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

Exercises


Ex. 1 → Implement a method __add__, which constructs a new polynomial p+q by adding two given polynomials p and q. In monomial form, polynomials are added by just adding the coefficients, whereas in Newton form, the coefficients depend on the abscissa xi of the interpolation points. Before adding the coefficients of both polynomials, the polynomial q has to get new interpolation points with the property that their abscissa xi coincides with those of p and the method __changepoints__ has to be provided for that. It should change the interpolation points and return a new set of coefficients.

Ex. 2 → Write conversion methods to convert a polynomial from Newton form into monomial form and vice versa.

Ex. 3 → Write a method called add_point that takes a polynomial q and a tuple (x,y) as parameters and returns a new polynomial that interpolates self.points and (x,y).

Ex. 4 → Write a class called LagrangePolynomial that implements polynomials in Lagrange form and inherits as much as...