Manipulating files and programs using Unix shell file programs can often be a bit of a pain, even for seasoned programmers. This can be due to how infrequently you use them, or because you are often moving between OS/X, Windows and Linux, and their subtle differences can often trip you up.
I used to be fairly proficient at them, but nowadays find I use them so rarely that I often have to revise what I used to know, even to achieve the most basic tasks. For many coders, the Unix shell programming language has become like an obscure language you only brush up on when you need to speak to a distant relative at Christmas time.
Fortunately, if you know and love Python, most of what you need to do with the Unix shell for filename searching, for-loops and file permissions can easily be done with iPython, without having to spend hours revising what you first learnt to do in Unix shell years ago.
This is the second post on how to accelerate Python with Cython. The previous post used Cython to declare static C data types in Python, to speed up the runtime of a prime number generator. In this post we shall be modifying a more complex program, one that performs an image transform on a map. This will allow me to demonstrate some more advanced Cython techniques, such as importing C functions from the C math library, using memory views of Numpy arrays and turning off the Python global interpreter lock (GIL).
As with the previous post, we shall be making a series of Cython modifications to Python code, noting the speed improvement with each step. As we go, we’ll be using the Cython compiler’s annotation feature to see which lines are converted into C at each stage, and which lines are still using Python objects and functions. And as we tune the code to run at increasingly higher speeds, we shall be profiling it to see what’s still holding us up, and where to refocus our attention.
Although I will be using Python 3 on a Mac, the instructions I give will mostly be platform agnostic: I will assume you have installed Cython on your system (on Windows, Linux or OS/X) and have followed and understood the installation and testing steps in my previous post. This will be essential if you are to follow the steps I outline below. As stated in the previous post, Cython is not for Python beginners.
This longer post will show you some of the coding skills you’ll need for turning your existing Python code into the Python-C hybrid we call Cython. In doing so, we’ll be digging into some C static data types, to see how much faster Python code will run, and restructuring some Python code along the way for maximum speed.
With Cython, all the benefits of Python are still yours – easily readable code, fast development cycles, powerful high level commands, maintainability, a suite of web development frameworks, a huge standard library for data science, machine learning, imaging, databases and security, plus easy manipulation of files, documents and strings. You should still use Python for all these things – these are what Python does best. But you should also consider combining them with Cython to speed up the computationally intensive Python functions that needs to be fast. Continue reading “From Python To Cython”
After using Spyder for a couple of years, I recently changed my Python IDE from Spyder to PyCharm Community Edition (CE). And since I’ve now used both, I thought I’d share my impressions of each with you.