Migrate input types to BEAST 3 spec interfaces

src/main/java/mascot/app/beauti/NeDynamicsListInputEditor.java

@@ -445,17 +445,22 @@ private void addParameter(RealParameter parameter, String pId, String dynamics,
 	}
 
 	private void removeParameters(NeDynamics neDynamics, MCMC mcmc) {
+		// The spec interface inputs (RealScalar / RealVector) don't extend
+		// StateNode, but at runtime the XML always binds a RealScalarParam /
+		// RealVectorParam (which do). Cast to StateNode at the call site so
+		// the BEAUti disconnect logic can use getID().
+		CompoundDistribution posterior = (CompoundDistribution) mcmc.posteriorInput.get();
 		if (neDynamics instanceof ConstantNe) {
-			removeParameter(((CompoundDistribution) mcmc.posteriorInput.get()), ((ConstantNe) neDynamics).NeInput.get());
+			removeParameter(posterior, (StateNode) ((ConstantNe) neDynamics).NeInput.get());
 		}else if (neDynamics instanceof ExponentialNe) {
-			removeParameter(((CompoundDistribution) mcmc.posteriorInput.get()), ((ExponentialNe) neDynamics).growthRateInput.get());
-			removeParameter(((CompoundDistribution) mcmc.posteriorInput.get()), ((ExponentialNe) neDynamics).logNeNullInput.get());			
+			removeParameter(posterior, (StateNode) ((ExponentialNe) neDynamics).growthRateInput.get());
+			removeParameter(posterior, (StateNode) ((ExponentialNe) neDynamics).logNeNullInput.get());
 		}else if (neDynamics instanceof Skygrowth) {
-			removeParameter(((CompoundDistribution) mcmc.posteriorInput.get()), ((Skygrowth) neDynamics).NeInput.get());
+			removeParameter(posterior, (StateNode) ((Skygrowth) neDynamics).NeInput.get());
 		}else if (neDynamics instanceof LogisticNe) {
-			removeParameter(((CompoundDistribution) mcmc.posteriorInput.get()), ((LogisticNe) neDynamics).capacityInput.get());
-			removeParameter(((CompoundDistribution) mcmc.posteriorInput.get()), ((LogisticNe) neDynamics).carryingProportionInput.get());
-			removeParameter(((CompoundDistribution) mcmc.posteriorInput.get()), ((LogisticNe) neDynamics).growthRateInput.get());
+			removeParameter(posterior, (StateNode) ((LogisticNe) neDynamics).capacityInput.get());
+			removeParameter(posterior, (StateNode) ((LogisticNe) neDynamics).carryingProportionInput.get());
+			removeParameter(posterior, (StateNode) ((LogisticNe) neDynamics).growthRateInput.get());
 		}
 	}
 

src/main/java/mascot/glmmodel/ErrorSmoothing.java

@@ -6,9 +6,11 @@
 import beast.base.inference.Distribution;
 import beast.base.inference.State;
 import beast.base.inference.StateNode;
-import beast.base.inference.distribution.ParametricDistribution;
+import beast.base.spec.domain.Real;
+import beast.base.spec.inference.distribution.ScalarDistribution;
 import beast.base.spec.inference.parameter.IntVectorParam;
 import beast.base.spec.inference.parameter.RealVectorParam;
+import beast.base.spec.type.RealVector;
 
 import java.util.ArrayList;
 import java.util.List;
@@ -19,14 +21,14 @@
         "If x is multidimensional, the components of x are assumed to be independent, " +
         "so the sum of log probabilities of all elements of x is returned as the prior.")
 public class ErrorSmoothing extends Distribution {
-    final public Input<Function> m_x = new Input<>("x", "point at which the density is calculated", Validate.REQUIRED);
+    final public Input<RealVector<? extends Real>> m_x = new Input<>("x", "point at which the density is calculated", Validate.REQUIRED);
 
-    final public Input<ParametricDistribution> distInput = new Input<>("distr", "distribution used to calculate prior, e.g. normal, beta, gamma.", Validate.REQUIRED);
+    final public Input<ScalarDistribution<?, Double>> distInput = new Input<>("distr", "distribution used to calculate prior, e.g. normal, beta, gamma.", Validate.REQUIRED);
 
     /**
      * shadows distInput *
      */
-    protected ParametricDistribution dist;
+    protected ScalarDistribution<?, Double> dist;
 
     @Override
     public void initAndValidate() {
@@ -36,9 +38,14 @@ public void initAndValidate() {
 
     @Override
     public double calculateLogP() {
-        Function x = m_x.get();
-        // spec types enforce bounds via domain, so no explicit check needed
-        logP = dist.calcLogP(x);
+        RealVector<? extends Real> x = m_x.get();
+        // Components of x are treated as iid draws from `dist` — sum the
+        // scalar log-densities. The spec ScalarDistribution doesn't have a
+        // single-shot logP-over-a-vector method; loop manually.
+        logP = 0;
+        for (int i = 0; i < x.size(); i++) {
+            logP += dist.logDensity(x.get(i));
+        }
         if (logP == Double.POSITIVE_INFINITY) {
             logP = Double.NEGATIVE_INFINITY;
         }
@@ -67,19 +74,17 @@ public void sample(State state, Random random) {
         sampleConditions(state, random);
 
         // sample distribution parameters
-        Function x = m_x.get();
+        RealVector<? extends Real> x = m_x.get();
 
-        Double[] newx;
         try {
-            newx = dist.sample(1)[0];
-
+            // Spec ScalarDistribution.sample() returns one value per call;
+            // for a vector x we draw size(x) iid samples. m_x is now typed
+            // RealVector<? extends Real>, so only the real-vector branch
+            // applies (the legacy code had an IntVectorParam branch that's
+            // unreachable under the new Input type).
             if (x instanceof RealVectorParam<?> rvp) {
-                for (int i = 0; i < newx.length; i++) {
-                    rvp.set(i, newx[i]);
-                }
-            } else if (x instanceof IntVectorParam<?> ivp) {
-                for (int i = 0; i < newx.length; i++) {
-                    ivp.set(i, (int)Math.round(newx[i]));
+                for (int i = 0; i < rvp.size(); i++) {
+                    rvp.set(i, dist.sample().get(0));
                 }
             }
 

src/main/java/mascot/glmmodel/GlmModel.java

@@ -4,12 +4,16 @@
 import beast.base.core.Input.Validate;
 import beast.base.core.Loggable;
 import beast.base.inference.CalculationNode;
+import beast.base.inference.StateNode;
+import beast.base.inference.StateNodeInitialiser;
 import beast.base.spec.inference.parameter.BoolVectorParam;
 import beast.base.spec.domain.Real;
 import beast.base.spec.inference.parameter.RealScalarParam;
 import beast.base.spec.inference.parameter.RealVectorParam;
 
-public abstract class GlmModel extends CalculationNode implements Loggable {
+import java.util.List;
+
+public abstract class GlmModel extends CalculationNode implements Loggable, StateNodeInitialiser {
 
     public Input<CovariateList> covariateListInput = new Input<>("covariateList", "input of covariates", Validate.REQUIRED);
     public Input<RealVectorParam<? extends Real>> scalerInput = new Input<>("scaler", "input of covariates scaler", Validate.REQUIRED);
@@ -84,6 +88,21 @@ public void setNrDummy(){
 		verticalEntries = 0;
 	}
 
+	// Subclasses (LogLinear, etc.) fix up the dimensions of scaler /
+	// indicator / error parameters during their own initAndValidate. The
+	// default initStateNodes is a no-op; getInitialisedStateNodes reports
+	// the parameters this class takes responsibility for so the framework
+	// can dedupe.
+	@Override
+	public void initStateNodes() {
+	}
 
+	@Override
+	public void getInitialisedStateNodes(List<StateNode> stateNodes) {
+		stateNodes.add(scalerInput.get());
+		stateNodes.add(indicatorInput.get());
+		if (errorInput.get() != null) stateNodes.add(errorInput.get());
+		if (constantErrorInput.get() != null) stateNodes.add(constantErrorInput.get());
+	}
 
 }

src/main/java/mascot/glmmodel/MaxRate.java

@@ -6,9 +6,7 @@
 import beast.base.core.Input.Validate;
 import beast.base.inference.Distribution;
 import beast.base.inference.State;
-import beast.base.inference.distribution.ParametricDistribution;
-import beast.base.spec.domain.Real;
-import beast.base.spec.inference.parameter.RealVectorParam;
+import beast.base.spec.inference.distribution.ScalarDistribution;
 import mascot.dynamics.GLM;
 
 import java.util.ArrayList;
@@ -21,14 +19,14 @@
         "so the sum of log probabilities of all elements of x is returned as the prior.")
 public class MaxRate extends Distribution {
     final public Input<GLM> GLMStepwiseModelInput = new Input<>("GLMmodel", "glm model input");
-    final public Input<ParametricDistribution> distInput = new Input<>("distr", "distribution used to calculate prior, e.g. normal, beta, gamma.", Validate.REQUIRED);
+    final public Input<ScalarDistribution<?, Double>> distInput = new Input<>("distr", "distribution used to calculate prior, e.g. normal, beta, gamma.", Validate.REQUIRED);
     final public Input<Boolean> migrationOnlyInput = new Input<>("migrationOnly", "put prior only on migration rates", false);
     final public Input<Boolean> NeOnlyInput = new Input<>("NeOnly", "put prior only on migration rates", false);
 
     /**
      * shadows distInput *
      */
-    protected ParametricDistribution dist;
+    protected ScalarDistribution<?, Double> dist;
 
 
     @Override
@@ -42,29 +40,28 @@ public double calculateLogP() {
     	Double[] mig = GLMStepwiseModelInput.get().getAllCoalescentRate();
 		Double[] coal = GLMStepwiseModelInput.get().getAllBackwardsMigration();
 
-    	RealVectorParam<Real> dCoal = new RealVectorParam<>(unbox(coal), Real.INSTANCE);
-    	RealVectorParam<Real> dMig = new RealVectorParam<>(unbox(mig), Real.INSTANCE);
-
     	logP = 0.0;
 
     	if (migrationOnlyInput.get()){
-	        logP += dist.calcLogP(dMig);
+	        logP += sumLogDensity(mig);
     	}else{
-	        logP += dist.calcLogP(dCoal);
-	        logP += dist.calcLogP(dMig);
+	        logP += sumLogDensity(coal);
+	        logP += sumLogDensity(mig);
     	}
         if (logP == Double.POSITIVE_INFINITY) {
             logP = Double.NEGATIVE_INFINITY;
         }
         return logP;
     }
 
-    private static double[] unbox(Double[] values) {
-        double[] result = new double[values.length];
-        for (int i = 0; i < values.length; i++) {
-            result[i] = values[i];
-        }
-        return result;
+    private double sumLogDensity(Double[] values) {
+        // Each entry is treated as an iid draw from `dist` (matches what
+        // legacy ParametricDistribution.calcLogP(Function) did over a
+        // multidimensional sample). Spec ScalarDistribution exposes
+        // logDensity per scalar; sum manually.
+        double sum = 0;
+        for (Double v : values) sum += dist.logDensity(v);
+        return sum;
     }
 
     /**

src/main/java/mascot/logger/StructuredTreeLogger.java

@@ -10,7 +10,7 @@
 import beast.base.evolution.tree.Tree;
 import beast.base.evolution.tree.TreeInterface;
 import beast.base.inference.StateNode;
-import beast.base.spec.inference.parameter.BoolVectorParam;
+import beast.base.spec.type.BoolVector;
 import mascot.distribution.Mascot;
 import mascot.ode.Euler2ndOrderTransitions;
 import mascot.ode.MascotODEUpDown;
@@ -48,7 +48,7 @@ public class StructuredTreeLogger extends Tree implements Loggable {
     public Input<BranchRateModel.Base> clockModelInput = new Input<BranchRateModel.Base>("branchratemodel", "rate to be logged with branches of the tree");
     public Input<List<Function>> parameterInput = new Input<List<Function>>("metadata", "meta data to be logged with the tree nodes", new ArrayList<>());
     public Input<Boolean> maxStateInput = new Input<Boolean>("maxState", "report branch lengths as substitutions (branch length times clock rate for the branch)", false);
-    public Input<BoolVectorParam> conditionalStateProbsInput = new Input<>("conditionalStateProbs", "report branch lengths as substitutions (branch length times clock rate for the branch)");
+    public Input<BoolVector> conditionalStateProbsInput = new Input<>("conditionalStateProbs", "report branch lengths as substitutions (branch length times clock rate for the branch)");
     public Input<Boolean> substitutionsInput = new Input<Boolean>("substitutions", "report branch lengths as substitutions (branch length times clock rate for the branch)", false);
     public Input<Integer> decimalPlacesInput = new Input<Integer>("dp", "the number of decimal places to use writing branch lengths and rates, use -1 for full precision (default = full precision)", -1);
 

src/main/java/mascot/logger/mappedProbLogger.java

@@ -7,7 +7,7 @@
 import beast.base.evolution.branchratemodel.BranchRateModel;
 import beast.base.evolution.tree.Node;
 import beast.base.inference.CalculationNode;
-import beast.base.spec.inference.parameter.BoolVectorParam;
+import beast.base.spec.type.BoolVector;
 import mascot.distribution.MappedMascot;
 
 import java.io.PrintStream;
@@ -34,7 +34,7 @@ public class mappedProbLogger extends CalculationNode implements Loggable {
 			"meta data to be logged with the tree nodes", new ArrayList<>());
 	public Input<Boolean> maxStateInput = new Input<Boolean>("maxState",
 			"report branch lengths as substitutions (branch length times clock rate for the branch)", false);
-	public Input<BoolVectorParam> conditionalStateProbsInput = new Input<>("conditionalStateProbs",
+	public Input<BoolVector> conditionalStateProbsInput = new Input<>("conditionalStateProbs",
 			"report branch lengths as substitutions (branch length times clock rate for the branch)");
 	public Input<Boolean> substitutionsInput = new Input<Boolean>("substitutions",
 			"report branch lengths as substitutions (branch length times clock rate for the branch)", false);

src/main/java/mascot/mapped/AncestralStateTreeLikelihood.java

@@ -5,7 +5,7 @@
 import beast.base.core.Loggable;
 import beast.base.evolution.alignment.Alignment;
 import beast.base.evolution.datatype.DataType;
-import beast.base.evolution.likelihood.TreeLikelihood;
+import beast.base.spec.evolution.likelihood.TreeLikelihood;
 import beast.base.evolution.tree.Node;
 import beast.base.evolution.tree.Tree;
 import beast.base.evolution.tree.TreeInterface;

src/main/java/mascot/parameterdynamics/ConstantNe.java

@@ -3,14 +3,14 @@
 import beast.base.core.Input;
 import beast.base.core.Input.Validate;
 import beast.base.spec.domain.Real;
-import beast.base.spec.inference.parameter.RealScalarParam;
+import beast.base.spec.type.RealScalar;
 
 public class ConstantNe extends NeDynamics {
 
-    public Input<RealScalarParam<Real>> NeInput = new Input<>(
+    public Input<RealScalar<Real>> NeInput = new Input<>(
     		"logNe", "input of the Ne at the time of the most recent sampled ancestor", Validate.REQUIRED);
 
-    RealScalarParam<Real> Ne;
+    RealScalar<Real> Ne;
 
 	@Override
 	public void initAndValidate() {
@@ -31,9 +31,6 @@ public double getNeTime(double t) {
 
 	@Override
 	public boolean isDirty() {
-		if (Ne.somethingIsDirty())
-			return true;
-
-		return false;
+		return isDirtyInput(Ne);
 	}
 }

src/main/java/mascot/parameterdynamics/ExponentialNe.java

@@ -3,20 +3,20 @@
 import beast.base.core.Input;
 import beast.base.core.Input.Validate;
 import beast.base.spec.domain.Real;
-import beast.base.spec.inference.parameter.RealScalarParam;
+import beast.base.spec.type.RealScalar;
 
 public class ExponentialNe extends NeDynamics {
 
-    public Input<RealScalarParam<Real>> logNeNullInput = new Input<>(
+    public Input<RealScalar<Real>> logNeNullInput = new Input<>(
     		"NeNull", "input of the Ne at the time of the most recent sampled ancestor", Validate.REQUIRED);
-    public Input<RealScalarParam<Real>> growthRateInput = new Input<>(
+    public Input<RealScalar<Real>> growthRateInput = new Input<>(
     		"growthRate", "input of the growth rate", Validate.REQUIRED);
 
     public Input<Double> minNeInput = new Input<>(
     		"minNe", "input of the minimal Ne", 0.0);
 
-    RealScalarParam<Real> logNeNull;
-    RealScalarParam<Real> growthRate;
+    RealScalar<Real> logNeNull;
+    RealScalar<Real> growthRate;
 
 	@Override
 	public void initAndValidate() {
@@ -39,13 +39,7 @@ public double getNeTime(double t) {
 
 	@Override
 	public boolean isDirty() {
-		if (logNeNull.somethingIsDirty())
-			return true;
-
-		if (growthRate.somethingIsDirty())
-			return true;
-
-		return false;
+		return isDirtyInput(logNeNull) || isDirtyInput(growthRate);
 	}
 
 

src/main/java/mascot/parameterdynamics/LogLinearGLM.java

@@ -2,13 +2,17 @@
 
 import beast.base.core.Input;
 import beast.base.core.Input.Validate;
+import beast.base.inference.StateNode;
+import beast.base.inference.StateNodeInitialiser;
 import beast.base.spec.inference.parameter.BoolVectorParam;
 import beast.base.spec.domain.Real;
 import beast.base.spec.inference.parameter.RealScalarParam;
 import beast.base.spec.inference.parameter.RealVectorParam;
 import mascot.glmmodel.CovariateList;
 
-public class LogLinearGLM extends NeDynamics {
+import java.util.List;
+
+public class LogLinearGLM extends NeDynamics implements StateNodeInitialiser {
     public Input<CovariateList> covariateListInput = new Input<>("covariateList", "input of covariates", Validate.REQUIRED);
     public Input<RealVectorParam<? extends Real>> scalerInput = new Input<>("scaler", "input of covariates scaler", Validate.REQUIRED);
     public Input<BoolVectorParam> indicatorInput = new Input<>("indicator", "input of covariates scaler", Validate.REQUIRED);
@@ -131,5 +135,15 @@ public void restore() {
 		valuesKnown = false;
 	}
 
+	@Override
+	public void initStateNodes() {
+	}
+
+	@Override
+	public void getInitialisedStateNodes(List<StateNode> stateNodes) {
+		stateNodes.add(scalerInput.get());
+		stateNodes.add(indicatorInput.get());
+		if (errorInput.get() != null) stateNodes.add(errorInput.get());
+	}
 
 }