{"id":1085643,"date":"2025-01-08T13:18:04","date_gmt":"2025-01-08T05:18:04","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1085643.html"},"modified":"2025-01-08T13:18:11","modified_gmt":"2025-01-08T05:18:11","slug":"python%e7%ad%89%e5%80%bc%e7%ba%bf%e5%9b%be%e5%a6%82%e4%bd%95%e5%8a%a0%e5%85%a5%e5%9b%be%e4%be%8b-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1085643.html","title":{"rendered":"python\u7b49\u503c\u7ebf\u56fe\u5982\u4f55\u52a0\u5165\u56fe\u4f8b"},"content":{"rendered":"

\"python\u7b49\u503c\u7ebf\u56fe\u5982\u4f55\u52a0\u5165\u56fe\u4f8b\"<\/p>\n

\u5728Python\u4e2d\u4f7f\u7528\u7b49\u503c\u7ebf\u56fe\uff08Contour Plot\uff09\u65f6\uff0c\u52a0\u5165\u56fe\u4f8b\u80fd\u591f\u4f7f\u56fe\u5f62\u66f4\u5177\u53ef\u8bfb\u6027\u548c\u4fe1\u606f\u6027\u3002\u4f7f\u7528matplotlib<\/code>\u5e93\u4e2d\u7684contour<\/code>\u548ccontourf<\/code>\u51fd\u6570\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe\uff0c\u5e76\u901a\u8fc7clabel<\/code>\u548ccolorbar<\/code>\u51fd\u6570\u6dfb\u52a0\u56fe\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u56fe\u5f62\u66f4\u5bb9\u6613\u7406\u89e3\u3002<\/strong><\/p>\n<\/p>\n

\u5728\u4f7f\u7528matplotlib<\/code>\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe\u65f6\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u6765\u6dfb\u52a0\u56fe\u4f8b\uff1a<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528contour<\/code>\u6216contourf<\/code>\u51fd\u6570\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe\u3002<\/li>\n
  2. \u4f7f\u7528clabel<\/code>\u51fd\u6570\u4e3a\u7b49\u503c\u7ebf\u6dfb\u52a0\u6807\u7b7e\u3002<\/li>\n
  3. \u4f7f\u7528colorbar<\/code>\u51fd\u6570\u6dfb\u52a0\u989c\u8272\u6761\u4f5c\u4e3a\u56fe\u4f8b\u3002<\/li>\n<\/ol>\n

    \u4ee5\u4e0b\u662f\u8be6\u7ec6\u4ecb\u7ecd\u53ca\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

    import numpy as np<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
    x = np.linspace(-3.0, 3.0, 100)<\/p>\n
    y = np.linspace(-3.0, 3.0, 100)<\/p>\n
    X, Y = np.meshgrid(x, y)<\/p>\n
    Z = np.sin(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
    \u7ed8\u5236\u7b49\u503c\u7ebf\u56fe<\/strong><\/h2>\n
    plt.figure()<\/p>\n
    contour_set = plt.contour(X, Y, Z, levels=10, cmap='viridis')<\/p>\n
    \u4e3a\u7b49\u503c\u7ebf\u6dfb\u52a0\u6807\u7b7e<\/strong><\/h2>\n
    plt.clabel(contour_set, inline=True, fontsize=8)<\/p>\n
    \u6dfb\u52a0\u989c\u8272\u6761\u4f5c\u4e3a\u56fe\u4f8b<\/strong><\/h2>\n
    plt.colorbar(contour_set)<\/p>\n
    \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
    plt.title('Contour Plot with Legend')<\/p>\n
    plt.xlabel('X-axis')<\/p>\n
    plt.ylabel('Y-axis')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e00\u3001\u521b\u5efa\u6570\u636e<\/h3>\n<\/p>\n
    \u5728\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u6570\u636e\u3002\u6570\u636e\u901a\u5e38\u662f\u4e8c\u7ef4\u7684\uff0c\u53ef\u4ee5\u901a\u8fc7\u7f51\u683c\u70b9\u751f\u6210\u3002\u6211\u4eec\u4f7f\u7528numpy<\/code>\u5e93\u4e2d\u7684linspace<\/code>\u548cmeshgrid<\/code>\u51fd\u6570\u751f\u6210\u4e8c\u7ef4\u7f51\u683c\u6570\u636e\uff0csin<\/code>\u51fd\u6570\u751f\u6210Z\u503c\u3002<\/p>\n<\/p>\n
    x = np.linspace(-3.0, 3.0, 100)<\/p>\n
    y = np.linspace(-3.0, 3.0, 100)<\/p>\n
    X, Y = np.meshgrid(x, y)<\/p>\n
    Z = np.sin(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe<\/h3>\n<\/p>\n
    \u4f7f\u7528matplotlib<\/code>\u5e93\u4e2d\u7684contour<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe\u3002\u901a\u8fc7levels<\/code>\u53c2\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u7b49\u503c\u7ebf\u7684\u6570\u91cf\u6216\u5177\u4f53\u7684\u503c\u3002<\/p>\n<\/p>\n
    plt.figure()<\/p>\n
    contour_set = plt.contour(X, Y, Z, levels=10, cmap='viridis')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u4e3a\u7b49\u503c\u7ebf\u6dfb\u52a0\u6807\u7b7e<\/h3>\n<\/p>\n
    clabel<\/code>\u51fd\u6570\u7528\u4e8e\u4e3a\u7b49\u503c\u7ebf\u56fe\u6dfb\u52a0\u6807\u7b7e\uff0c\u4f7f\u5f97\u5404\u7b49\u503c\u7ebf\u5bf9\u5e94\u7684\u6570\u503c\u663e\u793a\u5728\u56fe\u4e0a\uff0c\u589e\u52a0\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n
    plt.clabel(contour_set, inline=True, fontsize=8)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u6dfb\u52a0\u989c\u8272\u6761\u4f5c\u4e3a\u56fe\u4f8b<\/h3>\n<\/p>\n
    colorbar<\/code>\u51fd\u6570\u7528\u4e8e\u5728\u56fe\u7684\u4e00\u4fa7\u6dfb\u52a0\u989c\u8272\u6761\uff0c\u989c\u8272\u6761\u53ef\u4ee5\u76f4\u89c2\u5730\u663e\u793a\u5404\u7b49\u503c\u7ebf\u5bf9\u5e94\u7684\u6570\u503c\u8303\u56f4\uff0c\u4f5c\u4e3a\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
    plt.colorbar(contour_set)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u663e\u793a\u56fe\u5f62<\/h3>\n<\/p>\n
    \u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7show<\/code>\u51fd\u6570\u6765\u663e\u793a\u7ed8\u5236\u7684\u56fe\u5f62\uff0c\u5e76\u8bbe\u7f6e\u56fe\u7684\u6807\u9898\u548c\u5750\u6807\u8f74\u6807\u7b7e\u3002<\/p>\n<\/p>\n
    plt.title('Contour Plot with Legend')<\/p>\n
    plt.xlabel('X-axis')<\/p>\n
    plt.ylabel('Y-axis')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u6df1\u5165\u7406\u89e3\u7b49\u503c\u7ebf\u56fe\u7684\u56fe\u4f8b<\/h3>\n<\/p>\n
    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u7b49\u503c\u7ebf\u56fe\u7684\u56fe\u4f8b\u4e0d\u4ec5\u80fd\u591f\u63d0\u4f9b\u6570\u503c\u8303\u56f4\uff0c\u8fd8\u80fd\u901a\u8fc7\u989c\u8272\u7684\u53d8\u5316\u53cd\u6620\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u4e3a\u4e86\u8ba9\u56fe\u4f8b\u66f4\u6709\u610f\u4e49\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u81ea\u5b9a\u4e49\u8bbe\u7f6e\uff0c\u6bd4\u5982\u8c03\u6574\u989c\u8272\u6620\u5c04\u3001\u7b49\u503c\u7ebf\u7684\u6570\u91cf\u548c\u5177\u4f53\u503c\u3002<\/p>\n<\/p>\n
    \u81ea\u5b9a\u4e49\u989c\u8272\u6620\u5c04<\/h4>\n<\/p>\n
    matplotlib<\/code>\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u989c\u8272\u6620\u5c04\uff08cmap\uff09\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7279\u70b9\u9009\u62e9\u5408\u9002\u7684\u989c\u8272\u6620\u5c04\u3002<\/p>\n<\/p>\n
    cmap = plt.get_cmap('coolwarm')<\/p>\n
    contour_set = plt.contourf(X, Y, Z, levels=10, cmap=cmap)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u81ea\u5b9a\u4e49\u7b49\u503c\u7ebf\u6570\u91cf\u548c\u5177\u4f53\u503c<\/h4>\n<\/p>\n
    \u901a\u8fc7levels<\/code>\u53c2\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u81ea\u5b9a\u4e49\u7b49\u503c\u7ebf\u7684\u6570\u91cf\u6216\u5177\u4f53\u503c\uff0c\u4f7f\u5f97\u56fe\u5f62\u66f4\u7b26\u5408\u6570\u636e\u7684\u5b9e\u9645\u60c5\u51b5\u3002<\/p>\n<\/p>\n
    levels = np.linspace(-1, 1, 20)<\/p>\n
    contour_set = plt.contour(X, Y, Z, levels=levels, cmap='viridis')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e03\u3001\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
    \u7b49\u503c\u7ebf\u56fe\u5728\u79d1\u5b66\u7814\u7a76\u3001\u5de5\u7a0b\u5e94\u7528\u3001\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\u7b49\u9886\u57df\u6709\u5e7f\u6cdb\u5e94\u7528\u3002\u901a\u8fc7\u7b49\u503c\u7ebf\u56fe\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u6570\u636e\u7684\u7a7a\u95f4\u5206\u5e03\u548c\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
    \u6c14\u8c61\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
    \u5728\u6c14\u8c61\u6570\u636e\u5206\u6790\u4e2d\uff0c\u7b49\u503c\u7ebf\u56fe\u5e38\u7528\u4e8e\u663e\u793a\u6e29\u5ea6\u3001\u6c14\u538b\u7b49\u6570\u636e\u7684\u7a7a\u95f4\u5206\u5e03\u3002\u901a\u8fc7\u6dfb\u52a0\u56fe\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u5feb\u901f\u4e86\u89e3\u5404\u7b49\u503c\u7ebf\u5bf9\u5e94\u7684\u6570\u503c\u8303\u56f4\u3002<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u521b\u5efa\u6a21\u62df\u6c14\u6e29\u6570\u636e<\/strong><\/h2>\n
    x = np.linspace(-3.0, 3.0, 100)<\/p>\n
    y = np.linspace(-3.0, 3.0, 100)<\/p>\n
    X, Y = np.meshgrid(x, y)<\/p>\n
    Z = 15 + 10*np.sin(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
    \u7ed8\u5236\u7b49\u503c\u7ebf\u56fe<\/strong><\/h2>\n
    plt.figure()<\/p>\n
    contour_set = plt.contour(X, Y, Z, levels=10, cmap='coolwarm')<\/p>\n
    \u4e3a\u7b49\u503c\u7ebf\u6dfb\u52a0\u6807\u7b7e<\/strong><\/h2>\n
    plt.clabel(contour_set, inline=True, fontsize=8)<\/p>\n
    \u6dfb\u52a0\u989c\u8272\u6761\u4f5c\u4e3a\u56fe\u4f8b<\/strong><\/h2>\n
    plt.colorbar(contour_set, label='Temperature (\u00b0C)')<\/p>\n
    \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
    plt.title('Temperature Contour Plot with Legend')<\/p>\n
    plt.xlabel('Longitude')<\/p>\n
    plt.ylabel('Latitude')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5de5\u7a0b\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
    \u5728\u5de5\u7a0b\u6570\u636e\u5206\u6790\u4e2d\uff0c\u7b49\u503c\u7ebf\u56fe\u53ef\u4ee5\u7528\u4e8e\u663e\u793a\u5e94\u529b\u3001\u5e94\u53d8\u7b49\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u901a\u8fc7\u6dfb\u52a0\u56fe\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u4e86\u89e3\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u521b\u5efa\u6a21\u62df\u5e94\u529b\u6570\u636e<\/strong><\/h2>\n
    x = np.linspace(-3.0, 3.0, 100)<\/p>\n
    y = np.linspace(-3.0, 3.0, 100)<\/p>\n
    X, Y = np.meshgrid(x, y)<\/p>\n
    Z = np.cos(np.sqrt(X<strong>2 + Y<\/strong>2))<\/p>\n
    \u7ed8\u5236\u7b49\u503c\u7ebf\u56fe<\/strong><\/h2>\n
    plt.figure()<\/p>\n
    contour_set = plt.contour(X, Y, Z, levels=10, cmap='viridis')<\/p>\n
    \u4e3a\u7b49\u503c\u7ebf\u6dfb\u52a0\u6807\u7b7e<\/strong><\/h2>\n
    plt.clabel(contour_set, inline=True, fontsize=8)<\/p>\n
    \u6dfb\u52a0\u989c\u8272\u6761\u4f5c\u4e3a\u56fe\u4f8b<\/strong><\/h2>\n
    plt.colorbar(contour_set, label='Stress (Pa)')<\/p>\n
    \u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
    plt.title('Stress Contour Plot with Legend')<\/p>\n
    plt.xlabel('X-axis')<\/p>\n
    plt.ylabel('Y-axis')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u4ecb\u7ecd\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5728Python\u4e2d\u4f7f\u7528matplotlib<\/code>\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe\u5e76\u6dfb\u52a0\u56fe\u4f8b\u662f\u975e\u5e38\u7b80\u5355\u7684\u3002\u901a\u8fc7\u5408\u7406\u9009\u62e9\u989c\u8272\u6620\u5c04\u3001\u81ea\u5b9a\u4e49\u7b49\u503c\u7ebf\u6570\u91cf\u548c\u5177\u4f53\u503c\uff0c\u53ef\u4ee5\u4f7f\u7b49\u503c\u7ebf\u56fe\u66f4\u7b26\u5408\u6570\u636e\u7684\u5b9e\u9645\u60c5\u51b5\uff0c\u589e\u52a0\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u4fe1\u606f\u6027\u3002<\/strong> \u7b49\u503c\u7ebf\u56fe\u5728\u79d1\u5b66\u7814\u7a76\u3001\u5de5\u7a0b\u5e94\u7528\u3001\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\u7b49\u9886\u57df\u6709\u5e7f\u6cdb\u5e94\u7528\uff0c\u638c\u63e1\u5176\u7ed8\u5236\u65b9\u6cd5\u548c\u6280\u5de7\u5bf9\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u975e\u5e38\u91cd\u8981\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u4e3a\u7b49\u503c\u7ebf\u56fe\u6dfb\u52a0\u56fe\u4f8b\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u521b\u5efa\u7b49\u503c\u7ebf\u56fe\u5e76\u6dfb\u52a0\u56fe\u4f8b\u3002\u9996\u5148\uff0c\u4f7f\u7528contour<\/code>\u6216contourf<\/code>\u51fd\u6570\u7ed8\u5236\u7b49\u503c\u7ebf\uff0c\u7136\u540e\u901a\u8fc7plt.colorbar()<\/code>\u6dfb\u52a0\u56fe\u4f8b\u3002\u786e\u4fdd\u5728\u7ed8\u5236\u7b49\u503c\u7ebf\u65f6\u4e3a\u6bcf\u4e2a\u7ebf\u6761\u6307\u5b9a\u6807\u7b7e\uff0c\u8fd9\u6837\u5b83\u4eec\u5c31\u80fd\u5728\u56fe\u4f8b\u4e2d\u663e\u793a\u3002<\/p>\n
    \u7b49\u503c\u7ebf\u56fe\u7684\u989c\u8272\u6761\u4e0e\u56fe\u4f8b\u6709\u4ec0\u4e48\u533a\u522b\uff1f<\/strong>
    \u7b49\u503c\u7ebf\u56fe\u4e2d\u7684\u989c\u8272\u6761\uff08colorbar\uff09\u663e\u793a\u4e86\u4e0d\u540c\u7b49\u503c\u7ebf\u5bf9\u5e94\u7684\u6570\u503c\u8303\u56f4\uff0c\u800c\u56fe\u4f8b\u901a\u5e38\u7528\u4e8e\u63cf\u8ff0\u4e0d\u540c\u6570\u636e\u7cfb\u5217\u6216\u56fe\u5f62\u5143\u7d20\u7684\u6807\u7b7e\u3002\u5728\u7ed8\u5236\u7b49\u503c\u7ebf\u56fe\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u989c\u8272\u6761\u6765\u8868\u793a\u6570\u503c\u53d8\u5316\uff0c\u800c\u4f7f\u7528\u56fe\u4f8b\u6765\u533a\u5206\u4e0d\u540c\u7684\u7b49\u503c\u7ebf\u7ec4\u3002<\/p>\n
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