Fix derivative for the spectrum and introduce fit example
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@ -1,4 +1,5 @@
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import space.kscience.gradle.*
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import space.kscience.gradle.useApache2Licence
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import space.kscience.gradle.useSPCTeam
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plugins {
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id("space.kscience.gradle.project")
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@ -17,7 +18,7 @@ allprojects {
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val dataforgeVersion by extra("0.6.0-dev-15")
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val tablesVersion: String by extra("0.2.0-dev-3")
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val kmathVersion by extra("0.3.1-dev-6")
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val kmathVersion by extra("0.3.1-dev-9")
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val visionForgeVersion: String by rootProject.extra("0.3.0-dev-6")
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@ -13,4 +13,4 @@ org.gradle.parallel=true
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org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
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toolsVersion=0.13.3-kotlin-1.7.20
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compose.version=1.2.1
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compose.version=1.2.2
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@ -24,6 +24,7 @@ kotlin {
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implementation(project(":numass-data-model"))
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implementation("space.kscience:visionforge-core:$visionForgeVersion")
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implementation("space.kscience:visionforge-plotly:$visionForgeVersion")
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implementation(compose.runtime)
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}
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}
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jvmMain {
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@ -2,11 +2,8 @@ package ru.inr.mass.data.server
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import space.kscience.dataforge.context.Context
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import space.kscience.dataforge.misc.DFExperimental
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import space.kscience.visionforge.html.HtmlVisionFragment
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import space.kscience.visionforge.html.ResourceLocation
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import space.kscience.visionforge.html.VisionPage
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import space.kscience.visionforge.html.importScriptHeader
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import space.kscience.visionforge.makeFile
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import space.kscience.visionforge.html.*
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import space.kscience.visionforge.visionManager
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import java.awt.Desktop
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import java.nio.file.Path
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@ -19,7 +16,7 @@ public fun Context.makeNumassWebFile(
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show: Boolean = true,
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content: HtmlVisionFragment,
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): Unit {
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val actualPath = VisionPage(this, content = content).makeFile(path) { actualPath: Path ->
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val actualPath = VisionPage(visionManager, content = content).makeFile(path) { actualPath: Path ->
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mapOf(
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"title" to VisionPage.title(title),
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"numassWeb" to VisionPage.importScriptHeader("js/numass-web.js", resourceLocation, actualPath)
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@ -32,8 +32,8 @@ public class NBkgSpectrum(public val source: Spectrum) : DifferentiableSpectrum
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override fun derivativeOrNull(symbols: List<Symbol>): Spectrum? = when {
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symbols.isEmpty() -> this
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symbols.size == 1 -> when (symbols.first()) {
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norm -> Spectrum { x, arguments -> source(x, arguments) + (arguments[bkg] ?: 0.0) }
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bkg -> Spectrum { x, arguments -> (arguments[norm] ?: 1.0) * source(x, arguments) }
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norm -> Spectrum { x, arguments -> source(x, arguments) }
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bkg -> Spectrum { x, _ -> 1.0 }
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else -> (source as? DifferentiableSpectrum)?.derivativeOrNull(symbols)?.let { NBkgSpectrum(it) }
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}
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else -> null
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@ -3,6 +3,8 @@
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* To change this template file, choose Tools | Templates
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* and open the template in the editor.
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*/
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@file:Suppress("PARAMETER_NAME_CHANGED_ON_OVERRIDE")
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package ru.inr.mass.models
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import space.kscience.kmath.expressions.Symbol
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@ -12,6 +12,8 @@ import kotlin.math.sqrt
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/**
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* @author [Alexander Nozik](mailto:altavir@gmail.com)
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*/
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@Suppress("PARAMETER_NAME_CHANGED_ON_OVERRIDE")
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public class NumassResolution(
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public val resA: Double = 8.3e-5,
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public val resB: Double = 0.0,
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@ -43,12 +45,12 @@ public class NumassResolution(
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// else -> ResolutionFunction.getConstantTail()
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// }
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private fun getValueFast(E: Double, U: Double): Double {
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val delta = resA * E
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private fun getValueFast(e: Double, u: Double): Double {
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val delta = resA * e
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return when {
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E - U < 0 -> 0.0
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E - U > delta -> tailFunction(E, U)
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else -> (E - U) / delta
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e - u < 0 -> 0.0
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e - u > delta -> tailFunction(e, u)
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else -> (e - u) / delta
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}
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}
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@ -57,15 +59,15 @@ public class NumassResolution(
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override val x: Symbol get() = e
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override val y: Symbol get() = u
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override fun invoke(E: Double, U: Double, arguments: Map<Symbol, Double>): Double {
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override fun invoke(e: Double, u: Double, arguments: Map<Symbol, Double>): Double {
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if (resB <= 0) {
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return this.getValueFast(E, U)
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return this.getValueFast(e, u)
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}
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val delta = resA * E
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val delta = resA * e
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return when {
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E - U < 0 -> 0.0
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E - U > delta -> tailFunction(E, U)
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else -> (1 - sqrt(1 - (E - U) / E * resB)) / (1 - sqrt(1 - resA * resB))
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e - u < 0 -> 0.0
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e - u > delta -> tailFunction(e, u)
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else -> (1 - sqrt(1 - (e - u) / e * resB)) / (1 - sqrt(1 - resA * resB))
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}
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}
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@ -274,13 +274,13 @@ public class NumassTransmission(
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* Значение полной производной функции потерь с учетом всех неисчезающих
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* порядков
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*
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* @param X
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* @param thickness
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* @param eIn
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* @param eOut
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* @return
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*/
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private fun getTotalLossDeriv(X: Double, eIn: Double, eOut: Double): Double {
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val probs = getLossProbDerivs(X)
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private fun getTotalLossDeriv(thickness: Double, eIn: Double, eOut: Double): Double {
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val probs = getLossProbDerivs(thickness)
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var sum = 0.0
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for (i in 1 until probs.size) {
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@ -399,8 +399,8 @@ public class NumassTransmission(
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// return getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio)
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// }
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public val trapFunction: (Double, Double) -> Double = { Ei: Double, Ef: Double ->
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val eps = Ei - Ef
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public val trapFunction: (Double, Double) -> Double = { ei: Double, ef: Double ->
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val eps = ei - ef
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if (eps > 10) {
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1.86e-04 * exp(-eps / 25.0) + 5.5e-05
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} else {
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@ -5,6 +5,7 @@ import ru.inr.mass.workspace.buffer
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import ru.inr.mass.workspace.fitWith
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import ru.inr.mass.workspace.generate
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import space.kscience.kmath.expressions.Symbol
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import space.kscience.kmath.expressions.derivative
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.operations.asSequence
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import space.kscience.kmath.optimization.*
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@ -28,10 +29,14 @@ suspend fun main() {
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NumassBeta.e0 to 18575.0,
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NumassBeta.msterile2 to 1000.0.pow(2),
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NumassBeta.u2 to 1e-2,
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NumassTransmission.thickness to 0.1,
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NumassTransmission.thickness to 0.3,
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NumassTransmission.trap to 1.0
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)
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listOf(NBkgSpectrum.norm, NBkgSpectrum.bkg, NumassBeta.e0).forEach {
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println("$it: ${spectrum.derivative(it).invoke(14000.0, args)}")
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}
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val timePerPoint = 30.0
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val strategy = (12000.0..19000.0 step 100.0).asSequence().associateWith { timePerPoint }
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@ -41,11 +46,12 @@ suspend fun main() {
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val fit: XYFit = generatedData.fitWith(
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optimizer = QowOptimizer,
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modelExpression = spectrum,
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startingPoint = args,
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OptimizationParameters(NBkgSpectrum.norm, NBkgSpectrum.bkg, NumassBeta.e0)
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startingPoint = args + mapOf(NBkgSpectrum.norm to 8.1e5),
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OptimizationParameters(NBkgSpectrum.norm, NBkgSpectrum.bkg, NumassBeta.e0),
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OptimizationIterations(20)
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)
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val fitResult = fit.resultPoint
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println("Chi squared/dof: ${fit.chiSquaredOrNull}/${fit.dof}")
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Plotly.plot {
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scatter {
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@ -66,7 +72,7 @@ suspend fun main() {
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name = "Fit"
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mode = ScatterMode.lines
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x.buffer = 12000.0..18600.0 step 10.0
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y.numbers = x.doubles.map { spectrum(it, fitResult) }
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y.numbers = x.doubles.map { spectrum(it, args + fit.resultPoint) }
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}
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}.makeFile()
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}
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@ -11,8 +11,8 @@ import space.kscience.kmath.misc.FeatureSet
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import space.kscience.kmath.misc.Loggable
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import space.kscience.kmath.misc.UnstableKMathAPI
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import space.kscience.kmath.optimization.*
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import space.kscience.kmath.random.RandomGenerator
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import space.kscience.kmath.samplers.PoissonSampler
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import space.kscience.kmath.stat.RandomGenerator
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import space.kscience.kmath.stat.next
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import space.kscience.kmath.structures.asBuffer
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import kotlin.math.sqrt
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