Mastering Python Scientific Computing电子书

Mastering Python Scientific Computing

Table of Contents

Mastering Python Scientific Computing

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

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

Error analysis

Conditioning, stability, and accuracy

Backward and forward error analysis

Is it okay to ignore these errors?

Computer arithmetic and floating-point numbers

The background of the Python programming language

The guiding principles of the Python language

Why Python for scientific computing?

Compact and readable code

Holistic language design

Free and open source

Language interoperability

Portable and extensible

Hierarchical module system

Graphical user interface packages

Data structures

Python's testing framework

Available libraries

The downsides of Python

Summary

2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes

Mathematical components of scientific computations

A system of linear equations

A system of nonlinear equations

Optimization

Interpolation

Extrapolation

Numerical integration

Numerical differentiation

Differential equations

The initial value problem

The boundary value problem

Random number generator

Python scientific computing

Introduction to NumPy

The SciPy library

The SciPy Subpackage

Data analysis using pandas

A brief idea of interactive programming using IPython

IPython parallel computing

IPython Notebook

Symbolic computing using SymPy

The features of SymPy

Why SymPy?

The plotting library

Summary

3. Efficiently Fabricating and Managing Scientific Data

The basic concepts of data

Data storage software and toolkits

Files

Structured files

Unstructured files

Database

Possible operations on data

Scientific data format

Ready-to-use standard datasets

Data generation

Synthetic data generation (fabrication)

Using Python's built-in functions for random number generation

Bookkeeping functions

Functions for integer random number generation

Functions for sequences

Statistical-distribution-based functions

Nondeterministic random number generator

Designing and implementing random number generators based on statistical distributions

A program with simple logic to generate five-digit random numbers

A brief note about large-scale datasets

Summary

4. Scientific Computing APIs for Python

Numerical scientific computing in Python

The NumPy package

The ndarrays data structure

File handling

Some sample NumPy programs

The SciPy package

The optimization package

The interpolation package

Integration and differential equations in SciPy

The stats module

Clustering package and spatial algorithms in SciPy

Image processing in SciPy

Sample SciPy programs

Statistics using SciPy

Optimization in SciPy

Image processing using SciPy

Symbolic computations using SymPy

Computer Algebra System

Features of a general-purpose CAS

A brief idea of SymPy

Core capability

Polynomials

Calculus

Solving equations

Discrete math

Matrices

Geometry

Plotting

Physics

Statistics

Printing

SymPy modules

Simple exemplary programs

Basic symbol manipulation

Expression expansion in SymPy

Simplification of an expression or formula

Simple integrations

APIs and toolkits for data analysis and visualization

Data analysis and manipulation using pandas

Important data structures of pandas

Special features of pandas

Data visualization using matplotlib

Interactive computing in Python using IPython

Sample data analysis and visualization programs

Summary

5. Performing Numerical Computing

The NumPy fundamental objects

The ndarray object

The attributes of an array

Basic operations on arrays

Special operations on arrays (shape change and conversion)

Classes associated with arrays

The matrix sub class

Masked array

The structured/recor array

The universal function object

Attributes

Methods

Various available ufunc

The NumPy mathematical modules

Introduction to SciPy

Mathematical functions in SciPy

Advanced modules/packages

Integration

Signal processing (scipy.signal)

Fourier transforms (scipy.fftpack)

Spatial data structures and algorithms (scipy.spatial)

Optimization (scipy.optimize)

Interpolation (scipy.interpolate)

Linear algebra (scipy.linalg)

Sparse eigenvalue problems with ARPACK

Statistics (scipy.stats)

Multidimensional image processing (scipy.ndimage)

Clustering

Curve fitting

File I/O (scipy.io)

Summary

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

Hydrogen wave functions

Matrices and Pauli algebra

The quantum harmonic oscillator in 1-D and 3-D

Second quantization

High-energy Physics

Mechanics

Pretty printing

LaTeX 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

Matplotlib

The architecture of matplotlib

The scripting layer (pyplot)

The artist layer

The backend layer

Graphics with matplotlib

Output generation

The pandas library

Series

DataFrame

Panel

The common functionality among the data structures

Time series and date functions

Handling missing data

I/O operations

Working on CSV files

Ready-to-eat datasets

The pandas plotting

IPython

The IPython console and system shell

The operating system interface

Nonblocking plotting

Debugging

IPython Notebook

Summary

8. Parallel and Large-scale Scientific Computing

Parallel computing using IPython

The architecture of IPython parallel computing

The components of parallel computing

The IPython engine

The IPython controller

IPython view and interfaces

The IPython client

Example of performing parallel computing

A parallel decorator

IPython's magic functions

Activating specific views

Engines and QtConsole

Advanced features of IPython

Fault-tolerant execution

Dynamic load balancing

Pushing and pulling objects between clients and engines

Database support for storing the requests and results

Using MPI in IPython

Managing dependencies among tasks

Functional dependency

Decorators for functional dependency

Graph dependency

Impossible dependencies

The DAG dependency and the NetworkX library

Using IPython on an Amazon EC2 cluster with StarCluster

A note on security of IPython

Well-known parallel programming styles

Issues in parallel programming

Parallel programming

Concurrent programming

Distributed programming

Multiprocessing in Python

Multithreading in Python

Hadoop-based MapReduce in Python

Spark in Python

Summary

9. Revisiting Real-life Case Studies

Scientific computing applications developed in Python

The one Laptop per Child project used Python for their user interface

ExpEYES – eyes for science

A weather prediction application in Python

An aircraft conceptual designing tool and API in Python

OpenQuake Engine

SMS Siemag AG application for energy efficiency

Automated code generator for analysis of High-energy Physics data

Python for computational chemistry applications

Python for developing a Blind Audio Tactile Mapping System

TAPTools for air traffic control

Energy-efficient lights with an embedded system

Scientific computing libraries developed in Python

A maritime designing API by Tribon

Molecular Modeling Toolkit

Standard Python packages

Summary

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