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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

19.5 Exercises

Ex. 1: Implement a method __add__ that constructs a new polynomial  by adding two given polynomials  and . In monomial form, polynomials are added by just adding the coefficients, whereas in Newton form, the coefficients depend on the abscissas  of the interpolation points. Before adding the coefficients of both polynomials, the polynomial  has to get new interpolation points with the property that their abscissas  coincide with those of , 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  as parameters and returns a new polynomial that interpolates self.points and .

Ex. 4: Write a class called LagrangePolynomial...

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