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Scientific Computing with Python 3

You're reading from  Scientific Computing with Python 3

Product type Book
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Pages 332 pages
Edition 1st Edition
Languages
Authors (3):
Claus Führer Claus Führer
Profile icon Claus Führer
Jan Erik Solem Jan Erik Solem
Olivier Verdier Olivier Verdier
View More author details

Table of Contents (23) Chapters

Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
1. Getting Started 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Functions as decorators


In the partial application section, we saw how a function can be used to modify another function. A decorator is a syntax element in Python that conveniently allows us to alter the behavior of a function without changing the definition of the function itself. Let us start with the following situation:

Assume that we have a function that determines the degree of sparsity of a matrix:

def how_sparse(A):
    return len(A.reshape(-1).nonzero()[0])

This function returns an error if it is not called with an array object as input. More precisely, it will not work with an object that does not implement the reshape method. For instance, the how_sparse function will not work with a list, because lists have no reshape method. The following helper function modifies any function with one input parameter so that it tries to make a type conversion to an array:

def cast2array(f):
    def new_function(obj):
        fA = f(array(obj))
        return fA
    return...
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