Examples with Logger org.apache.logging.log4j.Logger used on opensource projects

Example 71 with Logger

use of org.apache.logging.log4j.Logger in project gatk by broadinstitute.

the class HDF5PCACoveragePoNCreationUtils method normalizeAndLogReadCounts.

/**
     * Final pre-panel normalization that consists of dividing all counts by the median of
     * its column and log it with base 2.
     * <p>
     *     The normalization occurs in-place.
     * </p>
     *
     * @param readCounts the input counts to normalize.
     */
@VisibleForTesting
static void normalizeAndLogReadCounts(final ReadCountCollection readCounts, final Logger logger) {
    final RealMatrix counts = readCounts.counts();
    final Median medianCalculator = new Median();
    final double[] medians = IntStream.range(0, counts.getColumnDimension()).mapToDouble(col -> medianCalculator.evaluate(counts.getColumn(col))).toArray();
    counts.walkInOptimizedOrder(new DefaultRealMatrixChangingVisitor() {

        @Override
        public double visit(final int row, final int column, final double value) {
            return Math.log(Math.max(EPSILON, value / medians[column])) * INV_LN_2;
        }
    });
    logger.info("Counts normalized by the column median and log2'd.");
}

Example 72 with Logger

use of org.apache.logging.log4j.Logger in project gatk by broadinstitute.

the class ReferenceAPISourceUnitTest method testDummy.

@Test(groups = "cloud")
public void testDummy() {
    String referenceName = HS37D5_REF_ID;
    final String expected = "AAACAGGTTA";
    // -1 because we're using closed intervals
    SimpleInterval interval = new SimpleInterval("1", 50001, 50001 + expected.length() - 1);
    Logger logger = LogManager.getLogger(ReferenceAPISourceUnitTest.class);
    GenomicsOptions options = PipelineOptionsFactory.create().as(GenomicsOptions.class);
    options.setApiKey(getGCPTestApiKey());
    options.setProject(getGCPTestProject());
    // We don't use GATKTestPipeline because we need specific options.
    final Pipeline p = TestPipeline.create(options);
    ReferenceAPISource refAPISource = makeReferenceAPISource(referenceName, p);
    ReferenceBases bases = refAPISource.getReferenceBases(p.getOptions(), interval);
    final String actual = new String(bases.getBases());
    Assert.assertEquals(actual, expected, "Wrong bases returned");
    p.run();
}

Example 73 with Logger

use of org.apache.logging.log4j.Logger in project gatk by broadinstitute.

the class CoverageModelEMWorkspace method initializeWorkersWithPCA.

/**
     * Initialize model parameters by performing PCA.
     */
@EvaluatesRDD
@UpdatesRDD
@CachesRDD
private void initializeWorkersWithPCA() {
    logger.info("Initializing model parameters using PCA...");
    /* initially, set m_t, Psi_t and W_tl to zero to get an estimate of the read depth */
    final int numLatents = config.getNumLatents();
    mapWorkers(cb -> cb.cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.m_t, Nd4j.zeros(new int[] { 1, cb.getTargetSpaceBlock().getNumElements() })).cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.Psi_t, Nd4j.zeros(new int[] { 1, cb.getTargetSpaceBlock().getNumElements() })));
    if (biasCovariatesEnabled) {
        mapWorkers(cb -> cb.cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.W_tl, Nd4j.zeros(new int[] { cb.getTargetSpaceBlock().getNumElements(), numLatents })));
    }
    /* update read depth without taking into account correction from bias covariates */
    updateReadDepthPosteriorExpectations(1.0, true);
    /* fetch sample covariance matrix */
    final int minPCAInitializationReadCount = config.getMinPCAInitializationReadCount();
    mapWorkers(cb -> cb.cloneWithPCAInitializationData(minPCAInitializationReadCount, Integer.MAX_VALUE));
    cacheWorkers("PCA initialization");
    final INDArray targetCovarianceMatrix = mapWorkersAndReduce(CoverageModelEMComputeBlock::calculateTargetCovarianceMatrixForPCAInitialization, INDArray::add);
    /* perform eigen-decomposition on the target covariance matrix */
    final ImmutablePair<INDArray, INDArray> targetCovarianceEigensystem = CoverageModelEMWorkspaceMathUtils.eig(targetCovarianceMatrix, false, logger);
    /* the eigenvalues of sample covariance matrix can be immediately inferred by scaling */
    final INDArray sampleCovarianceEigenvalues = targetCovarianceEigensystem.getLeft().div(numSamples);
    /* estimate the isotropic unexplained variance -- see Bishop 12.46 */
    final int residualDim = numTargets - numLatents;
    final double isotropicVariance = sampleCovarianceEigenvalues.get(NDArrayIndex.interval(numLatents, numSamples)).sumNumber().doubleValue() / residualDim;
    logger.info(String.format("PCA estimate of isotropic unexplained variance: %f", isotropicVariance));
    /* estimate bias factors -- see Bishop 12.45 */
    final INDArray scaleFactors = Transforms.sqrt(sampleCovarianceEigenvalues.get(NDArrayIndex.interval(0, numLatents)).sub(isotropicVariance), false);
    final INDArray biasCovariatesPCA = Nd4j.create(new int[] { numTargets, numLatents });
    for (int li = 0; li < numLatents; li++) {
        final INDArray v = targetCovarianceEigensystem.getRight().getColumn(li);
        /* calculate [Delta_PCA_st]^T v */
        /* note: we do not need to broadcast vec since it is small and lambda capture is just fine */
        final INDArray unnormedBiasCovariate = CoverageModelSparkUtils.assembleINDArrayBlocksFromCollection(mapWorkersAndCollect(cb -> ImmutablePair.of(cb.getTargetSpaceBlock(), cb.getINDArrayFromCache(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.Delta_PCA_st).transpose().mmul(v))), 0);
        final double norm = unnormedBiasCovariate.norm1Number().doubleValue();
        final INDArray normedBiasCovariate = unnormedBiasCovariate.divi(norm).muli(scaleFactors.getDouble(li));
        biasCovariatesPCA.getColumn(li).assign(normedBiasCovariate);
    }
    if (ardEnabled) {
        /* a better estimate of ARD coefficients */
        biasCovariatesARDCoefficients.assign(Nd4j.zeros(new int[] { 1, numLatents }).addi(config.getInitialARDPrecisionRelativeToNoise() / isotropicVariance));
    }
    final CoverageModelParameters modelParamsFromPCA = new CoverageModelParameters(processedTargetList, Nd4j.zeros(new int[] { 1, numTargets }), Nd4j.zeros(new int[] { 1, numTargets }).addi(isotropicVariance), biasCovariatesPCA, biasCovariatesARDCoefficients);
    /* clear PCA initialization data from workers */
    mapWorkers(CoverageModelEMComputeBlock::cloneWithRemovedPCAInitializationData);
    /* push model parameters to workers */
    initializeWorkersWithGivenModel(modelParamsFromPCA);
    /* update bias latent posterior expectations without admixing */
    updateBiasLatentPosteriorExpectations(1.0);
}

Example 74 with Logger

use of org.apache.logging.log4j.Logger in project gatk-protected by broadinstitute.

the class CoverageModelEMWorkspace method initializeWorkersWithPCA.

/**
     * Initialize model parameters by performing PCA.
     */
@EvaluatesRDD
@UpdatesRDD
@CachesRDD
private void initializeWorkersWithPCA() {
    logger.info("Initializing model parameters using PCA...");
    /* initially, set m_t, Psi_t and W_tl to zero to get an estimate of the read depth */
    final int numLatents = config.getNumLatents();
    mapWorkers(cb -> cb.cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.m_t, Nd4j.zeros(new int[] { 1, cb.getTargetSpaceBlock().getNumElements() })).cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.Psi_t, Nd4j.zeros(new int[] { 1, cb.getTargetSpaceBlock().getNumElements() })));
    if (biasCovariatesEnabled) {
        mapWorkers(cb -> cb.cloneWithUpdatedPrimitive(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.W_tl, Nd4j.zeros(new int[] { cb.getTargetSpaceBlock().getNumElements(), numLatents })));
    }
    /* update read depth without taking into account correction from bias covariates */
    updateReadDepthPosteriorExpectations(1.0, true);
    /* fetch sample covariance matrix */
    final int minPCAInitializationReadCount = config.getMinPCAInitializationReadCount();
    mapWorkers(cb -> cb.cloneWithPCAInitializationData(minPCAInitializationReadCount, Integer.MAX_VALUE));
    cacheWorkers("PCA initialization");
    final INDArray targetCovarianceMatrix = mapWorkersAndReduce(CoverageModelEMComputeBlock::calculateTargetCovarianceMatrixForPCAInitialization, INDArray::add);
    /* perform eigen-decomposition on the target covariance matrix */
    final ImmutablePair<INDArray, INDArray> targetCovarianceEigensystem = CoverageModelEMWorkspaceMathUtils.eig(targetCovarianceMatrix, false, logger);
    /* the eigenvalues of sample covariance matrix can be immediately inferred by scaling */
    final INDArray sampleCovarianceEigenvalues = targetCovarianceEigensystem.getLeft().div(numSamples);
    /* estimate the isotropic unexplained variance -- see Bishop 12.46 */
    final int residualDim = numTargets - numLatents;
    final double isotropicVariance = sampleCovarianceEigenvalues.get(NDArrayIndex.interval(numLatents, numSamples)).sumNumber().doubleValue() / residualDim;
    logger.info(String.format("PCA estimate of isotropic unexplained variance: %f", isotropicVariance));
    /* estimate bias factors -- see Bishop 12.45 */
    final INDArray scaleFactors = Transforms.sqrt(sampleCovarianceEigenvalues.get(NDArrayIndex.interval(0, numLatents)).sub(isotropicVariance), false);
    final INDArray biasCovariatesPCA = Nd4j.create(new int[] { numTargets, numLatents });
    for (int li = 0; li < numLatents; li++) {
        final INDArray v = targetCovarianceEigensystem.getRight().getColumn(li);
        /* calculate [Delta_PCA_st]^T v */
        /* note: we do not need to broadcast vec since it is small and lambda capture is just fine */
        final INDArray unnormedBiasCovariate = CoverageModelSparkUtils.assembleINDArrayBlocksFromCollection(mapWorkersAndCollect(cb -> ImmutablePair.of(cb.getTargetSpaceBlock(), cb.getINDArrayFromCache(CoverageModelEMComputeBlock.CoverageModelICGCacheNode.Delta_PCA_st).transpose().mmul(v))), 0);
        final double norm = unnormedBiasCovariate.norm1Number().doubleValue();
        final INDArray normedBiasCovariate = unnormedBiasCovariate.divi(norm).muli(scaleFactors.getDouble(li));
        biasCovariatesPCA.getColumn(li).assign(normedBiasCovariate);
    }
    if (ardEnabled) {
        /* a better estimate of ARD coefficients */
        biasCovariatesARDCoefficients.assign(Nd4j.zeros(new int[] { 1, numLatents }).addi(config.getInitialARDPrecisionRelativeToNoise() / isotropicVariance));
    }
    final CoverageModelParameters modelParamsFromPCA = new CoverageModelParameters(processedTargetList, Nd4j.zeros(new int[] { 1, numTargets }), Nd4j.zeros(new int[] { 1, numTargets }).addi(isotropicVariance), biasCovariatesPCA, biasCovariatesARDCoefficients);
    /* clear PCA initialization data from workers */
    mapWorkers(CoverageModelEMComputeBlock::cloneWithRemovedPCAInitializationData);
    /* push model parameters to workers */
    initializeWorkersWithGivenModel(modelParamsFromPCA);
    /* update bias latent posterior expectations without admixing */
    updateBiasLatentPosteriorExpectations(1.0);
}

Example 75 with Logger

use of org.apache.logging.log4j.Logger in project jabref by JabRef.

the class FallbackExceptionHandler method uncaughtException.

@Override
public void uncaughtException(Thread thread, Throwable exception) {
    Logger logger = LogManager.getLogger(FallbackExceptionHandler.class);
    logger.error(UncaughtException_MARKER, "Uncaught exception Occurred in " + thread, exception);
}