About the Book
Key FeaturesBook DescriptionThis book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.What you will 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
Who this book is forThis book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.
Table of Contents:
Table of Contents- Introduction to SciPy
- Top-level SciPy
- SciPy for Linear Algebra
- SciPy for Numerical Analysis
- SciPy for Signal Processing
- SciPy for Data Mining-
- SciPy for Computational Geometry
- Interaction with Other Languages
About the Author :
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. C# (LINQ, .NET Framework, WPF, FinMath) (2008-), Python (NumPy, SciPy, Pandas, MatPlotLib) 2.7/3.3 (2011-), C++/C++11 (2007-), Java (2011-11) Functional: F# (2014-) Machine Learning PyML (2013-), MatLab Database: SQL (SSMS) (2008-), Hadoop/HDInsight (2014-) Shell: C-Shell (1990-2000), PowerShell ISE (2012-) Stat/Packages: MatLab (2013-) Quant/Packages: QuantOffice (2013) Source Control: GitHub (2013-) IDE: VS2012, SSMS (2008-) Agile: Scrum 2.1 (2012-) Office: Excel/VBA (2007-) I will always be indebted to Bradley J. Lucier and Rodrigo Bañuelos, for being a constant inspiration, for their guidance and teachings. Special thanks to my editors, Sriram Neelakantam, Bharat Patil, Nikhil Potdukhe, and Mohammad Rizvi. Many colleagues have contributed with encouragement and fruitful discussions. In particular, I would like to mention Parsa Bakhtary, Aaron Dutle, Edsel Peña, Pablo Sprechmann, Adam Taylor, and Holly Watson.
But the most special thanks go without a doubt to my wife and daughter. Grace's love and smiles alone provided all the motivation, enthusiasm and skills to overcome any difficulties encountered during the pursuit of this book, and everything life threw at me ever since she was born.
