branches from the master branch; these feature branches are then ultimately merged back into master via Pull Requests then deleted.
  • Changes in patch, major and minor versions are defined solely by creating an annotated tag for a particular commit. The name of this tag should be of the form v<major>.<minor>.<patch> i.e the version preceded by a v. The version does not need to then be specified anywhere in the code (other than in links to mybinder in the Sphinx docs): whenever an installable release of MuMoT is created the setuptools_scm package will embed version information using the most recent tag on the current branch plus extra information derived from the output of git describe if the most recent commit does not have an annotated tag associated with it.

To create a release:

  1. Decide on the type of the next release (patch, major or minor), which depends on the nature of the changes.

  2. (Related) determine the appropriate version number for this pending release.

  3. Create a draft item for this release in ORDA (The University of Sheffield’s Research Data Catalogue and Repository), so as to reserve a DOI for it.

  4. Major/minor release only: ensure all GitHub Issues tagged with the pending release (Milestone) have either been addressed or are reassigned to a different Milestone. Ensure all pull requests against master relating to the pending Milestone have been merged and all CI tests pass.

  5. If necessary, create a pull request against master to change the version in links to e.g. in

    ensure VERSION is master or a particular current or future tagged version, preceded by a v e.g. v0.9.0.

    Also, check/update citation info (including the DOI and contributors) for this pending release in docs/source/about.rst.

    Also, update the file with changes since the last release. You can derive this list of changes from commits made since the last release; if the last release was tagged in git with v0.8.0 then you can see the first line of all commit comments since then with:

    $ git checkout master
    $ git log --pretty=oneline --abbrev-commit v0.8.0..HEAD


    $ git commit -a -m "Preparing for release of version 0.9.0"

    where 0.9.0 is the version of the new release. Next, create the Pull Request.

  6. Merge this Pull Request into master then create an annotated tag:

    $ git checkout master
    $ git fetch --prune --all
    $ git merge --ff-only upstream/master
    $ git tag -a v0.9.0 -m "Release 0.9.0"
    $ git push upstream --tags
    $ git push

    Here we assume that you’ve set up your local git repository with a remote called upstream that points at e.g.

    $ git remote -v
    origin (fetch)
    origin (push)
    upstream (fetch)
    upstream (push)

    NB annotated tags are are often used within git repositories to identify the commit corresponding to a particular release.

  7. The pushing of a tagged commit to causes GitHub Actions to:

    1. Run through the standard tasks performed for Pull Requests (see .github/workflows/test-and-release.yml) then
    2. Build several distributions for this release of MuMoT
      • One or more binary ‘wheel’ packages e.g. mumot-0.9.0-py3-none-any.whl
      • A source package e.g. mumot-0.9.0.tar.gz
    3. Upload these files to PyPI using environment variables stored as encrypted credentials in this GitHub repo.
  8. You can monitor the progress of building packages for MuMoT and uploading them to PyPI using the GitHub Actions dashboard.

  9. Attach an archive of the code/docs for this release to the draft item in ORDA. Create this archive using:

    git archive VERSION | gzip > mumot-VERSION.tar.gz

    For example:

    git archive v1.2.2 | gzip > mumot-v1.2.2.tar.gz
  10. Publish the item in ORDA to ensure:

    • The release being referenceable/citable by DOI.
    • The release being discoverable via the University’s Library Catalogue.