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Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
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Authors (4):
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Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Fuhrer Claus Fuhrer
Author Profile Icon Claus Fuhrer
Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
Author Profile Icon Claus Führer
Claus Führer
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Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Automatic testing

The correct way to develop any piece of code is to use automatic testing. The advantages are:

  • The automated repetition of a large number of tests after every code refactoring and before any new versions are launched.
  • A silent documentation of the use of the code.
  • A documentation of the test coverage of your code: Did things work before a change or was a certain aspect never tested?

Note

Changes in the program and in particular in its structure which do not affect its functionality are called code refactoring.

We suggest developing tests in parallel to the code. Good design of tests is an art of its own and there is rarely an investment which guarantees such a good pay-off in development time savings as the investment in good tests.

Now we will go through the implementation of a simple algorithm with the automated testing methods in mind.

Testing the bisection algorithm

Let us examine automated testing for the bisection algorithm. With this algorithm, a zero of a real valued...

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