package data

import (
	"encoding/json"
	"errors"
	"eta_gn/eta_api/models/data_manage"
	"eta_gn/eta_api/utils"
	"fmt"
	"github.com/shopspring/decimal"
	"math"
	"sort"
	"strings"
	"time"
)

func CheckFormula(formula string) map[string]string {
	mathFormula := []string{"MAX", "MIN", "ABS", "ACOS", "ASIN", "CEIL", "MOD", "POW", "ROUND", "SIGN", "SIN", "TAN", "LOG10", "LOG2", "LOG", "LN", "EXP"}

	str := strings.ToUpper(formula)
	for _, v := range mathFormula {
		str = strings.Replace(str, v, "", -1)
	}
	str = strings.Replace(str, "(", "", -1)
	str = strings.Replace(str, ")", "", -1)

	byteMap := make(map[string]string)
	for i := 0; i < len(str); i++ {
		byteInt := str[i]
		if byteInt >= 65 && byteInt <= 90 {
			byteStr := string(byteInt)
			if _, ok := byteMap[byteStr]; !ok {
				byteMap[byteStr] = byteStr
			}
		}
	}
	return byteMap
}

type FormulaListItem struct {
	Formula string `json:"f"`
	Date    string `json:"d"`
}

// CheckFormulaJson 检测计算公式json串是否异常
func CheckFormulaJson(formula string) (formulaSlice []string, err error) {
	list := make([]FormulaListItem, 0)
	err = json.Unmarshal([]byte(formula), &list)
	if err != nil {
		err = fmt.Errorf("公式串解析失败： json.Unmarshal Err: %v", err)
		return
	}
	formulaSlice = make([]string, 0)
	// 日期排序
	for _, v := range list {
		formulaSlice = append(formulaSlice, v.Formula)
	}
	return
}

type CalculateItems struct {
	EdbInfoId int
	DataMap   map[string]float64
}

// handleDataByLinearRegression 插值法补充数据(线性方程式)
func handleDataByLinearRegression(edbInfoDataList []*data_manage.EdbDataList, handleDataMap map[string]float64) (err error) {
	if len(edbInfoDataList) < 2 {
		return
	}

	var startEdbInfoData *data_manage.EdbDataList
	for _, v := range edbInfoDataList {
		handleDataMap[v.DataTime] = v.Value

		// 第一个数据就给过滤了,给后面的试用
		if startEdbInfoData == nil {
			startEdbInfoData = v
			continue
		}

		// 获取两条数据之间相差的天数
		startDataTime, _ := time.ParseInLocation(utils.FormatDate, startEdbInfoData.DataTime, time.Local)
		currDataTime, _ := time.ParseInLocation(utils.FormatDate, v.DataTime, time.Local)
		betweenHour := int(currDataTime.Sub(startDataTime).Hours())
		betweenDay := betweenHour / 24

		// 如果相差一天,那么过滤
		if betweenDay <= 1 {
			startEdbInfoData = v
			continue
		}

		// 生成线性方程式
		var a, b float64
		{
			coordinateData := make([]utils.Coordinate, 0)
			tmpCoordinate1 := utils.Coordinate{
				X: 1,
				Y: startEdbInfoData.Value,
			}
			coordinateData = append(coordinateData, tmpCoordinate1)
			tmpCoordinate2 := utils.Coordinate{
				X: float64(betweenDay) + 1,
				Y: v.Value,
			}
			coordinateData = append(coordinateData, tmpCoordinate2)

			a, b = utils.GetLinearResult(coordinateData)
			if math.IsNaN(a) || math.IsNaN(b) {
				err = errors.New("线性方程公式生成失败")
				return
			}
		}

		// 生成对应的值
		{
			for i := 1; i < betweenDay; i++ {
				tmpDataTime := startDataTime.AddDate(0, 0, i)
				aDecimal := decimal.NewFromFloat(a)
				xDecimal := decimal.NewFromInt(int64(i) + 1)
				bDecimal := decimal.NewFromFloat(b)

				val, _ := aDecimal.Mul(xDecimal).Add(bDecimal).Round(4).Float64()
				handleDataMap[tmpDataTime.Format(utils.FormatDate)] = val
			}
		}

		startEdbInfoData = v
	}

	return
}

// HandleDataByLinearRegression 插值法补充数据(线性方程式)
func HandleDataByLinearRegression(edbInfoDataList []*data_manage.EdbDataList, handleDataMap map[string]float64) (err error) {
	return handleDataByLinearRegression(edbInfoDataList, handleDataMap)
}

// CallCalculateComputeCorrelation 调用计算拟合残差的相关系数
func CallCalculateComputeCorrelation(data *data_manage.EdbInfoCalculateBatchSaveReqByEdbLib, lang string) (val string, err error, errMsg string) {
	errMsg = "计算失败"
	// 调用指标库去更新
	reqJson, err := json.Marshal(data)
	if err != nil {
		errMsg = "计算相关系数参数解析异常！"
		err = errors.New("参数解析失败，Err:" + err.Error())
		return
	}
	respItem, err := CalculateComputeCorrelation(string(reqJson), lang)
	if err != nil {
		return
	}

	if respItem.Ret == 200 {
		val = respItem.Data
	}

	return
}

// HandleDataByLinearRegressionToList 插值法补充数据(线性方程式)
func HandleDataByLinearRegressionToList(edbInfoDataList []*data_manage.EdbDataList, handleDataMap map[string]float64) (dataTimeList []string, valueList []float64, err error) {
	if len(edbInfoDataList) < 2 {
		return
	}

	var startEdbInfoData *data_manage.EdbDataList
	for _, v := range edbInfoDataList {
		handleDataMap[v.DataTime] = v.Value
		dataTimeList = append(dataTimeList, v.DataTime)
		// 第一个数据就给过滤了,给后面的试用
		if startEdbInfoData == nil {
			startEdbInfoData = v
			//startEdbInfoData.DataTime = startEdbInfoData.DataTime[:5]+ "01-01"
			continue
		}

		// 获取两条数据之间相差的天数
		startDataTime, _ := time.ParseInLocation(utils.FormatDate, startEdbInfoData.DataTime, time.Local)
		currDataTime, _ := time.ParseInLocation(utils.FormatDate, v.DataTime, time.Local)
		betweenHour := int(currDataTime.Sub(startDataTime).Hours())
		betweenDay := betweenHour / 24

		// 如果相差一天,那么过滤
		if betweenDay <= 1 {
			startEdbInfoData = v
			continue
		}

		// 生成线性方程式
		var a, b float64
		{
			coordinateData := make([]utils.Coordinate, 0)
			tmpCoordinate1 := utils.Coordinate{
				X: 1,
				Y: startEdbInfoData.Value,
			}
			coordinateData = append(coordinateData, tmpCoordinate1)
			tmpCoordinate2 := utils.Coordinate{
				X: float64(betweenDay) + 1,
				Y: v.Value,
			}
			coordinateData = append(coordinateData, tmpCoordinate2)

			a, b = utils.GetLinearResult(coordinateData)
			if math.IsNaN(a) || math.IsNaN(b) {
				err = errors.New("线性方程公式生成失败")
				return
			}
		}

		// 生成对应的值
		{
			for i := 1; i < betweenDay; i++ {
				tmpDataTime := startDataTime.AddDate(0, 0, i)
				aDecimal := decimal.NewFromFloat(a)
				xDecimal := decimal.NewFromInt(int64(i) + 1)
				bDecimal := decimal.NewFromFloat(b)

				val, _ := aDecimal.Mul(xDecimal).Add(bDecimal).Round(4).Float64()
				handleDataMap[tmpDataTime.Format(utils.FormatDate)] = val
				dataTimeList = append(dataTimeList, tmpDataTime.Format(utils.FormatDate))
				valueList = append(valueList, val)
			}
		}

		startEdbInfoData = v
	}

	return
}

// HandleDataByLinearRegressionToList 保证生成365个数据点的线性插值法
func HandleDataByLinearRegressionToListV2(edbInfoDataList []*data_manage.EdbDataList, handleDataMap map[string]float64) (dataTimeList []string, valueList []float64, err error) {
	if len(edbInfoDataList) < 2 {
		return
	}

	// 确保至少有两天数据来生成线性方程
	if len(edbInfoDataList) < 2 {
		err = errors.New("至少需要两天的数据来执行线性插值")
		return
	}

	// 对数据按日期排序，确保顺序正确
	sort.Slice(edbInfoDataList, func(i, j int) bool {
		t1, _ := time.ParseInLocation(utils.FormatDate, edbInfoDataList[i].DataTime, time.Local)
		t2, _ := time.ParseInLocation(utils.FormatDate, edbInfoDataList[j].DataTime, time.Local)
		return t1.Before(t2)
	})

	startEdbInfoData := edbInfoDataList[0]
	endEdbInfoData := edbInfoDataList[len(edbInfoDataList)-1]

	// 计算起始和结束日期间实际的天数
	startDate, _ := time.ParseInLocation(utils.FormatDate, startEdbInfoData.DataTime, time.Local)
	endDate, _ := time.ParseInLocation(utils.FormatDate, endEdbInfoData.DataTime, time.Local)
	actualDays := endDate.Sub(startDate).Hours() / 24

	// 生成365个数据点，首先处理已有数据
	for _, v := range edbInfoDataList {
		handleDataMap[v.DataTime] = v.Value
		dataTimeList = append(dataTimeList, v.DataTime)
		valueList = append(valueList, v.Value)
	}

	// 如果已有数据跨越天数不足365天，则对缺失的日期进行线性插值
	if actualDays < 365 {
		// 使用已有数据点生成线性方程（这里简化处理，实际可能需更细致处理边界情况）
		var a, b float64
		coordinateData := []utils.Coordinate{
			{X: 1, Y: startEdbInfoData.Value},
			{X: float64(len(edbInfoDataList)), Y: endEdbInfoData.Value},
		}
		a, b = utils.GetLinearResult(coordinateData)
		if math.IsNaN(a) || math.IsNaN(b) {
			err = errors.New("线性方程公式生成失败")
			return
		}

		// 对剩余日期进行插值
		for i := 1; i < 365; i++ {
			day := startDate.AddDate(0, 0, i)
			if _, exists := handleDataMap[day.Format(utils.FormatDate)]; !exists {
				aDecimal := decimal.NewFromFloat(a)
				xDecimal := decimal.NewFromInt(int64(i) + 1)
				bDecimal := decimal.NewFromFloat(b)

				val, _ := aDecimal.Mul(xDecimal).Add(bDecimal).Round(4).Float64()
				handleDataMap[day.Format(utils.FormatDate)] = val
				dataTimeList = append(dataTimeList, day.Format(utils.FormatDate))
				valueList = append(valueList, val)
			}
		}
	}

	return
}

// HandleDataByLinearRegressionToListV3 插值法补充数据(线性方程式)-直接补充指标起始日期间的所有数据
func HandleDataByLinearRegressionToListV3(edbInfoDataList []*data_manage.EdbDataList, handleDataMap map[string]float64) (newEdbInfoDataList []*data_manage.EdbDataList, dataTimeList []string, valueList []float64, err error) {
	if len(edbInfoDataList) < 2 {
		return
	}

	var startEdbInfoData *data_manage.EdbDataList
	for _, v := range edbInfoDataList {
		handleDataMap[v.DataTime] = v.Value
		newEdbInfoDataList = append(newEdbInfoDataList, v)
		dataTimeList = append(dataTimeList, v.DataTime)
		// 第一个数据就给过滤了,给后面的试用
		if startEdbInfoData == nil {
			startEdbInfoData = v
			//startEdbInfoData.DataTime = startEdbInfoData.DataTime[:5]+ "01-01"
			continue
		}

		// 获取两条数据之间相差的天数
		startDataTime, _ := time.ParseInLocation(utils.FormatDate, startEdbInfoData.DataTime, time.Local)
		currDataTime, _ := time.ParseInLocation(utils.FormatDate, v.DataTime, time.Local)
		betweenHour := int(currDataTime.Sub(startDataTime).Hours())
		betweenDay := betweenHour / 24

		// 如果相差一天,那么过滤
		if betweenDay <= 1 {
			startEdbInfoData = v
			continue
		}

		// 生成线性方程式
		var a, b float64
		{
			coordinateData := make([]utils.Coordinate, 0)
			tmpCoordinate1 := utils.Coordinate{
				X: 1,
				Y: startEdbInfoData.Value,
			}
			coordinateData = append(coordinateData, tmpCoordinate1)
			tmpCoordinate2 := utils.Coordinate{
				X: float64(betweenDay) + 1,
				Y: v.Value,
			}
			coordinateData = append(coordinateData, tmpCoordinate2)

			a, b = utils.GetLinearResult(coordinateData)
			if math.IsNaN(a) || math.IsNaN(b) {
				err = errors.New("线性方程公式生成失败")
				return
			}
		}

		// 生成对应的值
		{
			for i := 1; i < betweenDay; i++ {
				tmpDataTime := startDataTime.AddDate(0, 0, i)
				aDecimal := decimal.NewFromFloat(a)
				xDecimal := decimal.NewFromInt(int64(i) + 1)
				bDecimal := decimal.NewFromFloat(b)

				val, _ := aDecimal.Mul(xDecimal).Add(bDecimal).Round(4).Float64()
				handleDataMap[tmpDataTime.Format(utils.FormatDate)] = val
				dataTimeList = append(dataTimeList, tmpDataTime.Format(utils.FormatDate))
				valueList = append(valueList, val)
				newEdbInfoDataList = append(newEdbInfoDataList, &data_manage.EdbDataList{
					EdbDataId:     v.EdbDataId,
					EdbInfoId:     v.EdbInfoId,
					DataTime:      tmpDataTime.Format(utils.FormatDate),
					DataTimestamp: tmpDataTime.UnixNano() / 1e6,
					Value:         val,
				})
			}
		}

		startEdbInfoData = v
	}

	return
}