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 java.util.List;
import java.util.Vector;
public class PCAModel
{
- private volatile PCA pca;
-
- int top;
+ /*
+ * inputs
+ */
+ private AlignmentView inputData;
- AlignmentView seqstrings;
+ private final SequenceI[] seqs;
- SequenceI[] seqs;
+ private final SimilarityParamsI similarityParams;
/*
- * Score model used to calculate PCA
+ * options - score model, nucleotide / protein
*/
- ScoreModelI scoreModel;
+ private ScoreModelI scoreModel;
private boolean nucleotide = false;
- private Vector<SequencePoint> points;
+ /*
+ * outputs
+ */
+ private PCA pca;
- private boolean jvCalcMode = true;
+ int top;
- private SimilarityParamsI similarityParams;
+ private List<SequencePoint> points;
/**
* Constructor given sequence data, score model and score calculation
* @param seqData
* @param sqs
* @param nuc
- * @param sm
+ * @param modelName
* @param params
*/
- public PCAModel(AlignmentView seqData, SequenceI[] sqs, boolean nuc, ScoreModelI sm,
- SimilarityParamsI params)
+ public PCAModel(AlignmentView seqData, SequenceI[] sqs, boolean nuc,
+ ScoreModelI modelName, SimilarityParamsI params)
{
- seqstrings = seqData;
+ inputData = seqData;
seqs = sqs;
nucleotide = nuc;
- scoreModel = sm;
+ 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, scoreModel, similarityParams);
- 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;
// top = pca.getM().height() - 1;
top = height - 1;
- points = new Vector<SequencePoint>();
- 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 < height; i++)
{
SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
- points.addElement(sp);
+ points.add(sp);
}
}
}
/**
+ * 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 ..)
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.getHeight(); i++)
{
- points.elementAt(i).coord = scores[i];
+ points.get(i).coord = scores[i];
}
}
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,
}
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<SequencePoint> getSequencePoints()
{
- jvCalcMode = state;
+ return points;
}
- public String getScoreModelName()
+ public void setSequencePoints(List<SequencePoint> sp)
{
- return scoreModel == null ? "" : scoreModel.getName();
+ points = sp;
}
- public void setScoreModel(ScoreModelI sm)
+ /**
+ * Answers the object holding the values of the computed PCA
+ *
+ * @return
+ */
+ public PCA getPcaData()
{
- this.scoreModel = sm;
+ return pca;
}
+ public void setPCA(PCA data)
+ {
+ pca = data;
+ }
}