Book Image

Mastering IPython 4.0

By : Thomas Bitterman, Dipanjan Deb
Book Image

Mastering IPython 4.0

By: Thomas Bitterman, Dipanjan Deb

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.

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Mastering IPython 4.0
Thomas Bitterman | Dipanjan Deb
May, 2016 | 382 pages
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Table of Contents (18 chapters)
Mastering IPython 4.0
Mastering IPython 4.0
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Using IPython for HPC
Using IPython for HPC
The need for speed
FORTRAN to the rescue – the problems FORTRAN addressed
Choosing between IPython and Fortran
An example case – Fast Fourier Transform
High Performance Computing
Going parallel
Summary
2
Advanced Shell Topics
Advanced Shell Topics
What is IPython?
Installing IPython
All-in-one distributions
What happened to the Notebook?
Starting out with the terminal
IPython beyond Python
Magic commands
Cython
Configuring IPython
Debugging
Read-Eval-Print Loop (REPL) and IPython architecture
Alternative development environments
Summary
3
Stepping Up to IPython for Parallel Computing
Stepping Up to IPython for Parallel Computing
Serial processes
Threading
Using multiple processors
The IPython parallel architecture
Getting started with ipyparallel
Parallel magic commands
Types of parallelism
Data parallelism
Application steering
Summary
4
Messaging with ZeroMQ and MPI
Messaging with ZeroMQ and MPI
The storage hierarchy
ZeroMQ
MPI
ZeroMQ and IPython
Summary
5
Opening the Toolkit – The IPython API
Opening the Toolkit – The IPython API
Performance profiling
The AsyncResult class
The Client class
The View class
Summary
6
Works Well with Others – IPython and Third-Party Tools
Works Well with Others – IPython and Third-Party Tools
R
Octave
Hy
Summary
7
Seeing Is Believing– Visualization
Seeing Is Believing– Visualization
Matplotlib
Bokeh
R
Python-nvd3
Summary
8
But It Worked in the Demo! – Testing
But It Worked in the Demo! – Testing
Unit testing
unittest
pytest
nose2
Summary
9
Documentation
Documentation
Inline comments
Docstrings
reStructuredText
Docutils
Sphinx
Summary
10
Visiting Jupyter
Visiting Jupyter
Installation and startup
The Dashboard
Creating a notebook
Interacting with Python scripts
Working with cells
Being graphic
Format conversion
Summary
11
Into the Future
Into the Future
Some history
The Jupyter project
IPython
The rise of parallelism
Growing professionalism
Summary
Index
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 times

Another...

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