A lot of minor fixes. Moving some code to kotlin
This commit is contained in:
commit
c6b928face
@ -6,6 +6,7 @@ import inr.numass.data.api.NumassBlock;
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import inr.numass.data.api.NumassEvent;
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import inr.numass.data.api.NumassFrame;
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import inr.numass.data.api.NumassPoint;
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import inr.numass.data.legacy.NumassFileEnvelope;
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import org.jetbrains.annotations.NotNull;
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import org.slf4j.LoggerFactory;
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@ -13,6 +14,7 @@ import java.io.IOException;
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import java.nio.ByteBuffer;
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import java.nio.ByteOrder;
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import java.nio.channels.ReadableByteChannel;
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import java.nio.file.Path;
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import java.time.Duration;
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import java.time.Instant;
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import java.util.Iterator;
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@ -23,6 +25,10 @@ import java.util.stream.StreamSupport;
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* Created by darksnake on 08.07.2017.
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*/
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public class ClassicNumassPoint implements NumassPoint {
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public static ClassicNumassPoint readFile(Path path) {
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return new ClassicNumassPoint(NumassFileEnvelope.open(path, true));
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}
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private final Envelope envelope;
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public ClassicNumassPoint(Envelope envelope) {
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@ -81,7 +81,7 @@ public class ProtoNumassPoint implements NumassPoint {
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@Override
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public Duration getLength() {
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return Duration.ofNanos((long) (getMeta().getInt("b_size") / getMeta().getInt("sample_freq") * 1e9));
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return Duration.ofNanos((long) (getMeta().getDouble("params.b_size") / getMeta().getDouble("params.sample_freq") * 1e9));
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}
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@Override
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@ -172,9 +172,9 @@ class TimeAnalyzer @JvmOverloads constructor(private val processor: SignalProces
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val inverted = config.getBoolean("inverted",false)
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return super.getEvents(block, config).asSequence().zipWithNext { prev, next ->
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val delay = Math.max(next.timeOffset - prev.timeOffset, 0)
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if(inverted) {
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if(inverted){
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Pair(next, delay)
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} else{
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} else {
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Pair(prev, delay)
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}
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}.asStream()
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@ -8,6 +8,7 @@ import org.apache.commons.math3.random.RandomGenerator
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import java.time.Duration
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import java.time.Instant
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import java.util.*
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import kotlin.collections.ArrayList
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interface ChainGenerator {
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@ -30,27 +31,84 @@ interface ChainGenerator {
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}
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}
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private fun RandomGenerator.nextExp(mean: Double): Double {
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return -mean * Math.log(1 - nextDouble())
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}
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private fun RandomGenerator.nextDeltaTime(cr: Double): Long {
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return (nextExp(1.0 / cr) * 1e9).toLong()
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}
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class SimpleChainGenerator(
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val cr: Double,
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private val rnd: RandomGenerator = JDKRandomGenerator(),
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private val amp: (Long) -> Short = { 1 }
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private val amp: RandomGenerator.(NumassEvent?, Long) -> Short = { _, _ -> ((nextDouble() + 2.0) * 100).toShort() }
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) : ChainGenerator {
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override fun next(event: NumassEvent?): NumassEvent {
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return if (event == null) {
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NumassEvent(amp(0), Instant.EPOCH, 0)
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NumassEvent(rnd.amp(null, 0), Instant.EPOCH, 0)
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} else {
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val deltaT = generateDeltaTime()
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NumassEvent(amp(deltaT), event.blockTime, event.timeOffset + deltaT)
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val deltaT = rnd.nextDeltaTime(cr)
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NumassEvent(rnd.amp(event, deltaT), event.blockTime, event.timeOffset + deltaT)
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}
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}
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private fun nextExpDecay(mean: Double): Double {
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return -mean * Math.log(1 - rnd.nextDouble())
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}
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private fun generateDeltaTime(): Long {
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return (nextExpDecay(1.0 / cr) * 1e9).toLong()
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}
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}
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class BunchGenerator(
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private val cr: Double,
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private val bunchRate: Double,
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private val bunchLength: RandomGenerator.() -> Long,
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private val rnd: RandomGenerator = JDKRandomGenerator(),
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private val amp: RandomGenerator.(NumassEvent?, Long) -> Short = { _, _ -> ((nextDouble() + 2.0) * 100).toShort() }
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) : ChainGenerator {
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private val internalGenerator = SimpleChainGenerator(cr, rnd, amp)
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var bunchStart: Long = 0
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var bunchEnd: Long = 0
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override fun next(event: NumassEvent?): NumassEvent {
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if (event?.timeOffset ?: 0 >= bunchEnd) {
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bunchStart = bunchEnd + rnd.nextExp(bunchRate).toLong()
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bunchEnd = bunchStart + rnd.bunchLength()
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return NumassEvent(rnd.amp(null, 0), Instant.EPOCH, bunchStart)
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} else {
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return internalGenerator.next(event)
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}
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}
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override fun generateBlock(start: Instant, length: Long, filter: (NumassEvent, NumassEvent) -> Boolean): NumassBlock {
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bunchStart = 0
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bunchEnd = 0
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return super.generateBlock(start, length, filter)
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}
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}
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class MergingGenerator(private vararg val generators: ChainGenerator) : ChainGenerator {
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private val waiting: TreeSet<Pair<ChainGenerator, NumassEvent>> = TreeSet(Comparator.comparing<Pair<ChainGenerator, NumassEvent>, Long> { it.second.timeOffset })
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init {
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generators.forEach { generator ->
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waiting.add(Pair(generator, generator.next(null)))
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}
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}
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override fun next(event: NumassEvent?): NumassEvent {
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val pair = waiting.first()
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waiting.remove(pair)
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waiting.add(Pair(pair.first, pair.first.next(pair.second)))
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return pair.second
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}
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override fun generateBlock(start: Instant, length: Long, filter: (NumassEvent, NumassEvent) -> Boolean): NumassBlock {
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generators.forEach { generator ->
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waiting.add(Pair(generator, generator.next(null)))
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}
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return super.generateBlock(start, length, filter)
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}
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}
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46
numass-main/src/main/kotlin/inr/numass/scripts/Bunches.kt
Normal file
46
numass-main/src/main/kotlin/inr/numass/scripts/Bunches.kt
Normal file
@ -0,0 +1,46 @@
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package inr.numass.scripts
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import hep.dataforge.fx.plots.PlotManager
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import hep.dataforge.kodex.buildContext
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import hep.dataforge.kodex.buildMeta
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import inr.numass.NumassPlugin
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import inr.numass.data.BunchGenerator
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import inr.numass.data.MergingGenerator
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import inr.numass.data.SimpleChainGenerator
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import inr.numass.data.analyzers.NumassAnalyzer
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import inr.numass.data.analyzers.SmartAnalyzer
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import inr.numass.data.api.SimpleNumassPoint
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import java.time.Instant
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fun main(args: Array<String>) {
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val context = buildContext("NUMASS", NumassPlugin::class.java, PlotManager::class.java)
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val cr = 10.0
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val length = 30e9.toLong()
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val num = 50;
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val dt = 6.5
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val regularGenerator = SimpleChainGenerator(cr)
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val bunchGenerator = BunchGenerator(40.0, 0.1, { 2e9.toLong() })
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val generator = MergingGenerator(regularGenerator, bunchGenerator)
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val blocks = (1..num).map {
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generator.generateBlock(Instant.now().plusNanos(it * length), length)
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}
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val point = SimpleNumassPoint(10000.0, blocks)
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val meta = buildMeta {
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"t0.crFraction" to 0.1
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}
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println("actual count rate: ${point.events.count() / point.length.seconds}")
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val res = SmartAnalyzer().analyze(point,meta)
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.getDouble(NumassAnalyzer.COUNT_RATE_KEY)
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println("estimated count rate: ${res}")
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}
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@ -16,77 +16,72 @@
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package inr.numass.scripts
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import hep.dataforge.description.DescriptorUtils
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import hep.dataforge.fx.plots.PlotManager
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import hep.dataforge.kodex.buildContext
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import hep.dataforge.kodex.buildMeta
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import hep.dataforge.kodex.replaceColumn
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import hep.dataforge.plots.data.DataPlot
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import hep.dataforge.tables.Table
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import inr.numass.NumassPlugin
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import inr.numass.data.NumassDataUtils
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import inr.numass.data.analyzers.NumassAnalyzer
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import inr.numass.data.analyzers.TimeAnalyzer
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import inr.numass.data.analyzers.getAmplitudeSpectrum
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import inr.numass.data.api.NumassPoint
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import inr.numass.data.analyzers.SmartAnalyzer
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import inr.numass.data.api.NumassSet
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import inr.numass.data.storage.NumassStorageFactory
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fun main(args: Array<String>) {
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val context = buildContext("NUMASS", NumassPlugin::class.java, PlotManager::class.java) {
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rootDir = "D:\\Work\\Numass\\sterile\\2017_05"
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dataDir = "D:\\Work\\Numass\\data\\2017_05"
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rootDir = "D:\\Work\\Numass\\sterile\\2017_11"
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dataDir = "D:\\Work\\Numass\\data\\2017_11"
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}
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//val rootDir = File("D:\\Work\\Numass\\data\\2017_05\\Fill_2")
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val storage = NumassStorageFactory.buildLocal(context, "Fill_2", true, false);
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val sets = (2..14).map { "set_$it" }
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val sets = (1..24).map { "set_$it" }
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val loaders = sets.mapNotNull { set ->
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storage.provide("loader::$set", NumassSet::class.java).orElse(null)
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}
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val analyzer = SmartAnalyzer()
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val all = NumassDataUtils.join("sum", loaders)
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val t0 = 20e3
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val meta = buildMeta {
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"t0" to 30e3
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"inverted" to true
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"window.lo" to 400
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"window.up" to 1800
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"t0" to t0
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}
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val filter: (NumassPoint) -> Table = { point ->
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val analyzer = TimeAnalyzer()
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val sequence = analyzer.zipEvents(point, meta).filter { it.second.timeOffset - it.first.timeOffset > t0 }.map { it.second }
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//val sequence = analyzer.getEvents(point,meta).asSequence()
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getAmplitudeSpectrum(sequence, point.length.toMillis().toDouble() / 1000.0, meta)
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"window.up" to 1600
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}
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val plots = context.getFeature(PlotManager::class.java)
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val frame = plots.getPlotFrame("differential")
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val frame = plots.getPlotFrame("differential").apply {
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this.plots.descriptor = DescriptorUtils.buildDescriptor(DataPlot::class)
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this.plots.configureValue("showLine", true)
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}
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val integralFrame = plots.getPlotFrame("integral")
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for (hv in arrayOf(14000.0, 14500.0, 15000.0, 15500.0)) {
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val basePoint = all.optPoint(hv).get()
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for (hv in arrayOf(14000.0, 14500.0, 15000.0, 15500.0, 16050.0)) {
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val point1 = all.optPoint(hv).get()
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val subPoint = all.optPoint(hv + 200.0).get()
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val point0 = all.optPoint(hv + 200.0).get()
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with(NumassAnalyzer) {
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val baseSpectrum = filter(basePoint).withBinning(50)
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val spectrum1 = analyzer.getAmplitudeSpectrum(point1, meta).withBinning(50)
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val subSpectrum = filter(subPoint).withBinning(50)
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val spectrum0 = analyzer.getAmplitudeSpectrum(point0, meta).withBinning(50)
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val res = subtractAmplitudeSpectrum(baseSpectrum, subSpectrum)
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val res = subtractAmplitudeSpectrum(spectrum1, spectrum0)
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val norm = res.getColumn(COUNT_RATE_KEY).stream().mapToDouble { it.doubleValue() }.sum()
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integralFrame.add(DataPlot.plot("point_$hv", AMPLITUDE_ADAPTER, baseSpectrum))
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integralFrame.add(DataPlot.plot("point_$hv", AMPLITUDE_ADAPTER, spectrum0))
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frame.add(DataPlot.plot("point_$hv", AMPLITUDE_ADAPTER, res.replaceColumn(COUNT_RATE_KEY) { getDouble(COUNT_RATE_KEY) / norm }))
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}
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@ -16,108 +16,66 @@
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package inr.numass.scripts
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import hep.dataforge.description.DescriptorUtils
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import hep.dataforge.fx.plots.PlotManager
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import hep.dataforge.kodex.buildContext
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import hep.dataforge.kodex.buildMeta
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import hep.dataforge.kodex.replaceColumn
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import hep.dataforge.meta.Meta
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import hep.dataforge.plots.PlotPlugin
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import hep.dataforge.plots.data.DataPlot
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import inr.numass.NumassPlugin
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import inr.numass.data.NumassDataUtils
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import inr.numass.data.analyzers.NumassAnalyzer
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import inr.numass.data.analyzers.TimeAnalyzer
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import inr.numass.data.analyzers.getAmplitudeSpectrum
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import inr.numass.data.analyzers.NumassAnalyzer.Companion.AMPLITUDE_ADAPTER
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import inr.numass.data.analyzers.NumassAnalyzer.Companion.withBinning
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import inr.numass.data.analyzers.SmartAnalyzer
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import inr.numass.data.api.NumassSet
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import inr.numass.data.storage.NumassStorageFactory
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import kotlin.streams.asSequence
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fun main(args: Array<String>) {
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val context = buildContext("NUMASS", NumassPlugin::class.java, PlotManager::class.java){
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rootDir = "D:\\Work\\Numass\\sterile2017_05"
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val context = buildContext("NUMASS", NumassPlugin::class.java, PlotManager::class.java) {
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rootDir = "D:\\Work\\Numass\\sterile\\2017_11"
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dataDir = "D:\\Work\\Numass\\data\\2017_11"
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}
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//val rootDir = File("D:\\Work\\Numass\\data\\2017_05\\Fill_2")
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val storage = NumassStorageFactory.buildLocal(context, "D:\\Work\\Numass\\data\\2017_05\\Fill_2", true, false);
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val storage = NumassStorageFactory.buildLocal(context, "Fill_2", true, false);
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val sets = (2..14).map { "set_$it" }
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val sets = (10..24).map { "set_$it" }
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val loaders = sets.mapNotNull { set ->
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storage.provide("loader::$set", NumassSet::class.java).orElse(null)
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}
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val all = NumassDataUtils.join("sum", loaders)
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val point = all.optPoint(14000.0).get()
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val t0 = 20e3.toLong()
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val analyzer = TimeAnalyzer()
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val seconds = point.length.toMillis().toDouble() / 1000.0
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val binning = 20
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val set = NumassDataUtils.join("sum", loaders)
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val plots = context.getFeature(PlotPlugin::class.java);
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val analyzer = SmartAnalyzer()
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val meta = buildMeta {
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node("window"){
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"lo" to 300
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"up" to 2600
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}
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// "t0" to 30e3
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// "inverted" to true
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"window.lo" to 400
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"window.up" to 1600
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}
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with(NumassAnalyzer) {
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val events = getAmplitudeSpectrum(analyzer.getEvents(point).asSequence(), seconds, meta)
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.withBinning(binning)
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val metaForChain = meta.builder.setValue("t0", 15e3)
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val eventsNorming = events.getColumn(COUNT_RATE_KEY).stream().mapToDouble{it.doubleValue()}.sum()
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println("The norming factor for unfiltered count rate is $eventsNorming")
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val filtered = getAmplitudeSpectrum(
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analyzer.zipEvents(point, Meta.empty()).filter { it.second.timeOffset - it.first.timeOffset > t0 }.map { it.second },
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seconds,
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meta
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).withBinning(binning)
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val filteredNorming = filtered.getColumn(COUNT_RATE_KEY).stream().mapToDouble{it.doubleValue()}.sum()
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println("The norming factor for filtered count rate is $filteredNorming")
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val defaultFiltered = getAmplitudeSpectrum(
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analyzer.getEvents(point, buildMeta {"t0" to t0}).asSequence(),
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seconds,
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meta
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).withBinning(binning)
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val defaultFilteredNorming = defaultFiltered.getColumn(COUNT_RATE_KEY).stream().mapToDouble{it.doubleValue()}.sum()
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println("The norming factor for default filtered count rate is $defaultFilteredNorming")
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val metaForChainInverted = metaForChain.builder.setValue("inverted", true)
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plots.getPlotFrame("amps").apply {
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add(DataPlot.plot("events", AMPLITUDE_ADAPTER, events.replaceColumn(COUNT_RATE_KEY){getDouble(COUNT_RATE_KEY)/eventsNorming}))
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add(DataPlot.plot("filtered", AMPLITUDE_ADAPTER, filtered.replaceColumn(COUNT_RATE_KEY){getDouble(COUNT_RATE_KEY)/filteredNorming}))
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add(DataPlot.plot("defaultFiltered", AMPLITUDE_ADAPTER, defaultFiltered.replaceColumn(COUNT_RATE_KEY){getDouble(COUNT_RATE_KEY)/defaultFilteredNorming}))
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val plots = context.getFeature(PlotManager::class.java)
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for (hv in arrayOf(14000.0, 14500.0, 15000.0, 15500.0, 16050.0)) {
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val frame = plots.getPlotFrame("integral[$hv]").apply {
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this.plots.descriptor = DescriptorUtils.buildDescriptor(DataPlot::class)
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this.plots.configureValue("showLine", true)
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}
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// plots.getPlotFrame("ratio").apply {
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//
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// add(
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// DataPlot.plot(
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// "ratio",
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// Adapters.DEFAULT_XY_ADAPTER,
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// events.zip(filtered) { f, s ->
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// Adapters.buildXYDataPoint(f.getDouble(CHANNEL_KEY), f.getDouble(COUNT_RATE_KEY) / s.getDouble(COUNT_RATE_KEY))
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// }
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// )
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// )
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// }
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val point = set.optPoint(hv).get()
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frame.add(DataPlot.plot("raw", AMPLITUDE_ADAPTER, analyzer.getAmplitudeSpectrum(point, meta).withBinning(20)))
|
||||
frame.add(DataPlot.plot("filtered", AMPLITUDE_ADAPTER, analyzer.getAmplitudeSpectrum(point, metaForChain).withBinning(20)))
|
||||
frame.add(DataPlot.plot("invertedFilter", AMPLITUDE_ADAPTER, analyzer.getAmplitudeSpectrum(point, metaForChainInverted).withBinning(20)))
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
@ -0,0 +1,61 @@
|
||||
/*
|
||||
* Copyright 2017 Alexander Nozik.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package inr.numass.scripts
|
||||
|
||||
import hep.dataforge.description.DescriptorUtils
|
||||
import hep.dataforge.fx.plots.PlotManager
|
||||
import hep.dataforge.kodex.buildContext
|
||||
import hep.dataforge.kodex.buildMeta
|
||||
import hep.dataforge.plots.data.DataPlot
|
||||
import inr.numass.NumassPlugin
|
||||
import inr.numass.data.analyzers.NumassAnalyzer.Companion.AMPLITUDE_ADAPTER
|
||||
import inr.numass.data.analyzers.NumassAnalyzer.Companion.withBinning
|
||||
import inr.numass.data.analyzers.SmartAnalyzer
|
||||
import inr.numass.data.storage.ProtoNumassPoint
|
||||
import java.nio.file.Paths
|
||||
|
||||
|
||||
fun main(args: Array<String>) {
|
||||
|
||||
val context = buildContext("NUMASS", NumassPlugin::class.java, PlotManager::class.java)
|
||||
|
||||
val analyzer = SmartAnalyzer()
|
||||
|
||||
val meta = buildMeta {
|
||||
"window.lo" to 800
|
||||
"window.up" to 5600
|
||||
}
|
||||
|
||||
val metaForChain = meta.builder.setValue("t0", 15e3)
|
||||
|
||||
val metaForChainInverted = metaForChain.builder.setValue("inverted", true)
|
||||
|
||||
|
||||
val plots = context.getFeature(PlotManager::class.java)
|
||||
|
||||
val point = ProtoNumassPoint.readFile(Paths.get("D:\\Work\\Numass\\data\\2017_05_frames\\Fill_3_events\\set_33\\p36(30s)(HV1=17000).df"))
|
||||
|
||||
val frame = plots.getPlotFrame("integral").apply {
|
||||
this.plots.descriptor = DescriptorUtils.buildDescriptor(DataPlot::class)
|
||||
this.plots.configureValue("showLine", true)
|
||||
}
|
||||
|
||||
frame.add(DataPlot.plot("raw", AMPLITUDE_ADAPTER, analyzer.getAmplitudeSpectrum(point, meta).withBinning(80)))
|
||||
frame.add(DataPlot.plot("filtered", AMPLITUDE_ADAPTER, analyzer.getAmplitudeSpectrum(point, metaForChain).withBinning(80)))
|
||||
frame.add(DataPlot.plot("invertedFilter", AMPLITUDE_ADAPTER, analyzer.getAmplitudeSpectrum(point, metaForChainInverted).withBinning(80)))
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user