Contributor quickstart guide#

After getting the source code from GitHub, there are three steps to start contributing:

  1. Set up a development environment

    Using 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.

  2. Build SciPy

    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.

  3. Perform development tasks

    These can include any changes you want to make to the source code, running tests, building the documentation, running benchmarks, etc.

Basic workflow#

Note

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 git@github.com:YOURUSERNAME/scipy.git scipy
cd scipy
git submodule update --init
git remote add upstream https://github.com/scipy/scipy.git

Next, set up your development environment. With system-level dependencies installed, execute the instructions in Building from source.

For details on how to test your changes, see the more complete setup walkthrough in Development workflow.

Other workflows#

There are many possible ways to set up your development environment. For more detailed instructions, see the SciPy contributor guide.

Note

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.

Another alternative is to use Gitpod. We do not maintain this solution anymore but some information can be found in previous versions of our docs.

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