IPython Interactive Computing and Visualization Cookbook

Getting started with Sage

Analyzing a nonlinear differential system – Lotka-Volterra (predator-prey) equations

Finding a Boolean propositional formula from a truth table

A bit of number theory with SymPy

Computing exact probabilities and manipulating random variables

Analyzing real-valued functions

Solving equations and inequalities

Diving into symbolic computing with SymPy

Introduction

Chapter 15. Symbolic and Numerical Mathematics

Creating a route planner for a road network

Manipulating geospatial data with Shapely and basemap

Computing the Voronoi diagram of a set of points

Computing connected components in an image

Resolving dependencies in a directed acyclic graph with a topological sort

Analyzing a social network with NetworkX

Manipulating and visualizing graphs with NetworkX

Introduction

Chapter 14. Graphs Geometry and Geographic Information Systems

Simulating a stochastic differential equation

Simulating a Brownian motion

Simulating a Poisson process

Simulating a discrete-time Markov chain

Introduction

Chapter 13. Stochastic Dynamical Systems

Simulating a partial differential equation – reaction-diffusion systems and Turing patterns

Simulating an ordinary differential equation with SciPy

Simulating an elementary cellular automaton

Plotting the bifurcation diagram of a chaotic dynamical system

Introduction

Chapter 12. Deterministic Dynamical Systems

Creating a sound synthesizer in the notebook

Applying digital filters to speech sounds

Detecting faces in an image with OpenCV

Finding points of interest in an image

Segmenting an image

Applying filters on an image

Manipulating the exposure of an image

Introduction

Chapter 11. Image and Audio Processing

Computing the autocorrelation of a time series

Applying a linear filter to a digital signal

Analyzing the frequency components of a signal with a Fast Fourier Transform

Introduction

Chapter 10. Signal Processing

Finding the equilibrium state of a physical system by minimizing its potential energy

Fitting a function to data with nonlinear least squares

Minimizing a mathematical function

Finding the root of a mathematical function

Introduction

Chapter 9. Numerical Optimization

Detecting hidden structures in a dataset with clustering

Reducing the dimensionality of a dataset with a principal component analysis

Using a random forest to select important features for regression

Using support vector machines for classification tasks

Learning from text – Naive Bayes for Natural Language Processing

Learning to recognize handwritten digits with a K-nearest neighbors classifier

Predicting who will survive on the Titanic with logistic regression

Getting started with scikit-learn

Introduction

Chapter 8. Machine Learning

Analyzing data with the R programming language in the IPython notebook

Fitting a Bayesian model by sampling from a posterior distribution with a Markov chain Monte Carlo method

Estimating a probability distribution nonparametrically with a kernel density estimation

Fitting a probability distribution to data with the maximum likelihood method

Estimating the correlation between two variables with a contingency table and a chi-squared test

Getting started with Bayesian methods

Getting started with statistical hypothesis testing – a simple z-test

Exploring a dataset with pandas and matplotlib

Introduction

Chapter 7. Statistical Data Analysis

Getting started with Vispy for high-performance interactive data visualizations

Converting matplotlib figures to D3.js visualizations with mpld3

Visualizing a NetworkX graph in the IPython notebook with D3.js

Creating interactive web visualizations with Bokeh

Creating beautiful statistical plots with seaborn

Making nicer matplotlib figures with prettyplotlib

Introduction

Chapter 6. Advanced Visualization

Trying the Julia language in the notebook

Parallelizing code with MPI in IPython

Interacting with asynchronous parallel tasks in IPython

Distributing Python code across multiple cores with IPython

Writing massively parallel code for heterogeneous platforms with OpenCL

Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA

Releasing the GIL to take advantage of multicore processors with Cython and OpenMP

Optimizing Cython code by writing less Python and more C

Accelerating Python code with Cython

Wrapping a C library in Python with ctypes

Accelerating array computations with Numexpr

Accelerating pure Python code with Numba and just-in-time compilation

Introduction

Chapter 5. High-performance Computing

Manipulating large heterogeneous tables with HDF5 and PyTables

Manipulating large arrays with HDF5 and PyTables

Processing huge NumPy arrays with memory mapping

Making efficient array selections in NumPy

Implementing an efficient rolling average algorithm with stride tricks

Using stride tricks with NumPy

Understanding the internals of NumPy to avoid unnecessary array copying

Profiling the memory usage of your code with memory_profiler

Profiling your code line-by-line with line_profiler

Profiling your code easily with cProfile and IPython

Evaluating the time taken by a statement in IPython

Introduction

Chapter 4. Profiling and Optimization

Processing webcam images in real time from the notebook

Creating a custom JavaScript widget in the notebook – a spreadsheet editor for pandas

Using interactive widgets – a piano in the notebook

Customizing the CSS style in the notebook

Adding custom controls in the notebook toolbar

Converting an IPython notebook to other formats with nbconvert

Teaching programming in the notebook with IPython blocks

Introduction

Chapter 3. Mastering the Notebook

Debugging your code with IPython

Writing unit tests with nose

Writing high-quality Python code

Ten tips for conducting reproducible interactive computing experiments

A typical workflow with Git branching

Learning the basics of the distributed version control system Git

Efficient interactive computing workflows with IPython

Choosing (or not) between Python 2 and Python 3

Introduction

Chapter 2. Best Practices in Interactive Computing

Creating a simple kernel for IPython

Mastering IPython's configuration system

Creating an IPython extension with custom magic commands

Introducing the multidimensional array in NumPy for fast array computations

Getting started with exploratory data analysis in IPython

Introducing the IPython notebook

Introduction

Chapter 1. A Tour of Interactive Computing with IPython

Customer support

Reader feedback

Conventions

Who this book is for

What you need for this book

What this book covers

What this book is

Preface

Support files eBooks discount offers and more

www.PacktPub.com

About the Reviewers

About the Author

Credits

版权页

封面

封面

版权页

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files eBooks discount offers and more

Preface

What this book is

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Chapter 1. A Tour of Interactive Computing with IPython

Introduction

Introducing the IPython notebook

Getting started with exploratory data analysis in IPython

Introducing the multidimensional array in NumPy for fast array computations

Creating an IPython extension with custom magic commands

Mastering IPython's configuration system

Creating a simple kernel for IPython

Chapter 2. Best Practices in Interactive Computing

Introduction

Choosing (or not) between Python 2 and Python 3

Efficient interactive computing workflows with IPython

Learning the basics of the distributed version control system Git

A typical workflow with Git branching

Ten tips for conducting reproducible interactive computing experiments

Writing high-quality Python code

Writing unit tests with nose

Debugging your code with IPython

Chapter 3. Mastering the Notebook

Introduction

Teaching programming in the notebook with IPython blocks

Converting an IPython notebook to other formats with nbconvert

Adding custom controls in the notebook toolbar

Customizing the CSS style in the notebook

Using interactive widgets – a piano in the notebook

Creating a custom JavaScript widget in the notebook – a spreadsheet editor for pandas

Processing webcam images in real time from the notebook

Chapter 4. Profiling and Optimization

Introduction

Evaluating the time taken by a statement in IPython

Profiling your code easily with cProfile and IPython

Profiling your code line-by-line with line_profiler

Profiling the memory usage of your code with memory_profiler

Understanding the internals of NumPy to avoid unnecessary array copying

Using stride tricks with NumPy

Implementing an efficient rolling average algorithm with stride tricks

Making efficient array selections in NumPy

Processing huge NumPy arrays with memory mapping

Manipulating large arrays with HDF5 and PyTables

Manipulating large heterogeneous tables with HDF5 and PyTables

Chapter 5. High-performance Computing

Introduction

Accelerating pure Python code with Numba and just-in-time compilation

Accelerating array computations with Numexpr

Wrapping a C library in Python with ctypes

Accelerating Python code with Cython

Optimizing Cython code by writing less Python and more C

Releasing the GIL to take advantage of multicore processors with Cython and OpenMP

Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA

Writing massively parallel code for heterogeneous platforms with OpenCL

Distributing Python code across multiple cores with IPython

Interacting with asynchronous parallel tasks in IPython

Parallelizing code with MPI in IPython

Trying the Julia language in the notebook

Chapter 6. Advanced Visualization

Introduction

Making nicer matplotlib figures with prettyplotlib

Creating beautiful statistical plots with seaborn

Creating interactive web visualizations with Bokeh

Visualizing a NetworkX graph in the IPython notebook with D3.js

Converting matplotlib figures to D3.js visualizations with mpld3

Getting started with Vispy for high-performance interactive data visualizations

Chapter 7. Statistical Data Analysis

Introduction

Exploring a dataset with pandas and matplotlib

Getting started with statistical hypothesis testing – a simple z-test

Getting started with Bayesian methods

Estimating the correlation between two variables with a contingency table and a chi-squared test

Fitting a probability distribution to data with the maximum likelihood method

Estimating a probability distribution nonparametrically with a kernel density estimation

Fitting a Bayesian model by sampling from a posterior distribution with a Markov chain Monte Carlo method

Analyzing data with the R programming language in the IPython notebook

Chapter 8. Machine Learning

Introduction

Getting started with scikit-learn

Predicting who will survive on the Titanic with logistic regression

Learning to recognize handwritten digits with a K-nearest neighbors classifier

Learning from text – Naive Bayes for Natural Language Processing

Using support vector machines for classification tasks

Using a random forest to select important features for regression

Reducing the dimensionality of a dataset with a principal component analysis

Detecting hidden structures in a dataset with clustering

Chapter 9. Numerical Optimization

Introduction

Finding the root of a mathematical function

Minimizing a mathematical function

Fitting a function to data with nonlinear least squares

Finding the equilibrium state of a physical system by minimizing its potential energy

Chapter 10. Signal Processing

Introduction

Analyzing the frequency components of a signal with a Fast Fourier Transform

Applying a linear filter to a digital signal

Computing the autocorrelation of a time series

Chapter 11. Image and Audio Processing

Introduction

Manipulating the exposure of an image

Applying filters on an image

Segmenting an image

Finding points of interest in an image

Detecting faces in an image with OpenCV

Applying digital filters to speech sounds

Creating a sound synthesizer in the notebook

Chapter 12. Deterministic Dynamical Systems

Introduction

Plotting the bifurcation diagram of a chaotic dynamical system

Simulating an elementary cellular automaton

Simulating an ordinary differential equation with SciPy

Simulating a partial differential equation – reaction-diffusion systems and Turing patterns

Chapter 13. Stochastic Dynamical Systems

Introduction

Simulating a discrete-time Markov chain

Simulating a Poisson process

Simulating a Brownian motion

Simulating a stochastic differential equation

Chapter 14. Graphs Geometry and Geographic Information Systems

Introduction

Manipulating and visualizing graphs with NetworkX

Analyzing a social network with NetworkX

Resolving dependencies in a directed acyclic graph with a topological sort

Computing connected components in an image

Computing the Voronoi diagram of a set of points

Manipulating geospatial data with Shapely and basemap

Creating a route planner for a road network

Chapter 15. Symbolic and Numerical Mathematics

Introduction

Diving into symbolic computing with SymPy

Solving equations and inequalities

Analyzing real-valued functions

Computing exact probabilities and manipulating random variables

A bit of number theory with SymPy

Finding a Boolean propositional formula from a truth table

Analyzing a nonlinear differential system – Lotka-Volterra (predator-prey) equations

Getting started with Sage