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

Shape mismatch

It is not possible to automatically broadcast a vector v of length n to the shape (n,m). This is illustrated in Figure 5.2.

Figure 5.2: Failure of broadcasting due to shape mismatch

The broadcasting fails, because the shape (n,) may not be automatically broadcast to the shape (m, n). The solution is to manually reshape v to the shape (n,1). The broadcasting will now work as usual (by extension only):

M + v.reshape(-1,1)

This is illustrated by the following example.

Define a matrix with:

M = array([[11, 12, 13, 14],
           [21, 22, 23, 24],
           [31, 32, 33, 34]])

and a vector with:

v = array([100, 200, 300])

Now automatic broadcasting will fail because automatic reshaping does not work:

M + v # shape mismatch error

The solution is thus to take care of the reshaping manually. What we want in that case is to add 1 on the right, that is, transform the vector into a column matrix. The broadcasting...