Book Image

Scientific Computing with Python - Second Edition

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python - Second Edition

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
20
About Packt
22
References

15.1 Manual testing

During code development, you do a lot of small tests in order to test its functionality. This could be called manual testing. Typically, you would test if a given function does what it is supposed to do, by manually testing the function in an interactive environment. For instance, suppose that you implement the bisection algorithm. It is an algorithm that finds a zero (root) of a scalar non-linear function. To start the algorithm, an interval has to be given with the property that the function takes different signs on the interval boundaries (see Exercise 4 in Section 7.10: Exercises, for more information).

You will then test an implementation of that algorithm, typically by checking that:

  • A solution is found when the function has opposite signs at the interval boundaries.
  • An exception is raised when the function has the same sign at the interval boundaries.

Manual testing, as necessary as it may seem to be, is unsatisfactory. Once you have convinced yourself...