Mastering Python Scientific Computing

A complete guide for Python programmers to master scientific computing using Python APIs and tools
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Mastering Python Scientific Computing

Hemant Kumar Mehta

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A complete guide for Python programmers to master scientific computing using Python APIs and tools

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Book Details

ISBN 139781783288823
Paperback300 pages

Book Description

In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing.

At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python.

The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs.

Table of Contents

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

What You Will Learn

  • Fundamentals and components of scientific computing
  • Scientific computing data management
  • Performing numerical computing using NumPy and SciPy
  • Concepts and programming for symbolic computing using SymPy
  • Using the plotting library matplotlib for data visualization
  • Data analysis and visualization using Pandas, matplotlib, and IPython
  • Performing parallel and high performance computing
  • Real-life case studies and best practices of scientific computing

Authors

Table of Contents

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

Book Details

ISBN 139781783288823
Paperback300 pages
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