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

A complete process: subprocess.run

We demonstrate this tool with the most standard and simple UNIX command, ls—the command for listing the content of a directory. It comes with various optional arguments; for example, ls -l displays the list with extended information.

To execute this command within a Python script, we use subprocess.run. The simplest usage is using only one argument, a list with the Linux command split into several text strings:

import subprocess as sp
res = sp.run(['ls','-l'])

The module shlex provides a special tool for performing this split: 

_import shlex
command_list = shlex.split('ls -l') # returns ['ls', '-l']

It also respects empty spaces in filenames and does not use those as separators.

The command run displays the result of the Linux command and the subprocess.CompletedProcess object res.

To execute UNIX commands in this way is quite useless. Mostly, you want to process the output. Therefore...