From: amwaterhouse <Andrew Waterhouse>
Date: Fri, 23 Sep 2005 16:17:33 +0000 (+0000)
Subject: Capture output as details
X-Git-Tag: Release_2_06~13
X-Git-Url: http://source.jalview.org/gitweb/?a=commitdiff_plain;h=7b90c1cf89c40a68253ab60fa517031c302dbf45;p=jalview.git

Capture output as details
---

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