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Mastering Python Scientific Computing

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Mastering Python Scientific Computing

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Mastering Python Scientific Computing
Sep, 2015 | 300 pages
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Table of Contents (17 chapters)
Mastering Python Scientific Computing
Mastering Python Scientific Computing
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
The Landscape of Scientific Computing – and Why Python?
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
2
A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes
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
3
Efficiently Fabricating and Managing Scientific Data
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
4
Scientific Computing APIs for Python
Scientific Computing APIs for Python
Numerical scientific computing in Python
Symbolic computations using SymPy
APIs and toolkits for data analysis and visualization
Summary
5
Performing Numerical Computing
Performing Numerical Computing
The NumPy fundamental objects
Introduction to SciPy
Summary
6
Applying Python for Symbolic Computing
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
7
Data Analysis and Visualization
Data Analysis and Visualization
Matplotlib
The pandas library
I/O operations
IPython
Summary
8
Parallel and Large-scale Scientific Computing
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
9
Revisiting Real-life Case Studies
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
10
Best Practices for Scientific Computing
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
Index

Summary

In this chapter, we discussed the concepts, the features, and some selected sample programs of various scientific computing APIs and toolkits. The chapter started with a discussion of NumPy and SciPy. After covering NymPy, we discussed the concepts associated with symbolic computing and SymPy.

In the remaining chapter, we discussed interactive computing and data analysis and visualization, along with their APIs or toolkits. IPython is the Python toolkit for interactive computing. We also discussed the data analysis package named pandas and the data visualization API named matplotlib.

In next chapter, we will have a detailed discussion on the numerical computing API—NumPy. We will cover various functions of NumPy and the associated mathematical concepts with the help of sample programs.

Previous Section
End of Chapter 4
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