package com.flightfeather.uav.biz.dataprocess
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import com.flightfeather.uav.lightshare.bean.DataVo
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import com.flightfeather.uav.socket.eunm.FactorType
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import java.math.BigDecimal
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import kotlin.math.max
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import kotlin.math.min
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import kotlin.math.round
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import kotlin.math.sqrt
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/**
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* 预处理数据
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* 统计数据的"记录数", "均值", "标准差", "最小值", "最大值", "10%分位值", "25%分位值", "50%分位值", "75%分位值", "90%分位值"
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* @Date 2022.2.5
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*/
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class PreData(
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//统计时间
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private val time: String?,
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//需要统计的监测因子
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private val needFactor: List<FactorType> = listOf(
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FactorType.NO2,
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FactorType.CO,
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FactorType.H2S,
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FactorType.SO2,
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FactorType.O3,
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FactorType.PM25,
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FactorType.PM10,
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FactorType.VOC
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),
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//是否需要计算分位值
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private val needQuartile: Boolean = true,
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) {
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inner class TempData {
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//数据平方和（用于计算标准差）
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var sumOfSquares: BigDecimal = BigDecimal.ZERO
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//数据和
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var total: BigDecimal = BigDecimal.ZERO
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//计数
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var count: Int = 0
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var minV: Double = Double.MAX_VALUE
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var maxV: Double = Double.MIN_VALUE
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//数据值记录，按照升序排列，用于获取分位值
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// "10%分位值",
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// "25%分位值",
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// "50%分位值",
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// "75%分位值",
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// "90%分位值"
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val dataList = mutableListOf<Double>()
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}
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inner class ResultData {
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var count: Int = 0
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var avg: Double? = null
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// 标准差
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var std: Double? = null
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var min: Double? = null
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var max: Double? = null
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var q10: Double? = null
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var q25: Double? = null
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var q50: Double? = null
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var q75: Double? = null
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var q90: Double? = null
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}
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//按照每种监测因子记录当前时间下的统计中间数据
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private val dataMap = mutableMapOf<String?, TempData>()
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fun add(data: DataVo) {
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data.values?.forEach {
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if (!dataMap.containsKey(it.factorName)) {
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dataMap[it.factorName] = TempData()
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}
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dataMap[it.factorName]?.apply {
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sumOfSquares = sumOfSquares.plus(BigDecimal.valueOf(
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(it.factorData ?: .0) * (it.factorData ?: .0)
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))
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total = total.plus(BigDecimal.valueOf(it.factorData ?: .0))
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count++
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minV = min(minV, it.factorData?: .0)
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maxV = max(maxV, it.factorData?: .0)
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if (needQuartile) {
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dataList.add(it.factorData ?: .0)
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}
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}
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}
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}
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/**
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* 输出当前统计时间下的一行统计数据
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*/
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fun getOutPutContent(): Array<Any> {
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//每种监测因子都有对应的一系列统计项
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val content = mutableListOf<Any>()
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//第一列是统计时间
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content.add(time ?: "错误时间")
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needFactor.forEach {
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dataMap[it.name]?.let {t ->
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content.apply {
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//"记录数",
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val c = BigDecimal("${t.count}")
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add(t.count)
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// "均值",
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val avg = t.total.div(c)
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add(avg.toString())
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// "标准差",
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add(
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sqrt(
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t.sumOfSquares.minus(
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avg.times(BigDecimal(2)).times(t.total)
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).plus(
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avg.times(avg).times(c)
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).div(c).toDouble()
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).toString()
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)
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// "最小值",
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add(t.minV)
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// "最大值",
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add(t.maxV)
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//分位值
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if (needQuartile) {
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t.dataList.sortBy { d -> d }
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val size = t.dataList.size
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// "10%分位值",
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add(t.dataList[round(size * .1).toInt()])
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// "25%分位值",
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add(t.dataList[round(size * .25).toInt()])
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// "50%分位值",
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add(t.dataList[round(size * .5).toInt()])
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// "75%分位值",
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add(t.dataList[round(size * .75).toInt()])
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// "90%分位值"
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add(t.dataList[round(size * .9).toInt()])
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} else {
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add("")
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add("")
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add("")
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add("")
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add("")
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}
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}
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}
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}
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clear()
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return content.toTypedArray()
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}
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/**
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* 计算数据的各项统计值
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*/
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fun calculate(): Map<FactorType, ResultData> {
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//每种监测因子都有对应的一系列统计项
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val resMap = mutableMapOf<FactorType, ResultData>()
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needFactor.forEach {
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dataMap[it.name]?.let {t ->
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if (!resMap.containsKey(it)) {
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resMap[it] = ResultData()
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}
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resMap[it]?.apply {
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//"记录数",
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val c = BigDecimal("${t.count}")
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count = t.count
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// "均值",
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avg = if (c == BigDecimal.ZERO) .0 else t.total.div(c).toDouble()
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// "标准差",
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std = sqrt(
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t.sumOfSquares.minus(
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BigDecimal(avg!!).times(BigDecimal(2)).times(t.total)
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).plus(
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BigDecimal(avg!!).times(BigDecimal(avg!!)).times(c)
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).div(c).toDouble()
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)
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// "最小值",
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min = t.minV
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// "最大值",
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max = t.maxV
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//分位值
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if (needQuartile) {
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t.dataList.sortBy { d -> d }
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val size = t.dataList.size
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// "10%分位值",
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q10 = t.dataList[round(size * .1).toInt()]
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// "25%分位值",
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q25 = t.dataList[round(size * .25).toInt()]
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// "50%分位值",
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q50 = t.dataList[round(size * .5).toInt()]
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// "75%分位值",
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q75 = t.dataList[round(size * .75).toInt()]
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// "90%分位值"
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q90 = t.dataList[round(size * .9).toInt()]
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}
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}
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}
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}
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clear()
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return resMap
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}
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/**
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* 释放内存
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*/
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private fun clear() {
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dataMap.clear()
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}
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}
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