eta_index_lib/logic/chart_correlation.go

package logic

import (
	"eta/eta_index_lib/models"
	"eta/eta_index_lib/utils"
	"fmt"
)

// CalculateCorrelation 计算相关性-获取x轴和y轴
func CalculateCorrelation(leadValue int, leadUnit, frequencyA, frequencyB string, dataListA, dataListB []*models.EdbInfoSearchData) (xEdbIdValue []int, yDataList []YData, err error) {
	xData := make([]int, 0)
	yData := make([]float64, 0)
	if leadValue == 0 {
		xData = append(xData, 0)
	}
	if leadValue > 0 {
		leadMin := 0 - leadValue
		xLen := 2*leadValue + 1
		for i := 0; i < xLen; i++ {
			n := leadMin + i
			xData = append(xData, n)
		}
	}

	// 计算窗口，不包含第一天
	//startDateTime, _ := time.ParseInLocation(utils.FormatDate, startDate, time.Local)
	//startDate = startDateTime.AddDate(0, 0, 1).Format(utils.FormatDate)

	//// 2023-03-02 时间序列始终以指标B为基准, 始终是A进行平移
	//baseEdbInfo := edbInfoMappingB
	//changeEdbInfo := edbInfoMappingA
	// 2023-03-17 时间序列始终以指标A为基准, 始终是B进行平移
	//baseEdbInfo := edbInfoMappingA
	//changeEdbInfo := edbInfoMappingB

	// 获取时间基准指标在时间区间内的值
	//aDataList := make([]*EdbDataList, 0)
	//switch baseEdbInfo.EdbInfoCategoryType {
	//case 0:
	//	aDataList, err = GetEdbDataList(baseEdbInfo.Source, baseEdbInfo.SubSource, baseEdbInfo.EdbInfoId, startDate, endDate)
	//case 1:
	//	_, aDataList, _, _, err, _ = data.GetPredictDataListByPredictEdbInfoId(baseEdbInfo.EdbInfoId, startDate, endDate, false)
	//default:
	//	err = errors.New("指标base类型异常")
	//	return
	//}
	//
	//// 获取变频指标所有日期的值, 插值法完善数据
	//bDataList := make([]*EdbDataList, 0)
	//switch changeEdbInfo.EdbInfoCategoryType {
	//case 0:
	//	bDataList, err = GetEdbDataList(changeEdbInfo.Source, changeEdbInfo.SubSource, changeEdbInfo.EdbInfoId, "", "")
	//case 1:
	//	_, bDataList, _, _, err, _ = data.GetPredictDataListByPredictEdbInfoId(changeEdbInfo.EdbInfoId, "", "", false)
	//default:
	//	err = errors.New("指标change类型异常")
	//	return
	//}
	//changeDataMap := make(map[string]float64)
	//newChangeDataList, e := HandleDataByLinearRegression(bDataList, changeDataMap)
	//if e != nil {
	//	err = fmt.Errorf("获取变频指标插值法Map失败, Err: %s", e.Error())
	//	return
	//}

	// 2023-03-17 时间序列始终以指标A为基准, 始终是B进行平移
	baseDataList := make([]*models.EdbInfoSearchData, 0)
	baseDataMap := make(map[string]float64)
	changeDataList := make([]*models.EdbInfoSearchData, 0)
	changeDataMap := make(map[string]float64)

	// 先把低频指标升频为高频
	{
		frequencyIntMap := map[string]int{
			"日度": 1,
			"周度": 2,
			"旬度": 3,
			"月度": 4,
			"季度": 5,
			"年度": 6,
		}

		// 如果A指标是高频，那么就需要对B指标进行升频
		if frequencyIntMap[frequencyA] < frequencyIntMap[frequencyB] {
			tmpNewChangeDataList, e := models.HandleDataByLinearRegression(dataListB, changeDataMap)
			if e != nil {
				err = fmt.Errorf("获取变频指标插值法Map失败, Err: %s", e.Error())
				return
			}
			changeDataList = tmpNewChangeDataList
			baseDataList = dataListA
			for _, v := range baseDataList {
				baseDataMap[v.DataTime] = v.Value
			}

		} else if frequencyIntMap[frequencyA] > frequencyIntMap[frequencyB] {
			// 如果B指标是高频，那么就需要对A指标进行升频
			tmpNewChangeDataList, e := models.HandleDataByLinearRegression(dataListA, baseDataMap)
			if e != nil {
				err = fmt.Errorf("获取变频指标插值法Map失败, Err: %s", e.Error())
				return
			}
			baseDataList = tmpNewChangeDataList

			changeDataList = dataListB
			for _, v := range changeDataList {
				changeDataMap[v.DataTime] = v.Value
			}
		} else {
			baseDataList = dataListA
			for _, v := range baseDataList {
				baseDataMap[v.DataTime] = v.Value
			}
			changeDataList = dataListB
			for _, v := range changeDataList {
				changeDataMap[v.DataTime] = v.Value
			}
		}

	}

	// 计算不领先也不滞后时的相关系数
	baseCalculateData := make([]float64, 0)
	baseDataTimeArr := make([]string, 0)
	for i := range baseDataList {
		baseDataTimeArr = append(baseDataTimeArr, baseDataList[i].DataTime)
		baseCalculateData = append(baseCalculateData, baseDataList[i].Value)
	}

	//zeroBaseData := make([]float64, 0)
	//zeroCalculateData := make([]float64, 0)
	//for i := range baseDataTimeArr {
	//	tmpBaseVal, ok1 := baseDataMap[baseDataTimeArr[i]]
	//	tmpCalculateVal, ok2 := changeDataMap[baseDataTimeArr[i]]
	//	if ok1 && ok2 {
	//		zeroBaseData = append(zeroBaseData, tmpBaseVal)
	//		zeroCalculateData = append(zeroCalculateData, tmpCalculateVal)
	//	}
	//}
	//if len(zeroBaseData) != len(zeroCalculateData) {
	//	err = fmt.Errorf("相关系数两组序列元素数不一致, %d-%d", len(baseCalculateData), len(zeroCalculateData))
	//	return
	//}
	//zeroRatio := utils.CalculateCorrelationByIntArr(zeroBaseData, zeroCalculateData)
	//if leadValue == 0 {
	//	yData = append(yData, zeroRatio)
	//}

	// 计算领先/滞后N期
	if leadValue > 0 {
		// 平移变频指标领先/滞后的日期(单位天)
		moveUnitDays := utils.FrequencyDaysMap[leadUnit]

		for i := range xData {
			//if xData[i] == 0 {
			//	yData = append(yData, zeroRatio)
			//	continue
			//}
			xCalculateData := make([]float64, 0)
			yCalculateData := make([]float64, 0)

			// 平移指定天数
			mDays := int(moveUnitDays) * xData[i]
			_, dMap := models.MoveDataDaysToNewDataList(changeDataList, mDays)

			// 取出对应的基准日期的值
			for i2 := range baseDataTimeArr {
				tmpDate := baseDataTimeArr[i2]
				if yVal, ok := dMap[tmpDate]; ok {
					xCalculateData = append(xCalculateData, baseCalculateData[i2])
					yCalculateData = append(yCalculateData, yVal)
				}
			}
			if len(yCalculateData) <= 0 {
				//err = fmt.Errorf("领先滞后相关系数两组序列元素数不一致, %d-%d", len(baseCalculateData), len(yCalculateData))
				//return
				// 领先滞后后，没有可以计算的数据了
				continue
			}

			// 公式计算出领先/滞后频度对应点的相关性系数
			ratio := utils.CalculateCorrelationByIntArr(xCalculateData, yCalculateData)
			yData = append(yData, ratio)
		}
	}

	xEdbIdValue = xData
	yDataList = make([]YData, 0)
	yDate := "0000-00-00"
	yDataList = append(yDataList, YData{
		Date:  yDate,
		Value: yData,
	})
	return
}