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

import org.broadinstitute.gatk.utils.commandline.*;
import org.broadinstitute.gatk.engine.CommandLineGATK;
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.engine.walkers.PartitionBy;
import org.broadinstitute.gatk.engine.walkers.PartitionType;
import org.broadinstitute.gatk.engine.walkers.RodWalker;
import org.broadinstitute.gatk.engine.walkers.TreeReducible;
import org.broadinstitute.gatk.engine.SampleUtils;
import org.broadinstitute.gatk.utils.help.HelpConstants;
import org.broadinstitute.gatk.engine.GATKVCFUtils;
import htsjdk.variant.vcf.*;
import org.broadinstitute.gatk.utils.exceptions.UserException;
import org.broadinstitute.gatk.utils.help.DocumentedGATKFeature;
import htsjdk.variant.variantcontext.VariantContext;
import htsjdk.variant.variantcontext.VariantContextBuilder;
import htsjdk.variant.variantcontext.writer.VariantContextWriter;
import org.broadinstitute.gatk.utils.variant.GATKVCFConstants;
import org.broadinstitute.gatk.utils.variant.GATKVCFHeaderLines;

import java.io.File;
import java.util.*;

/**
 * Apply a score cutoff to filter variants based on a recalibration table
 *
 * <p>
 * This tool performs the second pass in a two-stage process called VQSR; the first pass is performed by the
 * <a href='https://www.broadinstitute.org/gatk/guide/tooldocs/org_broadinstitute_gatk_tools_walkers_variantrecalibration_VariantRecalibrator.php'>VariantRecalibrator</a> tool.
 * In brief, the first pass consists of creating a Gaussian mixture model by looking at the distribution of annotation
 * values over a high quality subset of the input call set, and then scoring all input variants according to the model.
 * The second pass consists of filtering variants based on score cutoffs identified in the first pass.
 *</p>
 *
 * <p>
 * Using the tranche file and recalibration table generated by the previous step, the ApplyRecalibration tool looks at each variant's VQSLOD value
 * and decides which tranche it falls in. Variants in tranches that fall below the specified truth sensitivity filter level
 * have their FILTER field annotated with the corresponding tranche level. This will result in a call set that is filtered
 * to the desired level but retains the information necessary to increase sensitivity if needed.</p>
 *
 * <p>To be clear, please note that by "filtered", we mean that variants failing the requested tranche cutoff are <b>marked
 * as filtered</b> in the output VCF; they are <b>not discarded</b>.</p>
 *
 * <p>VQSR is probably the hardest part of the Best Practices to get right, so be sure to read the
 * <a href='https://www.broadinstitute.org/gatk/guide/article?id=39'>method documentation</a>,
 * <a href='https://www.broadinstitute.org/gatk/guide/article?id=1259'>parameter recommendations</a> and
 * <a href='https://www.broadinstitute.org/gatk/guide/article?id=2805'>tutorial</a> to really understand what these
 * tools and how to use them for best results on your own data.</p>
 *
 * <h3>Input</h3>
 * <ul>
 * <li>The raw input variants to be filtered.</li>
 * <li>The recalibration table file that was generated by the VariantRecalibrator tool.</li>
 * <li>The tranches file that was generated by the VariantRecalibrator tool.</li>
 * </ul>
 *
 * <h3>Output</h3>
 * <ul>
 * <li>A recalibrated VCF file in which each variant of the requested type is annotated with its VQSLOD and marked as filtered if the score is below the desired quality level.</li>
 * </ul>
 *
 * <h3>Usage example for filtering SNPs</h3>
 * <pre>
 * java -Xmx3g -jar GenomeAnalysisTK.jar \
 *   -T ApplyRecalibration \
 *   -R reference.fasta \
 *   -input NA12878.HiSeq.WGS.bwa.cleaned.raw.subset.b37.vcf \
 *   --ts_filter_level 99.0 \
 *   -tranchesFile path/to/output.tranches \
 *   -recalFile path/to/output.recal \
 *   -mode SNP \
 *   -o path/to/output.recalibrated.filtered.vcf
 * </pre>
 *
 * <h3>Caveats</h3>
 *
 * <ul>
 * <li>The tranche values used in the example above is only a general example. You should determine the level of sensitivity
 * that is appropriate for your specific project. Remember that higher sensitivity (more power to detect variants, yay!) comes
 * at the cost of specificity (more false negatives, boo!). You have to choose at what point you want to set the tradeoff.</li>
 * <li>In order to create the tranche reporting plots (which are only generated for SNPs, not indels!) Rscript needs to be
 * in your environment PATH (this is the scripting version of R, not the interactive version).
 * See <a target="r-project" href="http://www.r-project.org">http://www.r-project.org</a> for more info on how to download and install R.</li>
 * </ul>
 */

@DocumentedGATKFeature( groupName = HelpConstants.DOCS_CAT_VARDISC, extraDocs = {CommandLineGATK.class} )
@PartitionBy(PartitionType.LOCUS)
public class ApplyRecalibration extends RodWalker<Integer, Integer> implements TreeReducible<Integer> {

    public static final String LOW_VQSLOD_FILTER_NAME = "LOW_VQSLOD";
    private final double DEFAULT_VQSLOD_CUTOFF = 0.0;

    /////////////////////////////
    // Inputs
    /////////////////////////////
    /**
     * These calls should be unfiltered and annotated with the error covariates that are intended to use for modeling.
     */
    @Input(fullName="input", shortName = "input", doc="The raw input variants to be recalibrated", required=true)
    public List<RodBinding<VariantContext>> input;
    @Input(fullName="recal_file", shortName="recalFile", doc="The input recal file used by ApplyRecalibration", required=true)
    protected RodBinding<VariantContext> recal;
    @Input(fullName="tranches_file", shortName="tranchesFile", doc="The input tranches file describing where to cut the data", required=false)
    protected File TRANCHES_FILE;

    /////////////////////////////
    // Outputs
    /////////////////////////////
    @Output( doc="The output filtered and recalibrated VCF file in which each variant is annotated with its VQSLOD value")
    private VariantContextWriter vcfWriter = null;

    /////////////////////////////
    // Command Line Arguments
    /////////////////////////////
    @Argument(fullName="ts_filter_level", shortName="ts_filter_level", doc="The truth sensitivity level at which to start filtering", required=false)
    protected Double TS_FILTER_LEVEL = null;
    @Advanced
    @Argument(fullName="lodCutoff", shortName="lodCutoff", doc="The VQSLOD score below which to start filtering", required=false)
    protected Double VQSLOD_CUTOFF = null;

    /**
     * For this to work properly, the -ignoreFilter argument should also be applied to the VariantRecalibration command.
     */
    @Argument(fullName="ignore_filter", shortName="ignoreFilter", doc="If specified, the recalibration will be applied to variants marked as filtered by the specified filter name in the input VCF file", required=false)
    private String[] IGNORE_INPUT_FILTERS = null;
    @Argument(fullName="ignore_all_filters", shortName="ignoreAllFilters", doc="If specified, the variant recalibrator will ignore all input filters. Useful to rerun the VQSR from a filtered output file.", required=false)
    private boolean IGNORE_ALL_FILTERS = false;
    @Argument(fullName="excludeFiltered", shortName="ef", doc="Don't output filtered loci after applying the recalibration", required=false)
    protected boolean EXCLUDE_FILTERED = false;
    @Argument(fullName = "mode", shortName = "mode", doc = "Recalibration mode to employ: 1.) SNP for recalibrating only SNPs (emitting indels untouched in the output VCF); 2.) INDEL for indels; and 3.) BOTH for recalibrating both SNPs and indels simultaneously.", required = false)
    public VariantRecalibratorArgumentCollection.Mode MODE = VariantRecalibratorArgumentCollection.Mode.SNP;

    /////////////////////////////
    // Private Member Variables
    /////////////////////////////
    final private List<Tranche> tranches = new ArrayList<>();
    final private Set<String> inputNames = new HashSet<>();
    final private Set<String> ignoreInputFilterSet = new TreeSet<>();

    //---------------------------------------------------------------------------------------------------------------
    //
    // initialize
    //
    //---------------------------------------------------------------------------------------------------------------

    public void initialize() {
        if( TS_FILTER_LEVEL != null ) {
            for ( final Tranche t : Tranche.readTranches(TRANCHES_FILE) ) {
                if ( t.ts >= TS_FILTER_LEVEL ) {
                    tranches.add(t);
                }
                logger.info(String.format("Read tranche " + t));
            }
            Collections.reverse(tranches); // this algorithm wants the tranches ordered from best (lowest truth sensitivity) to worst (highest truth sensitivity)
        }

        for( final RodBinding rod : input ) {
            inputNames.add( rod.getName() );
        }

        if( IGNORE_INPUT_FILTERS != null ) {
            ignoreInputFilterSet.addAll( Arrays.asList(IGNORE_INPUT_FILTERS) );
        }

        // setup the header fields
        final Set<VCFHeaderLine> hInfo = new HashSet<>();
        hInfo.addAll(GATKVCFUtils.getHeaderFields(getToolkit(), inputNames));
        addVQSRStandardHeaderLines(hInfo);
        final TreeSet<String> samples = new TreeSet<>();
        samples.addAll(SampleUtils.getUniqueSamplesFromRods(getToolkit(), inputNames));

        if( TS_FILTER_LEVEL != null ) {
            // if the user specifies both ts_filter_level and lodCutoff then throw a user error
            if( VQSLOD_CUTOFF != null ) {
                throw new UserException("Arguments --ts_filter_level and --lodCutoff are mutually exclusive. Please only specify one option.");
            }

            if( tranches.size() >= 2 ) {
                for( int iii = 0; iii < tranches.size() - 1; iii++ ) {
                    final Tranche t = tranches.get(iii);
                    hInfo.add(new VCFFilterHeaderLine(t.name, String.format("Truth sensitivity tranche level for " + t.model.toString() + " model at VQS Lod: " + t.minVQSLod + " <= x < " + tranches.get(iii+1).minVQSLod)));
                }
            }
            if( tranches.size() >= 1 ) {
                hInfo.add(new VCFFilterHeaderLine(tranches.get(0).name + "+", String.format("Truth sensitivity tranche level for " + tranches.get(0).model.toString() + " model at VQS Lod < " + tranches.get(0).minVQSLod)));
            } else {
                throw new UserException("No tranches were found in the file or were above the truth sensitivity filter level " + TS_FILTER_LEVEL);
            }

            logger.info("Keeping all variants in tranche " + tranches.get(tranches.size()-1));
        } else {
            if( VQSLOD_CUTOFF == null ) {
                VQSLOD_CUTOFF = DEFAULT_VQSLOD_CUTOFF;
            }
            hInfo.add(new VCFFilterHeaderLine(LOW_VQSLOD_FILTER_NAME, "VQSLOD < " + VQSLOD_CUTOFF));
            logger.info("Keeping all variants with VQSLOD >= " + VQSLOD_CUTOFF);
        }

        final VCFHeader vcfHeader = new VCFHeader(hInfo, samples);
        vcfWriter.writeHeader(vcfHeader);
    }

    public static void addVQSRStandardHeaderLines(final Set<VCFHeaderLine> hInfo) {
        hInfo.add(VCFStandardHeaderLines.getInfoLine(VCFConstants.END_KEY));
        hInfo.add(GATKVCFHeaderLines.getInfoLine(GATKVCFConstants.VQS_LOD_KEY));
        hInfo.add(GATKVCFHeaderLines.getInfoLine(GATKVCFConstants.CULPRIT_KEY));
        hInfo.add(GATKVCFHeaderLines.getInfoLine(GATKVCFConstants.POSITIVE_LABEL_KEY));
        hInfo.add(GATKVCFHeaderLines.getInfoLine(GATKVCFConstants.NEGATIVE_LABEL_KEY));
    }

    //---------------------------------------------------------------------------------------------------------------
    //
    // map
    //
    //---------------------------------------------------------------------------------------------------------------

    public Integer map( RefMetaDataTracker tracker, ReferenceContext ref, AlignmentContext context ) {

        if( tracker == null ) { // For some reason RodWalkers get map calls with null trackers
            return 1;
        }

        final List<VariantContext> VCs =  tracker.getValues(input, context.getLocation());
        final List<VariantContext> recals =  tracker.getValues(recal, context.getLocation());

        for( final VariantContext vc : VCs ) {

            if( VariantDataManager.checkVariationClass( vc, MODE ) && (IGNORE_ALL_FILTERS || vc.isNotFiltered() || ignoreInputFilterSet.containsAll(vc.getFilters())) ) {

                final VariantContext recalDatum = getMatchingRecalVC(vc, recals);
                if( recalDatum == null ) {
                    throw new UserException("Encountered input variant which isn't found in the input recal file. Please make sure VariantRecalibrator and ApplyRecalibration were run on the same set of input variants. First seen at: " + vc );
                }

                final String lodString = recalDatum.getAttributeAsString(GATKVCFConstants.VQS_LOD_KEY, null);
                if( lodString == null ) {
                    throw new UserException("Encountered a malformed record in the input recal file. There is no lod for the record at: " + vc );
                }
                final double lod;
                try {
                    lod = Double.valueOf(lodString);
                } catch (NumberFormatException e) {
                    throw new UserException("Encountered a malformed record in the input recal file. The lod is unreadable for the record at: " + vc );
                }

                VariantContextBuilder builder = new VariantContextBuilder(vc);

                // Annotate the new record with its VQSLOD and the worst performing annotation
                builder.attribute(GATKVCFConstants.VQS_LOD_KEY, lod);
                builder.attribute(GATKVCFConstants.CULPRIT_KEY, recalDatum.getAttribute(GATKVCFConstants.CULPRIT_KEY));
                if ( recalDatum.hasAttribute(GATKVCFConstants.POSITIVE_LABEL_KEY))
                    builder.attribute(GATKVCFConstants.POSITIVE_LABEL_KEY, true);
                if ( recalDatum.hasAttribute(GATKVCFConstants.NEGATIVE_LABEL_KEY))
                    builder.attribute(GATKVCFConstants.NEGATIVE_LABEL_KEY, true);

                final String filterString = generateFilterString(lod);

                if( filterString.equals(VCFConstants.PASSES_FILTERS_v4) ) {
                    builder.passFilters();
                } else {
                    builder.filters(filterString);
                }

                final VariantContext outputVC = builder.make();
                if( !EXCLUDE_FILTERED || outputVC.isNotFiltered() ) {
                    vcfWriter.add( outputVC );
                }
            } else { // valid VC but not compatible with this mode, so just emit the variant untouched
                vcfWriter.add( vc );
            }
        }

        return 1; // This value isn't used for anything
    }

    /**
     * Generate the VCF filter string for this record based on the provided lod score
     * @param lod non-null double
     * @return the String to use as the VCF filter field
     */
    protected String generateFilterString( final double lod ) {
        String filterString = null;
        if( TS_FILTER_LEVEL != null ) {
            for( int i = tranches.size() - 1; i >= 0; i-- ) {
                final Tranche tranche = tranches.get(i);
                if( lod >= tranche.minVQSLod ) {
                    if( i == tranches.size() - 1 ) {
                        filterString = VCFConstants.PASSES_FILTERS_v4;
                    } else {
                        filterString = tranche.name;
                    }
                    break;
                }
            }

            if( filterString == null ) {
                filterString = tranches.get(0).name+"+";
            }
        } else {
            filterString = ( lod < VQSLOD_CUTOFF ? LOW_VQSLOD_FILTER_NAME : VCFConstants.PASSES_FILTERS_v4 );
        }

        return filterString;
    }

    private static VariantContext getMatchingRecalVC(final VariantContext target, final List<VariantContext> recalVCs) {
        for( final VariantContext recalVC : recalVCs ) {
            if ( target.getEnd() == recalVC.getEnd() ) {
                return recalVC;
            }
        }

        return null;
    }

    //---------------------------------------------------------------------------------------------------------------
    //
    // reduce
    //
    //---------------------------------------------------------------------------------------------------------------

    public Integer reduceInit() {
        return 1; // This value isn't used for anything
    }

    public Integer reduce( final Integer mapValue, final Integer reduceSum ) {
        return 1; // This value isn't used for anything
    }

    public Integer treeReduce( final Integer lhs, final Integer rhs ) {
        return 1; // This value isn't used for anything
    }

    public void onTraversalDone( final Integer reduceSum ) {
    }
}

