Book Image

Scientific Computing with Python 3

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python 3

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (23 chapters)
Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
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...