//设置package包名称以及导入依赖的类
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions.max

object DrawdownCalculator {
  def calculate(sparkSession: SparkSession, df: DataFrame): Double = {

    val windowUptoCurrentRow = Window.orderBy("date").rowsBetween(Long.MinValue, 0)
    val dfWithRollingMaxPrice = df.withColumn("rolling_max_price",
                                              max(df("price")).over(windowUptoCurrentRow))

    val dfWithRollingDrawdowns = dfWithRollingMaxPrice.withColumn("rolling_dd",
      max(dfWithRollingMaxPrice("rolling_max_price") - dfWithRollingMaxPrice("price")).over(windowUptoCurrentRow))

    dfWithRollingDrawdowns.createOrReplaceTempView("DrawdownCalculation")

    val dfWithOrderedDrawndowns = sparkSession.sql("SELECT date, price, rolling_dd, rolling_max_price, " +
                                                   "(rolling_dd / rolling_max_price) as drawdown_pct " +
                                                   "FROM DrawdownCalculation ORDER BY drawdown_pct ASC")

    dfWithOrderedDrawndowns.show()

    val rollingDrawdown = dfWithOrderedDrawndowns.first().getDouble(2)
    val rollingMaxPrice = dfWithOrderedDrawndowns.first().getDouble(3)
    val maxDrawdownPct = dfWithOrderedDrawndowns.first().getDouble(4)

    maxDrawdownPct
  }
} 

//设置package包名称以及导入依赖的类
package org.alghimo.spark.dimensionalModelling

import org.apache.spark.sql._
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions.{lit, max, row_number}


  def naturalKeyColumns: Seq[String]

  def enrichedDimensionsToDimensions(enrichedDims: EnrichedDimensions, refreshDimensionTable: Boolean = false): Dimensions = {
    val dimensions = dimensionTable(refreshDimensionTable)
    val maxSk = dimensions.select(max(surrogateKeyColumn)).as[Long].collect().head
    val rankWindow = Window.partitionBy().orderBy(naturalKeyColumns.head, naturalKeyColumns.tail:_*)

    enrichedDims
      .withColumn(surrogateKeyColumnName, lit(maxSk) + (row_number() over rankWindow))
      .withColumn(startTimestampColumnName, timestampColumn)
      .withColumn(endTimestampColumnName, lit(null).cast("timestamp"))
      .withColumn(isCurrentColumnName, lit(true))
      .selectExpr(dimensions.columns:_*)
      .as[DIM]
  }

  def keepOnlyMostRecentEvents(enrichedDimensions: EnrichedDimensions): EnrichedDimensions = {
    val naturalKeyWindow = Window.partitionBy(naturalKeyColumns.map(new Column(_)):_*).orderBy(timestampColumn.desc)

    enrichedDimensions
      .withColumn("row_num", row_number() over naturalKeyWindow)
      .filter("row_num = 1")
      .drop("row_num")
      .as[ENRICHED_DIM]
  }
} 

//设置package包名称以及导入依赖的类
package com.esri

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.{max, min}

object TrackApp extends App {

  val spark = SparkSession
    .builder()
    .appName("Path SOM")
    .master("local[*]")
    .config("spark.ui.enabled", "false")
    .getOrCreate()

  import spark.implicits._

  try {

    val df = spark
      .read
      .json("Paths")
      .as[TrackCells]
      .cache()

    val qrAgg = df
      .flatMap(_.cells)
      .distinct()
      .agg(min("q").as("qmin"), max("q").alias("qmax"), min("r").alias("rmin"), max("r").alias("rmax"))
      .as[QRMinMax]
      .head

    val qrMin = Cell(qrAgg.qmin, qrAgg.rmin)
    val qrMax = Cell(qrAgg.qmax, qrAgg.rmax)
    val qrDel = (qrMax - qrMin) + 1
    val qrSize = qrDel size

    val trainingArr = df
      .rdd
      .map(trackCells => trackCells.toBreeze(qrMin, qrDel, qrSize))
      .collect()

    val rnd = new java.security.SecureRandom()
    val somSize = 3
    val nodes = for {
      q <- 0 until somSize
      r <- 0 until somSize
    } yield Node(q, r, trainingArr(rnd.nextInt(trainingArr.length)))


    val som = SOM(nodes)
    val epochMax = trainingArr.length * 400
    implicit val progressBar = TerminalProgressBar(epochMax)
    som.train(trainingArr, epochMax, 2.5, initialAlpha = 0.4)
    som.saveAsFig("/tmp/fig.png", qrDel)

  } finally {
    spark.stop()
  }
}