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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
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
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Index
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Index

Using a logarithmic scale

When visualizing data that varies across a very wide range, a logarithmic scale allows us to visualize variations that would otherwise be barely visible. In this recipe, we are going to show you how to manipulate the scaling system of a figure.

How to do it...

There are several ways to set up a logarithmic scale. The way it is done here works for any kind of figure and not only curve plots. In the following example, we set up a logarithmic scale that will apply to all plot elements:

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(1, 10, 1024)

plt.yscale('log')
plt.plot(X, X, c = 'k', lw = 2., label = r'$f(x)=x$')
plt.plot(X, 10 ** X, c = '.75', ls = '--', lw = 2., label = r'$f(x)=e^x$')
plt.plot(X, np.log(X), c = '.75', lw = 2., label = r'$f(x)=\log(x)$')

plt.legend()
plt.show()

Several curves are shown in the following figure, with the vertical axis using a logarithmic scale:

How it works...

In this example, we display three functions, with the...

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matplotlib Plotting Cookbook
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