通过ssr-echarts,服务端生成echarts图

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ssr-echarts ：一个开源项目，它能够服务端渲染 ECharts 图表，并直接生成 PNG 图片返回。该项目提供了多种主题，并且支持 GET 和 POST 请求。如果参数较多，建议使用 POST 方法。用户可以自己部署这个服务。

1. 服务端安装ssr-echarts服务.

gitlab地址:ssr-echarts: 服务端渲染echarts，直接生成PNG图片返回，可用于发送邮件等。可以设置宽度、高度、主题，包含15种主题。

1.1 SSR-ECHARTS介绍

用于服务端渲染生成echarts图片。可以使用Get或Post。如果是GET需要UrlEncode。如果参数较多建议使用POST，通过后端转发buffer。

主题：light，dark，chalk，essos，halloween，infographic，macarons，purple-passion，roma，romantic，shine，vintage，walden，westeros，wonderland

需要自己部署，源网站已经关闭无法访问。

1.2 部署方式:

Docker部署

环境：Docker

指令：

docker pull lz1998/ssr-echarts

docker run -d -p 3000:3000 lz1998/ssr-echarts

1.3 本地npm部署

环境：nodejs 12

安装依赖：npm instsall

运行方式：PORT=3000 npm run serve

1.4 使用说明

发送请求：

METHOD：POST/GET

URL：http://localhost:3000/

HEADER：Content-Type:application/json

BODY：

{
    "theme": "macarons",
    "width": 800,
    "height": 600,
    "xAxis": {
        "type": "category",
        "data": ["1","1","1","1","1","1","1"]
    },
    "yAxis": {
        "type": "value"
    },
    "series": [{
        "data": [820, 932, 901, 934, 1290, 1330, 1320],
        "type": "line"
    }]
}

2. 使用示例

2.1 曲线图

请求curl:

curl --location --request POST 'http://172.16.8.51:3000/' \
--header 'Content-Type: application/json' \
--data-raw '{
    "theme": "macarons",
    "width": 800,
    "height": 600,
    "xAxis": {
        "type": "category",
        "data": ["1","1","1","1","1","1","1"]
    },
    "yAxis": {
        "type": "value"
    },
    "series": [{
        "data": [820, 932, 901, 934, 1290, 1330, 1320],
        "type": "line"
    }]
}'

2.2 环装饼图

请求curl:

curl --location --request POST 'http://172.16.8.51:3000/' \
--header 'Content-Type: application/json' \
--data-raw ' {
  "angleAxis": {},
  "radiusAxis": {
    "type": "category",
    "data": ["Mon", "Tue", "Wed","Thu"],
    "z": 10
  },
  "polar": {},
  "series": [
    {
      "type": "bar",
      "data": [1, 2, 3, 4],
      "coordinateSystem": "polar",
      "name": "A",
      "stack": "a",
      "emphasis": {
        "focus": "series"
      }
    },
	
	    {
      "type": "bar",
     "data": [2, 4, 6, 8],
      "coordinateSystem": "polar",
      "name": "B",
      "stack": "a",
      "emphasis": {
        "focus": "series"
      }
    },
	
	    {
      "type": "bar",
      "data": [1, 2, 3, 4],
      "coordinateSystem": "polar",
      "name": "C",
      "stack": "a",
      "emphasis": {
        "focus": "series"
      }
    }
  ],
  "legend": {
    "show": true,
    "data": ["A","B", "C"]
  }
}'

2.3 桑基图

curl --location --request POST 'http://172.16.8.51:3000/' \
--header 'Content-Type: application/json' \
--data-raw ' {
      "title": {
        "text": "Node Align Left"
      },
      "tooltip": {
        "trigger": "item",
        "triggerOn": "mousemove"
      },
      "animation": false,
      "series": [
        {
          "type": "sankey",
          "emphasis": {
            "focus": "adjacency"
          },
          "nodeAlign": "right",
          "data": [
{"name":"Agricultural '\''waste'\''"},
{"name":"Bio-conversion"},
{"name":"Liquid"},
{"name":"Losses"},
{"name":"Solid"},
{"name":"Gas"},
{"name":"Biofuel imports"},
{"name":"Biomass imports"},
{"name":"Coal imports"},
{"name":"Coal"},
{"name":"Coal reserves"},
{"name":"District heating"},
{"name":"Industry"},
{"name":"Heating and cooling - commercial"},
{"name":"Heating and cooling - homes"},
{"name":"Electricity grid"},
{"name":"Over generation / exports"},
{"name":"H2 conversion"},
{"name":"Road transport"},
{"name":"Agriculture"},
{"name":"Rail transport"},
{"name":"Lighting & appliances - commercial"},
{"name":"Lighting & appliances - homes"},
{"name":"Gas imports"},
{"name":"Ngas"},
{"name":"Gas reserves"},
{"name":"Thermal generation"},
{"name":"Geothermal"},
{"name":"H2"},
{"name":"Hydro"},
{"name":"International shipping"},
{"name":"Domestic aviation"},
{"name":"International aviation"},
{"name":"National navigation"},
{"name":"Marine algae"},
{"name":"Nuclear"},
{"name":"Oil imports"},
{"name":"Oil"},
{"name":"Oil reserves"},
{"name":"Other waste"},
{"name":"Pumped heat"},
{"name":"Solar PV"},
{"name":"Solar Thermal"},
{"name":"Solar"},
{"name":"Tidal"},
{"name":"UK land based bioenergy"},
{"name":"Wave"},
{"name":"Wind"}
],
          "links": [
{"source": "Agricultural '\''waste'\''", "target": "Bio-conversion",  "value": 124.729},
{"source": "Bio-conversion", "target": "Liquid",  "value": 0.597},
{"source": "Bio-conversion",  "target": "Losses",  "value": 26.862},
{"source": "Bio-conversion",  "target": "Solid",  "value": 280.322},
{"source": "Bio-conversion", "target": "Gas",  "value": 81.144},
{"source": "Biofuel imports", "target": "Liquid",  "value": 35},
{"source": "Biomass imports", "target": "Solid",  "value": 35},
{"source": "Coal imports", "target": "Coal", "value": 11.606},
{"source": "Coal reserves","target": "Coal", "value": 63.965},
{"source": "Coal", "target": "Solid", "value": 75.571},
{"source": "District heating", "target": "Industry", "value": 10.639},
{"source": "District heating", "target": "Heating and cooling - commercial", "value": 22.505},
{"source": "District heating", "target": "Heating and cooling - homes", "value": 46.184},
{"source": "Electricity grid", "target": "Over generation / exports", "value": 104.453},
{"source": "Electricity grid", "target": "Heating and cooling - homes", "value": 113.726},
{"source": "Electricity grid", "target": "H2 conversion", "value": 27.14},
{"source": "Electricity grid", "target": "Industry", "value": 342.165},
{"source": "Electricity grid", "target": "Road transport", "value": 37.797},
{"source": "Electricity grid", "target": "Agriculture", "value": 4.412},
{"source": "Electricity grid", "target": "Heating and cooling - commercial", "value": 40.858},
{"source": "Electricity grid", "target": "Losses", "value": 56.691},
{"source": "Electricity grid", "target": "Rail transport", "value": 7.863},
{"source": "Electricity grid", "target": "Lighting & appliances - commercial", "value": 90.008},
{"source": "Electricity grid", "target": "Lighting & appliances - homes", "value": 93.494},
{"source": "Gas imports", "target": "Ngas", "value": 40.719},
{"source": "Gas reserves", "target": "Ngas", "value": 82.233},
{"source": "Gas", "target": "Heating and cooling - commercial", "value": 0.129},
{"source": "Gas", "target": "Losses", "value": 1.401},
{"source": "Gas", "target": "Thermal generation", "value": 151.891},
{"source": "Gas", "target": "Agriculture", "value": 2.096},
{"source": "Gas", "target": "Industry", "value": 48.58},
{"source": "Geothermal", "target": "Electricity grid", "value": 7.013},
{"source": "H2 conversion", "target": "H2", "value": 20.897},
{"source": "H2 conversion", "target": "Losses", "value": 6.242},
{"source": "H2", "target": "Road transport", "value": 20.897},
{"source": "Hydro", "target": "Electricity grid", "value": 6.995},
{"source": "Liquid", "target": "Industry", "value": 121.066},
{"source": "Liquid", "target": "International shipping", "value": 128.69},
{"source": "Liquid", "target": "Road transport", "value": 135.835},
{"source": "Liquid", "target": "Domestic aviation", "value": 14.458},
{"source": "Liquid", "target": "International aviation", "value": 206.267},
{"source": "Liquid", "target": "Agriculture", "value": 3.64},
{"source": "Liquid", "target": "National navigation", "value": 33.218},
{"source": "Liquid", "target": "Rail transport", "value": 4.413},
{"source": "Marine algae", "target": "Bio-conversion", "value": 4.375},
{"source": "Ngas", "target": "Gas", "value": 122.952},
{"source": "Nuclear", "target": "Thermal generation", "value": 839.978},
{"source": "Oil imports", "target": "Oil", "value": 504.287},
{"source": "Oil reserves", "target": "Oil", "value": 107.703},
{"source": "Oil", "target": "Liquid", "value": 611.99},
{"source": "Other waste", "target": "Solid", "value": 56.587},
{"source": "Other waste", "target": "Bio-conversion", "value": 77.81},
{"source": "Pumped heat", "target": "Heating and cooling - homes", "value": 193.026},
{"source": "Pumped heat", "target": "Heating and cooling - commercial", "value": 70.672},
{"source": "Solar PV", "target": "Electricity grid", "value": 59.901},
{"source": "Solar Thermal", "target": "Heating and cooling - homes", "value": 19.263},
{"source": "Solar", "target": "Solar Thermal", "value": 19.263},
{"source": "Solar", "target": "Solar PV", "value": 59.901},
{"source": "Solid", "target": "Agriculture", "value": 0.882},
{"source": "Solid", "target": "Thermal generation", "value": 400.12},
{"source": "Solid", "target": "Industry", "value": 46.477},
{"source": "Thermal generation", "target": "Electricity grid", "value": 525.531},
{"source": "Thermal generation", "target": "Losses", "value": 787.129},
{"source": "Thermal generation", "target": "District heating", "value": 79.329},
{"source": "Tidal", "target": "Electricity grid", "value": 9.452},
{"source": "UK land based bioenergy", "target": "Bio-conversion", "value": 182.01},
{"source": "Wave", "target": "Electricity grid", "value": 19.013},
{"source": "Wind", "target": "Electricity grid", "value": 289.366}
],
          "lineStyle": {
            "color": "source",
            "curveness": 0.5
          }
        }
      ]
    }'

2.4 散点图

curl --location --request POST 'http://172.16.8.51:3000/' \
--header 'Content-Type: application/json' \
--data-raw '{
     "theme": "light",
     "backgroundColor": "#FFFFFF",
"title": {
"text": "Male and female height and weight distribution",
"subtext": "Data from: Heinz 2003"
},
"grid": {
"left": "3%",
"right": "7%",
"bottom": "7%",
"containLabel": true
},
"tooltip": {

"showDelay": 0,

"axisPointer": {
"show": true,
"type": "cross",
"lineStyle": {
"type": "dashed",
"width": 1
}
}
},
"toolbox": {

"feature": {
"dataZoom": {},
"brush": {
"type": ["rect", "polygon", "clear"]
}
}
},
"brush": {},
"legend": {
"data": ["Female", "Male"],
"left": "center",
"bottom": 10
},
"xAxis": [
{
"type": "value",

"scale": true,
"axisLabel": {
"formatter": "{value} cm"
},
"splitLine": {
"show": false
}
}
],
"yAxis": [
{
"type": "value",
"scale": true,
"axisLabel": {

"formatter": "{value} kg"
},
"splitLine": {
"show": false
}
}
],
"series": [
{
 "name": "Female",
"type": "scatter",
"emphasis": {
"focus": "series"
},

"data": [[161.2, 51.6], [167.5, 59.0], [159.5, 49.2], [157.0, 63.0], [155.8, 53.6],
[170.0, 59.0], [159.1, 47.6], [166.0, 69.8], [176.2, 66.8], [160.2, 75.2],
[172.5, 55.2], [170.9, 54.2], [172.9, 62.5], [153.4, 42.0], [160.0, 50.0],
[147.2, 49.8], [168.2, 49.2], [175.0, 73.2], [157.0, 47.8], [167.6, 68.8],
[159.5, 50.6], [175.0, 82.5], [166.8, 57.2], [176.5, 87.8], [170.2, 72.8],
[174.0, 54.5], [173.0, 59.8], [179.9, 67.3], [170.5, 67.8], [160.0, 47.0],
[154.4, 46.2], [162.0, 55.0], [176.5, 83.0], [160.0, 54.4], [152.0, 45.8],
[162.1, 53.6], [170.0, 73.2], [160.2, 52.1], [161.3, 67.9], [166.4, 56.6],
[168.9, 62.3], [163.8, 58.5], [167.6, 54.5], [160.0, 50.2], [161.3, 60.3],
[167.6, 58.3], [165.1, 56.2], [160.0, 50.2], [170.0, 72.9], [157.5, 59.8],
[167.6, 61.0], [160.7, 69.1], [163.2, 55.9], [152.4, 46.5], [157.5, 54.3],
[168.3, 54.8], [180.3, 60.7], [165.5, 60.0], [165.0, 62.0], [164.5, 60.3],
[156.0, 52.7], [160.0, 74.3], [163.0, 62.0], [165.7, 73.1], [161.0, 80.0],
[162.0, 54.7], [166.0, 53.2], [174.0, 75.7], [172.7, 61.1], [167.6, 55.7],
[151.1, 48.7], [164.5, 52.3], [163.5, 50.0], [152.0, 59.3], [169.0, 62.5],
[164.0, 55.7], [161.2, 54.8], [155.0, 45.9], [170.0, 70.6], [176.2, 67.2],
[170.0, 69.4], [162.5, 58.2], [170.3, 64.8], [164.1, 71.6], [169.5, 52.8],
[163.2, 59.8], [154.5, 49.0], [159.8, 50.0], [173.2, 69.2], [170.0, 55.9],
[161.4, 63.4], [169.0, 58.2], [166.2, 58.6], [159.4, 45.7], [162.5, 52.2],
[159.0, 48.6], [162.8, 57.8], [159.0, 55.6], [179.8, 66.8], [162.9, 59.4],
[161.0, 53.6], [151.1, 73.2], [168.2, 53.4], [168.9, 69.0], [173.2, 58.4],
[171.8, 56.2], [178.0, 70.6], [164.3, 59.8], [163.0, 72.0], [168.5, 65.2],
[166.8, 56.6], [172.7, 105.2], [163.5, 51.8], [169.4, 63.4], [167.8, 59.0],
[159.5, 47.6], [167.6, 63.0], [161.2, 55.2], [160.0, 45.0], [163.2, 54.0],
[162.2, 50.2], [161.3, 60.2], [149.5, 44.8], [157.5, 58.8], [163.2, 56.4],
[172.7, 62.0], [155.0, 49.2], [156.5, 67.2], [164.0, 53.8], [160.9, 54.4],
[162.8, 58.0], [167.0, 59.8], [160.0, 54.8], [160.0, 43.2], [168.9, 60.5],
[158.2, 46.4], [156.0, 64.4], [160.0, 48.8], [167.1, 62.2], [158.0, 55.5],
[167.6, 57.8], [156.0, 54.6], [162.1, 59.2], [173.4, 52.7], [159.8, 53.2],
[170.5, 64.5], [159.2, 51.8], [157.5, 56.0], [161.3, 63.6], [162.6, 63.2],
[160.0, 59.5], [168.9, 56.8], [165.1, 64.1], [162.6, 50.0], [165.1, 72.3],
[166.4, 55.0], [160.0, 55.9], [152.4, 60.4], [170.2, 69.1], [162.6, 84.5],
[170.2, 55.9], [158.8, 55.5], [172.7, 69.5], [167.6, 76.4], [162.6, 61.4],
[167.6, 65.9], [156.2, 58.6], [175.2, 66.8], [172.1, 56.6], [162.6, 58.6],
[160.0, 55.9], [165.1, 59.1], [182.9, 81.8], [166.4, 70.7], [165.1, 56.8],
[177.8, 60.0], [165.1, 58.2], [175.3, 72.7], [154.9, 54.1], [158.8, 49.1],
[172.7, 75.9], [168.9, 55.0], [161.3, 57.3], [167.6, 55.0], [165.1, 65.5],
[175.3, 65.5], [157.5, 48.6], [163.8, 58.6], [167.6, 63.6], [165.1, 55.2],
[165.1, 62.7], [168.9, 56.6], [162.6, 53.9], [164.5, 63.2], [176.5, 73.6],
[168.9, 62.0], [175.3, 63.6], [159.4, 53.2], [160.0, 53.4], [170.2, 55.0],
[162.6, 70.5], [167.6, 54.5], [162.6, 54.5], [160.7, 55.9], [160.0, 59.0],
[157.5, 63.6], [162.6, 54.5], [152.4, 47.3], [170.2, 67.7], [165.1, 80.9],
[172.7, 70.5], [165.1, 60.9], [170.2, 63.6], [170.2, 54.5], [170.2, 59.1],
[161.3, 70.5], [167.6, 52.7], [167.6, 62.7], [165.1, 86.3], [162.6, 66.4],
[152.4, 67.3], [168.9, 63.0], [170.2, 73.6], [175.2, 62.3], [175.2, 57.7],
[160.0, 55.4], [165.1, 104.1], [174.0, 55.5], [170.2, 77.3], [160.0, 80.5],
[167.6, 64.5], [167.6, 72.3], [167.6, 61.4], [154.9, 58.2], [162.6, 81.8],
[175.3, 63.6], [171.4, 53.4], [157.5, 54.5], [165.1, 53.6], [160.0, 60.0],
[174.0, 73.6], [162.6, 61.4], [174.0, 55.5], [162.6, 63.6], [161.3, 60.9],
[156.2, 60.0], [149.9, 46.8], [169.5, 57.3], [160.0, 64.1], [175.3, 63.6],
[169.5, 67.3], [160.0, 75.5], [172.7, 68.2], [162.6, 61.4], [157.5, 76.8],
[176.5, 71.8], [164.4, 55.5], [160.7, 48.6], [174.0, 66.4], [163.8, 67.3]
],
"markArea": {
"silent": true,
"itemStyle": {
"color": "transparent",
"borderWidth": 1,
"borderType": "dashed"
},
"data": [
[
{
 "name": "Female Data Range",
"xAxis": "min",
"yAxis": "min"
},
{
"xAxis": "max",
"yAxis": "max"
}
]
]
},
"markPoint": {
"data": [
{ "type": "max",  "name": "Max" },
{ "type": "min",  "name": "Min" }
]
},
"markLine": {
"lineStyle": {
"type": "solid"
},
"data": [{ "type": "average",  "name": "AVG" }, { "xAxis": 160 }]
}
},
{
 "name": "Male",
"type": "scatter",
"emphasis": {

"focus": "series"
},

"data": [[174.0, 65.6], [175.3, 71.8], [193.5, 80.7], [186.5, 72.6], [187.2, 78.8],
[181.5, 74.8], [184.0, 86.4], [184.5, 78.4], [175.0, 62.0], [184.0, 81.6],
[180.0, 76.6], [177.8, 83.6], [192.0, 90.0], [176.0, 74.6], [174.0, 71.0],
[184.0, 79.6], [192.7, 93.8], [171.5, 70.0], [173.0, 72.4], [176.0, 85.9],
[176.0, 78.8], [180.5, 77.8], [172.7, 66.2], [176.0, 86.4], [173.5, 81.8],
[178.0, 89.6], [180.3, 82.8], [180.3, 76.4], [164.5, 63.2], [173.0, 60.9],
[183.5, 74.8], [175.5, 70.0], [188.0, 72.4], [189.2, 84.1], [172.8, 69.1],
[170.0, 59.5], [182.0, 67.2], [170.0, 61.3], [177.8, 68.6], [184.2, 80.1],
[186.7, 87.8], [171.4, 84.7], [172.7, 73.4], [175.3, 72.1], [180.3, 82.6],
[182.9, 88.7], [188.0, 84.1], [177.2, 94.1], [172.1, 74.9], [167.0, 59.1],
[169.5, 75.6], [174.0, 86.2], [172.7, 75.3], [182.2, 87.1], [164.1, 55.2],
[163.0, 57.0], [171.5, 61.4], [184.2, 76.8], [174.0, 86.8], [174.0, 72.2],
[177.0, 71.6], [186.0, 84.8], [167.0, 68.2], [171.8, 66.1], [182.0, 72.0],
[167.0, 64.6], [177.8, 74.8], [164.5, 70.0], [192.0, 101.6], [175.5, 63.2],
[171.2, 79.1], [181.6, 78.9], [167.4, 67.7], [181.1, 66.0], [177.0, 68.2],
[174.5, 63.9], [177.5, 72.0], [170.5, 56.8], [182.4, 74.5], [197.1, 90.9],
[180.1, 93.0], [175.5, 80.9], [180.6, 72.7], [184.4, 68.0], [175.5, 70.9],
[180.6, 72.5], [177.0, 72.5], [177.1, 83.4], [181.6, 75.5], [176.5, 73.0],
[175.0, 70.2], [174.0, 73.4], [165.1, 70.5], [177.0, 68.9], [192.0, 102.3],
[176.5, 68.4], [169.4, 65.9], [182.1, 75.7], [179.8, 84.5], [175.3, 87.7],
[184.9, 86.4], [177.3, 73.2], [167.4, 53.9], [178.1, 72.0], [168.9, 55.5],
[157.2, 58.4], [180.3, 83.2], [170.2, 72.7], [177.8, 64.1], [172.7, 72.3],
[165.1, 65.0], [186.7, 86.4], [165.1, 65.0], [174.0, 88.6], [175.3, 84.1],
[185.4, 66.8], [177.8, 75.5], [180.3, 93.2], [180.3, 82.7], [177.8, 58.0],
[177.8, 79.5], [177.8, 78.6], [177.8, 71.8], [177.8, 116.4], [163.8, 72.2],
[188.0, 83.6], [198.1, 85.5], [175.3, 90.9], [166.4, 85.9], [190.5, 89.1],
[166.4, 75.0], [177.8, 77.7], [179.7, 86.4], [172.7, 90.9], [190.5, 73.6],
[185.4, 76.4], [168.9, 69.1], [167.6, 84.5], [175.3, 64.5], [170.2, 69.1],
[190.5, 108.6], [177.8, 86.4], [190.5, 80.9], [177.8, 87.7], [184.2, 94.5],
[176.5, 80.2], [177.8, 72.0], [180.3, 71.4], [171.4, 72.7], [172.7, 84.1],
[172.7, 76.8], [177.8, 63.6], [177.8, 80.9], [182.9, 80.9], [170.2, 85.5],
[167.6, 68.6], [175.3, 67.7], [165.1, 66.4], [185.4, 102.3], [181.6, 70.5],
[172.7, 95.9], [190.5, 84.1], [179.1, 87.3], [175.3, 71.8], [170.2, 65.9],
[193.0, 95.9], [171.4, 91.4], [177.8, 81.8], [177.8, 96.8], [167.6, 69.1],
[167.6, 82.7], [180.3, 75.5], [182.9, 79.5], [176.5, 73.6], [186.7, 91.8],
[188.0, 84.1], [188.0, 85.9], [177.8, 81.8], [174.0, 82.5], [177.8, 80.5],
[171.4, 70.0], [185.4, 81.8], [185.4, 84.1], [188.0, 90.5], [188.0, 91.4],
[182.9, 89.1], [176.5, 85.0], [175.3, 69.1], [175.3, 73.6], [188.0, 80.5],
[188.0, 82.7], [175.3, 86.4], [170.5, 67.7], [179.1, 92.7], [177.8, 93.6],
[175.3, 70.9], [182.9, 75.0], [170.8, 93.2], [188.0, 93.2], [180.3, 77.7],
[177.8, 61.4], [185.4, 94.1], [168.9, 75.0], [185.4, 83.6], [180.3, 85.5],
[174.0, 73.9], [167.6, 66.8], [182.9, 87.3], [160.0, 72.3], [180.3, 88.6],
[167.6, 75.5], [186.7, 101.4], [175.3, 91.1], [175.3, 67.3], [175.9, 77.7],
[175.3, 81.8], [179.1, 75.5], [181.6, 84.5], [177.8, 76.6], [182.9, 85.0],
[177.8, 102.5], [184.2, 77.3], [179.1, 71.8], [176.5, 87.9], [188.0, 94.3],
[174.0, 70.9], [167.6, 64.5], [170.2, 77.3], [167.6, 72.3], [188.0, 87.3],
[174.0, 80.0], [176.5, 82.3], [180.3, 73.6], [167.6, 74.1], [188.0, 85.9],
[180.3, 73.2], [167.6, 76.3], [183.0, 65.9], [183.0, 90.9], [179.1, 89.1],
[170.2, 62.3], [177.8, 82.7], [179.1, 79.1], [190.5, 98.2], [177.8, 84.1],
[180.3, 83.2], [180.3, 83.2]
],
"markArea": {
"silent": true,
"itemStyle": {
"color": "transparent",
"borderWidth": 1,
"borderType": "dashed"
},
"data": [
[
{
 "name": "Male Data Range",
"xAxis": "min",
"yAxis": "min"
},
{
"xAxis": "max",
"yAxis": "max"
}
]
]
},
"markPoint": {
"data": [
{ "type": "max",  "name": "Max" },
{ "type": "min",  "name": "Min" }
]
},
"markLine": {
"lineStyle": {
"type": "solid"
},
"data": [{ "type": "average", "name": "Average" }, { "xAxis": 170 }]
}
}
]
}'

3. 备注

支持多个主题,可以修改传参中的 theme 用来修改不同主题.

"theme": "light"

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