Java Code Examples for com.google.common.primitives.Doubles#concat()

@VisibleForTesting
static Map<String, Double> getContigToMedianCRMap(final ReadCountCollection readCountCollection) {
    final List<String> allContigsPresent = retrieveAllContigsPresent(readCountCollection);
    final Map<String, Double> contigToMedian = new LinkedHashMap<>();
    for (String contig: allContigsPresent) {
        final ReadCountCollection oneContigReadCountCollection = readCountCollection.subsetTargets(readCountCollection.targets().stream().filter(t -> t.getContig().equals(contig)).collect(Collectors.toSet()));
        final double[] flatCounts = Doubles.concat(oneContigReadCountCollection.counts().getData());

        // Put into CRSpace
        final double[] flatCountsInCRSpace = DoubleStream.of(flatCounts).map(d -> Math.pow(2, d)).toArray();
        contigToMedian.put(contig, new Median().evaluate(flatCountsInCRSpace));
    }
    return contigToMedian;
}
 

/**
 * Truncates the extreme count values in the input read-count collection.
 * Values are forced to be bound by the percentile indicated with the input {@code percentile} which must be
 * in the range [0 .. 50.0]. Values under that percentile and the complementary (1 - percentile) are set to the
 * corresponding threshold value.
 *
 * <p>The imputation is done in-place, thus the input matrix is modified as a result of this call.</p>
 *
 * @param readCounts the input and output read-count matrix.
 */
public static void truncateExtremeCounts(final ReadCountCollection readCounts, final double percentile, final Logger logger) {

    final RealMatrix counts = readCounts.counts();
    final int targetCount = counts.getRowDimension();
    final int columnCount = counts.getColumnDimension();

    // Create a row major array of the counts.
    final double[] values = Doubles.concat(counts.getData());

    final Percentile bottomPercentileEvaluator = new Percentile(percentile);
    final Percentile topPercentileEvaluator = new Percentile(100.0 - percentile);
    final double bottomPercentileThreshold = bottomPercentileEvaluator.evaluate(values);
    final double topPercentileThreshold = topPercentileEvaluator.evaluate(values);
    long totalCounts = 0;
    long bottomTruncatedCounts = 0;
    long topTruncatedCounts = 0;

    for (int i = 0; i < targetCount; i++) {
        final double[] rowCounts = counts.getRow(i);
        for (int j = 0; j < columnCount; j++) {
            final double count = rowCounts[j];
            totalCounts++;
            if (count < bottomPercentileThreshold) {
                counts.setEntry(i, j, bottomPercentileThreshold);
                bottomTruncatedCounts++;
            } else if (count > topPercentileThreshold) {
                counts.setEntry(i, j, topPercentileThreshold);
                topTruncatedCounts++;
            }
        }
    }
    if (topTruncatedCounts == 0 && bottomTruncatedCounts == 0) {
        logger.info(String.format("None of the %d counts were truncated as they all fall in the non-extreme range " +
                "[%.2f, %.2f]", totalCounts, bottomPercentileThreshold, topPercentileThreshold));
    } else {
        final double truncatedPercentage = ((double)(topTruncatedCounts + bottomTruncatedCounts) / totalCounts) * 100;
        logger.info(String.format("Some counts (%d out of %d, %.2f%%) were truncated as they fall out of the " +
                "non-extreme range [%.2f, %.2f]", topTruncatedCounts + bottomTruncatedCounts, totalCounts,
                truncatedPercentage, bottomPercentileThreshold, topPercentileThreshold));
    }
}
 

public double[] getArrayCopy() {
  return Doubles.concat(this.doubleArray);
}
 

public double[] getArrayCopy() {
    return Doubles.concat(this.doubleArray);
}