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

11.2.1 Updating curve parameters with a button

So far, we have updated the curve when the slider value changed and used the method on_changed for this. A complicated graphical output might take some computing time to update. In such a case, you would like to design the GUI in such a way that first, the curve parameters are set by sliders, and then a button is pressed to initiate the updating of the curve.

This can be achieved by the Button widget:

from matplotlib.widgets import Button
button_ax = axes([0.85, 0.01, 0.05, 0.05]) # axes for update button
btn = Button(button_ax, 'Update', hovercolor='red')

The coordinates in this example are set in such a way that the button is located under the two sliders. It is labeled by Update and its color turns to red when the mouse is placed over the button.

This widget has a method, on_clicked, that is used instead of the slider method on_changed:

def update(event):
lines.set_ydata(sld_amp.val*sin(2.*pi*sld_omega...