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

6.1.1 Using the plot command and some of its variants

The standard plotting function is plot. Calling plot(x,y) creates a figure window with a plot of  as a function of . The input arguments are arrays (or lists) of equal length. It is also possible to use plot(y), in which case the values in  will be plotted against their index, that is, plot(y) is a short form of plot(range(len(y)),y).

Here is an example that shows how to plot  using 200 sample points and with markers at every fourth point:

# plot sin(x) for some interval
x = linspace(-2*pi,2*pi,200)
plot(x,sin(x))

# plot marker for every 4th point
samples = x[::4]
plot(samples,sin(samples),'r*')

# add title and grid lines
title('Function sin(x) and some points plotted')
grid()

The result is shown in the following figure (Figure 6.1):

Figure 6.1: A plot of the function sin(x) with grid lines shown

As you can see, the standard plot is a solid blue...