src/Pure/ML/ml_statistics.scala

more compact ML_Statistics, to make build_status work with less than 2GB heap;

/*  Title:      Pure/ML/ml_statistics.scala
    Author:     Makarius

ML runtime statistics.
*/

package isabelle


import scala.annotation.tailrec
import scala.collection.mutable
import scala.collection.immutable.{SortedSet, SortedMap}
import scala.swing.{Frame, Component}

import org.jfree.data.xy.{XYSeries, XYSeriesCollection}
import org.jfree.chart.{JFreeChart, ChartPanel, ChartFactory}
import org.jfree.chart.plot.PlotOrientation


object ML_Statistics
{
  /* properties */

  val Now = new Properties.Double("now")
  def now(props: Properties.T): Double = Now.unapply(props).get


  /* heap */

  val HEAP_SIZE = "size_heap"

  def heap_scale(x: Long): Long = x / 1024 / 1024
  def heap_scale(x: Double): Double = heap_scale(x.toLong).toLong


  /* standard fields */

  type Fields = (String, List[String])

  val tasks_fields: Fields =
    ("Future tasks",
      List("tasks_ready", "tasks_pending", "tasks_running", "tasks_passive", "tasks_urgent"))

  val workers_fields: Fields =
    ("Worker threads", List("workers_total", "workers_active", "workers_waiting"))

  val GC_fields: Fields =
    ("GCs", List("partial_GCs", "full_GCs"))

  val heap_fields: Fields =
    ("Heap", List(HEAP_SIZE, "size_allocation", "size_allocation_free",
      "size_heap_free_last_full_GC", "size_heap_free_last_GC"))

  val threads_fields: Fields =
    ("Threads", List("threads_total", "threads_in_ML", "threads_wait_condvar",
      "threads_wait_IO", "threads_wait_mutex", "threads_wait_signal"))

  val time_fields: Fields =
    ("Time", List("time_CPU", "time_GC"))

  val speed_fields: Fields =
    ("Speed", List("speed_CPU", "speed_GC"))


  val all_fields: List[Fields] =
    List(tasks_fields, workers_fields, GC_fields, heap_fields, threads_fields,
      time_fields, speed_fields)

  val main_fields: List[Fields] =
    List(tasks_fields, workers_fields, heap_fields)


  /* content interpretation */

  final case class Entry(time: Double, data: Map[String, Double])
  {
    def get(field: String): Double = data.getOrElse(field, 0.0)
  }

  val empty: ML_Statistics = apply(Nil)

  def apply(ml_statistics: List[Properties.T], heading: String = "",
    domain: String => Boolean = (key: String) => true): ML_Statistics =
  {
    require(ml_statistics.forall(props => Now.unapply(props).isDefined))

    val time_start = if (ml_statistics.isEmpty) 0.0 else now(ml_statistics.head)
    val duration = if (ml_statistics.isEmpty) 0.0 else now(ml_statistics.last) - time_start

    val fields =
      SortedSet.empty[String] ++
        (for {
          props <- ml_statistics.iterator
          (x, _) <- props.iterator
          if x != Now.name && domain(x) } yield x)

    val content =
    {
      var last_edge = Map.empty[String, (Double, Double, Double)]
      val result = new mutable.ListBuffer[ML_Statistics.Entry]
      for (props <- ml_statistics) {
        val time = now(props) - time_start
        require(time >= 0.0)

        // rising edges -- relative speed
        val speeds =
          (for {
            (key, value) <- props.iterator
            a <- Library.try_unprefix("time", key)
            b = "speed" + a if domain(b)
          }
          yield {
            val (x0, y0, s0) = last_edge.getOrElse(a, (0.0, 0.0, 0.0))

            val x1 = time
            val y1 = java.lang.Double.parseDouble(value)
            val s1 = if (x1 == x0) 0.0 else (y1 - y0) / (x1 - x0)

            if (y1 > y0) {
              last_edge += (a -> (x1, y1, s1))
              (b, s1.toString)
            }
            else (b, s0.toString)
          }).toList

        val data =
          SortedMap.empty[String, Double] ++
            (for {
              (x, y) <- props.iterator ++ speeds.iterator
              if x != Now.name && domain(x)
              z = java.lang.Double.parseDouble(y) if z != 0.0
            } yield { (x.intern, if (heap_fields._2.contains(x)) heap_scale(z) else z) })

        result += ML_Statistics.Entry(time, data)
      }
      result.toList
    }

    new ML_Statistics(heading, fields, content, time_start, duration)
  }
}

final class ML_Statistics private(
  val heading: String,
  val fields: Set[String],
  val content: List[ML_Statistics.Entry],
  val time_start: Double,
  val duration: Double)
{
  /* content */

  def maximum(field: String): Double =
    (0.0 /: content)({ case (m, e) => m max e.get(field) })

  def average(field: String): Double =
  {
    @tailrec def sum(t0: Double, list: List[ML_Statistics.Entry], acc: Double): Double =
      list match {
        case Nil => acc
        case e :: es =>
          val t = e.time
          sum(t, es, (t - t0) * e.get(field) + acc)
      }
    content match {
      case Nil => 0.0
      case List(e) => e.get(field)
      case e :: es => sum(e.time, es, 0.0) / duration
    }
  }

  def maximum_heap_size: Long = maximum(ML_Statistics.HEAP_SIZE).toLong
  def average_heap_size: Long = average(ML_Statistics.HEAP_SIZE).toLong


  /* charts */

  def update_data(data: XYSeriesCollection, selected_fields: List[String])
  {
    data.removeAllSeries
    for {
      field <- selected_fields.iterator
      series = new XYSeries(field)
    } {
      content.foreach(entry => series.add(entry.time, entry.get(field)))
      data.addSeries(series)
    }
  }

  def chart(title: String, selected_fields: List[String]): JFreeChart =
  {
    val data = new XYSeriesCollection
    update_data(data, selected_fields)

    ChartFactory.createXYLineChart(title, "time", "value", data,
      PlotOrientation.VERTICAL, true, true, true)
  }

  def chart(fields: ML_Statistics.Fields): JFreeChart =
    chart(fields._1, fields._2)

  def show_frames(fields: List[ML_Statistics.Fields] = ML_Statistics.main_fields): Unit =
    fields.map(chart(_)).foreach(c =>
      GUI_Thread.later {
        new Frame {
          iconImage = GUI.isabelle_image()
          title = heading
          contents = Component.wrap(new ChartPanel(c))
          visible = true
        }
      })
}