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matplotlib Plotting Cookbook

By : Alexandre Devert
Book Image

matplotlib Plotting Cookbook

By: Alexandre Devert

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matplotlib Plotting Cookbook
Alexandre Devert
Mar, 2014 | 222 pages
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Table of Contents (15 chapters)
matplotlib Plotting Cookbook
matplotlib Plotting Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
First Steps
First Steps
Introduction
Installing matplotlib
Plotting one curve
Using NumPy
Plotting multiple curves
Plotting curves from file data
Plotting points
Plotting bar charts
Plotting multiple bar charts
Plotting stacked bar charts
Plotting back-to-back bar charts
Plotting pie charts
Plotting histograms
Plotting boxplots
Plotting triangulations
2
Customizing the Color and Styles
Customizing the Color and Styles
Introduction
Defining your own colors
Using custom colors for scatter plots
Using custom colors for bar charts
Using custom colors for pie charts
Using custom colors for boxplots
Using colormaps for scatter plots
Using colormaps for bar charts
Controlling a line pattern and thickness
Controlling a fill pattern
Controlling a marker's style
Controlling a marker's size
Creating your own markers
Getting more control over markers
Creating your own color scheme
3
Working with Annotations
Working with Annotations
Introduction
Adding a title
Using LaTeX-style notations
Adding a label to each axis
Adding text
Adding arrows
Adding a legend
Adding a grid
Adding lines
Adding shapes
Controlling tick spacing
Controlling tick labeling
4
Working with Figures
Working with Figures
Introduction
Compositing multiple figures
Scaling both the axes equally
Setting an axis range
Setting the aspect ratio
Inserting subfigures
Using a logarithmic scale
Using polar coordinates
5
Working with a File Output
Working with a File Output
Introduction
Generating a PNG picture file
Handling transparency
Controlling the output resolution
Generating PDF or SVG documents
Handling multiple-page PDF documents
6
Working with Maps
Working with Maps
Introduction
Visualizing the content of a 2D array
Adding a colormap legend to a figure
Visualizing nonuniform 2D data
Visualizing a 2D scalar field
Visualizing contour lines
Visualizing a 2D vector field
Visualizing the streamlines of a 2D vector field
7
Working with 3D Figures
Working with 3D Figures
Introduction
Creating 3D scatter plots
Creating 3D curve plots
Plotting a scalar field in 3D
Plotting a parametric 3D surface
Embedding 2D figures in a 3D figure
Creating a 3D bar plot
8
User Interface
User Interface
Introduction
Making a user-controllable plot
Integrating a plot to a Tkinter user interface
Integrating a plot to a wxWidgets user interface
Integrating a plot to a GTK user interface
Integrating a plot in a Pyglet application
Index
Index

Introduction

matplotlib has ever-increasing support for three-dimensional plots. Since Version 1.2, the API to make 3D figures has been very similar to the 2D API. Adding one more dimension to your plots can help you to visualize more information at a glance. Also, 3D plots are quite an attention-grabber on a presentation or during a class. In this chapter, we are going to explore what matplotlib can do with a third dimension.

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