{"id":1072055,"date":"2025-01-08T11:12:47","date_gmt":"2025-01-08T03:12:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1072055.html"},"modified":"2025-01-08T11:12:49","modified_gmt":"2025-01-08T03:12:49","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e8%8f%b1%e5%bd%a2%e5%8d%81%e4%ba%8c%e9%9d%a2%e4%bd%93-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1072055.html","title":{"rendered":"\u5982\u4f55\u7528Python\u753b\u83f1\u5f62\u5341\u4e8c\u9762\u4f53"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u753b\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\"<\/p>\n

\u8981\u7528Python\u753b\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\uff0c\u53ef\u4ee5\u4f7f\u75283D\u56fe\u5f62\u5e93\uff0c\u4f8b\u5982Matplotlib\u3001Mayavi\u6216Vpython\u3002\u4f7f\u7528Matplotlib\u6bd4\u8f83\u5e38\u89c1\uff0c\u56e0\u4e3a\u5b83\u6613\u4e8e\u4f7f\u7528\u5e76\u4e14\u53ef\u4ee5\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u56fe\u5f62\u3002\u4f60\u9700\u8981\u5b9a\u4e49\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u7684\u9876\u70b9\u548c\u9762\uff0c\u7136\u540e\u5c06\u5b83\u4eec\u7ed8\u5236\u51fa\u6765\u3002<\/strong> \u5176\u4e2d\uff0c\u5b9a\u4e49\u9876\u70b9\u548c\u9762\u7684\u8fc7\u7a0b\u6bd4\u8f83\u5173\u952e\uff0c\u56e0\u4e3a\u8fd9\u662f\u51b3\u5b9a\u56fe\u5f62\u662f\u5426\u6b63\u786e\u7684\u57fa\u7840\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Matplotlib\u548cNumPy\u5e93\u3002Matplotlib\u7528\u4e8e\u7ed8\u56fe\uff0c\u800cNumPy\u7528\u4e8e\u5904\u7406\u6570\u7ec4\u548c\u6570\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
from mpl_toolkits.mplot3d.art3d import Poly3DCollection<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5b9a\u4e49\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u7684\u9876\u70b9<\/h3>\n<\/p>\n
\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u753112\u4e2a\u83f1\u5f62\u9762\u7ec4\u6210\uff0c\u6bcf\u4e2a\u9762\u7531\u56db\u4e2a\u9876\u70b9\u6784\u6210\u3002\u6211\u4eec\u9700\u8981\u5b9a\u4e49\u8fd9\u4e9b\u9876\u70b9\u7684\u5750\u6807\u3002\u4e00\u4e2a\u65b9\u6cd5\u662f\u4f7f\u7528\u9ec4\u91d1\u6bd4\u4f8b\u03c6(\u7ea6\u4e3a1.618)\u6765\u786e\u5b9a\u9876\u70b9\u7684\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
phi = (1 + np.sqrt(5)) \/ 2  # \u9ec4\u91d1\u6bd4\u4f8b<\/p>\n
vertices = np.array([<\/p>\n
    [1, 1, 1],<\/p>\n
    [1, 1, -1],<\/p>\n
    [1, -1, 1],<\/p>\n
    [1, -1, -1],<\/p>\n
    [-1, 1, 1],<\/p>\n
    [-1, 1, -1],<\/p>\n
    [-1, -1, 1],<\/p>\n
    [-1, -1, -1],<\/p>\n
    [0, 1\/phi, phi],<\/p>\n
    [0, 1\/phi, -phi],<\/p>\n
    [0, -1\/phi, phi],<\/p>\n
    [0, -1\/phi, -phi],<\/p>\n
    [1\/phi, phi, 0],<\/p>\n
    [1\/phi, -phi, 0],<\/p>\n
    [-1\/phi, phi, 0],<\/p>\n
    [-1\/phi, -phi, 0],<\/p>\n
    [phi, 0, 1\/phi],<\/p>\n
    [phi, 0, -1\/phi],<\/p>\n
    [-phi, 0, 1\/phi],<\/p>\n
    [-phi, 0, -1\/phi]<\/p>\n
])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5b9a\u4e49\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u7684\u9762<\/h3>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u5b9a\u4e49\u6bcf\u4e2a\u9762\u3002\u6211\u4eec\u5c06\u9762\u5b9a\u4e49\u4e3a\u7531\u9876\u70b9\u6570\u7ec4\u7684\u7d22\u5f15\u7ec4\u6210\u7684\u5217\u8868\u3002<\/p>\n<\/p>\n
faces = [<\/p>\n
    [0, 8, 4, 14],<\/p>\n
    [0, 12, 1, 8],<\/p>\n
    [0, 16, 2, 12],<\/p>\n
    [0, 14, 6, 16],<\/p>\n
    [1, 17, 9, 8],<\/p>\n
    [1, 12, 3, 17],<\/p>\n
    [2, 10, 18, 16],<\/p>\n
    [2, 13, 3, 10],<\/p>\n
    [3, 15, 17, 13],<\/p>\n
    [4, 8, 9, 5],<\/p>\n
    [4, 19, 6, 14],<\/p>\n
    [5, 11, 19, 9],<\/p>\n
    [5, 15, 7, 11],<\/p>\n
    [6, 18, 10, 2],<\/p>\n
    [7, 13, 15, 3],<\/p>\n
    [7, 11, 10, 13],<\/p>\n
    [11, 7, 3, 17],<\/p>\n
    [12, 0, 8, 1],<\/p>\n
    [14, 0, 16, 6],<\/p>\n
    [16, 18, 2, 0],<\/p>\n
    [18, 6, 14, 4],<\/p>\n
    [19, 4, 14, 6]<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7ed8\u5236\u83f1\u5f62\u5341\u4e8c\u9762\u4f53<\/h3>\n<\/p>\n
\u73b0\u5728\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7ed8\u5236\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u3002\u6211\u4eec\u5c06\u4f7f\u75283D\u7ed8\u56fe\u5de5\u5177\uff0c\u5e76\u4e3a\u6bcf\u4e2a\u9762\u6dfb\u52a0\u591a\u8fb9\u5f62\u3002<\/p>\n<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
\u7ed8\u5236\u6bcf\u4e2a\u9762<\/strong><\/h2>\n
for face in faces:<\/p>\n
    polygon = [vertices[vertice] for vertice in face]<\/p>\n
    poly = Poly3DCollection([polygon], alpha=.25, edgecolor='k')<\/p>\n
    ax.add_collection3d(poly)<\/p>\n
\u8bbe\u7f6e\u5750\u6807\u8f74\u8303\u56f4<\/strong><\/h2>\n
ax.set_xlim([-2, 2])<\/p>\n
ax.set_ylim([-2, 2])<\/p>\n
ax.set_zlim([-2, 2])<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u8fdb\u4e00\u6b65\u4f18\u5316\u548c\u7f8e\u5316<\/h3>\n<\/p>\n
\u4e3a\u4e86\u4f7f\u56fe\u5f62\u66f4\u7f8e\u89c2\uff0c\u53ef\u4ee5\u8c03\u6574\u989c\u8272\u3001\u900f\u660e\u5ea6\u548c\u89c6\u89d2\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
    \n
  1. \u989c\u8272\u548c\u900f\u660e\u5ea6<\/strong>\uff1a\u53ef\u4ee5\u4e3a\u6bcf\u4e2a\u9762\u8bbe\u7f6e\u4e0d\u540c\u7684\u989c\u8272\uff0c\u5e76\u8c03\u6574\u900f\u660e\u5ea6\uff0c\u4f7f\u5176\u770b\u8d77\u6765\u66f4\u52a0\u751f\u52a8\u3002<\/li>\n<\/ol>\n
    colors = ['cyan', 'magenta', 'yellow', 'blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'blue', 'green', 'red']<\/p>\n
    for i, face in enumerate(faces):<\/p>\n
        polygon = [vertices[vertice] for vertice in face]<\/p>\n
        poly = Poly3DCollection([polygon], alpha=.5, edgecolor='k', facecolor=colors[i % len(colors)])<\/p>\n
        ax.add_collection3d(poly)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u89c6\u89d2\u8c03\u6574<\/strong>\uff1a\u53ef\u4ee5\u8bbe\u7f6e\u521d\u59cb\u89c6\u89d2\uff0c\u4f7f\u56fe\u5f62\u4ece\u6700\u4f73\u89d2\u5ea6\u5448\u73b0\u3002<\/li>\n<\/ol>\n
      ax.view_init(elev=20, azim=30)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u6dfb\u52a0\u5750\u6807\u8f74\u6807\u7b7e<\/strong>\uff1a\u4e3a\u6bcf\u4e2a\u8f74\u6dfb\u52a0\u6807\u7b7e\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u56fe\u5f62\u3002<\/li>\n<\/ol>\n
        ax.set_xlabel('X')<\/p>\n
        ax.set_ylabel('Y')<\/p>\n
        ax.set_zlabel('Z')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u901a\u8fc7\u8fd9\u4e9b\u8c03\u6574\uff0c\u4f60\u53ef\u4ee5\u751f\u6210\u66f4\u52a0\u7f8e\u89c2\u548c\u4e13\u4e1a\u7684\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u56fe\u5f62\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        from mpl_toolkits.mplot3d.art3d import Poly3DCollection<\/p>\n
        import numpy as np<\/p>\n
        phi = (1 + np.sqrt(5)) \/ 2  # \u9ec4\u91d1\u6bd4\u4f8b<\/p>\n
        vertices = np.array([<\/p>\n
            [1, 1, 1],<\/p>\n
            [1, 1, -1],<\/p>\n
            [1, -1, 1],<\/p>\n
            [1, -1, -1],<\/p>\n
            [-1, 1, 1],<\/p>\n
            [-1, 1, -1],<\/p>\n
            [-1, -1, 1],<\/p>\n
            [-1, -1, -1],<\/p>\n
            [0, 1\/phi, phi],<\/p>\n
            [0, 1\/phi, -phi],<\/p>\n
            [0, -1\/phi, phi],<\/p>\n
            [0, -1\/phi, -phi],<\/p>\n
            [1\/phi, phi, 0],<\/p>\n
            [1\/phi, -phi, 0],<\/p>\n
            [-1\/phi, phi, 0],<\/p>\n
            [-1\/phi, -phi, 0],<\/p>\n
            [phi, 0, 1\/phi],<\/p>\n
            [phi, 0, -1\/phi],<\/p>\n
            [-phi, 0, 1\/phi],<\/p>\n
            [-phi, 0, -1\/phi]<\/p>\n
        ])<\/p>\n
        faces = [<\/p>\n
            [0, 8, 4, 14],<\/p>\n
            [0, 12, 1, 8],<\/p>\n
            [0, 16, 2, 12],<\/p>\n
            [0, 14, 6, 16],<\/p>\n
            [1, 17, 9, 8],<\/p>\n
            [1, 12, 3, 17],<\/p>\n
            [2, 10, 18, 16],<\/p>\n
            [2, 13, 3, 10],<\/p>\n
            [3, 15, 17, 13],<\/p>\n
            [4, 8, 9, 5],<\/p>\n
            [4, 19, 6, 14],<\/p>\n
            [5, 11, 19, 9],<\/p>\n
            [5, 15, 7, 11],<\/p>\n
            [6, 18, 10, 2],<\/p>\n
            [7, 13, 15, 3],<\/p>\n
            [7, 11, 10, 13],<\/p>\n
            [11, 7, 3, 17],<\/p>\n
            [12, 0, 8, 1],<\/p>\n
            [14, 0, 16, 6],<\/p>\n
            [16, 18, 2, 0],<\/p>\n
            [18, 6, 14, 4],<\/p>\n
            [19, 4, 14, 6]<\/p>\n
        ]<\/p>\n
        fig = plt.figure()<\/p>\n
        ax = fig.add_subplot(111, projection='3d')<\/p>\n
        colors = ['cyan', 'magenta', 'yellow', 'blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'blue', 'green', 'red']<\/p>\n
        for i, face in enumerate(faces):<\/p>\n
            polygon = [vertices[vertice] for vertice in face]<\/p>\n
            poly = Poly3DCollection([polygon], alpha=.5, edgecolor='k', facecolor=colors[i % len(colors)])<\/p>\n
            ax.add_collection3d(poly)<\/p>\n
        ax.set_xlim([-2, 2])<\/p>\n
        ax.set_ylim([-2, 2])<\/p>\n
        ax.set_zlim([-2, 2])<\/p>\n
        ax.view_init(elev=20, azim=30)<\/p>\n
        ax.set_xlabel('X')<\/p>\n
        ax.set_ylabel('Y')<\/p>\n
        ax.set_zlabel('Z')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Python\u6210\u529f\u7ed8\u5236\u51fa\u4e00\u4e2a\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\uff0c\u5e76\u8fdb\u884c\u7f8e\u5316\u5904\u7406\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff01<\/p>\n<\/p>\n
        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
         \u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\uff1f<\/strong><\/p>\n
        \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u7ed8\u5236\u4e09\u7ef4\u56fe\u5f62\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fMatplotlib\u548cMayavi\u3002Matplotlib\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u63a5\u53e3\u6765\u7ed8\u5236\u57fa\u672c\u7684\u4e09\u7ef4\u5f62\u72b6\uff0c\u800cMayavi\u5219\u9002\u5408\u66f4\u590d\u6742\u7684\u4e09\u7ef4\u53ef\u89c6\u5316\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u4f7f\u7ed8\u5236\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u7684\u8fc7\u7a0b\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n
        \u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u7684\u6570\u5b66\u57fa\u7840\u662f\u4ec0\u4e48\uff1f<\/strong><\/p>\n
        \u83f1\u5f62\u5341\u4e8c\u9762\u4f53\u662f\u4e00\u79cd\u5177\u670912\u4e2a\u83f1\u5f62\u9762\u7684\u591a\u9762\u4f53\u3002\u5176\u9876\u70b9\u548c\u8fb9\u7684\u914d\u7f6e\u53ef\u4ee5\u901a\u8fc7\u51e0\u4f55\u5b66\u4e2d\u7684\u516c\u5f0f\u6765\u5b9a\u4e49\u3002\u5728\u7ed8\u5236\u4e4b\u524d\uff0c\u4e86\u89e3\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\u5c06\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u5728\u4ee3\u7801\u4e2d\u5b9e\u73b0\u5b83\u4eec\u3002<\/p>\n
        \u5982\u4f55\u4f18\u5316Python\u7ed8\u56fe\u4ee3\u7801\u4ee5\u63d0\u9ad8\u6027\u80fd\uff1f<\/strong><\/p>\n
        \u5728\u7ed8\u5236\u590d\u6742\u5f62\u72b6\u65f6\uff0c\u4ee3\u7801\u7684\u6027\u80fd\u53ef\u80fd\u4f1a\u5f71\u54cd\u6700\u7ec8\u7684\u663e\u793a\u6548\u679c\u3002\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u7ec4\u8fd0\u7b97\u3001\u907f\u514d\u91cd\u590d\u8ba1\u7b97\uff0c\u4ee5\u53ca\u5728\u6e32\u67d3\u65f6\u51cf\u5c11\u4e0d\u5fc5\u8981\u7684\u56fe\u5f62\u66f4\u65b0\u90fd\u662f\u63d0\u5347\u6027\u80fd\u7684\u6709\u6548\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u5408\u7406\u8bbe\u7f6e\u7ed8\u56fe\u7684\u53c2\u6570\u548c\u4f7f\u7528\u77e2\u91cf\u56fe\u5f62\u683c\u5f0f\u4e5f\u80fd\u663e\u8457\u6539\u5584\u7ed8\u56fe\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u7528Python\u753b\u83f1\u5f62\u5341\u4e8c\u9762\u4f53\uff0c\u53ef\u4ee5\u4f7f\u75283D\u56fe\u5f62\u5e93\uff0c\u4f8b\u5982Matplotlib\u3001Mayavi\u6216Vpython\u3002\u4f7f […]","protected":false},"author":3,"featured_media":1072064,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1072055"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1072055"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1072055\/revisions"}],"predecessor-version":[{"id":1072066,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1072055\/revisions\/1072066"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1072064"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1072055"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1072055"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1072055"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}