IPython Interactive Computing and Visualization Cookbook by Rossant, Cyrille -- Read -- Imperial Library of Trantor

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Cover Table of Contents IPython Interactive Computing and Visualization Cookbook IPython Interactive Computing and Visualization Cookbook Credits About the Author About the Reviewers www.PacktPub.com Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support 1. A Tour of Interactive Computing with IPython 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 2. Best Practices in Interactive Computing 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 3. Mastering the Notebook 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 4. Profiling and Optimization 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 5. High-performance Computing 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 6. Advanced Visualization 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 7. Statistical Data Analysis 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 8. Machine Learning 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 9. Numerical Optimization 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 10. Signal Processing 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 11. Image and Audio Processing 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 12. Deterministic Dynamical Systems 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 13. Stochastic Dynamical Systems Simulating a discrete-time Markov chain Simulating a Poisson process Simulating a Brownian motion Simulating a stochastic differential equation 14. Graphs, Geometry, and Geographic Information Systems 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 15. Symbolic and Numerical Mathematics 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 Index

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