2018年1月23日 星期二

Sample plots in Matplotlib


Sample plots in Matplotlib

Here you’ll find a host of example plots with the code that generated them.

Line Plot

Here’s how to create a line plot with text labels using plot().
../../_images/sphx_glr_simple_plot_0011.png
Simple Plot

Multiple subplots in one figure

Multiple axes (i.e. subplots) are created with the subplot() function:
../../_images/sphx_glr_subplot_0011.png
Subplot

Images

Matplotlib can display images (assuming equally spaced horizontal dimensions) using the imshow() function.
../../_images/sphx_glr_image_demo_0031.png
Example of using imshow() to display a CT scan

Contouring and pseudocolor

The pcolormesh() function can make a colored representation of a two-dimensional array, even if the horizontal dimensions are unevenly spaced. The contour() function is another way to represent the same data:
../../_images/sphx_glr_pcolormesh_levels_0011.png
Example comparing pcolormesh() and contour() for plotting two-dimensional data

Histograms

The hist() function automatically generates histograms and returns the bin counts or probabilities:
../../_images/sphx_glr_histogram_features_0011.png
Histogram Features

Paths

You can add arbitrary paths in Matplotlib using the matplotlib.path module:
../../_images/sphx_glr_path_patch_0011.png
Path Patch

Three-dimensional plotting

The mplot3d toolkit (see Getting started and mplot3d toolkit) has support for simple 3d graphs including surface, wireframe, scatter, and bar charts.
../../_images/sphx_glr_surface3d_0011.png
Surface3d
Thanks to John Porter, Jonathon Taylor, Reinier Heeres, and Ben Root for the mplot3d toolkit. This toolkit is included with all standard Matplotlib installs.

Streamplot

The streamplot() function plots the streamlines of a vector field. In addition to simply plotting the streamlines, it allows you to map the colors and/or line widths of streamlines to a separate parameter, such as the speed or local intensity of the vector field.
../../_images/sphx_glr_plot_streamplot_0011.png
Streamplot with various plotting options.
This feature complements the quiver() function for plotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the streamplot function.

Ellipses

In support of the Phoenix mission to Mars (which used Matplotlib to display ground tracking of spacecraft), Michael Droettboom built on work by Charlie Moad to provide an extremely accurate 8-spline approximation to elliptical arcs (see Arc), which are insensitive to zoom level.
../../_images/sphx_glr_ellipse_demo_0011.png
Ellipse Demo

Bar charts

Use the bar() function to make bar charts, which includes customizations such as error bars:
../../_images/sphx_glr_barchart_demo_0011.png
Barchart Demo
You can also create stacked bars (bar_stacked.py), or horizontal bar charts (barh.py).

Pie charts

The pie() function allows you to create pie charts. Optional features include auto-labeling the percentage of area, exploding one or more wedges from the center of the pie, and a shadow effect. Take a close look at the attached code, which generates this figure in just a few lines of code.
../../_images/sphx_glr_pie_features_0011.png
Pie Features

Tables

The table() function adds a text table to an axes.
../../_images/sphx_glr_table_demo_0011.png
Table Demo

Scatter plots

The scatter() function makes a scatter plot with (optional) size and color arguments. This example plots changes in Google’s stock price, with marker sizes reflecting the trading volume and colors varying with time. Here, the alpha attribute is used to make semitransparent circle markers.
../../_images/sphx_glr_scatter_demo2_0011.png
Scatter Demo2

GUI widgets

Matplotlib has basic GUI widgets that are independent of the graphical user interface you are using, allowing you to write cross GUI figures and widgets. See matplotlib.widgets and the widget examples.
../../_images/sphx_glr_slider_demo_0011.png
Slider and radio-button GUI.

Filled curves

The fill() function lets you plot filled curves and polygons:
../../_images/sphx_glr_fill_0011.png
Fill
Thanks to Andrew Straw for adding this function.

Date handling

You can plot timeseries data with major and minor ticks and custom tick formatters for both.
../../_images/sphx_glr_date_0011.png
Date
See matplotlib.ticker and matplotlib.dates for details and usage.

Log plots

The semilogx()semilogy() and loglog() functions simplify the creation of logarithmic plots.
../../_images/sphx_glr_log_demo_0011.png
Log Demo
Thanks to Andrew Straw, Darren Dale and Gregory Lielens for contributions log-scaling infrastructure.

Polar plots

The polar() function generates polar plots.
../../_images/sphx_glr_polar_demo_0011.png
Polar Demo

Legends

The legend() function automatically generates figure legends, with MATLAB-compatible legend-placement functions.
../../_images/sphx_glr_legend_0011.png
Legend
Thanks to Charles Twardy for input on the legend function.

TeX-notation for text objects

Below is a sampling of the many TeX expressions now supported by Matplotlib’s internal mathtext engine. The mathtext module provides TeX style mathematical expressions using FreeType and the DejaVu, BaKoMa computer modern, or STIX fonts. See the matplotlib.mathtext module for additional details.
../../_images/sphx_glr_mathtext_examples_0011.png
Mathtext Examples
Matplotlib’s mathtext infrastructure is an independent implementation and does not require TeX or any external packages installed on your computer. See the tutorial at Writing mathematical expressions.

Native TeX rendering

Although Matplotlib’s internal math rendering engine is quite powerful, sometimes you need TeX. Matplotlib supports external TeX rendering of strings with the usetex option.
../../_images/sphx_glr_tex_demo_0011.png
Tex Demo

EEG GUI

You can embed Matplotlib into pygtk, wx, Tk, or Qt applications. Here is a screenshot of an EEG viewer called pbrain.
../../_images/eeg_small.png
The lower axes uses specgram() to plot the spectrogram of one of the EEG channels.
For examples of how to embed Matplotlib in different toolkits, see:

XKCD-style sketch plots

Just for fun, Matplotlib supports plotting in the style of xkcd.
../../_images/sphx_glr_xkcd_0011.png
Xkcd

Subplot example

Many plot types can be combined in one figure to create powerful and flexible representations of data.
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(19680801)
data = np.random.randn(2, 100)

fig, axs = plt.subplots(2, 2, figsize=(5, 5))
axs[0, 0].hist(data[0])
axs[1, 0].scatter(data[0], data[1])
axs[0, 1].plot(data[0], data[1])
axs[1, 1].hist2d(data[0], data[1])

plt.show()
../../_images/sphx_glr_sample_plots_001.png
Total running time of the script: ( 0 minutes 0.075 seconds)

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