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matplotlib Plotting Cookbook

matplotlib Plotting Cookbook

By : Alexandre Devert
3.7 (10)
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matplotlib Plotting Cookbook

matplotlib Plotting Cookbook

3.7 (10)
By: Alexandre Devert
Buy this Book

Overview of this book

Table of Contents (15 chapters)
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matplotlib Plotting Cookbook
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matplotlib Plotting Cookbook
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
First Steps
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First Steps
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Introduction
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Installing matplotlib
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Plotting one curve
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Using NumPy
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Plotting multiple curves
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Plotting curves from file data
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Plotting points
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Plotting bar charts
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Plotting multiple bar charts
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Plotting stacked bar charts
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Plotting back-to-back bar charts
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Plotting pie charts
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Plotting histograms
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Plotting boxplots
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Plotting triangulations
2
Customizing the Color and Styles
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Customizing the Color and Styles
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Introduction
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Defining your own colors
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Using custom colors for scatter plots
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Using custom colors for bar charts
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Using custom colors for pie charts
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Using custom colors for boxplots
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Using colormaps for scatter plots
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Using colormaps for bar charts
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Controlling a line pattern and thickness
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Controlling a fill pattern
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Controlling a marker's style
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Controlling a marker's size
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Creating your own markers
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Getting more control over markers
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Creating your own color scheme
3
Working with Annotations
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Working with Annotations
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Introduction
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Adding a title
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Using LaTeX-style notations
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Adding a label to each axis
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Adding text
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Adding arrows
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Adding a legend
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Adding a grid
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Adding lines
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Adding shapes
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Controlling tick spacing
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Controlling tick labeling
4
Working with Figures
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Working with Figures
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Introduction
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Compositing multiple figures
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Scaling both the axes equally
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Setting an axis range
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Setting the aspect ratio
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Inserting subfigures
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Using a logarithmic scale
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Using polar coordinates
5
Working with a File Output
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Working with a File Output
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Introduction
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Generating a PNG picture file
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Handling transparency
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Controlling the output resolution
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Generating PDF or SVG documents
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Handling multiple-page PDF documents
6
Working with Maps
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Working with Maps
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Introduction
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Visualizing the content of a 2D array
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Adding a colormap legend to a figure
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Visualizing nonuniform 2D data
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Visualizing a 2D scalar field
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Visualizing contour lines
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Visualizing a 2D vector field
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Visualizing the streamlines of a 2D vector field
7
Working with 3D Figures
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Working with 3D Figures
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Introduction
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Creating 3D scatter plots
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Creating 3D curve plots
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Plotting a scalar field in 3D
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Plotting a parametric 3D surface
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Embedding 2D figures in a 3D figure
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Creating a 3D bar plot
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User Interface
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User Interface
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Introduction
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Making a user-controllable plot
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Integrating a plot to a Tkinter user interface
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Integrating a plot to a wxWidgets user interface
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Integrating a plot to a GTK user interface
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Integrating a plot in a Pyglet application
1
Index
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Index

Installing matplotlib

Before experimenting with matplotlib, you need to install it. Here we introduce some tips to get matplotlib up and running without too much trouble.

How to do it...

We have three likely scenarios: you might be using Linux, OS X, or Windows.

Linux

Most Linux distributions have Python installed by default, and provide matplotlib in their standard package list. So all you have to do is use the package manager of your distribution to install matplotlib automatically. In addition to matplotlib, we highly recommend that you install NumPy, SciPy, and SymPy, as they are supposed to work together. The following list consists of commands to enable the default packages available in different versions of Linux:

  • Ubuntu: The default Python packages are compiled for Python 2.7. In a command terminal, enter the following command:

    sudo apt-get install python-matplotlib python-numpy python-scipy python-sympy

  • ArchLinux: The default Python packages are compiled for Python 3. In a command terminal, enter the following command:

    sudo pacman -S python-matplotlib python-numpy python-scipy python-sympy

    If you prefer using Python 2.7, replace python by python2 in the package names

  • Fedora: The default Python packages are compiled for Python 2.7. In a command terminal, enter the following command:

    sudo yum install python-matplotlib numpy scipy sympy

    Note

    There are other ways to install these packages; in this chapter, we propose the most simple and seamless ways to do it.

Windows and OS X

Windows and OS X do not have a standard package system for software installation. We have two options—using a ready-made self-installing package or compiling matplotlib from the code source. The second option involves much more work; it is worth the effort to have the latest, bleeding edge version of matplotlib installed. Therefore, in most cases, using a ready-made package is a more pragmatic choice.

You have several choices for ready-made packages: Anaconda, Enthought Canopy, Algorete Loopy, and more! All these packages provide Python, SciPy, NumPy, matplotlib, and more (a text editor and fancy interactive shells) in one go. Indeed, all these systems install their own package manager and from there you install/uninstall additional packages as you would do on a typical Linux distribution. For the sake of brevity, we will provide instructions only for Enthought Canopy. All the other systems have extensive documentation online, so installing them should not be too much of a problem.

So, let's install Enthought Canopy by performing the following steps:

  1. Download the Enthought Canopy installer from https://www.enthought.com/products/canopy. You can choose the free Express edition. The website can guess your operating system and propose the right installer for you.

  2. Run the Enthought Canopy installer. You do not need to be an administrator to install the package if you do not want to share the installed software with other users.

  3. When installing, just click on Next to keep the defaults. You can find additional information about the installation process at http://docs.enthought.com/canopy/quick-start.html.

That's it! You will have Python 2.7, NumPy, SciPy, and matplotlib installed and ready to run.

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