package jalview.analysis;

import jalview.datamodel.AlignmentI;
import jalview.datamodel.SequenceFeature;
import jalview.datamodel.SequenceGroup;
import jalview.datamodel.SequenceI;

import java.util.Enumeration;
import java.util.Hashtable;
import java.util.Vector;

/**
 * various methods for defining groups on an alignment based on some other properties
 * @author JimP
 *
 */
public class Grouping
{
  /**
   * Divide the given sequences based on the equivalence of their corresponding selectedChars string. If exgroups is provided, existing groups will be subdivided.
   * @param sequences
   * @param selectedChars
   * @param exgroups
   * @return
   */
  public static SequenceGroup[] makeGroupsFrom(SequenceI[] sequences, String[] selectedChars, Vector exgroups)          
  {
    // TODO: determine how to get/recover input data for group generation 
    Hashtable gps = new Hashtable();
    int width = 0,i;
    Hashtable pgroup = new Hashtable();
    if (exgroups!=null)
    {
      SequenceGroup sg;
      for (Enumeration g=exgroups.elements(); g.hasMoreElements(); )
      {
        sg = (SequenceGroup) g.nextElement();
        for (Enumeration sq = sg.getSequences(null).elements(); sq.hasMoreElements(); )
          pgroup.put(sq.nextElement().toString(), sg);
      }
    }
    for (i = 0; i < sequences.length; i++)
    {
      String schar = selectedChars[i];
      SequenceGroup pgp = (SequenceGroup) pgroup.get(((Object) sequences[i]).toString());
      if (pgp!=null)
      {
        schar = pgp.getName()+":"+schar;
      }
      Vector svec = (Vector) gps.get(schar);
      if (svec == null)
      {
        svec = new Vector();
        gps.put(schar, svec);
      }
      if (width<sequences[i].getLength())
      {
        width = sequences[i].getLength();
      }
      svec.addElement(sequences[i]);
    }
    // make some groups
    java.util.Enumeration sge = gps.keys();
    SequenceGroup[] groups = new SequenceGroup[gps.size()];
    i=0;
    while (sge.hasMoreElements()) {
      String key = (String) sge.nextElement();
      SequenceGroup group = new SequenceGroup((Vector) gps.get(key), "Subseq: "+key, null, true, true,
              false, 0, width - 1);
      
      groups[i++] = group;
    }
    gps.clear();
    pgroup.clear();
    return groups;
  }
  /**
   * subdivide the given sequences based on the distribution of features 
   * @param featureLabels - null or one or more feature types to filter on.
   * @param groupLabels - null or set of groups to filter features on
   * @param start - range for feature filter
   * @param stop - range for feature filter
   * @param sequences - sequences to be divided
   * @param exgroups - existing groups to be subdivided
   * @param method - density, description, score
   */
  public static void divideByFeature(String[] featureLabels, String[] groupLabels, int start, int stop, 
          SequenceI[] sequences, Vector exgroups, String method)
  {
    // TODO implement divideByFeature
    /*
    if (method!=AlignmentSorter.FEATURE_SCORE && method!=AlignmentSorter.FEATURE_LABEL && method!=AlignmentSorter.FEATURE_DENSITY)
    {
      throw new Error("Implementation Error - sortByFeature method must be one of FEATURE_SCORE, FEATURE_LABEL or FEATURE_DENSITY.");
    }
    boolean ignoreScore=method!=AlignmentSorter.FEATURE_SCORE;
    StringBuffer scoreLabel = new StringBuffer();
    scoreLabel.append(start+stop+method);
    // This doesn't work yet - we'd like to have a canonical ordering that can be preserved from call to call
    for (int i=0;featureLabels!=null && i<featureLabels.length; i++)
    {
      scoreLabel.append(featureLabels[i]==null ? "null" : featureLabels[i]);
    }
    for (int i=0;groupLabels!=null && i<groupLabels.length; i++)
    {
      scoreLabel.append(groupLabels[i]==null ? "null" : groupLabels[i]);
    }
    SequenceI[] seqs = alignment.getSequencesArray();
    
    boolean[] hasScore = new boolean[seqs.length]; // per sequence score
                                                    // presence
    int hasScores = 0; // number of scores present on set
    double[] scores = new double[seqs.length];
    int[] seqScores = new int[seqs.length];
    Object[] feats = new Object[seqs.length];
    double min = 0, max = 0;
    for (int i = 0; i < seqs.length; i++)
    {
      SequenceFeature[] sf = seqs[i].getSequenceFeatures();
      if (sf==null && seqs[i].getDatasetSequence()!=null)
      {
        sf = seqs[i].getDatasetSequence().getSequenceFeatures();
      }
      if (sf==null)
      {
        sf = new SequenceFeature[0];
      } else {
        SequenceFeature[] tmp = new SequenceFeature[sf.length];
        for (int s=0; s<tmp.length;s++)
        {
          tmp[s] = sf[s];
        }
        sf = tmp;
      }
      int sstart = (start==-1) ? start : seqs[i].findPosition(start);
      int sstop = (stop==-1) ? stop : seqs[i].findPosition(stop);
      seqScores[i]=0;
      scores[i]=0.0;
      int n=sf.length;
      for (int f=0;f<sf.length;f++)
      {
        // filter for selection criteria
        if (
        // ignore features outwith alignment start-stop positions.
        (sf[f].end < sstart || sf[f].begin > sstop)
                ||
                // or ignore based on selection criteria
                (featureLabels != null && !AlignmentSorter.containsIgnoreCase(sf[f].type, featureLabels))
                || (groupLabels != null
                        // problem here: we cannot eliminate null feature group features
                        && (sf[f].getFeatureGroup() != null 
                                && !AlignmentSorter.containsIgnoreCase(sf[f].getFeatureGroup(), groupLabels))))
        {
          // forget about this feature
          sf[f] = null;
          n--;
        } else {
          // or, also take a look at the scores if necessary.
          if (!ignoreScore && sf[f].getScore()!=Float.NaN)
          {
            if (seqScores[i]==0)
            {
              hasScores++;
            }
            seqScores[i]++;
            hasScore[i] = true;
            scores[i] += sf[f].getScore(); // take the first instance of this
                                            // score.
          }
        }
      }
      SequenceFeature[] fs;
      feats[i] = fs = new SequenceFeature[n];
      if (n>0)
      {
        n=0;
        for (int f=0;f<sf.length;f++)
        {
          if (sf[f]!=null)
          {
            ((SequenceFeature[]) feats[i])[n++] = sf[f];
          }
        }
        if (method==FEATURE_LABEL)
        {
          // order the labels by alphabet
          String[] labs = new String[fs.length];
          for (int l=0;l<labs.length; l++)
          {
            labs[l] = (fs[l].getDescription()!=null ? fs[l].getDescription() : fs[l].getType());
          }
          jalview.util.QuickSort.sort(labs, ((Object[]) feats[i]));
        }
      }
      if (hasScore[i])
      {      
        // compute average score
        scores[i]/=seqScores[i];
        // update the score bounds.
        if (hasScores == 1)
        {
          max = min = scores[i];
        }
        else
        {
          if (max < scores[i])
          {
            max = scores[i];
          }
          if (min > scores[i])
          {
            min = scores[i];
          }
        }
      }
    }
    
    if (method==FEATURE_SCORE)
    {
      if (hasScores == 0)
    {
      return; // do nothing - no scores present to sort by.
    }
    // pad score matrix 
    if (hasScores < seqs.length)
    {
      for (int i = 0; i < seqs.length; i++)
      {
        if (!hasScore[i])
        {
          scores[i] = (max + i);
        } else {
          int nf=(feats[i]==null) ? 0 :((SequenceFeature[]) feats[i]).length;
          System.err.println("Sorting on Score: seq "+seqs[i].getName()+ " Feats: "+nf+" Score : "+scores[i]);
        }
      }
    }

    jalview.util.QuickSort.sort(scores, seqs);
    }
    else 
      if (method==FEATURE_DENSITY)
      {
        
        // break ties between equivalent numbers for adjacent sequences by adding 1/Nseq*i on the original order
        double fr = 0.9/(1.0*seqs.length);
        for (int i=0;i<seqs.length; i++)
        {
          double nf;
          scores[i] = (0.05+fr*i)+(nf=((feats[i]==null) ? 0.0 :1.0*((SequenceFeature[]) feats[i]).length));
          System.err.println("Sorting on Density: seq "+seqs[i].getName()+ " Feats: "+nf+" Score : "+scores[i]);
        }
        jalview.util.QuickSort.sort(scores, seqs);
      }
      else {
        if (method==FEATURE_LABEL)
        {
          throw new Error("Not yet implemented.");
        }
      }
    if (lastSortByFeatureScore ==null || scoreLabel.equals(lastSortByFeatureScore))
    {
      setOrder(alignment, seqs);
    }
    else
    {
      setReverseOrder(alignment, seqs);
    }
    lastSortByFeatureScore = scoreLabel.toString(); */
  }


}