diff --git a/numass-main/src/main/groovy/inr/numass/scripts/TritiumTest.groovy b/numass-main/src/main/groovy/inr/numass/scripts/TritiumTest.groovy
index b0f79c7e..80927d7e 100644
--- a/numass-main/src/main/groovy/inr/numass/scripts/TritiumTest.groovy
+++ b/numass-main/src/main/groovy/inr/numass/scripts/TritiumTest.groovy
@@ -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();
diff --git a/numass-main/src/main/groovy/inr/numass/scripts/models/TristanModel.groovy b/numass-main/src/main/groovy/inr/numass/scripts/models/TristanModel.groovy
index af5fd8eb..88af28dd 100644
--- a/numass-main/src/main/groovy/inr/numass/scripts/models/TristanModel.groovy
+++ b/numass-main/src/main/groovy/inr/numass/scripts/models/TristanModel.groovy
@@ -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)
diff --git a/numass-main/src/main/java/inr/numass/Numass.java b/numass-main/src/main/java/inr/numass/Numass.java
index 7a06de38..9451f045 100644
--- a/numass-main/src/main/java/inr/numass/Numass.java
+++ b/numass-main/src/main/java/inr/numass/Numass.java
@@ -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");
diff --git a/numass-main/src/main/java/inr/numass/data/SpectrumGenerator.java b/numass-main/src/main/java/inr/numass/data/SpectrumGenerator.java
index 8554b912..2ddfcd26 100644
--- a/numass-main/src/main/java/inr/numass/data/SpectrumGenerator.java
+++ b/numass-main/src/main/java/inr/numass/data/SpectrumGenerator.java
@@ -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
diff --git a/numass-main/src/main/java/inr/numass/models/LossCalculator.java b/numass-main/src/main/java/inr/numass/models/LossCalculator.java
index fa62d412..a9e0ba0a 100644
--- a/numass-main/src/main/java/inr/numass/models/LossCalculator.java
+++ b/numass-main/src/main/java/inr/numass/models/LossCalculator.java
@@ -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));
         }
 
     }
diff --git a/numass-main/src/main/kotlin/inr/numass/data/Generator.kt b/numass-main/src/main/kotlin/inr/numass/data/Generator.kt
index 16b9e3cb..fd69a995 100644
--- a/numass-main/src/main/kotlin/inr/numass/data/Generator.kt
+++ b/numass-main/src/main/kotlin/inr/numass/data/Generator.kt
@@ -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(
diff --git a/numass-main/src/main/kotlin/inr/numass/models/mc/BetaSampler.kt b/numass-main/src/main/kotlin/inr/numass/models/mc/BetaSampler.kt
new file mode 100644
index 00000000..8d1ca7de
--- /dev/null
+++ b/numass-main/src/main/kotlin/inr/numass/models/mc/BetaSampler.kt
@@ -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)
+    }
+
+}
\ No newline at end of file
diff --git a/numass-main/src/main/kotlin/inr/numass/models/misc/FunctionSupport.kt b/numass-main/src/main/kotlin/inr/numass/models/misc/FunctionSupport.kt
index eec46c5b..d0c3a6c2 100644
--- a/numass-main/src/main/kotlin/inr/numass/models/misc/FunctionSupport.kt
+++ b/numass-main/src/main/kotlin/inr/numass/models/misc/FunctionSupport.kt
@@ -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 {
diff --git a/numass-main/src/main/kotlin/inr/numass/models/sterile/TestModels.kt b/numass-main/src/main/kotlin/inr/numass/models/sterile/TestModels.kt
deleted file mode 100644
index c522ae66..00000000
--- a/numass-main/src/main/kotlin/inr/numass/models/sterile/TestModels.kt
+++ /dev/null
@@ -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)
-    }
-
-}
diff --git a/numass-main/src/main/kotlin/inr/numass/scripts/ScanTree.kt b/numass-main/src/main/kotlin/inr/numass/scripts/utils/ScanTree.kt
similarity index 95%
rename from numass-main/src/main/kotlin/inr/numass/scripts/ScanTree.kt
rename to numass-main/src/main/kotlin/inr/numass/scripts/utils/ScanTree.kt
index f043c353..9cd0e2af 100644
--- a/numass-main/src/main/kotlin/inr/numass/scripts/ScanTree.kt
+++ b/numass-main/src/main/kotlin/inr/numass/scripts/utils/ScanTree.kt
@@ -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)
diff --git a/numass-main/src/test/java/inr/numass/run/NumassSpectrumTest.java b/numass-main/src/test/java/inr/numass/models/NumassSpectrumTest.java
similarity index 93%
rename from numass-main/src/test/java/inr/numass/run/NumassSpectrumTest.java
rename to numass-main/src/test/java/inr/numass/models/NumassSpectrumTest.java
index c01e7210..5a000b89 100644
--- a/numass-main/src/test/java/inr/numass/run/NumassSpectrumTest.java
+++ b/numass-main/src/test/java/inr/numass/models/NumassSpectrumTest.java
@@ -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;
diff --git a/numass-main/src/test/java/inr/numass/models/TestModels.kt b/numass-main/src/test/java/inr/numass/models/TestModels.kt
new file mode 100644
index 00000000..8378a20d
--- /dev/null
+++ b/numass-main/src/test/java/inr/numass/models/TestModels.kt
@@ -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)
+}
+
diff --git a/numass-main/src/test/java/inr/numass/models/TestNeLossParametrisation.java b/numass-main/src/test/java/inr/numass/models/TestNeLossParametrisation.java
index f658a5e9..44cd5bef 100644
--- a/numass-main/src/test/java/inr/numass/models/TestNeLossParametrisation.java
+++ b/numass-main/src/test/java/inr/numass/models/TestNeLossParametrisation.java
@@ -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(
diff --git a/numass-main/src/test/java/inr/numass/models/TransmissionInterpolatorTest.java b/numass-main/src/test/java/inr/numass/models/TransmissionInterpolatorTest.java
index b2f4f3a6..dea75d03 100644
--- a/numass-main/src/test/java/inr/numass/models/TransmissionInterpolatorTest.java
+++ b/numass-main/src/test/java/inr/numass/models/TransmissionInterpolatorTest.java
@@ -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);
     }