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

15.2.3 Test setUp and tearDown methods

The class unittest.TestCase provides two special methods, setUp and tearDown, which run before and after every call to a test method. This is needed when testing generators, which are exhausted after every test. We demonstrate this by testing a program that checks the line in a file in which a given string occurs for the first time:

class StringNotFoundException(Exception):
    pass

def find_string(file, string):
    for i,lines in enumerate(file.readlines()):
        if string in lines:
            return i
    raise StringNotFoundException(
f'String {string} not found in File {file.name}.')

We assume that this code is saved in a file named find_in_file.py.

A test has to prepare a file and open it and remove it after the test as given in the following example:

import unittest
import os # used for, for example, deleting files

from find_in_file import find_string, StringNotFoundException...