Ronan Lamy - Instant SymPy Starter
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Copyright 2013 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: May 2013
Production Reference: 1170513
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78216-362-6
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Author
Ronan Lamy
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Douglas B. Meade
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To obtain his Ph.D. in Physics, Ronan Lamy spent four years analyzing data in a proprietary computer algebra system. This taught him the power of symbolic computation. After quitting academia, and losing access to all his work, he discovered the beauty of Python and understood, a little late, the benefits of using a well-designed, general-purpose programming language with a free license.
He is now an open source developer and Python consultant, and remains fascinated by symbolic computation. He has been a core developer of SymPy since 2009.
Douglas B. Meade is an Associate Professor and Director of Undergraduate Studies in the Department of Mathematics at The University of South Carolina in Columbia, SC. His research interests include numerical PDEs, mathematical modeling, and applications of computer algebra. He has been using symbolic mathematics, primarily Maple, for more than 25 years. He is a co-author of Getting Started with Maple (2009, third edition, Wiley, ISBN: 978-0470455548).
Prof. Meade's current project is Maplets for Calculus, a collection of drill-and-practice maplets on nearly 200 topics from precalculus through vector calculus. He is intrigued by the possibilities of SymPy to facilitate the use of symbolic mathematics to an even wider range of academic and educational applications.
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Welcome to Instant SymPy Starter . This book has been especially created to provide you with all the information that you need to get up to speed with SymPy. You will learn the basics of SymPy, build your first SymPy code, and discover functions and tricks of the trade when it comes to using SymPy.
This document contains the following sections:
So, what is SymPy? helps you find out what SymPy actually is, what you can do with it, and why its so great.
Installation teaches you how to try out SymPy in your browser, download and install it with minimum fuss, and then set up its optional dependencies so that you can use all its functionalities.
Quick start Automated curve sketching will show you how to perform one of the core tasks of SymPy, automating mathematical tasks, by guiding you through the creation of a commonplace curve sketching application.
Top 5 features you need to know about will teach you how to perform five tasks with the most important features of SymPy. By the end of this section you will be able to create symbolic expressions, manipulate them, convert them to efficient numerical functions, compute limits, integrals and derivatives, and solve equations.
People and places you should get to know provides you with many useful links to the open source project as it is centered around a community. You will find the project page and forums, as well as a number of helpful articles, tutorials, blogs, and the Twitter feeds of SymPy contributors.
SymPy is a library for symbolic computation written entirely in Python and distributed under a BSD-type permissive free software license. Its core symbolic capabilities include:
- Elementary algebra
- Pattern matching and substitution
- Symbolic transformations and simplifications
- All the common mathematical functions and many special ones
- Calculus: limits, derivatives, integrals, Taylor series, and so on
- Solving many kinds of equations, including differential equations
- Linear algebra
- Polynomials
- Infinite sums and products
- Planar geometry
- Physical constants and units
- Numeric computation in arbitrary precision
SymPy also includes subpackages dealing with advanced, specialized topics such as category theory, quantum logic, mechanical dynamics, or statistics. Even if you have no use for their functionalities, they can serve as useful examples of how to encode complex domain knowledge.
SymPy is well integrated with the rest of the scientific Python ecosystem. Using it inside the IPython notebook provides a productive environment for prototyping and exploration of symbolic models and calculations. Powerful numerical librariessuch as NumPy, SciPy, pandas, and sklearnare also just a few import statements away, and SymPy has features to help smooth the transition between the symbolic and numeric worlds.
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