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Mastering Matplotlib
Mastering Matplotlib

Mastering Matplotlib: A practical guide that takes you beyond the basics of matplotlib and gives solutions to plot complex data

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Profile Icon Duncan M McGreggor Profile Icon Duncan M. McGreggor
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (8 Ratings)
Paperback Jun 2015 292 pages 1st Edition
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Profile Icon Duncan M McGreggor Profile Icon Duncan M. McGreggor
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (8 Ratings)
Paperback Jun 2015 292 pages 1st Edition
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Mex$504.99 Mex$721.99
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Mex$504.99 Mex$721.99
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Mastering Matplotlib

Chapter 2. The matplotlib Architecture

As software systems age, they tend to undergo a natural evolution through processes such as feature addition and debugging. The resultant codebase embodies the familiar tension between maintaining the old code and at the same time offering the end users an improved product. Architectures for long-term projects are not something that were originally carved in stone and adhered to monomaniacally ever since. Rather, they are living, adaptive concepts that guide the plans and activities of a project's contributors.

The matplotlib module arose out of such an environment, and it has continuous goals of refining and improving its architecture and updating its older bits to follow the best practices of and the latest advances in not only the project itself, but also the wider Python community over the years since its inception.

In this chapter, we will perform the following tasks:

  • Review the original design goals of matplotlib and explore its evolution...

The original design goals

As mentioned in Chapter 1, Getting Up to Speed, the creators of matplotlib were originally focused on building a GTK+ application for researchers and providing a command interface for the interactive plotting of data, not unlike that provided by MATLAB.

Both of these aims helped drive the development of improved abstractions for matplotlib. It was in this dual crucible that the top-level object of the rendered plots in matplotlib gained its rightful prominence—the Figure. These ideas led to various foundational objects in matplotlib, and the relationships between them ultimately provided the basis for the architecture of this library.

The current matplotlib architecture

The current matplotlib architecture revolves around the operations that are necessary for the users to create, render, and update the Figure objects. Figures can be displayed and interacted with via common user interface events such as the keyboard and mouse inputs. This layer of interaction with common user interface is called the backend layer. A Figure needs to be composed of multiple objects that should be individually modifiable, but it should be implemented in such a way that it has a positive and predictable impact on the other aspects of the Figure. This logical layer is responsible for the abstraction of each visual component that one sees in a Figure. Due to its highly visual nature, this layer was identified as the more general concept of creating visual art and is thus referred to as the artist layer. Lastly, the Figure needs to support programmatic interaction and provide the users with the ability to manipulate Figures with a syntax that...

The backend layer

Seasoned computer scientists, engineers, and software developers all know that one of the subtler and trickier problems that arise in our industry is naming. It sounds a bit silly and it is repeatedly the subject of jokes, but the difficulty remains—how do you speak or write explicitly on a subject whose very nature requires exquisite precision and yet has great ambiguity that arises in different contexts?

We have the same problem with the term backend. Here, as in so many other instances, the context is everything. Our context is matplotlib, a set of tools, and a framework where everything is done in support of the visualizing of data and their relationships. The term backend has to be viewed from this perspective to support the generation of plots. The matplotlib backend has nothing to do with other noteworthy backends such as databases, servers, messaging systems, or dispatchers of various sorts. The backend of matplotlib is an abstraction layer over various components...

The artist layer

The artist layer constitutes the bulk of what matplotlib actually does—the generation of the plots for the purpose of display, manipulation, and publication. Most work in the artist layer is performed by a number of classes, most of which are derived from the Artist base class.

The artist layer is concerned with things such as the lines, shapes, axes, text, and so on. These are the subclasses of the Artist class that define things such as the following:

  • A canvas-artist coordinate transformation
  • Visibility
  • A clip box that defines the paintable area
  • Labels
  • A callback registry instance to handle user interaction events

The Artist subclasses can be classified into one of the following two groups:

  • Primitives
  • Containers

The following two sections provide more details about these groups.

Primitives

The matplotlib artist primitives are classes of graphical objects that are supposed to be painted on a figure's canvas. These include, but are not limited to, the following:

  • Line2D
  • Shape...

The scripting layer

While the backend layer focuses on providing a common interface to the toolkits and rendering the primitives and containers of the artist layer, the scripting layer is the user-facing interface that simplifies the task of working with other layers.

Programmers who integrate matplotlib with application servers will often find it more convenient to work directly with the backend and artist layers. However, for the scientists' daily use, data visualization, or exploratory interactions, pyplot—the scripting layer—is a better option. This is what we use in most of the IPython Notebooks in this book.

The pyplot interface is much less verbose; one can get insights into one's data in very few steps. Under the covers, pyplot uses module-level objects to track the state of the data so that the user does not have to create things like figures, axes, canvases, figure canvas managers, or preferred backends.

We will take a quick look at pyplot's internals...

The original design goals


As mentioned in Chapter 1, Getting Up to Speed, the creators of matplotlib were originally focused on building a GTK+ application for researchers and providing a command interface for the interactive plotting of data, not unlike that provided by MATLAB.

Both of these aims helped drive the development of improved abstractions for matplotlib. It was in this dual crucible that the top-level object of the rendered plots in matplotlib gained its rightful prominence—the Figure. These ideas led to various foundational objects in matplotlib, and the relationships between them ultimately provided the basis for the architecture of this library.

The current matplotlib architecture


The current matplotlib architecture revolves around the operations that are necessary for the users to create, render, and update the Figure objects. Figures can be displayed and interacted with via common user interface events such as the keyboard and mouse inputs. This layer of interaction with common user interface is called the backend layer. A Figure needs to be composed of multiple objects that should be individually modifiable, but it should be implemented in such a way that it has a positive and predictable impact on the other aspects of the Figure. This logical layer is responsible for the abstraction of each visual component that one sees in a Figure. Due to its highly visual nature, this layer was identified as the more general concept of creating visual art and is thus referred to as the artist layer. Lastly, the Figure needs to support programmatic interaction and provide the users with the ability to manipulate Figures with a syntax that...

The backend layer


Seasoned computer scientists, engineers, and software developers all know that one of the subtler and trickier problems that arise in our industry is naming. It sounds a bit silly and it is repeatedly the subject of jokes, but the difficulty remains—how do you speak or write explicitly on a subject whose very nature requires exquisite precision and yet has great ambiguity that arises in different contexts?

We have the same problem with the term backend. Here, as in so many other instances, the context is everything. Our context is matplotlib, a set of tools, and a framework where everything is done in support of the visualizing of data and their relationships. The term backend has to be viewed from this perspective to support the generation of plots. The matplotlib backend has nothing to do with other noteworthy backends such as databases, servers, messaging systems, or dispatchers of various sorts. The backend of matplotlib is an abstraction layer over various components...

The artist layer


The artist layer constitutes the bulk of what matplotlib actually does—the generation of the plots for the purpose of display, manipulation, and publication. Most work in the artist layer is performed by a number of classes, most of which are derived from the Artist base class.

The artist layer is concerned with things such as the lines, shapes, axes, text, and so on. These are the subclasses of the Artist class that define things such as the following:

  • A canvas-artist coordinate transformation

  • Visibility

  • A clip box that defines the paintable area

  • Labels

  • A callback registry instance to handle user interaction events

The Artist subclasses can be classified into one of the following two groups:

  • Primitives

  • Containers

The following two sections provide more details about these groups.

Primitives

The matplotlib artist primitives are classes of graphical objects that are supposed to be painted on a figure's canvas. These include, but are not limited to, the following:

  • Line2D

  • Shape (patch) classes...

The scripting layer


While the backend layer focuses on providing a common interface to the toolkits and rendering the primitives and containers of the artist layer, the scripting layer is the user-facing interface that simplifies the task of working with other layers.

Programmers who integrate matplotlib with application servers will often find it more convenient to work directly with the backend and artist layers. However, for the scientists' daily use, data visualization, or exploratory interactions, pyplot—the scripting layer—is a better option. This is what we use in most of the IPython Notebooks in this book.

The pyplot interface is much less verbose; one can get insights into one's data in very few steps. Under the covers, pyplot uses module-level objects to track the state of the data so that the user does not have to create things like figures, axes, canvases, figure canvas managers, or preferred backends.

We will take a quick look at pyplot's internals later in this chapter (as well...

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Description

If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.

Who is this book for?

If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.

What you will learn

  • Analyze the matplotlib code base and its internals
  • Rerender visualized data on the fly based on changes in the user interface
  • Take advantage of sophisticated thirdparty libraries to plot complex data relationships
  • Create custom styles for use in specialize publications, presentations, or online media
  • Generate consolidated master plots comprising many subplots for dashboardlike results
  • Deploy matplotlib in Cloud environments
  • Utilize matplotlib in big data projects

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Length: 292 pages
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Table of Contents

10 Chapters
1. Getting Up to Speed Chevron down icon Chevron up icon
2. The matplotlib Architecture Chevron down icon Chevron up icon
3. matplotlib APIs and Integrations Chevron down icon Chevron up icon
4. Event Handling and Interactive Plots Chevron down icon Chevron up icon
5. High-level Plotting and Data Analysis Chevron down icon Chevron up icon
6. Customization and Configuration Chevron down icon Chevron up icon
7. Deploying matplotlib in Cloud Environments Chevron down icon Chevron up icon
8. matplotlib and Big Data Chevron down icon Chevron up icon
9. Clustering for matplotlib Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
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4 star 12.5%
3 star 25%
2 star 12.5%
1 star 12.5%
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Justin Marley Nov 24, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good book with a lot of material including code, graphs and hardware/cloud discussion to take the reader to the next level.
Amazon Verified review Amazon
Loris Aug 11, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I bought this book with the goal of improving my basic knowledge of matplotlib: while the creation of basic plots is straightforward, I found it difficult to modify the default behaviors of the library, and I faced problems in plotting large amount of data. I can safely say that this book helped me to solve both issues. As the author mentions in the book, a prior knowledge of matplotlib will definitely makes the reading more enjoiable.The book begins by giving an historical overview of matplotlib and by introducing two popular projects, seaborn and pandas. The chapters that follow describes the matplotlib internal architecture and its API. Next the author illustrates how events are handled in matplotlib and how to create interactive plots. The fifth chapter is dedicated to high-level plotting and shows how to create plots with third-party libraries, such as networkX, pandas, and seaborn, which wrap matplotlib functionality. The chapter also briefly introduces Bokeh, a library that offers a series of improvements over matplotlib and focus its attention on the web browser. In this chapter the author did a very good job in showing how a good visualization of the data is crucial in data analysis. The next chapter covers the customization (and the configuration) of matplotlib. Here the author shows how to create complex layouts where different plots are combined in the same figure. In the eight chapter the author explains how to plot huge amount of data by illustrating different strategies that range from using tools such as numpy's memmap function and pytables, to decimating data (removal of a fraction of the data). The last chapter shows how it is possible to improve the performance of matplotlib by using a clustered environment.Last but not least, the authors provided a GitHub repository with the example code and notebooks of each chapter of the book.
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Ro'eh Nävee May 12, 2017
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'Mastering matplotlib' is a textbook for someone acquainted with using the Python library named 'matplotlib.' Also the IPython/Jupyter interpreter, since the 'nbagg' IPython Notebook backend to matplotlib will also be used. The 'nbagg' backend is for working with plots in a Web browser. Other Python libraries were SciPy, NumPy, Pandas and Seaborn.Most Jupyter notebooks in the tutorials start with:import matplotlibmatplotlib.use('nbagg')%matplotlib inlineimport matplotlib.pyplot as pltOne convenience to appreciate is how the author listed dependent libraries featured in the textbook, and even stored them in a text file included within the accompanying code, which can be downloaded for free from Packt Publishing. The generous author even states: "...you are welcome to...utilize the matplotlib library and the provided code in whatever way you see fit." Free practical, usable code. Once the free code has been downloaded, the github.com repo masteringmatplotlib/notebooks is optional. Mastering matplotlib
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G. A. Patino Aug 12, 2015
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This is the definite book to learn the most advanced aspects of matplotlib. Even though the book covers installation and gives the fundamentals about basic plots, it is best suited for intermediate users in both Python and matplotlib. In fact, the authors are very class-oriented, making a fair familiarity with object-oriented programming something of a prerequisite. However, if you have those prerequisites the book is a worthy investment as you will be able to take full advantage of matplotlib.Even though the topics covered are advanced, from the matplotlib architecture to deploying it in Docker and implementing in parallel computing, they are presented in a clear and concise way. Yet the breadth of applications covered is quite comprehensive, and the authors are able to articulate all the different chapters so that the learning feels like a natural progression instead of trying to cram very disparate subjects. The code is elegant and relatively short, facilitating its reading; and the authors explanations for it are very easy to follow. In particular, the chapter of big data visualization definitely goes beyond what is presented in other books that also cover the same topic, and the implementation explanations are much better. The fact that the book is under 300 pages long is a huge plus. The only chapter I felt that wasn't as easy to follow is the one on GUI deployment.One aspect I really enjoyed about the book is the multiple explanations about the different approaches to creating figures with matplotlib. When you are learning Python and matplotlib you see some books that use pyplot, while others use pylab. Or some like the ax. synthax while others stick to the plt. one. This is the first book in which I see a presentation of all those possibilities, along with their advantages and disadvantages. By the same token, if you are confused as to when to use Seaborn vs yhat ggplot, what's the point of NetworkX, what is ModGrapher, etc. you will find all those explanations here, along with suggestions for their appropriate application.
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NRK Aug 14, 2018
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I was hoping for a systematic understanding of the package. Didn't get it.
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