Development guide¶

Dependencies¶

Mr Mustard requires the following libraries be installed:

Python >= 3.9

as well as the following Python packages:

If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.

Installation¶

For development purposes, it is recommended to install the Mr Mustard source code using development mode:

git clone https://github.com/XanaduAI/MrMustard
cd MrMustard
pip install -e .

The -e flag ensures that edits to the source code will be reflected when importing Mr Mustard in Python.

Increased numerical stability using Julia [optional]¶

Converting phase space objects to Fock space can be numerically unstable due to accumulating floating point errors. To resolve this, the conversion can be performed with extended-precision arithmetic. To use this feature, an installation of Julia is required (version 1.9.3 recommended). If no valid version of Julia is found, it will be installed automatically before trying to run any Julia code.

Development environment¶

Mr Mustard uses a pytest suite for testing and black for formatting. These dependencies can be installed via poetry:

poetry install --with dev

Software tests¶

The Mr Mustard test suite includes pytest, pytest-cov for coverage reports and hypothesis for property-based testing.

To ensure that Mr Mustard is working correctly after installation, the test suite can be run by navigating to the source code folder and running

make test

Individual test modules are run by invoking pytest directly from the command line:

pytest tests/test_fidelity.py

The --backend flag allows specifying the backend used when running the tests. To use the numpy backend, type pytest tests/test_fidelity.py or pytest tests/test_fidelity.py --backend=numpy. To use the tensorflow backend, run the command pytest tests/test_fidelity.py --backend=tensorflow.

Note

Run options for Mr Mustard tests

When running tests, it can be useful to examine a single failing test. The following command stops at the first failing test:

pytest -x

For further useful options (e.g. -k, -s, --tb=short, etc.) refer to the pytest --help command line usage description or the pytest online documentation.

Test coverage¶

Test coverage can be checked by running

make coverage

The output of the above command will show the coverage percentage of each file, as well as the line numbers of any lines missing test coverage.

To obtain coverage, the pytest-cov plugin is needed.

The coverage of a specific file can also be checked by generating a report:

pytest tests/test_fidelity.py --cov=mrmustard/location/to/module --cov-report=term-missing

Here the coverage report will be created relative to the module specified by the path passed to the --cov= option.

The previously mentioned pytest options can be combined with the coverage options. As an example, the -k option allows you to pass a boolean string using file names, test class/test function names, and marks. Using -k in the following command we can get the report of a specific file while also filtering out certain tests:

pytest tests/test_fidelity.py --cov --cov-report=term-missing -k 'not test_fidelity_coherent_state'

Passing the --cov option without any modules specified will generate a coverage report for all modules of Mr Mustard.

Format and code style¶

Contributions are checked for format alignment in the pipeline. With black installed, changes can be formatted locally using:

make format

Contributors without make installed can run black directly using:

black -l 100 mrmustard

Contributions are checked for format alignment in the pipeline. Changes can be formatted and linted locally using:

make lint

To run both linting and formatting use

make format lint

Documentation¶

Additional packages are required to build the documentation, as specified in pyproject.toml under the group doc. These packages can be installed using:

poetry install --with doc

from within the top-level directory. To then build the HTML documentation, run

make docs

The documentation can be found in the doc/_build/html/ directory.

Submitting a pull request¶

Before submitting a pull request, please make sure the following is done:

All new features must include a unit test. If you’ve fixed a bug or added code that should be tested, add a test to the tests directory.
All new functions and code must be clearly commented and documented.

Have a look through the source code at some of the existing function docstrings— the easiest approach is to simply copy an existing docstring and modify it as appropriate.

If you do make documentation changes, make sure that the docs build and render correctly by running make docs.
Ensure that the test suite passes, by running make test.
Make sure the modified code in the pull request conforms to the PEP8 coding standard.

Mr Mustard’s source code conforms to PEP8 standards. Before submitting the PR, you can autoformat your code changes using the Black Python autoformatter, with max-line length set to 120:
```
black -l 100 mrmustard/path/to/modified/file.py
```
We check all of our code against Pylint for errors. To lint modified files, simply pip install pylint, and then from the source code directory, run
```
pylint mrmustard/path/to/modified/file.py
```

When ready, submit your fork as a pull request to the Mr Mustard repository, filling out the pull request template. This template is added automatically to the comment box when you create a new issue.

When describing the pull request, please include as much detail as possible regarding the changes made/new features added/performance improvements. If including any bug fixes, mention the issue numbers associated with the bugs.
Once you have submitted the pull request, three things will automatically occur:
- The test suite will automatically run on GitHub Actions to ensure that all tests continue to pass.
- Once the test suite is finished, a code coverage report will be generated on Codecov. This will calculate the percentage of Mr Mustard covered by the test suite, to ensure that all new code additions are adequately tested.
- Finally, the code quality is calculated by Codefactor, to ensure all new code additions adhere to our code quality standards.

Based on these reports, we may ask you to make small changes to your branch before merging the pull request into the master branch. Alternatively, you can also grant us permission to make changes to your pull request branch.

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