X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fviewmodel%2FPCAModel.java;h=1693294bf556920989784beaa734246bbbc6a468;hb=bc18effe68ba80213a6d03ca7e6175adc6be71d6;hp=cb6deed5c05aa0b3e2493ccf31b9311a77ee9bc6;hpb=a8f483d04205bb8273ee311c12968b7e86d205fa;p=jalview.git
diff --git a/src/jalview/viewmodel/PCAModel.java b/src/jalview/viewmodel/PCAModel.java
index cb6deed..1693294 100644
--- a/src/jalview/viewmodel/PCAModel.java
+++ b/src/jalview/viewmodel/PCAModel.java
@@ -1,76 +1,92 @@
/*
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.2)
- * Copyright (C) 2014 The Jalview Authors
+ * 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.
+ * 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 .
+ * 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;
- score_matrix = nucleotide2 ? "PID" : "BLOSUM62";
- }
-
- private volatile PCA pca;
+ private final SequenceI[] seqs;
- int top;
+ private final SimilarityParamsI similarityParams;
- AlignmentView seqstrings;
-
- SequenceI[] seqs;
-
- /**
- * Score matrix used to calculate PC
+ /*
+ * options - score model, nucleotide / protein
*/
- String score_matrix;
+ private ScoreModelI scoreModel;
- /**
- * use the identity matrix for calculating similarity between sequences.
- */
private boolean nucleotide = false;
- private Vector points;
+ /*
+ * outputs
+ */
+ private PCA pca;
+
+ int top;
- private boolean jvCalcMode = true;
+ private List points;
- public boolean isJvCalcMode()
+ /**
+ * Constructor given sequence data, score model and score calculation
+ * parameter options.
+ *
+ * @param seqData
+ * @param sqs
+ * @param nuc
+ * @param modelName
+ * @param params
+ */
+ public PCAModel(AlignmentView seqData, SequenceI[] sqs, boolean nuc,
+ ScoreModelI modelName, SimilarityParamsI params)
{
- return jvCalcMode;
+ 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, score_matrix);
- pca.setJvCalcMode(jvCalcMode);
- 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;
@@ -80,32 +96,23 @@ public class PCAModel
ii++;
}
- double[][] comps = new double[ii][ii];
-
- for (int i = 0; i < ii; i++)
- {
- if (pca.getEigenvalue(i) > 1e-4)
- {
- comps[i] = pca.component(i);
- }
- }
-
- top = pca.getM().rows - 1;
+ int height = pca.getHeight();
+ // top = pca.getM().height() - 1;
+ top = height - 1;
- points = new Vector();
- float[][] scores = pca.getComponents(top - 1, top - 2, top - 3, 100);
+ points = new Vector<>();
+ Point[] 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()
@@ -119,17 +126,22 @@ public class PCAModel
}
/**
+ * 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
+ * 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 ..)
@@ -141,11 +153,11 @@ public class PCAModel
public void updateRcView(int dim1, int dim2, int dim3)
{
// note: actual indices for components are dim1-1, etc (patch for JAL-1123)
- float[][] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 100);
+ 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];
}
}
@@ -154,9 +166,14 @@ 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,
@@ -197,47 +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()
{
- 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 similarityParams;
}
- public void setJvCalcMode(boolean state)
+ public List getSequencePoints()
{
- jvCalcMode = state;
+ return points;
}
- public String getScore_matrix()
+ public void setSequencePoints(List sp)
+ {
+ points = sp;
+ }
+
+ /**
+ * Answers the object holding the values of the computed PCA
+ *
+ * @return
+ */
+ public PCA getPcaData()
{
- return score_matrix;
+ return pca;
}
- public void setScore_matrix(String score_matrix)
+ public void setPCA(PCA data)
{
- this.score_matrix = score_matrix;
+ pca = data;
}
-
}