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

1.7.1 Simple modules – collecting functions

Often, you collect functions in a script. This creates a module with additional Python functionality. To demonstrate this, we create a module by collecting functions in a single file, for example, smartfunctions.py:

def f(x): 
return 2*x + 1
def g(x):
return x**2 + 4*x - 5
def h(x):
return 1/f(x)
  • These functions can now be used by any external script or directly in the IPython environment.
  • Functions within the module can depend on each other._
  • Grouping functions with a common theme or purpose gives modules that can be shared and used by others.

Again, the command exec(open('smartfunctions.py').read()) makes these functions available to your IPython shell (note that there is also the IPython magic function, run). In Python terminology, you say that they are put into the actual namespace.