SciPy contributor guide¶
This guide is designed to help you quickly find the information you need about SciPy development after you’ve reviewed the introductory material in Ways to Contribute. If you’re new to this and want to start coding ASAP, you’ve found the right place.
Development environment - how to set up and maintain a development environment, including installing compilers and SciPy dependencies, creating a personal fork of the SciPy repository on GitHub, using git to manage a local repository with development branches, performing an in-place build of SciPy, and creating a virtual environment that adds this development version of SciPy to the Python path
Editing SciPy - how to edit SciPy Python code, with tips on finding which module contains SciPy functionality to be edited, adding new modules to SciPy, and complying with PEP8 style standards
Unit tests - how to write and run unit tests for SciPy with the pytest framework
Documentation - how to write reStructuredText documentation that complies with docstring standards, build documentation locally with Sphinx, and view documentation built during continuous integration checks
Benchmarks - how to benchmark code with airspeed velocity
Compiled code - how to add fast, compiled code to SciPy
Development environment¶
Development environment quickstart guide using pip on Ubuntu Linux presents an overview of setting up the development environment using pip on Linux
Development environment quickstart guide (macOS) presents a step-by-step process for setting up a convenient SciPy development environment in macOS
Development environment quickstart guide (Ubuntu) presents a step-by-step process for setting up a convenient SciPy development environment in Ubuntu
Building from source on Windows presents a step-by-step process for building SciPy on Windows
Development environment quickstart guide (Docker) presents a step-by-step process for building SciPy using Docker; if you have trouble with the instructions above, this may be your best option
Building from sources may have some helpful hints if you need to deviate from the guides above
Recommended development setup includes additional notes about the development setup; all of this information is contained elsewhere, but it is retained as a legacy document
Editing SciPy¶
Development workflow lays out what to do after your development environment is set up
SciPy Development Workflow is a five-minute video example of fixing a bug and submitting a pull request
PEP8 and SciPy gives some tips for ensuring that your code is PEP8 compliant
Git for development is a guide to using
git, the distributed version-control system used to manage the changes made to SciPy code from around the worldSciPy API contains some important notes about how SciPy code is organized and documents the structure of the SciPy API; if you are going to import other SciPy code, read this first
Reviewing Pull Requests explains how to review another author’s SciPy code locally
NumPy Distutils - Users Guide - check this out before adding any new files to SciPy
Adding New Methods, Functions, and Classes has information on how to add new methods, functions and classes
SciPy Core Developer Guide has background information including how decisions are made and how a release is prepared; it’s geared toward Core Developers, but contains useful information for all contributors
Unit tests¶
Testing Guidelines is the definitive guide to writing unit tests of SciPy code
Running SciPy Tests Locally documents
runtests.py, a convenient script for building SciPy and running tests locally
Documentation¶
A Guide to NumPy Documentation contains everything you need to know about writing docstrings, which are rendered to produce HTML documentation using Sphinx
Rendering Documentation with Sphinx it’s important to check how changes to the documentation render before merging a PR; this document explains how you can do that
Benchmarks¶
Benchmarking SciPy with airspeed velocity explains how to add benchmarks to SciPy using airspeed velocity
Compiled code¶
Adding Cython to SciPy extending and compiling Python code with Cython can significantly improve its performance; this document helps you get started
Beyond Python discusses the use of C, C++, and Fortran code in SciPy
Public Cython APIs on guidelines on exposing public Cython APIs