想在代码中嵌入图片怎么办? 比如,想要嵌入这里维基上的链接对应的在线图片,

链接地址, 可以Import下载(不一定能在浏览器中直接打开):

ImageResize[Import["http://upload.wikimedia.org/wikipedia/commons/thumb/6/6c/Star_Wars_Logo.svg/694px-Star_Wars_Logo.svg.png"], 300]

代码都是文本格式,居然可以压缩为文本形式:

Uncompress@"1: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"

然后运行,得到星战电影开头的动态文本效果

logo=;
Composition[CellPrint,Cell[#,Deployed->True,Selectable->False]&,BoxData,ToBoxes]@With[{size={500,500}},DynamicModule[{graphics,pol,proc1,proc2,procR,proc3,LOGO,shading},Dynamic[Refresh[Overlay[{graphics,LOGO,shading},Dynamic@obj,Alignment->Center],None]],Initialization:>(obj=All;
op=0;
h=-1.3;
LOGO=Graphics[{Inset[Image[logo,ImageSize->Dynamic[Round[300 (-op+1)]]]]},ImageSize->size];
shading=Graphics[Dynamic@{Opacity@op,Rectangle[]},ImageSize->size];
pol=TextCell[Fold[StringReplace,text,{"\n  "->" ","\n "->"\n\n"}],"Text",TextJustification->1,CellSize->{320,540},FontFamily->"Helvetica",18,Bold,Yellow,LineIndent->0,Background->Black,CellFrame->10];
graphics=Graphics3D[{Texture@pol,EdgeForm@Black,Polygon[{{0,0,0},{0,1,0},{2,1,0},{2,0,0}},VertexTextureCoordinates->{{.98,.02},{.02,.02},{.02,.98},{.98,.98}}]},Boxed->False,Background->Black,ViewVertical->{0,0,1},ImageSize->{500,500},ViewVector->Dynamic@({{-h,.5,1},{2-h,.5,0}}),ViewAngle->1,Lighting->"Neutral"];
RemoveScheduledTask@ScheduledTasks[];
procR[]:=(RemoveScheduledTask@ScheduledTasks[]);
proc1[]:=RunScheduledTask[If[op<1,op+=.005;,procR[];obj={1,3};op=0;proc2[]],.05];
proc2[]:=(RunScheduledTask[If[h<1,h+=.002,procR[];proc3[]];,.05];);
proc3[]:=RunScheduledTask[If[op<1,op+=.05;,procR[];],.02];
RunScheduledTask[proc1[]];)]]

运行效果

代码来自:
http://mathematica.stackexchange.com/questions/47268/may-the-fourth-be-with-you/47273#47273

Mathematica里面总有一些炫的特征相关推荐

  1. [EULAR文摘] 在总人群中监测ACPA能否预测早期关节炎

    标签: 类风湿关节炎; 抗CCP抗体; 预测因子; 病程演变 在总人群中监测ACPA能否预测早期关节炎 Verstappen SM, et al. EULAR 2015. Present ID: OP ...

  2. 小世界网络模型代码 c 语言,新的小世界网络模型实现文本特征的提取方法与流程...

    本发明涉及语义网络技术领域,具体涉及新的小世界网络模型实现文本特征的提取方法. 背景技术: 目前常用的文本特征提取方法,包括词频-反文档频率方法-TF-IDF.信息增益方法.互信息等方法:TF-IDF ...

  3. 金融风控实战——信贷特征衍生与筛选(中国移动人群画像赛TOP1)

    运营商变量的深度挖掘 我们以本次比赛的特征为例进行展开的描述: 1.用户实名制是否通过核实 1为是0为否 目前国内基本上是手机卡绑定身份证,当然有部分落后地区仍旧存在着买卖所谓流量卡这类的经营活动,一 ...

  4. [机器学习] 树模型(xgboost,lightgbm)特征重要性原理总结

    在使用GBDT.RF.Xgboost等树类模型建模时,往往可以通过 feature_importance 来返回特征重要性,各模型输出特征重要性的原理与方法 一 计算特征重要性方法 首先,目前计算特征 ...

  5. 机器学习-有监督学习-分类算法:决策树算法【CART树:分类树(基于信息熵;分类依据:信息增益、信息增益率、基尼系数)、回归树(基于均方误差)】【损失函数:叶节点信息熵和】【对特征具有很好的分析能力】

    一.决策树概述 注:生产实践中,不使用决策树,太简单,而是使用决策树的升级版:集成学习算法. 集成学习算法有: Random Forest(随机森林) Extremely Randomized For ...

  6. 机器学习(决策树五)——案例:鸢尾花数据分类 及 据特征属性比较

    本文实现两个案例,分别是:鸢尾花数据分类 和 鸢尾花数据特征属性比较. 用到的数据集跟前面博客中KNN中的数据集是一样: 数据链接:https://download.csdn.net/download ...

  7. 数据挖掘--特征工程

    (一) 特征工程需要根据实际的业务场景进行处理 ---数据与特征处理 1. 数据选择/清洗/采样 2. 数值型/类别型/日期型/文本型特征处理 3. 组合特征处理 ---特征选择 1. Filter/ ...

  8. MLK | 那些常见的特征工程

    "MLK,即Machine Learning Knowledge,本专栏在于对机器学习的重点知识做一次梳理,便于日后温习,内容主要来自于<百面机器学习>一书,结合自己的经验与思考 ...

  9. 图像特征检测描述:SIFT、SURF、ORB、HOG、LBP特征的原理概述

    版权声明:本文为博主原创文章,转载请标明原始博文地址: https://blog.csdn.net/yuanlulu/article/details/82148429 </div>< ...

最新文章

  1. axios不发起请求_重复的ajax请求让人很受伤
  2. java 从mysql 导出到excel_JAVA实现在数据库导出到EXCEL并下载
  3. 【MM模块】Physics Inventory 库存盘点差异
  4. troubleshoot之:GC调优到底是什么
  5. mac 安装使用 webp 来压缩图片
  6. 洛谷P1061 Jam的计数法
  7. 使用.NET Core+Docker 开发微服务
  8. java synchronized 关键字(1)对象监视器为Object
  9. MySQL定义数据库对象之指定definer
  10. 【STM32】【STM32CubeMX】STM32CubeMX的使用之二:外部中断
  11. python generator
  12. Response.Redirect()和Response.RedirectPermanent()区别
  13. pandas多行合并一行_Pandas函数妙用
  14. antd table表格删除末页数据,跳回上一页
  15. pulseaudio如何开通系统日志来debug
  16. 2021-05-14 linux下用root 登录ftp连接
  17. Change Log - 更改日志
  18. 让单个单元格显示两个数据
  19. VS集成Qt环境搭建
  20. 计算机网络之CPT实验

热门文章

  1. Python--模块和包
  2. oracle runInstaller报错SEVERE: Remote ‘AttachHome‘ on node ‘rac102‘ failed
  3. YUV图解 (YUV444, YUV422, YUV420, YV12, NV12, NV21)
  4. 第十四届蓝桥杯大赛软件赛省赛-试题 B---01 串的熵 解题思路+完整代码
  5. Linux/Unix-stty命令详解
  6. 从执行顺序看for循环(深入理解)
  7. MAC如何安装pfx
  8. 【图论】中国邮递员问题、平面图上最大割问题的多项式时间算法
  9. 【matlab】拟合直线的方法
  10. linux打印机验证密码,HP LaserJet Pro打印机远程管理员密码泄露漏洞