JASERIES is a scala API for time numeric series operations. ( ** project page **)

Latest changes :
  • now using sbt 0.12
  • now using sbteclipse 2.1.0
  • now using sbt-assembly 0.8.3
  • now using scalatest 1.8
  • Series factories enhancements
  • showLegend boolean added to chartConfig.
  • Add support for QuoteCell (for stock series processing)
  • Various Code cleanup and enhancements.
  • Add date automatic support for google finance historical data (although the API will be shutdown ~ october 2012)
  • CSV2Series now supports direct parsing of Quote Series ( CSV2Series.quoteFromURL )
  • Automatic supports of CSV with all cells in quotes
  • new tests cases (quotes)
  • duration based takeRight and dropRight method added
  • chart classes refactoring
  • stacked chart type added : StackedChart class (BUT still work in progress as TableXYDataSet implies the same number of cells in each series !!!)
  • Series sample method now preserves cell type !! (was previously returning a StatCell!!)
  • statSample method added to Series to allow sampling using StatCell
  • toSeries method added to Series to allow cell type conversions
  • Series count2Rate method renamed to toRate

Apache total accesses counter to hit rate

The following example demonstrates how to convert a global http hit counter, into a hitrate series:

import fr.janalyse.series._
val csv = CSV2Series.fromURL("http://dnld.crosson.org/modstatus-totalaccesses.csv")
val totalaccesses=csv.values.head

val hitrate=
.rename("http hit rate")

CSV2Series.toFile(hitrate, "hitrate.csv")

JVM processing time to JVM cpu usage

The following example demonstrates how to convert a java process cpu time counter (mbean : java.lang:type=OperatingSystem__ProcessCpuTime), into a cpu usage metric (here I didn't take into account the number of CPU, so max value = cpu count * 100):

import fr.janalyse.series._
val csv = CSV2Series.fromURL("http://dnld.crosson.org/processcputime.csv")
val cputime=csv.values.head

val cpucells =
for(Seq(a,b) <- (cputime / 1000d / 1000d).sliding(2).toIterable )
yield a.time->(b.value-a.value)/(b.time-a.time)*100d

val cpuusage = Series[StatCell]("cpu usage percent", "5m") <<< cpucells

CSV2Series.toFile(cpuusage, "cpuusage.csv")