X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=42a168dab78a24d40c6f572fd4e10d404f6dcaab;hb=47fdef81050c615ce519a0deaa8a5f4c67b83f0b;hp=d978451457e07a51034cd33148c07a75463648fa;hpb=35679161e01fac267b1a58bec4244baae94d7fd2;p=jalview.git

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index d978451..42a168d 100755
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -20,9 +20,10 @@
  */
 package jalview.analysis;
 
+import jalview.api.analysis.ScoreModelI;
+import jalview.api.analysis.SimilarityParamsI;
+import jalview.datamodel.AlignmentView;
 import jalview.math.MatrixI;
-import jalview.schemes.ResidueProperties;
-import jalview.schemes.ScoreMatrix;
 
 import java.io.PrintStream;
 
@@ -31,8 +32,6 @@ import java.io.PrintStream;
  */
 public class PCA implements Runnable
 {
-  boolean jvCalcMode = true;
-
   MatrixI symm;
 
   double[] eigenvalue;
@@ -41,108 +40,20 @@ public class PCA implements Runnable
 
   StringBuilder details = new StringBuilder(1024);
 
-  private String[] seqs;
+  final private AlignmentView seqs;
 
-  private ScoreMatrix scoreMatrix;
+  private ScoreModelI scoreModel;
 
-  /**
-   * 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
-   */
-  public PCA(String[] s)
-  {
-    this(s, false);
-  }
+  private SimilarityParamsI similarityParams;
 
-  /**
-   * 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.
-   */
-  public PCA(String[] s, boolean nucleotides)
-  {
-    this(s, nucleotides, null);
-  }
-
-  public PCA(String[] s, boolean nucleotides, String s_m)
+  public PCA(AlignmentView s, ScoreModelI sm, SimilarityParamsI options)
   {
     this.seqs = s;
+    this.similarityParams = options;
+    this.scoreModel = sm;
 
-    // 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].encode();
-    // ii++;
-    // }
-    //
-    // BinarySequence[] bs2 = new BinarySequence[s.length];
-    scoreMatrix = null;
-    String sm = s_m;
-    if (sm != null)
-    {
-      scoreMatrix = ResidueProperties.getScoreMatrix(sm);
-    }
-    if (scoreMatrix == null)
-    {
-      // either we were given a non-existent score matrix or a scoremodel that
-      // isn't based on a pairwise symbol score matrix
-      scoreMatrix = ResidueProperties
-              .getScoreMatrix(sm = (nucleotides ? "DNA" : "BLOSUM62"));
-    }
-    details.append("PCA calculation using " + sm
+    details.append("PCA calculation using " + sm.getName()
             + " sequence similarity matrix\n========\n\n");
-    // ii = 0;
-    // while ((ii < s.length) && (s[ii] != null))
-    // {
-    // bs2[ii] = new BinarySequence(s[ii], nucleotides);
-    // if (scoreMatrix != null)
-    // {
-    // try
-    // {
-    // bs2[ii].matrixEncode(scoreMatrix);
-    // } catch (InvalidSequenceTypeException x)
-    // {
-    // details.append("Unexpected mismatch of sequence type and score matrix. Calculation will not be valid!\n\n");
-    // }
-    // }
-    // ii++;
-    // }
-    //
-    // int count = 0;
-    // while ((count < bs.length) && (bs[count] != null))
-    // {
-    // count++;
-    // }
-    //
-    // double[][] seqmat = new double[count][];
-    // double[][] seqmat2 = new double[count][];
-    //
-    // int i = 0;
-    // while (i < count)
-    // {
-    // seqmat[i] = bs[i].getDBinary();
-    // seqmat2[i] = bs2[i].getDBinary();
-    // i++;
-    // }
-    //
-    // /*
-    // * using a SparseMatrix to hold the encoded sequences matrix
-    // * greatly speeds up matrix multiplication as these are mostly zero
-    // */
-    // m = new SparseMatrix(seqmat);
-    // m2 = new Matrix(seqmat2);
-
   }
 
   /**
@@ -258,11 +169,7 @@ public class PCA implements Runnable
     // long now = System.currentTimeMillis();
     try
     {
-      details.append("PCA Calculation Mode is "
-              + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
-              + "\n");
-
-      eigenvector = scoreMatrix.computePairwiseScores(seqs);
+      eigenvector = scoreModel.findSimilarities(seqs, similarityParams);
 
       details.append(" --- OrigT * Orig ---- \n");
       eigenvector.print(ps, "%8.2f");
@@ -286,7 +193,8 @@ public class PCA implements Runnable
       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(
+              "*** Matrices below may not be fully diagonalised. ***\n");
     }
 
     details.append(" --- New diagonalization matrix ---\n");
@@ -304,11 +212,6 @@ public class PCA implements Runnable
     // + (System.currentTimeMillis() - now) + "ms"));
   }
 
-  public void setJvCalcMode(boolean calcMode)
-  {
-    this.jvCalcMode = calcMode;
-  }
-
   /**
    * Answers the N dimensions of the NxN PCA matrix. This is the number of
    * sequences involved in the pairwise score calculation.
@@ -318,6 +221,6 @@ public class PCA implements Runnable
   public int getHeight()
   {
     // TODO can any of seqs[] be null?
-    return seqs.length;
+    return seqs.getSequences().length;
   }
 }