{"id":1128600,"date":"2025-01-08T20:22:09","date_gmt":"2025-01-08T12:22:09","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1128600.html"},"modified":"2025-01-08T20:22:12","modified_gmt":"2025-01-08T12:22:12","slug":"python%e7%94%bb%e7%9a%84%e4%b8%89%e7%bb%b4%e5%9b%be%e5%a6%82%e4%bd%95%e4%bf%9d%e5%ad%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1128600.html","title":{"rendered":"python\u753b\u7684\u4e09\u7ef4\u56fe\u5982\u4f55\u4fdd\u5b58"},"content":{"rendered":"

\"python\u753b\u7684\u4e09\u7ef4\u56fe\u5982\u4f55\u4fdd\u5b58\"<\/p>\n

Python \u753b\u7684\u4e09\u7ef4\u56fe\u5982\u4f55\u4fdd\u5b58<\/strong>\uff1a\u4f7f\u7528Matplotlib\u7684savefig\u65b9\u6cd5\u3001\u4f7f\u7528Mayavi\u7684mlab.savefig\u65b9\u6cd5\u3001\u786e\u4fdd\u6b63\u786e\u8bbe\u7f6e\u6587\u4ef6\u8def\u5f84\u548c\u683c\u5f0f<\/strong>\u3002\u5728\u8be6\u7ec6\u63cf\u8ff0\u4e2d\uff0c\u6211\u4eec\u5c06\u63a2\u8ba8\u5982\u4f55\u4f7f\u7528Matplotlib\u548cMayavi\u8fd9\u4e24\u4e2a\u5e38\u7528\u5de5\u5177\u6765\u4fdd\u5b58\u4e09\u7ef4\u56fe\uff0c\u4ee5\u53ca\u4e00\u4e9b\u5728\u4fdd\u5b58\u8fc7\u7a0b\u4e2d\u9700\u8981\u6ce8\u610f\u7684\u6280\u5de7\u548c\u6700\u4f73\u5b9e\u8df5\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u7684savefig\u65b9\u6cd5<\/h3>\n<\/p>\n

Matplotlib \u662f Python \u4e2d\u975e\u5e38\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u4e8c\u7ef4\u548c\u4e09\u7ef4\u56fe\u5f62\u7684\u7ed8\u5236\u3002\u8981\u4fdd\u5b58\u4e09\u7ef4\u56fe\u5f62\uff0c\u9996\u5148\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u56fe\u5f62\uff0c\u7136\u540e\u4f7f\u7528 savefig<\/code> \u65b9\u6cd5\u5c06\u5176\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6\u3002<\/p>\n<\/p>\n

1.1 \u521b\u5efa\u4e09\u7ef4\u56fe\u5f62<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757\u5e76\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
from mpl_toolkits.mplot3d import Axes3D<\/p>\n
import numpy as np<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(-5, 5, 100)<\/p>\n
y = np.linspace(-5, 5, 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\u56fe\u5f62<\/strong><\/h2>\n
ax.plot_surface(X, Y, Z, cmap='viridis')<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u4f7f\u7528savefig\u65b9\u6cd5\u4fdd\u5b58\u56fe\u5f62<\/h4>\n<\/p>\n
\u5b8c\u6210\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528 savefig<\/code> \u65b9\u6cd5\u5c06\u5176\u4fdd\u5b58\u4e3a\u6587\u4ef6\u3002savefig<\/code> \u65b9\u6cd5\u5141\u8bb8\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u683c\u5f0f\uff0c\u4f8b\u5982 .png<\/code>\u3001.jpg<\/code> \u6216 .pdf<\/code>\u3002\u4ee5\u4e0b\u662f\u4fdd\u5b58\u56fe\u5f62\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
plt.savefig('3d_plot.png')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u8bbe\u7f6e\u56fe\u5f62\u7684\u5206\u8fa8\u7387\u548c\u8d28\u91cf<\/h4>\n<\/p>\n
\u5728\u4fdd\u5b58\u56fe\u5f62\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7 dpi<\/code> \u53c2\u6570\u8bbe\u7f6e\u56fe\u5f62\u7684\u5206\u8fa8\u7387\uff0c\u4ee5\u63d0\u9ad8\u56fe\u50cf\u7684\u8d28\u91cf\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528 bbox_inches<\/code> \u53c2\u6570\u6765\u786e\u4fdd\u56fe\u5f62\u7684\u8fb9\u754c\u88ab\u6b63\u786e\u4fdd\u5b58\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
plt.savefig('3d_plot_high_res.png', dpi=300, bbox_inches='tight')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Mayavi\u7684mlab.savefig\u65b9\u6cd5<\/h3>\n<\/p>\n
Mayavi \u662f\u53e6\u4e00\u4e2a\u7528\u4e8e\u4e09\u7ef4\u6570\u636e\u53ef\u89c6\u5316\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u5c24\u5176\u9002\u5408\u5904\u7406\u590d\u6742\u7684\u4e09\u7ef4\u56fe\u5f62\u3002Mayavi \u63d0\u4f9b\u4e86 mlab.savefig<\/code> \u65b9\u6cd5\u6765\u4fdd\u5b58\u4e09\u7ef4\u56fe\u5f62\u3002<\/p>\n<\/p>\n
2.1 \u521b\u5efa\u4e09\u7ef4\u56fe\u5f62<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757\u5e76\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u56fe\u5f62\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from mayavi import mlab<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x, y = np.mgrid[-5:5:100j, -5:5:100j]<\/p>\n
z = np.sin(np.sqrt(x<strong>2 + y<\/strong>2))<\/p>\n
\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
mlab.figure(size=(800, 600))<\/p>\n
mlab.surf(x, y, z, colormap='viridis')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u4f7f\u7528mlab.savefig\u65b9\u6cd5\u4fdd\u5b58\u56fe\u5f62<\/h4>\n<\/p>\n
\u5b8c\u6210\u4e09\u7ef4\u56fe\u5f62\u7ed8\u5236\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528 mlab.savefig<\/code> \u65b9\u6cd5\u5c06\u5176\u4fdd\u5b58\u4e3a\u6587\u4ef6\u3002mlab.savefig<\/code> \u65b9\u6cd5\u5141\u8bb8\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u683c\u5f0f\uff0c\u4f8b\u5982 .png<\/code> \u6216 .jpg<\/code>\u3002\u4ee5\u4e0b\u662f\u4fdd\u5b58\u56fe\u5f62\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
mlab.savefig('mayavi_3d_plot.png')<\/p>\n
mlab.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3 \u8bbe\u7f6e\u56fe\u5f62\u7684\u5206\u8fa8\u7387\u548c\u8d28\u91cf<\/h4>\n<\/p>\n
\u5728\u4fdd\u5b58\u56fe\u5f62\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7 size<\/code> \u53c2\u6570\u8bbe\u7f6e\u56fe\u5f62\u7684\u5206\u8fa8\u7387\uff0c\u4ee5\u63d0\u9ad8\u56fe\u50cf\u7684\u8d28\u91cf\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
mlab.savefig('mayavi_3d_plot_high_res.png', size=(1600, 1200))<\/p>\n
mlab.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u786e\u4fdd\u6b63\u786e\u8bbe\u7f6e\u6587\u4ef6\u8def\u5f84\u548c\u683c\u5f0f<\/h3>\n<\/p>\n
\u5728\u4fdd\u5b58\u56fe\u5f62\u65f6\uff0c\u786e\u4fdd\u6587\u4ef6\u8def\u5f84\u548c\u683c\u5f0f\u8bbe\u7f6e\u6b63\u786e\u975e\u5e38\u91cd\u8981\u3002\u8fd9\u4e0d\u4ec5\u80fd\u5e2e\u52a9\u4f60\u7ba1\u7406\u6587\u4ef6\uff0c\u8fd8\u80fd\u786e\u4fdd\u56fe\u50cf\u7684\u517c\u5bb9\u6027\u548c\u8d28\u91cf\u3002<\/p>\n<\/p>\n
3.1 \u8bbe\u7f6e\u6587\u4ef6\u8def\u5f84<\/h4>\n<\/p>\n
\u5728\u4fdd\u5b58\u56fe\u5f62\u65f6\uff0c\u6587\u4ef6\u8def\u5f84\u53ef\u4ee5\u662f\u76f8\u5bf9\u8def\u5f84\u6216\u7edd\u5bf9\u8def\u5f84\u3002\u76f8\u5bf9\u8def\u5f84\u662f\u57fa\u4e8e\u5f53\u524d\u5de5\u4f5c\u76ee\u5f55\u7684\uff0c\u800c\u7edd\u5bf9\u8def\u5f84\u662f\u4ece\u6839\u76ee\u5f55\u5f00\u59cb\u7684\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u4f7f\u7528\u76f8\u5bf9\u8def\u5f84<\/p>\n
plt.savefig('.\/images\/3d_plot.png')<\/p>\n
\u4f7f\u7528\u7edd\u5bf9\u8def\u5f84<\/strong><\/h2>\n
plt.savefig('\/home\/user\/images\/3d_plot.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u9009\u62e9\u5408\u9002\u7684\u6587\u4ef6\u683c\u5f0f<\/h4>\n<\/p>\n
\u4e0d\u540c\u7684\u6587\u4ef6\u683c\u5f0f\u6709\u4e0d\u540c\u7684\u7528\u9014\u548c\u4f18\u7f3a\u70b9\u3002\u5e38\u89c1\u7684\u6587\u4ef6\u683c\u5f0f\u5305\u62ec .png<\/code>\u3001.jpg<\/code>\u3001.pdf<\/code> \u548c .svg<\/code>\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u683c\u5f0f\u7684\u8bf4\u660e\uff1a<\/p>\n<\/p>\n
    \n
  • PNG<\/strong>\uff1a\u65e0\u635f\u538b\u7f29\uff0c\u9002\u5408\u9ad8\u8d28\u91cf\u7684\u56fe\u50cf\u3002<\/li>\n
  • JPG<\/strong>\uff1a\u6709\u635f\u538b\u7f29\uff0c\u9002\u5408\u7167\u7247\u548c\u8f83\u5c0f\u6587\u4ef6\u5927\u5c0f\u7684\u56fe\u50cf\u3002<\/li>\n
  • PDF<\/strong>\uff1a\u9002\u5408\u77e2\u91cf\u56fe\u5f62\u548c\u9ad8\u8d28\u91cf\u6253\u5370\u3002<\/li>\n
  • SVG<\/strong>\uff1a\u77e2\u91cf\u683c\u5f0f\uff0c\u9002\u5408\u7f51\u9875\u548c\u9ad8\u8d28\u91cf\u7684\u56fe\u5f62\u3002<\/li>\n<\/ul>\n
    \u56db\u3001\u6ce8\u610f\u4e8b\u9879\u548c\u6700\u4f73\u5b9e\u8df5<\/h3>\n<\/p>\n
    \u5728\u4fdd\u5b58\u4e09\u7ef4\u56fe\u5f62\u65f6\uff0c\u6709\u4e00\u4e9b\u6ce8\u610f\u4e8b\u9879\u548c\u6700\u4f73\u5b9e\u8df5\u53ef\u4ee5\u5e2e\u52a9\u4f60\u83b7\u5f97\u66f4\u597d\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n
    4.1 \u8c03\u6574\u56fe\u5f62\u7684\u89c6\u89d2\u548c\u5e03\u5c40<\/h4>\n<\/p>\n
    \u5728\u4fdd\u5b58\u56fe\u5f62\u524d\uff0c\u786e\u4fdd\u8c03\u6574\u56fe\u5f62\u7684\u89c6\u89d2\u548c\u5e03\u5c40\uff0c\u4ee5\u83b7\u5f97\u6700\u4f73\u7684\u663e\u793a\u6548\u679c\u3002\u4f8b\u5982\uff0c\u5728 Matplotlib \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 view_init<\/code> \u65b9\u6cd5\u8c03\u6574\u89c6\u89d2\uff1a<\/p>\n<\/p>\n
    ax.view_init(elev=30, azim=45)<\/p>\n
    plt.savefig('3d_plot_adjusted.png')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728 Mayavi \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 mlab.view<\/code> \u65b9\u6cd5\u8c03\u6574\u89c6\u89d2\uff1a<\/p>\n<\/p>\n
    mlab.view(azimuth=45, elevation=30, distance=10)<\/p>\n
    mlab.savefig('mayavi_3d_plot_adjusted.png')<\/p>\n
    mlab.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.2 \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/h4>\n<\/p>\n
    \u4e3a\u56fe\u5f62\u6dfb\u52a0\u6807\u9898\u548c\u8f74\u6807\u7b7e\uff0c\u6709\u52a9\u4e8e\u63d0\u9ad8\u56fe\u5f62\u7684\u53ef\u8bfb\u6027\u548c\u4fe1\u606f\u91cf\u3002\u5728 Matplotlib \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 title<\/code>\u3001xlabel<\/code> \u548c ylabel<\/code> \u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    ax.set_title('3D Surface Plot')<\/p>\n
    ax.set_xlabel('X Axis')<\/p>\n
    ax.set_ylabel('Y Axis')<\/p>\n
    ax.set_zlabel('Z Axis')<\/p>\n
    plt.savefig('3d_plot_with_labels.png')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728 Mayavi \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 mlab.title<\/code> \u548c mlab.xlabel<\/code> \u7b49\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    mlab.title('3D Surface Plot')<\/p>\n
    mlab.xlabel('X Axis')<\/p>\n
    mlab.ylabel('Y Axis')<\/p>\n
    mlab.zlabel('Z Axis')<\/p>\n
    mlab.savefig('mayavi_3d_plot_with_labels.png')<\/p>\n
    mlab.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.3 \u5904\u7406\u5927\u578b\u6570\u636e\u96c6<\/h4>\n<\/p>\n
    \u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\uff0c\u7ed8\u5236\u548c\u4fdd\u5b58\u4e09\u7ef4\u56fe\u5f62\u53ef\u80fd\u4f1a\u8017\u8d39\u8f83\u591a\u7684\u5185\u5b58\u548c\u65f6\u95f4\u3002\u53ef\u4ee5\u901a\u8fc7\u51cf\u5c11\u6570\u636e\u70b9\u7684\u6570\u91cf\u6216\u4f7f\u7528\u66f4\u9ad8\u6548\u7684\u7b97\u6cd5\u6765\u4f18\u5316\u6027\u80fd\u3002\u4f8b\u5982\uff0c\u5728 Matplotlib \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 plot_wireframe<\/code> \u65b9\u6cd5\u800c\u4e0d\u662f plot_surface<\/code> \u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
    ax.plot_wireframe(X, Y, Z, color='black')<\/p>\n
    plt.savefig('3d_wireframe_plot.png')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728 Mayavi \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 mlab.pipeline.reduce_points<\/code> \u65b9\u6cd5\u51cf\u5c11\u6570\u636e\u70b9\u7684\u6570\u91cf\uff1a<\/p>\n<\/p>\n
    plot = mlab.surf(x, y, z, colormap='viridis')<\/p>\n
    plot.actor.property.point_size = 2<\/p>\n
    mlab.savefig('mayavi_3d_plot_optimized.png')<\/p>\n
    mlab.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
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