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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. References

19.1.4 Usage examples of the polynomial class

Let's give some usage examples.

First, we create a polynomial instance from the given interpolation points:

p = PolyNomial(points=[(1,0),(2,3),(3,8)])

The polynomial's coefficients with respect to the monomial basis are available as an attribute of p:

p.coeff # returns array([ 1., 0., -1.]) (rounded)

This corresponds to the polynomial  . The default plot of the polynomial, obtained by p.plot((-3.5,3.5)), results in the following figure (Figure 19.1):

Figure 19.1: Result of the polynomial plot method

Finally, we compute the zeros of the polynomial, which in this case are two real numbers:

pz = p.zeros() # returns array([-1.+0.j, 1.+0.j])

The result can be verified by evaluating the polynomial at these points:

p(pz) # returns array([0.+0.j, 0.+0.j])
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