Examples with Taxa dr.evolution.util.Taxa used on opensource projects

Example 16 with Taxa

use of dr.evolution.util.Taxa in project beast-mcmc by beast-dev.

the class ClockModelGenerator method writeBranchRatesModel.

/**
 * Write the relaxed clock branch rates block.
 *
 * @param clockModel  PartitionClockModel
 * @param writer the writer
 */
public void writeBranchRatesModel(PartitionClockModel clockModel, XMLWriter writer) {
    Attribute[] attributes;
    PartitionTreeModel treeModel = clockModel.getPartitionTreeModel();
    String treePrefix = treeModel.getPrefix();
    String prefix = clockModel.getPrefix();
    String tag;
    switch(clockModel.getClockType()) {
        case STRICT_CLOCK:
            tag = StrictClockBranchRatesParser.STRICT_CLOCK_BRANCH_RATES;
            writer.writeComment("The strict clock (Uniform rates across branches)");
            writer.writeOpenTag(tag, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + BranchRateModel.BRANCH_RATES) });
            writeParameter("rate", "clock.rate", clockModel, writer);
            writer.writeCloseTag(tag);
            writeMeanRateStatistic(writer, tag, prefix, treePrefix);
            break;
        case UNCORRELATED:
            if (clockModel.performModelAveraging()) {
                tag = MixtureModelBranchRatesParser.MIXTURE_MODEL_BRANCH_RATES;
                writer.writeComment("Bayesian Model Averaging (BMA) of the available relaxed clock models as described by" + " Li & Drummond (2012) MBE 29:751-61.");
                // Bayesian Model Averaging uses the continuous quantile implementation by default
                writer.writeComment("  Continuous quantile implementation (Li & Drummond (2012) Mol Biol Evol 29:751-61)");
                attributes = new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + BranchRateModel.BRANCH_RATES) };
                writer.writeOpenTag(tag, attributes);
                // tree
                writer.writeIDref(DefaultTreeModel.TREE_MODEL, treePrefix + DefaultTreeModel.TREE_MODEL);
                writer.writeOpenTag("distribution");
                writer.writeOpenTag(LogNormalDistributionModelParser.LOGNORMAL_DISTRIBUTION_MODEL, new Attribute.Default<String>(LogNormalDistributionModelParser.MEAN_IN_REAL_SPACE, "true"));
                writeParameter("mean", ClockType.UCLD_MEAN, clockModel, writer);
                writeParameter("stdev", ClockType.UCLD_STDEV, clockModel, writer);
                writer.writeCloseTag(LogNormalDistributionModelParser.LOGNORMAL_DISTRIBUTION_MODEL);
                writer.writeCloseTag("distribution");
                writer.writeOpenTag("distribution");
                writer.writeOpenTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
                writeParameter("mean", ClockType.UCGD_MEAN, clockModel, writer);
                writeParameter("shape", ClockType.UCGD_SHAPE, clockModel, writer);
                writer.writeCloseTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
                writer.writeCloseTag("distribution");
                writer.writeOpenTag("distribution");
                writer.writeOpenTag(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL);
                writeParameter("mean", ClockType.UCED_MEAN, clockModel, writer);
                writer.writeCloseTag(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL);
                writer.writeCloseTag("distribution");
                writer.writeOpenTag(MixtureModelBranchRatesParser.DISTRIBUTION_INDEX);
                writeParameter(clockModel.getParameter("branchRates.distributionIndex"), -1, writer);
                writer.writeCloseTag(MixtureModelBranchRatesParser.DISTRIBUTION_INDEX);
                writer.writeOpenTag(MixtureModelBranchRatesParser.RATE_CATEGORY_QUANTILES);
                writeParameter(clockModel.getParameter("branchRates.quantiles"), -1, writer);
                writer.writeCloseTag(MixtureModelBranchRatesParser.RATE_CATEGORY_QUANTILES);
                writer.writeCloseTag(tag);
                writeMeanRateStatistic(writer, tag, prefix, treePrefix);
                writeCoefficientOfVariationStatistic(writer, tag, prefix, treePrefix);
                writeCovarianceStatistic(writer, tag, prefix, treePrefix);
            } else {
                tag = DiscretizedBranchRatesParser.DISCRETIZED_BRANCH_RATES;
                writer.writeComment("The uncorrelated relaxed clock (Drummond, Ho, Phillips & Rambaut (2006) PLoS Biology 4, e88 )");
                if (clockModel.isContinuousQuantile()) {
                    writer.writeComment("  Continuous quantile implementation (Li & Drummond (2012) Mol Biol Evol 29:751-61)");
                    tag = ContinuousBranchRatesParser.CONTINUOUS_BRANCH_RATES;
                }
                attributes = new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + BranchRateModel.BRANCH_RATES) };
                writer.writeOpenTag(tag, attributes);
                // tree
                writer.writeIDref(DefaultTreeModel.TREE_MODEL, treePrefix + DefaultTreeModel.TREE_MODEL);
                writer.writeOpenTag("distribution");
                switch(clockModel.getClockDistributionType()) {
                    case LOGNORMAL:
                        writer.writeOpenTag(LogNormalDistributionModelParser.LOGNORMAL_DISTRIBUTION_MODEL, new Attribute.Default<String>(LogNormalDistributionModelParser.MEAN_IN_REAL_SPACE, "true"));
                        writeParameter("mean", ClockType.UCLD_MEAN, clockModel, writer);
                        writeParameter("stdev", ClockType.UCLD_STDEV, clockModel, writer);
                        writer.writeCloseTag(LogNormalDistributionModelParser.LOGNORMAL_DISTRIBUTION_MODEL);
                        break;
                    case GAMMA:
                        writer.writeOpenTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
                        writeParameter("mean", ClockType.UCGD_MEAN, clockModel, writer);
                        writeParameter("shape", ClockType.UCGD_SHAPE, clockModel, writer);
                        writer.writeCloseTag(GammaDistributionModel.GAMMA_DISTRIBUTION_MODEL);
                        break;
                    case CAUCHY:
                        throw new UnsupportedOperationException("Uncorrelated Cauchy model not implemented yet");
                    // break;
                    case EXPONENTIAL:
                        writer.writeOpenTag(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL);
                        writeParameter("mean", ClockType.UCED_MEAN, clockModel, writer);
                        writer.writeCloseTag(ExponentialDistributionModel.EXPONENTIAL_DISTRIBUTION_MODEL);
                        break;
                }
                writer.writeCloseTag("distribution");
                if (clockModel.isContinuousQuantile()) {
                    writer.writeOpenTag(ContinuousBranchRatesParser.RATE_QUANTILES);
                    writeParameter(clockModel.getParameter("branchRates.quantiles"), -1, writer);
                    writer.writeCloseTag(ContinuousBranchRatesParser.RATE_QUANTILES);
                    writer.writeCloseTag(tag);
                } else {
                    writer.writeOpenTag(DiscretizedBranchRatesParser.RATE_CATEGORIES);
                    writeParameter(clockModel.getParameter("branchRates.categories"), -1, writer);
                    writer.writeCloseTag(DiscretizedBranchRatesParser.RATE_CATEGORIES);
                    writer.writeCloseTag(tag);
                }
                writeMeanRateStatistic(writer, tag, prefix, treePrefix);
                writeCoefficientOfVariationStatistic(writer, tag, prefix, treePrefix);
                writeCovarianceStatistic(writer, tag, prefix, treePrefix);
            }
            break;
        case AUTOCORRELATED:
            writer.writeComment("The autocorrelated relaxed clock (Rannala & Yang, 2007)");
            tag = ACLikelihoodParser.AC_LIKELIHOOD;
            attributes = new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + BranchRateModel.BRANCH_RATES), new Attribute.Default<String>("episodic", "false"), new Attribute.Default<String>("logspace", "true") };
            writer.writeOpenTag(tag, attributes);
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, treePrefix + DefaultTreeModel.TREE_MODEL);
            // if (!model.isEstimatedRate()) { //TODO move to options or panel select method
            // Parameter parameter = tree.getParameter(DefaultTreeModel.TREE_MODEL + "." + RateEvolutionLikelihood.ROOTRATE);//"treeModel.rootRate"
            // parameter.isFixed = true;
            // parameter.initial = model.getRate();
            // }
            writer.writeOpenTag(RateEvolutionLikelihood.RATES, new Attribute[] { new Attribute.Default<String>(TreeModelParser.ROOT_NODE, "false"), new Attribute.Default<String>(TreeModelParser.INTERNAL_NODES, "true"), new Attribute.Default<String>(TreeModelParser.LEAF_NODES, "true") });
            writer.writeTag(ParameterParser.PARAMETER, new Attribute.Default<String>(XMLParser.ID, treePrefix + DefaultTreeModel.TREE_MODEL + "." + TreeModelParser.NODE_RATES), true);
            writer.writeCloseTag(RateEvolutionLikelihood.RATES);
            writer.writeOpenTag(RateEvolutionLikelihood.ROOTRATE, new Attribute[] { new Attribute.Default<String>(TreeModelParser.ROOT_NODE, "true"), new Attribute.Default<String>(TreeModelParser.INTERNAL_NODES, "false"), new Attribute.Default<String>(TreeModelParser.LEAF_NODES, "false") });
            writer.writeTag(ParameterParser.PARAMETER, new Attribute.Default<String>(XMLParser.ID, treePrefix + DefaultTreeModel.TREE_MODEL + "." + RateEvolutionLikelihood.ROOTRATE), true);
            writer.writeCloseTag(RateEvolutionLikelihood.ROOTRATE);
            // writeParameterRef("rates", treePrefix + "treeModel.nodeRates", writer);
            // writeParameterRef(RateEvolutionLikelihood.ROOTRATE, treePrefix + "treeModel.rootRate", writer);
            writeParameter("variance", "branchRates.var", treeModel, writer);
            writer.writeCloseTag(tag);
            // if (model.isEstimatedRate()) {//TODO
            writer.writeText("");
            writer.writeOpenTag(CompoundParameterParser.COMPOUND_PARAMETER, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, treePrefix + DefaultTreeModel.TREE_MODEL + "." + "allRates") });
            writer.writeIDref(ParameterParser.PARAMETER, treePrefix + DefaultTreeModel.TREE_MODEL + "." + TreeModelParser.NODE_RATES);
            writer.writeIDref(ParameterParser.PARAMETER, treePrefix + DefaultTreeModel.TREE_MODEL + "." + RateEvolutionLikelihood.ROOTRATE);
            writer.writeCloseTag(CompoundParameterParser.COMPOUND_PARAMETER);
            // }
            writeMeanRateStatistic(writer, tag, prefix, treePrefix);
            writeCoefficientOfVariationStatistic(writer, tag, prefix, treePrefix);
            writeCovarianceStatistic(writer, tag, prefix, treePrefix);
        case // 1 random local clock CANNOT have different tree models
        RANDOM_LOCAL_CLOCK:
            writer.writeComment("The random local clock model (Drummond & Suchard, 2010)");
            tag = RandomLocalClockModelParser.LOCAL_BRANCH_RATES;
            writer.writeOpenTag(tag, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + BranchRateModel.BRANCH_RATES), new Attribute.Default<String>("ratesAreMultipliers", "false") });
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, treePrefix + DefaultTreeModel.TREE_MODEL);
            writer.writeOpenTag("rates");
            writer.writeTag(ParameterParser.PARAMETER, new Attribute.Default<String>(XMLParser.ID, prefix + ClockType.LOCAL_CLOCK + ".relativeRates"), true);
            writer.writeCloseTag("rates");
            writer.writeOpenTag("rateIndicator");
            writer.writeTag(ParameterParser.PARAMETER, new Attribute.Default<String>(XMLParser.ID, prefix + ClockType.LOCAL_CLOCK + ".changes"), true);
            writer.writeCloseTag("rateIndicator");
            writeParameter("clockRate", "clock.rate", clockModel, writer);
            writer.writeCloseTag(tag);
            writer.writeText("");
            writer.writeOpenTag(SumStatisticParser.SUM_STATISTIC, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + "rateChanges"), new Attribute.Default<String>("name", prefix + "rateChangeCount"), new Attribute.Default<String>("elementwise", "true") });
            writer.writeIDref(ParameterParser.PARAMETER, prefix + ClockType.LOCAL_CLOCK + ".changes");
            writer.writeCloseTag(SumStatisticParser.SUM_STATISTIC);
            writeMeanRateStatistic(writer, tag, prefix, treePrefix);
            writeCoefficientOfVariationStatistic(writer, tag, prefix, treePrefix);
            writeCovarianceStatistic(writer, tag, prefix, treePrefix);
            break;
        case FIXED_LOCAL_CLOCK:
            writer.writeComment("The a priori local clock model (Yoder & Yang, 2000)");
            tag = LocalClockModelParser.LOCAL_CLOCK_MODEL;
            writer.writeOpenTag(tag, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + BranchRateModel.BRANCH_RATES) });
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, treePrefix + DefaultTreeModel.TREE_MODEL);
            writeParameter(LocalClockModelParser.RATE, "clock.rate", clockModel, writer);
            for (Taxa taxonSet : options.taxonSets) {
                if (options.taxonSetsMono.get(taxonSet)) {
                    String parameterName = taxonSet.getId() + ".rate";
                    writer.writeOpenTag(LocalClockModelParser.CLADE, new Attribute[] { new Attribute.Default<String>("includeStem", options.taxonSetsIncludeStem.get(taxonSet).toString()) });
                    writeParameter(parameterName, clockModel, writer);
                    writer.writeIDref(TaxaParser.TAXA, taxonSet.getId());
                    writer.writeCloseTag(LocalClockModelParser.CLADE);
                }
            }
            writer.writeCloseTag(tag);
            writeMeanRateStatistic(writer, tag, prefix, treePrefix);
            writeCoefficientOfVariationStatistic(writer, tag, prefix, treePrefix);
            writeCovarianceStatistic(writer, tag, prefix, treePrefix);
            break;
        default:
            throw new IllegalArgumentException("Unknown clock model");
    }
}

Example 17 with Taxa

use of dr.evolution.util.Taxa in project beast-mcmc by beast-dev.

the class LogGenerator method writeDemographicLogToFile.

public void writeDemographicLogToFile(XMLWriter writer, TreePriorGenerator treePriorGenerator, ClockModelGenerator clockModelGenerator, SubstitutionModelGenerator substitutionModelGenerator, TreeLikelihoodGenerator treeLikelihoodGenerator) {
    writer.writeComment("demographic log file");
    if (options.demographicLogFileName == null) {
        options.demographicLogFileName = options.fileNameStem + ".demo.log";
    }
    String header = "Demographic Model: " + options.demographicModelName;
    writer.writeOpenTag(LoggerParser.LOG, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, "fileLog"), new Attribute.Default<String>(LoggerParser.HEADER, header + ""), new Attribute.Default<String>(LoggerParser.LOG_EVERY, options.logEvery + ""), new Attribute.Default<String>(LoggerParser.FILE_NAME, options.logFileName), new Attribute.Default<Boolean>(LoggerParser.ALLOW_OVERWRITE_LOG, options.allowOverwriteLog) });
    if (options.hasData()) {
        writer.writeIDref(CompoundLikelihoodParser.JOINT, "joint");
    }
    writer.writeIDref(CompoundLikelihoodParser.PRIOR, "prior");
    for (PartitionTreeModel model : options.getPartitionTreeModels()) {
        writer.writeIDref(ParameterParser.PARAMETER, model.getPrefix() + DefaultTreeModel.TREE_MODEL + "." + TreeModelParser.ROOT_HEIGHT);
    }
    for (Taxa taxa : options.taxonSets) {
        // make tmrca(tree.name) eay to read in log for Tracer
        PartitionTreeModel treeModel = options.taxonSetsTreeModel.get(taxa);
        writer.writeIDref(TMRCAStatisticParser.TMRCA_STATISTIC, "tmrca(" + treeModel.getPrefix() + taxa.getId() + ")");
    }
    // } else { // no species
    for (PartitionTreePrior prior : options.getPartitionTreePriors()) {
        // treePriorGenerator.setModelPrefix(prior.getPrefix()); // priorName.treeModel
        treePriorGenerator.writeParameterLog(prior, writer);
    }
    for (PartitionSubstitutionModel model : options.getPartitionSubstitutionModels()) {
        substitutionModelGenerator.writeLog(model, writer);
    }
    for (PartitionClockModel model : options.getPartitionClockModels()) {
        // if (model.getRateTypeOption() == FixRateType.FIXED_MEAN) {
        // writer.writeIDref(ParameterParser.PARAMETER, model.getName());
        // if (model.getClockType() == ClockType.UNCORRELATED) {
        // switch (model.getClockDistributionType()) {
        // case LOGNORMAL:
        // writer.writeIDref(ParameterParser.PARAMETER, model.getPrefix() + ClockType.UCLD_STDEV);
        // break;
        // case GAMMA:
        // throw new UnsupportedOperationException("Uncorrelated gamma model not implemented yet");
        // //                            break;
        // case CAUCHY:
        // throw new UnsupportedOperationException("Uncorrelated Cauchy model not implemented yet");
        // //                            break;
        // case EXPONENTIAL:
        // // nothing required
        // break;
        // }
        // }
        // }
        clockModelGenerator.writeLog(model, writer);
    }
    for (PartitionClockModel model : options.getPartitionClockModels()) {
        clockModelGenerator.writeLogStatistic(model, writer);
    }
    generateInsertionPoint(ComponentGenerator.InsertionPoint.IN_FILE_LOG_PARAMETERS, writer);
    treeLikelihoodGenerator.writeTreeLikelihoodReferences(writer);
    clockModelGenerator.writeClockLikelihoodReferences(writer);
    generateInsertionPoint(ComponentGenerator.InsertionPoint.IN_FILE_LOG_LIKELIHOODS, writer);
    // coalescentLikelihood
    for (PartitionTreeModel model : options.getPartitionTreeModels()) {
        PartitionTreePrior prior = model.getPartitionTreePrior();
        treePriorGenerator.writePriorLikelihoodReferenceLog(prior, model, writer);
        writer.writeText("");
    }
    for (PartitionTreePrior prior : options.getPartitionTreePriors()) {
        if (prior.getNodeHeightPrior() == TreePriorType.EXTENDED_SKYLINE) {
            // only 1 coalescent
            writer.writeIDref(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD, prior.getPrefix() + COALESCENT);
        } else if (prior.getNodeHeightPrior() == TreePriorType.SKYGRID) {
            writer.writeIDref(GMRFSkyrideLikelihoodParser.SKYGRID_LIKELIHOOD, prior.getPrefix() + "skygrid");
        }
    }
    writer.writeCloseTag(LoggerParser.LOG);
    generateInsertionPoint(ComponentGenerator.InsertionPoint.AFTER_FILE_LOG, writer);
}

Example 18 with Taxa

use of dr.evolution.util.Taxa in project beast-mcmc by beast-dev.

the class InitialTreeGenerator method writeStartingTree.

/**
 * Generate XML for the starting tree
 * @param model  PartitionTreeModel
 *
 * @param writer the writer
 */
public void writeStartingTree(PartitionTreeModel model, XMLWriter writer) {
    String prefix = model.getPrefix();
    switch(model.getStartingTreeType()) {
        case USER:
        case UPGMA:
            Parameter rootHeight = model.getParameter("treeModel.rootHeight");
            // generate a rescaled starting tree
            writer.writeComment("Construct a starting tree that is compatible with specified clade heights");
            Attribute[] attributes = (rootHeight.priorType != PriorType.NONE_TREE_PRIOR ? new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + STARTING_TREE), new Attribute.Default<String>(RescaledTreeParser.HEIGHT, "" + rootHeight.getInitial()) } : new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + STARTING_TREE) });
            writer.writeOpenTag(RescaledTreeParser.RESCALED_TREE, attributes);
            writeSourceTree(model, writer);
            if (options.taxonSets != null && options.taxonSets.size() > 0) {
                for (Taxa taxa : options.taxonSets) {
                    Double height = options.taxonSetsHeights.get(taxa);
                    if (height != null) {
                        writer.writeOpenTag(RescaledTreeParser.CLADE, new Attribute.Default<String>(RescaledTreeParser.HEIGHT, height.toString()));
                        writer.writeTag("taxa", new Attribute.Default<String>(XMLParser.IDREF, taxa.getId()), true);
                        writer.writeCloseTag(RescaledTreeParser.CLADE);
                    } else if (options.taxonSetsMono.get(taxa)) {
                        // if monophyly is enforced then placing this clade element here will force BEAST to check
                        // the clade exists in the tree.
                        writer.writeOpenTag(RescaledTreeParser.CLADE);
                        writer.writeTag("taxa", new Attribute.Default<String>(XMLParser.IDREF, taxa.getId()), true);
                        writer.writeCloseTag(RescaledTreeParser.CLADE);
                    }
                }
            }
            writer.writeCloseTag(RescaledTreeParser.RESCALED_TREE);
            break;
        case RANDOM:
            // generate a coalescent tree
            String simulatorId = prefix + STARTING_TREE;
            String taxaId = TaxaParser.TAXA;
            AbstractPartitionData partition = options.getDataPartitions(model).get(0);
            if (!options.hasIdenticalTaxa()) {
                taxaId = partition.getPartitionTreeModel().getPrefix() + TaxaParser.TAXA;
            }
            if (partition instanceof PartitionPattern && ((PartitionPattern) partition).getPatterns().hasMask()) {
                taxaId = partition.getPrefix() + TaxaParser.TAXA;
            }
            writer.writeComment("Generate a random starting tree under the coalescent process");
            if (options.taxonSets != null && options.taxonSets.size() > 0) {
                writeSubTree(simulatorId, taxaId, options.taxonList, model, writer);
            } else {
                writer.writeOpenTag(CoalescentSimulatorParser.COALESCENT_SIMULATOR, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, simulatorId) });
                writeTaxaRef(taxaId, model, writer);
                writeInitialDemoModelRef(model, writer);
                writer.writeCloseTag(CoalescentSimulatorParser.COALESCENT_SIMULATOR);
            }
            break;
        default:
            throw new IllegalArgumentException("Unknown StartingTreeType");
    }
}

Example 19 with Taxa

use of dr.evolution.util.Taxa in project beast-mcmc by beast-dev.

the class InitialTreeGenerator method writeTaxaRef.

private void writeTaxaRef(String taxaId, PartitionTreeModel model, XMLWriter writer) {
    Attribute[] taxaAttribute = { new Attribute.Default<String>(XMLParser.IDREF, taxaId) };
    if (options.taxonSets != null && options.taxonSets.size() > 0) {
        // *BEAST case is in STARBEASTGenerator.writeStartingTreeForCalibration(XMLWriter writer)
        writer.writeOpenTag(OldCoalescentSimulatorParser.CONSTRAINED_TAXA);
        writer.writeTag(TaxaParser.TAXA, taxaAttribute, true);
        for (Taxa taxa : options.taxonSets) {
            if (options.taxonSetsTreeModel.get(taxa).equals(model)) {
                Parameter statistic = options.getStatistic(taxa);
                Attribute mono = new Attribute.Default<Boolean>(OldCoalescentSimulatorParser.IS_MONOPHYLETIC, options.taxonSetsMono.get(taxa));
                writer.writeOpenTag(OldCoalescentSimulatorParser.TMRCA_CONSTRAINT, mono);
                writer.writeIDref(TaxaParser.TAXA, taxa.getId());
                if (model.getPartitionTreePrior().getNodeHeightPrior() == TreePriorType.YULE_CALIBRATION && statistic.priorType == PriorType.UNIFORM_PRIOR) {
                    writeDistribution(statistic, false, writer);
                }
                writer.writeCloseTag(OldCoalescentSimulatorParser.TMRCA_CONSTRAINT);
            }
        }
        writer.writeCloseTag(OldCoalescentSimulatorParser.CONSTRAINED_TAXA);
    } else {
        writer.writeTag(TaxaParser.TAXA, taxaAttribute, true);
    }
}

Example 20 with Taxa

use of dr.evolution.util.Taxa in project beast-mcmc by beast-dev.

the class TMRCAStatisticsGenerator method writeTMRCAStatisticReferences.

public void writeTMRCAStatisticReferences(XMLWriter writer) {
    for (Taxa taxa : options.taxonSets) {
        // make tmrca(tree.name) eay to read in log for Tracer
        PartitionTreeModel treeModel = options.taxonSetsTreeModel.get(taxa);
        writer.writeIDref(TMRCAStatisticParser.TMRCA_STATISTIC, "tmrca(" + treeModel.getPrefix() + taxa.getId() + ")");
    }
    for (Taxa taxa : options.taxonSets) {
        // make tmrca(tree.name) eay to read in log for Tracer
        PartitionTreeModel treeModel = options.taxonSetsTreeModel.get(taxa);
        if (treeModel.hasTipCalibrations()) {
            writer.writeIDref(TMRCAStatisticParser.TMRCA_STATISTIC, "age(" + treeModel.getPrefix() + taxa.getId() + ")");
        }
    }
}