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  • Book Overview & Buying matplotlib Plotting Cookbook
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matplotlib Plotting Cookbook

matplotlib Plotting Cookbook

By : Alexandre Devert
3.7 (10)
Buy this Book
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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
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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
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Index
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Index

Plotting a parametric 3D surface

In the previous recipe, we used plot_surface() to plot a scalar field: that is, a function of the f(x, y) = z form. However, matplotlib is able to plot a generic, parametric 3D surface. Let's demonstrate this by plotting a torus, which is a fairly simple parametric surface.

How to do it...

We are going to use plot_surface() again to display a torus, using the following code:

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

# Generate torus mesh
angle = np.linspace(0, 2 * np.pi, 32)
theta, phi = np.meshgrid(angle, angle)
r, R = .25, 1.
X = (R + r * np.cos(phi)) * np.cos(theta)
Y = (R + r * np.cos(phi)) * np.sin(theta)
Z = r * np.sin(phi)

# Display the mesh
fig = plt.figure()
ax = fig.gca(projection = '3d')
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
ax.plot_surface(X, Y, Z, color = 'w', rstride = 1, cstride = 1)
plt.show()

The preceding code will display our torus as follows:

How it works...

A torus...

CONTINUE READING
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Tech Concepts
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Programming languages
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matplotlib Plotting Cookbook
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