Example usage for org.apache.commons.lang ArrayUtils toPrimitive

Introduction

In this page you can find the example usage for org.apache.commons.lang ArrayUtils toPrimitive.

Prototype

public static boolean[] toPrimitive(Boolean[] array)

Source Link

Document

Converts an array of object Booleans to primitives.

Usage

From source file:org.broadinstitute.gatk.tools.walkers.genotyper.afcalc.ExactCallLogger.java

/**
 * Read in a list of ExactCall objects from reader, keeping only those
 * with starts in startsToKeep or all sites (if this is empty)
 *
 * @param reader a just-opened reader sitting at the start of the file
 * @param startsToKeep a list of start position of the calls to keep, or empty if all calls should be kept
 * @param parser a genome loc parser to create genome locs
 * @return a list of ExactCall objects in reader
 * @throws IOException//from w w w. j  a  v a 2s  .c o  m
 */
public static List<ExactCall> readExactLog(final BufferedReader reader, final List<Integer> startsToKeep,
        GenomeLocParser parser) throws IOException {
    if (reader == null)
        throw new IllegalArgumentException("reader cannot be null");
    if (startsToKeep == null)
        throw new IllegalArgumentException("startsToKeep cannot be null");
    if (parser == null)
        throw new IllegalArgumentException("GenomeLocParser cannot be null");

    List<ExactCall> calls = new LinkedList<ExactCall>();

    // skip the header line
    reader.readLine();

    // skip the first "type" line
    reader.readLine();

    while (true) {
        final VariantContextBuilder builder = new VariantContextBuilder();
        final List<Allele> alleles = new ArrayList<Allele>();
        final List<Genotype> genotypes = new ArrayList<Genotype>();
        final double[] posteriors = new double[2];
        final double[] priors = MathUtils.normalizeFromLog10(new double[] { 0.5, 0.5 }, true);
        final List<Integer> mle = new ArrayList<Integer>();
        final Map<Allele, Double> log10pRefByAllele = new HashMap<Allele, Double>();
        long runtimeNano = -1;

        GenomeLoc currentLoc = null;
        while (true) {
            final String line = reader.readLine();
            if (line == null)
                return calls;

            final String[] parts = line.split("\t");
            final GenomeLoc lineLoc = parser.parseGenomeLoc(parts[0]);
            final String variable = parts[1];
            final String key = parts[2];
            final String value = parts[3];

            if (currentLoc == null)
                currentLoc = lineLoc;

            if (variable.equals("type")) {
                if (startsToKeep.isEmpty() || startsToKeep.contains(currentLoc.getStart())) {
                    builder.alleles(alleles);
                    final int stop = currentLoc.getStart() + alleles.get(0).length() - 1;
                    builder.chr(currentLoc.getContig()).start(currentLoc.getStart()).stop(stop);
                    builder.genotypes(genotypes);
                    final int[] mleInts = ArrayUtils.toPrimitive(mle.toArray(new Integer[] {}));
                    final AFCalculationResult result = new AFCalculationResult(mleInts, 1, alleles, posteriors,
                            priors, log10pRefByAllele);
                    calls.add(new ExactCall(builder.make(), runtimeNano, result));
                }
                break;
            } else if (variable.equals("allele")) {
                final boolean isRef = key.equals("0");
                alleles.add(Allele.create(value, isRef));
            } else if (variable.equals("PL")) {
                final GenotypeBuilder gb = new GenotypeBuilder(key);
                gb.PL(GenotypeLikelihoods.fromPLField(value).getAsPLs());
                genotypes.add(gb.make());
            } else if (variable.equals("log10PosteriorOfAFEq0")) {
                posteriors[0] = Double.valueOf(value);
            } else if (variable.equals("log10PosteriorOfAFGt0")) {
                posteriors[1] = Double.valueOf(value);
            } else if (variable.equals("MLE")) {
                mle.add(Integer.valueOf(value));
            } else if (variable.equals("pRefByAllele")) {
                final Allele a = Allele.create(key);
                log10pRefByAllele.put(a, Double.valueOf(value));
            } else if (variable.equals("runtime.nano")) {
                runtimeNano = Long.valueOf(value);
            } else {
                // nothing to do
            }
        }
    }
}

From source file:org.broadinstitute.gatk.tools.walkers.variantrecalibration.VariantDataManager.java

public void normalizeData() {
    boolean foundZeroVarianceAnnotation = false;
    for (int iii = 0; iii < meanVector.length; iii++) {
        final double theMean = mean(iii, true);
        final double theSTD = standardDeviation(theMean, iii, true);
        logger.info(annotationKeys.get(iii)
                + String.format(": \t mean = %.2f\t standard deviation = %.2f", theMean, theSTD));
        if (Double.isNaN(theMean)) {
            throw new UserException.BadInput("Values for " + annotationKeys.get(iii)
                    + " annotation not detected for ANY training variant in the input callset. VariantAnnotator may be used to add these annotations.");
        }//from w w  w.j  a  v  a2  s  .com

        foundZeroVarianceAnnotation = foundZeroVarianceAnnotation || (theSTD < 1E-5);
        meanVector[iii] = theMean;
        varianceVector[iii] = theSTD;
        for (final VariantDatum datum : data) {
            // Transform each data point via: (x - mean) / standard deviation
            datum.annotations[iii] = (datum.isNull[iii] ? 0.1 * Utils.getRandomGenerator().nextGaussian()
                    : (datum.annotations[iii] - theMean) / theSTD);
        }
    }
    if (foundZeroVarianceAnnotation) {
        throw new UserException.BadInput(
                "Found annotations with zero variance. They must be excluded before proceeding.");
    }

    // trim data by standard deviation threshold and mark failing data for exclusion later
    for (final VariantDatum datum : data) {
        boolean remove = false;
        for (final double val : datum.annotations) {
            remove = remove || (Math.abs(val) > VRAC.STD_THRESHOLD);
        }
        datum.failingSTDThreshold = remove;
    }

    // re-order the data by increasing standard deviation so that the results don't depend on the order things were specified on the command line
    // standard deviation over the training points is used as a simple proxy for information content, perhaps there is a better thing to use here
    final List<Integer> theOrder = calculateSortOrder(meanVector);
    annotationKeys = reorderList(annotationKeys, theOrder);
    varianceVector = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(varianceVector), theOrder));
    meanVector = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(meanVector), theOrder));
    for (final VariantDatum datum : data) {
        datum.annotations = ArrayUtils
                .toPrimitive(reorderArray(ArrayUtils.toObject(datum.annotations), theOrder));
        datum.isNull = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(datum.isNull), theOrder));
    }
    logger.info("Annotations are now ordered by their information content: " + annotationKeys.toString());
}

From source file:org.broadinstitute.gatk.tools.walkers.variantrecalibration.VariantDataManagerUnitTest.java

@Test
public final void testCalculateSortOrder() {
    final double passingQual = 400.0;
    final VariantRecalibratorArgumentCollection VRAC = new VariantRecalibratorArgumentCollection();

    VariantDataManager vdm = new VariantDataManager(new ArrayList<String>(), VRAC);

    final List<VariantDatum> theData = new ArrayList<>();
    final VariantDatum datum1 = new VariantDatum();
    datum1.atTrainingSite = true;//  w w w  . j a  va  2s . c  o m
    datum1.failingSTDThreshold = false;
    datum1.originalQual = passingQual;
    datum1.annotations = new double[] { 0.0, -10.0, 10.0 };
    datum1.isNull = new boolean[] { false, false, false };
    theData.add(datum1);

    final VariantDatum datum2 = new VariantDatum();
    datum2.atTrainingSite = true;
    datum2.failingSTDThreshold = false;
    datum2.originalQual = passingQual;
    datum2.annotations = new double[] { 0.0, -9.0, 15.0 };
    datum2.isNull = new boolean[] { false, false, false };
    theData.add(datum2);

    final VariantDatum datum3 = new VariantDatum();
    datum3.atTrainingSite = false;
    datum3.failingSTDThreshold = false;
    datum3.originalQual = passingQual;
    datum3.annotations = new double[] { 0.0, 1.0, 999.0 };
    datum3.isNull = new boolean[] { false, false, false };
    theData.add(datum3);

    final VariantDatum datum4 = new VariantDatum();
    datum4.atTrainingSite = false;
    datum4.failingSTDThreshold = false;
    datum4.originalQual = passingQual;
    datum4.annotations = new double[] { 0.015, 2.0, 1001.11 };
    datum4.isNull = new boolean[] { false, false, false };
    theData.add(datum4);

    vdm.setData(theData);

    final double[] meanVector = new double[3];
    for (int iii = 0; iii < meanVector.length; iii++) {
        meanVector[iii] = vdm.mean(iii, true);
    }
    final List<Integer> order = vdm.calculateSortOrder(meanVector);
    Assert.assertTrue(Arrays.equals(new int[] { 2, 1, 0 },
            ArrayUtils.toPrimitive(order.toArray(new Integer[order.size()]))));
}

From source file:org.broadinstitute.gatk.utils.MathUtilsUnitTest.java

@Test(dataProvider = "ArrayMinData")
public void testArrayMinIntArray(final List<Integer> values, final int expected) {
    final int[] asArray = ArrayUtils.toPrimitive(values.toArray(new Integer[values.size()]));
    final int actual = MathUtils.arrayMin(asArray);
    Assert.assertEquals(actual, expected, "Failed with " + values);
}

From source file:org.broadinstitute.sting.gatk.walkers.genotyper.afcalc.ExactCallLogger.java

/**
 * Read in a list of ExactCall objects from reader, keeping only those
 * with starts in startsToKeep or all sites (if this is empty)
 *
 * @param reader a just-opened reader sitting at the start of the file
 * @param startsToKeep a list of start position of the calls to keep, or empty if all calls should be kept
 * @param parser a genome loc parser to create genome locs
 * @return a list of ExactCall objects in reader
 * @throws IOException//from  w ww .  ja va 2 s  .  co  m
 */
public static List<ExactCall> readExactLog(final BufferedReader reader, final List<Integer> startsToKeep,
        GenomeLocParser parser) throws IOException {
    if (reader == null)
        throw new IllegalArgumentException("reader cannot be null");
    if (startsToKeep == null)
        throw new IllegalArgumentException("startsToKeep cannot be null");
    if (parser == null)
        throw new IllegalArgumentException("GenomeLocParser cannot be null");

    List<ExactCall> calls = new LinkedList<ExactCall>();

    // skip the header line
    reader.readLine();

    // skip the first "type" line
    reader.readLine();

    while (true) {
        final VariantContextBuilder builder = new VariantContextBuilder();
        final List<Allele> alleles = new ArrayList<Allele>();
        final List<Genotype> genotypes = new ArrayList<Genotype>();
        final double[] posteriors = new double[2];
        final double[] priors = MathUtils.normalizeFromLog10(new double[] { 0.5, 0.5 }, true);
        final List<Integer> mle = new ArrayList<Integer>();
        final Map<Allele, Double> log10pRefByAllele = new HashMap<Allele, Double>();
        long runtimeNano = -1;

        GenomeLoc currentLoc = null;
        while (true) {
            final String line = reader.readLine();
            if (line == null)
                return calls;

            final String[] parts = line.split("\t");
            final GenomeLoc lineLoc = parser.parseGenomeLoc(parts[0]);
            final String variable = parts[1];
            final String key = parts[2];
            final String value = parts[3];

            if (currentLoc == null)
                currentLoc = lineLoc;

            if (variable.equals("type")) {
                if (startsToKeep.isEmpty() || startsToKeep.contains(currentLoc.getStart())) {
                    builder.alleles(alleles);
                    final int stop = currentLoc.getStart() + alleles.get(0).length() - 1;
                    builder.chr(currentLoc.getContig()).start(currentLoc.getStart()).stop(stop);
                    builder.genotypes(genotypes);
                    final int[] mleInts = ArrayUtils.toPrimitive(mle.toArray(new Integer[] {}));
                    final AFCalcResult result = new AFCalcResult(mleInts, 1, alleles, posteriors, priors,
                            log10pRefByAllele);
                    calls.add(new ExactCall(builder.make(), runtimeNano, result));
                }
                break;
            } else if (variable.equals("allele")) {
                final boolean isRef = key.equals("0");
                alleles.add(Allele.create(value, isRef));
            } else if (variable.equals("PL")) {
                final GenotypeBuilder gb = new GenotypeBuilder(key);
                gb.PL(GenotypeLikelihoods.fromPLField(value).getAsPLs());
                genotypes.add(gb.make());
            } else if (variable.equals("log10PosteriorOfAFEq0")) {
                posteriors[0] = Double.valueOf(value);
            } else if (variable.equals("log10PosteriorOfAFGt0")) {
                posteriors[1] = Double.valueOf(value);
            } else if (variable.equals("MLE")) {
                mle.add(Integer.valueOf(value));
            } else if (variable.equals("pRefByAllele")) {
                final Allele a = Allele.create(key);
                log10pRefByAllele.put(a, Double.valueOf(value));
            } else if (variable.equals("runtime.nano")) {
                runtimeNano = Long.valueOf(value);
            } else {
                // nothing to do
            }
        }
    }
}

From source file:org.broadinstitute.sting.gatk.walkers.variantrecalibration.VariantDataManager.java

public void normalizeData() {
    boolean foundZeroVarianceAnnotation = false;
    for (int iii = 0; iii < meanVector.length; iii++) {
        final double theMean = mean(iii, true);
        final double theSTD = standardDeviation(theMean, iii, true);
        logger.info(annotationKeys.get(iii)
                + String.format(": \t mean = %.2f\t standard deviation = %.2f", theMean, theSTD));
        if (Double.isNaN(theMean)) {
            throw new UserException.BadInput("Values for " + annotationKeys.get(iii)
                    + " annotation not detected for ANY training variant in the input callset. VariantAnnotator may be used to add these annotations. See "
                    + HelpConstants.forumPost("discussion/49/using-variant-annotator"));
        }//from  w  ww .ja v  a 2 s.  c om

        foundZeroVarianceAnnotation = foundZeroVarianceAnnotation || (theSTD < 1E-5);
        meanVector[iii] = theMean;
        varianceVector[iii] = theSTD;
        for (final VariantDatum datum : data) {
            // Transform each data point via: (x - mean) / standard deviation
            datum.annotations[iii] = (datum.isNull[iii]
                    ? 0.1 * GenomeAnalysisEngine.getRandomGenerator().nextGaussian()
                    : (datum.annotations[iii] - theMean) / theSTD);
        }
    }
    if (foundZeroVarianceAnnotation) {
        throw new UserException.BadInput(
                "Found annotations with zero variance. They must be excluded before proceeding.");
    }

    // trim data by standard deviation threshold and mark failing data for exclusion later
    for (final VariantDatum datum : data) {
        boolean remove = false;
        for (final double val : datum.annotations) {
            remove = remove || (Math.abs(val) > VRAC.STD_THRESHOLD);
        }
        datum.failingSTDThreshold = remove;
    }

    // re-order the data by increasing standard deviation so that the results don't depend on the order things were specified on the command line
    // standard deviation over the training points is used as a simple proxy for information content, perhaps there is a better thing to use here
    final List<Integer> theOrder = calculateSortOrder(meanVector);
    annotationKeys = reorderList(annotationKeys, theOrder);
    varianceVector = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(varianceVector), theOrder));
    meanVector = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(meanVector), theOrder));
    for (final VariantDatum datum : data) {
        datum.annotations = ArrayUtils
                .toPrimitive(reorderArray(ArrayUtils.toObject(datum.annotations), theOrder));
        datum.isNull = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(datum.isNull), theOrder));
    }
    logger.info("Annotations are now ordered by their information content: " + annotationKeys.toString());
}

From source file:org.broadinstitute.sting.gatk.walkers.variantrecalibration.VariantDataManagerUnitTest.java

@Test
public final void testCalculateSortOrder() {
    final double passingQual = 400.0;
    final VariantRecalibratorArgumentCollection VRAC = new VariantRecalibratorArgumentCollection();

    VariantDataManager vdm = new VariantDataManager(new ArrayList<String>(), VRAC);

    final List<VariantDatum> theData = new ArrayList<>();
    final VariantDatum datum1 = new VariantDatum();
    datum1.atTrainingSite = true;/* w  ww.j a v a 2s.co  m*/
    datum1.failingSTDThreshold = false;
    datum1.originalQual = passingQual;
    datum1.annotations = new double[] { 0.0, -10.0, 10.0 };
    datum1.isNull = new boolean[] { false, false, false };
    theData.add(datum1);

    final VariantDatum datum2 = new VariantDatum();
    datum2.atTrainingSite = true;
    datum2.failingSTDThreshold = false;
    datum2.originalQual = passingQual;
    datum2.annotations = new double[] { 0.0, -9.0, 15.0 };
    datum2.isNull = new boolean[] { false, false, false };
    theData.add(datum2);

    final VariantDatum datum3 = new VariantDatum();
    datum3.atTrainingSite = false;
    datum3.failingSTDThreshold = false;
    datum3.originalQual = passingQual;
    datum3.annotations = new double[] { 0.0, 1.0, 999.0 };
    datum3.isNull = new boolean[] { false, false, false };
    theData.add(datum3);

    final VariantDatum datum4 = new VariantDatum();
    datum4.atTrainingSite = false;
    datum4.failingSTDThreshold = false;
    datum4.originalQual = passingQual;
    datum4.annotations = new double[] { 0.015, 2.0, 1001.11 };
    datum4.isNull = new boolean[] { false, false, false };
    theData.add(datum4);

    vdm.setData(theData);

    final double[] meanVector = new double[3];
    for (int iii = 0; iii < meanVector.length; iii++) {
        meanVector[iii] = vdm.mean(iii, true);
    }
    final List<Integer> order = vdm.calculateSortOrder(meanVector);
    Assert.assertArrayEquals(new int[] { 2, 1, 0 },
            ArrayUtils.toPrimitive(order.toArray(new Integer[order.size()])));
}

From source file:org.broadinstitute.sting.utils.codecs.bcf2.BCF2EncoderDecoderUnitTest.java

@Test(dataProvider = "BestIntTypeTests")
public void determineBestEncoding(final List<Integer> ints, final BCF2Type expectedType) throws IOException {
    BCF2Encoder encoder = new BCF2Encoder();
    Assert.assertEquals(BCF2Utils.determineIntegerType(ints), expectedType);
    Assert.assertEquals(BCF2Utils.determineIntegerType(ArrayUtils.toPrimitive(ints.toArray(new Integer[0]))),
            expectedType);//from  www .  j  a va 2 s  . c om
}

From source file:org.carbondata.core.util.CarbonUtil.java

/**
 * This API will record the indexes of the dimension which doesnt have
 * Dictionary values.// ww  w.  j  a  va 2 s .c  o m
 *
 * @param currentDims .
 * @return
 */
public static int[] getNoDictionaryColIndex(Dimension[] currentDims) {
    List<Integer> dirSurrogateList = new ArrayList<Integer>(currentDims.length);
    for (Dimension dim : currentDims) {
        if (dim.isNoDictionaryDim()) {
            dirSurrogateList.add(dim.getOrdinal());
        }
    }
    int[] noDictionaryValIndex = ArrayUtils
            .toPrimitive(dirSurrogateList.toArray(new Integer[dirSurrogateList.size()]));
    return noDictionaryValIndex;
}

From source file:org.carbondata.core.util.CarbonUtil.java

/**
 * Below method will be used to get the dimension
 *
 * @param tableDimensionList table dimension list
 * @return boolean array specifying true if dimension is dictionary
 * and false if dimension is not a dictionary column
 *//* w  w w. ja  va 2  s . c  o  m*/
public static boolean[] identifyDimensionType(List<CarbonDimension> tableDimensionList) {
    List<Boolean> isDictionaryDimensions = new ArrayList<Boolean>();
    Set<Integer> processedColumnGroup = new HashSet<Integer>();
    for (CarbonDimension carbonDimension : tableDimensionList) {
        if (carbonDimension.isColumnar() && hasEncoding(carbonDimension.getEncoder(), Encoding.DICTIONARY)) {
            isDictionaryDimensions.add(true);
        } else if (!carbonDimension.isColumnar()) {
            if (processedColumnGroup.add(carbonDimension.columnGroupId())) {
                isDictionaryDimensions.add(true);
            }
        } else {
            isDictionaryDimensions.add(false);
        }
    }
    boolean[] primitive = ArrayUtils
            .toPrimitive(isDictionaryDimensions.toArray(new Boolean[isDictionaryDimensions.size()]));
    return primitive;
}