{"id":961347,"date":"2024-12-27T03:57:53","date_gmt":"2024-12-26T19:57:53","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/961347.html"},"modified":"2024-12-27T03:57:55","modified_gmt":"2024-12-26T19:57:55","slug":"python%e5%a6%82%e4%bd%95%e7%94%bb%e8%9e%ba%e6%97%8b%e6%9b%b2%e7%ba%bf","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/961347.html","title":{"rendered":"python\u5982\u4f55\u753b\u87ba\u65cb\u66f2\u7ebf"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25103609\/4afdb298-db62-4de8-96c9-dfd8b74ec97e.webp\" alt=\"python\u5982\u4f55\u753b\u87ba\u65cb\u66f2\u7ebf\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u548cnumpy\u5e93\u753b\u87ba\u65cb\u66f2\u7ebf\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u6781\u5750\u6807\u3001\u4f7f\u7528\u53c2\u6570\u65b9\u7a0b\u3001\u8c03\u6574\u53c2\u6570\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u6b65\u9aa4\uff1a<\/strong><\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u4f7f\u7528\u6781\u5750\u6807\u7cfb\u7ed8\u5236<\/strong>\uff1a\u5229\u7528matplotlib\u7684\u6781\u5750\u6807\u7cfb\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u51fa\u87ba\u65cb\u66f2\u7ebf\u3002\u6781\u5750\u6807\u7cfb\u662f\u4ee5\u4e00\u4e2a\u70b9\u4e3a\u57fa\u51c6\uff0c\u4f7f\u7528\u89d2\u5ea6\u548c\u534a\u5f84\u6765\u5b9a\u4e49\u4f4d\u7f6e\u7684\u5750\u6807\u7cfb\u3002\u901a\u8fc7\u8bbe\u5b9a\u89d2\u5ea6\u548c\u534a\u5f84\u7684\u5173\u7cfb\uff0c\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u87ba\u65cb\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u53c2\u6570\u65b9\u7a0b\u6cd5<\/strong>\uff1a\u901a\u8fc7\u4f7f\u7528\u53c2\u6570\u65b9\u7a0b\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u6240\u9700\u7684\u87ba\u65cb\u66f2\u7ebf\u3002\u53c2\u6570\u65b9\u7a0b\u5141\u8bb8\u6211\u4eec\u5b9a\u4e49\u6bcf\u4e2a\u70b9\u7684\u5750\u6807\uff0c\u5e76\u6839\u636e\u8fd9\u4e9b\u70b9\u7ed8\u5236\u66f2\u7ebf\u3002\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u65b9\u7a0b\u4e2d\u7684\u53c2\u6570\uff0c\u53ef\u4ee5\u751f\u6210\u4e0d\u540c\u5f62\u72b6\u548c\u7c7b\u578b\u7684\u87ba\u65cb\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u8c03\u6574\u53c2\u6570<\/strong>\uff1a\u5728\u7ed8\u5236\u87ba\u65cb\u66f2\u7ebf\u65f6\uff0c\u8c03\u6574\u53c2\u6570\u53ef\u4ee5\u6539\u53d8\u87ba\u65cb\u7684\u5f62\u72b6\u548c\u5bc6\u5ea6\u3002\u4f8b\u5982\uff0c\u6539\u53d8\u89d2\u5ea6\u7684\u6b65\u957f\u6216\u534a\u5f84\u7684\u589e\u957f\u901f\u7387\uff0c\u53ef\u4ee5\u5f97\u5230\u4e0d\u540c\u7684\u87ba\u65cb\u6548\u679c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><h3>\u4e00\u3001\u4f7f\u7528\u6781\u5750\u6807\u7cfb\u7ed8\u5236\u87ba\u65cb\u66f2\u7ebf<\/h3>\n<\/p>\n<p><p>\u4f7f\u7528matplotlib\u5e93\u7684\u6781\u5750\u6807\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u51fa\u87ba\u65cb\u66f2\u7ebf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528\u6781\u5750\u6807\u7cfb\u7ed8\u5236\u7b80\u5355\u87ba\u65cb\u66f2\u7ebf\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n<p>theta = np.linspace(0, 4 * np.pi, 1000)  # \u89d2\u5ea6\u4ece0\u52304\u03c0<\/p>\n<p>r = theta  # \u534a\u5f84\u968f\u7740\u89d2\u5ea6\u589e\u52a0<\/p>\n<h2><strong>\u521b\u5efa\u6781\u5750\u6807\u5b50\u56fe<\/strong><\/h2>\n<p>ax = plt.subplot(111, projection=&#39;polar&#39;)<\/p>\n<p>ax.plot(theta, r)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86numpy\u7684linspace\u51fd\u6570\u751f\u6210\u4ece0\u52304\u03c0\u7684\u89d2\u5ea6\u6570\u7ec4\uff0c\u5e76\u5c06\u534a\u5f84\u8bbe\u5b9a\u4e3a\u4e0e\u89d2\u5ea6\u76f8\u540c\u7684\u503c\u3002\u8fd9\u6837\uff0c\u968f\u7740\u89d2\u5ea6\u7684\u589e\u52a0\uff0c\u534a\u5f84\u4e5f\u589e\u52a0\uff0c\u5f62\u6210\u4e00\u4e2a\u7b80\u5355\u7684\u963f\u57fa\u7c73\u5fb7\u87ba\u7ebf\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u4f7f\u7528\u53c2\u6570\u65b9\u7a0b\u6cd5\u7ed8\u5236\u87ba\u65cb\u66f2\u7ebf<\/h3>\n<\/p>\n<p><p>\u53c2\u6570\u65b9\u7a0b\u6cd5\u5141\u8bb8\u6211\u4eec\u5b9a\u4e49\u6bcf\u4e2a\u70b9\u7684\u5750\u6807\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528\u53c2\u6570\u65b9\u7a0b\u6cd5\u7ed8\u5236\u87ba\u65cb\u66f2\u7ebf\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u53c2\u6570\u65b9\u7a0b<\/strong><\/h2>\n<p>t = np.linspace(0, 4 * np.pi, 1000)<\/p>\n<p>x = t * np.cos(t)<\/p>\n<p>y = t * np.sin(t)<\/p>\n<h2><strong>\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n<p>plt.plot(x, y)<\/p>\n<p>plt.axis(&#39;equal&#39;)  # \u4fdd\u6301\u5750\u6807\u8f74\u6bd4\u4f8b<\/p>\n<p>plt.title(&#39;Parametric Spiral&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u53c2\u6570t\uff0c\u5e76\u4f7f\u7528\u5b83\u6765\u8ba1\u7b97\u6bcf\u4e2a\u70b9\u7684x\u548cy\u5750\u6807\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e00\u6761\u87ba\u65cb\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u8c03\u6574\u53c2\u6570\u4ee5\u6539\u53d8\u87ba\u65cb\u5f62\u72b6<\/h3>\n<\/p>\n<p><p>\u901a\u8fc7\u8c03\u6574\u53c2\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u6539\u53d8\u87ba\u65cb\u7684\u5f62\u72b6\u548c\u5bc6\u5ea6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u8c03\u6574\u53c2\u6570\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u53c2\u6570\u65b9\u7a0b<\/strong><\/h2>\n<p>t = np.linspace(0, 10 * np.pi, 1000)<\/p>\n<p>a = 0.1  # \u8c03\u6574\u8fd9\u4e2a\u53c2\u6570\u4ee5\u6539\u53d8\u87ba\u65cb\u7684\u5bc6\u5ea6<\/p>\n<p>b = 0.2  # \u8c03\u6574\u8fd9\u4e2a\u53c2\u6570\u4ee5\u6539\u53d8\u87ba\u65cb\u7684\u589e\u957f\u901f\u7387<\/p>\n<p>x = (a + b * t) * np.cos(t)<\/p>\n<p>y = (a + b * t) * np.sin(t)<\/p>\n<h2><strong>\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n<p>plt.plot(x, y)<\/p>\n<p>plt.axis(&#39;equal&#39;)<\/p>\n<p>plt.title(&#39;Adjusted Parametric Spiral&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u8c03\u6574\u53c2\u6570a\u548cb\u6765\u6539\u53d8\u87ba\u65cb\u66f2\u7ebf\u7684\u5bc6\u5ea6\u548c\u589e\u957f\u901f\u7387\u3002\u901a\u8fc7\u8c03\u6574\u8fd9\u4e9b\u53c2\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u4e0d\u540c\u7c7b\u578b\u548c\u5f62\u72b6\u7684\u87ba\u65cb\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u4f7f\u75283D\u7ed8\u56fe\u7ed8\u5236\u87ba\u65cb\u66f2\u7ebf<\/h3>\n<\/p>\n<p><p>\u9664\u4e86\u57282D\u5e73\u9762\u4e0a\u7ed8\u5236\u87ba\u65cb\u66f2\u7ebf\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528matplotlib\u76843D\u7ed8\u56fe\u529f\u80fd\u6765\u7ed8\u52363D\u87ba\u65cb\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<p>from mpl_toolkits.mplot3d import Axes3D<\/p>\n<h2><strong>\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n<p>t = np.linspace(0, 10 * np.pi, 1000)<\/p>\n<p>x = np.sin(t)<\/p>\n<p>y = np.cos(t)<\/p>\n<p>z = t<\/p>\n<h2><strong>\u521b\u5efa3D\u5b50\u56fe<\/strong><\/h2>\n<p>fig = plt.figure()<\/p>\n<p>ax = fig.add_subplot(111, projection=&#39;3d&#39;)<\/p>\n<p>ax.plot(x, y, z)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u57283D\u7a7a\u95f4\u4e2d\u7ed8\u5236\u4e86\u4e00\u6761\u87ba\u65cb\u66f2\u7ebf\uff0c\u5176\u4e2dx\u548cy\u5750\u6807\u662f\u6b63\u5f26\u548c\u4f59\u5f26\u51fd\u6570\uff0c\u800cz\u5750\u6807\u662f\u53c2\u6570t\u7684\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u751f\u6210\u7f8e\u89c2\u76843D\u87ba\u65cb\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<p><h3>\u4e94\u3001\u5e94\u7528\u6848\u4f8b\uff1a\u7ed8\u5236\u53cc\u87ba\u65cbDNA\u6a21\u578b<\/h3>\n<\/p>\n<p><p>\u4f5c\u4e3a\u87ba\u65cb\u66f2\u7ebf\u7684\u4e00\u4e2a\u5b9e\u9645\u5e94\u7528\uff0c\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u53cc\u87ba\u65cbDNA\u6a21\u578b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u53cc\u87ba\u65cbDNA\u6a21\u578b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>import numpy as np<\/p>\n<h2><strong>\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n<p>t = np.linspace(0, 10 * np.pi, 1000)<\/p>\n<p>x1 = np.sin(t)<\/p>\n<p>y1 = np.cos(t)<\/p>\n<p>z1 = t<\/p>\n<p>x2 = np.sin(t + np.pi)<\/p>\n<p>y2 = np.cos(t + np.pi)<\/p>\n<p>z2 = t<\/p>\n<h2><strong>\u521b\u5efa3D\u5b50\u56fe<\/strong><\/h2>\n<p>fig = plt.figure()<\/p>\n<p>ax = fig.add_subplot(111, projection=&#39;3d&#39;)<\/p>\n<p>ax.plot(x1, y1, z1, label=&#39;Strand 1&#39;)<\/p>\n<p>ax.plot(x2, y2, z2, label=&#39;Strand 2&#39;)<\/p>\n<h2><strong>\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n<p>plt.legend()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e24\u4e2a\u76f8\u4f4d\u76f8\u5dee\u03c0\u7684\u87ba\u65cb\uff0c\u4ee5\u6a21\u62dfDNA\u7684\u53cc\u87ba\u65cb\u7ed3\u6784\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u7528\u4e8e\u53ef\u89c6\u5316DNA\u7b49\u590d\u6742\u7684\u751f\u7269\u7ed3\u6784\u3002<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u7684\u6b65\u9aa4\u548c\u793a\u4f8b\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u7ed8\u5236\u591a\u79cd\u7c7b\u578b\u7684\u87ba\u65cb\u66f2\u7ebf\uff0c\u5e76\u6839\u636e\u9700\u8981\u8c03\u6574\u5176\u5f62\u72b6\u548c\u53c2\u6570\u3002\u65e0\u8bba\u662f\u7b80\u5355\u76842D\u87ba\u65cb\uff0c\u8fd8\u662f\u590d\u6742\u76843D\u87ba\u65cb\uff0cPython\u7684matplotlib\u548cnumpy\u5e93\u90fd\u80fd\u63d0\u4f9b\u5f3a\u5927\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236\u7b80\u5355\u7684\u87ba\u65cb\u66f2\u7ebf\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u7b80\u5355\u7684\u87ba\u65cb\u66f2\u7ebf\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\uff0c\u7136\u540e\u4f7f\u7528\u6781\u5750\u6807\u7cfb\u7edf\u7ed8\u5236\u87ba\u65cb\u3002\u4ee3\u7801\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n<pre><code class=\"language-python\">import numpy as np\nimport matplotlib.pyplot as plt\n\ntheta = np.linspace(0, 4 * np.pi, 100)  # \u89d2\u5ea6\u8303\u56f4\nr = theta  # \u87ba\u65cb\u534a\u5f84\nx = r * np.cos(theta)  # x\u5750\u6807\ny = r * np.sin(theta)  # y\u5750\u6807\n\nplt.figure()\nplt.plot(x, y)\nplt.title(&quot;\u87ba\u65cb\u66f2\u7ebf&quot;)\nplt.show()\n<\/code><\/pre>\n<p>\u8fd9\u6bb5\u4ee3\u7801\u5c06\u751f\u6210\u4e00\u4e2a\u7b80\u5355\u7684\u87ba\u65cb\u66f2\u7ebf\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u9700\u8981\u8c03\u6574\u53c2\u6570\u4ee5\u6539\u53d8\u87ba\u65cb\u7684\u5f62\u72b6\u3002<\/p>\n<p><strong>\u4f7f\u7528Python\u7ed8\u5236\u4e0d\u540c\u7c7b\u578b\u7684\u87ba\u65cb\u66f2\u7ebf\u6709\u54ea\u4e9b\u5e93\u63a8\u8350\uff1f<\/strong><br \/>\u9664\u4e86Matplotlib\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528Seaborn\u548cPlotly\u7b49\u5e93\u6765\u7ed8\u5236\u87ba\u65cb\u66f2\u7ebf\u3002Seaborn\u63d0\u4f9b\u4e86\u7f8e\u89c2\u7684\u56fe\u5f62\u6837\u5f0f\uff0c\u800cPlotly\u80fd\u591f\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u5982\u679c\u60a8\u60f3\u8981\u66f4\u590d\u6742\u7684\u87ba\u65cb\u6548\u679c\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528Mayavi\u6216Pygame\u7b49\u5e93\uff0c\u8fd9\u4e9b\u5e93\u652f\u63013D\u7ed8\u56fe\u548c\u52a8\u753b\u6548\u679c\u3002<\/p>\n<p><strong>\u5982\u4f55\u81ea\u5b9a\u4e49\u87ba\u65cb\u66f2\u7ebf\u7684\u989c\u8272\u548c\u6837\u5f0f\uff1f<\/strong><br \/>\u5728Matplotlib\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u7ebf\u6761\u7684\u989c\u8272\u3001\u5bbd\u5ea6\u548c\u6837\u5f0f\u6765\u5b9a\u5236\u87ba\u65cb\u66f2\u7ebf\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u5728<code>plt.plot()<\/code>\u51fd\u6570\u4e2d\u6dfb\u52a0\u53c2\u6570\uff0c\u5982<code>color=&#39;red&#39;<\/code>\u548c<code>linestyle=&#39;--&#39;<\/code>\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<pre><code class=\"language-python\">plt.plot(x, y, color=&#39;red&#39;, linewidth=2, linestyle=&#39;--&#39;)\n<\/code><\/pre>\n<p>\u8fd9\u5c06\u751f\u6210\u4e00\u6761\u7ea2\u8272\u7684\u865a\u7ebf\u87ba\u65cb\u66f2\u7ebf\u3002\u60a8\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u559c\u597d\u9009\u62e9\u4e0d\u540c\u7684\u989c\u8272\u548c\u7ebf\u578b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u548cnumpy\u5e93\u753b\u87ba\u65cb\u66f2\u7ebf\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u6781\u5750\u6807\u3001\u4f7f\u7528\u53c2\u6570\u65b9\u7a0b\u3001 [&hellip;]","protected":false},"author":3,"featured_media":961355,"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\/961347"}],"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=961347"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/961347\/revisions"}],"predecessor-version":[{"id":961356,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/961347\/revisions\/961356"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/961355"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=961347"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=961347"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=961347"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}