X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=06a139bb6df27db1c145b93309e3dff54063298d;hb=37de9310bec3501cbc6381e0c3dcb282fcaad812;hp=18ff3816253a59b4d85c28ffa3ff8e970ddec048;hpb=47168f025aefdaa044802bd5f8f510ffe43a4808;p=jalview.git

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 18ff381..06a139b 100755
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -1,6 +1,6 @@
 /*
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.2)
- * Copyright (C) 2014 The Jalview Authors
+ * Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
+ * Copyright (C) $$Year-Rel$$ The Jalview Authors
  * 
  * This file is part of Jalview.
  * 
@@ -20,14 +20,14 @@
  */
 package jalview.analysis;
 
-import java.io.*;
-
-import jalview.datamodel.*;
+import jalview.datamodel.BinarySequence;
 import jalview.datamodel.BinarySequence.InvalidSequenceTypeException;
-import jalview.math.*;
+import jalview.math.Matrix;
 import jalview.schemes.ResidueProperties;
 import jalview.schemes.ScoreMatrix;
 
+import java.io.PrintStream;
+
 /**
  * Performs Principal Component Analysis on given sequences
  * 
@@ -74,6 +74,7 @@ public class PCA implements Runnable
   {
     this(s, nucleotides, null);
   }
+
   public PCA(String[] s, boolean nucleotides, String s_m)
   {
 
@@ -90,15 +91,17 @@ public class PCA implements Runnable
     BinarySequence[] bs2 = new BinarySequence[s.length];
     ii = 0;
     ScoreMatrix smtrx = null;
-    String sm=s_m;
-    if (sm!=null)
+    String sm = s_m;
+    if (sm != null)
     {
       smtrx = ResidueProperties.getScoreMatrix(sm);
     }
-    if (smtrx==null)
+    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"));
+      // 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");
@@ -264,41 +267,45 @@ public class PCA implements Runnable
       }
     };
 
-    try {
-    details.append("PCA Calculation Mode is "
-            + (jvCalcMode ? "Jalview variant" : "Original SeqSpace") + "\n");
-    Matrix mt = m.transpose();
-
-    details.append(" --- OrigT * Orig ---- \n");
-    if (!jvCalcMode)
+    try
     {
-      eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
-    }
-    else
-    {
-      eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace method
-    }
+      details.append("PCA Calculation Mode is "
+              + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
+              + "\n");
+      Matrix mt = m.transpose();
 
-    eigenvector.print(ps);
+      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
+      }
 
-    symm = eigenvector.copy();
+      eigenvector.print(ps);
 
-    eigenvector.tred();
+      symm = eigenvector.copy();
 
-    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();
+      eigenvector.tred();
+
+      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();
+      // 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("\n*** Unexpected exception when performing PCA ***\n"
+              + q.getLocalizedMessage());
       details.append("*** Matrices below may not be fully diagonalised. ***\n");
     }