JAL-1645 Version-Rel Version 2.9 Year-Rel 2015 Licensing glob

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

diff --git a/src/jalview/viewmodel/PCAModel.java b/src/jalview/viewmodel/PCAModel.java
index 30f8783..54a4925 100644 (file)
--- a/src/jalview/viewmodel/PCAModel.java
+++ b/src/jalview/viewmodel/PCAModel.java
@@ -1,12 +1,32 @@
+/*
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.9)
+ * Copyright (C) 2015 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.datamodel.AlignmentView;
 import jalview.datamodel.SequenceI;
 import jalview.datamodel.SequencePoint;
-import jalview.api.RotatableCanvasI;
+
+import java.util.Vector;
 
 public class PCAModel
 {
@@ -14,30 +34,45 @@ public class PCAModel
   public PCAModel(AlignmentView seqstrings2, SequenceI[] seqs2,
           boolean nucleotide2)
   {
-    seqstrings=seqstrings2;
-    seqs=seqs2;
-    nucleotide=nucleotide2;
+    seqstrings = seqstrings2;
+    seqs = seqs2;
+    nucleotide = nucleotide2;
+    score_matrix = nucleotide2 ? "PID" : "BLOSUM62";
   }
 
-  PCA pca;
-  
+  private volatile PCA pca;
+
   int top;
-  
+
   AlignmentView seqstrings;
 
   SequenceI[] seqs;
 
   /**
-   * use the identity matrix for calculating similarity between sequences. 
+   * Score matrix used to calculate PC
    */
-  private boolean nucleotide=false;
+  String score_matrix;
+
+  /**
+   * use the identity matrix for calculating similarity between sequences.
+   */
+  private boolean nucleotide = false;
 
   private Vector<SequencePoint> points;
 
+  private boolean jvCalcMode = true;
+
+  public boolean isJvCalcMode()
+  {
+    return jvCalcMode;
+  }
+
   public void run()
   {
-    
-    pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide);
+
+    pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide,
+            score_matrix);
+    pca.setJvCalcMode(jvCalcMode);
     pca.run();
 
     // Now find the component coordinates
@@ -68,7 +103,7 @@ public class PCAModel
       SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
       points.addElement(sp);
     }
-    
+
   }
 
   public void updateRc(RotatableCanvasI rc)
@@ -80,9 +115,10 @@ public class PCAModel
   {
     return nucleotide;
   }
+
   public void setNucleotide(boolean nucleotide)
   {
-    this.nucleotide=nucleotide;
+    this.nucleotide = nucleotide;
   }
 
   /**
@@ -97,15 +133,18 @@ public class PCAModel
 
   /**
    * update the 2d coordinates for the list of points to the given dimensions
-   * Principal dimension is getTop(). Next greated eigenvector is getTop()-1.
-   * Note - pca.getComponents starts counting the spectrum from zero rather than one, so getComponents(dimN ...)  == updateRcView(dimN+1 ..)  
-   * @param dim1 
+   * 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
    * @param dim2
    * @param dim3
    */
   public void updateRcView(int dim1, int dim2, int dim3)
   {
-    float[][] scores = pca.getComponents(dim1-1, dim2-1, dim3-1, 100);
+    // note: actual indices for components are dim1-1, etc (patch for JAL-1123)
+    float[][] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 100);
 
     for (int i = 0; i < pca.getM().rows; i++)
     {
@@ -122,7 +161,9 @@ 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\"");
@@ -187,4 +228,19 @@ public class PCAModel
     return pts;
   }
 
+  public void setJvCalcMode(boolean state)
+  {
+    jvCalcMode = state;
+  }
+
+  public String getScore_matrix()
+  {
+    return score_matrix;
+  }
+
+  public void setScore_matrix(String score_matrix)
+  {
+    this.score_matrix = score_matrix;
+  }
+
 }