NPM/numass-framework
4
0
Fork 1
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Restored kodex. A lot of minor fixes.

Browse Source
This commit is contained in:
Alexander Nozik 2018-01-10 17:01:33 +03:00
parent d5ea5190fa
commit 01c910682e
14 changed files with 178 additions and 133 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
numass-main/src/main/groovy/inr/numass/scripts/TritiumTest.groovy
View File

@ -28,7 +28,6 @@ import inr.numass.models.BetaSpectrum
import inr.numass.models.ModularSpectrum
import inr.numass.models.NBkgSpectrum
import static hep.dataforge.maths.RandomUtils.setSeed
import static inr.numass.utils.DataModelUtils.getUniformSpectrumConfiguration
PrintWriter out = Global.out();

20
numass-main/src/main/groovy/inr/numass/scripts/models/TristanModel.groovy
View File

@ -36,26 +36,32 @@ new GrindShell(ctx).eval {
def response = new ModGauss(5.0)
ParametricFunction spectrum = NumassModelsKt.convolute(beta, response)
def model = new XYModel(Meta.empty(), new SpectrumAdapter(Meta.empty()), new NBkgSpectrum(spectrum));
def model = new XYModel(Meta.empty(), new SpectrumAdapter(Meta.empty()), new NBkgSpectrum(spectrum))
ParamSet params = morph(ParamSet, [:], "params") {
N(value: 1e+14, err: 30, lower: 0)
bkg(value: 5.0, err: 0.1)
E0(value: 18575.0, err: 0.1)
mnu2(value: 0, err: 0.01)
msterile2(value: 1000**2, err: 1)
msterile2(value: 7000**2, err: 1)
U2(value: 0.0, err: 1e-3)
//X(value: 0.0, err: 0.01, lower: 0)
//trap(value: 1.0, err: 0.05)
w(value: 150, err: 5)
//shift(value: 1, err: 1e-2)
tailAmp(value: 0.01, err: 1e-2)
//tailAmp(value: 0.01, err: 1e-2)
tailW(value: 300, err: 1)
}
ph.plotFunction(-2000d, 500d, 400, "actual", "response") { double x ->
response.value(x, params)
}
// double norm = NumassIntegrator.defaultIntegrator.integrate(1000d, 18500d) {
// model.value(it, params)
// }
// println("The total number of events is $norm")
//
// ph.plotFunction(-2000d, 500d, 400, "actual", "response") { double x ->
// response.value(x, params)
// }
SpectrumGenerator generator = new SpectrumGenerator(model, params, 12316);
@ -65,7 +71,7 @@ new GrindShell(ctx).eval {
.configure(showLine: true, showSymbol: false, showErrors: false, thickness: 2, connectionType: "spline", color: "red")
Table data = generator.generateData(DataModelUtils.getUniformSpectrumConfiguration(10000, 19500, 1, 950));
Table data = generator.generateData(DataModelUtils.getUniformSpectrumConfiguration(7000, 19500, 1, 1000));
//params.setParValue("w", 151)
//params.setParValue("tailAmp", 0.011)

4
numass-main/src/main/java/inr/numass/Numass.java
View File

@ -48,7 +48,7 @@ public class Numass {
.ln()
.text("\t")
.content(
MarkupUtils.markupDescriptor(DescriptorUtils.buildDescriptor("method::hep.dataforge.data.DataManager.read"))
MarkupUtils.INSTANCE.markupDescriptor(DescriptorUtils.buildDescriptor("method::hep.dataforge.data.DataManager.read"))
)
.ln()
.text("***Allowed actions***", "red")
@ -60,7 +60,7 @@ public class Numass {
am.getAllActions()
.map(name -> am.optAction(name).get())
.map(ActionDescriptor::build).forEach(descriptor ->
builder.text("\t").content(MarkupUtils.markupDescriptor(descriptor))
builder.text("\t").content(MarkupUtils.INSTANCE.markupDescriptor(descriptor))
);
builder.text("***End of actions list***", "red");

4
numass-main/src/main/java/inr/numass/data/SpectrumGenerator.java
View File

@ -16,6 +16,7 @@
package inr.numass.data;
import hep.dataforge.meta.Meta;
import hep.dataforge.stat.RandomKt;
import hep.dataforge.stat.fit.ParamSet;
import hep.dataforge.stat.models.Generator;
import hep.dataforge.stat.models.XYModel;
@ -27,7 +28,6 @@ import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomDataGenerator;
import org.apache.commons.math3.random.RandomGenerator;
import static hep.dataforge.maths.RandomUtils.getDefaultRandomGenerator;
import static java.lang.Double.isNaN;
import static java.lang.Math.sqrt;
@ -62,7 +62,7 @@ public class SpectrumGenerator implements Generator {
}
public SpectrumGenerator(XYModel source, ParamSet params) {
this(source, params, INSTANCE.getDefaultRandomGenerator());
this(source, params, RandomKt.getDefaultGenerator());
}
@Override

8
numass-main/src/main/java/inr/numass/models/LossCalculator.java
View File

@ -180,12 +180,12 @@ public class LossCalculator {
final LossCalculator loss = LossCalculator.instance;
final List<Double> probs = loss.getGunLossProbabilities(set.getDouble("X"));
UnivariateFunction single = (double e) -> probs.get(1) * scatterFunction.value(e);
frame.add(XYFunctionPlot.plotFunction("Single scattering", single::value, 0, 100, 1000));
frame.add(XYFunctionPlot.plotFunction("Single scattering", 0, 100, 1000, single::value));
for (int i = 2; i < probs.size(); i++) {
final int j = i;
UnivariateFunction scatter = (double e) -> probs.get(j) * loss.getLossValue(j, e, 0d);
frame.add(XYFunctionPlot.plotFunction(j + " scattering", scatter::value, 0, 100, 1000));
frame.add(XYFunctionPlot.plotFunction(j + " scattering", 0, 100, 1000, scatter::value));
}
UnivariateFunction total = (eps) -> {
@ -199,11 +199,11 @@ public class LossCalculator {
return sum;
};
frame.add(XYFunctionPlot.plotFunction("Total loss", total::value, 0, 100, 1000));
frame.add(XYFunctionPlot.plotFunction("Total loss", 0, 100, 1000, total::value));
} else {
frame.add(XYFunctionPlot.plotFunction("Differential crosssection", scatterFunction::value, 0, 100, 2000));
frame.add(XYFunctionPlot.plotFunction("Differential crosssection", 0, 100, 2000, scatterFunction::value));
}
}

6
numass-main/src/main/kotlin/inr/numass/data/Generator.kt
View File

@ -3,13 +3,13 @@ package inr.numass.data
import hep.dataforge.maths.chain.Chain
import hep.dataforge.maths.chain.MarkovChain
import hep.dataforge.maths.chain.StatefulChain
import hep.dataforge.stat.defaultGenerator
import inr.numass.data.api.NumassBlock
import inr.numass.data.api.NumassEvent
import inr.numass.data.api.SimpleBlock
import kotlinx.coroutines.experimental.channels.takeWhile
import kotlinx.coroutines.experimental.channels.toList
import kotlinx.coroutines.experimental.runBlocking
import org.apache.commons.math3.random.JDKRandomGenerator
import org.apache.commons.math3.random.RandomGenerator
import java.time.Duration
import java.time.Instant
@ -24,12 +24,10 @@ private fun RandomGenerator.nextDeltaTime(cr: Double): Long {
fun generateBlock(start: Instant, length: Long, chain: Chain<NumassEvent>): NumassBlock {
val events = runBlocking { chain.asChannel().takeWhile { it.timeOffset < length }.toList()}
val events = runBlocking { chain.channel.takeWhile { it.timeOffset < length }.toList()}
return SimpleBlock(start, Duration.ofNanos(length), events)
}
internal val defaultGenerator = JDKRandomGenerator()
internal val defaultAmplitudeGenerator: RandomGenerator.(NumassEvent?, Long) -> Short = { _, _ -> ((nextDouble() + 2.0) * 100).toShort() }
fun buildSimpleEventChain(

55
numass-main/src/main/kotlin/inr/numass/models/mc/BetaSampler.kt Normal file
View File

@ -0,0 +1,55 @@
package inr.numass.models.mc
import hep.dataforge.fx.plots.PlotManager
import hep.dataforge.kodex.buildMeta
import hep.dataforge.kodex.global
import hep.dataforge.maths.chain.Chain
import hep.dataforge.plots.data.XYFunctionPlot
import hep.dataforge.stat.PolynomialDistribution
import hep.dataforge.stat.fit.ParamSet
import inr.numass.NumassPlugin
import inr.numass.models.sterile.SterileNeutrinoSpectrum
fun sampleBeta(params: ParamSet): Chain<Double> {
TODO()
}
fun main(args: Array<String>) {
NumassPlugin().startGlobal()
val pm = PlotManager().apply { startGlobal() }
val meta = buildMeta("model") {
"fast" to true
node("resolution") {
"width" to 1.22e-4
"tailAlpha" to 1e-2
}
}
val allPars = ParamSet()
.setPar("N", 7e+05, 1.8e+03, 0.0, java.lang.Double.POSITIVE_INFINITY)
.setPar("bkg", 1.0, 0.050)
.setPar("E0", 18575.0, 1.4)
.setPar("mnu2", 0.0, 1.0)
.setPar("msterile2", 1000.0 * 1000.0, 0.0)
.setPar("U2", 0.0, 1e-4, -1.0, 1.0)
.setPar("X", 0.0, 0.01, 0.0, java.lang.Double.POSITIVE_INFINITY)
.setPar("trap", 1.0, 0.01, 0.0, java.lang.Double.POSITIVE_INFINITY)
val sp = SterileNeutrinoSpectrum(global, meta)
val spectrumPlot = XYFunctionPlot.plotFunction("spectrum", 14000.0, 18600.0, 500) {
sp.value(it, allPars)
}
val distribution = PolynomialDistribution(0.0, 5000.0, 3.0);
val distributionPlot = XYFunctionPlot.plotFunction("distribution", 14000.0, 18500.0, 500) {
50*distribution.density(18575.0 - it)
}
pm.getPlotFrame("beta").apply {
add(spectrumPlot)
add(distributionPlot)
}
}

2
numass-main/src/main/kotlin/inr/numass/models/misc/FunctionSupport.kt
View File

@ -1,6 +1,6 @@
package inr.numass.models.misc
import hep.dataforge.maths.HyperSquareDomain
import hep.dataforge.maths.domains.HyperSquareDomain
import hep.dataforge.values.Values
interface FunctionSupport {

104
numass-main/src/main/kotlin/inr/numass/models/sterile/TestModels.kt
View File

@ -1,104 +0,0 @@
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
package inr.numass.models.sterile
import hep.dataforge.context.Context
import hep.dataforge.maths.MathPlugin
import hep.dataforge.meta.Meta
import hep.dataforge.meta.MetaBuilder
import hep.dataforge.stat.fit.ParamSet
import hep.dataforge.stat.parametric.ParametricFunction
import inr.numass.Numass
import inr.numass.models.BetaSpectrum
import inr.numass.models.ModularSpectrum
import inr.numass.models.NBkgSpectrum
import inr.numass.models.ResolutionFunction
import org.apache.commons.math3.analysis.BivariateFunction
/**
*
* @author Alexander Nozik
*/
object TestModels {
/**
* @param args the command line arguments
*/
@JvmStatic
fun main(args: Array<String>) {
val context = Numass.buildContext()
/*
<model modelName="sterile" fssFile="FS.txt">
<resolution width = "1.22e-4" tailAlpha="1e-2"/>
<transmission trapping="numass.trap2016"/>
</model>
*/
val meta = MetaBuilder("model")
.putNode(MetaBuilder("resolution")
.putValue("width", 1.22e-4)
.putValue("tailAlpha", 1e-2)
)
.putNode(MetaBuilder("transmission")
.putValue("trapping", "numass.trap2016")
)
val oldFunc = oldModel(context, meta)
val newFunc = newModel(context, meta)
val allPars = ParamSet()
.setPar("N", 7e+05, 1.8e+03, 0.0, java.lang.Double.POSITIVE_INFINITY)
.setPar("bkg", 1.0, 0.050)
.setPar("E0", 18575.0, 1.4)
.setPar("mnu2", 0.0, 1.0)
.setPar("msterile2", 1000.0 * 1000.0, 0.0)
.setPar("U2", 0.0, 1e-4, -1.0, 1.0)
.setPar("X", 0.04, 0.01, 0.0, java.lang.Double.POSITIVE_INFINITY)
.setPar("trap", 1.0, 0.01, 0.0, java.lang.Double.POSITIVE_INFINITY)
var u = 14000.0
while (u < 18600) {
// double oldVal = oldFunc.value(u, allPars);
// double newVal = newFunc.value(u, allPars);
val oldVal = oldFunc.derivValue("trap", u, allPars)
val newVal = newFunc.derivValue("trap", u, allPars)
System.out.printf("%f\t%g\t%g\t%g%n", u, oldVal, newVal, 1.0 - oldVal / newVal)
u += 100.0
}
}
private fun oldModel(context: Context, meta: Meta): ParametricFunction {
val A = meta.getDouble("resolution", meta.getDouble("resolution.width", 8.3e-5)!!)!!//8.3e-5
val from = meta.getDouble("from", 13900.0)!!
val to = meta.getDouble("to", 18700.0)!!
context.chronicle.report("Setting up tritium model with real transmission function")
val resolutionTail: BivariateFunction
if (meta.hasValue("resolution.tailAlpha")) {
resolutionTail = ResolutionFunction.getAngledTail(meta.getDouble("resolution.tailAlpha")!!, meta.getDouble("resolution.tailBeta", 0.0)!!)
} else {
resolutionTail = ResolutionFunction.getRealTail()
}
//RangedNamedSetSpectrum beta = new BetaSpectrum(context.io().getFile("FS.txt"));
val beta = BetaSpectrum()
val sp = ModularSpectrum(beta, ResolutionFunction(A, resolutionTail), from, to)
if (meta.getBoolean("caching", false)!!) {
context.chronicle.report("Caching turned on")
sp.setCaching(true)
}
//Adding trapping energy dependence
if (meta.hasValue("transmission.trapping")) {
val trap = MathPlugin.buildFrom(context).buildBivariateFunction(meta.getString("transmission.trapping"))
sp.setTrappingFunction(trap)
}
return NBkgSpectrum(sp)
}
private fun newModel(context: Context, meta: Meta): ParametricFunction {
val sp = SterileNeutrinoSpectrum(context, meta)
return NBkgSpectrum(sp)
}
}

4
numass-main/src/main/kotlin/inr/numass/scripts/ScanTree.kt → numass-main/src/main/kotlin/inr/numass/scripts/utils/ScanTree.kt
View File

@ -1,4 +1,4 @@
package inr.numass.scripts
package inr.numass.scripts.utils
import hep.dataforge.io.XMLMetaWriter
import hep.dataforge.kodex.buildMeta
@ -74,7 +74,7 @@ fun main(args: Array<String>) {
val directory = if (args.isNotEmpty()) {
args.first()
} else {
"."
""
}
val path = Paths.get(directory)

4
numass-main/src/test/java/inr/numass/run/NumassSpectrumTest.java → numass-main/src/test/java/inr/numass/models/NumassSpectrumTest.java
View File

@ -13,13 +13,11 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package inr.numass.run;
package inr.numass.models;
import hep.dataforge.exceptions.NamingException;
import hep.dataforge.stat.fit.MINUITPlugin;
import hep.dataforge.stat.fit.ParamSet;
import inr.numass.models.BetaSpectrum;
import inr.numass.models.ModularSpectrum;
import java.io.File;
import java.io.FileNotFoundException;

93
numass-main/src/test/java/inr/numass/models/TestModels.kt Normal file
View File

@ -0,0 +1,93 @@
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
package inr.numass.models
import hep.dataforge.context.Context
import hep.dataforge.kodex.step
import hep.dataforge.maths.MathPlugin
import hep.dataforge.meta.Meta
import hep.dataforge.meta.MetaBuilder
import hep.dataforge.stat.fit.ParamSet
import hep.dataforge.stat.parametric.ParametricFunction
import inr.numass.Numass
import inr.numass.models.sterile.SterileNeutrinoSpectrum
import org.apache.commons.math3.analysis.BivariateFunction
/**
* @param args the command line arguments
*/
fun main(args: Array<String>) {
val context = Numass.buildContext()
/*
<model modelName="sterile" fssFile="FS.txt">
<resolution width = "1.22e-4" tailAlpha="1e-2"/>
<transmission trapping="numass.trap2016"/>
</model>
*/
val meta = MetaBuilder("model")
.putNode(MetaBuilder("resolution")
.putValue("width", 1.22e-4)
.putValue("tailAlpha", 1e-2)
)
.putNode(MetaBuilder("transmission")
// .putValue("trapping", "function::numass.trap.nominal")
)
val oldFunc = oldModel(context, meta)
val newFunc = newModel(context, meta)
val allPars = ParamSet()
.setPar("N", 7e+05, 1.8e+03, 0.0, java.lang.Double.POSITIVE_INFINITY)
.setPar("bkg", 1.0, 0.050)
.setPar("E0", 18575.0, 1.4)
.setPar("mnu2", 0.0, 1.0)
.setPar("msterile2", 1000.0 * 1000.0, 0.0)
.setPar("U2", 0.0, 1e-4, -1.0, 1.0)
.setPar("X", 0.0, 0.01, 0.0, java.lang.Double.POSITIVE_INFINITY)
.setPar("trap", 1.0, 0.01, 0.0, java.lang.Double.POSITIVE_INFINITY)
for (u in 14000.0..18600.0 step 100.0) {
val oldVal = oldFunc.value(u, allPars);
val newVal = newFunc.value(u, allPars);
// val oldVal = oldFunc.derivValue("trap", u, allPars)
// val newVal = newFunc.derivValue("trap", u, allPars)
System.out.printf("%f\t%g\t%g\t%g%n", u, oldVal, newVal, 1.0 - oldVal / newVal)
}
}
private fun oldModel(context: Context, meta: Meta): ParametricFunction {
val A = meta.getDouble("resolution", meta.getDouble("resolution.width", 8.3e-5))//8.3e-5
val from = meta.getDouble("from", 13900.0)
val to = meta.getDouble("to", 18700.0)
context.chronicle.report("Setting up tritium model with real transmission function")
val resolutionTail: BivariateFunction = if (meta.hasValue("resolution.tailAlpha")) {
ResolutionFunction.getAngledTail(meta.getDouble("resolution.tailAlpha"), meta.getDouble("resolution.tailBeta", 0.0))
} else {
ResolutionFunction.getRealTail()
}
//RangedNamedSetSpectrum beta = new BetaSpectrum(context.io().getFile("FS.txt"));
val sp = ModularSpectrum(BetaSpectrum(), ResolutionFunction(A, resolutionTail), from, to)
if (meta.getBoolean("caching", false)) {
context.chronicle.report("Caching turned on")
sp.setCaching(true)
}
//Adding trapping energy dependence
if (meta.hasValue("transmission.trapping")) {
val trap = MathPlugin.buildFrom(context).buildBivariateFunction(meta.getString("transmission.trapping"))
sp.setTrappingFunction(trap)
}
return NBkgSpectrum(sp)
}
private fun newModel(context: Context, meta: Meta): ParametricFunction {
val sp = SterileNeutrinoSpectrum(context, meta)
return NBkgSpectrum(sp)
}

4
numass-main/src/test/java/inr/numass/models/TestNeLossParametrisation.java
View File

@ -41,8 +41,8 @@ public class TestNeLossParametrisation {
System.out.println(norm);
frame.add(XYFunctionPlot.plotFunction("old", oldFunction::value, 0, 30, 300));
frame.add(XYFunctionPlot.plotFunction("new", newFunction::value, 0, 30, 300));
frame.add(XYFunctionPlot.plotFunction("old", 0, 30, 300, oldFunction::value));
frame.add(XYFunctionPlot.plotFunction("new", 0, 30, 300, newFunction::value));
}
public static UnivariateFunction getSingleScatterFunction(

2
numass-main/src/test/java/inr/numass/models/TransmissionInterpolatorTest.java
View File

@ -33,7 +33,7 @@ public class TransmissionInterpolatorTest {
TransmissionInterpolator interpolator = TransmissionInterpolator.fromFile(Global.instance(),
"d:\\sterile-new\\loss2014-11\\.dataforge\\merge\\empty_sum.onComplete", "Uset", "CR", 15, 0.8, 19002d);
frame.add(DataPlot.plot("data", interpolator.getX(), interpolator.getY()));
frame.add(XYFunctionPlot.plotFunction("interpolated", interpolator::value, interpolator.getXmin(), interpolator.getXmax(), 2000));
frame.add(XYFunctionPlot.plotFunction("interpolated", interpolator.getXmin(), interpolator.getXmax(), 2000, interpolator::value));
// PrintFunction.printFuntionSimple(new PrintWriter(System.onComplete), interpolator, interpolator.getXmin(), interpolator.getXmax(), 500);
}