Examples with Attribute dr.util.Attribute used on opensource projects

Example 26 with Attribute

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

the class TreePriorGenerator method writeTreePriorModel.

//    void writeTreePrior(PartitionTreePrior prior, PartitionTreeModel model, XMLWriter writer) {    // for species, partitionName.treeModel
//        setModelPrefix(prior.getPrefix()); // only has prefix, if (options.getPartitionTreePriors().size() > 1)
//
//        writePriorLikelihood(prior, model, writer);
//    }
/**
     * Write a tree prior (coalescent or speciational) model
     *
     * @param prior  the partition tree prior
     * @param writer the writer
     */
void writeTreePriorModel(PartitionTreePrior prior, XMLWriter writer) {
    // only has prefix, if (options.getPartitionTreePriors().size() > 1)
    setModelPrefix(prior.getPrefix());
    String initialPopSize = null;
    TreePriorType nodeHeightPrior = prior.getNodeHeightPrior();
    Units.Type units = options.units;
    TreePriorParameterizationType parameterization = prior.getParameterization();
    switch(nodeHeightPrior) {
        case CONSTANT:
            writer.writeComment("A prior assumption that the population size has remained constant", "throughout the time spanned by the genealogy.");
            writer.writeOpenTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "constant"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writeParameter("constant.popSize", prior, writer);
            writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
            break;
        case EXPONENTIAL:
            // generate an exponential prior tree
            writer.writeComment("A prior assumption that the population size has grown exponentially", "throughout the time spanned by the genealogy.");
            writer.writeOpenTag(ExponentialGrowthModelParser.EXPONENTIAL_GROWTH_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "exponential"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // write pop size socket
            writer.writeOpenTag(ExponentialGrowthModelParser.POPULATION_SIZE);
            writeParameter("exponential.popSize", prior, writer);
            writer.writeCloseTag(ExponentialGrowthModelParser.POPULATION_SIZE);
            if (parameterization == TreePriorParameterizationType.GROWTH_RATE) {
                // write growth rate socket
                writer.writeOpenTag(ExponentialGrowthModelParser.GROWTH_RATE);
                writeParameter("exponential.growthRate", prior, writer);
                writer.writeCloseTag(ExponentialGrowthModelParser.GROWTH_RATE);
            } else {
                // write doubling time socket
                writer.writeOpenTag(ExponentialGrowthModelParser.DOUBLING_TIME);
                writeParameter("exponential.doublingTime", prior, writer);
                writer.writeCloseTag(ExponentialGrowthModelParser.DOUBLING_TIME);
            }
            writer.writeCloseTag(ExponentialGrowthModelParser.EXPONENTIAL_GROWTH_MODEL);
            break;
        case LOGISTIC:
            // generate an exponential prior tree
            writer.writeComment("A prior assumption that the population size has grown logistically", "throughout the time spanned by the genealogy.");
            writer.writeOpenTag(LogisticGrowthModelParser.LOGISTIC_GROWTH_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "logistic"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // write pop size socket
            writer.writeOpenTag(LogisticGrowthModelParser.POPULATION_SIZE);
            writeParameter("logistic.popSize", prior, writer);
            writer.writeCloseTag(LogisticGrowthModelParser.POPULATION_SIZE);
            if (parameterization == TreePriorParameterizationType.GROWTH_RATE) {
                // write growth rate socket
                writer.writeOpenTag(LogisticGrowthModelParser.GROWTH_RATE);
                writeParameter("logistic.growthRate", prior, writer);
                writer.writeCloseTag(LogisticGrowthModelParser.GROWTH_RATE);
            } else {
                // write doubling time socket
                writer.writeOpenTag(LogisticGrowthModelParser.DOUBLING_TIME);
                writeParameter("logistic.doublingTime", prior, writer);
                writer.writeCloseTag(LogisticGrowthModelParser.DOUBLING_TIME);
            }
            // write logistic t50 socket
            writer.writeOpenTag(LogisticGrowthModelParser.TIME_50);
            //	            if (options.clockModelOptions.getRateOptionClockModel() == FixRateType.FIX_MEAN
            //	        			|| options.clockModelOptions.getRateOptionClockModel() == FixRateType.RELATIVE_TO) {
            //            		writer.writeComment("No calibration");
            //                writer.writeComment("logistic.t50 initial always has to < treeRootHeight initial");
            //                dr.app.beauti.options.Parameter priorPara = prior.getParameter("logistic.t50");
            //
            //                double initRootHeight;
            //                if (options.isShareSameTreePrior()) {
            //                    initRootHeight = priorPara.initial;
            //                    for (PartitionTreeModel tree : options.getPartitionTreeModels()) {
            //                        double tmpRootHeight = tree.getParameter("treeModel.rootHeight").initial;
            //                        if (initRootHeight > tmpRootHeight) { // take min
            //                            initRootHeight = tmpRootHeight;
            //                        }
            //                    }
            //                } else {
            //                    initRootHeight = prior.getTreeModel().getParameter("treeModel.rootHeight").initial;
            //                }
            //                // logistic.t50 initial always has to < treeRootHeight initial
            //                if (priorPara.initial >= initRootHeight) {
            //                    priorPara.initial = initRootHeight / 2; // tree prior.initial has to < treeRootHeight.initial
            //                }
            //	            } else {
            //	            	writer.writeComment("Has calibration");
            //
            //	            	throw new IllegalArgumentException("This function is not available in this release !");
            //	            }
            writeParameter("logistic.t50", prior, writer);
            writer.writeCloseTag(LogisticGrowthModelParser.TIME_50);
            writer.writeCloseTag(LogisticGrowthModelParser.LOGISTIC_GROWTH_MODEL);
            initialPopSize = "logistic.popSize";
            break;
        case EXPANSION:
            // generate an exponential prior tree
            writer.writeComment("A prior assumption that the population size has grown exponentially", "from some ancestral population size in the past.");
            writer.writeOpenTag(ExpansionModelParser.EXPANSION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "expansion"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // write pop size socket
            writeParameter(ExpansionModelParser.POPULATION_SIZE, "expansion.popSize", prior, writer);
            if (parameterization == TreePriorParameterizationType.GROWTH_RATE) {
                // write growth rate socket
                writeParameter(ExpansionModelParser.GROWTH_RATE, "expansion.growthRate", prior, writer);
            } else {
                // write doubling time socket
                writeParameter(ExpansionModelParser.DOUBLING_TIME, "expansion.doublingTime", prior, writer);
            }
            // write ancestral proportion socket
            writeParameter(ExpansionModelParser.ANCESTRAL_POPULATION_PROPORTION, "expansion.ancestralProportion", prior, writer);
            writer.writeCloseTag(ExpansionModelParser.EXPANSION_MODEL);
            initialPopSize = "expansion.popSize";
            break;
        case YULE:
        case YULE_CALIBRATION:
            if (nodeHeightPrior == TreePriorType.YULE_CALIBRATION) {
                writer.writeComment("Calibrated Yule: Heled J, Drummond AJ (2011), Syst Biol, doi: " + "10.1093/sysbio/syr087");
            } else {
                writer.writeComment("A prior on the distribution node heights defined given", "a Yule speciation process (a pure birth process).");
            }
            writer.writeOpenTag(YuleModelParser.YULE_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + YuleModelParser.YULE), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
            writeParameter(YuleModelParser.BIRTH_RATE, "yule.birthRate", prior, writer);
            writer.writeCloseTag(YuleModelParser.YULE_MODEL);
            break;
        case BIRTH_DEATH:
        case BIRTH_DEATH_INCOMPLETE_SAMPLING:
            writer.writeComment("A prior on the distribution node heights defined given");
            writer.writeComment(nodeHeightPrior == TreePriorType.BIRTH_DEATH_INCOMPLETE_SAMPLING ? BirthDeathModelParser.getCitationRHO() : BirthDeathModelParser.getCitation());
            writer.writeOpenTag(BirthDeathModelParser.BIRTH_DEATH_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + BirthDeathModelParser.BIRTH_DEATH), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
            writeParameter(BirthDeathModelParser.BIRTHDIFF_RATE, BirthDeathModelParser.MEAN_GROWTH_RATE_PARAM_NAME, prior, writer);
            writeParameter(BirthDeathModelParser.RELATIVE_DEATH_RATE, BirthDeathModelParser.RELATIVE_DEATH_RATE_PARAM_NAME, prior, writer);
            if (nodeHeightPrior == TreePriorType.BIRTH_DEATH_INCOMPLETE_SAMPLING) {
                writeParameter(BirthDeathModelParser.SAMPLE_PROB, BirthDeathModelParser.BIRTH_DEATH + "." + BirthDeathModelParser.SAMPLE_PROB, prior, writer);
            }
            writer.writeCloseTag(BirthDeathModelParser.BIRTH_DEATH_MODEL);
            break;
        case BIRTH_DEATH_SERIAL_SAMPLING:
            writer.writeComment(BirthDeathSerialSamplingModelParser.getCitationPsiOrg());
            writer.writeOpenTag(BirthDeathSerialSamplingModelParser.BIRTH_DEATH_SERIAL_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + BirthDeathSerialSamplingModelParser.BDSS), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)), new Attribute.Default<Boolean>(BirthDeathSerialSamplingModelParser.HAS_FINAL_SAMPLE, false) });
            writeParameter(BirthDeathSerialSamplingModelParser.LAMBDA, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.LAMBDA, prior, writer);
            writeParameter(BirthDeathSerialSamplingModelParser.RELATIVE_MU, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.RELATIVE_MU, prior, writer);
            //                writeParameter(BirthDeathSerialSamplingModelParser.SAMPLE_PROBABILITY,
            //                        BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.SAMPLE_PROBABILITY, prior, writer);
            writeParameter(BirthDeathSerialSamplingModelParser.PSI, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.PSI, prior, writer);
            writeParameter(BirthDeathSerialSamplingModelParser.ORIGIN, BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.ORIGIN, prior, writer);
            writer.writeCloseTag(BirthDeathSerialSamplingModelParser.BIRTH_DEATH_SERIAL_MODEL);
            break;
        case BIRTH_DEATH_BASIC_REPRODUCTIVE_NUMBER:
            writer.writeComment(BirthDeathSerialSamplingModelParser.getCitationRT());
            writer.writeOpenTag(BirthDeathEpidemiologyModelParser.BIRTH_DEATH_EPIDEMIOLOGY, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + BirthDeathEpidemiologyModelParser.BIRTH_DEATH_EPIDEMIOLOGY), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
            writeParameter(BirthDeathEpidemiologyModelParser.R0, BirthDeathEpidemiologyModelParser.R0, prior, writer);
            writeParameter(BirthDeathEpidemiologyModelParser.RECOVERY_RATE, BirthDeathEpidemiologyModelParser.RECOVERY_RATE, prior, writer);
            writeParameter(BirthDeathEpidemiologyModelParser.SAMPLING_PROBABILITY, BirthDeathEpidemiologyModelParser.SAMPLING_PROBABILITY, prior, writer);
            writeParameter(BirthDeathEpidemiologyModelParser.ORIGIN, BirthDeathEpidemiologyModelParser.ORIGIN, prior, writer);
            writer.writeCloseTag(BirthDeathEpidemiologyModelParser.BIRTH_DEATH_EPIDEMIOLOGY);
            break;
        case SPECIES_BIRTH_DEATH:
        case SPECIES_YULE:
        case SPECIES_YULE_CALIBRATION:
            writer.writeComment("A prior assumption that the population size has remained constant");
            writer.writeComment("throughout the time spanned by the genealogy.");
            if (nodeHeightPrior == TreePriorType.SPECIES_YULE_CALIBRATION)
                writer.writeComment("Calibrated Yule: Heled J, Drummond AJ (2011), Syst Biol, doi: " + "10.1093/sysbio/syr087");
            writer.writeOpenTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "constant"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(options.units)) });
            // initial value for pop mean is the same as what used to be the value for the population size
            Parameter para = options.starBEASTOptions.getParameter(TraitData.TRAIT_SPECIES + "." + options.starBEASTOptions.POP_MEAN);
            prior.getParameter("constant.popSize").setInitial(para.getInitial());
            writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writeParameter("constant.popSize", prior, writer);
            writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
            writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
            break;
    }
    if ((!options.useStarBEAST) && nodeHeightPrior != TreePriorType.CONSTANT && nodeHeightPrior != TreePriorType.EXPONENTIAL) {
        // If the node height prior is not one of these two then we need to simulate a
        // random starting tree under a constant size coalescent.
        writer.writeComment("This is a simple constant population size coalescent model", "that is used to generate an initial tree for the chain.");
        writer.writeOpenTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, modelPrefix + "initialDemo"), new Attribute.Default<String>("units", Units.Utils.getDefaultUnitName(units)) });
        writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
        if (initialPopSize != null) {
            writer.writeIDref(ParameterParser.PARAMETER, modelPrefix + initialPopSize);
        } else {
            writeParameter(modelPrefix + "initialDemo.popSize", 1, 100.0, Double.NaN, Double.NaN, writer);
        }
        writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
        writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
    }
//        if (nodeHeightPrior == TreePriorType.BIRTH_DEATH_BASIC_REPRODUCTIVE_NUMBER) {
//            writer.writeComment("R0 = b/(b*d+s*r)");
//            writer.writeOpenTag(RPNcalculatorStatisticParser.RPN_STATISTIC,
//                    new Attribute[]{
//                            new Attribute.Default<String>(XMLParser.ID, modelPrefix + "R0")
//                    });
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "b")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.LAMBDA, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "d")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.RELATIVE_MU, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "s")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.PSI, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.VARIABLE,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "r")
//                    });
//            writeParameterRef(modelPrefix + BirthDeathSerialSamplingModelParser.BDSS + "." + BirthDeathSerialSamplingModelParser.R, writer);
//            writer.writeCloseTag(RPNcalculatorStatisticParser.VARIABLE);
//
//            writer.writeOpenTag(RPNcalculatorStatisticParser.EXPRESSION,
//                    new Attribute[]{
//                            new Attribute.Default<String>(Statistic.NAME, modelPrefix + "R0")
//                    });
//            writer.writeText(modelPrefix + "b " + modelPrefix + "b " + modelPrefix + "d " + "* " + modelPrefix + "s " + modelPrefix + "r " + "* + /");
//            writer.writeCloseTag(RPNcalculatorStatisticParser.EXPRESSION);
//
//            writer.writeCloseTag(RPNcalculatorStatisticParser.RPN_STATISTIC);
//        }
}

Example 27 with Attribute

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

the class InitialTreeGenerator method writeSubTree.

private void writeSubTree(String treeId, String taxaId, Taxa taxa, PartitionTreeModel model, XMLWriter writer) {
    Double height = options.taxonSetsHeights.get(taxa);
    if (height == null) {
        height = Double.NaN;
    }
    Attribute[] attributes = new Attribute[] {};
    if (treeId != null) {
        if (Double.isNaN(height)) {
            attributes = new Attribute[] { new Attribute.Default<String>(XMLParser.ID, treeId) };
        } else {
            attributes = new Attribute[] { new Attribute.Default<String>(XMLParser.ID, treeId), new Attribute.Default<String>(CoalescentSimulatorParser.HEIGHT, "" + height) };
        }
    } else {
        if (!Double.isNaN(height)) {
            attributes = new Attribute[] { new Attribute.Default<String>(CoalescentSimulatorParser.HEIGHT, "" + height) };
        }
    }
    // construct a subtree
    writer.writeOpenTag(CoalescentSimulatorParser.COALESCENT_SIMULATOR, attributes);
    List<Taxa> subsets = new ArrayList<Taxa>();
    for (Taxa taxa2 : options.taxonSets) {
        boolean sameTree = model.equals(options.taxonSetsTreeModel.get(taxa2));
        boolean isMono = options.taxonSetsMono.get(taxa2);
        boolean hasHeight = options.taxonSetsHeights.get(taxa2) != null;
        boolean isSubset = taxa.containsAll(taxa2);
        if (sameTree && (isMono || hasHeight) && taxa2 != taxa && isSubset) {
            subsets.add(taxa2);
        }
    }
    List<Taxa> toRemove = new ArrayList<Taxa>();
    for (Taxa taxa3 : subsets) {
        boolean isSubSubSet = false;
        for (Taxa taxa4 : subsets) {
            if (!taxa4.equals(taxa3) && taxa4.containsAll(taxa3)) {
                isSubSubSet = true;
            }
        }
        if (isSubSubSet) {
            toRemove.add(taxa3);
        }
    }
    subsets.removeAll(toRemove);
    for (Taxa taxa5 : subsets) {
        //            remainingTaxa.removeTaxa(taxa5);
        writeSubTree(null, null, taxa5, model, writer);
    }
    if (taxaId == null) {
        writer.writeIDref(TaxaParser.TAXA, taxa.getId());
    } else {
        writer.writeIDref(TaxaParser.TAXA, taxaId);
    }
    writeInitialDemoModelRef(model, writer);
    writer.writeCloseTag(CoalescentSimulatorParser.COALESCENT_SIMULATOR);
}

Example 28 with Attribute

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

the class LogGenerator method writeLogToFile.

/**
     * write log to file
     *
     * @param writer                     XMLWriter
     * @param treePriorGenerator         TreePriorGenerator
     * @param clockModelGenerator  ClockModelGenerator
     * @param substitutionModelGenerator SubstitutionModelGenerator
     * @param treeLikelihoodGenerator    TreeLikelihoodGenerator
     */
public void writeLogToFile(XMLWriter writer, TreePriorGenerator treePriorGenerator, ClockModelGenerator clockModelGenerator, SubstitutionModelGenerator substitutionModelGenerator, TreeLikelihoodGenerator treeLikelihoodGenerator) {
    writer.writeComment("write log to file");
    if (options.logFileName == null) {
        options.logFileName = options.fileNameStem + ".log";
    }
    writer.writeOpenTag(LoggerParser.LOG, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, "fileLog"), 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.POSTERIOR, "posterior");
    }
    writer.writeIDref(CompoundLikelihoodParser.PRIOR, "prior");
    if (options.hasData()) {
        writer.writeIDref(CompoundLikelihoodParser.LIKELIHOOD, "likelihood");
    }
    if (options.useStarBEAST) {
        // species
        // coalescent prior
        writer.writeIDref(MultiSpeciesCoalescentParser.SPECIES_COALESCENT, TraitData.TRAIT_SPECIES + "." + COALESCENT);
        // prior on population sizes
        //            if (options.speciesTreePrior == TreePriorType.SPECIES_YULE) {
        writer.writeIDref(MixedDistributionLikelihoodParser.DISTRIBUTION_LIKELIHOOD, SPOPS);
        //            } else {
        //                writer.writeIDref(SpeciesTreeBMPrior.STPRIOR, STP);
        //            }
        // prior on species tree
        writer.writeIDref(SpeciationLikelihoodParser.SPECIATION_LIKELIHOOD, SPECIATION_LIKE);
        writer.writeIDref(ParameterParser.PARAMETER, TraitData.TRAIT_SPECIES + "." + options.starBEASTOptions.POP_MEAN);
        writer.writeIDref(ParameterParser.PARAMETER, SpeciesTreeModelParser.SPECIES_TREE + "." + SPLIT_POPS);
        if (options.getPartitionTreePriors().get(0).getNodeHeightPrior() == TreePriorType.SPECIES_BIRTH_DEATH) {
            writer.writeIDref(ParameterParser.PARAMETER, TraitData.TRAIT_SPECIES + "." + BirthDeathModelParser.MEAN_GROWTH_RATE_PARAM_NAME);
            writer.writeIDref(ParameterParser.PARAMETER, TraitData.TRAIT_SPECIES + "." + BirthDeathModelParser.RELATIVE_DEATH_RATE_PARAM_NAME);
        } else if (options.getPartitionTreePriors().get(0).getNodeHeightPrior() == TreePriorType.SPECIES_YULE || options.getPartitionTreePriors().get(0).getNodeHeightPrior() == TreePriorType.SPECIES_YULE_CALIBRATION) {
            writer.writeIDref(ParameterParser.PARAMETER, TraitData.TRAIT_SPECIES + "." + YuleModelParser.YULE + "." + YuleModelParser.BIRTH_RATE);
        } else {
            throw new IllegalArgumentException("Get wrong species tree prior using *BEAST : " + options.getPartitionTreePriors().get(0).getNodeHeightPrior().toString());
        }
        //Species Tree: tmrcaStatistic
        writer.writeIDref(TMRCAStatisticParser.TMRCA_STATISTIC, SpeciesTreeModelParser.SPECIES_TREE + "." + TreeModelParser.ROOT_HEIGHT);
    }
    for (PartitionTreeModel model : options.getPartitionTreeModels()) {
        writer.writeIDref(ParameterParser.PARAMETER, model.getPrefix() + TreeModel.TREE_MODEL + "." + TreeModelParser.ROOT_HEIGHT);
    }
    // @todo check for redundancy with rootHeight - if no tip dates or given as heights (time before present)
    for (PartitionTreeModel model : options.getPartitionTreeModels()) {
        if (model.hasTipCalibrations()) {
            writer.writeIDref(TMRCAStatisticParser.TMRCA_STATISTIC, model.getPrefix() + TreeModel.TREE_MODEL + ".rootAge");
        }
    }
    if (options.useStarBEAST) {
        for (Taxa taxa : options.speciesSets) {
            // make tmrca(tree.name) eay to read in log for Tracer
            writer.writeIDref(TMRCAStatisticParser.TMRCA_STATISTIC, "tmrca(" + taxa.getId() + ")");
        }
    } else {
        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 (PartitionTreePrior prior : options.getPartitionTreePriors()) {
        treePriorGenerator.writeParameterLog(prior, writer);
    }
    for (PartitionSubstitutionModel model : options.getPartitionSubstitutionModels()) {
        substitutionModelGenerator.writeLog(model, writer);
    }
    for (PartitionClockModel model : options.getPartitionClockModels()) {
        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 29 with Attribute

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

the class PatternListGenerator method writePatternList.

/**
     * Writes the pattern lists
     *
     * @param partition the partition data to write the pattern lists for
     * @param writer    the writer
     */
public void writePatternList(PartitionData partition, XMLWriter writer) {
    writer.writeText("");
    AncestralStatesComponentOptions ancestralStatesOptions = (AncestralStatesComponentOptions) options.getComponentOptions(AncestralStatesComponentOptions.class);
    SequenceErrorModelComponentOptions sequenceErrorOptions = (SequenceErrorModelComponentOptions) options.getComponentOptions(SequenceErrorModelComponentOptions.class);
    PartitionSubstitutionModel model = partition.getPartitionSubstitutionModel();
    String codonHeteroPattern = model.getCodonHeteroPattern();
    int partitionCount = model.getCodonPartitionCount();
    boolean isAncestralStatesModel = (!ancestralStatesOptions.usingAncestralStates(partition) && !sequenceErrorOptions.usingSequenceErrorModel(partition));
    boolean isCovarionModel = model.getDataType().getType() == DataType.COVARION && model.getBinarySubstitutionModel() == BinaryModelType.BIN_COVARION;
    boolean unique = isAncestralStatesModel || isCovarionModel;
    boolean strip = isAncestralStatesModel || isCovarionModel;
    if (model.getDataType().getType() == DataType.NUCLEOTIDES && codonHeteroPattern != null && partitionCount > 1) {
        if (codonHeteroPattern.equals("112")) {
            writer.writeComment("The " + (unique ? "unique " : "") + "patterns for codon positions 1 & 2");
            writer.writeOpenTag(MergePatternsParser.MERGE_PATTERNS, new Attribute[] { new Attribute.Default<String>(XMLParser.ID, partition.getPrefix() + model.getPrefixCodon(1) + SitePatternsParser.PATTERNS) });
            writePatternList(partition, 0, 3, null, unique, strip, writer);
            writePatternList(partition, 1, 3, null, unique, strip, writer);
            writer.writeCloseTag(MergePatternsParser.MERGE_PATTERNS);
            writer.writeComment("The " + (unique ? "unique " : "") + "patterns for codon position 3");
            writePatternList(partition, 2, 3, model.getPrefixCodon(2), unique, strip, writer);
        } else {
            // pattern is 123
            for (int i = 1; i <= 3; i++) {
                writer.writeComment("The " + (unique ? "unique " : "") + "patterns for codon position " + i);
                writePatternList(partition, i - 1, 3, model.getPrefixCodon(i), unique, strip, writer);
            }
        }
    // END: pattern is 123
    } else {
        writePatternList(partition, 0, 1, "", unique, strip, writer);
    }
}

Example 30 with Attribute

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

the class PatternListGenerator method writePatternList.

/**
     * Write a single pattern list
     *
     * @param partition the partition to write a pattern list for
     * @param offset    offset by
     * @param every     skip every
     * @param writer    the writer
     */
private void writePatternList(final PartitionData partition, int offset, int every, final String codonPrefix, final boolean unique, final boolean strip, final XMLWriter writer) {
    Alignment alignment = partition.getAlignment();
    int from = partition.getFromSite();
    int to = partition.getToSite();
    int partEvery = partition.getEvery();
    if (partEvery > 1 && every > 1)
        throw new IllegalArgumentException();
    if (from < 1)
        from = 1;
    every = Math.max(partEvery, every);
    from += offset;
    // this object is created solely to calculate the number of patterns in the alignment
    SitePatterns patterns = new SitePatterns(alignment, null, from - 1, to - 1, every, strip, unique);
    writer.writeComment("The " + (unique ? "unique " : "") + "patterns from " + from + " to " + (to > 0 ? to : "end") + ((every > 1) ? " every " + every : ""), "npatterns=" + patterns.getPatternCount());
    List<Attribute> attributes = new ArrayList<Attribute>();
    // no 11 of 112 codon, which uses mergePatterns id instead
    if (codonPrefix != null) {
        attributes.add(new Attribute.Default<String>(XMLParser.ID, partition.getPrefix() + codonPrefix + SitePatternsParser.PATTERNS));
    }
    attributes.add(new Attribute.Default<String>("from", "" + from));
    if (to >= 0)
        attributes.add(new Attribute.Default<String>("to", "" + to));
    if (every > 1) {
        attributes.add(new Attribute.Default<String>("every", "" + every));
    }
    if (!unique) {
        attributes.add(new Attribute.Default<Boolean>(SitePatternsParser.UNIQUE, false));
    }
    if (strip) {
        // default true
        attributes.add(new Attribute.Default<Boolean>(SitePatternsParser.STRIP, false));
    }
    // generate <patterns>
    writer.writeOpenTag(SitePatternsParser.PATTERNS, attributes);
    writer.writeIDref(AlignmentParser.ALIGNMENT, alignment.getId());
    writer.writeCloseTag(SitePatternsParser.PATTERNS);
}