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

import org.broadinstitute.gatk.tools.walkers.genotyper.UnifiedGenotyper;
import org.broadinstitute.gatk.utils.Utils;
import org.broadinstitute.gatk.utils.contexts.AlignmentContext;
import org.broadinstitute.gatk.utils.contexts.ReferenceContext;
import org.broadinstitute.gatk.utils.refdata.RefMetaDataTracker;
import org.broadinstitute.gatk.tools.walkers.annotator.interfaces.ActiveRegionBasedAnnotation;
import org.broadinstitute.gatk.tools.walkers.annotator.interfaces.AnnotatorCompatible;
import org.broadinstitute.gatk.tools.walkers.annotator.interfaces.InfoFieldAnnotation;
import org.broadinstitute.gatk.tools.walkers.annotator.interfaces.StandardAnnotation;
import org.broadinstitute.gatk.utils.MathUtils;
import org.broadinstitute.gatk.utils.genotyper.PerReadAlleleLikelihoodMap;
import org.broadinstitute.gatk.utils.variant.GATKVCFConstants;
import org.broadinstitute.gatk.utils.variant.GATKVCFHeaderLines;
import org.broadinstitute.gatk.utils.variant.GATKVariantContextUtils;
import htsjdk.variant.vcf.VCFInfoHeaderLine;
import htsjdk.variant.variantcontext.Genotype;
import htsjdk.variant.variantcontext.GenotypesContext;
import htsjdk.variant.variantcontext.VariantContext;

import java.util.*;

/**
 * Variant confidence normalized by unfiltered depth of variant samples
 *
 * <p>This annotation puts the variant confidence QUAL score into perspective by normalizing for the amount of coverage available. Because each read contributes a little to the QUAL score, variants in regions with deep coverage can have artificially inflated QUAL scores, giving the impression that the call is supported by more evidence than it really is. To compensate for this, we normalize the variant confidence by depth, which gives us a more objective picture of how well supported the call is.</p>
 *
 * <h3>Statistical notes</h3>
 * <p>The QD is the QUAL score normalized by allele depth (AD) for a variant. For a single sample, the HaplotypeCaller calculates the QD by taking QUAL/AD. For multiple samples, HaplotypeCaller and GenotypeGVCFs calculate the QD by taking QUAL/AD of samples with a non hom-ref genotype call. The reason we leave out the samples with a hom-ref call is to not penalize the QUAL for the other samples with the variant call.</p>
 * <p>Here is a single sample example:</p>
 * <pre>2	37629	.	C	G	1063.77	.	AC=2;AF=1.00;AN=2;DP=31;FS=0.000;MLEAC=2;MLEAF=1.00;MQ=58.50;QD=34.32;SOR=2.376	GT:AD:DP:GQ:PL:QSS	1/1:0,31:31:93:1092,93,0:0,960</pre>
   <p>QUAL/AD = 1063.77/31 = 34.32 = QD</p>
 * <p>Here is a multi-sample example:</p>
 * <pre>10	8046	.	C	T	4107.13	.	AC=1;AF=0.167;AN=6;BaseQRankSum=-3.717;DP=1063;FS=1.616;MLEAC=1;MLEAF=0.167;QD=11.54
   GT:AD:DP:GQ:PL:QSS	0/0:369,4:373:99:0,1007,12207:10548,98	    0/0:331,1:332:99:0,967,11125:9576,27	    0/1:192,164:356:99:4138,0,5291:5501,4505</pre>
 * <p>QUAL/AD = 4107.13/356 = 11.54 = QD</p>
 * <p>Note that currently, when HaplotypeCaller is run with `-ERC GVCF`, the QD calculation is invoked before AD itself has been calculated, due to a technical constraint. In that case, HaplotypeCaller uses the number of overlapping reads from the haplotype likelihood calculation in place of AD to calculate QD, which generally yields a very similar number. This does not cause any measurable problems, but can cause some confusion since the number may be slightly different than what you would expect to get if you did the calculation manually. For that reason, this behavior will be modified in an upcoming version.</p>
 *
 * <h3>Caveat</h3>
 * <p>This annotation can only be calculated for sites for which at least one sample was genotyped as carrying a variant allele.</p>
 *
 * <h3>Related annotations</h3>
 * <ul>
 *     <li><b><a href="https://www.broadinstitute.org/gatk/guide/tooldocs/org_broadinstitute_gatk_tools_walkers_annotator_Coverage.php">Coverage</a></b> gives the filtered depth of coverage for each sample and the unfiltered depth across all samples.</li>
 *     <li><b><a href="https://www.broadinstitute.org/gatk/guide/tooldocs/org_broadinstitute_gatk_tools_walkers_annotator_DepthPerAlleleBySample.php">DepthPerAlleleBySample</a></b> calculates depth of coverage for each allele per sample (AD).</li>
 * </ul>
 */
public class QualByDepth extends InfoFieldAnnotation implements StandardAnnotation, ActiveRegionBasedAnnotation {
//    private final static Logger logger = Logger.getLogger(QualByDepth.class);

    public Map<String, Object> annotate(final RefMetaDataTracker tracker,
                                        final AnnotatorCompatible walker,
                                        final ReferenceContext ref,
                                        final Map<String, AlignmentContext> stratifiedContexts,
                                        final VariantContext vc,
                                        final Map<String, PerReadAlleleLikelihoodMap> perReadAlleleLikelihoodMap ) {
        if ( !vc.hasLog10PError() )
            return null;

        final GenotypesContext genotypes = vc.getGenotypes();
        if ( genotypes == null || genotypes.size() == 0 )
            return null;

        int standardDepth = 0;
        int ADrestrictedDepth = 0;

        for ( final Genotype genotype : genotypes ) {

            // we care only about variant calls with likelihoods
            if ( !genotype.isHet() && !genotype.isHomVar() )
                continue;

            // if we have the AD values for this sample, let's make sure that the variant depth is greater than 1!
            // TODO -- If we like how this is working and want to apply it to a situation other than the single sample HC pipeline,
            // TODO --  then we will need to modify the annotateContext() - and related - routines in the VariantAnnotatorEngine
            // TODO --  so that genotype-level annotations are run first (to generate AD on the samples) and then the site-level
            // TODO --  annotations must come afterwards (so that QD can use the AD).
            if ( genotype.hasAD() ) {
                final int[] AD = genotype.getAD();
                final int totalADdepth = (int)MathUtils.sum(AD);
                if ( totalADdepth - AD[0] > 1 )
                    ADrestrictedDepth += totalADdepth;
                standardDepth += totalADdepth;
                continue;
            }

            if (stratifiedContexts!= null && !stratifiedContexts.isEmpty()) {
                final AlignmentContext context = stratifiedContexts.get(genotype.getSampleName());
                if ( context == null )
                    continue;
                standardDepth += context.getBasePileup().depthOfCoverage();

            } else if (perReadAlleleLikelihoodMap != null) {
                final PerReadAlleleLikelihoodMap perReadAlleleLikelihoods = perReadAlleleLikelihoodMap.get(genotype.getSampleName());
                if (perReadAlleleLikelihoods == null || perReadAlleleLikelihoods.isEmpty())
                    continue;

                standardDepth += perReadAlleleLikelihoods.getNumberOfStoredElements();
            } else if ( genotype.hasDP() ) {
                standardDepth += genotype.getDP();
            }
        }

        // if the AD-restricted depth is a usable value (i.e. not zero), then we should use that one going forward
        if ( ADrestrictedDepth > 0 )
            standardDepth = ADrestrictedDepth;

        if ( standardDepth == 0 )
            return null;

        final double altAlleleLength = GATKVariantContextUtils.getMeanAltAlleleLength(vc);
        // Hack: UnifiedGenotyper (but not HaplotypeCaller or GenotypeGVCFs) over-estimates the quality of long indels
        //       Penalize the QD calculation for UG indels to compensate for this
        double QD = -10.0 * vc.getLog10PError() / ((double)standardDepth * indelNormalizationFactor(altAlleleLength, walker instanceof UnifiedGenotyper));

        // Hack: see note in the fixTooHighQD method below
        QD = fixTooHighQD(QD);

        final Map<String, Object> map = new HashMap<>();
        map.put(getKeyNames().get(0), String.format("%.2f", QD));
        return map;
    }

    /**
     * Generate the indel normalization factor.
     *
     * @param altAlleleLength  the average alternate allele length for the call
     * @param increaseNormalizationAsLengthIncreases should we apply a normalization factor based on the allele length?
     * @return a possitive double
     */
    private double indelNormalizationFactor(final double altAlleleLength, final boolean increaseNormalizationAsLengthIncreases) {
        return ( increaseNormalizationAsLengthIncreases ? Math.max(altAlleleLength / 3.0, 1.0) : 1.0);
    }

    /**
     * The haplotype caller generates very high quality scores when multiple events are on the
     * same haplotype.  This causes some very good variants to have unusually high QD values,
     * and VQSR will filter these out.  This code looks at the QD value, and if it is above
     * threshold we map it down to the mean high QD value, with some jittering
     *
     * // TODO -- remove me when HaplotypeCaller bubble caller is live
     *
     * @param QD the raw QD score
     * @return a QD value
     */
    private double fixTooHighQD(final double QD) {
        if ( QD < MAX_QD_BEFORE_FIXING ) {
            return QD;
        } else {
            return IDEAL_HIGH_QD + Utils.getRandomGenerator().nextGaussian() * JITTER_SIGMA;
        }
    }

    private final static double MAX_QD_BEFORE_FIXING = 35;
    private final static double IDEAL_HIGH_QD = 30;
    private final static double JITTER_SIGMA = 3;

    public List<String> getKeyNames() { return Arrays.asList(GATKVCFConstants.QUAL_BY_DEPTH_KEY); }

    public List<VCFInfoHeaderLine> getDescriptions() {
        return Arrays.asList(GATKVCFHeaderLines.getInfoLine(getKeyNames().get(0)));
    }


}
