Learning SciPy for Numerical and Scientific Computing - Second Edition

More Information
Learn
  • Get to know the benefits of using the combination of Python, NumPy, SciPy, and matplotlib as a programming environment for scientific purposes
  • Create and manipulate an object array used by SciPy
  • Use SciPy with large matrices to compute eigenvalues and eigenvectors
  • Focus on construction, acquisition, quality improvement, compression, and feature extraction of signals
  • Make use of SciPy to collect, organize, analyze, and interpret data, with examples taken from statistics and clustering
  • Acquire the skill of constructing a triangulation of points, convex hulls, Voronoi diagrams, and many similar applications
  • Find out ways that SciPy can be used with other languages such as C/C++, Fortran, and MATLAB/Octave
About

SciPy is an open source Python library used to perform scientific computing. The SciPy (Scientific Python) package extends the functionality of NumPy with a substantial collection of useful algorithms.

The book starts with a brief description of the SciPy libraries, followed by a chapter that is a fun and fast-paced primer on array creation, manipulation, and problem-solving. You will also learn how to use SciPy in linear algebra, which includes topics such as computation of eigenvalues and eigenvectors. Furthermore, the book is based on interesting subjects such as definition and manipulation of functions, computation of derivatives, integration, interpolation, and regression. You will also learn how to use SciPy in signal processing and how applications of SciPy can be used to collect, organize, analyze, and interpret data.

By the end of the book, you will have fast, accurate, and easy-to-code solutions for numerical and scientific computing applications.

Features
  • Use different modules and routines from the SciPy library quickly and efficiently
  • Create vectors and matrices and learn how to perform standard mathematical operations between them or on the respective array in a functional form
  • A step-by-step tutorial that will help users solve research-based problems from various areas of science using Scipy
Page Count 188
Course Length 5 hours 38 minutes
ISBN 9781783987702
Date Of Publication 26 Feb 2015

Authors

Francisco J. Blanco-Silva

Francisco J. Blanco-Silva is the owner of a scientific consulting company—Tizona Scientific Solutions—and adjunct faculty in the Department of Mathematics of the University of South Carolina. He obtained his formal training as an applied mathematician at Purdue University. He enjoys problem solving, learning, and teaching. Being an avid programmer and blogger, when it comes to writing, he relishes finding that common denominator among his passions and skills and making it available to everyone. He coauthored Modeling Nanoscale Imaging in Electron Microscopy, Springer along with Peter Binev, Wolfgang Dahmen, and Thomas Vogt.

Sergio J. Rojas G.

Sergio J. Rojas G. is currently a full professor of physics at Universidad Simón Bolívar, Venezuela. Regarding his formal studies, in 1991, he earned a BS in physics with his thesis on numerical relativity from the Universidad de Oriente, Estado Sucre, Venezuela, and then, in 1998, he earned a PhD in physics from the Department of Physics at City College of the City University of New York, where he worked on the applications of fluid dynamics in the flow of fluids in porous media, gaining and developing since then a vast experience in programming as an aid to scientific research via Fortran77/90 and C/C++. In 2001, he also earned a master's degree in computational finance from the Oregon Graduate Institute of Science and Technology.

Sergio's teaching activities involve lecturing undergraduate and graduate physics courses at his home university, Universidad Simón Bolívar, Venezuela, including a course on Monte Carlo methods and another on computational finance. His research interests include physics education research, fluid flow in porous media, and the application of the theory of complex systems and statistical mechanics in financial engineering. More recently, Sergio has been involved in machine learning and its applications in science and engineering via the Python programming language.

Erik A Christensen

Erik A Christensen is a quant analyst/developer in finance and creative industries. He has a PhD from the Technical University of Denmark, with postdoctoral studies at the Levich Institute at the City College of the City University of New York and the Courant Institute of Mathematical Sciences at New York University. His interests in technology span from Python to F# and Cassandra/Spark. He is active in the meet-up communities in London!