package data

import (
	"errors"
	"eta/eta_chart_lib/models"
	"eta/eta_chart_lib/utils"
	"github.com/shopspring/decimal"
	"math"
	"time"
)

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

	var startEdbInfoData *models.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
}