routines for creating groups over an alignment

diff --git a/src/jalview/analysis/Grouping.java b/src/jalview/analysis/Grouping.java
new file mode 100644 (file)
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+++ b/src/jalview/analysis/Grouping.java
@@ -0,0 +1,273 @@
+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(); */
+  }
+
+
+}