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

Example 21 with Taxa

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

the class TMRCAStatisticsGenerator method writeTaxonSets.

/**
 * Generate additional taxon sets
 *
 * @param writer    the writer
 * @param taxonSets a list of taxa to write
 */
public void writeTaxonSets(XMLWriter writer, List<Taxa> taxonSets) {
    writer.writeText("");
    for (Taxa taxa : taxonSets) {
        writer.writeOpenTag(TaxaParser.TAXA, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, taxa.getId()) });
        for (int j = 0; j < taxa.getTaxonCount(); j++) {
            writer.writeIDref(TaxonParser.TAXON, taxa.getTaxon(j).getId());
        }
        writer.writeCloseTag(TaxaParser.TAXA);
    }
}

Example 22 with Taxa

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

the class TMRCAStatisticsGenerator method writeTMRCAStatistics.

/**
 * Generate tmrca statistics
 *
 * @param writer       the writer
 */
public void writeTMRCAStatistics(XMLWriter writer) {
    List<Taxa> taxonSets;
    Map<Taxa, Boolean> taxonSetsMono;
    taxonSets = options.taxonSets;
    taxonSetsMono = options.taxonSetsMono;
    writer.writeComment("Taxon Sets");
    writer.writeText("");
    for (Taxa taxa : taxonSets) {
        PartitionTreeModel treeModel = options.taxonSetsTreeModel.get(taxa);
        String id = "tmrca(" + treeModel.getPrefix() + taxa.getId() + ")";
        writeTMRCAStatistic(writer, id, taxa, treeModel, false, options.taxonSetsIncludeStem.get(taxa));
        if (treeModel.hasTipCalibrations()) {
            id = "age(" + treeModel.getPrefix() + taxa.getId() + ")";
            writeTMRCAStatistic(writer, id, taxa, treeModel, true, options.taxonSetsIncludeStem.get(taxa));
        }
        if (taxonSetsMono.get(taxa)) {
            // && treeModel.getPartitionTreePrior().getNodeHeightPrior() != TreePriorType.YULE
            // && options.getKeysFromValue(options.taxonSetsTreeModel, treeModel).size() > 1) {
            writer.writeOpenTag(MonophylyStatisticParser.MONOPHYLY_STATISTIC, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, "monophyly(" + taxa.getId() + ")") });
            writer.writeOpenTag(MonophylyStatisticParser.MRCA);
            writer.writeIDref(TaxaParser.TAXA, taxa.getId());
            writer.writeCloseTag(MonophylyStatisticParser.MRCA);
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, treeModel.getPrefix() + DefaultTreeModel.TREE_MODEL);
            writer.writeCloseTag(MonophylyStatisticParser.MONOPHYLY_STATISTIC);
        }
    }
}

Example 23 with Taxa

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

the class TreePriorGenerator method writePriorLikelihood.

/**
 * Write the prior on node heights (coalescent or speciational models)
 *
 * @param model  PartitionTreeModel
 * @param writer the writer
 */
void writePriorLikelihood(PartitionTreeModel model, XMLWriter writer) {
    // tree model prefix
    String prefix = model.getPrefix();
    PartitionTreePrior prior = model.getPartitionTreePrior();
    TreePriorType treePrior = prior.getNodeHeightPrior();
    switch(treePrior) {
        case YULE:
        case BIRTH_DEATH:
        case BIRTH_DEATH_INCOMPLETE_SAMPLING:
        case BIRTH_DEATH_SERIAL_SAMPLING:
        case BIRTH_DEATH_BASIC_REPRODUCTIVE_NUMBER:
        case YULE_CALIBRATION:
            // generate a speciational process
            writer.writeComment("Generate a speciation likelihood for Yule or Birth Death");
            writer.writeOpenTag(SpeciationLikelihoodParser.SPECIATION_LIKELIHOOD, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + "speciation") });
            // write pop size socket
            writer.writeOpenTag(SpeciationLikelihoodParser.MODEL);
            writeNodeHeightPriorModelRef(prior, writer);
            writer.writeCloseTag(SpeciationLikelihoodParser.MODEL);
            writer.writeOpenTag(SpeciationLikelihoodParser.TREE);
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, prefix + DefaultTreeModel.TREE_MODEL);
            writer.writeCloseTag(SpeciationLikelihoodParser.TREE);
            if (treePrior == TreePriorType.YULE_CALIBRATION) {
                if (options.treeModelOptions.isNodeCalibrated(model) == 0) {
                    writer.writeOpenTag(SpeciationLikelihoodParser.CALIBRATION, new Attribute[] { new Attribute.Default<String>(SpeciationLikelihoodParser.CORRECTION, prior.getCalibCorrectionType().toString()) });
                    writer.writeOpenTag(SpeciationLikelihoodParser.POINT);
                    String taxaId;
                    if (options.hasIdenticalTaxa()) {
                        taxaId = TaxaParser.TAXA;
                    } else {
                        taxaId = options.getDataPartitions(model).get(0).getPrefix() + TaxaParser.TAXA;
                    }
                    writer.writeIDref(TaxaParser.TAXA, taxaId);
                    writeDistribution(model.getParameter("treeModel.rootHeight"), true, writer);
                    writer.writeCloseTag(SpeciationLikelihoodParser.POINT);
                    writer.writeCloseTag(SpeciationLikelihoodParser.CALIBRATION);
                } else if (options.treeModelOptions.isNodeCalibrated(model) == 1) {
                    // should be only 1 calibrated internal node with monophyletic for each tree at moment
                    Taxa t = (Taxa) options.getKeysFromValue(options.taxonSetsTreeModel, model).get(0);
                    Parameter nodeCalib = options.getStatistic(t);
                    writer.writeOpenTag(SpeciationLikelihoodParser.CALIBRATION, new Attribute[] { new Attribute.Default<String>(SpeciationLikelihoodParser.CORRECTION, prior.getCalibCorrectionType().toString()) });
                    writer.writeOpenTag(SpeciationLikelihoodParser.POINT);
                    writer.writeIDref(TaxaParser.TAXA, t.getId());
                    writeDistribution(nodeCalib, true, writer);
                    writer.writeCloseTag(SpeciationLikelihoodParser.POINT);
                    writer.writeCloseTag(SpeciationLikelihoodParser.CALIBRATION);
                    if (!options.treeModelOptions.isNodeCalibrated(nodeCalib)) {
                        throw new IllegalArgumentException("Calibrated Yule model requires a calibration to be specified for node, " + nodeCalib.getName() + ".");
                    }
                }
            }
            writer.writeCloseTag(SpeciationLikelihoodParser.SPECIATION_LIKELIHOOD);
            break;
        case SKYLINE:
            // generate a Bayesian skyline plot
            writer.writeComment("Generate a generalizedSkyLineLikelihood for Bayesian Skyline");
            writer.writeOpenTag(BayesianSkylineLikelihoodParser.SKYLINE_LIKELIHOOD, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + "skyline"), new Attribute.Default<String>("linear", prior.getSkylineModel() == TreePriorParameterizationType.LINEAR_SKYLINE ? "true" : "false") });
            // write pop size socket
            writer.writeOpenTag(BayesianSkylineLikelihoodParser.POPULATION_SIZES);
            if (prior.getSkylineModel() == TreePriorParameterizationType.LINEAR_SKYLINE) {
                writeParameter(prior.getParameter("skyline.popSize"), prior.getSkylineGroupCount() + 1, writer);
            } else {
                writeParameter(prior.getParameter("skyline.popSize"), prior.getSkylineGroupCount(), writer);
            }
            writer.writeCloseTag(BayesianSkylineLikelihoodParser.POPULATION_SIZES);
            // write group size socket
            writer.writeOpenTag(BayesianSkylineLikelihoodParser.GROUP_SIZES);
            writeParameter(prior.getParameter("skyline.groupSize"), prior.getSkylineGroupCount(), writer);
            writer.writeCloseTag(BayesianSkylineLikelihoodParser.GROUP_SIZES);
            writer.writeOpenTag(CoalescentLikelihoodParser.POPULATION_TREE);
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, prefix + DefaultTreeModel.TREE_MODEL);
            writer.writeCloseTag(CoalescentLikelihoodParser.POPULATION_TREE);
            writer.writeCloseTag(BayesianSkylineLikelihoodParser.SKYLINE_LIKELIHOOD);
            writer.writeText("");
            writeExponentialMarkovLikelihood(prior, writer);
            break;
        case EXTENDED_SKYLINE:
            // different format
            break;
        case GMRF_SKYRIDE:
            writer.writeComment("Generate a gmrfSkyrideLikelihood for GMRF Bayesian Skyride process");
            writer.writeOpenTag(GMRFSkyrideLikelihoodParser.SKYLINE_LIKELIHOOD, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + "skyride"), new Attribute.Default<String>(GMRFSkyrideLikelihoodParser.TIME_AWARE_SMOOTHING, prior.getSkyrideSmoothing() == TreePriorParameterizationType.TIME_AWARE_SKYRIDE ? "true" : "false"), new Attribute.Default<String>(GMRFSkyrideLikelihoodParser.RANDOMIZE_TREE, // TODO For GMRF, tree model/tree prior combination not implemented by BEAST yet. The validation is in BeastGenerator.checkOptions()
            options.getPartitionTreeModels(prior).get(0).getStartingTreeType() == StartingTreeType.UPGMA ? "true" : "false") });
            int skyrideIntervalCount = options.taxonList.getTaxonCount() - 1;
            writer.writeOpenTag(GMRFSkyrideLikelihoodParser.POPULATION_PARAMETER);
            writer.writeComment("skyride.logPopSize is in log units unlike other popSize");
            writeParameter(prior.getParameter("skyride.logPopSize"), skyrideIntervalCount, writer);
            writer.writeCloseTag(GMRFSkyrideLikelihoodParser.POPULATION_PARAMETER);
            writer.writeOpenTag(GMRFSkyrideLikelihoodParser.GROUP_SIZES);
            writeParameter(prior.getParameter("skyride.groupSize"), skyrideIntervalCount, writer);
            writer.writeCloseTag(GMRFSkyrideLikelihoodParser.GROUP_SIZES);
            writer.writeOpenTag(GMRFSkyrideLikelihoodParser.PRECISION_PARAMETER);
            writeParameter(prior.getParameter("skyride.precision"), 1, writer);
            writer.writeCloseTag(GMRFSkyrideLikelihoodParser.PRECISION_PARAMETER);
            writer.writeOpenTag(GMRFSkyrideLikelihoodParser.POPULATION_TREE);
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, prefix + DefaultTreeModel.TREE_MODEL);
            writer.writeCloseTag(GMRFSkyrideLikelihoodParser.POPULATION_TREE);
            writer.writeCloseTag(GMRFSkyrideLikelihoodParser.SKYLINE_LIKELIHOOD);
            break;
        case SKYGRID:
            break;
        default:
            // generate a coalescent process
            writer.writeComment("Generate a coalescent likelihood");
            writer.writeOpenTag(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, prefix + COALESCENT) });
            writer.writeOpenTag(CoalescentLikelihoodParser.MODEL);
            writeNodeHeightPriorModelRef(prior, writer);
            writer.writeCloseTag(CoalescentLikelihoodParser.MODEL);
            writer.writeOpenTag(CoalescentLikelihoodParser.POPULATION_TREE);
            writer.writeIDref(DefaultTreeModel.TREE_MODEL, prefix + DefaultTreeModel.TREE_MODEL);
            writer.writeCloseTag(CoalescentLikelihoodParser.POPULATION_TREE);
            writer.writeCloseTag(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD);
    }
}

Example 24 with Taxa

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

the class PartitionClockModel method selectParameters.

// From PartitionClockModelTreeModelLink
// public List<Parameter> selectParameters(List<Parameter> params) {
// setAvgRootAndRate();
// getParameter("branchRates.categories");
// getParameter("treeModel.rootRate");
// getParameter("treeModel.nodeRates");
// getParameter("treeModel.allRates");
// 
// if (options.hasData()) {
// // if not fixed then do mutation rate move and up/down move
// boolean fixed = !model.isEstimatedRate();
// 
// Parameter rateParam;
// 
// switch (model.getClockType()) {
// case AUTOCORRELATED:
// rateParam = getParameter("treeModel.rootRate");
// rateParam.isFixed = fixed;
// if (!fixed) params.add(rateParam);
// 
// params.add(getParameter("branchRates.var"));
// break;
// }
// }
// return params;
// }
@Override
public List<Parameter> selectParameters(List<Parameter> params) {
    // setAvgRootAndRate();
    double rate = 1.0;
    if (options.hasData()) {
        switch(clockType) {
            case STRICT_CLOCK:
                params.add(getClockRateParameter());
                break;
            case RANDOM_LOCAL_CLOCK:
                params.add(getClockRateParameter());
                getParameter(ClockType.LOCAL_CLOCK + ".changes");
                params.add(getParameter("rateChanges"));
                params.add(getParameter(ClockType.LOCAL_CLOCK + ".relativeRates"));
                break;
            case FIXED_LOCAL_CLOCK:
                params.add(getClockRateParameter());
                for (Taxa taxonSet : options.taxonSets) {
                    if (options.taxonSetsMono.get(taxonSet)) {
                        String parameterName = taxonSet.getId() + ".rate";
                        if (!hasParameter(parameterName)) {
                            new Parameter.Builder(parameterName, "substitution rate").prior(PriorType.CTMC_RATE_REFERENCE_PRIOR).initial(rate).isCMTCRate(true).isNonNegative(true).partitionOptions(this).taxonSet(taxonSet).build(parameters);
                            createScaleOperator(parameterName, demoTuning, rateWeights);
                        }
                        params.add(getParameter(taxonSet.getId() + ".rate"));
                    }
                }
                break;
            case UNCORRELATED:
                // in getClockRateParameter.
                switch(clockDistributionType) {
                    case LOGNORMAL:
                        params.add(getClockRateParameter());
                        params.add(getParameter(ClockType.UCLD_STDEV));
                        break;
                    case GAMMA:
                        params.add(getClockRateParameter());
                        params.add(getParameter(ClockType.UCGD_SHAPE));
                        break;
                    case CAUCHY:
                        throw new UnsupportedOperationException("Uncorrelated Cauchy clock not implemented yet");
                    // break;
                    case EXPONENTIAL:
                        params.add(getClockRateParameter());
                        break;
                    case MODEL_AVERAGING:
                        params.add(getClockRateParameter(ClockType.UNCORRELATED, ClockDistributionType.LOGNORMAL));
                        params.add(getParameter(ClockType.UCLD_STDEV));
                        params.add(getClockRateParameter(ClockType.UNCORRELATED, ClockDistributionType.GAMMA));
                        params.add(getParameter(ClockType.UCGD_SHAPE));
                        params.add(getClockRateParameter(ClockType.UNCORRELATED, ClockDistributionType.EXPONENTIAL));
                        params.add(getParameter("branchRates.quantiles"));
                        params.add(getParameter("branchRates.distributionIndex"));
                        break;
                }
                break;
            case AUTOCORRELATED:
                throw new UnsupportedOperationException("Autocorrelated clock not implemented yet");
            default:
                throw new IllegalArgumentException("Unknown clock model");
        }
    // Parameter rateParam = getClockRateParameter();
    // params.add(rateParam);
    }
    return params;
}

Example 25 with Taxa

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

the class TaxonSetPanel method setupTaxonSetsComboBox.

protected void setupTaxonSetsComboBox(JComboBox comboBox, List<Taxon> availableTaxa) {
    comboBox.removeAllItems();
    comboBox.addItem(TAXON.toLowerCase() + "...");
    for (Taxa taxa : options.taxonSets) {
        // AR - as these comboboxes are just intended to be handy ways of selecting taxa, I have removed
        // these requirements (it was just confusing why they weren't in the lists.
        // if (taxa != currentTaxonSet) {
        // if (isCompatible(taxa, availableTaxa)) {
        // have to add String, otherwise it will throw Exception to cast "taxa..." into Taxa
        comboBox.addItem(taxa.getId());
    // }
    // }
    }
}