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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

Converting a symbolic expression into a numeric function


As we have seen the numerical evaluation of a symbolic expression is done in three steps, first we do some symbolic computations and then we substitute values by numbers and do an evaluation to a floating point number by evalf.

The reason for symbolic computations is often that one wants to make parameter studies. This requires that the parameter is modified within a given parameter range. This requires that an symbolic expression is eventually turned into a numeric function.

A study on the parameter dependency of polynomial coefficients

We demonstrate a symbolic/ numeric parameter study by an interpolation example to introduce the SymPy command lambdify. Let us consider the task to interpolate the data x = [0, t, 1] and y = [0, 1,-1]. Here, t is a free parameter, which we will vary over the interval [-0.4, 1.4]. The quadratic interpolation polynomial has coefficients depending on this parameter:

.

Using SymPy and the monomial approach...

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