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Scientific Computing with Python

Scientific Computing with Python

By : Führer, Claus Fuhrer, Solem, Verdier
4.5 (15)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (15)
By: Führer, Claus Fuhrer, Solem, 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)
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20
About Packt
22
References

12.2.3 The Python debugger

Python comes with its own built-in debugger called pdb. Some development environments come with the debugger integrated. The following process still holds in most of these cases.

The easiest way to use the debugger is to enable stack tracing at the point in your code that you want to investigate. Here is a simple example of triggering the debugger based on the example mentioned in Section 7.3: Return values:

import pdb

def complex_to_polar(z):
    pdb.set_trace() 
    r = sqrt(z.real ** 2 + z.imag ** 2)
    phi = arctan2(z.imag, z.real)
    return (r,phi)
z = 3 + 5j 
r,phi = complex_to_polar(z)

print(r,phi)

The command pdb.set_trace() starts the debugger and enables the tracing of subsequent commands. The preceding code will show this:

> debugging_example.py(7)complex_to_polar()
-> r = sqrt(z.real ** 2 + z.imag ** 2) 
(Pdb)

The debugger prompt is indicated with (Pdb). The debugger stops the program execution and...

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