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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
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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
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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
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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
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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
12
Index
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Index

Performance profiling

Before optimizing a system, it is handy to know exactly which part is slow. IPython adds some specialized tools for this purpose, along with the tools that Python provides.

Using utils.timing

IPython provides a library called IPython.utils.timing to time code execution. The library can be useful as relatively lightweight calls to include in code. Here is an example:

In [64]: import IPython

In [65]: IPython.utils.timing.clocku()
Out[65]: 218.533777

In [67]: IPython.utils.timing.clocku()
Out[67]: 218.542776

This library distinguishes between two categories of time—user CPU time and system CPU time—as evidenced in the following functions:

Function

Description

clocku( )

Returns the user CPU time in seconds since the start of the process

clocks( )

Returns the system CPU time in seconds since the start of the process

clock( )

Returns the total (user + system) CPU time in seconds since the start of the process

clock2( )

Returns a tuple of user/system...

CONTINUE READING
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Tech Concepts
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Programming languages
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