Reader small image

You're reading from  Learning Cython Programming (Second Edition) - Second Edition

Product typeBook
Published inFeb 2016
Reading LevelBeginner
PublisherPackt
ISBN-139781783551675
Edition2nd Edition
Languages
Tools
Right arrow
Author (1)
Philip Herron
Philip Herron
author image
Philip Herron

Philip Herron is a developer who focuses his passion toward compilers and virtual machine implementations. When he was first accepted to Google Summer of Code 2010, he used inspiration from Paul Biggar's PhD on the optimization of dynamic languages to develop a proof of the concept GCC frontend to compile Python. This project sparked his deep interest in how Python works. After completing a consecutive year on the same project in 2011, Philip applied to Cython under the Python foundation to gain a deeper appreciation of the standard Python implementation. Through this he started leveraging the advantages of Python to control the logic in systems or even add more high-level interfaces, such as embedding Flask web servers in a REST API to a system-level piece of software, without writing any C code. Philip currently works as a software consultant for Instil Software based in Northern Ireland. He develops mobile applications with embedded native code for video streaming. Instil has given him a lot of support in becoming a better engineer. He has written several tutorials for the UK-based Linux Format magazine on Python and loves to share his passion for the Python programming language.
Read more about Philip Herron

Right arrow

Cython and NumPy


NumPy is a scientific library designed to provide functionality similar to or on par with MATLAB, which is a paid proprietary mathematics package. NumPy has a lot of popularity with Cython users since you can seek out more performance from your highly computational code using C types. In Cython, you can import this library as follows:

import numpy as np
cimport numpy as np

np.import_array()

You can access full Python APIs as follows:

np.PyArray_ITER_NOTDONE

So, you can integrate with iterators at a very native area of the API. This allows NumPy users to get a lot of speed when working with native types via something as follows:

cdef double * val = (<double*>np.PyArray_MultiIter_DATA(it, 0))[0]

We can cast the data from the array to double, and it's a cdef type in Cython to work with now. For more information and NumPy tutorials, visit https://github.com/cython/cython/wiki/tutorials-numpy.

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
Learning Cython Programming (Second Edition) - Second Edition
Published in: Feb 2016Publisher: PacktISBN-13: 9781783551675

Author (1)

author image
Philip Herron

Philip Herron is a developer who focuses his passion toward compilers and virtual machine implementations. When he was first accepted to Google Summer of Code 2010, he used inspiration from Paul Biggar's PhD on the optimization of dynamic languages to develop a proof of the concept GCC frontend to compile Python. This project sparked his deep interest in how Python works. After completing a consecutive year on the same project in 2011, Philip applied to Cython under the Python foundation to gain a deeper appreciation of the standard Python implementation. Through this he started leveraging the advantages of Python to control the logic in systems or even add more high-level interfaces, such as embedding Flask web servers in a REST API to a system-level piece of software, without writing any C code. Philip currently works as a software consultant for Instil Software based in Northern Ireland. He develops mobile applications with embedded native code for video streaming. Instil has given him a lot of support in becoming a better engineer. He has written several tutorials for the UK-based Linux Format magazine on Python and loves to share his passion for the Python programming language.
Read more about Philip Herron