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

import org.apache.commons.lang.ArrayUtils;
import org.apache.log4j.Logger;
import org.broadinstitute.gatk.utils.Utils;
import org.broadinstitute.gatk.utils.refdata.RefMetaDataTracker;
import org.broadinstitute.gatk.utils.GenomeLoc;
import org.broadinstitute.gatk.utils.MathUtils;
import htsjdk.variant.vcf.VCFConstants;
import org.broadinstitute.gatk.utils.exceptions.ReviewedGATKException;
import htsjdk.variant.variantcontext.writer.VariantContextWriter;
import org.broadinstitute.gatk.utils.help.HelpConstants;
import org.broadinstitute.gatk.utils.collections.ExpandingArrayList;
import org.broadinstitute.gatk.utils.exceptions.UserException;
import htsjdk.variant.variantcontext.Allele;
import htsjdk.variant.variantcontext.VariantContext;
import htsjdk.variant.variantcontext.VariantContextBuilder;
import org.broadinstitute.gatk.utils.variant.GATKVCFConstants;

import java.util.*;

/**
 * Created by IntelliJ IDEA.
 * User: rpoplin
 * Date: Mar 4, 2011
 */

public class VariantDataManager {
    private List<VariantDatum> data = Collections.emptyList();
    private double[] meanVector;
    private double[] varianceVector; // this is really the standard deviation
    public List<String> annotationKeys;
    private final VariantRecalibratorArgumentCollection VRAC;
    protected final static Logger logger = Logger.getLogger(VariantDataManager.class);
    protected final List<TrainingSet> trainingSets;

    public VariantDataManager( final List<String> annotationKeys, final VariantRecalibratorArgumentCollection VRAC ) {
        this.data = Collections.emptyList();
        this.annotationKeys = new ArrayList<>( annotationKeys );
        this.VRAC = VRAC;
        meanVector = new double[this.annotationKeys.size()];
        varianceVector = new double[this.annotationKeys.size()];
        trainingSets = new ArrayList<>();
    }

    public void setData( final List<VariantDatum> data ) {
        this.data = data;
    }

    public List<VariantDatum> getData() {
        return data;
    }

    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"));
            }

            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());
    }

    /**
     * Get a list of indices which give the ascending sort order of the data array
     * @param inputVector the data to consider
     * @return a non-null list of integers with length matching the length of the input array
     */
    protected List<Integer> calculateSortOrder(final double[] inputVector) {
        final List<Integer> theOrder = new ArrayList<>(inputVector.length);
        final List<MyDoubleForSorting> toBeSorted = new ArrayList<>(inputVector.length);
        int count = 0;
        for( int iii = 0; iii < inputVector.length; iii++ ) {
            toBeSorted.add(new MyDoubleForSorting(-1.0 * Math.abs(inputVector[iii] - mean(iii, false)), count++));
        }
        Collections.sort(toBeSorted);
        for( final MyDoubleForSorting d : toBeSorted ) {
            theOrder.add(d.originalIndex); // read off the sort order by looking at the index field
        }
        return theOrder;
    }

    // small private class to assist in reading off the new ordering of the annotation array
    private class MyDoubleForSorting implements Comparable<MyDoubleForSorting> {
        final Double myData;
        final int originalIndex;

        public MyDoubleForSorting(final double myData, final int originalIndex) {
            this.myData = myData;
            this.originalIndex = originalIndex;
        }

        @Override
        public int compareTo(final MyDoubleForSorting other) {
            return myData.compareTo(other.myData);
        }
    }

    /**
     * Convenience connector method to work with arrays instead of lists. See ##reorderList##
     */
    private <T> T[] reorderArray(final T[] data, final List<Integer> order) {
        return reorderList(Arrays.asList(data), order).toArray(data);
    }

    /**
     * Reorder the given data list to be in the specified order
     * @param data the data to reorder
     * @param order the new order to use
     * @return a reordered list of data
     */
    private <T> List<T> reorderList(final List<T> data, final List<Integer> order) {
        final List<T> returnList = new ArrayList<>(data.size());
        for( final int index : order ) {
            returnList.add( data.get(index) );
        }
        return returnList;
    }

    /**
     * Convert a normalized point to it's original annotation value
     *
     * norm = (orig - mu) / sigma
     * orig = norm * sigma + mu
     *
     * @param normalizedValue the normalized value of the ith annotation
     * @param annI the index of the annotation value
     * @return the denormalized value for the annotation
     */
    public double denormalizeDatum(final double normalizedValue, final int annI) {
        final double mu = meanVector[annI];
        final double sigma = varianceVector[annI];
        return normalizedValue * sigma + mu;
    }

    public void addTrainingSet( final TrainingSet trainingSet ) {
        trainingSets.add( trainingSet );
    }

    public List<String> getAnnotationKeys() {
        return annotationKeys;
    }

    public boolean checkHasTrainingSet() {
        for( final TrainingSet trainingSet : trainingSets ) {
            if( trainingSet.isTraining ) { return true; }
        }
        return false;
    }

    public boolean checkHasTruthSet() {
        for( final TrainingSet trainingSet : trainingSets ) {
            if( trainingSet.isTruth ) { return true; }
        }
        return false;
    }

    public List<VariantDatum> getTrainingData() {
        final List<VariantDatum> trainingData = new ExpandingArrayList<>();
        for( final VariantDatum datum : data ) {
            if( datum.atTrainingSite && !datum.failingSTDThreshold ) {
                trainingData.add( datum );
            }
        }
        logger.info( "Training with " + trainingData.size() + " variants after standard deviation thresholding." );
        if( trainingData.size() < VRAC.MIN_NUM_BAD_VARIANTS ) {
            logger.warn( "WARNING: Training with very few variant sites! Please check the model reporting PDF to ensure the quality of the model is reliable." );
        } else if( trainingData.size() > VRAC.MAX_NUM_TRAINING_DATA ) {
            logger.warn( "WARNING: Very large training set detected. Downsampling to " + VRAC.MAX_NUM_TRAINING_DATA + " training variants." );
            Collections.shuffle(trainingData, Utils.getRandomGenerator());
            return trainingData.subList(0, VRAC.MAX_NUM_TRAINING_DATA);
        }
        return trainingData;
    }

    public List<VariantDatum> selectWorstVariants() {
        final List<VariantDatum> trainingData = new ExpandingArrayList<>();

        for( final VariantDatum datum : data ) {
            if( datum != null && !datum.failingSTDThreshold && !Double.isInfinite(datum.lod) && datum.lod < VRAC.BAD_LOD_CUTOFF ) {
                datum.atAntiTrainingSite = true;
                trainingData.add( datum );
            }
        }

        logger.info( "Training with worst " + trainingData.size() + " scoring variants --> variants with LOD <= " + String.format("%.4f", VRAC.BAD_LOD_CUTOFF) + "." );

        return trainingData;
    }

    public List<VariantDatum> getEvaluationData() {
        final List<VariantDatum> evaluationData = new ExpandingArrayList<>();

        for( final VariantDatum datum : data ) {
            if( datum != null && !datum.failingSTDThreshold && !datum.atTrainingSite && !datum.atAntiTrainingSite ) {
                evaluationData.add( datum );
            }
        }

        return evaluationData;
    }

    /**
     * Remove all VariantDatum's from the data list which are marked as aggregate data
     */
    public void dropAggregateData() {
        final Iterator<VariantDatum> iter = data.iterator();
        while (iter.hasNext()) {
            final VariantDatum datum = iter.next();
            if( datum.isAggregate ) {
                iter.remove();
            }
        }
    }

    public List<VariantDatum> getRandomDataForPlotting( final int numToAdd, final List<VariantDatum> trainingData, final List<VariantDatum> antiTrainingData, final List<VariantDatum> evaluationData ) {
        final List<VariantDatum> returnData = new ExpandingArrayList<>();
        Collections.shuffle(trainingData, Utils.getRandomGenerator());
        Collections.shuffle(antiTrainingData, Utils.getRandomGenerator());
        Collections.shuffle(evaluationData, Utils.getRandomGenerator());
        returnData.addAll(trainingData.subList(0, Math.min(numToAdd, trainingData.size())));
        returnData.addAll(antiTrainingData.subList(0, Math.min(numToAdd, antiTrainingData.size())));
        returnData.addAll(evaluationData.subList(0, Math.min(numToAdd, evaluationData.size())));
        Collections.shuffle(returnData, Utils.getRandomGenerator());
        return returnData;
    }

    protected double mean( final int index, final boolean trainingData ) {
        double sum = 0.0;
        int numNonNull = 0;
        for( final VariantDatum datum : data ) {
            if( (trainingData == datum.atTrainingSite) && !datum.isNull[index] ) { sum += datum.annotations[index]; numNonNull++; }
        }
        return sum / ((double) numNonNull);
    }

    protected double standardDeviation( final double mean, final int index, final boolean trainingData ) {
        double sum = 0.0;
        int numNonNull = 0;
        for( final VariantDatum datum : data ) {
            if( (trainingData == datum.atTrainingSite) && !datum.isNull[index] ) { sum += ((datum.annotations[index] - mean)*(datum.annotations[index] - mean)); numNonNull++; }
        }
        return Math.sqrt( sum / ((double) numNonNull) );
    }

    public void decodeAnnotations( final VariantDatum datum, final VariantContext vc, final boolean jitter ) {
        final double[] annotations = new double[annotationKeys.size()];
        final boolean[] isNull = new boolean[annotationKeys.size()];
        int iii = 0;
        for( final String key : annotationKeys ) {
            isNull[iii] = false;
            annotations[iii] = decodeAnnotation( key, vc, jitter );
            if( Double.isNaN(annotations[iii]) ) { isNull[iii] = true; }
            iii++;
        }
        datum.annotations = annotations;
        datum.isNull = isNull;
    }

    private static double decodeAnnotation( final String annotationKey, final VariantContext vc, final boolean jitter ) {
        double value;

        final double LOG_OF_TWO = 0.6931472;

        try {
            value = vc.getAttributeAsDouble( annotationKey, Double.NaN );
            if( Double.isInfinite(value) ) { value = Double.NaN; }
            if( jitter && annotationKey.equalsIgnoreCase("HaplotypeScore") && MathUtils.compareDoubles(value, 0.0, 0.01) == 0 ) { value += 0.01 * Utils.getRandomGenerator().nextGaussian(); }
            if( jitter && annotationKey.equalsIgnoreCase("FS") && MathUtils.compareDoubles(value, 0.0, 0.01) == 0 ) { value += 0.01 * Utils.getRandomGenerator().nextGaussian(); }
            if( jitter && annotationKey.equalsIgnoreCase("InbreedingCoeff") && MathUtils.compareDoubles(value, 0.0, 0.01) == 0 ) { value += 0.01 * Utils.getRandomGenerator().nextGaussian(); }
            if( jitter && annotationKey.equalsIgnoreCase("SOR") && MathUtils.compareDoubles(value, LOG_OF_TWO, 0.01) == 0 ) { value += 0.01 * Utils.getRandomGenerator().nextGaussian(); }   //min SOR is 2.0, then we take ln
        } catch( Exception e ) {
            value = Double.NaN; // The VQSR works with missing data by marginalizing over the missing dimension when evaluating the Gaussian mixture model
        }

        return value;
    }

    public void parseTrainingSets( final RefMetaDataTracker tracker, final GenomeLoc genomeLoc, final VariantContext evalVC, final VariantDatum datum, final boolean TRUST_ALL_POLYMORPHIC ) {
        datum.isKnown = false;
        datum.atTruthSite = false;
        datum.atTrainingSite = false;
        datum.atAntiTrainingSite = false;
        datum.prior = 2.0;

        for( final TrainingSet trainingSet : trainingSets ) {
            for( final VariantContext trainVC : tracker.getValues(trainingSet.rodBinding, genomeLoc) ) {
                if( isValidVariant( evalVC, trainVC, TRUST_ALL_POLYMORPHIC ) ) {
                    datum.isKnown = datum.isKnown || trainingSet.isKnown;
                    datum.atTruthSite = datum.atTruthSite || trainingSet.isTruth;
                    datum.atTrainingSite = datum.atTrainingSite || trainingSet.isTraining;
                    datum.prior = Math.max( datum.prior, trainingSet.prior );
                    datum.consensusCount += ( trainingSet.isConsensus ? 1 : 0 );
                }
                if( trainVC != null ) {
                    datum.atAntiTrainingSite = datum.atAntiTrainingSite || trainingSet.isAntiTraining;
                }
            }
        }
    }

    private boolean isValidVariant( final VariantContext evalVC, final VariantContext trainVC, final boolean TRUST_ALL_POLYMORPHIC) {
        return trainVC != null && trainVC.isNotFiltered() && trainVC.isVariant() && checkVariationClass( evalVC, trainVC ) &&
                (TRUST_ALL_POLYMORPHIC || !trainVC.hasGenotypes() || trainVC.isPolymorphicInSamples());
    }

    protected static boolean checkVariationClass( final VariantContext evalVC, final VariantContext trainVC ) {
        switch( trainVC.getType() ) {
            case SNP:
            case MNP:
                return checkVariationClass( evalVC, VariantRecalibratorArgumentCollection.Mode.SNP );
            case INDEL:
            case MIXED:
            case SYMBOLIC:
                return checkVariationClass( evalVC, VariantRecalibratorArgumentCollection.Mode.INDEL );
            default:
                return false;
        }
    }

    protected static boolean checkVariationClass( final VariantContext evalVC, final VariantRecalibratorArgumentCollection.Mode mode ) {
        switch( mode ) {
            case SNP:
                return evalVC.isSNP() || evalVC.isMNP();
            case INDEL:
                return evalVC.isStructuralIndel() || evalVC.isIndel() || evalVC.isMixed() || evalVC.isSymbolic();
            case BOTH:
                return true;
            default:
                throw new ReviewedGATKException( "Encountered unknown recal mode: " + mode );
        }
    }

    public void writeOutRecalibrationTable( final VariantContextWriter recalWriter ) {
        // we need to sort in coordinate order in order to produce a valid VCF
        Collections.sort( data, new Comparator<VariantDatum>() {
            public int compare(VariantDatum vd1, VariantDatum vd2) {
                return vd1.loc.compareTo(vd2.loc);
            }} );

        // create dummy alleles to be used
        final List<Allele> alleles = Arrays.asList(Allele.create("N", true), Allele.create("<VQSR>", false));

        for( final VariantDatum datum : data ) {
            VariantContextBuilder builder = new VariantContextBuilder("VQSR", datum.loc.getContig(), datum.loc.getStart(), datum.loc.getStop(), alleles);
            builder.attribute(VCFConstants.END_KEY, datum.loc.getStop());
            builder.attribute(GATKVCFConstants.VQS_LOD_KEY, String.format("%.4f", datum.lod));
            builder.attribute(GATKVCFConstants.CULPRIT_KEY, (datum.worstAnnotation != -1 ? annotationKeys.get(datum.worstAnnotation) : "NULL"));

            if ( datum.atTrainingSite ) builder.attribute(GATKVCFConstants.POSITIVE_LABEL_KEY, true);
            if ( datum.atAntiTrainingSite ) builder.attribute(GATKVCFConstants.NEGATIVE_LABEL_KEY, true);

            recalWriter.add(builder.make());
        }
    }
}
