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IPython Interactive Computing and Visualization Cookbook

IPython Interactive Computing and Visualization Cookbook

By : Cyrille Rossant
4.5 (13)
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IPython Interactive Computing and Visualization Cookbook

IPython Interactive Computing and Visualization Cookbook

4.5 (13)
By: Cyrille Rossant

Overview of this book

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
Table of Contents (22 chapters)
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IPython Interactive Computing and Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Implementing an efficient rolling average algorithm with stride tricks


Stride tricks can be useful for local computations on arrays, when the computed value at a given position depends on the neighboring values. Examples include dynamical systems, digital filters, and cellular automata.

In this recipe, we will implement an efficient rolling average algorithm (a particular type of convolution-based linear filter) with NumPy stride tricks. A rolling average of a 1D vector contains, at each position, the average of the elements around this position in the original vector. Roughly speaking, this process filters out the noisy components of a signal so as to keep only the slower components.

Getting ready

Make sure to reuse the id() function from the Understanding the internals of NumPy to avoid unnecessary array copying recipe. This function returns the memory address of the internal data buffer of a NumPy array.

How to do it...

The idea is to start from a 1D vector, and make a virtual 2D array where...

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