Capture output as details

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 4d691c0..4e7100d 100755 (executable)
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -22,6 +22,7 @@ import jalview.datamodel.*;
 \r
 import jalview.math.*;\r
 \r
+import java.io.*;\r
 \r
 /**\r
  * Performs Principal Component Analysis on given sequences\r
@@ -36,6 +37,7 @@ public class PCA implements Runnable
     Matrix m2;\r
     double[] eigenvalue;\r
     Matrix eigenvector;\r
+    StringBuffer details = new StringBuffer();\r
 \r
 \r
     /**\r
@@ -181,6 +183,11 @@ public class PCA implements Runnable
         return out / eigenvector.d[n];\r
     }\r
 \r
+    public String getDetails()\r
+    {\r
+      return details.toString();\r
+    }\r
+\r
 \r
     /**\r
      * DOCUMENT ME!\r
@@ -189,47 +196,43 @@ public class PCA implements Runnable
     {\r
         Matrix mt = m.transpose();\r
 \r
-        //    System.out.println(" --- OrigT * Orig ---- ");\r
+        details.append(" --- OrigT * Orig ---- \n");\r
         eigenvector = mt.preMultiply(m2);\r
 \r
-        //  eigenvector.print(System.out);\r
+        PrintStream  ps = new PrintStream(System.out)\r
+           {\r
+              public void print(String x) {\r
+                   details.append(x);\r
+               }\r
+               public void println()\r
+               {\r
+                 details.append("\n");\r
+               }\r
+            };\r
+\r
+\r
+        eigenvector.print( ps );\r
+\r
         symm = eigenvector.copy();\r
 \r
-        //TextArea ta = new TextArea(25,72);\r
-        //TextAreaPrintStream taps = new TextAreaPrintStream(System.out,ta);\r
-        //Frame f = new Frame("PCA output");\r
-        //f.resize(500,500);\r
-        //f.setLayout(new BorderLayout());\r
-        //f.add("Center",ta);\r
-        //f.show();\r
-        //symm.print(taps);\r
-        long tstart = System.currentTimeMillis();\r
         eigenvector.tred();\r
 \r
-        long tend = System.currentTimeMillis();\r
+        details.append(" ---Tridiag transform matrix ---\n");\r
+        details.append(" --- D vector ---\n");\r
+        eigenvector.printD(ps);\r
+        ps.println();\r
+        details.append("--- E vector ---\n");\r
+        eigenvector.printE(ps);\r
+        ps.println();\r
 \r
-        //taps.println("Time take for tred = " + (tend-tstart) + "ms");\r
-        //taps.println(" ---Tridiag transform matrix ---");\r
-        //taps.println(" --- D vector ---");\r
-        //eigenvector.printD(taps);\r
-        //taps.println();\r
-        //taps.println(" --- E vector ---");\r
-        //    eigenvector.printE(taps);\r
-        //taps.println();\r
         // Now produce the diagonalization matrix\r
-        tstart = System.currentTimeMillis();\r
         eigenvector.tqli();\r
-        tend = System.currentTimeMillis();\r
-\r
-        //System.out.println("Time take for tqli = " + (tend-tstart) + " ms");\r
-        //System.out.println(" --- New diagonalization matrix ---");\r
-        //System.out.println(" --- Eigenvalues ---");\r
-        //eigenvector.printD(taps);\r
-        //System.out.println();\r
-        // for (int i=0; i < eigenvector.cols; i++) {\r
-        // checkEigenvector(i,taps);\r
-        // taps.println();\r
-        // }\r
+\r
+\r
+        details.append(" --- New diagonalization matrix ---\n");\r
+        details.append(" --- Eigenvalues ---\n");\r
+        eigenvector.printD(ps);\r
+        ps.println();\r
         //  taps.println();\r
         //  taps.println("Transformed sequences = ");\r
         // Matrix trans =  m.preMultiply(eigenvector);\r