## Numerical Analysis and Signal Processing

I've covered a great deal of the topics associated with data analysis and visualization: reading and writing files, text processing and converting text to numerical data, plotting and graphing, writing scripts, and implementing algorithms. It's time to take a deeper dive and analyze numerical data.

This chapter deals with two important topics: numerical analysis and signal processing. These two topics appear in many sciences: mathematics, computing, engineering, and more. From a simplistic point of view, numerical analysis is concerned with algorithms that yield numerical values: a solution to a nonlinear equation, the decimal representation of re, and more. Signal processing deals with processing signals, that is, values that change over time. Signal processing includes such topics as detection and filtering.

Most universities and colleges offer undergraduate courses that teach these topics. But you don't have to be an engineer or a computer scientist to use the methods and ideas discussed in the chapter. Most of the topics are easy to follow, as I've tried to keep the math to a minimum.

If you have a strong numerical analysis and signal processing background, this chapter should prove a good starting point for these topics in Python. If you're new to the ideas of numerical analysis and signal processing, I hope to shed some light so that you can pick it up from here with relevant scientific literature. In particular, I'd like to point out one of the books that made a great deal of impact on me (and many others), Numerical Recipes: The Art of Scientific Computing, Third Edition by William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery (Cambridge University Press, 2007; for more information, see http:// www.nr.com). Although the book implements algorithms using C/C++ (my original copy was in the Pascal programming language), it provides a wealth of information on numerical algorithms and should prove easy enough to port to Python.

In my view, the field of numerical analysis is a cookbook of algorithms to numerically solve mathematical problems. And so in a sense, that's how the chapter is organized as well: as a list of problems and solutions. Each topic will be explored with examples in hopes that you'll modify the examples to fit your needs. And that's also how I suggest you refer to the chapter:

as a cookbook of algorithms. While it's quite possible to read through and learn the algorithms one at a time, it's probably easier to read specific sections as you engage problems associated with them in real life. So my suggestion is this: skim through the table of contents to acquaint yourself with what's available, and then try to solve a specific problem by reading the relevant section.

In this chapter, I've used SciPy, matplotlib, and NumPy extensively. These three packages are rich and complex, and as a result, I was only able to cover some of the functionality, not all of it. I therefore chose to cover topics and show examples of problems I personally encountered. I hope you'll find the examples of value.

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