JAL-2738 copy to spikes/mungo

[jalview.git] / src / jalview / viewmodel / PCAModel.java

diff --git a/src/jalview/viewmodel/PCAModel.java b/src/jalview/viewmodel/PCAModel.java
index 6002c8a..5e7fca2 100644 (file)
--- a/src/jalview/viewmodel/PCAModel.java
+++ b/src/jalview/viewmodel/PCAModel.java
@@ -1,43 +1,79 @@
+/*
+ * Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
+ * Copyright (C) $$Year-Rel$$ The Jalview Authors
+ * 
+ * This file is part of Jalview.
+ * 
+ * Jalview is free software: you can redistribute it and/or
+ * modify it under the terms of the GNU General Public License 
+ * as published by the Free Software Foundation, either version 3
+ * of the License, or (at your option) any later version.
+ *  
+ * Jalview is distributed in the hope that it will be useful, but 
+ * WITHOUT ANY WARRANTY; without even the implied warranty 
+ * of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
+ * PURPOSE.  See the GNU General Public License for more details.
+ * 
+ * You should have received a copy of the GNU General Public License
+ * along with Jalview.  If not, see <http://www.gnu.org/licenses/>.
+ * The Jalview Authors are detailed in the 'AUTHORS' file.
+ */
 package jalview.viewmodel;
 
-import java.util.Vector;
-
 import jalview.analysis.PCA;
+import jalview.api.RotatableCanvasI;
+import jalview.api.analysis.ScoreModelI;
+import jalview.api.analysis.SimilarityParamsI;
 import jalview.datamodel.AlignmentView;
 import jalview.datamodel.SequenceI;
 import jalview.datamodel.SequencePoint;
-import jalview.api.RotatableCanvasI;
+
+import java.util.Vector;
 
 public class PCAModel
 {
+  private volatile PCA pca;
 
-  public PCAModel(AlignmentView seqstrings2, SequenceI[] seqs2,
-          boolean nucleotide2)
-  {
-    seqstrings=seqstrings2;
-    seqs=seqs2;
-    nucleotide=nucleotide2;
-  }
-
-  PCA pca;
-  
   int top;
-  
+
   AlignmentView seqstrings;
 
   SequenceI[] seqs;
 
-  /**
-   * use the identity matrix for calculating similarity between sequences. 
+  /*
+   * Name of score model used to calculate PCA
    */
-  private boolean nucleotide=false;
+  ScoreModelI scoreModel;
+
+  private boolean nucleotide = false;
 
   private Vector<SequencePoint> points;
 
+  private SimilarityParamsI similarityParams;
+
+  /**
+   * Constructor given sequence data, score model and score calculation
+   * parameter options.
+   * 
+   * @param seqData
+   * @param sqs
+   * @param nuc
+   * @param modelName
+   * @param params
+   */
+  public PCAModel(AlignmentView seqData, SequenceI[] sqs, boolean nuc,
+          ScoreModelI modelName, SimilarityParamsI params)
+  {
+    seqstrings = seqData;
+    seqs = sqs;
+    nucleotide = nuc;
+    scoreModel = modelName;
+    similarityParams = params;
+  }
+
   public void run()
   {
-    
-    pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide);
+    pca = new PCA(seqstrings, scoreModel, similarityParams);
     pca.run();
 
     // Now find the component coordinates
@@ -48,41 +84,33 @@ public class PCAModel
       ii++;
     }
 
-    double[][] comps = new double[ii][ii];
-
-    for (int i = 0; i < ii; i++)
-    {
-      if (pca.getEigenvalue(i) > 1e-4)
-      {
-        comps[i] = pca.component(i);
-      }
-    }
-
-    top = pca.getM().rows - 1;
+    int height = pca.getHeight();
+    // top = pca.getM().height() - 1;
+    top = height - 1;
 
     points = new Vector<SequencePoint>();
     float[][] scores = pca.getComponents(top - 1, top - 2, top - 3, 100);
 
-    for (int i = 0; i < pca.getM().rows; i++)
+    for (int i = 0; i < height; i++)
     {
       SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
       points.addElement(sp);
     }
-    
   }
 
   public void updateRc(RotatableCanvasI rc)
   {
-    rc.setPoints(points, pca.getM().rows);
+    rc.setPoints(points, pca.getHeight());
   }
 
   public boolean isNucleotide()
   {
     return nucleotide;
   }
+
   public void setNucleotide(boolean nucleotide)
   {
-    this.nucleotide=nucleotide;
+    this.nucleotide = nucleotide;
   }
 
   /**
@@ -98,19 +126,21 @@ public class PCAModel
   /**
    * update the 2d coordinates for the list of points to the given dimensions
    * Principal dimension is getTop(). Next greatest eigenvector is getTop()-1.
-   * Note - pca.getComponents starts counting the spectrum from rank-2 to zero, rather than rank-1, so getComponents(dimN ...)  == updateRcView(dimN+1 ..)  
-   * @param dim1 
+   * Note - pca.getComponents starts counting the spectrum from rank-2 to zero,
+   * rather than rank-1, so getComponents(dimN ...) == updateRcView(dimN+1 ..)
+   * 
+   * @param dim1
    * @param dim2
    * @param dim3
    */
   public void updateRcView(int dim1, int dim2, int dim3)
   {
     // note: actual indices for components are dim1-1, etc (patch for JAL-1123)
-    float[][] scores = pca.getComponents(dim1-1, dim2-1, dim3-1, 100);
+    float[][] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 100);
 
-    for (int i = 0; i < pca.getM().rows; i++)
+    for (int i = 0; i < pca.getHeight(); i++)
     {
-      ((SequencePoint) points.elementAt(i)).coord = scores[i];
+      points.elementAt(i).coord = scores[i];
     }
   }
 
@@ -123,7 +153,9 @@ public class PCAModel
   {
     return seqstrings;
   }
-  public String getPointsasCsv(boolean transformed, int xdim, int ydim, int zdim)
+
+  public String getPointsasCsv(boolean transformed, int xdim, int ydim,
+          int zdim)
   {
     StringBuffer csv = new StringBuffer();
     csv.append("\"Sequence\"");
@@ -188,4 +220,14 @@ public class PCAModel
     return pts;
   }
 
+  public String getScoreModelName()
+  {
+    return scoreModel == null ? "" : scoreModel.getName();
+  }
+
+  public void setScoreModel(ScoreModelI sm)
+  {
+    this.scoreModel = sm;
+  }
+
 }