Name: Mastering Python Scientific Computing
Author: Hemant Kumar Mehta
ISBN: 9781783288830

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Mastering Python Scientific Computing

Hemant Kumar Mehta

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Mastering Python Scientific Computing
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. The Landscape of Scientific Computing – and Why Python?
Definition of scientific computing
A simple flow of the scientific computation process
Examples from scientific/engineering domains
A strategy for solving complex problems
Approximation, errors, and associated concepts and terms
Computer arithmetic and floating-point numbers
The background of the Python programming language
Summary
Chapter 2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes
Mathematical components of scientific computations
Python scientific computing
A brief idea of interactive programming using IPython
Symbolic computing using SymPy
Summary
Chapter 3. Efficiently Fabricating and Managing Scientific Data
The basic concepts of data
Data storage software and toolkits
Possible operations on data
Scientific data format
Ready-to-use standard datasets
Data generation
Synthetic data generation (fabrication)
A brief note about large-scale datasets
Summary
Chapter 4. Scientific Computing APIs for Python
Numerical scientific computing in Python
Symbolic computations using SymPy
APIs and toolkits for data analysis and visualization
Summary
Chapter 5. Performing Numerical Computing
The NumPy fundamental objects
Introduction to SciPy
Summary
Chapter 6. Applying Python for Symbolic Computing
Symbols, expressions, and basic arithmetic
Equation solving
Functions for rational numbers, exponentials, and logarithms
Polynomials
Trigonometry and complex numbers
Linear algebra
Calculus
Vectors
The physics module
Pretty printing
The cryptography module
Parsing input
The logic module
The geometry module
Symbolic integrals
Polynomial manipulation
Sets
The simplify and collect operations
Summary
Chapter 7. Data Analysis and Visualization
Matplotlib
The pandas library
I/O operations
IPython
Summary
Chapter 8. Parallel and Large-scale Scientific Computing
Parallel computing using IPython
The architecture of IPython parallel computing
Example of performing parallel computing
Advanced features of IPython
A note on security of IPython
Summary
Chapter 9. Revisiting Real-life Case Studies
Scientific computing applications developed in Python
Python for developing a Blind Audio Tactile Mapping System
Scientific computing libraries developed in Python
Summary
Chapter 10. Best Practices for Scientific Computing
The best practices for designing
The implementation of best practices
The best practices for data management and application deployment
The best practices to achieving high performance
The best practices for data privacy and security
Testing and maintenance best practices
General Python best practices
Summary
Index

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Mastering Python Scientific Computing