echarts交互组件与数据的视觉映射

交互组件

ECharts 提供了很多交互组件：例组件 legend、标题组件 title、视觉映射组件 visualMap、数据区域缩放组件 dataZoom、时间线组件 timeline。

接下来的内容我们将介绍如何使用数据区域缩放组件 dataZoom。

dataZoom

dataZoom 组件可以实现通过鼠标滚轮滚动，放大缩小图表的功能。

默认情况下 dataZoom 控制 x 轴，即对 x 轴进行数据窗口缩放和数据窗口平移操作。

option = {
    xAxis: {
        type: 'value'
    },
    yAxis: {
        type: 'value'
    },
    dataZoom: [
        {   // 这个dataZoom组件，默认控制x轴。
            type: 'slider', // 这个 dataZoom 组件是 slider 型 dataZoom 组件
            start: 10,      // 左边在 10% 的位置。
            end: 60         // 右边在 60% 的位置。
        }
    ],
    series: [
        {
            type: 'scatter', // 这是个『散点图』
            itemStyle: {
                opacity: 0.8
            },
            symbolSize: function (val) {
                return val[2] * 40;
            },
            data: [["14.616","7.241","0.896"],["3.958","5.701","0.955"],["2.768","8.971","0.669"],["9.051","9.710","0.171"],["14.046","4.182","0.536"],["12.295","1.429","0.962"],["4.417","8.167","0.113"],["0.492","4.771","0.785"],["7.632","2.605","0.645"],["14.242","5.042","0.368"]]
        }
    ]
}

上面的实例只能拖动 dataZoom 组件来缩小或放大图表。如果想在坐标系内进行拖动，以及用鼠标滚轮（或移动触屏上的两指滑动）进行缩放，那么需要 再再加上一个 inside 型的 dataZoom 组件。

在以上实例基础上我们再增加 type: 'inside' 的配置信息：

option = {
    ...,
    dataZoom: [
        {   // 这个dataZoom组件，默认控制x轴。
            type: 'slider', // 这个 dataZoom 组件是 slider 型 dataZoom 组件
            start: 10,      // 左边在 10% 的位置。
            end: 60         // 右边在 60% 的位置。
        },
        {   // 这个dataZoom组件，也控制x轴。
            type: 'inside', // 这个 dataZoom 组件是 inside 型 dataZoom 组件
            start: 10,      // 左边在 10% 的位置。
            end: 60         // 右边在 60% 的位置。
        }
    ],
    ...
}

当然我们可以通过 dataZoom.xAxisIndex 或 dataZoom.yAxisIndex 来指定 dataZoom 控制哪个或哪些数轴。

var data1 = [];
var data2 = [];
var data3 = [];

var random = function (max) {
    return (Math.random() * max).toFixed(3);
};

for (var i = 0; i < 500; i++) {
    data1.push([random(15), random(10), random(1)]);
    data2.push([random(10), random(10), random(1)]);
    data3.push([random(15), random(10), random(1)]);
}

option = {
    animation: false,
    legend: {
        data: ['scatter', 'scatter2', 'scatter3']
    },
    tooltip: {
    },
    xAxis: {
        type: 'value',
        min: 'dataMin',
        max: 'dataMax',
        splitLine: {
            show: true
        }
    },
    yAxis: {
        type: 'value',
        min: 'dataMin',
        max: 'dataMax',
        splitLine: {
            show: true
        }
    },
    dataZoom: [
        {
            type: 'slider',
            show: true,
            xAxisIndex: [0],
            start: 1,
            end: 35
        },
        {
            type: 'slider',
            show: true,
            yAxisIndex: [0],
            left: '93%',
            start: 29,
            end: 36
        },
        {
            type: 'inside',
            xAxisIndex: [0],
            start: 1,
            end: 35
        },
        {
            type: 'inside',
            yAxisIndex: [0],
            start: 29,
            end: 36
        }
    ],
    series: [
        {
            name: 'scatter',
            type: 'scatter',
            itemStyle: {
                normal: {
                    opacity: 0.8
                }
            },
            symbolSize: function (val) {
                return val[2] * 40;
            },
            data: data1
        },
        {
            name: 'scatter2',
            type: 'scatter',
            itemStyle: {
                normal: {
                    opacity: 0.8
                }
            },
            symbolSize: function (val) {
                return val[2] * 40;
            },
            data: data2
        },
        {
            name: 'scatter3',
            type: 'scatter',
            itemStyle: {
                normal: {
                    opacity: 0.8,
                }
            },
            symbolSize: function (val) {
                return val[2] * 40;
            },
            data: data3
        }
    ]
}

数据的视觉映射

数据可视化简单来讲就是将数据用图表的形式来展示，专业的表达方式就是数据到视觉元素的映射过程。

ECharts 的每种图表本身就内置了这种映射过程，我们之前学习到的柱形图就是将数据映射到长度。

此外，ECharts 还提供了 visualMap 组件 来提供通用的视觉映射。visualMap 组件中可以使用的视觉元素有：

• 图形类别（symbol）
• 图形大小（symbolSize）
• 颜色（color）
• 透明度（opacity）
• 颜色透明度（colorAlpha）
• 颜色明暗度（colorLightness）
• 颜色饱和度（colorSaturation）
• 色调（colorHue）

一、数据和维度

ECharts 中的数据，一般存放于 series.data 中。

不同的图表类型，数据格式有所不一样，但是他们的共同特点就都是数据项（dataItem） 的集合。每个数据项含有 数据值（value） 和其他信息（可选）。每个数据值，可以是单一的数值（一维）或者一个数组（多维）。

series.data 最常见的形式 是线性表，即一个普通数组：

series: {
    data: [
        {       // 这里每一个项就是数据项（dataItem）
            value: 2323, // 这是数据项的数据值（value）
            itemStyle: {...}
        },
        1212,   // 也可以直接是 dataItem 的 value，这更常见。
        2323,   // 每个 value 都是『一维』的。
        4343,
        3434
    ]
}
series: {
    data: [
        {                        // 这里每一个项就是数据项（dataItem）
            value: [3434, 129,  '圣马力诺'], // 这是数据项的数据值（value）
            itemStyle: {...}
        },
        [1212, 5454, '梵蒂冈'],   // 也可以直接是 dataItem 的 value，这更常见。
        [2323, 3223, '瑙鲁'],     // 每个 value 都是『三维』的，每列是一个维度。
        [4343, 23,   '图瓦卢']    // 假如是『气泡图』，常见第一维度映射到x轴，
                                 // 第二维度映射到y轴，
                                 // 第三维度映射到气泡半径（symbolSize）
    ]
}

在图表中，往往默认把 value 的前一两个维度进行映射，比如取第一个维度映射到x轴，取第二个维度映射到y轴。如果想要把更多的维度展现出来，可以借助 visualMap 。

二、visualMap 组件

visualMap 组件定义了把数据的指定维度映射到对应的视觉元素上。

visualMap 组件可以定义多个，从而可以同时对数据中的多个维度进行视觉映射。

visualMap 组件可以定义为 分段型（visualMapPiecewise） 或 连续型（visualMapContinuous），通过 type 来区分。例如：

option = {
    visualMap: [
        { // 第一个 visualMap 组件
            type: 'continuous', // 定义为连续型 visualMap
            ...
        },
        { // 第二个 visualMap 组件
            type: 'piecewise', // 定义为分段型 visualMap
            ...
        }
    ],
    ...
};

分段型视觉映射组件，有三种模式：

• 连续型数据平均分段: 依据 visualMap-piecewise.splitNumber 来自动平均分割成若干块。
• 连续型数据自定义分段: 依据 visualMap-piecewise.pieces 来定义每块范围。
• 离散数据根据类别分段: 类别定义在 visualMap-piecewise.categories 中。

分段型视觉映射组件，展现形式如下图：

实例

<!DOCTYPE html>
<html style="height: 100%">
   <head>
       <meta charset="utf-8">
   </head>
   <body style="height: 100%; margin: 0">
       <div id="container" style="height: 100%"></div>
       <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/dist/echarts.min.js"></script>
       <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts-gl/dist/echarts-gl.min.js"></script>
       <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts-stat/dist/ecStat.min.js"></script>
       <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/dist/extension/dataTool.min.js"></script>
       <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/map/js/china.js"></script>
       <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/map/js/world.js"></script>
       <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/dist/extension/bmap.min.js"></script>
       <script type="text/javascript">
var dom = document.getElementById("container");
var myChart = echarts.init(dom);
var app = {};
option = null;
var geoCoordMap = {
    "海门":[121.15,31.89],
    "鄂尔多斯":[109.781327,39.608266],
    "招远":[120.38,37.35],
    "舟山":[122.207216,29.985295],
    "齐齐哈尔":[123.97,47.33],
    "盐城":[120.13,33.38],
    "赤峰":[118.87,42.28],
    "青岛":[120.33,36.07],
    "乳山":[121.52,36.89],
    "金昌":[102.188043,38.520089],
    "泉州":[118.58,24.93],
    "莱西":[120.53,36.86],
    "日照":[119.46,35.42],
    "胶南":[119.97,35.88],
    "南通":[121.05,32.08],
    "拉萨":[91.11,29.97],
    "云浮":[112.02,22.93],
    "梅州":[116.1,24.55],
    "文登":[122.05,37.2],
    "上海":[121.48,31.22],
    "攀枝花":[101.718637,26.582347],
    "威海":[122.1,37.5],
    "承德":[117.93,40.97],
    "厦门":[118.1,24.46],
    "汕尾":[115.375279,22.786211],
    "潮州":[116.63,23.68],
    "丹东":[124.37,40.13],
    "太仓":[121.1,31.45],
    "曲靖":[103.79,25.51],
    "烟台":[121.39,37.52],
    "福州":[119.3,26.08],
    "瓦房店":[121.979603,39.627114],
    "即墨":[120.45,36.38],
    "抚顺":[123.97,41.97],
    "玉溪":[102.52,24.35],
    "张家口":[114.87,40.82],
    "阳泉":[113.57,37.85],
    "莱州":[119.942327,37.177017],
    "湖州":[120.1,30.86],
    "汕头":[116.69,23.39],
    "昆山":[120.95,31.39],
    "宁波":[121.56,29.86],
    "湛江":[110.359377,21.270708],
    "揭阳":[116.35,23.55],
    "荣成":[122.41,37.16],
    "连云港":[119.16,34.59],
    "葫芦岛":[120.836932,40.711052],
    "常熟":[120.74,31.64],
    "东莞":[113.75,23.04],
    "河源":[114.68,23.73],
    "淮安":[119.15,33.5],
    "泰州":[119.9,32.49],
    "南宁":[108.33,22.84],
    "营口":[122.18,40.65],
    "惠州":[114.4,23.09],
    "江阴":[120.26,31.91],
    "蓬莱":[120.75,37.8],
    "韶关":[113.62,24.84],
    "嘉峪关":[98.289152,39.77313],
    "广州":[113.23,23.16],
    "延安":[109.47,36.6],
    "太原":[112.53,37.87],
    "清远":[113.01,23.7],
    "中山":[113.38,22.52],
    "昆明":[102.73,25.04],
    "寿光":[118.73,36.86],
    "盘锦":[122.070714,41.119997],
    "长治":[113.08,36.18],
    "深圳":[114.07,22.62],
    "珠海":[113.52,22.3],
    "宿迁":[118.3,33.96],
    "咸阳":[108.72,34.36],
    "铜川":[109.11,35.09],
    "平度":[119.97,36.77],
    "佛山":[113.11,23.05],
    "海口":[110.35,20.02],
    "江门":[113.06,22.61],
    "章丘":[117.53,36.72],
    "肇庆":[112.44,23.05],
    "大连":[121.62,38.92],
    "临汾":[111.5,36.08],
    "吴江":[120.63,31.16],
    "石嘴山":[106.39,39.04],
    "沈阳":[123.38,41.8],
    "苏州":[120.62,31.32],
    "茂名":[110.88,21.68],
    "嘉兴":[120.76,30.77],
    "长春":[125.35,43.88],
    "胶州":[120.03336,36.264622],
    "银川":[106.27,38.47],
    "张家港":[120.555821,31.875428],
    "三门峡":[111.19,34.76],
    "锦州":[121.15,41.13],
    "南昌":[115.89,28.68],
    "柳州":[109.4,24.33],
    "三亚":[109.511909,18.252847],
    "自贡":[104.778442,29.33903],
    "吉林":[126.57,43.87],
    "阳江":[111.95,21.85],
    "泸州":[105.39,28.91],
    "西宁":[101.74,36.56],
    "宜宾":[104.56,29.77],
    "呼和浩特":[111.65,40.82],
    "成都":[104.06,30.67],
    "大同":[113.3,40.12],
    "镇江":[119.44,32.2],
    "桂林":[110.28,25.29],
    "张家界":[110.479191,29.117096],
    "宜兴":[119.82,31.36],
    "北海":[109.12,21.49],
    "西安":[108.95,34.27],
    "金坛":[119.56,31.74],
    "东营":[118.49,37.46],
    "牡丹江":[129.58,44.6],
    "遵义":[106.9,27.7],
    "绍兴":[120.58,30.01],
    "扬州":[119.42,32.39],
    "常州":[119.95,31.79],
    "潍坊":[119.1,36.62],
    "重庆":[106.54,29.59],
    "台州":[121.420757,28.656386],
    "南京":[118.78,32.04],
    "滨州":[118.03,37.36],
    "贵阳":[106.71,26.57],
    "无锡":[120.29,31.59],
    "本溪":[123.73,41.3],
    "克拉玛依":[84.77,45.59],
    "渭南":[109.5,34.52],
    "马鞍山":[118.48,31.56],
    "宝鸡":[107.15,34.38],
    "焦作":[113.21,35.24],
    "句容":[119.16,31.95],
    "北京":[116.46,39.92],
    "徐州":[117.2,34.26],
    "衡水":[115.72,37.72],
    "包头":[110,40.58],
    "绵阳":[104.73,31.48],
    "乌鲁木齐":[87.68,43.77],
    "枣庄":[117.57,34.86],
    "杭州":[120.19,30.26],
    "淄博":[118.05,36.78],
    "鞍山":[122.85,41.12],
    "溧阳":[119.48,31.43],
    "库尔勒":[86.06,41.68],
    "安阳":[114.35,36.1],
    "开封":[114.35,34.79],
    "济南":[117,36.65],
    "德阳":[104.37,31.13],
    "温州":[120.65,28.01],
    "九江":[115.97,29.71],
    "邯郸":[114.47,36.6],
    "临安":[119.72,30.23],
    "兰州":[103.73,36.03],
    "沧州":[116.83,38.33],
    "临沂":[118.35,35.05],
    "南充":[106.110698,30.837793],
    "天津":[117.2,39.13],
    "富阳":[119.95,30.07],
    "泰安":[117.13,36.18],
    "诸暨":[120.23,29.71],
    "郑州":[113.65,34.76],
    "哈尔滨":[126.63,45.75],
    "聊城":[115.97,36.45],
    "芜湖":[118.38,31.33],
    "唐山":[118.02,39.63],
    "平顶山":[113.29,33.75],
    "邢台":[114.48,37.05],
    "德州":[116.29,37.45],
    "济宁":[116.59,35.38],
    "荆州":[112.239741,30.335165],
    "宜昌":[111.3,30.7],
    "义乌":[120.06,29.32],
    "丽水":[119.92,28.45],
    "洛阳":[112.44,34.7],
    "秦皇岛":[119.57,39.95],
    "株洲":[113.16,27.83],
    "石家庄":[114.48,38.03],
    "莱芜":[117.67,36.19],
    "常德":[111.69,29.05],
    "保定":[115.48,38.85],
    "湘潭":[112.91,27.87],
    "金华":[119.64,29.12],
    "岳阳":[113.09,29.37],
    "长沙":[113,28.21],
    "衢州":[118.88,28.97],
    "廊坊":[116.7,39.53],
    "菏泽":[115.480656,35.23375],
    "合肥":[117.27,31.86],
    "武汉":[114.31,30.52],
    "大庆":[125.03,46.58]
};

var convertData = function (data) {
    var res = [];
    for (var i = 0; i < data.length; i++) {
        var geoCoord = geoCoordMap[data[i].name];
        if (geoCoord) {
            res.push(geoCoord.concat(data[i].value));
        }
    }
    return res;
};

option = {
    backgroundColor: '#404a59',
    title: {
        text: '全国主要城市空气质量',
        subtext: 'data from PM25.in',
        sublink: 'http://www.pm25.in',
        left: 'center',
        textStyle: {
            color: '#fff'
        }
    },
    tooltip: {
        trigger: 'item'
    },
    legend: {
        orient: 'vertical',
        top: 'bottom',
        left: 'right',
        data:['pm2.5'],
        textStyle: {
            color: '#fff'
        }
    },
    visualMap: {
        min: 0,
        max: 300,
        splitNumber: 5,
        color: ['#d94e5d','#eac736','#50a3ba'],
        textStyle: {
            color: '#fff'
        }
    },
    geo: {
        map: 'china',
        label: {
            emphasis: {
                show: false
            }
        },
        itemStyle: {
            normal: {
                areaColor: '#323c48',
                borderColor: '#111'
            },
            emphasis: {
                areaColor: '#2a333d'
            }
        }
    },
    series: [
        {
            name: 'pm2.5',
            type: 'scatter',
            coordinateSystem: 'geo',
            data: convertData([
                {name: "海门", value: 9},
                {name: "鄂尔多斯", value: 12},
                {name: "招远", value: 12},
                {name: "舟山", value: 12},
                {name: "齐齐哈尔", value: 14},
                {name: "盐城", value: 15},
                {name: "赤峰", value: 16},
                {name: "青岛", value: 18},
                {name: "乳山", value: 18},
                {name: "金昌", value: 19},
                {name: "泉州", value: 21},
                {name: "莱西", value: 21},
                {name: "日照", value: 21},
                {name: "胶南", value: 22},
                {name: "南通", value: 23},
                {name: "拉萨", value: 24},
                {name: "云浮", value: 24},
                {name: "梅州", value: 25},
                {name: "文登", value: 25},
                {name: "上海", value: 25},
                {name: "攀枝花", value: 25},
                {name: "威海", value: 25},
                {name: "承德", value: 25},
                {name: "厦门", value: 26},
                {name: "汕尾", value: 26},
                {name: "潮州", value: 26},
                {name: "丹东", value: 27},
                {name: "太仓", value: 27},
                {name: "曲靖", value: 27},
                {name: "烟台", value: 28},
                {name: "福州", value: 29},
                {name: "瓦房店", value: 30},
                {name: "即墨", value: 30},
                {name: "抚顺", value: 31},
                {name: "玉溪", value: 31},
                {name: "张家口", value: 31},
                {name: "阳泉", value: 31},
                {name: "莱州", value: 32},
                {name: "湖州", value: 32},
                {name: "汕头", value: 32},
                {name: "昆山", value: 33},
                {name: "宁波", value: 33},
                {name: "湛江", value: 33},
                {name: "揭阳", value: 34},
                {name: "荣成", value: 34},
                {name: "连云港", value: 35},
                {name: "葫芦岛", value: 35},
                {name: "常熟", value: 36},
                {name: "东莞", value: 36},
                {name: "河源", value: 36},
                {name: "淮安", value: 36},
                {name: "泰州", value: 36},
                {name: "南宁", value: 37},
                {name: "营口", value: 37},
                {name: "惠州", value: 37},
                {name: "江阴", value: 37},
                {name: "蓬莱", value: 37},
                {name: "韶关", value: 38},
                {name: "嘉峪关", value: 38},
                {name: "广州", value: 38},
                {name: "延安", value: 38},
                {name: "太原", value: 39},
                {name: "清远", value: 39},
                {name: "中山", value: 39},
                {name: "昆明", value: 39},
                {name: "寿光", value: 40},
                {name: "盘锦", value: 40},
                {name: "长治", value: 41},
                {name: "深圳", value: 41},
                {name: "珠海", value: 42},
                {name: "宿迁", value: 43},
                {name: "咸阳", value: 43},
                {name: "铜川", value: 44},
                {name: "平度", value: 44},
                {name: "佛山", value: 44},
                {name: "海口", value: 44},
                {name: "江门", value: 45},
                {name: "章丘", value: 45},
                {name: "肇庆", value: 46},
                {name: "大连", value: 47},
                {name: "临汾", value: 47},
                {name: "吴江", value: 47},
                {name: "石嘴山", value: 49},
                {name: "沈阳", value: 50},
                {name: "苏州", value: 50},
                {name: "茂名", value: 50},
                {name: "嘉兴", value: 51},
                {name: "长春", value: 51},
                {name: "胶州", value: 52},
                {name: "银川", value: 52},
                {name: "张家港", value: 52},
                {name: "三门峡", value: 53},
                {name: "锦州", value: 54},
                {name: "南昌", value: 54},
                {name: "柳州", value: 54},
                {name: "三亚", value: 54},
                {name: "自贡", value: 56},
                {name: "吉林", value: 56},
                {name: "阳江", value: 57},
                {name: "泸州", value: 57},
                {name: "西宁", value: 57},
                {name: "宜宾", value: 58},
                {name: "呼和浩特", value: 58},
                {name: "成都", value: 58},
                {name: "大同", value: 58},
                {name: "镇江", value: 59},
                {name: "桂林", value: 59},
                {name: "张家界", value: 59},
                {name: "宜兴", value: 59},
                {name: "北海", value: 60},
                {name: "西安", value: 61},
                {name: "金坛", value: 62},
                {name: "东营", value: 62},
                {name: "牡丹江", value: 63},
                {name: "遵义", value: 63},
                {name: "绍兴", value: 63},
                {name: "扬州", value: 64},
                {name: "常州", value: 64},
                {name: "潍坊", value: 65},
                {name: "重庆", value: 66},
                {name: "台州", value: 67},
                {name: "南京", value: 67},
                {name: "滨州", value: 70},
                {name: "贵阳", value: 71},
                {name: "无锡", value: 71},
                {name: "本溪", value: 71},
                {name: "克拉玛依", value: 72},
                {name: "渭南", value: 72},
                {name: "马鞍山", value: 72},
                {name: "宝鸡", value: 72},
                {name: "焦作", value: 75},
                {name: "句容", value: 75},
                {name: "北京", value: 79},
                {name: "徐州", value: 79},
                {name: "衡水", value: 80},
                {name: "包头", value: 80},
                {name: "绵阳", value: 80},
                {name: "乌鲁木齐", value: 84},
                {name: "枣庄", value: 84},
                {name: "杭州", value: 84},
                {name: "淄博", value: 85},
                {name: "鞍山", value: 86},
                {name: "溧阳", value: 86},
                {name: "库尔勒", value: 86},
                {name: "安阳", value: 90},
                {name: "开封", value: 90},
                {name: "济南", value: 92},
                {name: "德阳", value: 93},
                {name: "温州", value: 95},
                {name: "九江", value: 96},
                {name: "邯郸", value: 98},
                {name: "临安", value: 99},
                {name: "兰州", value: 99},
                {name: "沧州", value: 100},
                {name: "临沂", value: 103},
                {name: "南充", value: 104},
                {name: "天津", value: 105},
                {name: "富阳", value: 106},
                {name: "泰安", value: 112},
                {name: "诸暨", value: 112},
                {name: "郑州", value: 113},
                {name: "哈尔滨", value: 114},
                {name: "聊城", value: 116},
                {name: "芜湖", value: 117},
                {name: "唐山", value: 119},
                {name: "平顶山", value: 119},
                {name: "邢台", value: 119},
                {name: "德州", value: 120},
                {name: "济宁", value: 120},
                {name: "荆州", value: 127},
                {name: "宜昌", value: 130},
                {name: "义乌", value: 132},
                {name: "丽水", value: 133},
                {name: "洛阳", value: 134},
                {name: "秦皇岛", value: 136},
                {name: "株洲", value: 143},
                {name: "石家庄", value: 147},
                {name: "莱芜", value: 148},
                {name: "常德", value: 152},
                {name: "保定", value: 153},
                {name: "湘潭", value: 154},
                {name: "金华", value: 157},
                {name: "岳阳", value: 169},
                {name: "长沙", value: 175},
                {name: "衢州", value: 177},
                {name: "廊坊", value: 193},
                {name: "菏泽", value: 194},
                {name: "合肥", value: 229},
                {name: "武汉", value: 273},
                {name: "大庆", value: 279}
            ]),
            symbolSize: 12,
            label: {
                normal: {
                    show: false
                },
                emphasis: {
                    show: false
                }
            },
            itemStyle: {
                emphasis: {
                    borderColor: '#fff',
                    borderWidth: 1
                }
            }
        }
    ]
};
if (option && typeof option === "object") {
    myChart.setOption(option, true);
}
       </script>
   </body>
</html>

三、视觉映射方式的配置

visualMap 中可以指定数据的指定维度映射到对应的视觉元素上。

实例 1

option = {
    visualMap: [
        {
            type: 'piecewise'
            min: 0,
            max: 5000,
            dimension: 3,       // series.data 的第四个维度（即 value[3]）被映射
            seriesIndex: 4,     // 对第四个系列进行映射。
            inRange: {          // 选中范围中的视觉配置
                color: ['blue', '#121122', 'red'], // 定义了图形颜色映射的颜色列表，
                                                    // 数据最小值映射到'blue'上，
                                                    // 最大值映射到'red'上，
                                                    // 其余自动线性计算。
                symbolSize: [30, 100]               // 定义了图形尺寸的映射范围，
                                                    // 数据最小值映射到30上，
                                                    // 最大值映射到100上，
                                                    // 其余自动线性计算。
            },
            outOfRange: {       // 选中范围外的视觉配置
                symbolSize: [30, 100]
            }
        },
        ...
    ]
};

实例 2

option = {
    visualMap: [
        {
            ...,
            inRange: {          // 选中范围中的视觉配置
                colorLightness: [0.2, 1], // 映射到明暗度上。也就是对本来的颜色进行明暗度处理。
                                          // 本来的颜色可能是从全局色板中选取的颜色，visualMap组件并不关心。
                symbolSize: [30, 100]
            },
            ...
        },
        ...
    ]
};

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持我们。