多种颜色填充 matplotlib 标记

Multiple color fills in matplotlib markers

我想在用 matplotlib 制作的标记中使用多种颜色。按照 this example, and with some additional info from this documentation,做两种颜色并不难。但是,我想知道是否可以制作超过 2 种颜色的标记。我处于一种情况,我希望单个标记实际上获得 3 种不同的颜色(地图上的一个点指的是三个不同的观察结果)。

您可以按照此处显示的 matplotlib 示例执行此操作:

https://matplotlib.org/stable/gallery/lines_bars_and_markers/scatter_piecharts.html

下面我稍微更改了示例以使用 ax.plot 而不是 ax.scatter

基本上这意味着您的所有标记必须具有相同的大小,而不是对 scatter 使用 s kwarg,您使用 ms(或 markersize ) kwarg plot.

此外,您需要定义 markerfacecolor.

而不是 facecolor

除这些变化外,其他一切都与原始示例相同。

"""
This example makes custom 'pie charts' as the markers for a scatter plot
    
Thanks to Manuel Metz for the example
"""
import numpy as np
import matplotlib.pyplot as plt

# first define the ratios
r1 = 0.2       # 20%
r2 = r1 + 0.4  # 40%

# define some sizes of the scatter marker
sizes = np.array([60, 80, 120])

# calculate the points of the first pie marker
#
# these are just the origin (0,0) +
# some points on a circle cos,sin
x1 = np.cos(2 * np.pi * np.linspace(0, r1))
y1 = np.sin(2 * np.pi * np.linspace(0, r1))
xy1 = np.row_stack([[0, 0], np.column_stack([x1, y1])])
s1 = np.abs(xy1).max()

x2 = np.cos(2 * np.pi * np.linspace(r1, r2))
y2 = np.sin(2 * np.pi * np.linspace(r1, r2))
xy2 = np.row_stack([[0, 0], np.column_stack([x2, y2])])
s2 = np.abs(xy2).max()

x3 = np.cos(2 * np.pi * np.linspace(r2, 1))
y3 = np.sin(2 * np.pi * np.linspace(r2, 1))
xy3 = np.row_stack([[0, 0], np.column_stack([x3, y3])])
s3 = np.abs(xy3).max()

fig, ax = plt.subplots()

# Here's where I made changes
ax.plot(np.arange(3), np.arange(3), marker=xy1,
           ms=20, markerfacecolor='blue', markeredgecolor='None', linestyle='None')   # I changed this line
ax.plot(np.arange(3), np.arange(3), marker=xy2,
           ms=20, markerfacecolor='green', markeredgecolor='None', linestyle='None')  # I changed this line
ax.plot(np.arange(3), np.arange(3), marker=xy3,
           ms=20, markerfacecolor='red', markeredgecolor='None', linestyle='None')    # I changed this line


plt.margins(0.05)
    
plt.show()