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

Creating processes: subprocess.Popen

What happens when you apply subprocess.run on a Linux command that starts a process that requires user input to terminate?

A simple example of such a program is xclock. It opens a new window displaying a clock until the window is closed by the user.

As the command subprocess.run creates a CompletedProcess object, the following Python script:

import subprocess as sp
res=sp.run(['xclock'])

starts a process and waits until it ends, that is, until somebody closes the window with the clock; see Figure 17.9:

Figure 17.9: The xclock window

This makes a difference to subprocess.Popen. It creates a _Popen object. The process itself becomes a Python object. It need not be completed to become an accessible Python object:

import subprocess as sp
p=sp.Popen(['xclock'])

The process is completed by either a user action on the clock window or by explicitly terminating the process with:

p.terminate()

With Popen, we can construct Linux pipes in Python...