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Description

The SciPy stack is a collection of open source libraries of the powerful scripting language Python, together with its interactive shells. This environment offers a cutting-edge platform for numerical computation, programming, visualization and publishing, and is used by some of the world’s leading mathematicians, scientists, and engineers. It works on any operating system that supports Python and is very easy to install, and completely free of charge! It can effectively transform into a data-processing and system-prototyping environment, directly rivalling MATLAB and Octave. This book goes beyond a mere description of the different built-in functions coded in the libraries from the SciPy stack. It presents you with a solid mathematical and computational background to help you identify the right tools for each problem in scientific computing and visualization. You will gain an insight into the best practices with numerical methods depending on the amount or type of data, properties of the mathematical tools employed, or computer architecture, among other factors. The book kicks off with a concise exploration of the basics of numerical linear algebra and graph theory for the treatment of problems that handle large data sets or matrices. In the subsequent chapters, you will delve into the depths of algorithms in symbolic algebra and numerical analysis to address modeling/simulation of various real-world problems with functions (through interpolation, approximation, or creation of systems of differential equations), and extract their representing features (zeros, extrema, integration or differentiation). Lastly, you will move on to advanced concepts of data analysis, image/signal processing, and computational geometry.

What you will learn

Master relevant algorithms used in symbolic or numerical mathematics to address the approximation, interpolation, and optimization of scalar or multivariate functions

Develop different algorithms and strategies to effectively store and manipulate large matrices of data, with a view to solving various problems in numerical linear algebra

Understand how to model physical problems with systems of differential equations and distinguish the factors that dictate the strategies to solve them numerically

Perform statistical analysis, inference, data mining, and machine learning at higher level, and apply these to realworld problems

Adapt valuable ideas in computational geometry like Delaunay triangulations, Voronoi diagrams, geometric query problems, or Bezier curves, and apply them to various engineering problems

Familiarize yourself with different methods to represent and compress images, as well as techniques used in image processing, including edition, restoration, inpainting, segmentation, or feature recognition

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Learning SciPy for Numerical and Scientific Computing Second Edition

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Raiyan Kamal Dec 29, 2015

I'm one of the technical reviewers of this book. The book is written for readers who are already familiar with Python and some basic knowledge on various numerical methods. This book will help you a great deal to familiarize with the various Python modules for well known scientific computing use cases.Many important topics of scientific computing are covered, including:- Differential Equations- Optimization- Computational Geometry- Image processingEach chapter takes the reader through steps working on an example problem with gradually increasing complexity. Frequent code snippets from IPython notebook and matplotlib charts make the examples easy to follow. Diagrams, textual and image output is also shown whenever necessary.I'm happy to have reviewed this book and I'm very well pleased with the result. I believe this book would prove to be useful for anyone interested in harnessing the power of SciPy stack for their scientific or engineering profession or graduate level research.

Amazon Verified review

William J. Brown Nov 23, 2021

There is much that can be learned from the book, but it was written in 2015 and the code hasn't been updated for python3, so much of it is unusable at this point. Also, so much math is required to exercise SciPy that it's difficult write a book that shows off the library without wandering down a very large number of paths into arcane algorithms each with a limited audience.

Mastering SciPy: Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack

What do you get with Print?

Mastering SciPy

Chapter 2. Interpolation and Approximation

Motivation

Interpolation

Least squares approximation

Linear least squares approximation

Summary

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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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