matplotlib实现自定义散点形状marker的3种方法

import matplotlib.pyplot as plt 
import numpy as np 

data = np.random.rand(3,2)
fig = plt.figure() 
ax = fig.add_subplot(111) 
markers = ["$\u266B$", r"$\frac{1}{2}$", "$\heartsuit$"]
for i in range(data.shape[0]):
    ax.scatter(data[i,0], data[i,1], marker=markers[i], s=400) 
plt.show()

import matplotlib.pyplot as plt
import matplotlib.path as mpath
import numpy as np

star = mpath.Path.unit_regular_star(6)  # 星型Path
circle = mpath.Path.unit_circle()  # 圆形Path 
# 整合两个路径对象的点
verts = np.concatenate([circle.vertices, star.vertices[::-1, ...]])
# 整合两个路径对象点的类型
codes = np.concatenate([circle.codes, star.codes])

# 根据路径点和点的类型重新生成一个新的Path对象 
cut_star = mpath.Path(verts, codes)

plt.plot(np.arange(10)**2, '--r', marker=cut_star, markersize=15)

plt.show()

import matplotlib.pyplot as plt
import matplotlib.path as mpath
import numpy as np

circle = mpath.Path.unit_circle()  # 获得一个圆 
verts_part= circle.wedge(340,220).vertices  # 按逆时针从340度到220度部分圆的路径点
codes_part = circle.wedge(340,220).codes  # 点类型 
verts_part = verts_part[1:-2]  # 去除第一个点和最后两个点 
codes_part = codes_part[1:-2] 
# 整合新的点 
verts = np.concatenate([np.array([[0,-2]]), verts_part, np.array([[0,-2]])]) 
codes = [mpath.Path.MOVETO] + codes_part.tolist() +  [mpath.Path.CLOSEPOLY] 

icon = mpath.Path(vertices=verts, codes=codes)

plt.plot(verts[:,0], verts[:,1]) 
plt.show() 

plt.scatter(data[:,0], data[:,1], marker=icon, s=300, facecolor="none",edgecolors="black")
plt.show()

import matplotlib as mpl 
from svgpath2mpl import parse_path
import xml.etree.ElementTree as etree 
from six import StringIO 
import re 

# svg格式图片的svg代码
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# 解析代码 
tree = etree.parse(StringIO(svg)) 
root = tree.getroot() 
width = int(re.match(r'\d+', root.attrib['width']).group())
height = int(re.match(r'\d+', root.attrib['height']).group())
path_elems = root.findall('.//{http://www.w3.org/2000/svg}path') 

# 解析出每个Path对象
paths = [parse_path(elem.attrib['d']) for elem in path_elems] 
facecolors = [elem.attrib.get('fill', 'none') for elem in path_elems] 
facecolors = [c if c !="" else "none" for c in facecolors]
edgecolors = [elem.attrib.get('stroke', 'none') for elem in path_elems]
linewidths = [elem.attrib.get('stroke_width', 1) for elem in path_elems]

#定义旋转矩阵
def rot(verts, az):
    #顺时针旋转
    rad = az / 180 * np.pi
    verts = np.array(verts)
    rotMat = np.array([[np.cos(rad), -np.sin(rad)], [np.sin(rad), np.cos(rad)]])
    transVerts = verts.dot(rotMat)
    return transVerts

# 合并每个Path对象
verts = paths[0].vertices 
codes = paths[0].codes 
for i in range(1,len(paths)): 
    verts = np.concatenate([verts, paths[i].vertices])
    codes = np.concatenate([codes, paths[i].codes])
verts = rot(verts, 180) 
fig = plt.figure() 
ax = fig.add_subplot(111) 
ax.plot(verts[:,0], verts[:,1]  , color="red")  
plt.show() 

# 定义解析函数 
def svg2path(svg):
    # 解析代码 
    tree = etree.parse(StringIO(svg)) 
    root = tree.getroot() 
    path_elems = root.findall('.//{http://www.w3.org/2000/svg}path') 

    # 解析出每个Path对象
    paths = [parse_path(elem.attrib['d']) for elem in path_elems] 
    
    # 合并path对象
    verts = paths[0].vertices 
    codes = paths[0].codes 
    for i in range(1, len(paths)): 
        verts = np.concatenate([verts, paths[i].vertices])
        codes = np.concatenate([codes, paths[i].codes])
    # 复原为原来的形状
    verts = rot(verts, 270) 
    verts = np.fliplr(verts)  # 水平翻转，镜像
    #verts = (verts - verts.min(0)) / (verts.max(0) - verts.min(0)) 
    icon = mpath.Path(vertices=verts, codes=codes)
    return icon 

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      """<svg t="1616821127367" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="2163" width="200" height="200"><path d="M227.3 551.5c-91.3 0-165.6 74.3-165.6 165.6S136 882.7 227.3 882.7s165.6-74.3 165.6-165.6-74.3-165.6-165.6-165.6z m0 287.2c-67.1 0-121.6-54.5-121.6-121.6s54.6-121.6 121.6-121.6S348.9 650 348.9 717.1s-54.5 121.6-121.6 121.6zM631.4 333.8c59.2 0 107.4-48.2 107.5-107.5 0-59.3-48.2-107.5-107.5-107.5S523.9 167 523.9 226.3s48.2 107.5 107.5 107.5z m0-171c35 0 63.4 28.5 63.5 63.5 0 35-28.5 63.5-63.5 63.5s-63.5-28.5-63.5-63.5 28.5-63.5 63.5-63.5zM789.7 563.5c-91.3 0-165.6 74.3-165.6 165.6s74.3 165.6 165.6 165.6 165.6-74.3 165.6-165.6S881 563.5 789.7 563.5z m0 287.2c-67.1 0-121.6-54.5-121.6-121.6s54.6-121.6 121.6-121.6c67.1 0 121.6 54.5 121.6 121.6s-54.5 121.6-121.6 121.6zM615 573.6l-112.1-95.1 26.3-39.2 118.7 61.5c9.1 4.7 18.8 6.9 28.4 6.9 19.9 0 39.4-9.7 51.2-27.3a61.74 61.74 0 0 0 8.6-49.6c-4.4-16.9-15.8-31.3-31.3-39.4L546.7 309c-29.6-15.4-65.8-6.2-84.4 21.5l-85.5 127.3c-18.5 27.6-13.3 64.7 12.1 86.2l99.6 84.1-53.8 80.1c-8.7 12.9-11.8 28.4-8.8 43.6s11.7 28.4 24.6 37c9.6 6.5 20.8 9.9 32.2 9.9 3.8 0 7.6-0.4 11.3-1.2 15.2-3 28.4-11.7 37-24.6l92.5-137.7c13.3-19.8 9.6-46.2-8.5-61.6z m-28 37.1l-92.6 137.7c-4.3 6.4-13.1 8.1-19.5 3.8s-8.1-13.1-3.8-19.5l64.8-96.5c6.2-9.3 4.5-21.9-4.1-29.1l-114.5-96.8c-8.3-7-10-19.1-3.9-28.1l85.5-127.3c6-9 17.8-12 27.3-7l158.1 82.4c4.6 2.4 7.7 6.4 9 11.3s0.4 10-2.4 14.2c-5 7.4-14.8 9.9-22.8 5.8L532 391c-9.9-5.1-22.2-2-28.4 7.3l-48.2 71.9c-6.3 9.3-4.5 21.8 4 29l127.1 108c1 0.9 1.3 2.4 0.5 3.5z" p-id="2164"></path></svg>"""]
icons = []
for s in svg:
    icons.append(svg2path(s)) 

fig = plt.figure() 
ax = fig.add_subplot(111) 
for i in range(data.shape[0]):
    ax.scatter(data[i,0], data[i,1], marker=icons[i], s=3000)
plt.show()