X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=18ff3816253a59b4d85c28ffa3ff8e970ddec048;hb=47168f025aefdaa044802bd5f8f510ffe43a4808;hp=109a5918bc30fe787376e12772f357df1ff2e20d;hpb=59d682209891099d46b960509907c79e3fb276fe;p=jalview.git

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 109a591..18ff381 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 2.8)
- * Copyright (C) 2012 J Procter, AM Waterhouse, LM Lui, J Engelhardt, G Barton, M Clamp, S Searle
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.2)
+ * 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;
 
@@ -69,6 +72,10 @@ public class PCA implements Runnable
    */
   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;
@@ -82,9 +89,17 @@ public class PCA implements Runnable
 
     BinarySequence[] bs2 = new BinarySequence[s.length];
     ii = 0;
-
-    String sm = nucleotides ? "DNA" : "BLOSUM62";
-    ScoreMatrix smtrx = ResidueProperties.getScoreMatrix(sm);
+    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))
@@ -236,6 +251,20 @@ public class PCA implements Runnable
    */
   public void run()
   {
+    PrintStream ps = new PrintStream(System.out)
+    {
+      public void print(String x)
+      {
+        details.append(x);
+      }
+
+      public void println()
+      {
+        details.append("\n");
+      }
+    };
+
+    try {
     details.append("PCA Calculation Mode is "
             + (jvCalcMode ? "Jalview variant" : "Original SeqSpace") + "\n");
     Matrix mt = m.transpose();
@@ -250,19 +279,6 @@ public class PCA implements Runnable
       eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace method
     }
 
-    PrintStream ps = new PrintStream(System.out)
-    {
-      public void print(String x)
-      {
-        details.append(x);
-      }
-
-      public void println()
-      {
-        details.append("\n");
-      }
-    };
-
     eigenvector.print(ps);
 
     symm = eigenvector.copy();
@@ -279,6 +295,12 @@ public class PCA implements Runnable
 
     // 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);