X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fviewmodel%2FPCAModel.java;h=1693294bf556920989784beaa734246bbbc6a468;hb=fd25ac2905528f9c2cf784317cc3d150c9167755;hp=30f87837ef26a41fab717122bf64efb63e1abcdc;hpb=55c883270e9f64da9562a24f09dfe5f2f079e59a;p=jalview.git
diff --git a/src/jalview/viewmodel/PCAModel.java b/src/jalview/viewmodel/PCAModel.java
index 30f8783..1693294 100644
--- a/src/jalview/viewmodel/PCAModel.java
+++ b/src/jalview/viewmodel/PCAModel.java
@@ -1,44 +1,92 @@
+/*
+ * Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
+ * Copyright (C) $$Year-Rel$$ 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.api.analysis.ScoreModelI;
+import jalview.api.analysis.SimilarityParamsI;
import jalview.datamodel.AlignmentView;
+import jalview.datamodel.Point;
import jalview.datamodel.SequenceI;
import jalview.datamodel.SequencePoint;
-import jalview.api.RotatableCanvasI;
+
+import java.util.List;
+import java.util.Vector;
public class PCAModel
{
+ /*
+ * inputs
+ */
+ private AlignmentView inputData;
- public PCAModel(AlignmentView seqstrings2, SequenceI[] seqs2,
- boolean nucleotide2)
- {
- seqstrings=seqstrings2;
- seqs=seqs2;
- nucleotide=nucleotide2;
- }
+ private final SequenceI[] seqs;
+
+ private final SimilarityParamsI similarityParams;
+
+ /*
+ * options - score model, nucleotide / protein
+ */
+ private ScoreModelI scoreModel;
+
+ private boolean nucleotide = false;
+
+ /*
+ * outputs
+ */
+ private PCA pca;
- PCA pca;
-
int top;
-
- AlignmentView seqstrings;
- SequenceI[] seqs;
+ private List points;
/**
- * use the identity matrix for calculating similarity between sequences.
+ * Constructor given sequence data, score model and score calculation
+ * parameter options.
+ *
+ * @param seqData
+ * @param sqs
+ * @param nuc
+ * @param modelName
+ * @param params
*/
- private boolean nucleotide=false;
-
- private Vector points;
+ public PCAModel(AlignmentView seqData, SequenceI[] sqs, boolean nuc,
+ ScoreModelI modelName, SimilarityParamsI params)
+ {
+ inputData = seqData;
+ seqs = sqs;
+ nucleotide = nuc;
+ scoreModel = modelName;
+ similarityParams = params;
+ }
- public void run()
+ /**
+ * Performs the PCA calculation (in the same thread) and extracts result data
+ * needed for visualisation by PCAPanel
+ */
+ public void calculate()
{
-
- pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide);
- pca.run();
+ pca = new PCA(inputData, scoreModel, similarityParams);
+ pca.run(); // executes in same thread, wait for completion
// Now find the component coordinates
int ii = 0;
@@ -48,68 +96,68 @@ public class PCAModel
ii++;
}
- double[][] comps = new double[ii][ii];
+ int height = pca.getHeight();
+ // top = pca.getM().height() - 1;
+ top = height - 1;
- for (int i = 0; i < ii; i++)
- {
- if (pca.getEigenvalue(i) > 1e-4)
- {
- comps[i] = pca.component(i);
- }
- }
-
- top = pca.getM().rows - 1;
+ points = new Vector<>();
+ Point[] scores = pca.getComponents(top - 1, top - 2, top - 3, 100);
- points = new Vector();
- float[][] scores = pca.getComponents(top - 1, top - 2, top - 3, 100);
-
- for (int i = 0; i < pca.getM().rows; i++)
+ for (int i = 0; i < height; i++)
{
SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
- points.addElement(sp);
+ points.add(sp);
}
-
}
public void updateRc(RotatableCanvasI rc)
{
- rc.setPoints(points, pca.getM().rows);
+ rc.setPoints(points, pca.getHeight());
}
public boolean isNucleotide()
{
return nucleotide;
}
+
public void setNucleotide(boolean nucleotide)
{
- this.nucleotide=nucleotide;
+ this.nucleotide = nucleotide;
}
/**
+ * Answers the index of the principal dimension of the PCA
*
- *
- * @return index of principle dimension of PCA
+ * @return
*/
public int getTop()
{
return top;
}
+ public void setTop(int t)
+ {
+ top = t;
+ }
+
/**
- * 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
+ * Updates the 3D coordinates for the list of points to the given dimensions.
+ * 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)
+ Point[] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 100);
- for (int i = 0; i < pca.getM().rows; i++)
+ for (int i = 0; i < pca.getHeight(); i++)
{
- ((SequencePoint) points.elementAt(i)).coord = scores[i];
+ points.get(i).coord = scores[i];
}
}
@@ -118,11 +166,18 @@ public class PCAModel
return pca.getDetails();
}
- public AlignmentView getSeqtrings()
+ public AlignmentView getInputData()
+ {
+ return inputData;
+ }
+
+ public void setInputData(AlignmentView data)
{
- return seqstrings;
+ inputData = data;
}
- 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\"");
@@ -159,32 +214,58 @@ public class PCAModel
}
else
{
- // output current x,y,z coords for points
- fl = getPointPosition(s);
- for (int d = 0; d < fl.length; d++)
- {
- csv.append(",");
- csv.append(fl[d]);
- }
+ Point p = points.get(s).coord;
+ csv.append(",").append(p.x);
+ csv.append(",").append(p.y);
+ csv.append(",").append(p.z);
}
csv.append("\n");
}
return csv.toString();
}
+ public String getScoreModelName()
+ {
+ return scoreModel == null ? "" : scoreModel.getName();
+ }
+
+ public void setScoreModel(ScoreModelI sm)
+ {
+ this.scoreModel = sm;
+ }
+
/**
+ * Answers the parameters configured for pairwise similarity calculations
*
- * @return x,y,z positions of point s (index into points) under current
- * transform.
+ * @return
*/
- public double[] getPointPosition(int s)
+ public SimilarityParamsI getSimilarityParameters()
+ {
+ return similarityParams;
+ }
+
+ public List getSequencePoints()
{
- double pts[] = new double[3];
- float[] p = points.elementAt(s).coord;
- pts[0] = p[0];
- pts[1] = p[1];
- pts[2] = p[2];
- return pts;
+ return points;
}
+ public void setSequencePoints(List sp)
+ {
+ points = sp;
+ }
+
+ /**
+ * Answers the object holding the values of the computed PCA
+ *
+ * @return
+ */
+ public PCA getPcaData()
+ {
+ return pca;
+ }
+
+ public void setPCA(PCA data)
+ {
+ pca = data;
+ }
}