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

4.11 Exercises

Ex. 1: Consider a  matrix:

  1. Construct this matrix in Python using the function array.
  2. Construct the same matrix using the function arange followed by a suitable reshape.
  3. What is the result of the expression M[2,:]? What is the result of the similar expression M[2:]?

Ex. 2: Given a vector x, construct in Python the following matrix:

Here, are the components of the vector  (numbered from zero). Given a vector , solve in Python the linear equation system . Let the components of  be denoted by . Write a function poly, which has  and  as input and computes the polynomial:

Plot this polynomial and depict in the same plot the points  as small stars. Try your code with the vectors:

Ex. 3: The matrix  in Ex. 2 is called a Vandermonde matrix. It can be set up in Python directly with the command vander. Evaluating a polynomial defined by a coefficient vector can be done with the...