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  • Book Overview & Buying Mastering IPython 4.0
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Mastering IPython 4.0

Mastering IPython 4.0

By : Bitterman, Dipanjan Deb
3 (3)
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Mastering IPython 4.0

Mastering IPython 4.0

3 (3)
By: Bitterman, Dipanjan Deb
Buy this Book

Overview of this book

IPython is an interactive computational environment in which you can combine code execution, rich text, mathematics, plots, and rich media. This book will get IPython developers up to date with the latest advancements in IPython and dive deep into interactive computing with IPython. This an advanced guide on interactive and parallel computing with IPython will explore advanced visualizations and high-performance computing with IPython in detail. You will quickly brush up your knowledge of IPython kernels and wrapper kernels, then we'?ll move to advanced concepts such as testing, Sphinx, JS events, interactive work, and the ZMQ cluster. The book will cover topics such as IPython Console Lexer, advanced configuration, and third-party tools. By the end of this book, you will be able to use IPython for interactive and parallel computing in a high-performance computing environment.
Table of Contents (13 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Using IPython for HPC
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1. Using IPython for HPC
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The need for speed
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FORTRAN to the rescue – the problems FORTRAN addressed
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Choosing between IPython and Fortran
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An example case – Fast Fourier Transform
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High Performance Computing
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Going parallel
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Summary
2
2. Advanced Shell Topics
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2. Advanced Shell Topics
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What is IPython?
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Installing IPython
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All-in-one distributions
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What happened to the Notebook?
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Starting out with the terminal
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IPython beyond Python
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Magic commands
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Cython
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Configuring IPython
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Debugging
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Read-Eval-Print Loop (REPL) and IPython architecture
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Alternative development environments
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Summary
3
3. Stepping Up to IPython for Parallel Computing
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3. Stepping Up to IPython for Parallel Computing
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Serial processes
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Threading
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Using multiple processors
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The IPython parallel architecture
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Getting started with ipyparallel
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Parallel magic commands
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Types of parallelism
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Data parallelism
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Application steering
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Summary
4
4. Messaging with ZeroMQ and MPI
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4. Messaging with ZeroMQ and MPI
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The storage hierarchy
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ZeroMQ
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MPI
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ZeroMQ and IPython
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Summary
5
5. Opening the Toolkit – The IPython API
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5. Opening the Toolkit – The IPython API
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Performance profiling
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The AsyncResult class
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The Client class
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The View class
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Summary
6
6. Works Well with Others – IPython and Third-Party Tools
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6. Works Well with Others – IPython and Third-Party Tools
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R
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Octave
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Hy
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Summary
7
7. Seeing Is Believing– Visualization
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7. Seeing Is Believing– Visualization
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Matplotlib
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Bokeh
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R
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Python-nvd3
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Summary
8
8. But It Worked in the Demo! – Testing
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8. But It Worked in the Demo! – Testing
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Unit testing
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unittest
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pytest
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nose2
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Summary
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9. Documentation
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9. Documentation
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Inline comments
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Docstrings
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reStructuredText
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Docutils
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Sphinx
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Summary
10
10. Visiting Jupyter
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10. Visiting Jupyter
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Installation and startup
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The Dashboard
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Creating a notebook
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Interacting with Python scripts
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Working with cells
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Being graphic
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Format conversion
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Summary
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11. Into the Future
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11. Into the Future
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Some history
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The Jupyter project
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IPython
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The rise of parallelism
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Growing professionalism
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Summary
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Index
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Index

Interacting with Python scripts

A simple way to take Python code that already exists and bring it into a notebook is to use the %load magic in a cell:

%load <filename>.p

Running this cell will load the contents of <filename>.py into the current cell:

Running the cell again does not run the script – the point of using %load was simply to include the script on the page, not execute it.

Using the %run magic runs the script and inserts its output into the notebook as the output of that cell:

Tip

Using a notebook with tests

A notebook full of calls to test functions can be a convenient and attractive way to maintain a test suite and records of its results over time.

Of course, if the only thing a notebook did was to allow one to display the output of programs on a web page, it would be of limited usefulness. The real power of Jupyter comes into focus when working with individual cells.

CONTINUE READING
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