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Mastering SciPy

You're reading from   Mastering SciPy Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781783984749
Length 404 pages
Edition 1st Edition
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Authors (2):
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Francisco Javier Blanco-Silva Francisco Javier Blanco-Silva
Author Profile Icon Francisco Javier Blanco-Silva
Francisco Javier Blanco-Silva
Francisco Javier B Silva Francisco Javier B Silva
Author Profile Icon Francisco Javier B Silva
Francisco Javier B Silva
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Toc

Interpolation

We have three different implementation methodologies to deal with interpolation problems:

  • A procedural mode that computes a set of data points (in the form of ndarray with the required dimension) representing the actual solution.
  • In a few special cases, a functional mode that provides us with numpy functions representing the solutions.
  • An object-oriented mode that creates classes for interpolation problems. Different classes have different methods, depending on the operations that the particular kinds of interpolants enjoy. The advantage of this mode is that, through these methods, we can request more information from the solutions: not only evaluation or representation, but also relevant operations like searching for roots, computing derivatives and antiderivatives, error checking, and calculating coefficients and knots.

The choice of mode to represent our interpolants is up to us, depending mostly on how much accuracy we require, and the information/operations that we need afterwards...

You have been reading a chapter from
Mastering SciPy
Published in: Nov 2015
Publisher:
ISBN-13: 9781783984749
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