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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

Elementary Functions


Examples for elementary functions in SymPy are trigonometric functions and their inverses. The following example shows how simplify acts on expression which include elementary function:

x = symbols('x')
simplify(cos(x)**2 + sin(x)**2)  # returns 1

Here is another example for the use of elementary functions:

atan(x).diff(x) - 1./(x**2+1)  # returns 0

If you use SciPy and SymPy together, we strongly recommend that you use them in different namespaces:

import scipy as sp
import sympy as sym
# working with numbers
x=3
y=sp.sin(x)
# working with symbols
x=sym.symbols('x')
y=sym.sin(x)   

Lambda - functions

In section Anonymous functions of Chapter 7, Functions, we saw how to define so-called anonymous functions in Python. The counterpart in SymPy is done by the Lambda command. Note the difference; lambda is a keyword while Lambda is a constructor.

The command Lambda takes two arguments, the symbol of the function's independent variable, and...

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