Contributing to PyBaMM#
If you’d like to contribute to PyBaMM (thanks!), please have a look at the guidelines below.
If you’re already familiar with our workflow, maybe have a quick look at the pre-commit checks directly below.
Before you commit any code, please perform the following checks:
Installing and using pre-commit#
PyBaMM uses a set of
pre-commit hooks and the
pre-commit bot to format and prettify the codebase. The hooks can be installed locally using -
pip install pre-commit pre-commit install
This would run the checks every time a commit is created locally. The checks will only run on the files modified by that commit, but the checks can be triggered for all the files using -
pre-commit run --all-files
If you would like to skip the failing checks and push the code for further discussion, use the
--no-verify option with
A. Before you begin#
Create an issue where new proposals can be discussed before any coding is done.
Download the source code onto your local system, by cloning the repository (or your fork of the repository).
Install PyBaMM with the developer options.
Test if your installation worked, using the test script:
$ python run-tests.py --unit.
You now have everything you need to start making changes!
B. Writing your code#
Make sure to follow our coding style guidelines.
Commit your changes to your branch with useful, descriptive commit messages: Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you’re working on as a place for discussion. Refer to your commits when discussing specific lines of code.
If you want to add a dependency on another library, or re-use code you found somewhere else, have a look at these guidelines.
C. Merging your changes with PyBaMM#
PyBaMM has online documentation at http://docs.pybamm.org/. To make sure any new methods or classes you added show up there, please read the documentation section.
If you added a major new feature, perhaps it should be showcased in an example notebook.
Once a PR has been created, it will be reviewed by any member of the community. Changes might be suggested which you can make by simply adding new commits to the branch. When everything’s finished, someone with the right GitHub permissions will merge your changes into PyBaMM main repository.
Finally, if you really, really, really love developing PyBaMM, have a look at the current project infrastructure.
Coding style guidelines#
PyBaMM follows the PEP8 recommendations for coding style. These are very common guidelines, and community tools have been developed to check how well projects implement them. We recommend using pre-commit hooks to check your code before committing it. See installing and using pre-commit section for more details.
We use ruff to check our PEP8 adherence. To try this on your system, navigate to the PyBaMM directory in a console and type
python -m pip install pre-commit pre-commit run ruff
ruff is configured inside the file
pre-commit-config.yaml, allowing us to ignore some errors. If you think this should be added or removed, please submit an issue
When you commit your changes they will be checked against ruff automatically (see Pre-commit checks).
Naming is hard. In general, we aim for descriptive class, method, and argument names. Avoid abbreviations when possible without making names overly long, so
mean is better than
mu, but a class name like
MyClass is fine.
Class names are CamelCase, and start with an upper case letter, for example
MyOtherClass. Method and variable names are lower case, and use underscores for word separation, for example
Dependencies and reusing code#
While it’s a bad idea for developers to “reinvent the wheel”, it’s important for users to get a reasonably sized download and an easy install. In addition, external libraries can sometimes cease to be supported, and when they contain bugs it might take a while before fixes become available as automatic downloads to PyBaMM users. For these reasons, all dependencies in PyBaMM should be thought about carefully, and discussed on GitHub.
Direct inclusion of code from other packages is possible, as long as their license permits it and is compatible with ours, but again should be considered carefully and discussed in the group. Snippets from blogs and stackoverflow can often be included without attribution, but if they solve a particularly nasty problem (or are very hard to read) it’s often a good idea to attribute (and document) them, by making a comment with a link in the source code.
On the other hand… We do want to compare several tools, to generate documentation, and to speed up development. For this reason, the dependency structure is split into 4 parts:
Core PyBaMM: A minimal set, including things like NumPy, SciPy, etc. All infrastructure should run against this set of dependencies, as well as any numerical methods we implement ourselves.
Extras: Other inference packages and their dependencies. Methods we don’t want to implement ourselves, but do want to provide an interface to can have their dependencies added here.
Documentation generating code: Everything you need to generate and work on the docs.
Development code: Everything you need to do PyBaMM development (so all of the above packages, plus ruff and other testing tools).
Only ‘core pybamm’ is installed by default. The others have to be specified explicitly when running the installation command.
Managing Optional Dependencies and Their Imports#
PyBaMM utilizes optional dependencies to allow users to choose which additional libraries they want to use. Managing these optional dependencies and their imports is essential to provide flexibility to PyBaMM users.
PyBaMM provides a utility function
have_optional_dependency, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here’s how to use it:
Optional dependencies should never be imported at the module level, but always inside methods. For example:
def use_pybtex(x,y,z): pybtex = have_optional_dependency("pybtex") ...
While importing a specific module instead of an entire package/library:
def use_parse_file(x, y, z): parse_file = have_optional_dependency("pybtex.database", "parse_file") ...
This allows people to (1) use PyBaMM without importing optional dependencies by default and (2) configure module-dependent functionalities in their scripts, which must be done before e.g.
print_citations method is first imported.
Writing Tests for Optional Dependencies
Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in
test_util.py. This ensures that an error is raised upon the absence of said dependency. Here’s an example:
from tests import TestCase import pybamm class TestUtil(TestCase): def test_optional_dependency(self): # Test that an error is raised when pybtex is not available with self.assertRaisesRegex( ModuleNotFoundError, "Optional dependency pybtex is not available" ): sys.modules["pybtex"] = None pybamm.function_using_pybtex(x, y, z) # Test that the function works when pybtex is available sys.modules["pybtex"] = pybamm.util.have_optional_dependency("pybtex") pybamm.function_using_pybtex(x, y, z)
All code requires testing. We use the unittest package for our tests. (These tests typically just check that the code runs without error, and so, are more debugging than testing in a strict sense. Nevertheless, they are very useful to have!)
If you have
nox installed, to run unit tests, type
nox -s unit
python run-tests.py --unit
Every new feature should have its own test. To create ones, have a look at the
test directory and see if there’s a test for a similar method. Copy-pasting this is a good way to start.
Next, add some simple (and speedy!) tests of your main features. If these run without exceptions that’s a good start! Next, check the output of your methods using any of these assert methods.
Running more tests#
The tests are divided into
unit tests, whose aim is to check individual bits of code (e.g. discretising a gradient operator, or solving a simple ODE), and
integration tests, which check how parts of the program interact as a whole (e.g. solving a full model).
If you want to check integration tests as well as unit tests, type
nox -s tests
When you commit anything to PyBaMM, these checks will also be run automatically (see infrastructure).
Testing the example notebooks#
To test all the example notebooks in the
docs/source/examples/ folder with
nox -s examples
Alternatively, you may use
pytest directly with the
which runs all the notebooks in the
docs/source/examples/notebooks/ folder in parallel by default, using the
Sometimes, debugging a notebook can be a hassle. To run a single notebook, pass the path to it to
pytest --nbmake docs/source/examples/notebooks/notebook-name.ipynb
or, alternatively, you can use posargs to pass the path to the notebook to
nox. For example:
nox -s examples -- docs/source/examples/notebooks/notebook-name.ipynb
You may also test multiple notebooks this way. Passing the path to a folder will run all the notebooks in that folder:
nox -s examples -- docs/source/examples/notebooks/models/
You may also use an appropriate glob pattern to run all notebooks matching a particular folder or name pattern.
To edit the structure and how the Jupyter notebooks get rendered in the Sphinx documentation (using
nbsphinx), install Pandoc on your system, either using
conda (through the
conda install -c conda-forge pandoc
or refer to the Pandoc installation instructions specific to your platform.
Testing the example scripts#
To test all the example scripts in the
examples/ folder, type
nox -s scripts
Often, the code you write won’t pass the tests straight away, at which stage it will become necessary to debug. The key to successful debugging is to isolate the problem by finding the smallest possible example that causes the bug. In practice, there are a few tricks to help you to do this, which we give below. Once you’ve isolated the issue, it’s a good idea to add a unit test that replicates this issue, so that you can easily check whether it’s been fixed, and make sure that it’s easily picked up if it crops up again. This also means that, if you can’t fix the bug yourself, it will be much easier to ask for help (by opening a bug-report issue).
Run individual test scripts instead of the whole test suite:
You can also run an individual test from a particular script, e.g.
python tests/unit/test_quick_plot.py TestQuickPlot.test_failure
If you want to run several, but not all, the tests from a script, you can restrict which tests are run from a particular script by using the skipping decorator:
@unittest.skip("") def test_bit_of_code(self): ...
or by just commenting out all the tests you don’t want to run.
Set break points, either in your IDE or using the Python debugging module. To use the latter, add the following line where you want to set the break point
import ipdb ipdb.set_trace()
This will start the Python interactive debugger. If you want to be able to use magic commands from
ipython, such as
%timeit, then set
from IPython import embed embed() import ipdb ipdb.set_trace()
at the break point instead. Figuring out where to start the debugger is the real challenge. Some good ways to set debugging break points are:
Try-except blocks. Suppose the line
do_something_complicated()is raising a
ValueError. Then you can put a try-except block around that line as:
try: do_something_complicated() except ValueError: import ipdb ipdb.set_trace()
This will start the debugger at the point where the
ValueErrorwas raised, and allow you to investigate further. Sometimes, it is more informative to put the try-except block further up the call stack than exactly where the error is raised.
Warnings. If functions are raising warnings instead of errors, it can be hard to pinpoint where this is coming from. Here, you can use the
warningsmodule to convert warnings to errors:
import warnings warnings.simplefilter("error")
Then you can use a try-except block, as in a., but with, for example,
Stepping through the expression tree. Most calls in PyBaMM are operations on expression trees. To view an expression tree in ipython, you can use the
You can then step through the expression tree, using the
childrenattribute, to pinpoint exactly where a bug is coming from. For example, if
expression_tree.jac(y)is failing, you can check
To isolate whether a bug is in a model, its Jacobian or its simplified version, you can set the
use_simplifyattributes of the model to
False(they are both
Trueby default for most models).
If a model isn’t giving the answer you expect, you can try comparing it to other models. For example, you can investigate parameter limits in which two models should give the same answer by setting some parameters to be small or zero. The
StandardOutputComparisonclass can be used to compare some standard outputs from battery models.
To get more information about what is going on under the hood, and hence understand what is causing the bug, you can set the logging level to
DEBUGby adding the following line to your test or script:
In models that inherit from
pybamm.BaseBatteryModel(i.e. any battery model), you can use
self.process_parameters_and_discretiseto process a symbol and see what it will look like.
Sometimes, a bit of code will take much longer than you expect to run. In this case, you can set
from IPython import embed embed() import ipdb ipdb.set_trace()
as above, and then use some of the profiling tools. In order of increasing detail:
Simple timer. In ipython, the command
tells you how long the line
command_to_time()takes. You can use
%timeitinstead to run the command several times and obtain more accurate timings.
Simple profiler. Using
%timewill give a brief profiling report 3. Detailed profiler. You can install the detailed profiler
pip install snakeviz
and then, in ipython, run
%load_ext snakeviz %snakeviz command_to_time()
This will open a window in your browser with detailed profiling information.
PyBaMM is documented in several ways.
First and foremost, every method and every class should have a docstring that describes in plain terms what it does, and what the expected input and output is.
These docstrings can be fairly simple, but can also make use of reStructuredText, a markup language designed specifically for writing technical documentation. For example, you can link to other classes and methods by writing
In addition, we write a (very) small bit of documentation in separate reStructuredText files in the
docs directory. Most of what these files do is simply import docstrings from the source code. But they also do things like add tables and indexes. If you’ve added a new class to a module, search the
docs directory for that module’s
.rst file and add your class (in alphabetical order) to its index. If you’ve added a whole new module, copy-paste another module’s file and add a link to your new file in the appropriate
Using Sphinx the documentation in
docs can be converted to HTML, PDF, and other formats. In particular, we use it to generate the documentation on http://docs.pybamm.org/
Building the documentation#
To test and debug the documentation, it’s best to build it locally. To do this, navigate to your PyBaMM directory in a console, and then type (on GNU/Linux, macOS, and Windows):
nox -s docs
And then visit the webpage served at
http://127.0.0.1:8000. Each time a change to the documentation source is detected, the HTML is rebuilt and the browser automatically reloaded. In CI, the docs are built and tested using the
docs session in the
noxfile.py file with warnings turned into errors, to fail the build. The warnings can be removed or ignored by adding the appropriate warning identifier to the
suppress_warnings list in
All example notebooks should be listed in docs/source/examples/index.rst. Please follow the (naming and writing) style of existing notebooks where possible.
All the notebooks are tested daily.
We aim to recognize all contributions by automatically generating citations to the relevant papers on which different parts of the code are built. These will change depending on what models and solvers you use. Adding the command
to the end of a script will print all citations that were used by that script. This will print BibTeX information to the terminal; passing a filename to
print_citations will print the BibTeX information to the specified file instead.
When you contribute code to PyBaMM, you can add your own papers that you would like to be cited if that code is used. First, add the BibTeX for your paper to CITATIONS.bib. Then, add the line
wherever code is called that uses that citation (for example, in functions or in the
__init__ method of a class such as a model or solver).
Installation of PyBaMM and its dependencies is handled via pip and setuptools. It uses
CMake to compile C++ extensions using
casadi. The installation process is described in detail in the source installation page and is configured through the
setup.py pyproject.toml MANIFEST.in
MANIFEST.in is used to include and exclude non-Python files and auxiliary package data for PyBaMM when distributing it. If a file is not included in
MANIFEST.in, it will not be included in the source distribution (SDist) and subsequently not be included in the binary distribution (wheel).
Continuous Integration using GitHub Actions#
Each change pushed to the PyBaMM GitHub repository will trigger the test and benchmark suites to be run, using GitHub Actions.
Tests are run for different operating systems, and for all Python versions officially supported by PyBaMM. If you opened a Pull Request, feedback is directly available on the corresponding page. If all tests pass, a green tick will be displayed next to the corresponding test run. If one or more test(s) fail, a red cross will be displayed instead.
Similarly, the benchmark suite is automatically run for the most recently pushed commit. Benchmark results are compared to the results available for the latest commit on the
develop branch. Should any significant performance regression be found, a red cross will be displayed next to the benchmark run.
In all cases, more details can be obtained by clicking on a specific run.
Configuration files for various GitHub actions workflow can be found in
Code coverage (how much of our code is actually seen by the (linux) unit tests) is tested using Codecov, a report is visible on https://codecov.io/gh/pybamm-team/PyBaMM.
Read the Docs#
Documentation is built using https://readthedocs.org/ and published on http://docs.pybamm.org/.
GitHub does some magic with particular filenames. In particular:
This CONTRIBUTING.md file, along with large sections of the code infrastructure, was copied from the excellent Pints GitHub repo