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Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
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Authors (4):
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Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Fuhrer Claus Fuhrer
Author Profile Icon Claus Fuhrer
Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
Author Profile Icon Claus Führer
Claus Führer
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Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 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 are objects


Functions are objects, like everything else in Python. One may pass functions as arguments, change their names, or delete them. For example:

def square(x):
    """
    Return the square of x
    """
    return x ** 2
square(4) # 16
sq = square # now sq is the same as square
sq(4) # 16
del square # square doesn't exist anymore
print(newton(sq, .2)) # passing as argument

Passing functions as arguments is very common when applying algorithms in scientific computing. The functions fsolve  in scipy.optimize for computing a zero of a given function or quad in scipy.integrate for computing integrals are typical examples.

A function itself can have a different number of arguments with differing types. So, when passing your function f to another function g as argument, make sure that f has exactly the form described in the docstring of g.

The docstring of fsolve  gives information about its func parameter:

func -- A Python function or...
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