Python for Scientists

Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets.

John M. Stewart (Author)

9781316641231, Cambridge University Press

Paperback / softback, published 20 July 2017

270 pages
24.5 x 17.4 x 1.4 cm, 0.55 kg

'This book is a rich resource for scientists who are familiar with programming and want to go beyond classical commercial scientific programming packages such as Mathematica and Maple … if you want to expand your research toolbox and computational flexibility, this book is a fantastic find … I highly recommend this book as a practical guide to real-life scientific programming. The book is well written, interspersed with great humor, and is presented from the viewpoint of a researcher who wants others to avoid suffering the same pitfalls and mistakes that he experienced.' Andreas Rueger, The Leading Edge

Scientific Python is a significant public domain alternative to expensive proprietary software packages. This book teaches from scratch everything the working scientist needs to know using copious, downloadable, useful and adaptable code snippets. Readers will discover how easy it is to implement and test non-trivial mathematical algorithms and will be guided through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the language's capabilities. The author also shows how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. In this new edition, several chapters have been re-written to reflect the IPython notebook style. With an extended index, an entirely new chapter discussing SymPy and a substantial increase in the number of code snippets, researchers and research students will be able to quickly acquire all the skills needed for using Python effectively.

1. Introduction
2. Getting started with IPython
3. A short Python tutorial
4. NumPy
5. Two-dimensional graphics
6. Multi-dimensional graphics
7. SymPy, a computer algebra system
8. Ordinary differential equations
9. Partial differential equations - a pseudospectral approach
10. Case study - multigrid
Appendix A. Installing a Python environment
Appendix B. Fortran77 subroutines for pseudospectral methods
References
Hints for using the index
Index.

Subject Areas: Programming & scripting languages: general [UMX], Information technology: general issues [UB], Numerical analysis [PBKS]