Index
A
- Advanced Message Queuing Protocol (AMQP) / The custom ZeroMQ cluster
- Amazon Machine Image (AMI) / Creating a host server on EC2
- analysis of precipitation
- about / Analysis of precipitation
- analysis of temperature
- about / Analysis of temperature
- Apache Kafka
- about / Distributed data
- Apache Spark
- about / Distributed data
- Apache Storm
- about / Distributed data
- artist layer
- about / The current matplotlib architecture, The artist layer
- Artist class / The artist layer
- Artist subclasses / The artist layer
- primitives / Primitives
- containers / Containers
- collections / Collections
- view / A view of the artist layer
- Automobile Data Set
- about / Subplots
B
- B-trees
- about / HDF5 and PyTables
- backend layer
- about / The current matplotlib architecture, The backend layer
- FigureCanvasBase / FigureCanvasBase
- RendererBase / RendererBase
- event / Event
- visualizing / Visualizing the backend layer
- backends
- functional categories / The backend layer
- user interface backends / The backend layer
- hardcopy backends / The backend layer
- baselines
- masteringmatplotlib/python / Configuration management
- masteringmatplotlib/scipy / Configuration management
- masteringmatplotlib/eros / Configuration management
- big data
- about / Big data
- big data, on filesystem
- about / Big data on the filesystem
- NumPy's memmap function / Big data on the filesystem
- PyTables / Big data on the filesystem
- Bokeh
- about / The grammar of graphics, Bokeh, Other visualization tools
- URL / Other visualization tools
- Bulk Download Application (BDA) / The data source
- Burr distribution / An example problem
C
- Chef / Configuration management
- clustering
- about / Clustering and parallel programming, More clustering
- clustering, with IPython
- about / Clustering with IPython, Getting started
- direct view / The direct view
- load-balanced view / The load-balanced view
- parallel magic functions / The parallel magic functions
- example / An example – estimating the value of π
- coding style / Coding style
- collections
- about / Collections
- compound event handling
- about / Compound event handling
- navigation toolbar / The navigation toolbar
- specialized events / Specialized events
- interactive and zooming / Interactive panning and zooming
- compound events
- about / Compound event handling
- configuration
- about / Configuration
- run control / The run control for matplotlib
- options in IPython / Options in IPython
- containers
- about / Containers
- controller client
- about / Clustering with IPython
- customization
- about / Customization
- custom style, creating / Creating a custom style
- subplots / Subplots
- exploring / Further explorations in customization
- custom ZeroMQ cluster
- about / The custom ZeroMQ cluster
- π value, estimating / Estimating the value of π
- ZeroMQ components, creating / Creating the ZeroMQ components
- results, working with / Working with the results
D
- data analysis
- about / Data analysis
- Pandas / Pandas, SciPy, and Seaborn
- SciPy / Pandas, SciPy, and Seaborn
- Seaborn / Pandas, SciPy, and Seaborn
- dataset, examining / Examining and shaping a dataset
- dataset, shaping / Examining and shaping a dataset
- analysis of temperature / Analysis of temperature
- analysis of precipitation / Analysis of precipitation
- data parallelization
- about / Clustering and parallel programming
- design patterns
- about / The grammar of graphics
- distributed data
- about / Distributed data
- MapReduce / MapReduce
- open source options / Open source options
- example / An example – working with data on EMR
- Docker / Configuration management
- Docker container / Dockerfiles and the Docker images
- Dockerfile / Dockerfiles and the Docker images
- Docker Hub / Dockerfiles and the Docker images
- Docker image / Dockerfiles and the Docker images
E
- Elastic Compute Cloud (EC2) / Types of deployment
- Elastic MapReduce (EMR) / Open source options
- event-based systems
- about / Event-based systems
- event handling
- about / Event handling
- mouse events / Mouse events
- keyboard events / Keyboard events
- axes and figure events / Axes and figure events
- object picking / Object picking
- compound event handling / Compound event handling
- event loops, matplotlib
- about / Event loops in matplotlib, The event loop
- event-based systems / Event-based systems
- GUI toolkit main loops / GUI toolkit main loops
- IPython Notebook event loops / IPython Notebook event loops
- matplotlib event loops / matplotlib event loops
- events
- about / Event
- Event class / Event
- ShowBase / Event
- TimerBase / Event
- example, AWS and Docker
- about / An example – AWS and Docker
- local setup / Getting set up locally
- requisites, for local setup / Requirements
- Dockerfiles / Dockerfiles and the Docker images
- Docker images / Dockerfiles and the Docker images
- Docker image, extending / Extending a Docker image
- new image, building / Building a new image
- deployment, preparing for / Preparing for deployment
- setup, obtaining on AWS / Getting the setup on AWS
- task, running / Running the task
- execution flow
- about / The execution flow
- overview of script / An overview of the script
- interactive session / An interactive session
F
- false color image / Defining a workflow
- fast Fourier transform (FFT) module
- about / The procedural pylab API
- FigureCanvasBase
- about / FigureCanvasBase
G
- ggplot / The intermediate matplotlib user
- ggplot2
- about / The grammar of graphics
- Git / Prerequisites for this book
- GNU Compiler Collection (gcc) / Prerequisites for this book
- GNU make / Prerequisites for this book
- GTK+ visualization tool / A brief historical overview of matplotlib
- GUI toolkit main loops
- about / GUI toolkit main loops
H
- hardcopy backends
- about / The backend layer
- HDF4
- about / HDF5 and PyTables
- HDF5
- about / HDF5 and PyTables
- Hierarchical Data Format (HDF)
- about / HDF5 and PyTables
- high-level plotting
- about / High-level plotting
- historical background / Historical background
- matplotlib / matplotlib
- NetworkX / NetworkX
- Pandas / Pandas
- grammar of graphics / The grammar of graphics
- Bokeh / Bokeh
- ŷhat ggplot / The ŷhat ggplot
- new styles, in matplotlib / New styles in matplotlib
- Seaborn / Seaborn
- ŷhat ggplot
- about / The ŷhat ggplot
I
- interactive backend
- setting up / Setting up the interactive backend
- joint plots, with Seaborn / Joint plots with Seaborn
- scatter plot matrix graphs, with Pandas / Scatter plot matrix graphs with Pandas
- intermediate matplotlib user / The intermediate matplotlib user
- IPython / The intermediate matplotlib user
- about / An important note on IPython
- built-in routing schemes / The load-balanced view
- IPython.parallel package
- IPython engine / Clustering with IPython
- IPython hub / Clustering with IPython
- IPython schedulers / Clustering with IPython
- controller client / Clustering with IPython
- IPython architecture
- about / Clustering with IPython
- IPython engine
- about / Clustering with IPython
- IPython hub
- about / Clustering with IPython
- IPython Notebook event loops
- about / IPython Notebook event loops
- IPython Notebooks
- using, with matplotlib / Using IPython Notebooks with matplotlib
- IPython schedulers
- about / Clustering with IPython
K
- keyboard events
- handling / Keyboard events
- key_press_event / Keyboard events
- key_release_event / Keyboard events
- Kinesis
- about / Distributed data
L
- Landsat 8 bands / Defining a workflow
- large data, visualizing
- about / Visualizing large data
- limits, finding / Finding the limits of matplotlib
- Agg rendering, with matplotlibrc / Agg rendering with matplotlibrc
- decimation / Decimation
- additional techniques / Additional techniques
- large data sources
- working with / Working with large data sources
- example problem / An example problem
- big data, on filesystem / Big data on the filesystem
- distributed data / Distributed data
- LaTeX
- about / The pyplot scripting API
- Lawrence Livermore National Laboratory (LLNL) / Estimating the value of π
- linear algebra module
- about / The procedural pylab API
- Lisp Flavored Erlang (LFE)
- about / MapReduce
M
- Macsyma / Historical background
- MapReduce
- about / MapReduce
- applications / MapReduce
- masked array module
- about / The procedural pylab API
- matplotlib
- historical overview / A brief historical overview of matplotlib
- prerequisites / Prerequisites for this book
- installing / Installing matplotlib
- IPython Notebooks, using with / Using IPython Notebooks with matplotlib
- advanced plots / Advanced plots – a preview
- original design goals / The original design goals
- current architecture / The current matplotlib architecture
- in other frameworks / matplotlib in other frameworks
- event loops / Event loops in matplotlib
- event handling / Event handling
- matplotlib 1.4
- features / What's new in matplotlib 1.4
- matplotlib architecture
- about / The current matplotlib architecture, The matplotlib architecture as it relates to this book
- backend layer / The current matplotlib architecture, The backend layer
- artist layer / The current matplotlib architecture, The artist layer
- scripting layer / The current matplotlib architecture, The scripting layer
- matplotlib event loops
- about / matplotlib event loops
- matplotlib in Cloud, use case
- creating / Making a use case for matplotlib in the Cloud
- data source / The data source
- workflow, defining / Defining a workflow
- technologies, selecting / Choosing technologies
- configuration management / Configuration management
- deployment types / Types of deployment
- matplotlib modules
- about / matplotlib modules
- filesystem, exploring / Exploring the filesystem
- imports, exploring visually / Exploring imports visually
- modulefinder module / ModuleFinder
- ModGrapher / ModGrapher
- matplotlib object-oriented API
- about / The matplotlib object-oriented API
- equations / Equations
- helper classes / Helper classes
- Plotter class / The Plotter class
- jobs, running / Running the jobs
- matplotlib stack
- supporting components / The supporting components of the matplotlib stack
- matplotlib Transformations Tutorial
- about / Further explorations in customization
- Mercator map projection
- about / Further explorations in customization
- Message Passing Interface (MPI)
- about / Clustering with IPython
- MIT (Massachusetts Institute of Technology) / Historical background
- ModGrapher
- about / ModGrapher
- modulefinder module
- about / ModuleFinder
- mouse events
- handling / Mouse events
- button_press_event / Mouse events
- button_release_event / Mouse events
- scroll_event / Mouse events
- motion_notify_event / Mouse events
- Multiplexed Information and Computing Service (Multics) / The run control for matplotlib
N
- National Center for Supercomputing Applications (NCSA)
- about / HDF5 and PyTables
- Near-Infrared (NIR) light / Defining a workflow
- NetworkX
- about / matplotlib in other frameworks, NetworkX
- NumPy's memmap function
- about / NumPy's memmap function
- nViZn
- about / The grammar of graphics
P
- Pandas / The intermediate matplotlib user
- used, for scatter plot matrix graphs / Scatter plot matrix graphs with Pandas
- about / Scatter plot matrix graphs with Pandas, matplotlib in other frameworks, Pandas
- Pandas data Series / Analysis of temperature
- parallelization
- problems / Clustering and parallel programming
- parallel programming
- about / Clustering and parallel programming
- data parallelization / Clustering and parallel programming
- task parallelization / Clustering and parallel programming
- ParaView
- about / Other visualization tools
- URL / Other visualization tools
- pattern language
- about / The grammar of graphics
- PEP 8 / Coding style
- PEP 3107 / Coding style
- primitives
- about / Primitives
- problems, parallelization
- N-body problems / Clustering and parallel programming
- structured grid problems / Clustering and parallel programming
- Monte Carlo simulation / Clustering and parallel programming
- combinational logic / Clustering and parallel programming
- graph traversal / Clustering and parallel programming
- dynamic programming / Clustering and parallel programming
- Bayesian networks / Clustering and parallel programming
- procedural pylab API
- about / The procedural pylab API
- drawbacks / The procedural pylab API
- motivating factors / The procedural pylab API
- interface / The procedural pylab API
- Pub-Sub synchronization / Creating the ZeroMQ components
- Puppet / Configuration management
- PyData
- about / HDF5 and PyTables
- pylab interface
- about / The scripting layer
- pyplot interface
- about / The scripting layer
- plot() function / The scripting layer
- title() function / The scripting layer
- savefig() function / The scripting layer
- draw() function / The scripting layer
- gcf() function / The scripting layer
- gca() function / The scripting layer
- get_current_fig_manager() function / The scripting layer
- figure() function / The scripting layer
- switch_backend() function / The scripting layer
- pyplot scripting API
- about / The pyplot scripting API
- PyTables
- about / HDF5 and PyTables
- Python 2 and Python 3
- syntactical differences / Python 3
- Python 3
- about / Python 3
- Python Imaging Library (PIL) / Configuration management
Q
- Qt5 backend / What's new in matplotlib 1.4
R
- read-eval-print loop (REPL)
- about / IPython Notebook event loops
- RendererBase
- about / RendererBase
- run control, for matplotlib
- file and directory locations / File and directory locations
- matplotlibrc file, using / Using the matplotlibrc file
- values, matplotlibrc file / Using the matplotlibrc file
- settings, updating dynamically / Updating the settings dynamically
S
- scikit-learn
- about / matplotlib in other frameworks
- scripting layer
- about / The current matplotlib architecture, The scripting layer
- pyplot interface / The scripting layer
- pylab interface / The scripting layer
- Seaborn / The intermediate matplotlib user
- about / Joint plots with Seaborn, matplotlib in other frameworks, Seaborn
- Secure Shell (SSH) / Creating a host server on EC2
- setup, on AWS
- about / Getting the setup on AWS
- source data, pushing to S3 / Pushing the source data to S3
- host server, creating on EC2 / Creating a host server on EC2
- Docker, using on EC2 / Using Docker on EC2
- reading, with S3 / Reading and writing with S3
- writing, with S3 / Reading and writing with S3
- Short-Wavelength Infrared (SWIR) light / Defining a workflow
- Singh-Maddala distribution / An example problem
- spline interpolation / Analysis of temperature
- StarCluster / The intermediate matplotlib user
- StarCluster project / More clustering
- Stereonets
- about / Further explorations in customization
- subplots
- about / Subplots
- Pandas, revisiting / Revisiting Pandas
- individual plots / Individual plots
- implementing / Bringing everything together
- Superhero Bootstrap theme / Creating a custom style
- supporting components, matplotlib stack
- about / The supporting components of the matplotlib stack
T
- task, Docker
- running / Running the task
- environment variables, setting / Environment variables and Docker
- Python module, updating / Changes to the Python module
- execution / Execution
- task parallelization
- about / Clustering and parallel programming
- Tornado / Event
U
- UCI Machine Learning Repository
- about / Subplots
- United States Geological Survey (USGS) / Making a use case for matplotlib in the Cloud
- United States Historical Climatology Network (USHCN)
- about / Examining and shaping a dataset
- user interface backends
- about / The backend layer
V
- VisIt
- about / Other visualization tools
- URL / Other visualization tools
- Vispy
- about / Other visualization tools
- URL / Other visualization tools
- visualization tools
- about / Other visualization tools
- ParaView / Other visualization tools
- VisIt / Other visualization tools
- Bokeh / Other visualization tools
- Vispy / Other visualization tools
W
- Wulff net
- about / Further explorations in customization
Z
- ZeroMQ components
- creating / Creating the ZeroMQ components