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model correction

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This commit is contained in:
Sabina 2023-07-09 13:53:13 +03:00
parent bbfa0483b6
commit 5fcff41db6
4 changed files with 123 additions and 45 deletions
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25
numass-data-proto/src/gen/kotlin/ru/inr/mass/data/proto/Point.kt
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@ -2,10 +2,29 @@
// Source: ru.inr.mass.data.proto.Point in numass-proto.proto
package ru.inr.mass.`data`.proto
import com.squareup.wire.*
import com.squareup.wire.FieldEncoding
import com.squareup.wire.Message
import com.squareup.wire.ProtoAdapter
import com.squareup.wire.ProtoReader
import com.squareup.wire.ProtoWriter
import com.squareup.wire.ReverseProtoWriter
import com.squareup.wire.Syntax
import com.squareup.wire.Syntax.PROTO_3
import com.squareup.wire.internal.immutableCopyOf
import com.squareup.wire.internal.redactElements
import com.squareup.wire.WireField
import com.squareup.wire.`internal`.immutableCopyOf
import com.squareup.wire.`internal`.redactElements
import kotlin.Any
import kotlin.AssertionError
import kotlin.Boolean
import kotlin.Deprecated
import kotlin.DeprecationLevel
import kotlin.Int
import kotlin.Long
import kotlin.Nothing
import kotlin.String
import kotlin.Unit
import kotlin.collections.List
import kotlin.jvm.JvmField
import okio.ByteString
public class Point(

5
numass-model/src/commonMain/kotlin/ru/inr/mass/models/NumassResolution.kt
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@ -15,9 +15,10 @@ import kotlin.math.sqrt
@Suppress("PARAMETER_NAME_CHANGED_ON_OVERRIDE")
public class NumassResolution(
public val resA: Double = 8.3e-5,
public val resA: Double = 1.7e-4,
public val resB: Double = 0.0,
public val tailFunction: (Double, Double) -> Double = { _, _ -> 1.0 },
// Recent formula for adiabacity
public val tailFunction: (Double, Double) -> Double = { e, u -> -1.75559e-13 * (e - u) * (e - u) * (e - u) - 5.97479e-11 * (e - u) * (e - u) + 3.26473e-7 * (e - u) + 1.0 },
) : DifferentiableKernel {
// private val tailFunction: Kernel = when {

12
numass-model/src/commonMain/kotlin/ru/inr/mass/models/NumassTransmission.kt
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@ -54,8 +54,10 @@ public class NumassTransmission(
}
sum
}
else -> null
}
else -> null
}
@ -63,6 +65,7 @@ public class NumassTransmission(
// loss part
val thickness = arguments[thickness] ?: 0.0
val loss = getTotalLossValue(thickness, ei, ef)
//val loss = 0.0
// double loss;
//
// if(eIn-eOut >= 300){
@ -76,7 +79,9 @@ public class NumassTransmission(
// }
//trapping part
val trap = (arguments[trap] ?: 1.0) * trapFunc(ei, ef, arguments)
//
//val trap = (arguments[trap] ?: 1.0) * trapFunc(ei, ef, arguments)
val trap = 0.0
return loss + trap
}
@ -86,7 +91,7 @@ public class NumassTransmission(
private val cache = HashMap<Int, Function1D<Double>>()
private const val ION_POTENTIAL = 15.4//eV
private const val ION_POTENTIAL = 13.6//eV
private fun getX(arguments: Map<Symbol, Double>, eIn: Double, adjustX: Boolean = false): Double {
@ -331,6 +336,7 @@ public class NumassTransmission(
val z = eps - pos1
A1 * exp(-2.0 * z * z / w1 / w1)
}
else -> {
val z = 4.0 * (eps - pos2) * (eps - pos2)
A2 / (1 + z / w2 / w2)
@ -399,7 +405,7 @@ public class NumassTransmission(
// return getSingleScatterFunction(exPos, ionPos, exW, ionW, exIonRatio)
// }
public val trapFunction: (Double, Double) -> Double = { ei: Double, ef: Double ->
public val trapFunction: Kernel = Kernel { ei: Double, ef: Double, _ ->
val eps = ei - ef
if (eps > 10) {
1.86e-04 * exp(-eps / 25.0) + 5.5e-05

108
numass-workspace/src/main/kotlin/ru/inr/mass/scripts/fit.kt
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@ -3,28 +3,51 @@ package ru.inr.mass.scripts
import ru.inr.mass.models.*
import ru.inr.mass.workspace.buffer
import ru.inr.mass.workspace.fitWith
import ru.inr.mass.workspace.generate
import space.kscience.kmath.data.XYColumnarData
import space.kscience.kmath.data.XYErrorColumnarData
import space.kscience.kmath.data.indices
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.asSequence
import space.kscience.kmath.optimization.*
import space.kscience.kmath.real.step
import space.kscience.kmath.structures.asBuffer
import space.kscience.plotly.*
import space.kscience.plotly.models.ScatterMode
import java.io.File
import kotlin.math.pow
fun parse(filename: String): XYErrorColumnarData<Double, Double, Double> {
val x: MutableList<Double> = mutableListOf()
val y: MutableList<Double> = mutableListOf()
val errors: MutableList<Double> = mutableListOf()
File(filename).forEachLine {
val array = it.split("\t").map { it.toDouble() }
x.add(array[0])
y.add(array[1])
//errors.add(array[2])
errors.add(1.0)
}
return XYErrorColumnarData.of(
x.asBuffer(),
y.asBuffer(),
errors.map { it }.asBuffer()
)
}
@OptIn(UnstableKMathAPI::class)
suspend fun main() {
val spectrum: NBkgSpectrum = SterileNeutrinoSpectrum(
fss = FSS.default,
// resolution = NumassResolution(8.2e-5)
// fss = FSS.default,
transmission = NumassTransmission(NumassTransmission.trapFunction),
resolution = NumassResolution(1.7e-4)
).withNBkg()
val args: Map<Symbol, Double> = mapOf(
NBkgSpectrum.norm to 8e5,
NBkgSpectrum.bkg to 2.0,
NBkgSpectrum.norm to 319034.0,
NBkgSpectrum.bkg to 0.029,
NumassBeta.mnu2 to 0.0,
NumassBeta.e0 to 18575.0,
NumassBeta.msterile2 to 1000.0.pow(2),
@ -33,46 +56,75 @@ suspend fun main() {
NumassTransmission.trap to 1.0
)
listOf(NBkgSpectrum.norm, NBkgSpectrum.bkg, NumassBeta.e0).forEach {
println("$it: ${spectrum.derivative(it).invoke(14000.0, args)}")
}
// listOf(NBkgSpectrum.norm, NBkgSpectrum.bkg, NumassBeta.e0).forEach {
// println("$it: ${spectrum.derivative(it).invoke(14000.0, args)}")
// }
val timePerPoint = 30.0
// val timePerPoint = 30.0
//
// val strategy = (12000.0..19000.0 step 100.0).asSequence().associateWith { timePerPoint }
val strategy = (12000.0..19000.0 step 100.0).asSequence().associateWith { timePerPoint }
val data = parse("/home/sabina/Numass/Fit/tr5wobkg")
val generatedData = spectrum.generate(strategy, args)
//val fit: XYFit = data.fitWith(
// optimizer = QowOptimizer,
// modelExpression = spectrum,
// startingPoint = args,
// OptimizationParameters(NBkgSpectrum.norm, NBkgSpectrum.bkg),
// OptimizationIterations(20)
//)
val fit: XYFit = generatedData.fitWith(
optimizer = QowOptimizer,
modelExpression = spectrum,
startingPoint = args + mapOf(NBkgSpectrum.norm to 8.1e5),
OptimizationParameters(NBkgSpectrum.norm, NBkgSpectrum.bkg, NumassBeta.e0),
OptimizationIterations(20)
)
//println("Chi squared/dof: ${fit.chiSquaredOrNull}/${fit.dof}")
println("Chi squared/dof: ${fit.chiSquaredOrNull}/${fit.dof}")
Plotly.plot {
scatter {
name = "Generated"
Plotly.page {
plot {
/* scatter {
name = "Data"
mode = ScatterMode.markers
x.buffer = generatedData.x
y.buffer = generatedData.y
}
x.buffer = data.x
y.buffer = data.y
}*/
scatter {
name = "Initial"
mode = ScatterMode.lines
x.buffer = 12000.0..18600.0 step 50.0
y.numbers = x.doubles.map { spectrum(it, args) }
File("/home/sabina/Numass/Fit/output").printWriter().use { out ->
y.numbers.forEach {
out.println(it)
}
scatter {
}
}
}
/* scatter {
name = "Fit"
mode = ScatterMode.lines
x.buffer = 12000.0..18600.0 step 10.0
y.numbers = x.doubles.map { spectrum(it, args + fit.resultPoint) }
}
}
plot{
scatter {
name = "Residuals"
mode = ScatterMode.markers
x.buffer = data.x
y.numbers = data.indices.map{
val value = spectrum(data.x[it], args + fit.resultPoint)
val dif = data.y[it] - value
dif/data.yErr[it]
}
}
}
plot{
histogram {
x.numbers = data.indices.map{
val value = spectrum(data.x[it], args + fit.resultPoint)
val dif = data.y[it] - value
dif/data.yErr[it]
}
name = "Res histo"
}
}*/
}.makeFile()
}