private void writeParameterIdref(XMLWriter writer, Parameter parameter) {
if (parameter.isStatistic) {
writer.writeIDref("statistic", parameter.getName());
} else {
writer.writeIDref(ParameterParser.PARAMETER, parameter.getName());
}
}
public void writeAdditionalLogToFile(
XMLWriter writer,
BranchRatesModelGenerator branchRatesModelGenerator,
SubstitutionModelGenerator substitutionModelGenerator) {
if (options.hasDiscreteIntegerTraitsExcludeSpecies()) {
writer.writeComment("write rate matrix log to file");
String fileName =
options.logFileName.substring(0, options.logFileName.indexOf(".log")) + "_rateMatrix.log";
writer.writeOpenTag(
LoggerParser.LOG,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, "rateMatrixLog"),
new Attribute.Default<String>(LoggerParser.LOG_EVERY, options.logEvery + ""),
new Attribute.Default<String>(LoggerParser.FILE_NAME, fileName)
});
for (PartitionSubstitutionModel model : options.getPartitionTraitsSubstitutionModels()) {
substitutionModelGenerator.writeRateLog(model, writer);
}
for (PartitionClockModel model : options.getPartitionTraitsClockModels()) {
branchRatesModelGenerator.writeLog(model, writer);
}
writer.writeCloseTag(LoggerParser.LOG);
}
}
/**
* Write the marginalLikelihoodEstimator, pathSamplingAnalysis and steppingStoneSamplingAnalysis
* blocks.
*
* @param writer XMLWriter
*/
public void writeMLE(XMLWriter writer, MarginalLikelihoodEstimationOptions options) {
if (options.performMLE) {
writer.writeComment("Define marginal likelihood estimator (PS/SS) settings");
List<Attribute> attributes = new ArrayList<Attribute>();
// attributes.add(new Attribute.Default<String>(XMLParser.ID, "mcmc"));
attributes.add(
new Attribute.Default<Integer>(
MarginalLikelihoodEstimator.CHAIN_LENGTH, options.mleChainLength));
attributes.add(
new Attribute.Default<Integer>(
MarginalLikelihoodEstimator.PATH_STEPS, options.pathSteps));
attributes.add(
new Attribute.Default<String>(
MarginalLikelihoodEstimator.PATH_SCHEME, options.pathScheme));
if (!options.pathScheme.equals(MarginalLikelihoodEstimator.LINEAR)) {
attributes.add(
new Attribute.Default<Double>(
MarginalLikelihoodEstimator.ALPHA, options.schemeParameter));
}
writer.writeOpenTag(MarginalLikelihoodEstimator.MARGINAL_LIKELIHOOD_ESTIMATOR, attributes);
writer.writeOpenTag("samplers");
writer.writeIDref("mcmc", "mcmc");
writer.writeCloseTag("samplers");
attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>(XMLParser.ID, "pathLikelihood"));
writer.writeOpenTag(PathLikelihood.PATH_LIKELIHOOD, attributes);
writer.writeOpenTag(PathLikelihood.SOURCE);
writer.writeIDref(CompoundLikelihoodParser.POSTERIOR, CompoundLikelihoodParser.POSTERIOR);
writer.writeCloseTag(PathLikelihood.SOURCE);
writer.writeOpenTag(PathLikelihood.DESTINATION);
writer.writeIDref(CompoundLikelihoodParser.PRIOR, CompoundLikelihoodParser.PRIOR);
writer.writeCloseTag(PathLikelihood.DESTINATION);
writer.writeCloseTag(PathLikelihood.PATH_LIKELIHOOD);
attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>(XMLParser.ID, "MLELog"));
attributes.add(new Attribute.Default<Integer>("logEvery", options.mleLogEvery));
attributes.add(new Attribute.Default<String>("fileName", options.mleFileName));
writer.writeOpenTag("log", attributes);
writer.writeIDref("pathLikelihood", "pathLikelihood");
writer.writeCloseTag("log");
writer.writeCloseTag(MarginalLikelihoodEstimator.MARGINAL_LIKELIHOOD_ESTIMATOR);
writer.writeComment("Path sampling estimator from collected samples");
attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>("fileName", options.mleFileName));
writer.writeOpenTag(PathSamplingAnalysis.PATH_SAMPLING_ANALYSIS, attributes);
writer.writeTag(
"likelihoodColumn", new Attribute.Default<String>("name", "pathLikelihood.delta"), true);
writer.writeTag(
"thetaColumn", new Attribute.Default<String>("name", "pathLikelihood.theta"), true);
writer.writeCloseTag(PathSamplingAnalysis.PATH_SAMPLING_ANALYSIS);
writer.writeComment("Stepping-stone sampling estimator from collected samples");
attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>("fileName", options.mleFileName));
writer.writeOpenTag(
SteppingStoneSamplingAnalysis.STEPPING_STONE_SAMPLING_ANALYSIS, attributes);
writer.writeTag(
"likelihoodColumn", new Attribute.Default<String>("name", "pathLikelihood.delta"), true);
writer.writeTag(
"thetaColumn", new Attribute.Default<String>("name", "pathLikelihood.theta"), true);
writer.writeCloseTag(SteppingStoneSamplingAnalysis.STEPPING_STONE_SAMPLING_ANALYSIS);
} else if (options.performMLEGSS) {
// First define necessary components for the tree working prior
if (options.choiceTreeWorkingPrior.equals("Product of exponential distributions")) {
// more general product of exponentials needs to be constructed
if (DEBUG) {
System.err.println("productOfExponentials selected: " + options.choiceTreeWorkingPrior);
}
List<Attribute> attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>(XMLParser.ID, "exponentials"));
attributes.add(new Attribute.Default<String>("fileName", beautiOptions.logFileName));
attributes.add(
new Attribute.Default<String>("burnin", "" + beautiOptions.chainLength * 0.10));
attributes.add(
new Attribute.Default<String>("parameterColumn", "coalescentEventsStatistic"));
attributes.add(
new Attribute.Default<String>(
"dimension", "" + (beautiOptions.taxonList.getTaxonCount() - 1)));
writer.writeOpenTag(
TreeWorkingPriorParsers.PRODUCT_OF_EXPONENTIALS_POSTERIOR_MEANS_LOESS, attributes);
writer.writeTag(
TreeModel.TREE_MODEL,
new Attribute.Default<String>(XMLParser.ID, TreeModel.TREE_MODEL),
true);
writer.writeCloseTag(TreeWorkingPriorParsers.PRODUCT_OF_EXPONENTIALS_POSTERIOR_MEANS_LOESS);
} else {
// matching coalescent model has to be constructed
// getting the coalescent model
if (DEBUG) {
System.err.println(
"matching coalescent model selected: " + options.choiceTreeWorkingPrior);
System.err.println(beautiOptions.getPartitionTreePriors().get(0).getNodeHeightPrior());
}
/*for (PartitionTreePrior prior : options.getPartitionTreePriors()) {
treePriorGenerator.writeTreePriorModel(prior, writer);
writer.writeText("");
}*/
// TODO: extend for more than 1 coalescent model?
TreePriorType nodeHeightPrior =
beautiOptions.getPartitionTreePriors().get(0).getNodeHeightPrior();
switch (nodeHeightPrior) {
case CONSTANT:
writer.writeComment("A working prior for the constant population size model.");
writer.writeOpenTag(
ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "constantReference"),
new Attribute.Default<String>(
"units", Units.Utils.getDefaultUnitName(beautiOptions.units))
});
writer.writeOpenTag(ConstantPopulationModelParser.POPULATION_SIZE);
writeParameter(
"constantReference.popSize",
"constant.popSize",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(ConstantPopulationModelParser.POPULATION_SIZE);
writer.writeCloseTag(ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL);
writer.writeComment("A working prior for the coalescent.");
writer.writeOpenTag(
CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "coalescentReference")
});
writer.writeOpenTag(CoalescentLikelihoodParser.MODEL);
writer.writeIDref(
ConstantPopulationModelParser.CONSTANT_POPULATION_MODEL,
beautiOptions.getPartitionTreePriors().get(0).getPrefix() + "constantReference");
writer.writeCloseTag(CoalescentLikelihoodParser.MODEL);
writer.writeOpenTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeIDref(TreeModel.TREE_MODEL, modelPrefix + TreeModel.TREE_MODEL);
writer.writeCloseTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeCloseTag(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD);
break;
case EXPONENTIAL:
writer.writeComment("A working prior for the exponential growth model.");
writer.writeOpenTag(
ExponentialGrowthModelParser.EXPONENTIAL_GROWTH_MODEL,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "exponentialReference"),
new Attribute.Default<String>(
"units", Units.Utils.getDefaultUnitName(beautiOptions.units))
});
writer.writeOpenTag(ExponentialGrowthModelParser.POPULATION_SIZE);
writeParameter(
"exponentialReference.popSize",
"exponential.popSize",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(ExponentialGrowthModelParser.POPULATION_SIZE);
writer.writeOpenTag(ExponentialGrowthModelParser.GROWTH_RATE);
writeParameter(
"exponentialReference.growthRate",
"exponential.growthRate",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(ExponentialGrowthModelParser.GROWTH_RATE);
writer.writeCloseTag(ExponentialGrowthModelParser.EXPONENTIAL_GROWTH_MODEL);
writer.writeComment("A working prior for the coalescent.");
writer.writeOpenTag(
CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "coalescentReference")
});
writer.writeOpenTag(CoalescentLikelihoodParser.MODEL);
writer.writeIDref(
ExponentialGrowthModelParser.EXPONENTIAL_GROWTH_MODEL,
beautiOptions.getPartitionTreePriors().get(0).getPrefix() + "constantReference");
writer.writeCloseTag(CoalescentLikelihoodParser.MODEL);
writer.writeOpenTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeIDref(TreeModel.TREE_MODEL, modelPrefix + TreeModel.TREE_MODEL);
writer.writeCloseTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeCloseTag(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD);
break;
case LOGISTIC:
writer.writeComment("A working prior for the logistic growth model.");
writer.writeOpenTag(
LogisticGrowthModelParser.LOGISTIC_GROWTH_MODEL,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "logisticReference"),
new Attribute.Default<String>(
"units", Units.Utils.getDefaultUnitName(beautiOptions.units))
});
writer.writeOpenTag(LogisticGrowthModelParser.POPULATION_SIZE);
writeParameter(
"logisticReference.popSize",
"logistic.popSize",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(LogisticGrowthModelParser.POPULATION_SIZE);
writer.writeOpenTag(LogisticGrowthModelParser.GROWTH_RATE);
writeParameter(
"logisticReference.growthRate",
"logistic.growthRate",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(LogisticGrowthModelParser.GROWTH_RATE);
writer.writeOpenTag(LogisticGrowthModelParser.TIME_50);
writeParameter(
"logisticReference.t50",
"logistic.t50",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(LogisticGrowthModelParser.TIME_50);
writer.writeCloseTag(LogisticGrowthModelParser.LOGISTIC_GROWTH_MODEL);
writer.writeComment("A working prior for the coalescent.");
writer.writeOpenTag(
CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "coalescentReference")
});
writer.writeOpenTag(CoalescentLikelihoodParser.MODEL);
writer.writeIDref(
LogisticGrowthModelParser.LOGISTIC_GROWTH_MODEL,
beautiOptions.getPartitionTreePriors().get(0).getPrefix() + "constantReference");
writer.writeCloseTag(CoalescentLikelihoodParser.MODEL);
writer.writeOpenTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeIDref(TreeModel.TREE_MODEL, modelPrefix + TreeModel.TREE_MODEL);
writer.writeCloseTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeCloseTag(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD);
break;
case EXPANSION:
writer.writeComment("A working prior for the expansion growth model.");
writer.writeOpenTag(
ExpansionModelParser.EXPANSION_MODEL,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "expansionReference"),
new Attribute.Default<String>(
"units", Units.Utils.getDefaultUnitName(beautiOptions.units))
});
writer.writeOpenTag(ExpansionModelParser.POPULATION_SIZE);
writeParameter(
"expansionReference.popSize",
"expansion.popSize",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(ExpansionModelParser.POPULATION_SIZE);
writer.writeOpenTag(ExpansionModelParser.GROWTH_RATE);
writeParameter(
"expansionReference.growthRate",
"expansion.growthRate",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(ExpansionModelParser.GROWTH_RATE);
writer.writeOpenTag(ExpansionModelParser.ANCESTRAL_POPULATION_PROPORTION);
writeParameter(
"expansionReference.ancestralProportion",
"expansion.ancestralProportion",
beautiOptions.logFileName,
(int) (options.mleChainLength * 0.10),
writer);
writer.writeCloseTag(ExpansionModelParser.ANCESTRAL_POPULATION_PROPORTION);
writer.writeCloseTag(ExpansionModelParser.EXPANSION_MODEL);
writer.writeComment("A working prior for the coalescent.");
writer.writeOpenTag(
CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, modelPrefix + "coalescentReference")
});
writer.writeOpenTag(CoalescentLikelihoodParser.MODEL);
writer.writeIDref(
ExpansionModelParser.EXPANSION_MODEL,
beautiOptions.getPartitionTreePriors().get(0).getPrefix() + "constantReference");
writer.writeCloseTag(CoalescentLikelihoodParser.MODEL);
writer.writeOpenTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeIDref(TreeModel.TREE_MODEL, modelPrefix + TreeModel.TREE_MODEL);
writer.writeCloseTag(CoalescentLikelihoodParser.POPULATION_TREE);
writer.writeCloseTag(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD);
break;
default:
// Do not switch to product of exponentials as the coalescentEventsStatistic has not
// been logged
// TODO: show menu that explains mismatch between prior and working prior?
// TODO: but show it when the MCM option is wrongfully being selected, don't do anything
// here
}
}
writer.writeComment("Define marginal likelihood estimator (GSS) settings");
List<Attribute> attributes = new ArrayList<Attribute>();
attributes.add(
new Attribute.Default<Integer>(
MarginalLikelihoodEstimator.CHAIN_LENGTH, options.mleChainLength));
attributes.add(
new Attribute.Default<Integer>(
MarginalLikelihoodEstimator.PATH_STEPS, options.pathSteps));
attributes.add(
new Attribute.Default<String>(
MarginalLikelihoodEstimator.PATH_SCHEME, options.pathScheme));
if (!options.pathScheme.equals(MarginalLikelihoodEstimator.LINEAR)) {
attributes.add(
new Attribute.Default<Double>(
MarginalLikelihoodEstimator.ALPHA, options.schemeParameter));
}
writer.writeOpenTag(MarginalLikelihoodEstimator.MARGINAL_LIKELIHOOD_ESTIMATOR, attributes);
writer.writeOpenTag("samplers");
writer.writeIDref("mcmc", "mcmc");
writer.writeCloseTag("samplers");
attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>(XMLParser.ID, "pathLikelihood"));
writer.writeOpenTag(PathLikelihood.PATH_LIKELIHOOD, attributes);
writer.writeOpenTag(PathLikelihood.SOURCE);
writer.writeIDref(CompoundLikelihoodParser.POSTERIOR, CompoundLikelihoodParser.POSTERIOR);
writer.writeCloseTag(PathLikelihood.SOURCE);
writer.writeOpenTag(PathLikelihood.DESTINATION);
writer.writeOpenTag(CompoundLikelihoodParser.WORKING_PRIOR);
ArrayList<Parameter> parameters = beautiOptions.selectParameters();
for (Parameter param : parameters) {
if (DEBUG) {
System.err.println(param.toString() + " " + param.priorType.toString());
}
// should leave out those parameters set by the coalescent
if (param.priorType != PriorType.NONE_TREE_PRIOR) {
// TODO: frequencies is multidimensional, is that automatically dealt with?
writer.writeOpenTag(
WorkingPriorParsers.NORMAL_REFERENCE_PRIOR,
new Attribute[] {
new Attribute.Default<String>("fileName", beautiOptions.logFileName),
new Attribute.Default<String>("parameterColumn", param.getName()),
new Attribute.Default<String>("burnin", "" + beautiOptions.chainLength * 0.10)
});
writeParameterIdref(writer, param);
writer.writeCloseTag(WorkingPriorParsers.NORMAL_REFERENCE_PRIOR);
}
}
if (options.choiceTreeWorkingPrior.equals("Product of exponential distributions")) {
writer.writeIDref("productOfExponentialsPosteriorMeansLoess", "exponentials");
} else {
writer.writeIDref(CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD, "coalescentReference");
}
writer.writeCloseTag(CompoundLikelihoodParser.WORKING_PRIOR);
writer.writeCloseTag(PathLikelihood.DESTINATION);
writer.writeCloseTag(PathLikelihood.PATH_LIKELIHOOD);
attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>(XMLParser.ID, "MLELog"));
attributes.add(new Attribute.Default<Integer>("logEvery", options.mleLogEvery));
attributes.add(new Attribute.Default<String>("fileName", options.mleFileName));
writer.writeOpenTag("log", attributes);
writer.writeIDref("pathLikelihood", "pathLikelihood");
writer.writeCloseTag("log");
writer.writeCloseTag(MarginalLikelihoodEstimator.MARGINAL_LIKELIHOOD_ESTIMATOR);
writer.writeComment("Generalized stepping-stone sampling estimator from collected samples");
attributes = new ArrayList<Attribute>();
attributes.add(new Attribute.Default<String>("fileName", options.mleFileName));
writer.writeOpenTag(
GeneralizedSteppingStoneSamplingAnalysis.GENERALIZED_STEPPING_STONE_SAMPLING_ANALYSIS,
attributes);
writer.writeTag(
"sourceColumn", new Attribute.Default<String>("name", "pathLikelihood.source"), true);
writer.writeTag(
"destinationColumn",
new Attribute.Default<String>("name", "pathLikelihood.destination"),
true);
writer.writeTag(
"thetaColumn", new Attribute.Default<String>("name", "pathLikelihood.theta"), true);
writer.writeCloseTag(
GeneralizedSteppingStoneSamplingAnalysis.GENERALIZED_STEPPING_STONE_SAMPLING_ANALYSIS);
}
}
/**
* write log to screen
*
* @param writer XMLWriter
* @param branchRatesModelGenerator BranchRatesModelGenerator
*/
public void writeLogToScreen(
XMLWriter writer,
BranchRatesModelGenerator branchRatesModelGenerator,
SubstitutionModelGenerator substitutionModelGenerator) {
writer.writeComment("write log to screen");
writer.writeOpenTag(
LoggerParser.LOG,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, "screenLog"),
new Attribute.Default<String>(LoggerParser.LOG_EVERY, options.echoEvery + "")
});
if (options.hasData()) {
writer.writeOpenTag(
ColumnsParser.COLUMN,
new Attribute[] {
new Attribute.Default<String>(ColumnsParser.LABEL, "Posterior"),
new Attribute.Default<String>(ColumnsParser.DECIMAL_PLACES, "4"),
new Attribute.Default<String>(ColumnsParser.WIDTH, "12")
});
writer.writeIDref(CompoundLikelihoodParser.POSTERIOR, "posterior");
writer.writeCloseTag(ColumnsParser.COLUMN);
}
writer.writeOpenTag(
ColumnsParser.COLUMN,
new Attribute[] {
new Attribute.Default<String>(ColumnsParser.LABEL, "Prior"),
new Attribute.Default<String>(ColumnsParser.DECIMAL_PLACES, "4"),
new Attribute.Default<String>(ColumnsParser.WIDTH, "12")
});
writer.writeIDref(CompoundLikelihoodParser.PRIOR, "prior");
writer.writeCloseTag(ColumnsParser.COLUMN);
if (options.hasData()) {
writer.writeOpenTag(
ColumnsParser.COLUMN,
new Attribute[] {
new Attribute.Default<String>(ColumnsParser.LABEL, "Likelihood"),
new Attribute.Default<String>(ColumnsParser.DECIMAL_PLACES, "4"),
new Attribute.Default<String>(ColumnsParser.WIDTH, "12")
});
writer.writeIDref(CompoundLikelihoodParser.LIKELIHOOD, "likelihood");
writer.writeCloseTag(ColumnsParser.COLUMN);
}
if (options.useStarBEAST) { // species
writer.writeOpenTag(
ColumnsParser.COLUMN,
new Attribute[] {
new Attribute.Default<String>(ColumnsParser.LABEL, "PopMean"),
new Attribute.Default<String>(ColumnsParser.DECIMAL_PLACES, "4"),
new Attribute.Default<String>(ColumnsParser.WIDTH, "12")
});
writer.writeIDref(
ParameterParser.PARAMETER,
TraitData.TRAIT_SPECIES + "." + options.starBEASTOptions.POP_MEAN);
writer.writeCloseTag(ColumnsParser.COLUMN);
}
for (PartitionTreeModel model : options.getPartitionTreeModels()) {
writer.writeOpenTag(
ColumnsParser.COLUMN,
new Attribute[] {
new Attribute.Default<String>(
ColumnsParser.LABEL, model.getPrefix() + TreeModelParser.ROOT_HEIGHT),
new Attribute.Default<String>(ColumnsParser.SIGNIFICANT_FIGURES, "6"),
new Attribute.Default<String>(ColumnsParser.WIDTH, "12")
});
writer.writeIDref(
ParameterParser.PARAMETER,
model.getPrefix() + TreeModel.TREE_MODEL + "." + TreeModelParser.ROOT_HEIGHT);
writer.writeCloseTag(ColumnsParser.COLUMN);
}
for (PartitionClockModel model : options.getPartitionClockModels()) {
writer.writeOpenTag(
ColumnsParser.COLUMN,
new Attribute[] {
new Attribute.Default<String>(
ColumnsParser.LABEL, branchRatesModelGenerator.getClockRateString(model)),
new Attribute.Default<String>(ColumnsParser.SIGNIFICANT_FIGURES, "6"),
new Attribute.Default<String>(ColumnsParser.WIDTH, "12")
});
branchRatesModelGenerator.writeAllClockRateRefs(model, writer);
// if (options.clockModelOptions.getRateOptionClockModel() == FixRateType.FIX_MEAN) {
// writer.writeIDref(ParameterParser.PARAMETER, "allClockRates");
// for (PartitionClockModel model : options.getPartitionClockModels()) {
// if (model.getClockType() == ClockType.UNCORRELATED_LOGNORMAL)
// writer.writeIDref(ParameterParser.PARAMETER, model.getPrefix() +
// ClockType.UCLD_STDEV);
// }
// } else {
// for (PartitionClockModel model : options.getPartitionClockModels()) {
// branchRatesModelGenerator.writeAllClockRateRefs(model, writer);
// }
// }
writer.writeCloseTag(ColumnsParser.COLUMN);
}
if (options.hasDiscreteIntegerTraitsExcludeSpecies()) {
for (PartitionSubstitutionModel model : options.getPartitionTraitsSubstitutionModels()) {
substitutionModelGenerator.writeStatisticLog(model, writer);
}
}
generateInsertionPoint(ComponentGenerator.InsertionPoint.IN_SCREEN_LOG, writer);
writer.writeCloseTag(LoggerParser.LOG);
generateInsertionPoint(ComponentGenerator.InsertionPoint.AFTER_SCREEN_LOG, writer);
}
/**
* write tree log to file
*
* @param writer XMLWriter
*/
public void writeTreeLogToFile(XMLWriter writer) {
writer.writeComment("write tree log to file");
if (options.useStarBEAST) { // species
// species tree log
writer.writeOpenTag(
TreeLoggerParser.LOG_TREE,
new Attribute[] {
new Attribute.Default<String>(
XMLParser.ID, TraitData.TRAIT_SPECIES + "." + TREE_FILE_LOG), // speciesTreeFileLog
new Attribute.Default<String>(TreeLoggerParser.LOG_EVERY, options.logEvery + ""),
new Attribute.Default<String>(TreeLoggerParser.NEXUS_FORMAT, "true"),
new Attribute.Default<String>(
TreeLoggerParser.FILE_NAME,
options.fileNameStem + "." + options.starBEASTOptions.SPECIES_TREE_FILE_NAME),
new Attribute.Default<String>(TreeLoggerParser.SORT_TRANSLATION_TABLE, "true")
});
writer.writeIDref(SpeciesTreeModelParser.SPECIES_TREE, SP_TREE);
if (options.hasData()) {
// we have data...
writer.writeIDref("posterior", "posterior");
}
writer.writeCloseTag(TreeLoggerParser.LOG_TREE);
}
// gene tree log
// TODO make code consistent to MCMCPanel
for (PartitionTreeModel tree : options.getPartitionTreeModels()) {
String treeFileName;
if (options.substTreeLog) {
treeFileName =
options.fileNameStem
+ "."
+ tree.getPrefix()
+ "(time)."
+ STARBEASTOptions.TREE_FILE_NAME;
} else {
treeFileName =
options.fileNameStem
+ "."
+ tree.getPrefix()
+ STARBEASTOptions.TREE_FILE_NAME; // stem.partitionName.tree
}
if (options.treeFileName.get(0).endsWith(".txt")) {
treeFileName += ".txt";
}
List<Attribute> attributes = new ArrayList<Attribute>();
attributes.add(
new Attribute.Default<String>(
XMLParser.ID, tree.getPrefix() + TREE_FILE_LOG)); // partionName.treeFileLog
attributes.add(
new Attribute.Default<String>(TreeLoggerParser.LOG_EVERY, options.logEvery + ""));
attributes.add(new Attribute.Default<String>(TreeLoggerParser.NEXUS_FORMAT, "true"));
attributes.add(new Attribute.Default<String>(TreeLoggerParser.FILE_NAME, treeFileName));
attributes.add(
new Attribute.Default<String>(TreeLoggerParser.SORT_TRANSLATION_TABLE, "true"));
// if (options.clockModelOptions.getRateOptionClockModel() == FixRateType.RElATIVE_TO &&
// tree.containsUncorrelatedRelaxClock()) { //TODO: Sibon's discretized branch length stuff
// double aveFixedRate =
// options.clockModelOptions.getSelectedRate(options.getPartitionClockModels());
// attributes.add(new Attribute.Default<String>(TreeLoggerParser.NORMALISE_MEAN_RATE_TO,
// Double.toString(aveFixedRate)));
// }
// generate <logTree>
writer.writeOpenTag(TreeLoggerParser.LOG_TREE, attributes);
// writer.writeOpenTag(TreeLoggerParser.LOG_TREE,
// new Attribute[]{
// new Attribute.Default<String>(XMLParser.ID, tree.getPrefix() +
// TREE_FILE_LOG), // partionName.treeFileLog
// new Attribute.Default<String>(TreeLoggerParser.LOG_EVERY,
// options.logEvery + ""),
// new Attribute.Default<String>(TreeLoggerParser.NEXUS_FORMAT,
// "true"),
// new Attribute.Default<String>(TreeLoggerParser.FILE_NAME,
// treeFileName),
// new
// Attribute.Default<String>(TreeLoggerParser.SORT_TRANSLATION_TABLE, "true")
// });
writer.writeIDref(TreeModel.TREE_MODEL, tree.getPrefix() + TreeModel.TREE_MODEL);
for (PartitionClockModel model :
options.getPartitionClockModels(options.getAllPartitionData(tree))) {
if (options.getAllPartitionData(model).get(0).getTraitType() == null) {
switch (model.getClockType()) {
case STRICT_CLOCK:
writer.writeIDref(
StrictClockBranchRatesParser.STRICT_CLOCK_BRANCH_RATES,
model.getPrefix() + BranchRateModel.BRANCH_RATES);
break;
case UNCORRELATED_EXPONENTIAL:
case UNCORRELATED_LOGNORMAL:
writer.writeIDref(
DiscretizedBranchRatesParser.DISCRETIZED_BRANCH_RATES,
options.noDuplicatedPrefix(model.getPrefix(), tree.getPrefix())
+ BranchRateModel.BRANCH_RATES);
break;
case RANDOM_LOCAL_CLOCK:
writer.writeIDref(
RandomLocalClockModelParser.LOCAL_BRANCH_RATES,
model.getPrefix() + BranchRateModel.BRANCH_RATES);
break;
case AUTOCORRELATED_LOGNORMAL:
writer.writeIDref(
ACLikelihoodParser.AC_LIKELIHOOD,
options.noDuplicatedPrefix(model.getPrefix(), tree.getPrefix())
+ BranchRateModel.BRANCH_RATES);
break;
default:
throw new IllegalArgumentException("Unknown clock model");
}
}
}
if (options.hasData()) {
// we have data...
writer.writeIDref("posterior", "posterior");
}
if (options.hasDiscreteIntegerTraitsExcludeSpecies()) {
for (PartitionData partitionData :
options.getAllPartitionData(
tree)) { // Each TD except Species has one AncestralTreeLikelihood
if (partitionData.getTraitType() != null
&& (!partitionData.getName().equalsIgnoreCase(TraitData.TRAIT_SPECIES.toString())))
writer.writeIDref(
AncestralStateTreeLikelihoodParser.RECONSTRUCTING_TREE_LIKELIHOOD,
partitionData.getPrefix() + TreeLikelihoodParser.TREE_LIKELIHOOD);
}
}
writer.writeCloseTag(TreeLoggerParser.LOG_TREE);
} // end For loop
generateInsertionPoint(ComponentGenerator.InsertionPoint.IN_TREES_LOG, writer);
if (options.substTreeLog) {
if (options.useStarBEAST) { // species
// TODO: species sub tree
}
// gene tree
for (PartitionTreeModel tree : options.getPartitionTreeModels()) {
// write tree log to file
writer.writeOpenTag(
TreeLoggerParser.LOG_TREE,
new Attribute[] {
new Attribute.Default<String>(XMLParser.ID, tree.getPrefix() + SUB_TREE_FILE_LOG),
new Attribute.Default<String>(TreeLoggerParser.LOG_EVERY, options.logEvery + ""),
new Attribute.Default<String>(TreeLoggerParser.NEXUS_FORMAT, "true"),
new Attribute.Default<String>(
TreeLoggerParser.FILE_NAME,
options.fileNameStem
+ "."
+ tree.getPrefix()
+ "(subst)."
+ STARBEASTOptions.TREE_FILE_NAME),
new Attribute.Default<String>(
TreeLoggerParser.BRANCH_LENGTHS, TreeLoggerParser.SUBSTITUTIONS)
});
writer.writeIDref(TreeModel.TREE_MODEL, tree.getPrefix() + TreeModel.TREE_MODEL);
for (PartitionClockModel model :
options.getPartitionClockModels(options.getAllPartitionData(tree))) {
if (options.getAllPartitionData(model).get(0).getTraitType() == null) {
switch (model.getClockType()) {
case STRICT_CLOCK:
writer.writeIDref(
StrictClockBranchRatesParser.STRICT_CLOCK_BRANCH_RATES,
model.getPrefix() + BranchRateModel.BRANCH_RATES);
break;
case UNCORRELATED_EXPONENTIAL:
case UNCORRELATED_LOGNORMAL:
writer.writeIDref(
DiscretizedBranchRatesParser.DISCRETIZED_BRANCH_RATES,
options.noDuplicatedPrefix(model.getPrefix(), tree.getPrefix())
+ BranchRateModel.BRANCH_RATES);
break;
case RANDOM_LOCAL_CLOCK:
writer.writeIDref(
RandomLocalClockModelParser.LOCAL_BRANCH_RATES,
model.getPrefix() + BranchRateModel.BRANCH_RATES);
break;
case AUTOCORRELATED_LOGNORMAL:
writer.writeIDref(
ACLikelihoodParser.AC_LIKELIHOOD,
options.noDuplicatedPrefix(model.getPrefix(), tree.getPrefix())
+ BranchRateModel.BRANCH_RATES);
break;
default:
throw new IllegalArgumentException("Unknown clock model");
}
}
}
writer.writeCloseTag(TreeLoggerParser.LOG_TREE);
}
}
generateInsertionPoint(ComponentGenerator.InsertionPoint.AFTER_TREES_LOG, writer);
}
/**
* write log to file
*
* @param writer XMLWriter
* @param treePriorGenerator TreePriorGenerator
* @param branchRatesModelGenerator BranchRatesModelGenerator
* @param substitutionModelGenerator SubstitutionModelGenerator
* @param treeLikelihoodGenerator TreeLikelihoodGenerator
* @param generalTraitGenerator
*/
public void writeLogToFile(
XMLWriter writer,
TreePriorGenerator treePriorGenerator,
BranchRatesModelGenerator branchRatesModelGenerator,
SubstitutionModelGenerator substitutionModelGenerator,
TreeLikelihoodGenerator treeLikelihoodGenerator,
GeneralTraitGenerator generalTraitGenerator) {
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) {
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);
}
for (Taxa taxa : options.taxonSets) {
// make tmrca(tree.name) eay to read in log for Tracer
writer.writeIDref(
TMRCAStatisticParser.TMRCA_STATISTIC,
"tmrca(" + taxa.getTreeModel().getPrefix() + taxa.getId() + ")");
}
// if ( options.shareSameTreePrior ) { // Share Same Tree Prior
// treePriorGenerator.setModelPrefix("");
// treePriorGenerator.writeParameterLog(options.activedSameTreePrior, writer);
// } 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(writer, model);
if (model.hasCodon()) {
writer.writeIDref(CompoundParameterParser.COMPOUND_PARAMETER, model.getPrefix() + "allMus");
}
}
if (options.clockModelOptions.getRateOptionClockModel() == FixRateType.FIX_MEAN) {
writer.writeIDref(ParameterParser.PARAMETER, "allClockRates");
for (PartitionClockModel model : options.getPartitionClockModels()) {
if (model.getClockType() == ClockType.UNCORRELATED_LOGNORMAL)
writer.writeIDref(ParameterParser.PARAMETER, model.getPrefix() + ClockType.UCLD_STDEV);
}
} else {
for (PartitionClockModel model : options.getPartitionClockModels()) {
branchRatesModelGenerator.writeLog(model, writer);
}
}
for (PartitionClockModel model : options.getPartitionClockModels()) {
branchRatesModelGenerator.writeLogStatistic(model, writer);
}
generateInsertionPoint(ComponentGenerator.InsertionPoint.IN_FILE_LOG_PARAMETERS, writer);
if (options.hasData()) {
treeLikelihoodGenerator.writeTreeLikelihoodReferences(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)
writer.writeIDref(
CoalescentLikelihoodParser.COALESCENT_LIKELIHOOD,
prior.getPrefix() + COALESCENT); // only 1 coalescent
}
if (options.hasDiscreteIntegerTraitsExcludeSpecies()) {
generalTraitGenerator.writeAncestralTreeLikelihoodReferences(writer);
}
writer.writeCloseTag(LoggerParser.LOG);
generateInsertionPoint(ComponentGenerator.InsertionPoint.AFTER_FILE_LOG, writer);
}