Examples with NewickImporter dr.evolution.io.NewickImporter used on opensource projects

Example 36 with NewickImporter

use of dr.evolution.io.NewickImporter in project beast-mcmc by beast-dev.

the class TreeStatFrame method processTreeFile.

protected void processTreeFile(File inFile, File outFile) throws IOException, Importer.ImportException {
    processTreeFileAction.setEnabled(false);
    BufferedReader r = new BufferedReader(new FileReader(inFile));
    String line = r.readLine();
    r.close();
    final ProgressMonitorInputStream in = new ProgressMonitorInputStream(this, "Reading " + inFile.getName(), new FileInputStream(inFile));
    in.getProgressMonitor().setMillisToDecideToPopup(0);
    in.getProgressMonitor().setMillisToPopup(0);
    final Reader reader = new InputStreamReader(new BufferedInputStream(in));
    //        final Reader reader = new FileReader(inFile);
    final TreeImporter importer;
    if (line.toUpperCase().startsWith("#NEXUS")) {
        importer = new NexusImporter(reader);
    } else {
        reader.close();
        importer = new NewickImporter(reader);
    }
    final Tree firstTree = importer.importNextTree();
    boolean isUltrametric = TreeUtils.isUltrametric(firstTree);
    boolean isBinary = TreeUtils.isBinary(firstTree);
    boolean stop = false;
    // check that the trees conform with the requirements of the selected statistics
    for (int i = 0; i < treeStatData.statistics.size(); i++) {
        TreeSummaryStatistic tss = (TreeSummaryStatistic) treeStatData.statistics.get(i);
        String label = tss.getSummaryStatisticName();
        if (!isUltrametric && !tss.allowsNonultrametricTrees()) {
            if (JOptionPane.showConfirmDialog(this, "Warning: These trees may not be ultrametric and this is\na requirement of the " + label + " statistic. Do you wish to continue?", "Warning", JOptionPane.YES_NO_OPTION) != JOptionPane.YES_OPTION) {
                stop = true;
                break;
            }
            // don't ask the question again...
            isUltrametric = true;
        }
        if (!isBinary && !tss.allowsPolytomies()) {
            if (JOptionPane.showConfirmDialog(this, "Warning: These trees may not be strictly bifurcating and this is\na requirement of the " + label + " statistic. Do you wish to continue?", "Warning", JOptionPane.YES_NO_OPTION) != JOptionPane.YES_OPTION) {
                stop = true;
                break;
            }
            // don't ask the question again...
            isBinary = true;
        }
    }
    if (stop) {
        processTreeFileAction.setEnabled(true);
        return;
    }
    final PrintWriter writer = new PrintWriter(new FileWriter(outFile));
    //        Thread readThread = new Thread() {
    //            public void run() {
    Tree tree = firstTree;
    writer.print("state");
    for (int i = 0; i < treeStatData.statistics.size(); i++) {
        TreeSummaryStatistic tss = (TreeSummaryStatistic) treeStatData.statistics.get(i);
        int dim = tss.getStatisticDimensions(tree);
        for (int j = 0; j < dim; j++) {
            writer.print("\t" + tss.getStatisticLabel(tree, j));
        }
    }
    writer.println();
    state = 0;
    do {
        writer.print(state);
        for (int i = 0; i < treeStatData.statistics.size(); i++) {
            TreeSummaryStatistic tss = (TreeSummaryStatistic) treeStatData.statistics.get(i);
            double[] stats = tss.getSummaryStatistic(tree);
            for (int j = 0; j < stats.length; j++) {
                writer.print("\t" + stats[j]);
            }
        }
        writer.println();
        state += 1;
        final int currentState = state;
        in.getProgressMonitor().setNote("Processing Tree " + currentState + "...");
        //                    EventQueue.invokeLater(
        //                            new Runnable() {
        //                                public void run() {
        //                                    progressLabel.setText("Processing Tree " + currentState + "...");
        //                                }
        //                            });
        //                    try {
        tree = importer.importNextTree();
    //                    } catch (final IOException e) {
    //                        EventQueue.invokeLater(
    //                                new Runnable() {
    //                                    public void run() {
    //                                        JOptionPane.showMessageDialog(TreeStatFrame.this, "File I/O Error: " + e.getMessage(),
    //                                                "File I/O Error",
    //                                                JOptionPane.ERROR_MESSAGE);
    //                                    }
    //                                });
    //                    } catch (final Importer.ImportException e) {
    //                        EventQueue.invokeLater(
    //                                new Runnable() {
    //                                    public void run() {
    //                                        JOptionPane.showMessageDialog(TreeStatFrame.this, "Error importing tree: " + e.getMessage(),
    //                                                "Tree Import Error",
    //                                                JOptionPane.ERROR_MESSAGE);
    //                                    }
    //                                });
    //                    }
    } while (tree != null);
    //            }
    //        };
    //
    //        readThread.start();
    //        while (readThread.isAlive()) {
    //            Thread.yield();
    //        }
    reader.close();
    writer.close();
    progressLabel.setText("" + state + " trees processed.");
    processTreeFileAction.setEnabled(true);
}

Example 37 with NewickImporter

use of dr.evolution.io.NewickImporter in project beast-mcmc by beast-dev.

the class TreeStatFrame method importFromFile.

protected void importFromFile(File file) throws IOException, Importer.ImportException {
    BufferedReader reader = new BufferedReader(new FileReader(file));
    String line = reader.readLine();
    Tree tree = null;
    if (line.toUpperCase().startsWith("#NEXUS")) {
        NexusImporter importer = new NexusImporter(reader);
        tree = importer.importTree(null);
    } else {
        reader.close();
        reader = new BufferedReader(new FileReader(file));
        NewickImporter importer = new NewickImporter(reader);
        tree = importer.importTree(null);
    }
    treeStatData.allTaxa = TreeUtils.getLeafSet(tree);
    statusLabel.setText(Integer.toString(treeStatData.allTaxa.size()) + " taxa loaded.");
    reader.close();
    fireDataChanged();
}

Example 38 with NewickImporter

use of dr.evolution.io.NewickImporter in project beast-mcmc by beast-dev.

the class KCPathDifferenceMetric method main.

public static void main(String[] args) {
    try {
        //4-taxa example
        NewickImporter importer = new NewickImporter("(('A':1.2,'B':0.8):0.5,('C':0.8,'D':1.0):1.1)");
        Tree treeOne = importer.importNextTree();
        System.out.println("4-taxa tree 1: " + treeOne);
        importer = new NewickImporter("((('A':0.8,'B':1.4):0.3,'C':0.7):0.9,'D':1.0)");
        Tree treeTwo = importer.importNextTree();
        System.out.println("4-taxa tree 2: " + treeTwo + "\n");
        ArrayList<Double> lambdaValues = new ArrayList<Double>();
        lambdaValues.add(0.0);
        lambdaValues.add(0.5);
        lambdaValues.add(1.0);
        List<Double> metric = (new KCPathDifferenceMetric().getMetric(treeOne, treeTwo, lambdaValues));
        List<Double> metric_old = (new KCPathDifferenceMetric().getMetric_old(treeOne, treeTwo, lambdaValues));
        System.out.println("\nPaired trees:");
        System.out.println("lambda (0.0) = " + metric.get(0) + " old = " + metric_old.get(0));
        System.out.println("lambda (0.5) = " + metric.get(1) + " old = " + metric_old.get(1));
        System.out.println("lambda (1.0) = " + metric.get(2) + " old = " + metric_old.get(2));
        //Additional test for comparing a collection of trees against a (fixed) focal tree
        metric = new KCPathDifferenceMetric(treeOne).getMetric(treeTwo, lambdaValues);
        metric_old = new KCPathDifferenceMetric(treeOne).getMetric_old(treeTwo, lambdaValues);
        System.out.println("\nFocal trees:");
        System.out.println("lambda (0.0) = " + metric.get(0) + " old = " + metric_old.get(0));
        System.out.println("lambda (0.5) = " + metric.get(1) + " old = " + metric_old.get(1));
        System.out.println("lambda (1.0) = " + metric.get(2) + " old = " + metric_old.get(2));
        //5-taxa example
        importer = new NewickImporter("(((('A':0.6,'B':0.6):0.1,'C':0.5):0.4,'D':0.7):0.1,'E':1.3)");
        treeOne = importer.importNextTree();
        System.out.println("5-taxa tree 1: " + treeOne);
        importer = new NewickImporter("((('A':0.8,'B':1.4):0.1,'C':0.7):0.2,('D':1.0,'E':0.9):1.3)");
        treeTwo = importer.importNextTree();
        System.out.println("5-taxa tree 2: " + treeTwo + "\n");
        //lambda = 0.0 should yield: sqrt(7) = 2.6457513110645907162
        //lambda = 1.0 should yield: sqrt(2.96) = 1.7204650534085252911
        lambdaValues = new ArrayList<Double>();
        lambdaValues.add(0.0);
        lambdaValues.add(0.5);
        lambdaValues.add(1.0);
        metric = (new KCPathDifferenceMetric().getMetric(treeOne, treeTwo, lambdaValues));
        System.out.println("\nPaired trees:");
        System.out.println("lambda (0.0) = " + metric.get(0) + " old = " + metric_old.get(0));
        System.out.println("lambda (0.5) = " + metric.get(1) + " old = " + metric_old.get(1));
        System.out.println("lambda (1.0) = " + metric.get(2) + " old = " + metric_old.get(2));
        //Additional test for comparing a collection of trees against a (fixed) focal tree
        metric = new KCPathDifferenceMetric(treeOne).getMetric(treeTwo, lambdaValues);
        System.out.println("\nFocal trees:");
        System.out.println("lambda (0.0) = " + metric.get(0) + " old = " + metric_old.get(0));
        System.out.println("lambda (0.5) = " + metric.get(1) + " old = " + metric_old.get(1));
        System.out.println("lambda (1.0) = " + metric.get(2) + " old = " + metric_old.get(2));
        //timings
        long startTime = System.currentTimeMillis();
        for (int i = 0; i < 1000000; i++) {
            new KCPathDifferenceMetric().getMetric_old(treeOne, treeTwo, lambdaValues);
        }
        System.out.println("Old algorithm: " + (System.currentTimeMillis() - startTime) + " ms");
        startTime = System.currentTimeMillis();
        for (int i = 0; i < 1000000; i++) {
            new KCPathDifferenceMetric().getMetric(treeOne, treeTwo, lambdaValues);
        }
        System.out.println("New algorithm: " + (System.currentTimeMillis() - startTime) + " ms");
    } catch (Importer.ImportException ie) {
        System.err.println(ie);
    } catch (IOException ioe) {
        System.err.println(ioe);
    }
}

Example 39 with NewickImporter

use of dr.evolution.io.NewickImporter in project beast-mcmc by beast-dev.

the class TreeMetrics method main.

public static final void main(String[] args) throws Exception {
    FileReader reader = new FileReader(args[0]);
    NewickImporter importer = new NewickImporter(reader);
    Tree[] trees = importer.importTrees(null);
    System.out.println("Imported " + trees.length + " trees...");
    analyze(trees, 1000);
}

Example 40 with NewickImporter

use of dr.evolution.io.NewickImporter in project beast-mcmc by beast-dev.

the class GMRFFixedGridLikelihood method run.

public static void run() throws Exception {
    NewickImporter importer = new NewickImporter("((((5:0.5,1:0.2):0.5,0:1):0.2,2:0.8):0.2,3:1.4)");
    Tree tree = importer.importNextTree();
    double[] data = new double[15];
    double[] times = new double[15];
    data[0] = 1.0;
    times[0] = 0.05;
    for (int i = 1; i < data.length; i++) {
        data[i] = data[i - 1] + 0.5;
        times[i] = times[i - 1] + 0.1;
    }
    GMRFFixedGridLikelihood like = new GMRFFixedGridLikelihood(tree, new Parameter.Default(data), new Parameter.Default(times), 4);
    System.out.println(like.getLogLikelihood());
}