{"id":928755,"date":"2024-12-26T16:41:59","date_gmt":"2024-12-26T08:41:59","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/928755.html"},"modified":"2024-12-26T16:42:02","modified_gmt":"2024-12-26T08:42:02","slug":"python%e5%a6%82%e4%bd%95%e7%94%bb%e6%97%8b%e6%b6%a1","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/928755.html","title":{"rendered":"python\u5982\u4f55\u753b\u65cb\u6da1"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25064303\/af6c6c39-578c-405a-a444-88e95c776a4b.webp\" alt=\"python\u5982\u4f55\u753b\u65cb\u6da1\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\u753b\u65cb\u6da1\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cNumPy\u5e93\u6765\u5b9e\u73b0\u3001\u901a\u8fc7\u6781\u5750\u6807\u7cfb\u751f\u6210\u65cb\u6da1\u56fe\u5f62\u3001\u8c03\u6574\u53c2\u6570\u6765\u63a7\u5236\u65cb\u6da1\u7684\u5f62\u72b6<\/strong>\u3002Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u751f\u6210\u591a\u79cd\u56fe\u5f62\uff0c\u800cNumPy\u5219\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u529f\u80fd\u3002\u901a\u8fc7\u8fd9\u4e24\u4e2a\u5e93\u7684\u7ed3\u5408\uff0c\u6211\u4eec\u80fd\u591f\u8f7b\u677e\u751f\u6210\u590d\u6742\u7684\u65cb\u6da1\u5f62\u72b6\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u6765\u7ed8\u5236\u65cb\u6da1\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u51c6\u5907\u5de5\u4f5c<\/p>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u7ed8\u5236\u65cb\u6da1\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u6240\u9700\u7684Python\u5e93\u3002\u4e3b\u8981\u7684\u5e93\u6709Matplotlib\u548cNumPy\u3002\u8fd9\u4e24\u4e2a\u5e93\u53ef\u4ee5\u901a\u8fc7Python\u7684\u5305\u7ba1\u7406\u5de5\u5177pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install matplotlib numpy<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u786e\u4fdd\u8fd9\u4e9b\u5e93\u5df2\u7ecf\u6210\u529f\u5b89\u88c5\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5f00\u59cb\u7f16\u5199Python\u4ee3\u7801\u6765\u7ed8\u5236\u65cb\u6da1\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u7406\u89e3\u6781\u5750\u6807\u7cfb<\/p>\n<\/p>\n<p><p>\u65cb\u6da1\u56fe\u5f62\u901a\u5e38\u4f7f\u7528\u6781\u5750\u6807\u7cfb\u6765\u751f\u6210\u3002\u6781\u5750\u6807\u7cfb\u662f\u4e00\u79cd\u4e8c\u7ef4\u5750\u6807\u7cfb\uff0c\u5176\u4e2d\u4e00\u4e2a\u70b9\u7684\u4f4d\u7f6e\u7531\u8ddd\u79bb\u539f\u70b9\u7684\u8ddd\u79bb\uff08\u5f84\u5411\u5750\u6807\uff09\u548c\u4e0e\u53c2\u8003\u65b9\u5411\u7684\u89d2\u5ea6\uff08\u89d2\u5750\u6807\uff09\u786e\u5b9a\u3002\u4f7f\u7528\u6781\u5750\u6807\u7cfb\uff0c\u6211\u4eec\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u751f\u6210\u87ba\u65cb\u7ebf\u6216\u65cb\u6da1\u5f62\u72b6\u3002<\/p>\n<\/p>\n<p><p>\u5728\u6781\u5750\u6807\u7cfb\u4e2d\uff0c\u87ba\u65cb\u7ebf\u7684\u57fa\u672c\u516c\u5f0f\u4e3a\uff1a<\/p>\n<\/p>\n<p><p>[ r = a + b\\theta ]<\/p>\n<\/p>\n<p><p>\u5176\u4e2d\uff0c( r ) \u662f\u5f84\u5411\u5750\u6807\uff0c( \\theta ) \u662f\u89d2\u5750\u6807\uff0c( a ) \u548c ( b ) \u662f\u63a7\u5236\u87ba\u65cb\u7ebf\u5f62\u72b6\u7684\u53c2\u6570\u3002\u8c03\u6574\u8fd9\u4e9b\u53c2\u6570\u53ef\u4ee5\u6539\u53d8\u65cb\u6da1\u7684\u7d27\u5bc6\u7a0b\u5ea6\u548c\u8d77\u59cb\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u65cb\u6da1<\/p>\n<\/p>\n<p><p>\u4e00\u65e6\u7406\u89e3\u4e86\u6781\u5750\u6807\u7cfb\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5728Python\u4e2d\u7ed8\u5236\u65cb\u6da1\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u65cb\u6da1\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u8bbe\u7f6e\u53c2\u6570<\/strong><\/h2>\n<p>a = 0.1  # \u63a7\u5236\u87ba\u65cb\u7ebf\u7684\u521d\u59cb\u534a\u5f84<\/p>\n<p>b = 0.2  # \u63a7\u5236\u87ba\u65cb\u7ebf\u7684\u7d27\u5bc6\u7a0b\u5ea6<\/p>\n<h2><strong>\u751f\u6210\u89d2\u5ea6\u6570\u636e<\/strong><\/h2>\n<p>theta = np.linspace(0, 4 * np.pi, 1000)<\/p>\n<h2><strong>\u8ba1\u7b97\u5f84\u5411\u5750\u6807<\/strong><\/h2>\n<p>r = a + b * theta<\/p>\n<h2><strong>\u7ed8\u5236\u65cb\u6da1\u56fe<\/strong><\/h2>\n<p>plt.figure(figsize=(6, 6))<\/p>\n<p>ax = plt.subplot(111, polar=True)  # \u4f7f\u7528\u6781\u5750\u6807\u7cfb<\/p>\n<p>ax.plot(theta, r, color=&#39;b&#39;)  # \u7ed8\u5236\u87ba\u65cb\u7ebf<\/p>\n<p>ax.set_title(&quot;Spiral Plot&quot;, va=&#39;bottom&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u751f\u6210\u4e86\u4e00\u7ec4\u89d2\u5ea6\u6570\u636e\uff0c\u5e76\u4f7f\u7528\u8fd9\u4e9b\u6570\u636e\u8ba1\u7b97\u5f84\u5411\u5750\u6807\u3002\u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u7684\u6781\u5750\u6807\u7cfb\u7ed8\u5236\u4e86\u65cb\u6da1\u56fe\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u8c03\u6574\u65cb\u6da1\u7684\u53c2\u6570<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u8c03\u6574\u516c\u5f0f\u4e2d\u7684\u53c2\u6570 ( a ) \u548c ( b )\uff0c\u6211\u4eec\u53ef\u4ee5\u6539\u53d8\u65cb\u6da1\u7684\u5f62\u72b6\u3002\u4f8b\u5982\uff0c\u589e\u52a0 ( a ) \u7684\u503c\u4f1a\u4f7f\u87ba\u65cb\u7ebf\u7684\u8d77\u59cb\u534a\u5f84\u589e\u5927\uff0c\u800c\u589e\u52a0 ( b ) \u7684\u503c\u4f1a\u4f7f\u87ba\u65cb\u7ebf\u53d8\u5f97\u66f4\u52a0\u7d27\u5bc6\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u6539\u53d8\u89d2\u5ea6\u6570\u636e\u7684\u8303\u56f4\u6765\u63a7\u5236\u65cb\u6da1\u7684\u5708\u6570\u3002<\/p>\n<\/p>\n<p><p>\u5c1d\u8bd5\u4ee5\u4e0b\u4ee3\u7801\uff0c\u89c2\u5bdf\u4e0d\u540c\u53c2\u6570\u4e0b\u65cb\u6da1\u7684\u53d8\u5316\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u4e0d\u540c\u53c2\u6570\u7684\u65cb\u6da1<\/p>\n<p>params = [(0.1, 0.1), (0.1, 0.2), (0.2, 0.1), (0.2, 0.2)]<\/p>\n<p>fig, axs = plt.subplots(2, 2, subplot_kw={&#39;projection&#39;: &#39;polar&#39;}, figsize=(10, 10))<\/p>\n<p>for ax, (a, b) in zip(axs.flat, params):<\/p>\n<p>    r = a + b * theta<\/p>\n<p>    ax.plot(theta, r)<\/p>\n<p>    ax.set_title(f&quot;a={a}, b={b}&quot;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u8fd9\u6bb5\u4ee3\u7801\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u56db\u4e2a\u5b50\u56fe\u7684\u56fe\u5f62\uff0c\u6bcf\u4e2a\u5b50\u56fe\u663e\u793a\u4e0d\u540c\u53c2\u6570\u4e0b\u7684\u65cb\u6da1\u5f62\u72b6\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u53c2\u6570\u5bf9\u65cb\u6da1\u5f62\u72b6\u7684\u5f71\u54cd\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u6dfb\u52a0\u989c\u8272\u548c\u6837\u5f0f<\/p>\n<\/p>\n<p><p>\u4e3a\u4e86\u4f7f\u65cb\u6da1\u56fe\u66f4\u52a0\u7f8e\u89c2\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u7ed8\u5236\u8fc7\u7a0b\u4e2d\u6dfb\u52a0\u989c\u8272\u548c\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5f84\u5411\u5750\u6807\u7684\u503c\u6765\u6539\u53d8\u7ebf\u6761\u7684\u989c\u8272\uff0c\u6216\u8005\u4f7f\u7528\u4e0d\u540c\u7684\u7ebf\u578b\u6765\u589e\u5f3a\u56fe\u5f62\u7684\u89c6\u89c9\u6548\u679c\u3002<\/p>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4e3a\u65cb\u6da1\u6dfb\u52a0\u6e10\u53d8\u989c\u8272\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u4e3a\u65cb\u6da1\u6dfb\u52a0\u6e10\u53d8\u989c\u8272<\/p>\n<p>colors = plt.cm.viridis(np.linspace(0, 1, len(theta)))<\/p>\n<p>plt.figure(figsize=(6, 6))<\/p>\n<p>ax = plt.subplot(111, polar=True)<\/p>\n<p>for i in range(len(theta) - 1):<\/p>\n<p>    ax.plot(theta[i:i+2], r[i:i+2], color=colors[i], lw=2)<\/p>\n<p>ax.set_title(&quot;Colored Spiral Plot&quot;, va=&#39;bottom&#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\u4f7f\u7528\u4e86Matplotlib\u7684\u989c\u8272\u6620\u5c04\u529f\u80fd\uff0c\u4e3a\u65cb\u6da1\u7684\u6bcf\u4e00\u6bb5\u5e94\u7528\u4e86\u4e0d\u540c\u7684\u989c\u8272\uff0c\u4ece\u800c\u4ea7\u751f\u4e86\u6e10\u53d8\u6548\u679c\u3002<\/p>\n<\/p>\n<p><p>\u516d\u3001\u5e94\u7528\u5b9e\u4f8b<\/p>\n<\/p>\n<p><p>\u7ed8\u5236\u65cb\u6da1\u56fe\u5f62\u5728\u8bb8\u591a\u9886\u57df\u90fd\u6709\u5e94\u7528\u3002\u4f8b\u5982\uff0c\u5728\u6570\u636e\u53ef\u89c6\u5316\u4e2d\uff0c\u65cb\u6da1\u56fe\u53ef\u4ee5\u7528\u4e8e\u5c55\u793a\u5468\u671f\u6027\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u5728\u827a\u672f\u8bbe\u8ba1\u4e2d\uff0c\u65cb\u6da1\u56fe\u5f62\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u5177\u6709\u5438\u5f15\u529b\u7684\u56fe\u6848\u3002\u6b64\u5916\uff0c\u5728\u79d1\u5b66\u7814\u7a76\u4e2d\uff0c\u65cb\u6da1\u56fe\u5f62\u4e5f\u53ef\u4ee5\u7528\u4e8e\u6a21\u62df\u548c\u5206\u6790\u6d41\u4f53\u52a8\u529b\u5b66\u4e2d\u7684\u6da1\u6d41\u73b0\u8c61\u3002<\/p>\n<\/p>\n<p><p>\u603b\u7ed3<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u4f7f\u7528Matplotlib\u548cNumPy\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u8f7b\u677e\u7ed8\u5236\u65cb\u6da1\u56fe\u5f62\u3002\u901a\u8fc7\u8c03\u6574\u6781\u5750\u6807\u7cfb\u4e0b\u7684\u53c2\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u4e0d\u540c\u5f62\u72b6\u7684\u65cb\u6da1\uff0c\u5e76\u901a\u8fc7\u6dfb\u52a0\u989c\u8272\u548c\u6837\u5f0f\u6765\u589e\u5f3a\u56fe\u5f62\u7684\u7f8e\u89c2\u6027\u3002\u65e0\u8bba\u662f\u5728\u6570\u636e\u53ef\u89c6\u5316\u3001\u827a\u672f\u8bbe\u8ba1\u8fd8\u662f\u79d1\u5b66\u7814\u7a76\u4e2d\uff0c\u65cb\u6da1\u56fe\u5f62\u90fd\u5177\u6709\u91cd\u8981\u7684\u5e94\u7528\u4ef7\u503c\u3002\u5e0c\u671b\u672c\u6587\u7684\u4ecb\u7ecd\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u65cb\u6da1\u56fe\u5f62\u7684\u7ed8\u5236\u6280\u672f\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236\u65cb\u6da1\u56fe\u5f62\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u7ed8\u5236\u65cb\u6da1\u56fe\u5f62\u3002\u901a\u8fc7\u751f\u6210\u6781\u5750\u6807\u4e0b\u7684\u87ba\u65cb\u7ebf\u6570\u636e\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u65cb\u6da1\u6548\u679c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\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)  # \u751f\u6210\u89d2\u5ea6\u6570\u636e\nr = theta**2  # \u5b9a\u4e49\u534a\u5f84\u7684\u53d8\u5316\nx = r * np.sin(theta)  # \u8ba1\u7b97x\u5750\u6807\ny = r * np.cos(theta)  # \u8ba1\u7b97y\u5750\u6807\n\nplt.figure(figsize=(6, 6))\nplt.plot(x, y)\nplt.title(&#39;\u65cb\u6da1\u56fe\u5f62&#39;)\nplt.xlabel(&#39;X\u8f74&#39;)\nplt.ylabel(&#39;Y\u8f74&#39;)\nplt.axis(&#39;equal&#39;)\nplt.show()\n<\/code><\/pre>\n<p>\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u751f\u6210\u4e00\u4e2a\u6f02\u4eae\u7684\u65cb\u6da1\u56fe\u5f62\u3002<\/p>\n<p><strong>\u4f7f\u7528\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7ed8\u5236\u65cb\u6da1\uff1f<\/strong><br \/>\u9664\u4e86Matplotlib\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528Seaborn\u3001Plotly\u548cPygame\u7b49\u5e93\u6765\u7ed8\u5236\u65cb\u6da1\u56fe\u5f62\u3002\u6bcf\u4e2a\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u529f\u80fd\u548c\u98ce\u683c\uff0c\u9009\u62e9\u65f6\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u53ef\u89c6\u5316\u7684\u7f8e\u89c2\u7a0b\u5ea6\u6765\u51b3\u5b9a\u3002<\/p>\n<p><strong>\u65cb\u6da1\u56fe\u5f62\u5728\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u6709\u4ec0\u4e48\u5e94\u7528\uff1f<\/strong><br \/>\u65cb\u6da1\u56fe\u5f62\u5e38\u7528\u4e8e\u5c55\u793a\u590d\u6742\u7684\u6570\u636e\u5173\u7cfb\u548c\u52a8\u6001\u53d8\u5316\u3002\u4f8b\u5982\uff0c\u5728\u7269\u7406\u5b66\u4e2d\u53ef\u4ee5\u7528\u6765\u6a21\u62df\u6d41\u4f53\u7684\u65cb\u8f6c\u8fd0\u52a8\uff0c\u5728\u6c14\u8c61\u5b66\u4e2d\u7528\u4ee5\u8868\u793a\u6c14\u65cb\u548c\u98ce\u573a\u3002\u5728\u827a\u672f\u548c\u8bbe\u8ba1\u4e2d\uff0c\u65cb\u6da1\u56fe\u5f62\u4e5f\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u521b\u610f\u4f5c\u54c1\u548c\u56fe\u5f62\u8bbe\u8ba1\u4e2d\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u753b\u65cb\u6da1\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cNumPy\u5e93\u6765\u5b9e\u73b0\u3001\u901a\u8fc7\u6781\u5750\u6807\u7cfb\u751f\u6210\u65cb\u6da1\u56fe\u5f62\u3001\u8c03\u6574\u53c2\u6570\u6765 [&hellip;]","protected":false},"author":3,"featured_media":928758,"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\/928755"}],"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=928755"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/928755\/revisions"}],"predecessor-version":[{"id":928760,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/928755\/revisions\/928760"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/928758"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=928755"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=928755"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=928755"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}