{"id":176533,"date":"2024-05-08T19:12:46","date_gmt":"2024-05-08T11:12:46","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/176533.html"},"modified":"2024-05-08T19:12:53","modified_gmt":"2024-05-08T11:12:53","slug":"python%e6%80%8e%e4%b9%88%e7%bb%98%e5%88%b6%e7%bb%9d%e5%af%b9%e5%85%89%e6%bb%91%e7%9a%84%e5%87%bd%e6%95%b0%e6%9b%b2%e7%ba%bf","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/176533.html","title":{"rendered":"python\u600e\u4e48\u7ed8\u5236\u7edd\u5bf9\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf"},"content":{"rendered":"

\"python\u600e\u4e48\u7ed8\u5236\u7edd\u5bf9\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\"<\/p>\n

\u5728Python\u4e2d\u7ed8\u5236\u7edd\u5bf9\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\u53ef\u901a\u8fc7\u63d0\u9ad8\u7ed8\u56fe\u5206\u8fa8\u7387\u3001\u4f7f\u7528\u5411\u91cf\u5316\u7684\u8ba1\u7b97\u65b9\u6cd5\u3001\u4ee5\u53ca\u5229\u7528\u9002\u5408\u7684\u6570\u5b66\u51fd\u6570\u6765\u5b9e\u73b0\u3002\u63d0\u5347\u7ed8\u56fe\u5206\u8fa8\u7387\u3001\u4f7f\u7528\u9ad8\u7cbe\u5ea6\u6570\u503c\u65b9\u6cd5\u3001\u9009\u62e9\u5408\u9002\u7684\u63d2\u503c\u7b97\u6cd5\u3001\u91c7\u7528\u6297\u952f\u9f7f\u6280\u672f<\/strong>\uff0c\u8fd9\u4e9b\u662f\u786e\u4fdd\u51fd\u6570\u66f2\u7ebf\u7edd\u5bf9\u5149\u6ed1\u7684\u5173\u952e\u6b65\u9aa4\u3002\u63d0\u5347\u7ed8\u56fe\u5206\u8fa8\u7387\u610f\u5473\u7740\u5728\u4e24\u4e2a\u91c7\u6837\u70b9\u4e4b\u95f4\u63d2\u5165\u66f4\u591a\u7684\u70b9\u6765\u7ed8\u5236\u66f2\u7ebf\uff0c\u8fd9\u6837\u53ef\u4ee5\u8ba9\u66f2\u7ebf\u770b\u8d77\u6765\u66f4\u5e73\u6ed1\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u63d0\u9ad8\u7ed8\u56fe\u5206\u8fa8\u7387<\/strong><\/p>\n<\/p>\n

\u8981\u4f7f\u7ed8\u5236\u7684\u51fd\u6570\u66f2\u7ebf\u66f4\u52a0\u5149\u6ed1\uff0c\u9996\u5148\u9700\u8981\u5173\u6ce8\u7684\u662f\u7ed8\u56fe\u7684\u5206\u8fa8\u7387\u3002\u5206\u8fa8\u7387\u8d8a\u9ad8\uff0c\u5728\u7ed8\u5236\u8fc7\u7a0b\u4e2d\u4f7f\u7528\u7684\u70b9\u5c31\u8d8a\u5bc6\u96c6\uff0c\u66f2\u7ebf\u81ea\u7136\u8d8a\u5e73\u6ed1\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bbe\u7f6e\u5206\u8fa8\u7387<\/strong><\/h2>\n
x = np.linspace(0, 2*np.pi, 1000)<\/p>\n
y = np.sin(x)<\/p>\n
plt.plot(x, y)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u8bbe\u7f6e\u4e861000\u4e2a\u6570\u636e\u70b9\u6765\u7ed8\u5236\u4e00\u4e2a\u5468\u671f\u7684\u6b63\u5f26\u66f2\u7ebf\uff0c\u901a\u8fc7\u589e\u52a0\u6570\u636e\u70b9\u7684\u6570\u91cf\uff08\u6bd4\u5982\u4ece1000\u589e\u52a0\u523010000\uff09\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u4f7f\u66f2\u7ebf\u66f4\u52a0\u5149\u6ed1\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u9ad8\u7cbe\u5ea6\u6570\u503c\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n
\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u548c\u7ed8\u56fe\u65f6\uff0c\u91c7\u7528\u9ad8\u7cbe\u5ea6\u7684\u6570\u503c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u786e\u4fdd\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\u7684\u820d\u5165\u8bef\u5dee\u6700\u5c0f\u5316\uff0c\u4ece\u800c\u4f7f\u5f97\u7ed8\u5236\u51fa\u7684\u66f2\u7ebf\u66f4\u52a0\u7cbe\u786e\u3002<\/p>\n<\/p>\n
# \u7ee7\u7eed\u4f7f\u7528\u524d\u6587\u5b9a\u4e49\u7684\u53d8\u91cfx, y<\/p>\n
\u9ad8\u7cbe\u5ea6\u7ed8\u56fe<\/strong><\/h2>\n
y = np.sin(np.double(x))<\/p>\n
plt.plot(x, y, lw=2)  # \u8bbe\u7f6e\u7ebf\u5bbd\u4e3a2\uff0c\u589e\u52a0\u66f2\u7ebf\u7c97\u7ec6\u6709\u52a9\u4e8e\u89c2\u5bdf\u5149\u6ed1\u7a0b\u5ea6<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u9009\u62e9\u5408\u9002\u7684\u63d2\u503c\u7b97\u6cd5<\/strong><\/p>\n<\/p>\n
\u4e3a\u4e86\u5728\u5c11\u91cf\u7684\u5df2\u77e5\u6570\u636e\u70b9\u4e4b\u95f4\u7ed8\u51fa\u5149\u6ed1\u7684\u66f2\u7ebf\uff0c\u53ef\u4ee5\u4f7f\u7528\u63d2\u503c\u7b97\u6cd5\u3002\u6bd4\u5982\uff0c\u4e09\u6b21\u6837\u6761\u63d2\u503c\u662f\u5e38\u7528\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u5728\u4fdd\u8bc1\u51fd\u6570\u66f2\u7ebf\u5e73\u6ed1\u7684\u540c\u65f6\uff0c\u4f7f\u5f97\u66f2\u7ebf\u901a\u8fc7\u6240\u6709\u6570\u636e\u70b9\u3002<\/p>\n<\/p>\n
from scipy.interpolate import interp1d<\/p>\n
\u6837\u672c\u70b9<\/strong><\/h2>\n
x = np.linspace(0, 2*np.pi, 10)<\/p>\n
y = np.sin(x)<\/p>\n
\u63d2\u503c<\/strong><\/h2>\n
f = interp1d(x, y, kind='cubic')<\/p>\n
x_new = np.linspace(0, 2*np.pi, 1000)<\/p>\n
y_smooth = f(x_new)<\/p>\n
plt.plot(x_new, y_smooth)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u91c7\u7528\u6297\u952f\u9f7f\u6280\u672f<\/strong><\/p>\n<\/p>\n
\u73b0\u4ee3\u7684\u56fe\u5f62\u5e93\u901a\u5e38\u4f1a\u63d0\u4f9b\u6297\u952f\u9f7f\u6280\u672f\uff0c\u53ef\u4ee5\u5728\u6e32\u67d3\u65f6\u51cf\u5c11\u952f\u9f7f\u6548\u5e94\uff0c\u4f7f\u7ebf\u6761\u770b\u4e0a\u53bb\u66f4\u67d4\u548c\u3001\u66f4\u5149\u6ed1\u3002<\/p>\n<\/p>\n
# \u7ee7\u7eed\u4f7f\u7528\u524d\u6587\u5b9a\u4e49\u7684\u53d8\u91cfx, y<\/p>\n
plt.plot(x, y, antialiased=True)  # \u5f00\u542f\u6297\u952f\u9f7f<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7efc\u4e0a\u6240\u8ff0\uff0c\u8981\u5728Python\u4e2d\u7ed8\u5236\u51fa\u7edd\u5bf9\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\uff0c\u9700\u8981\u7ed3\u5408\u4ee5\u4e0a\u63d0\u5230\u7684\u591a\u79cd\u7b56\u7565\u3002\u63d0\u9ad8\u7ed8\u56fe\u5206\u8fa8\u7387\u53ef\u4ee5\u8ba9\u66f2\u7ebf\u770b\u8d77\u6765\u8fde\u8d2f\uff0c\u4f7f\u7528\u9ad8\u7cbe\u5ea6\u6570\u503c\u65b9\u6cd5\u548c\u5408\u9002\u7684\u63d2\u503c\u7b97\u6cd5\u53ef\u4ee5\u4fdd\u8bc1\u66f2\u7ebf\u6570\u5b66\u4e0a\u7684\u5e73\u6ed1\uff0c\u800c\u6297\u952f\u9f7f\u6280\u672f\u4f1a\u5728\u89c6\u89c9\u4e0a\u8fdb\u4e00\u6b65\u589e\u52a0\u66f2\u7ebf\u7684\u5149\u6ed1\u611f\u3002\u901a\u8fc7\u8fd9\u6837\u7684\u7efc\u5408\u63aa\u65bd\uff0c\u53ef\u4ee5\u5728Python\u4e2d\u6709\u6548\u5730\u7ed8\u5236\u51fa\u9ad8\u8d28\u91cf\u7684\u6570\u5b66\u51fd\u6570\u56fe\u5f62\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u95ee\u98981\uff1a\u4e3a\u4ec0\u4e48\u6211\u7684Python\u7ed8\u5236\u7684\u51fd\u6570\u66f2\u7ebf\u4e0d\u591f\u5149\u6ed1\uff1f<\/strong><\/p>\n
\u56de\u7b54\uff1a\u53ef\u80fd\u662f\u56e0\u4e3a\u4f60\u4f7f\u7528\u7684\u7ed8\u56fe\u51fd\u6570\u7684\u7cbe\u5ea6\u4e0d\u591f\u9ad8\u3002\u53ef\u4ee5\u5c1d\u8bd5\u589e\u52a0\u7ed8\u56fe\u51fd\u6570\u7684\u7cbe\u5ea6\u8bbe\u7f6e\uff0c\u4f8b\u5982\u8bbe\u7f6e\u66f4\u5c0f\u7684\u6b65\u957f\uff0c\u8fd9\u6837\u53ef\u4ee5\u83b7\u5f97\u66f4\u591a\u7684\u6570\u636e\u70b9\u6765\u7ed8\u5236\u66f2\u7ebf\uff0c\u4ece\u800c\u4f7f\u66f2\u7ebf\u66f4\u5149\u6ed1\u3002<\/p>\n
\u95ee\u98982\uff1a\u6709\u6ca1\u6709\u4ec0\u4e48\u65b9\u6cd5\u53ef\u4ee5\u5728Python\u4e2d\u7ed8\u5236\u7edd\u5bf9\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\uff1f<\/strong><\/p>\n
\u56de\u7b54\uff1a\u662f\u7684\uff0c\u53ef\u4ee5\u4f7f\u7528\u66f4\u9ad8\u7ea7\u7684\u7ed8\u56fe\u5e93\uff0c\u4f8b\u5982Matplotlib\u4e2d\u7684\u63d2\u503c\u51fd\u6570\uff0c\u6765\u5b9e\u73b0\u7edd\u5bf9\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\u7ed8\u5236\u3002\u901a\u8fc7\u4f7f\u7528\u63d2\u503c\u51fd\u6570\uff0c\u53ef\u4ee5\u6839\u636e\u5df2\u77e5\u7684\u6570\u636e\u70b9\u751f\u6210\u4e00\u7cfb\u5217\u5e73\u6ed1\u7684\u66f2\u7ebf\u70b9\uff0c\u4ece\u800c\u5f97\u5230\u66f4\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\u3002<\/p>\n
\u95ee\u98983\uff1a\u6709\u6ca1\u6709\u529e\u6cd5\u4f7fPython\u7ed8\u5236\u7684\u51fd\u6570\u66f2\u7ebf\u66f4\u52a0\u5149\u6ed1\u548c\u81ea\u7136\uff1f<\/strong><\/p>\n
\u56de\u7b54\uff1a\u662f\u7684\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528\u6837\u6761\u63d2\u503c\u65b9\u6cd5\u6765\u83b7\u5f97\u66f4\u52a0\u5149\u6ed1\u548c\u81ea\u7136\u7684\u51fd\u6570\u66f2\u7ebf\u3002\u6837\u6761\u63d2\u503c\u65b9\u6cd5\u53ef\u4ee5\u5229\u7528\u5df2\u77e5\u7684\u6570\u636e\u70b9\u6765\u751f\u6210\u5149\u6ed1\u7684\u66f2\u7ebf\uff0c\u901a\u8fc7\u5728\u6570\u636e\u70b9\u4e4b\u95f4\u63d2\u5165\u989d\u5916\u7684\u70b9\u6765\u5b9e\u73b0\u66f2\u7ebf\u7684\u5e73\u6ed1\u3002\u5728Python\u4e2d\uff0cScipy\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e9b\u6837\u6761\u63d2\u503c\u7684\u51fd\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u51fd\u6570\u6765\u751f\u6210\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u7ed8\u5236\u7edd\u5bf9\u5149\u6ed1\u7684\u51fd\u6570\u66f2\u7ebf\u53ef\u901a\u8fc7\u63d0\u9ad8\u7ed8\u56fe\u5206\u8fa8\u7387\u3001\u4f7f\u7528\u5411\u91cf\u5316\u7684\u8ba1\u7b97\u65b9\u6cd5\u3001\u4ee5\u53ca\u5229\u7528\u9002\u5408\u7684\u6570\u5b66\u51fd\u6570\u6765\u5b9e\u73b0 […]","protected":false},"author":3,"featured_media":176542,"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\/176533"}],"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=176533"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/176533\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/176542"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=176533"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=176533"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=176533"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}