X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;ds=sidebyside;f=src%2Fjalview%2Fviewmodel%2FPCAModel.java;h=54a492569236b5c1e2e62c5c4b2b8429d033e9ed;hb=17e77c3f2949a0729322b4a8d907f3f34b6a9914;hp=30f87837ef26a41fab717122bf64efb63e1abcdc;hpb=55c883270e9f64da9562a24f09dfe5f2f079e59a;p=jalview.git
diff --git a/src/jalview/viewmodel/PCAModel.java b/src/jalview/viewmodel/PCAModel.java
index 30f8783..54a4925 100644
--- a/src/jalview/viewmodel/PCAModel.java
+++ b/src/jalview/viewmodel/PCAModel.java
@@ -1,12 +1,32 @@
+/*
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.9)
+ * Copyright (C) 2015 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.
+ *
+ * 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 .
+ * The Jalview Authors are detailed in the 'AUTHORS' file.
+ */
package jalview.viewmodel;
-import java.util.Vector;
-
import jalview.analysis.PCA;
+import jalview.api.RotatableCanvasI;
import jalview.datamodel.AlignmentView;
import jalview.datamodel.SequenceI;
import jalview.datamodel.SequencePoint;
-import jalview.api.RotatableCanvasI;
+
+import java.util.Vector;
public class PCAModel
{
@@ -14,30 +34,45 @@ public class PCAModel
public PCAModel(AlignmentView seqstrings2, SequenceI[] seqs2,
boolean nucleotide2)
{
- seqstrings=seqstrings2;
- seqs=seqs2;
- nucleotide=nucleotide2;
+ seqstrings = seqstrings2;
+ seqs = seqs2;
+ nucleotide = nucleotide2;
+ score_matrix = nucleotide2 ? "PID" : "BLOSUM62";
}
- PCA pca;
-
+ private volatile PCA pca;
+
int top;
-
+
AlignmentView seqstrings;
SequenceI[] seqs;
/**
- * use the identity matrix for calculating similarity between sequences.
+ * Score matrix used to calculate PC
*/
- private boolean nucleotide=false;
+ String score_matrix;
+
+ /**
+ * use the identity matrix for calculating similarity between sequences.
+ */
+ private boolean nucleotide = false;
private Vector points;
+ private boolean jvCalcMode = true;
+
+ public boolean isJvCalcMode()
+ {
+ return jvCalcMode;
+ }
+
public void run()
{
-
- pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide);
+
+ pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide,
+ score_matrix);
+ pca.setJvCalcMode(jvCalcMode);
pca.run();
// Now find the component coordinates
@@ -68,7 +103,7 @@ public class PCAModel
SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
points.addElement(sp);
}
-
+
}
public void updateRc(RotatableCanvasI rc)
@@ -80,9 +115,10 @@ public class PCAModel
{
return nucleotide;
}
+
public void setNucleotide(boolean nucleotide)
{
- this.nucleotide=nucleotide;
+ this.nucleotide = nucleotide;
}
/**
@@ -97,15 +133,18 @@ public class PCAModel
/**
* update the 2d coordinates for the list of points to the given dimensions
- * Principal dimension is getTop(). Next greated eigenvector is getTop()-1.
- * Note - pca.getComponents starts counting the spectrum from zero rather than one, so getComponents(dimN ...) == updateRcView(dimN+1 ..)
- * @param dim1
+ * Principal dimension is getTop(). Next greatest eigenvector is getTop()-1.
+ * Note - pca.getComponents starts counting the spectrum from rank-2 to zero,
+ * rather than rank-1, so getComponents(dimN ...) == updateRcView(dimN+1 ..)
+ *
+ * @param dim1
* @param dim2
* @param dim3
*/
public void updateRcView(int dim1, int dim2, int dim3)
{
- float[][] scores = pca.getComponents(dim1-1, dim2-1, dim3-1, 100);
+ // note: actual indices for components are dim1-1, etc (patch for JAL-1123)
+ float[][] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 100);
for (int i = 0; i < pca.getM().rows; i++)
{
@@ -122,7 +161,9 @@ public class PCAModel
{
return seqstrings;
}
- public String getPointsasCsv(boolean transformed, int xdim, int ydim, int zdim)
+
+ public String getPointsasCsv(boolean transformed, int xdim, int ydim,
+ int zdim)
{
StringBuffer csv = new StringBuffer();
csv.append("\"Sequence\"");
@@ -187,4 +228,19 @@ public class PCAModel
return pts;
}
+ public void setJvCalcMode(boolean state)
+ {
+ jvCalcMode = state;
+ }
+
+ public String getScore_matrix()
+ {
+ return score_matrix;
+ }
+
+ public void setScore_matrix(String score_matrix)
+ {
+ this.score_matrix = score_matrix;
+ }
+
}