Matplotlib 自定义标记/符号 [英] Matplotlib custom marker/symbol

问题描述

所以有这个指南:.

我使用 Inkscape 将

So there is this guide: http://matplotlib.org/examples/pylab_examples/scatter_symbol.html

# http://matplotlib.org/examples/pylab_examples/scatter_symbol.html
from matplotlib import pyplot as plt
import numpy as np
import matplotlib

x = np.arange(0.0, 50.0, 2.0)
y = x ** 1.3 + np.random.rand(*x.shape) * 30.0
s = np.random.rand(*x.shape) * 800 + 500

plt.scatter(x, y, s, c="g", alpha=0.5, marker=r'$clubsuit$',
            label="Luck")
plt.xlabel("Leprechauns")
plt.ylabel("Gold")
plt.legend(loc=2)
plt.show()

But what if you are like me and don't want to use a clubsuit marker...

How do you make your own marker _________?

UPDATE

What I like about this special marker type is that it's easy to adjust with simple matplotlib syntax:

from matplotlib import pyplot as plt
import numpy as np
import matplotlib

x = np.arange(0.0, 50.0, 2.0)
y = x ** 1.3 + np.random.rand(*x.shape) * 30.0
s = np.random.rand(*x.shape) * 800 + 500

plt.plot(x, y, "ro", alpha=0.5, marker=r'$clubsuit$', markersize=22)
plt.xlabel("Leprechauns")
plt.ylabel("Gold")
plt.show()

解决方案

The most flexible option for matplotlib is marker paths.

I used Inkscape to convert Smiley face svg into a single SVG path. Inkscape also has options to trace path in raster images. The I used svg path to convert it to matplotlib.path.Path using svgpath2mpl.

!pip install svgpath2mpl matplotlib
from svgpath2mpl import parse_path

import matplotlib.pyplot as plt      
import numpy as np                   
# Use Inkscape to edit SVG, 
# Path -> Combine to convert multiple paths into a single path
# Use Path -> Object to path to convert objects to SVG path
smiley = parse_path("""m 739.01202,391.98936 c 13,26 13,57 9,85 -6,27 -18,52 -35,68 -21,20 -50,23 -77,18 -15,-4 -28,-12 -39,-23 -18,-17 -30,-40 -36,-67 -4,-20 -4,-41 0,-60 l 6,-21 z m -302,-1 c 2,3 6,20 7,29 5,28 1,57 -11,83 -15,30 -41,52 -72,60 -29,7 -57,0 -82,-15 -26,-17 -45,-49 -50,-82 -2,-12 -2,-33 0,-45 1,-10 5,-26 8,-30 z M 487.15488,66.132209 c 121,21 194,115.000001 212,233.000001 l 0,8 25,1 1,18 -481,0 c -6,-13 -10,-27 -13,-41 -13,-94 38,-146 114,-193.000001 45,-23 93,-29 142,-26 z m -47,18 c -52,6 -98,28.000001 -138,62.000001 -28,25 -46,56 -51,87 -4,20 -1,57 5,70 l 423,1 c 2,-56 -39,-118 -74,-157 -31,-34 -72,-54.000001 -116,-63.000001 -11,-2 -38,-2 -49,0 z m 138,324.000001 c -5,6 -6,40 -2,58 3,16 4,16 10,10 14,-14 38,-14 52,0 15,18 12,41 -6,55 -3,3 -5,5 -5,6 1,4 22,8 34,7 42,-4 57.6,-40 66.2,-77 3,-17 1,-53 -4,-59 l -145.2,0 z m -331,-1 c -4,5 -5,34 -4,50 2,14 6,24 8,24 1,0 3,-2 6,-5 17,-17 47,-13 58,9 7,16 4,31 -8,43 -4,4 -7,8 -7,9 0,0 4,2 8,3 51,17 105,-20 115,-80 3,-15 0,-43 -3,-53 z m 61,-266 c 0,0 46,-40 105,-53.000001 66,-15 114,7 114,7 0,0 -14,76.000001 -93,95.000001 -76,18 -126,-49 -126,-49 z""")
smiley.vertices -= smiley.vertices.mean(axis=0)
x = np.linspace(-3, 3, 20)          
plt.plot(x, np.sin(x), marker=smiley, markersize=20, color='c')
plt.show()

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