Examples with Parameter dr.app.beauti.options.Parameter used on opensource projects

Example 1 with Parameter

use of dr.app.beauti.options.Parameter in project beast-mcmc by beast-dev.

the class LinkedParameterComponentGenerator method generateJointParameter.

private void generateJointParameter(LinkedParameter linkedParameter, List<Parameter> parameters, XMLWriter writer) {
    writer.writeOpenTag("jointParameter", new Attribute[] { new Attribute.Default<String>(XMLParser.ID, linkedParameter.getName()) });
    for (Parameter parameter : parameters) {
        writer.writeTag(ParameterParser.PARAMETER, new Attribute.Default<String>(XMLParser.IDREF, parameter.getName()), true);
    }
    writer.writeCloseTag("jointParameter");
}

Example 2 with Parameter

use of dr.app.beauti.options.Parameter in project beast-mcmc by beast-dev.

the class HierarchicalModelComponentGenerator method generateDistribution.

private void generateDistribution(HierarchicalPhylogeneticModel hpm, XMLWriter writer) {
    writer.writeOpenTag(DistributionLikelihood.DISTRIBUTION_LIKELIHOOD, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, getDistributionName(hpm)) });
    // Add parameters as data
    writer.writeOpenTag(DistributionLikelihoodParser.DATA);
    for (Parameter parameter : hpm.getArgumentParameterList()) {
        writeParameterRef(parameter.getName(), writer);
    }
    writer.writeCloseTag(DistributionLikelihoodParser.DATA);
    // Add HPM model
    writer.writeOpenTag(DistributionLikelihoodParser.DISTRIBUTION);
    writer.writeOpenTag(getModelTagName(hpm), getModelAttributes(hpm));
    writeParameter(NormalDistributionModelParser.MEAN, hpm.getConditionalParameterList().get(0).getName(), 1, hpm.getConditionalParameterList().get(0).getInitial(), Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, writer);
    writeParameter(NormalDistributionModelParser.PREC, hpm.getConditionalParameterList().get(1).getName(), 1, hpm.getConditionalParameterList().get(1).getInitial(), 0.0, Double.POSITIVE_INFINITY, writer);
    writer.writeCloseTag(getModelTagName(hpm));
    writer.writeCloseTag(DistributionLikelihoodParser.DISTRIBUTION);
    writer.writeCloseTag(DistributionLikelihood.DISTRIBUTION_LIKELIHOOD);
}

Example 3 with Parameter

use of dr.app.beauti.options.Parameter in project beast-mcmc by beast-dev.

the class SelectParametersDialog method showDialog.

public int showDialog(String message, List<Parameter> parameterList) {
    optionPanel.removeAll();
    if (message != null && !message.isEmpty()) {
        optionPanel.addSpanningComponent(new JLabel(message));
    }
    parameterCombo.removeAllItems();
    for (Parameter parameter : parameterList) {
        parameterCombo.addItem(parameter);
    }
    optionPanel.addComponentWithLabel("Parameter:", parameterCombo);
    JOptionPane optionPane = new JOptionPane(optionPanel, JOptionPane.QUESTION_MESSAGE, JOptionPane.OK_CANCEL_OPTION, null, null, null);
    optionPane.setBorder(new EmptyBorder(12, 12, 12, 12));
    int result = JOptionPane.CANCEL_OPTION;
    final JDialog dialog = optionPane.createDialog(frame, "Add Parameter");
    dialog.pack();
    dialog.setVisible(true);
    Integer value = (Integer) optionPane.getValue();
    if (value != null && value != -1) {
        result = value;
    }
    return result;
}

Example 4 with Parameter

use of dr.app.beauti.options.Parameter in project beast-mcmc by beast-dev.

the class STARBEASTGenerator method writeGeneUnderSpecies.

private void writeGeneUnderSpecies(XMLWriter writer) {
    writer.writeComment("Species Tree: Coalescent likelihood for gene trees under species tree");
    // speciesCoalescent id="coalescent"
    writer.writeOpenTag(MultiSpeciesCoalescentParser.SPECIES_COALESCENT, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, TraitData.TRAIT_SPECIES + "." + COALESCENT) });
    writer.writeIDref(TraitData.TRAIT_SPECIES, TraitData.TRAIT_SPECIES);
    writer.writeIDref(SpeciesTreeModelParser.SPECIES_TREE, SP_TREE);
    writer.writeCloseTag(MultiSpeciesCoalescentParser.SPECIES_COALESCENT);
    // exponentialDistributionModel id="pdist"
    //        writer.writeOpenTag(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL, new Attribute[]{
    //                new Attribute.Default<String>(XMLParser.ID, PDIST)});
    //
    //        writer.writeOpenTag(DistributionModelParser.MEAN);
    //
    //        Parameter para = options.getParameter(TraitGuesser.Traits.TRAIT_SPECIES + "." + options.POP_MEAN);
    //
    //        writer.writeTag(ParameterParser.PARAMETER, new Attribute[]{
    //                new Attribute.Default<String>(XMLParser.ID, TraitGuesser.Traits.TRAIT_SPECIES + "." + options.POP_MEAN),
    //                new Attribute.Default<String>(ParameterParser.VALUE, Double.toString(para.initial))}, true);
    //
    //        writer.writeCloseTag(DistributionModelParser.MEAN);
    //
    //        writer.writeCloseTag(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL);
    //        if (options.speciesTreePrior == TreePriorType.SPECIES_YULE) {
    writer.writeComment("Species tree prior: gama2 + gamma4");
    writer.writeOpenTag(MixedDistributionLikelihoodParser.DISTRIBUTION_LIKELIHOOD, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, SPOPS) });
    // change exponential + gamma2 into gama2 + gamma4
    // <distribution0>
    writer.writeOpenTag(MixedDistributionLikelihoodParser.DISTRIBUTION0);
    //        writer.writeIDref(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL, PDIST);
    writer.writeOpenTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
    writer.writeOpenTag(DistributionModelParser.SHAPE);
    writer.writeText("2");
    writer.writeCloseTag(DistributionModelParser.SHAPE);
    writer.writeOpenTag(DistributionModelParser.SCALE);
    Parameter para = options.starBEASTOptions.getParameter(TraitData.TRAIT_SPECIES + "." + options.starBEASTOptions.POP_MEAN);
    writeParameter(para, 1, writer);
    writer.writeCloseTag(DistributionModelParser.SCALE);
    writer.writeCloseTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
    writer.writeCloseTag(MixedDistributionLikelihoodParser.DISTRIBUTION0);
    // <distribution1>
    writer.writeOpenTag(MixedDistributionLikelihoodParser.DISTRIBUTION1);
    writer.writeOpenTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
    writer.writeOpenTag(DistributionModelParser.SHAPE);
    writer.writeText("4");
    writer.writeCloseTag(DistributionModelParser.SHAPE);
    writer.writeOpenTag(DistributionModelParser.SCALE);
    writer.writeIDref(ParameterParser.PARAMETER, TraitData.TRAIT_SPECIES + "." + options.starBEASTOptions.POP_MEAN);
    writer.writeCloseTag(DistributionModelParser.SCALE);
    writer.writeCloseTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
    writer.writeCloseTag(MixedDistributionLikelihoodParser.DISTRIBUTION1);
    // <data>
    writer.writeOpenTag(MixedDistributionLikelihoodParser.DATA);
    writer.writeIDref(ParameterParser.PARAMETER, SpeciesTreeModelParser.SPECIES_TREE + "." + SPLIT_POPS);
    writer.writeCloseTag(MixedDistributionLikelihoodParser.DATA);
    // <indicators>
    writer.writeOpenTag(MixedDistributionLikelihoodParser.INDICATORS);
    // Needs special treatment - you have to generate "NS" ones and 2(N-1) zeros, where N is the number of species.
    // N "1", 2(N-1) "0"
    writer.writeTag(ParameterParser.PARAMETER, new Attribute[] { new Attribute.Default<String>(ParameterParser.VALUE, getIndicatorsParaValue()) }, true);
    writer.writeCloseTag(MixedDistributionLikelihoodParser.INDICATORS);
    writer.writeCloseTag(MixedDistributionLikelihoodParser.DISTRIBUTION_LIKELIHOOD);
//        } else {
//            // STPopulationPrior id="stp" log_root="true"
//            writer.writeOpenTag(SpeciesTreeBMPrior.STPRIOR, new Attribute[]{
//                    new Attribute.Default<String>(XMLParser.ID, STP),
//                    new Attribute.Default<String>(SpeciesTreeBMPrior.LOG_ROOT, "true")});
//            writer.writeIDref(SpeciesTreeModelParser.SPECIES_TREE, SP_TREE);
//
//            writer.writeOpenTag(SpeciesTreeBMPrior.TIPS);
//
//            writer.writeIDref(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL, PDIST);
//
//            writer.writeCloseTag(SpeciesTreeBMPrior.TIPS);
//
//            writer.writeOpenTag(SpeciesTreeBMPrior.STSIGMA);
//
//            writer.writeTag(ParameterParser.PARAMETER, new Attribute[]{
//                    // <parameter id="stsigma" value="1" />
//                    new Attribute.Default<String>(XMLParser.ID, SpeciesTreeBMPrior.STSIGMA.toLowerCase()),
//                    new Attribute.Default<String>(ParameterParser.VALUE, "1")}, true);
//
//            writer.writeCloseTag(SpeciesTreeBMPrior.STSIGMA);
//
//            writer.writeCloseTag(SpeciesTreeBMPrior.STPRIOR);
//        }
}

Example 5 with Parameter

use of dr.app.beauti.options.Parameter in project beast-mcmc by beast-dev.

the class STARBEASTGenerator method writeStartingTreeForCalibration.

public void writeStartingTreeForCalibration(XMLWriter writer) {
    writer.writeComment("species starting tree for calibration");
    writer.writeText("");
    writer.writeOpenTag(OldCoalescentSimulatorParser.COALESCENT_TREE, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, SP_START_TREE) });
    Attribute[] taxaAttribute = { new Attribute.Default<String>(XMLParser.IDREF, ALL_SPECIES) };
    writer.writeOpenTag(OldCoalescentSimulatorParser.CONSTRAINED_TAXA);
    writer.writeTag(TaxaParser.TAXA, taxaAttribute, true);
    for (Taxa taxa : options.speciesSets) {
        Parameter statistic = options.getStatistic(taxa);
        Attribute mono = new Attribute.Default<Boolean>(OldCoalescentSimulatorParser.IS_MONOPHYLETIC, options.speciesSetsMono.get(taxa));
        writer.writeOpenTag(OldCoalescentSimulatorParser.TMRCA_CONSTRAINT, mono);
        writer.writeIDref(TaxaParser.TAXA, taxa.getId());
        if (options.getPartitionTreePriors().get(0).getNodeHeightPrior() == TreePriorType.SPECIES_YULE_CALIBRATION && statistic.priorType == PriorType.UNIFORM_PRIOR) {
            writeDistribution(statistic, false, writer);
        }
        writer.writeCloseTag(OldCoalescentSimulatorParser.TMRCA_CONSTRAINT);
    }
    writer.writeCloseTag(OldCoalescentSimulatorParser.CONSTRAINED_TAXA);
    writer.writeOpenTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, "spInitDemo"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
    writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
    // "initial" is "value"
    double popSizeValue = options.getPartitionTreePriors().get(0).getParameter("constant.popSize").getInitial();
    writer.writeTag(ParameterParser.PARAMETER, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, "sp.popSize"), new Attribute.Default<Double>(ParameterParser.VALUE, popSizeValue) }, true);
    writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
    writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
    writer.writeCloseTag(OldCoalescentSimulatorParser.COALESCENT_TREE);
}