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package org.broadinstitute.gatk.tools.walkers.indels;

import com.google.java.contract.Ensures;
import htsjdk.variant.variantcontext.Allele;
import org.broadinstitute.gatk.utils.contexts.ReferenceContext;
import org.broadinstitute.gatk.utils.genotyper.AlleleList;
import org.broadinstitute.gatk.utils.genotyper.IndexedAlleleList;
import org.broadinstitute.gatk.utils.genotyper.IndexedSampleList;
import org.broadinstitute.gatk.utils.MathUtils;
import org.broadinstitute.gatk.utils.clipping.ReadClipper;
import org.broadinstitute.gatk.utils.exceptions.UserException;
import org.broadinstitute.gatk.utils.genotyper.PerReadAlleleLikelihoodMap;
import org.broadinstitute.gatk.utils.genotyper.ReadLikelihoods;
import org.broadinstitute.gatk.utils.haplotype.Haplotype;
import org.broadinstitute.gatk.utils.pairhmm.ArrayLoglessPairHMM;
import org.broadinstitute.gatk.utils.pairhmm.Log10PairHMM;
import org.broadinstitute.gatk.utils.pairhmm.LoglessPairHMM;
import org.broadinstitute.gatk.utils.pairhmm.PairHMM;
import org.broadinstitute.gatk.utils.pileup.PileupElement;
import org.broadinstitute.gatk.utils.pileup.ReadBackedPileup;
import org.broadinstitute.gatk.utils.sam.GATKSAMRecord;
import org.broadinstitute.gatk.utils.sam.ReadUtils;

import java.util.*;


public class PairHMMIndelErrorModel {
    public static final int BASE_QUAL_THRESHOLD = 20;

    private boolean DEBUG = false;

    private static final int MAX_CACHED_QUAL = 127;

    private static final double baseMatchArray[];
    private static final double baseMismatchArray[];

    private static final int START_HRUN_GAP_IDX = 4;
    private static final int MAX_HRUN_GAP_IDX = 20;

    private static final byte MIN_GAP_OPEN_PENALTY = 30;
    private static final byte MIN_GAP_CONT_PENALTY = 10;
    private static final byte GAP_PENALTY_HRUN_STEP = 1; // each increase in hrun decreases gap penalty by this.

    private final byte[] GAP_OPEN_PROB_TABLE;
    private final byte[] GAP_CONT_PROB_TABLE;

    private final PairHMM pairHMM;

    /////////////////////////////
    // Private Member Variables
    /////////////////////////////

    static {
        baseMatchArray = new double[MAX_CACHED_QUAL+1];
        baseMismatchArray = new double[MAX_CACHED_QUAL+1];
        for (int k=1; k <= MAX_CACHED_QUAL; k++) {
            double baseProb = Math.pow(10, -k/10.);


            baseMatchArray[k] =  Math.log10(1-baseProb);
            baseMismatchArray[k] = Math.log10(baseProb);
        }
    }

    public PairHMMIndelErrorModel(byte indelGOP, byte indelGCP, boolean deb, final PairHMM.HMM_IMPLEMENTATION hmmType ) {
        this.DEBUG = deb;

        switch (hmmType) {
            case EXACT:
                pairHMM = new Log10PairHMM(true);
                break;
            case ORIGINAL:
                pairHMM = new Log10PairHMM(false);
                break;
            case LOGLESS_CACHING:
                pairHMM = new LoglessPairHMM();
                break;
            case ARRAY_LOGLESS:
                pairHMM = new ArrayLoglessPairHMM();
                break;
            default:
                throw new UserException.BadArgumentValue("pairHMM", "Specified pairHMM implementation is unrecognized or incompatible with the UnifiedGenotyper. Acceptable options are ORIGINAL, EXACT, LOGLESS_CACHING, or ARRAY_LOGLESS.");
        }

        // fill gap penalty table, affine naive model:
        this.GAP_CONT_PROB_TABLE = new byte[MAX_HRUN_GAP_IDX];
        this.GAP_OPEN_PROB_TABLE = new byte[MAX_HRUN_GAP_IDX];

        for (int i = 0; i < START_HRUN_GAP_IDX; i++) {
            GAP_OPEN_PROB_TABLE[i] = indelGOP;
            GAP_CONT_PROB_TABLE[i] = indelGCP;
        }

        double step = GAP_PENALTY_HRUN_STEP/10.0;

        // initialize gop and gcp to their default values
        byte gop = indelGOP;
        byte gcp = indelGCP;

        // all of the following is computed in QUal-space
        for (int i=START_HRUN_GAP_IDX; i < MAX_HRUN_GAP_IDX; i++) {
            gop -= GAP_PENALTY_HRUN_STEP;
            if (gop < MIN_GAP_OPEN_PENALTY)
                gop = MIN_GAP_OPEN_PENALTY;

            gcp -= step;
            if(gcp < MIN_GAP_CONT_PENALTY)
                gcp = MIN_GAP_CONT_PENALTY;
            GAP_OPEN_PROB_TABLE[i] = gop;
            GAP_CONT_PROB_TABLE[i] = gcp;
        }

    }

    static private void getContextHomopolymerLength(final byte[] refBytes, final int[] hrunArray) {
        // compute forward hrun length, example:
        // AGGTGACCCCCCTGAGAG
        // 001000012345000000
        hrunArray[0] = 0;
        int[] hforward = new int[hrunArray.length];
        int[] hreverse = new int[hrunArray.length];

        for (int i = 1; i < refBytes.length; i++) {
            if (refBytes[i] == refBytes[i-1])
                hforward[i] = hforward[i-1]+1;
            else
                hforward[i] = 0;
        }

        // do similar thing for reverse length, example:
        // AGGTGACCCCCCTGAGAG
        // 021000543210000000
        // and then accumulate with forward values.
        // Total:
        // AGGTGACCCCCCTGAGAG
        // 022000555555000000
        for (int i=refBytes.length-1; i > 0; i--) {
            if (refBytes[i-1] == refBytes[i])
                hreverse[i-1] += hreverse[i]+1;
        }

        for (int i = 1; i < refBytes.length; i++)
            hrunArray[i] = hforward[i]+hreverse[i];
    }


    private void fillGapProbabilities(final int[] hrunProfile,
                                      final byte[] contextLogGapOpenProbabilities,
                                      final byte[] contextLogGapContinuationProbabilities) {
        // fill based on lookup table
        for (int i = 0; i < hrunProfile.length; i++) {
            if (hrunProfile[i] >= MAX_HRUN_GAP_IDX) {
                contextLogGapOpenProbabilities[i] = GAP_OPEN_PROB_TABLE[MAX_HRUN_GAP_IDX-1];
                contextLogGapContinuationProbabilities[i] = GAP_CONT_PROB_TABLE[MAX_HRUN_GAP_IDX-1];
            }
            else {
                contextLogGapOpenProbabilities[i] = GAP_OPEN_PROB_TABLE[hrunProfile[i]];
                contextLogGapContinuationProbabilities[i] = GAP_CONT_PROB_TABLE[hrunProfile[i]];
            }
        }
    }

    /**
     * Trims the haplotypes in the given map to the provided start/stop.
     *
     * @param haplotypeMap                      the input map
     * @param startLocationInRefForHaplotypes   the start location of the trim
     * @param stopLocationInRefForHaplotypes    the stop location of the trim
     * @param ref                               the reference context (used for debugging only, so can be null)
     * @return a non-null mapping corresponding to the trimmed version of the original;
     *   some elements may be lost if trimming cannot be performed on them (e.g. they fall outside of the region to keep)
     */
    protected static Map<Allele, Haplotype> trimHaplotypes(final Map<Allele, Haplotype> haplotypeMap,
                                                           long startLocationInRefForHaplotypes,
                                                           long stopLocationInRefForHaplotypes,
                                                           final ReferenceContext ref) {
        if ( haplotypeMap == null ) throw new IllegalArgumentException("The input allele to haplotype map cannot be null");

        final LinkedHashMap<Allele, Haplotype> trimmedHaplotypeMap = new LinkedHashMap<>();
        for (final Allele a: haplotypeMap.keySet()) {

            final Haplotype haplotype = haplotypeMap.get(a);

            if (stopLocationInRefForHaplotypes > haplotype.getStopPosition())
                stopLocationInRefForHaplotypes = haplotype.getStopPosition();

            if (startLocationInRefForHaplotypes < haplotype.getStartPosition())
                startLocationInRefForHaplotypes = haplotype.getStartPosition();
            else if (startLocationInRefForHaplotypes > haplotype.getStopPosition())
                startLocationInRefForHaplotypes = haplotype.getStopPosition();

            final long indStart = startLocationInRefForHaplotypes - haplotype.getStartPosition();
            final long indStop =  stopLocationInRefForHaplotypes - haplotype.getStartPosition();
            if ( indStart >= indStop )
                continue;

            // commented out here because we need to make this method static for unit testing
            //if (DEBUG)
            //    System.out.format("indStart: %d indStop: %d WinStart:%d WinStop:%d start: %d stop: %d\n",
            //            indStart, indStop, ref.getWindow().getStart(), ref.getWindow().getStop(), startLocationInRefForHaplotypes, stopLocationInRefForHaplotypes);

            // get the trimmed haplotype-bases array and create a new haplotype based on it. Pack this into the new map
            final byte[] trimmedHaplotypeBases = Arrays.copyOfRange(haplotype.getBases(), (int)indStart, (int)indStop);
            final Haplotype trimmedHaplotype = new Haplotype(trimmedHaplotypeBases, haplotype.isReference());
            trimmedHaplotypeMap.put(a, trimmedHaplotype);
        }
        return trimmedHaplotypeMap;
    }


    public synchronized double[] computeDiploidReadHaplotypeLikelihoods(final ReadBackedPileup pileup,
                                                                        final LinkedHashMap<Allele, Haplotype> haplotypeMap,
                                                                        final ReferenceContext ref,
                                                                        final int eventLength,
                                                                        final PerReadAlleleLikelihoodMap perReadAlleleLikelihoodMap,
                                                                        final double downsamplingFraction) {
        final int numHaplotypes = haplotypeMap.size();

        final double[][] readLikelihoods = computeGeneralReadHaplotypeLikelihoods(pileup, haplotypeMap, ref, eventLength, perReadAlleleLikelihoodMap);
        perReadAlleleLikelihoodMap.performPerAlleleDownsampling(downsamplingFraction);
        return getDiploidHaplotypeLikelihoods(numHaplotypes, readLikelihoods);
        
    }

    /**
     * Should we clip a downstream portion of a read because it spans off the end of a haplotype?
     *
     * @param read               the read in question
     * @param refWindowStop      the end of the reference window
     * @return true if the read needs to be clipped, false otherwise
     */
    protected static boolean mustClipDownstream(final GATKSAMRecord read, final int refWindowStop) {
        return ( !read.isEmpty() && read.getSoftStart() < refWindowStop && read.getSoftStart() + read.getReadLength() - 1 > refWindowStop );
    }

    /**
     * Should we clip a upstream portion of a read because it spans off the end of a haplotype?
     *
     * @param read               the read in question
     * @param refWindowStart     the start of the reference window
     * @return true if the read needs to be clipped, false otherwise
     */
    protected static boolean mustClipUpstream(final GATKSAMRecord read, final int refWindowStart) {
        return ( !read.isEmpty() && read.getSoftStart() < refWindowStart && read.getSoftEnd() > refWindowStart );
    }

    @Ensures("result != null && result.length == pileup.getNumberOfElements()")
    public synchronized double[][] computeGeneralReadHaplotypeLikelihoods(final ReadBackedPileup pileup,
                                                                          final LinkedHashMap<Allele, Haplotype> haplotypeMap, 
                                                                          final ReferenceContext ref,
                                                                          final int eventLength, 
                                                                          final PerReadAlleleLikelihoodMap perReadAlleleLikelihoodMap) {
        final double readLikelihoods[][] = new double[pileup.getNumberOfElements()][haplotypeMap.size()];

        int readIdx=0;
        for (final PileupElement p: pileup) {

            // check if we've already computed likelihoods for this pileup element (i.e. for this read at this location)
            if (perReadAlleleLikelihoodMap.containsPileupElement(p)) {
                Map<Allele,Double> el = perReadAlleleLikelihoodMap.getLikelihoodsAssociatedWithPileupElement(p);
                int j=0;
                for (Allele a: haplotypeMap.keySet()) {
                    readLikelihoods[readIdx][j++] = el.get(a);
                }
            }
            else {
                // extra padding on candidate haplotypes to make sure reads are always strictly contained
                // in them - a value of 1 will in theory do but we use a slightly higher one just for safety sake, mostly
                // in case bases at edge of reads have lower quality.
                final int trailingBases = 3;
                final int refWindowStart = ref.getWindow().getStart() + trailingBases;
                final int refWindowStop  = ref.getWindow().getStop() - trailingBases;

                if (DEBUG) {
                    System.out.format("Read Name:%s, aln start:%d aln stop:%d orig cigar:%s\n",p.getRead().getReadName(), p.getRead().getAlignmentStart(), p.getRead().getAlignmentEnd(), p.getRead().getCigarString());
                }

                GATKSAMRecord read = ReadClipper.hardClipAdaptorSequence(p.getRead());

                // if the read extends beyond the downstream (right) end of the reference window, clip it
                if ( mustClipDownstream(read, refWindowStop) )
                    read = ReadClipper.hardClipByReadCoordinates(read, refWindowStop - read.getSoftStart() + 1, read.getReadLength() - 1);

                // if the read extends beyond the upstream (left) end of the reference window, clip it
                if ( mustClipUpstream(read, refWindowStart) )
                    read = ReadClipper.hardClipByReferenceCoordinatesLeftTail(read, refWindowStart);

                if (read.isEmpty())
                    continue;

                // hard-clip low quality ends - this may introduce extra H elements in CIGAR string
                read = ReadClipper.hardClipLowQualEnds(read, (byte) BASE_QUAL_THRESHOLD );

                if (read.isEmpty())
                    continue;

                // get bases of candidate haplotypes that overlap with reads
                final long readStart = read.getSoftStart();
                final long readEnd = read.getSoftEnd();

                // see if we want to use soft clipped bases. Aligners may soft clip all bases at insertions because they don't match,
                // but they're actually consistent with the insertion!
                // Rule: if a read starts in interval [eventStart-eventLength,eventStart+1] and we are at an insertion, we'll use all soft clipped bases at the beginning.
                // Conversely, if a read ends at [eventStart,eventStart+eventLength] we'll use all soft clipped bases in the end of the read.
                final long eventStartPos = ref.getLocus().getStart();

                // compute total number of clipped bases (soft or hard clipped) and only use them if necessary
                final boolean softClips = useSoftClippedBases(read, eventStartPos, eventLength);
                final int numStartSoftClippedBases = softClips ? read.getAlignmentStart()- read.getSoftStart() : 0;
                final int numEndSoftClippedBases = softClips ? read.getSoftEnd()- read.getAlignmentEnd() : 0 ;
                final byte [] unclippedReadBases = read.getReadBases();
                final byte [] unclippedReadQuals = read.getBaseQualities();

                /**
                 * Compute genomic locations that candidate haplotypes will span.
                 * Read start and stop locations (variables readStart and readEnd) are the original unclipped positions from SAMRecord,
                 * adjusted by hard clips from Cigar string and by qual-based soft-clipping performed above.
                 * We will propose haplotypes that overlap the read with some padding.
                 * True read start = readStart + numStartSoftClippedBases - ReadUtils.getFirstInsertionOffset(read)
                 * Last term is because if a read starts with an insertion then these bases are not accounted for in readStart.
                 * trailingBases is a padding constant(=3) and we additionally add abs(eventLength) to both sides of read to be able to
                 * differentiate context between two haplotypes
                 */
                final int absEventLength = Math.abs(eventLength);
                long startLocationInRefForHaplotypes = Math.max(readStart + numStartSoftClippedBases - trailingBases - ReadUtils.getFirstInsertionOffset(read) - absEventLength, 0);
                long stopLocationInRefForHaplotypes = readEnd - numEndSoftClippedBases + trailingBases + ReadUtils.getLastInsertionOffset(read) + absEventLength;

                if (DEBUG)
                    System.out.format("orig Start:%d orig stop: %d\n", startLocationInRefForHaplotypes, stopLocationInRefForHaplotypes);

                int readLength = read.getReadLength()-numStartSoftClippedBases-numEndSoftClippedBases;

                if (startLocationInRefForHaplotypes < ref.getWindow().getStart()) {
                    startLocationInRefForHaplotypes = ref.getWindow().getStart();                                       // read starts before haplotype: read will have to be cut numStartSoftClippedBases += ref.getWindow().getStart() - startLocationInRefForHaplotypes;
                }
                else if (startLocationInRefForHaplotypes > ref.getWindow().getStop()) {
                    startLocationInRefForHaplotypes = ref.getWindow().getStop();                                        // read starts after haplotype: read will have to be clipped completely;
                }

                // candidate haplotype cannot go beyond reference context
                if (stopLocationInRefForHaplotypes > ref.getWindow().getStop()) {
                    stopLocationInRefForHaplotypes = ref.getWindow().getStop();                                         // check also if end of read will go beyond reference context
                }

                if (stopLocationInRefForHaplotypes <= startLocationInRefForHaplotypes + readLength) {
                    stopLocationInRefForHaplotypes = startLocationInRefForHaplotypes + readLength-1;                    // if there's an insertion in the read, the read stop position will be less than start + read legnth, but we want to compute likelihoods in the whole region that a read might overlap
                }

                // ok, we now figured out the total number of clipped bases on both ends.
                // Figure out where we want to place the haplotype to score read against

                if (DEBUG)
                    System.out.format("numStartSoftClippedBases: %d numEndSoftClippedBases: %d WinStart:%d WinStop:%d start: %d stop: %d readLength: %d\n",
                            numStartSoftClippedBases, numEndSoftClippedBases, ref.getWindow().getStart(), ref.getWindow().getStop(), startLocationInRefForHaplotypes, stopLocationInRefForHaplotypes, read.getReadLength());

               // LinkedHashMap<Allele,Double> readEl = new LinkedHashMap<Allele,Double>();

                /**
                 * Check if we'll end up with an empty read once all clipping is done
                 */
                if (numStartSoftClippedBases + numEndSoftClippedBases >= unclippedReadBases.length) {
                    int j=0;
                    for (Allele a: haplotypeMap.keySet()) {
                        perReadAlleleLikelihoodMap.add(p,a,0.0);
                        readLikelihoods[readIdx][j++] = 0.0;
                    }
                }
                else {
                    final int endOfCopy = unclippedReadBases.length - numEndSoftClippedBases;
                    final byte[] readBases = Arrays.copyOfRange(unclippedReadBases, numStartSoftClippedBases, endOfCopy);
                    final byte[] readQuals = Arrays.copyOfRange(unclippedReadQuals, numStartSoftClippedBases, endOfCopy);

                    int j=0;

                    final byte[] contextLogGapOpenProbabilities = new byte[readBases.length];
                    final byte[] contextLogGapContinuationProbabilities  = new byte[readBases.length];

                    // get homopolymer length profile for current haplotype
                    final int[] hrunProfile = new int[readBases.length];
                    getContextHomopolymerLength(readBases,hrunProfile);
                    fillGapProbabilities(hrunProfile, contextLogGapOpenProbabilities, contextLogGapContinuationProbabilities);

                    // get the base insertion and deletion qualities to use
                    final byte[] baseInsertionQualities, baseDeletionQualities;
                    if ( read.hasBaseIndelQualities() ) {
                        baseInsertionQualities = Arrays.copyOfRange(read.getBaseInsertionQualities(), numStartSoftClippedBases, endOfCopy);
                        baseDeletionQualities = Arrays.copyOfRange(read.getBaseDeletionQualities(), numStartSoftClippedBases, endOfCopy);
                    } else {
                        baseInsertionQualities = contextLogGapOpenProbabilities;
                        baseDeletionQualities = contextLogGapOpenProbabilities;
                    }

                    // Create a new read based on the current one, but with trimmed bases/quals, for use in the HMM
                    final GATKSAMRecord processedRead = GATKSAMRecord.createQualityModifiedRead(read, readBases, readQuals, baseInsertionQualities, baseDeletionQualities);

                    // Pack the shortened read and its associated gap-continuation-penalty array into a map, as required by PairHMM
                    final Map<GATKSAMRecord,byte[]> readGCPArrayMap = Collections.singletonMap(processedRead,contextLogGapContinuationProbabilities);

                    // Create a map of alleles to a new set of haplotypes, whose bases have been trimmed to the appropriate genomic locations
                    final Map<Allele, Haplotype> trimmedHaplotypeMap = trimHaplotypes(haplotypeMap, startLocationInRefForHaplotypes, stopLocationInRefForHaplotypes, ref);

                    // Apparently more than one allele can map to the same haplotype after trimming
                    final Set<Haplotype> distinctHaplotypesSet = new LinkedHashSet<>(trimmedHaplotypeMap.values());
                    final AlleleList<Haplotype> distinctHaplotypesList = new IndexedAlleleList<>(distinctHaplotypesSet.toArray(new Haplotype[distinctHaplotypesSet.size()]));
                    // Get the likelihoods for our clipped read against each of our trimmed haplotypes.
                    final ReadLikelihoods<Haplotype> rl = new ReadLikelihoods<>(
                            new IndexedSampleList(Collections.singletonList("DUMMY_SAMPLE")),distinctHaplotypesList,Collections.singletonMap("DUMMY_SAMPLE",Collections.singletonList(processedRead)));

                    final ReadLikelihoods.Matrix<Haplotype> dummySampleLikelihoods = rl.sampleMatrix(0);
                    pairHMM.computeLikelihoods(rl.sampleMatrix(0), Collections.singletonList(processedRead), readGCPArrayMap);

                    // Pack the original pilup element, each allele, and each associated log10 likelihood into a final map, and add each likelihood to the array
                    for (final Allele a: trimmedHaplotypeMap.keySet()){
                        final Haplotype h = trimmedHaplotypeMap.get(a);
                        final int hIndex = rl.alleleIndex(h);
                        final double readLikelihood = dummySampleLikelihoods.get(hIndex,0);
                        readLikelihoods[readIdx][j++] = readLikelihood;
                        perReadAlleleLikelihoodMap.add(p,a,readLikelihood);
                    }
                }
            }
            readIdx++;
        }

        if (DEBUG) {
            System.out.println("\nLikelihood summary");
            for (readIdx=0; readIdx < pileup.getNumberOfElements(); readIdx++) {
                System.out.format("Read Index: %d ",readIdx);
                for (int i=0; i < readLikelihoods[readIdx].length; i++)
                    System.out.format("L%d: %f ",i,readLikelihoods[readIdx][i]);
                System.out.println();
            }

        }

        return readLikelihoods;
    }

    private boolean useSoftClippedBases(GATKSAMRecord read, long eventStartPos, int eventLength) {
        return !((read.getAlignmentStart() >= eventStartPos-eventLength && read.getAlignmentStart() <= eventStartPos+1) || (read.getAlignmentEnd() >= eventStartPos && read.getAlignmentEnd() <= eventStartPos + eventLength));
    }

//    private int computeFirstDifferingPosition(byte[] b1, byte[] b2) {
//        if (b1.length != b2.length)
//            return 0; // sanity check
//
//        for (int i=0; i < b1.length; i++ ){
//            if ( b1[i]!= b2[i] )
//                return i;
//        }
//        return b1.length;
//    }

    private static double[] getDiploidHaplotypeLikelihoods(final int numHaplotypes, final double readLikelihoods[][]) {
        final double[][] haplotypeLikehoodMatrix = new double[numHaplotypes][numHaplotypes];

        // todo: MAD 09/26/11 -- I'm almost certain this calculation can be simplified to just a single loop without the intermediate NxN matrix
        for (int i=0; i < numHaplotypes; i++) {
            for (int j=i; j < numHaplotypes; j++){
                // combine likelihoods of haplotypeLikelihoods[i], haplotypeLikelihoods[j]
                // L(Hi, Hj) = sum_reads ( Pr(R|Hi)/2 + Pr(R|Hj)/2)
                //readLikelihoods[k][j] has log10(Pr(R_k) | H[j] )
                for (int readIdx = 0; readIdx < readLikelihoods.length; readIdx++) {
                    // Compute log10(10^x1/2 + 10^x2/2) = log10(10^x1+10^x2)-log10(2)
                    // First term is approximated by Jacobian log with table lookup.
                    if (Double.isInfinite(readLikelihoods[readIdx][i]) && Double.isInfinite(readLikelihoods[readIdx][j]))
                        continue;
                    final double li = readLikelihoods[readIdx][i];
                    final double lj = readLikelihoods[readIdx][j];
                    haplotypeLikehoodMatrix[i][j] += MathUtils.approximateLog10SumLog10(li, lj) + MathUtils.LOG_ONE_HALF;
                }
            }
        }

        final double[] genotypeLikelihoods = new double[numHaplotypes*(numHaplotypes+1)/2];
        int k=0;
        for (int j=0; j < numHaplotypes; j++) {
            for (int i=0; i <= j; i++){
                genotypeLikelihoods[k++] = haplotypeLikehoodMatrix[i][j];
            }
        }

        // renormalize so that max element is zero.
        return MathUtils.normalizeFromLog10(genotypeLikelihoods, false, true);
    }
}
