Separated Input and Output management. Input moved to context

numass-core/src/main/kotlin/inr/numass/data/analyzers/TimeAnalyzer.kt

@@ -22,14 +22,12 @@ import hep.dataforge.meta.Meta
 import hep.dataforge.tables.Adapters.*
 import hep.dataforge.tables.TableFormat
 import hep.dataforge.tables.TableFormatBuilder
-import hep.dataforge.values.Value
+import hep.dataforge.values.*
-import hep.dataforge.values.ValueMap
-import hep.dataforge.values.ValueType
-import hep.dataforge.values.Values
 import inr.numass.data.analyzers.NumassAnalyzer.Companion.COUNT_KEY
 import inr.numass.data.analyzers.NumassAnalyzer.Companion.COUNT_RATE_ERROR_KEY
 import inr.numass.data.analyzers.NumassAnalyzer.Companion.COUNT_RATE_KEY
 import inr.numass.data.analyzers.NumassAnalyzer.Companion.LENGTH_KEY
+import inr.numass.data.analyzers.TimeAnalyzer.AveragingMethod.*
 import inr.numass.data.api.*
 import inr.numass.data.api.NumassPoint.Companion.HV_KEY
 import java.util.*
@@ -65,7 +63,7 @@ class TimeAnalyzer(processor: SignalProcessor? = null) : AbstractAnalyzer(proces
 
         val res = getEventsWithDelay(block, config).asSequence().chunked(chunkSize) {
             analyzeSequence(it.asSequence(), t0)
-        }.toList().average()
+        }.toList().mean(config.getEnum("mean", WEIGHTED))
 
         return ValueMap.Builder(res)
                 .putValue(NumassAnalyzer.WINDOW_KEY, arrayOf(loChannel, upChannel))
@@ -104,7 +102,7 @@ class TimeAnalyzer(processor: SignalProcessor? = null) : AbstractAnalyzer(proces
         val res = point.blocks.stream()
                 .map { it -> analyze(it, config) }
                 .toList()
-                .average()
+                .mean(config.getEnum("mean", WEIGHTED))
 
         val map = HashMap(res.asMap())
         if (point is NumassPoint) {
@@ -113,16 +111,36 @@ class TimeAnalyzer(processor: SignalProcessor? = null) : AbstractAnalyzer(proces
         return ValueMap(map)
     }
 
+    enum class AveragingMethod {
+        ARITHMETIC,
+        WEIGHTED,
+        GEOMETRIC
+    }
+
     /**
      * Combine multiple blocks from the same point into one
      *
      * @return
      */
-    private fun List<Values>.average(): Values {
+    private fun List<Values>.mean(method: AveragingMethod): Values {
 
         val totalTime = sumByDouble { it.getDouble(LENGTH_KEY) }
-        val countRate = sumByDouble { it.getDouble(COUNT_RATE_KEY) * it.getDouble(LENGTH_KEY) } / totalTime
+
-        val countRateDispersion = sumByDouble { Math.pow(it.getDouble(COUNT_RATE_ERROR_KEY) * it.getDouble(LENGTH_KEY) / totalTime, 2.0) }
+        val (countRate, countRateDispersion) = when (method) {
+            ARITHMETIC -> Pair(
+                    sumByDouble { it.getDouble(COUNT_RATE_KEY) } / size,
+                    sumByDouble { Math.pow(it.getDouble(COUNT_RATE_ERROR_KEY), 2.0) } / size / size
+            )
+            WEIGHTED -> Pair(
+                    sumByDouble { it.getDouble(COUNT_RATE_KEY) * it.getDouble(LENGTH_KEY) } / totalTime,
+                    sumByDouble { Math.pow(it.getDouble(COUNT_RATE_ERROR_KEY) * it.getDouble(LENGTH_KEY) / totalTime, 2.0) }
+            )
+            GEOMETRIC -> {
+                val mean = Math.exp(sumByDouble { Math.log(it.getDouble(COUNT_RATE_KEY)) } / size)
+                val variance = Math.pow(mean / size, 2.0) * sumByDouble { Math.pow(it.getDouble(COUNT_RATE_ERROR_KEY) / it.getDouble(COUNT_RATE_KEY), 2.0) }
+                Pair(mean, variance)
+            }
+        }
 
         return ValueMap.Builder(first())
                 .putValue(LENGTH_KEY, totalTime)

numass-main/src/main/kotlin/inr/numass/scripts/timeanalysis/TestAnalyzer.kt

@@ -9,11 +9,13 @@ import hep.dataforge.kodex.join
 import hep.dataforge.maths.chain.MarkovChain
 import inr.numass.NumassPlugin
 import inr.numass.actions.TimeAnalyzerAction
+import inr.numass.data.analyzers.TimeAnalyzer
 import inr.numass.data.api.OrphanNumassEvent
 import inr.numass.data.api.SimpleNumassPoint
 import inr.numass.data.generateBlock
 import org.apache.commons.math3.random.JDKRandomGenerator
 import org.apache.commons.math3.random.RandomGenerator
+import java.lang.Math.exp
 import java.time.Instant
 
 fun main(args: Array<String>) {
@@ -42,8 +44,8 @@ fun main(args: Array<String>) {
     val point = (1..num).map {
         Global.generate {
             MarkovChain(OrphanNumassEvent(1000, 0)) { event ->
-                //val deltaT = rnd.nextDeltaTime(cr * exp(- event.timeOffset / 1e11))
+                val deltaT = rnd.nextDeltaTime(cr * exp(- event.timeOffset.toDouble() / 5e10))
-                val deltaT = rnd.nextDeltaTime(cr)
+                //val deltaT = rnd.nextDeltaTime(cr)
                 OrphanNumassEvent(1000, event.timeOffset + deltaT)
             }.generateBlock(start.plusNanos(it * length), length)
         }
@@ -57,6 +59,7 @@ fun main(args: Array<String>) {
         "binNum" to 200
         "t0Step" to 200
         "chunkSize" to 5000
+        "mean" to TimeAnalyzer.AveragingMethod.ARITHMETIC
     }
 
     TimeAnalyzerAction().simpleRun(point, meta);

numass-main/src/main/kotlin/inr/numass/tasks/NumassTasks.kt

@@ -8,6 +8,8 @@ import hep.dataforge.description.ValueDef
 import hep.dataforge.description.ValueDefs
 import hep.dataforge.fx.plots.FXPlotManager
 import hep.dataforge.fx.plots.plus
+import hep.dataforge.io.output.Output.Companion.BINARY_MODE
+import hep.dataforge.io.output.stream
 import hep.dataforge.kodex.buildMeta
 import hep.dataforge.kodex.configure
 import hep.dataforge.kodex.task
@@ -105,7 +107,7 @@ val monitorTableTask = task("monitor") {
                         //add set markers
                         addSetMarkers(frame, data.values)
                     }
-                    context.output.get(name, stage = "numass.monitor", type = "dfp").render(PlotFrame.Wrapper().wrap(frame))
+                    context.output.get(name, "numass.monitor", BINARY_MODE).render(PlotFrame.Wrapper().wrap(frame))
 
                 }
             }
@@ -221,7 +223,7 @@ val fitTask = task("fit") {
         configure(meta.getMeta("fit"))
     }
     pipe<Table, FitResult> { data ->
-        context.output.stream(name, "numass.fit").use { out ->
+        context.output[name, "numass.fit"].stream.use { out ->
             val writer = PrintWriter(out)
             writer.printf("%n*** META ***%n")
             writer.println(meta.toString())