[Matplotlib-users] New matplotlib book: Mastering matplotlib

Hey all,

I wanted to let folks know that there is a new matplotlib book available,
having just been published:
 *
https://www.packtpub.com/big-data-and-business-intelligence/mastering-matplotlib

The IPython notebooks are listed here (with links to NBViewer as well as
the individual chapter repos):
 * https://github.com/masteringmatplotlib/notebooks

The book didn't ship with an Acknowledgements section, so I am attempting
to make up for that here:
 *
http://oubiwann.blogspot.com/2015/07/mastering-matplotlib-acknowledgments.html

The ToC for the book hasn't been updated on the publisher's (or Amazon's)
site, so for your reading pleasure I have included the text from the
section "What this book covers" below:

Chapter 1, Getting Up to Speed, covers some history and background of
matplotlib, goes over some of the latest features of the library, provides
a refresher on Python 3 and IPython Notebooks, and whets the reader's
appetite with some advanced plotting examples.

Chapter 2, matplotlib Architecture, reviews the original design goals of
matplotlib and then proceeds to discuss its current architecture in detail,
providing visualizations of the conceptual structure and relationships
between the Python modules.

Chapter 3, matplotlib APIs and Integrations, walks the reader through the
matplotlib APIs adapting a single example accordingly, examines how the
third-party libraries are integrated with matplotlib, and gives migration
advice to the advanced users of the deprecated pylab API.

Chapter 4, Event Handling and Interactive Plots, provides a review of the
event-based systems, covers event loops in matplotlib and IPython, goes
over a selection of matplotlib events, and shows how to take advantage of
these to create interactive plots.

Chapter 5, High-level Plotting and Data Analysis, combines the interrelated
topics, providing a historical background of plotting, a discussion on the
grammar of graphics, and an overview of high-level plotting libraries. This
is then put to use in a detailed analysis of weather-related data that
spans 120 years.

Chapter 6, Customization and Configuration, covers the custom styles in
matplotlib and the use of grid specs to create a dashboard effect with the
combined plots. The lesser-known configuration options are also discussed
with an eye to optimization.

Chapter 7, Deploying matplotlib in Cloud Environments, explores a use case
for matplotlib in a remote deployment, which is followed by a detailed
programmatic batch-job example using Docker and Amazon AWS.

Chapter 8, matplotlib and Big Data, provides detailed examples of working
with large local data sets as well as the distributed ones, covering
options such as numpy.memmap, HDF5, and Hadoop. Plots with millions of
points will also be demonstrated.

Chapter 9, Clustering for matplotlib, introduces parallel programming and
clusters that are designed for use with matplotlib, demonstrating how to
distribute parts of a problem and then assemble the results for analysis in
matplotlib.

Hope everyone's having a good time at SciPy 2015!

d