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这几天,看了一下ECharts官网的API和Demo发现很有意思,于是就利用模型预测产生的数据做一个伪实时的动态数据显示 。
首先,创建一个index.html的文件,我用的vscode打开的,进行编写。
1.插入一个标签
<div id="main" ></div>
设置他的一些style(可自行美化,我很懒!!!)。
2.在body下建一个<script>脚本(为什么要在body下写js脚本呢?因为这是提高用户体验,可自行百度深层次的原因~~~)。
3.脚本写啥呢?别急,慢慢来啊~~
(1)首先,我们来建一个echarts的对象,就叫他MyChart吧
var myChart = echarts.init(document.getElementById('main'));
(2)建一个setoption这是初始化图的,填一些基本参数(可通过此链接飞到Echarts官网的配置参考一下)
(3)初始化了之后,我们就可以ajax异步读取本地文件了~~~~
其中不懂堆栈的(链接在此这个是我Google的,有可能被墙~~)不懂push,shift操作数据的(链接在此这个应该不会被墙~~)
$.ajax({ type:"get", // async:true, url:"test_data.json", data:{}, dataType:"json", success:function(result){ var datas=result if(result){ var m=0; for(var i=0;i<result.length;i++){ if(m<1000){ datas.shift(); date.push(result[i]["time"]); data.push(result[i]["AM23SIG0206.AV"]); m+=1; } else{ break; } myChart.hideLoading(); setInterval(function(){ for(var n=0;n<2;n++){ date.shift(); date.push(datas[n]["time"]); data.shift(); data.push(datas[n]["AM23SIG0206.AV"]); } myChart.setOption({ xAxis:{ data:date }, series:[ { name:"参数", data:data } ]}); for(var nn=0;nn<2;nn++){ datas.shift() } },2000); } } }, error:function(errorMsg){ alert("图表请求数据失败!"); myChart.hideLoading(); myChart_2.hideLoading(); } })
我来解释一下,这个异步加载的原理是这样子的:
(1)我们现在加载一个.json文件,存在一个变量result里,开始进行数据的操作,利用堆栈的概念先存一个图上要显示的数据量,假设是1000个点,存到data里(这是一个队列)你如果要实现动态的实时的数据,看着数据他会动~~~~你需要存一个数据进去,但是呢这个队列只有1000个容量,放不下怎么办,没关系啊,你先取一个出来不就行了嘛,就这样循环下去~~~~
(2)但是啊,取一个存一个太麻烦了,我们在设置一个定时器setInterval()在这个里面,每过2s更新2个点(怎么更新呢,就是data.shift()
data.push()
模拟了堆栈~~~~
这样就达到了动态数据了~~~~
好吧,如果只是这样也太low了,我要实现一个框架+数据库+前端的真正数据动起来~~~~让我在研究几天~~~~~~
好了,不废话了,下面是源码,有需要的童鞋可以自己跑一下,看看可不可以动~~~~~
<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Charts</title> <script type="text/javascript" src="echarts.min.js"></script> <script type="text/javascript" src="http://ajax.googleapis.com/ajax/libs/jquery/1.2.6/jquery.min.js"></script> </head> <body> <div id="main" ></div> <div id="main_2" ></div> </body> <script type="text/javascript"> var myChart = echarts.init(document.getElementById('main')); var myChart_2 = echarts.init(document.getElementById('main_2')); myChart.setOption({ title:{ text:'异步数据加载' }, tooltip:{ show:true, }, xAxis:{ type:'category', boundaryGap:false, data:[], splitLine: { show: false }, }, yAxis:{ boundaryGap:[0,'100%'], type:'value', min:'dataMin', max:'dataMax', splitLine: { show: false }, }, series:[{ name:'参数', type:'line', data:[] }] }); myChart_2.setOption({ title:{ text:'异步数据加载' }, tooltip:{ show:true, }, xAxis:{ type:'category', boundaryGap:false, data:[], splitLine: { show: false }, }, yAxis:{ boundaryGap:[0,'100%'], type:'value', min:'dataMin', max:'dataMax', splitLine: { show: false }, }, series:[{ name:'参数', type:'line', data:[] }] }); myChart.showLoading(); myChart_2.showLoading(); var date = []; var data = []; $.ajax({ type:"get", // async:true, url:"test_data.json", data:{}, dataType:"json", success:function(result){ var datas=result if(result){ var m=0; for(var i=0;i<result.length;i++){ if(m<1000){ datas.shift(); date.push(result[i]["time"]); data.push(result[i]["AM23SIG0206.AV"]); m+=1; } else{ break; } myChart.hideLoading(); myChart_2.hideLoading(); setInterval(function(){ for(var n=0;n<2;n++){ date.shift(); date.push(datas[n]["time"]); data.shift(); data.push(datas[n]["AM23SIG0206.AV"]); } myChart.setOption({ xAxis:{ data:date }, series:[ { name:"参数", data:data } ]}); myChart_2.setOption({ xAxis:{ data:date }, series:[ { name:"参数", data:data } ]}); for(var nn=0;nn<2;nn++){ datas.shift() } },2000); } } }, error:function(errorMsg){ alert("图表请求数据失败!"); myChart.hideLoading(); myChart_2.hideLoading(); } }) </script> </html>
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"593.6537", "time": "2016-05-01 00:09:10" }, { "AM23SIG0206.AV": "593.5917", "time": "2016-05-01 00:09:13" }, { "AM23SIG0206.AV": "593.5568", "time": "2016-05-01 00:09:16" }, { "AM23SIG0206.AV": "593.573", "time": "2016-05-01 00:09:19" }, { "AM23SIG0206.AV": "593.5083", "time": "2016-05-01 00:09:22" }, { "AM23SIG0206.AV": "593.4922", "time": "2016-05-01 00:09:25" }, { "AM23SIG0206.AV": "593.4786", "time": "2016-05-01 00:09:28" }, { "AM23SIG0206.AV": "593.4463", "time": "2016-05-01 00:09:31" }, { "AM23SIG0206.AV": "593.4114", "time": "2016-05-01 00:09:34" }, { "AM23SIG0206.AV": "593.3953", "time": "2016-05-01 00:09:37" }, { "AM23SIG0206.AV": "593.3791", "time": "2016-05-01 00:09:40" }, { "AM23SIG0206.AV": "593.3306", "time": "2016-05-01 00:09:43" }, { "AM23SIG0206.AV": "593.3009", "time": "2016-05-01 00:09:46" }, { "AM23SIG0206.AV": "593.2983", "time": "2016-05-01 00:09:49" }, { "AM23SIG0206.AV": "593.266", "time": "2016-05-01 00:09:52" }, { "AM23SIG0206.AV": "593.2499", "time": "2016-05-01 00:09:55" }, { "AM23SIG0206.AV": "593.3145", "time": "2016-05-01 00:09:58" }, { "AM23SIG0206.AV": "593.2821", "time": "2016-05-01 00:10:01" }, { "AM23SIG0206.AV": "593.266", "time": "2016-05-01 00:10:04" }, { "AM23SIG0206.AV": "593.2485", "time": "2016-05-01 00:10:07" }, { "AM23SIG0206.AV": "593.2809", "time": "2016-05-01 00:10:10" }, { "AM23SIG0206.AV": "593.2485", "time": "2016-05-01 00:10:13" }, { "AM23SIG0206.AV": "593.2809", "time": "2016-05-01 00:10:16" }, { "AM23SIG0206.AV": "593.2809", "time": "2016-05-01 00:10:19" }, { "AM23SIG0206.AV": "593.2809", "time": "2016-05-01 00:10:22" }, { "AM23SIG0206.AV": "593.297", "time": "2016-05-01 00:10:25" }, { "AM23SIG0206.AV": "593.2646", "time": "2016-05-01 00:10:28" }, { "AM23SIG0206.AV": "593.297", "time": "2016-05-01 00:10:31" }, { "AM23SIG0206.AV": "593.297", "time": "2016-05-01 00:10:34" }, { "AM23SIG0206.AV": "593.3293", "time": "2016-05-01 00:10:37" }, { "AM23SIG0206.AV": "593.3131", "time": "2016-05-01 00:10:40" }, { "AM23SIG0206.AV": "593.3293", "time": "2016-05-01 00:10:43" }, { "AM23SIG0206.AV": "593.3293", "time": "2016-05-01 00:10:46" } ]
这个是.json文件(python的大数据模型预测产生的数据我拿来用一哈)
总结
以上所述是小编给大家介绍的HTML5+JS+JQuery+ECharts实现异步加载问题,希望对大家有所帮助,如果大家有任何问题欢迎给我留言,小编会及时回复大家的!
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