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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

Creation of universal functions

Your function will automatically be universal if you use only universal functions in it. If, however, your function uses functions that are not universal, you might get scalar results, or even an error when trying to apply them on an array:

def const(x):
    return 1
const(array([0, 2])) # returns 1 instead of array([1, 1])

Another example is the following:

def heaviside(x):
    if x >= 0:
        return 1.
    else: 
        return 0.
 
heaviside(array([-1, 2])) # error

The expected behavior would be that the heaviside function applied to a vector [a, b] would return [heaviside(a), heaviside(b)]. Alas, this does not work because the function always returns a scalar, no matter the size of the input argument. Besides, using the function with an array input would cause the statement if to raise an exception, as is explained in detail in Section 5.2.1: Boolean arrays.

The NumPy function vectorize...

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