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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
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
9
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

Threading

Experience with multitasking systems showed that a smaller unit of control was required than the process itself. Two inter-requirements presented themselves:

  • The need for a single process to perform multiple activities
  • The need for these activities to share data with each other

The process model, where each process has its own address space and program counter and an expensive context switch is required to change the instruction stream, was a poor fit for these requirements. In particular, the feature of the context switch, in which the process's memory was swapped out directly contradicted the need for data sharing.

The solution was the idea of threads. A thread is like a process in that it is a sequence of instructions with an associated process counter. The difference is that several threads can share the same address space. In general, threads are components of processes that share the entire process address space, but can be scheduled separately. Switching from one thread...

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