Physical Modeling With Python

The goal of this post and its predecessor is to provide some tools and tips for improving the performance of Python programs. In the previous post , we examined profiling tools — sophisticated stopwatches for timing programs as they execute. In this post, we will use these tools to demonstrate some general principles that make Python programs run faster. Remember: If your program already runs fast enough, you do not need to worry about profiling and optimization. Faster is not always better, especially if you end up with code that is difficult to read, modify, or maintain. Overview We can summarize our principles for optimizing performance as follows: Debug first. Never attempt to optimize a program that does not work. Focus on bottlenecks. Find out what takes the most time, and work on that. Look for algorithmic improvements. A different theoretical approach might speed up your program by orders of magnitude. Use library functions. The routines in NumPy, SciPy,

When people argue about programming languages, a common critique of Python is, “It’s slow.” This is occasionally followed by, “A program written in C will run a thousand times faster.” Such generalization carry little weight. Python is often fast enough, and a well-written Python program can run significantly faster than a poorly-written C program. Plus, Moore’s Law implies that computers today are over a thousand times faster than those of 15 years ago: You can do with Python today what was only possible with a highly optimized, compiled program in 2000. It is also important to consider development time. Suppose a C program takes a week to write and debug and 1 minute to run, and an equivalent Python program takes a day to write and debug and 1000 minutes (about a day) to run. The “slow” Python program will finish running five days earlier than the “fast” C program! If you already know Python and don’t know any C, then the time difference will be even greater. In short, you nee