' +
+ "
" +
+ selectCity +
+ "-" +
+ mapName +
+ "-" +
+ params[0].axisValue +
+ "年" +
+ "
";
+
+ // 获取排名、最大值、最小值
+ var rankInfo = {};
+ if (params[3].value == "-") {
+ // 预测值
+ rankInfo = getRanking(mapName, params[0].axisValue, false);
+ } else {
+ // 实际值
+ rankInfo = getRanking(mapName, params[0].axisValue, false);
+ }
+
+ for (let i = 0; i < params.length; i++) {
+ var item = params[i];
+ if (!(typeof item.value === "number" && !isNaN(item.value))) {
+ // 不是数字的时候
+ item.value = "-";
+ }
+ if (i == 1) {
+ // 预测最大值要加上预测最小值
+ var val = item.value + params[0].value;
+ str += "
" + item.marker + item.seriesName + "：" + val + "
";
+ } else {
+ str += "
" + item.marker + item.seriesName + "：" + item.value + "
";
+ }
+ }
+
+ if (params[3].value != "-") {
+ var trendStr = "";
+ if (rankInfo.hasOwnProperty("trend")) {
+ trendStr = "，较上一年" + rankInfo.trend;
+ }
+
+ str +=
+ "
排 名：" +
+ rankInfo.rankNum +
+ trendStr +
+ "
" +
+ "
最大值：" +
+ rankInfo.max +
+ "
" +
+ "
最小值：" +
+ rankInfo.min +
+ "
";
+ }
+
+ return str + "
' +
+ "
" +
+ selectCity +
+ "-" +
+ mapName +
+ "-" +
+ params[0].axisValue +
+ "年" +
+ "
";
+ var data = Object.create(params);
+ var arr1 = data.slice(0, data.length / 2);
+ var arr2 = data.slice(data.length / 2, data.length);
+ // 排序
+ ranking(arr1);
+ data = Object.create(arr1);
+
+ if (!(typeof params[8].value === "number" && !isNaN(params[8].value))) {
+ ranking(arr2);
+ data = Object.create(arr2);
+ // 没有实际值时
+ str += "
" + "预测值：" + "
";
+ }
+ for (let i = 0; i < data.length; i++) {
+ var item = data[i];
+ if (typeof item.value === "number" && !isNaN(item.value)) {
+ str += "
" + item.marker + item.seriesName + "：" + item.value + "
";
+ }
+ }
+ return str + "
';
+
+ str += "
" + params.seriesName + "
";
+
+ var correlationLevel = "";
+ var value = params.value[2];
+ if (value > 0) {
+ if (value >= 0.8) {
+ correlationLevel = "（正强相关）";
+ } else if (value >= 0.3 && value < 0.8) {
+ correlationLevel = "（正弱相关）";
+ } else if (value < 0.3) {
+ correlationLevel = "（没有相关性）";
+ }
+ } else {
+ if (-value >= 0.8) {
+ correlationLevel = "（负强相关）";
+ } else if (-value >= 0.3 && -value < 0.8) {
+ correlationLevel = "（负弱相关）";
+ } else if (-value < 0.3) {
+ correlationLevel = "（没有相关性）";
+ }
+ }
+ str += "
" + params.marker + dataX[params.value[0]] + "与" + dataY[params.value[1]] + "的相关性为：" + value + correlationLevel + "
";
+
+ return str + "
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+