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matplotlib实现自定义散点形状marker的3种方法

作者：R人

本文主要介绍了matplotlib实现自定义散点形状marker的3种方法，文中通过示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

matplotlib中marker支持的数据类型

marker有4种类型，分别是：

4种类型中前两种即无填充类型和填充类型都是matplotlib本身就有的，没有可扩充性。第三种tex字符串也只能表现一些特殊的形状，可扩充性也不是很强。最后一种Path对象可以自己定义形状，可扩充性比较强。

无填充形状和填充形状

使用这种形状只需给marker指定一个字符或者一个数字即可

Tex形状

通过自定义一个符合Latex格式的字符串复制给marker即可,因此如果想让makrer为一个数字或者单词字母或者特殊的符号便可以定义这样一个字符串

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()

在这里插入图片描述

Path对象

matplotlib中Path类的定义参考了图片格式svg格式的定义,具体不做阐述,可自行百度。Path对象给定制marker提供了极大的便利，可以使用Path模块中已经定义好的一些Path类供用户组合，用户也可以自定义Path类

使用Path模块中的Path对象

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()

在这里插入图片描述

自定义Path对象

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()

在这里插入图片描述

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

在这里插入图片描述

从svg格式转化为Path对象

既然Path类的定义起源于svg，那么引申出一个问题，能否将svg格式的图片转化为Path对象？这样我们就不用费力自己定义Path对象，而是可以用别人已经定义好的svg格式的图片转换为Path对象

答案是可以的，可以用svgpath2mpl库解析svg转化为matplotlib的Path对象

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
svg = """<svg t="1616741322150" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="8787" width="200" height="200"><path d="M292.306 214.793c40.673-59.469 98.47-89.114 123.74-99.877-50.841 19.771-94.015 54.996-123.74 99.877zM317.249 549.7l-0.009-0.017a0.095 0.095 0 0 1 0.009 0.017zM330.252 573.425l-0.889-1.589 0.889 1.589zM314.714 544.938zM312.366 540.488c-16.983-32.365-32.057-64.58-43.611-95.073 10.481 27.982 25.067 59.78 43.611 95.073z" fill="#8BF268" p-id="8788"></path><path d="M276.744 382.688C295.187 506.384 510.593 843.49 510.593 843.49S409.26 713.734 331.798 576.177c15.768 28.036 33.713 57.96 53.791 89.654 50.524 79.755 101.501 149.919 125.003 181.547a2836.34 2836.34 0 0 0 13.718-18.628c41.494-66.235 208.413-337.835 224.549-446.063 27.402-152.126-70.696-229.04-122.257-258.284-34.982-17.378-74.37-27.171-116.009-27.171-30.64 0-60.058 5.311-87.405 15.032-30.816 13.288-179.4 87.46-146.444 270.424z m233.849-157.864c78.389 0 142.163 63.774 142.163 142.163S588.982 509.15 510.593 509.15 368.43 445.376 368.43 366.987s63.773-142.163 142.163-142.163z" fill="#8BF268" p-id="8789"></path><path d="M497.201 885.762l13.392 17.442 13.392-17.442c0.701-0.913 70.898-92.56 140.138-201.86C758.272 535.28 806.01 425.928 806.01 358.882c0-39.874-7.813-78.564-23.222-114.995-14.88-35.18-36.178-66.772-63.303-93.897s-58.716-48.423-93.896-63.303c-36.432-15.409-75.121-23.222-114.995-23.222s-78.563 7.813-114.995 23.222c-35.18 14.88-66.772 36.178-93.897 63.303s-48.423 58.717-63.303 93.897c-15.409 36.431-23.222 75.121-23.222 114.995 0 67.046 47.738 176.399 141.888 325.02 69.238 109.3 139.435 200.947 140.136 201.86z m-68.507-775.74s-1.986 0.725-5.507 2.243c27.347-9.721 56.765-15.032 87.405-15.032 41.638 0 81.026 9.793 116.009 27.171-17.536-9.946-29.693-14.382-29.693-14.382s187.827 60.358 180.229 265.49C746.326 547.301 515.01 843.49 515.01 843.49s3.369-5.272 9.299-14.739a2790.949 2790.949 0 0 1-13.718 18.628c-23.502-31.628-74.479-101.792-125.003-181.547-20.078-31.694-38.022-61.618-53.791-89.654a1069.523 1069.523 0 0 1-2.435-4.341 1399.816 1399.816 0 0 1-12.114-22.136l-0.009-0.017c-0.847-1.582-1.689-3.164-2.527-4.745-0.685-1.291-1.36-2.574-2.035-3.855l-0.313-0.595c-18.543-35.293-33.13-67.091-43.611-95.073-9.363-24.711-16.414-48.291-20.29-69.902-2.548-68.796 16.885-121.307 43.842-160.72 29.725-44.881 72.899-80.106 123.74-99.877 7.92-3.374 12.649-4.895 12.649-4.895z" fill="" p-id="8790"></path><path d="M510.593 509.151c78.389 0 142.163-63.774 142.163-142.163s-63.774-142.163-142.163-142.163S368.43 288.599 368.43 366.988s63.773 142.163 142.163 142.163z m108.395-142.164c0 59.769-48.626 108.395-108.395 108.395s-108.395-48.626-108.395-108.395 48.626-108.395 108.395-108.395 108.395 48.627 108.395 108.395zM736.383 836.927c-20.041-9.489-47.483-17.342-81.565-23.342l-0.655 3.723a12.318 12.318 0 0 0-1.116-0.194c-6.864-0.816-13.127 4.408-13.99 11.668-0.849 7.137 3.843 13.572 10.522 14.564l-0.615 3.496c29.133 5.128 53.509 11.86 70.496 19.466 18.638 8.346 21.571 14.609 21.86 15.365-0.868 3.232-13.707 16.8-63.714 28.875-45.006 10.868-104.318 16.853-167.012 16.853s-122.007-5.985-167.013-16.853c-50.105-12.099-62.896-25.697-63.719-28.894 1.082-4.274 21.574-23.326 99.529-36.026l-0.62-3.806c6.158-1.477 10.323-7.675 9.466-14.463-0.899-7.125-6.996-12.235-13.711-11.594l-0.565-3.466c-35.641 5.806-65.645 13.94-86.77 23.521-27.28 12.374-41.112 27.795-41.112 45.835 0 14.81 9.313 27.887 27.679 38.869 13.882 8.302 33.26 15.631 57.594 21.786 48.085 12.162 111.741 18.86 179.241 18.86s131.155-6.698 179.241-18.86c24.334-6.155 43.712-13.484 57.594-21.786 18.366-10.982 27.679-24.06 27.679-38.869 0-17.513-13.029-32.562-38.724-44.728z" fill="" p-id="8791"></path><path d="M292.306 214.793c-26.956 39.413-46.389 91.924-43.842 160.72 3.876 21.611 10.928 45.191 20.29 69.902 11.553 30.493 26.628 62.708 43.611 95.073l0.313 0.595a1193.743 1193.743 0 0 0 4.562 8.6l0.009 0.017a1395.399 1395.399 0 0 0 14.549 26.477C409.26 713.734 510.593 843.49 510.593 843.49S295.187 506.384 276.744 382.688c-32.956-182.964 115.628-257.137 146.443-270.423 3.52-1.518 5.507-2.243 5.507-2.243s-4.729 1.521-12.649 4.894c-25.269 10.762-83.066 40.407-123.739 99.877z" fill="#FFFFFF" p-id="8792"></path><path d="M748.858 382.688c-16.136 108.228-183.054 379.827-224.549 446.063-5.93 9.467-9.299 14.739-9.299 14.739s231.316-296.189 262.128-467.977c7.597-205.132-180.229-265.49-180.229-265.49s12.156 4.435 29.693 14.382c51.56 29.243 149.658 106.157 122.256 258.283z" fill="#2FBC3C" p-id="8793"></path></svg>"""
# 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()

在这里插入图片描述

# 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 

svg = ["""<svg t="1616814589861" class="icon" viewBox="0 0 1151 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="1716" width="200" height="200"><path d="M234.141946 661.465839a30.020373 30.020373 0 0 1-6.190642-0.593624 33.412505 33.412505 0 0 1-22.303271-15.264596 770.692505 770.692505 0 0 0-47.913872-68.690683 33.921325 33.921325 0 0 1 5.851429-47.235445 921.727205 921.727205 0 0 1 569.030228-194.199586c19.759172 0 39.518344 0.593623 59.362319 1.865673a16.960663 16.960663 0 0 1 16.027826 16.960662v135.6853a16.960663 16.960663 0 0 1-18.911139 16.960663 760.770518 760.770518 0 0 0-534.26087 147.557764 33.921325 33.921325 0 0 1-20.692008 6.953872z m-49.016315-105.156108a789.264431 789.264431 0 0 1 49.185922 70.556356 787.822774 787.822774 0 0 1 475.66178-157.818965c21.116025 0 42.401656 0.848033 63.348075 2.5441V370.844886c-13.483727-0.593623-27.13706-0.932836-40.70559-0.932836a887.80588 887.80588 0 0 0-547.490187 186.567287z" p-id="1717"></path><path d="M234.141946 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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()

在这里插入图片描述

参考

https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.html#sphx-glr-gallery-lines-bars-and-markers-marker-reference-py

https://nbviewer.jupyter.org/github/nvictus/svgpath2mpl/blob/master/examples/homer.ipynb

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