X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=39ed19484aefc3cc9ef982b921ab78595381b1f4;hb=ab43013b7e357b84b4abade0dba949668dfb2a0e;hp=4441eebb2153e9add37d64f54ff2c7fdb1109700;hpb=3e9eba89979511361b51f1c78882c9877874f2ba;p=jalview.git

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 4441eeb..39ed194 100755
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -1,19 +1,22 @@
 /*
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.7)
- * Copyright (C) 2011 J Procter, AM Waterhouse, J Engelhardt, LM Lui, G Barton, M Clamp, S Searle
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.2b1)
+ * Copyright (C) 2014 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.
- * 
+ * 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/>.
+ * 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.analysis;
 
@@ -46,8 +49,9 @@ public class PCA implements Runnable
   StringBuffer details = new StringBuffer();
 
   /**
-   * Creates a new PCA object.
-   * By default, uses blosum62 matrix to generate sequence similarity matrices
+   * Creates a new PCA object. By default, uses blosum62 matrix to generate
+   * sequence similarity matrices
+   * 
    * @param s
    *          Set of amino acid sequences to perform PCA on
    */
@@ -55,34 +59,52 @@ public class PCA implements Runnable
   {
     this(s, false);
   }
-  
+
   /**
-   * Creates a new PCA object.
-   * By default, uses blosum62 matrix to generate sequence similarity matrices
+   * Creates a new PCA object. By default, uses blosum62 matrix to generate
+   * sequence similarity matrices
+   * 
    * @param s
    *          Set of sequences to perform PCA on
-   *          @param nucleotides if true, uses standard DNA/RNA matrix for sequence similarity calculation.
+   * @param nucleotides
+   *          if true, uses standard DNA/RNA matrix for sequence similarity
+   *          calculation.
    */
   public PCA(String[] s, boolean nucleotides)
   {
+    this(s, nucleotides, null);
+  }
+
+  public PCA(String[] s, boolean nucleotides, String s_m)
+  {
 
     BinarySequence[] bs = new BinarySequence[s.length];
     int ii = 0;
 
     while ((ii < s.length) && (s[ii] != null))
     {
-      bs[ii] = new BinarySequence(s[ii],nucleotides);
+      bs[ii] = new BinarySequence(s[ii], nucleotides);
       bs[ii].encode();
       ii++;
     }
 
     BinarySequence[] bs2 = new BinarySequence[s.length];
     ii = 0;
-
-    String sm=nucleotides ? "DNA" : "BLOSUM62";
-    ScoreMatrix smtrx=ResidueProperties.getScoreMatrix(sm);
-    details.append("PCA calculation using "+sm+" sequence similarity matrix\n========\n\n");
-    
+    ScoreMatrix smtrx = null;
+    String sm = s_m;
+    if (sm != null)
+    {
+      smtrx = ResidueProperties.getScoreMatrix(sm);
+    }
+    if (smtrx == null)
+    {
+      // either we were given a non-existent score matrix or a scoremodel that
+      // isn't based on a pairwise symbol score matrix
+      smtrx = ResidueProperties.getScoreMatrix(sm = (nucleotides ? "DNA"
+              : "BLOSUM62"));
+    }
+    details.append("PCA calculation using " + sm
+            + " sequence similarity matrix\n========\n\n");
     while ((ii < s.length) && (s[ii] != null))
     {
       bs2[ii] = new BinarySequence(s[ii], nucleotides);
@@ -232,19 +254,6 @@ public class PCA implements Runnable
    */
   public void run()
   {
-    details.append("PCA Calculation Mode is "+(jvCalcMode ? "Jalview variant" : "Original SeqSpace")+"\n");
-    Matrix mt = m.transpose();
-
-    details.append(" --- OrigT * Orig ---- \n");
-    if (!jvCalcMode)
-    {
-      eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
-    }
-    else
-    {
-      eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace method
-    }
-
     PrintStream ps = new PrintStream(System.out)
     {
       public void print(String x)
@@ -258,22 +267,47 @@ public class PCA implements Runnable
       }
     };
 
-    eigenvector.print(ps);
+    try
+    {
+      details.append("PCA Calculation Mode is "
+              + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
+              + "\n");
+      Matrix mt = m.transpose();
 
-    symm = eigenvector.copy();
+      details.append(" --- OrigT * Orig ---- \n");
+      if (!jvCalcMode)
+      {
+        eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
+      }
+      else
+      {
+        eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace
+                                          // method
+      }
 
-    eigenvector.tred();
+      eigenvector.print(ps);
 
-    details.append(" ---Tridiag transform matrix ---\n");
-    details.append(" --- D vector ---\n");
-    eigenvector.printD(ps);
-    ps.println();
-    details.append("--- E vector ---\n");
-    eigenvector.printE(ps);
-    ps.println();
+      symm = eigenvector.copy();
+
+      eigenvector.tred();
 
-    // Now produce the diagonalization matrix
-    eigenvector.tqli();
+      details.append(" ---Tridiag transform matrix ---\n");
+      details.append(" --- D vector ---\n");
+      eigenvector.printD(ps);
+      ps.println();
+      details.append("--- E vector ---\n");
+      eigenvector.printE(ps);
+      ps.println();
+
+      // Now produce the diagonalization matrix
+      eigenvector.tqli();
+    } catch (Exception q)
+    {
+      q.printStackTrace();
+      details.append("\n*** Unexpected exception when performing PCA ***\n"
+              + q.getLocalizedMessage());
+      details.append("*** Matrices below may not be fully diagonalised. ***\n");
+    }
 
     details.append(" --- New diagonalization matrix ---\n");
     eigenvector.print(ps);