Diving into NumPy Code, SciPy2013 Tutorial, Part 4 of 4

Summary

Presenters: David Cournapeau, Stefan Van der Walt

Description

Do you want to contribute to NumPy but find the codebase daunting ? Do you want to extend NumPy (e.g. adding support for decimal, or arbitrary precision) ? Are you curious to understand how NumPy works at all ? Then this tutorial is for you.

The goal of this tutorial is do dive into NumPy codebase, in particular the core C implementation. You will learn how to build NumPy from sources, how some of the core concepts such as data types and ufuncs are implemented at the C level and how it is hooked up to the Python runtime. You will also learn how to add a new ufunc and a new data type.

During the tutorial, we will also have a look at various tools (unix-oriented) that can help tracking bugs or follow a particular numpy expression from its python representation to its low-level implementation.

While a working knowledge of C and Python is required, we do not assume a preliminary knowledge of the NumPy codebase. An understanding of Python C extensions is a plus, but not required either.

Outline

The tutorial will be divided in 3 main sections:

Introduction: Why extending numpy in C ? (and perhaps more importantly, when you should not) being ready to develop on NumPy: building from sources, and building with different flags (optimisation and debug) Source code organisation: description of the numpy source tree and high-level description of what belongs where: core vs the rest, core.multiarray, core.ufunc, scalar arrays and support libraries (npysort, npymath)

The main data structures around ndarray:

the arrayobject and data type descriptor, and how they relate to each other. exercise to add a simple array method to the array object dealing with arbitrary array memory layout with iterators Adding a new dtype: Anatomy of the dtype: from a + a to a core C loop Simple example to wrap a software implementation of quadruple precision (revised version of IEEE 754 software) The current set of planned hand-on tasks/exercises:

building from sources with debug symbols adding an array method to compute a simple statistic (e.g. kurtosis) adding a new type to handle quadruple precision type Required Packages

You will need a working C compiler (gcc on unix/os x, Visual Studio 2008 on windows), and be familiar how to use it on your platform git if possible, gdb and cgdb on unix if possible: valgrind and kcachegrind for supported platforms (linux) Vagrant VM available here: https://s3.amazonaws.com/scipy-2013/divingintonumpy/numpy-tuto.box (use vagrant 1.2.1, as 1.2.2 has a serious bug for sharing files)