Contributor quickstart guide#
After getting the source code from GitHub, there are three steps to start contributing:
Set up a development environment
mamba, or some flavor of the many virtual environment management tools, you can make sure the development version of SciPy does not interfere with any other local installations of SciPy on your machine.
SciPy uses compiled code for speed, which means you might need extra dependencies to complete this step depending on your system - see Building from source.
Perform development tasks
These can include any changes you want to make to the source code, running tests, building the documentation, running benchmarks, etc.
We strongly recommend using a user-activated environment setup, such as a conda or virtual environment.
Since SciPy contains parts written in C, C++, and Fortran that need to be
compiled before use, make sure you have the necessary compilers and Python
development headers installed. If you are using
mamba, these will be
installed automatically. If you are using
pip, check which
system-level dependencies you might need.
First, fork a copy of the main SciPy repository in GitHub onto your own account and then create your local repository via:
git clone email@example.com:YOURUSERNAME/scipy.git scipy cd scipy git submodule update --init git remote add upstream https://github.com/scipy/scipy.git
# Create an environment with all development dependencies mamba env create -f environment.yml # works with `conda` too # Activate the environment mamba activate scipy-dev
# Create the virtual environment python -m venv $HOME/.venvs/scipy-dev # Activate the environment source $HOME/.venvs/scipy-dev/bin/activate # Install python-level dependencies python -m pip install numpy pytest cython pythran pybind11 meson ninja pydevtool rich-click hypothesis
Your command prompt now lists the name of your new environment, like so:
Finally, build SciPy for development and run the test suite with:
python dev.py test # this will always (re)build as needed first
Notice that this will take a few minutes (and some really slow tests are
disabled by default), so you might want to test only the part of SciPy you will
be working on. For details on how to do that, see the more complete setup
walkthrough in Development workflow, or
python dev.py test --help.
This is only one possible way to set up your development environment out of many. For more detailed instructions, see the SciPy contributor guide.
If you are having trouble building SciPy from source or setting up your local development environment, you can try to build SciPy with GitHub Codespaces. It allows you to create the correct development environment right in your browser, reducing the need to install local development environments and deal with incompatible dependencies.
If you have good internet connectivity and want a temporary set-up, it is
often faster to work on SciPy in a Codespaces environment. For
documentation on how to get started with Codespaces, see
the Codespaces docs.
When creating a codespace for the
scipy/scipy repository, the default
2-core machine type works; 4-core will build and work a bit faster (but of
course at a cost of halving your number of free usage hours). Once your
codespace has started, you can run
conda activate scipy-dev and your
development environment is completely set up - you can then follow the
relevant parts of the SciPy documentation to build, test, develop, write
docs, and contribute to SciPy.