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Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
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Authors (4):
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Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Fuhrer Claus Fuhrer
Author Profile Icon Claus Fuhrer
Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
Author Profile Icon Claus Führer
Claus Führer
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 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

Newton polynomial

The NewtonPolyNomial class defines a polynomial described with respect to the Newton basis. We let it inherit some common methods from the polynomial base class, for example, polynomial.plot, polynomial.zeros, and even parts of the __init__ method, by using the super command (refer to section Subclassing and Inheritance in Chapter 8, Classes):

class NewtonPolynomial(PolyNomial):
    base = 'Newton'
    def __init__(self,**args):
        if 'coeff' in args:
            try:
                self.xi = array(args['xi'])
            except KeyError: 
                raise ValueError('Coefficients need to be given'
                'together with abscissae values xi')
        super(NewtonPolynomial, self).__init__(**args)

Once the interpolation points are given, the computation of the coefficients is performed by:

def point_2_coeff(self):
    return array(list(self.divdiff()))

Here we used divided differences for computing the...

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