Coverage Report
File SpatialModel.java
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52% of files have more coverage
Classes
| Class | Line # | Actions | |||
|---|---|---|---|---|---|
| SpatialModel | 21 | 84 | 39 |
Contributing tests
This file is covered by 1 test.
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| 1 | package jalview.viewmodel; | |
| 2 | ||
| 3 | import java.util.Arrays; | |
| 4 | import java.util.List; | |
| 5 | import java.util.Set; | |
| 6 | import java.util.Vector; | |
| 7 | ||
| 8 | import jalview.analysis.Clusterer; | |
| 9 | import jalview.analysis.SpatialCalculationI; | |
| 10 | import jalview.api.RotatableCanvasI; | |
| 11 | import jalview.api.analysis.ScoreModelI; | |
| 12 | import jalview.api.analysis.SimilarityParamsI; | |
| 13 | import jalview.datamodel.AlignmentView; | |
| 14 | import jalview.datamodel.Point; | |
| 15 | import jalview.datamodel.SequenceGroup; | |
| 16 | import jalview.datamodel.SequenceI; | |
| 17 | import jalview.datamodel.SequencePoint; | |
| 18 | import jalview.math.Matrix; | |
| 19 | import jalview.math.MatrixI; | |
| 20 | ||
| 21 | public abstract class SpatialModel<T extends SpatialCalculationI> | |
| 22 | { | |
| 23 | /* | |
| 24 | * inputs | |
| 25 | */ | |
| 26 | protected AlignmentViewport av; | |
| 27 | ||
| 28 | protected AlignmentView inputData; | |
| 29 | ||
| 30 | protected final SequenceI[] seqs; | |
| 31 | ||
| 32 | protected final SimilarityParamsI similarityParams; | |
| 33 | ||
| 34 | /* | |
| 35 | * options - score model, nucleotide / protein | |
| 36 | */ | |
| 37 | protected ScoreModelI scoreModel; | |
| 38 | ||
| 39 | protected boolean nucleotide = false; | |
| 40 | ||
| 41 | protected T outputModel; | |
| 42 | ||
| 43 | private int top; | |
| 44 | ||
| 45 | private List<SequencePoint> points; | |
| 46 | ||
| 47 | /** | |
| 48 | * Constructor given sequence data, score model and score calculation | |
| 49 | * parameter options. | |
| 50 | * | |
| 51 | * @param seqData | |
| 52 | * @param sqs | |
| 53 | * @param nuc | |
| 54 | * @param modelName | |
| 55 | * @param params | |
| 56 | */ | |
| 57 | 2 |  public SpatialModel(AlignmentViewport av, AlignmentView seqData, SequenceI[] sqs, boolean nuc, |
| 58 | ScoreModelI modelName, SimilarityParamsI params) | |
| 59 | { | |
| 60 | 2 | this.av = av; |
| 61 | 2 | inputData = seqData; |
| 62 | 2 | seqs = sqs; |
| 63 | 2 | nucleotide = nuc; |
| 64 | 2 | scoreModel = modelName; |
| 65 | 2 | similarityParams = params; |
| 66 | } | |
| 67 | ||
| 68 | public abstract T constructModel(); | |
| 69 | ||
| 70 | 1 | public void calculate() |
| 71 | { | |
| 72 | 1 | outputModel = constructModel(); |
| 73 | 1 | outputModel.run(); // executes in same thread, wait for completion |
| 74 | 1 | if (outputModel.isCancelled()) |
| 75 | { | |
| 76 | // no more work to do | |
| 77 | 0 | return; |
| 78 | } | |
| 79 | ||
| 80 | // Now find the component coordinates | |
| 81 | // // delete this ? | |
| 82 | // int ii = 0; | |
| 83 | // | |
| 84 | // while ((ii < seqs.length) && (seqs[ii] != null)) | |
| 85 | // { | |
| 86 | // ii++; | |
| 87 | // } | |
| 88 | ||
| 89 | // NB height for PaSiMap is Width. | |
| 90 | 1 | int height = outputModel.getHeight(); |
| 91 | 1 | int width = outputModel.getWidth(); |
| 92 | ||
| 93 | // top = pca.getM().height() - 1; | |
| 94 | // top = pasimap.getWidth() - 1; | |
| 95 | 1 | top = outputModel.getTop(); |
| 96 | ||
| 97 | 1 | points = new Vector<>(); |
| 98 | 1 | Point[] scores = outputModel.getComponents(top - 1, top - 2, top - 3, 1f); |
| 99 | ||
| 100 | 16 | for (int i = 0; i < height; i++) |
| 101 | { | |
| 102 | 15 | SequencePoint sp = new SequencePoint(seqs[i], scores[i]); |
| 103 | 15 | points.add(sp); |
| 104 | } | |
| 105 | } | |
| 106 | ||
| 107 | 1 | public void updateRc(RotatableCanvasI rc) |
| 108 | { | |
| 109 | 1 | rc.setPoints(points, outputModel.getHeight()); |
| 110 | } | |
| 111 | ||
| 112 | 0 | public boolean isNucleotide() |
| 113 | { | |
| 114 | 0 | return nucleotide; |
| 115 | } | |
| 116 | ||
| 117 | 0 | public void setNucleotide(boolean nucleotide) |
| 118 | { | |
| 119 | 0 | this.nucleotide = nucleotide; |
| 120 | } | |
| 121 | ||
| 122 | /** | |
| 123 | * Answers the index of the principal dimension of the PCA | |
| 124 | * | |
| 125 | * @return | |
| 126 | */ | |
| 127 | 1 | public int getTop() |
| 128 | { | |
| 129 | 1 | return top; |
| 130 | } | |
| 131 | /** ? used ? | |
| 132 | * | |
| 133 | * @param t | |
| 134 | */ | |
| 135 | 1 | public void setTop(int t) |
| 136 | { | |
| 137 | 1 | top = t; |
| 138 | } | |
| 139 | ||
| 140 | /** | |
| 141 | * Updates the 3D coordinates for the list of points to the given dimensions. | |
| 142 | * Principal dimension is getTop(). Next greatest eigenvector is getTop()-1. | |
| 143 | * Note - pca.getComponents starts counting the spectrum from rank-2 to zero, | |
| 144 | * rather than rank-1, so getComponents(dimN ...) == updateRcView(dimN+1 ..) | |
| 145 | * | |
| 146 | * @param dim1 | |
| 147 | * @param dim2 | |
| 148 | * @param dim3 | |
| 149 | */ | |
| 150 | 0 | public void updateRcView(int dim1, int dim2, int dim3) |
| 151 | { | |
| 152 | // note: actual indices for components are dim1-1, etc (patch for JAL-1123) | |
| 153 | 0 | Point[] scores = outputModel.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 1); |
| 154 | ||
| 155 | 0 | for (int i = 0; i < outputModel.getHeight(); i++) |
| 156 | { | |
| 157 | 0 | points.get(i).coord = scores[i]; |
| 158 | } | |
| 159 | } | |
| 160 | ||
| 161 | 0 | public String getDetails() |
| 162 | { | |
| 163 | 0 | return outputModel.getDetails(); |
| 164 | } | |
| 165 | ||
| 166 | 0 | public String getAlignmentOutput() |
| 167 | { | |
| 168 | 0 | return outputModel.getAlignmentOutput(); |
| 169 | } | |
| 170 | ||
| 171 | 0 | public AlignmentViewport getInputAlignmentView() |
| 172 | { | |
| 173 | 0 | return av; |
| 174 | } | |
| 175 | 0 | public AlignmentView getInputData() |
| 176 | { | |
| 177 | 0 | return inputData; |
| 178 | } | |
| 179 | ||
| 180 | 0 | public String getPointsasCsv(boolean transformed, int xdim, int ydim, |
| 181 | int zdim) | |
| 182 | { | |
| 183 | 0 | StringBuffer csv = new StringBuffer(); |
| 184 | 0 | csv.append("\"Sequence\""); |
| 185 | 0 | if (transformed) |
| 186 | { | |
| 187 | 0 | csv.append(","); |
| 188 | 0 | csv.append(xdim); |
| 189 | 0 | csv.append(","); |
| 190 | 0 | csv.append(ydim); |
| 191 | 0 | csv.append(","); |
| 192 | 0 | csv.append(zdim); |
| 193 | } | |
| 194 | else | |
| 195 | { | |
| 196 | // pca.component(1).length == pasimap.getDim | |
| 197 | 0 | for (int d = 1, dmax = (int) outputModel.getDim(); d <= dmax; d++) |
| 198 | { | |
| 199 | 0 | csv.append("," + d); |
| 200 | } | |
| 201 | } | |
| 202 | 0 | csv.append("\n"); |
| 203 | 0 | for (int s = 0; s < seqs.length; s++) |
| 204 | { | |
| 205 | 0 | csv.append("\"" + seqs[s].getName() + "\""); |
| 206 | 0 | if (!transformed) |
| 207 | { | |
| 208 | 0 | double[] fl = outputModel.component(s); |
| 209 | 0 | for (int d = fl.length - 1; d >= 0; d--) |
| 210 | { | |
| 211 | 0 | csv.append(","); |
| 212 | 0 | csv.append(fl[d]); |
| 213 | } | |
| 214 | } | |
| 215 | else | |
| 216 | { | |
| 217 | 0 | Point p = points.get(s).coord; |
| 218 | 0 | csv.append(",").append(p.x); |
| 219 | 0 | csv.append(",").append(p.y); |
| 220 | 0 | csv.append(",").append(p.z); |
| 221 | } | |
| 222 | 0 | csv.append("\n"); |
| 223 | } | |
| 224 | 0 | return csv.toString(); |
| 225 | } | |
| 226 | ||
| 227 | 3 | public String getScoreModelName() |
| 228 | { | |
| 229 | 3 | return scoreModel == null ? "" : scoreModel.getName(); |
| 230 | } | |
| 231 | ||
| 232 | 0 | public void setScoreModel(ScoreModelI sm) |
| 233 | { | |
| 234 | 0 | this.scoreModel = sm; |
| 235 | } | |
| 236 | ||
| 237 | /** | |
| 238 | * Answers the parameters configured for pairwise similarity calculations | |
| 239 | * | |
| 240 | * @return | |
| 241 | */ | |
| 242 | 1 | public SimilarityParamsI getSimilarityParameters() |
| 243 | { | |
| 244 | 1 | return similarityParams; |
| 245 | } | |
| 246 | ||
| 247 | 1 | public List<SequencePoint> getSequencePoints() |
| 248 | { | |
| 249 | 1 | return points; |
| 250 | } | |
| 251 | ||
| 252 | 1 | public void setSequencePoints(List<SequencePoint> sp) |
| 253 | { | |
| 254 | 1 | points = sp; |
| 255 | } | |
| 256 | 0 | public T getOutputModel() { |
| 257 | 0 | return outputModel; |
| 258 | } | |
| 259 | 0 | public void setOutputModel(T newModel) |
| 260 | { | |
| 261 | 0 | outputModel = newModel; |
| 262 | } | |
| 263 | ||
| 264 | 0 | public boolean isCancelled() |
| 265 | { | |
| 266 | 0 | if (outputModel==null || outputModel.isCancelled()) |
| 267 | { | |
| 268 | 0 | return true; |
| 269 | } | |
| 270 | 0 | return false; |
| 271 | } | |
| 272 | ||
| 273 | 0 | public void cancel() |
| 274 | { | |
| 275 | 0 | outputModel.cancel(); |
| 276 | } | |
| 277 | ||
| 278 | 0 | public boolean canCancel() |
| 279 | { | |
| 280 | 0 | return (!isCancelled() && outputModel.isCancellable()); |
| 281 | } | |
| 282 | ||
| 283 | List<SequenceGroup> groups = Arrays.asList(new SequenceGroup[] {}); | |
| 284 | 0 | List<SequenceGroup> getGroups() { |
| 285 | 0 | return groups; |
| 286 | } | |
| 287 | ||
| 288 | 0 | public void markGroups() |
| 289 | { | |
| 290 | 0 | av.getAlignment().deleteAllGroups(); |
| 291 | 0 | int w = av.getAlignment().getWidth(); |
| 292 | // replace all groups with new groups | |
| 293 | // assign a random(ish) idColour | |
| 294 | 0 | for (jalview.datamodel.SequenceGroup sg : groups) |
| 295 | { | |
| 296 | 0 | sg.setStartRes(0); |
| 297 | 0 | sg.setEndRes(w); |
| 298 | 0 | sg.idColour = new java.awt.Color((int) (Math.random() * 255), |
| 299 | (int) (Math.random() * 255), (int) (Math.random() * 255)); | |
| 300 | 0 | sg.recalcConservation(); |
| 301 | 0 | av.getAlignment().addGroup(sg); |
| 302 | 0 | for (SequenceI sq : sg.getSequences()) |
| 303 | { | |
| 304 | 0 | av.setSequenceColour(sq, sg.getIdColour()); |
| 305 | } | |
| 306 | } | |
| 307 | } | |
| 308 | 1 | public List<String> getClusterOptions() |
| 309 | { | |
| 310 | 1 | return Arrays.asList(outputModel.getDistanceMatrices().keySet().toArray(new String[0])); |
| 311 | } | |
| 312 | 0 | public MatrixI getSimilarityMatrixFor(String key) |
| 313 | { | |
| 314 | 0 | return outputModel.getDistanceMatrices().get(key); |
| 315 | } | |
| 316 | ||
| 317 | 0 | public void doClustering(String method) |
| 318 | { | |
| 319 | 0 | Clusterer ourClusterer = new Clusterer(); |
| 320 | 0 | MatrixI distanceMatrx = outputModel.getDistanceMatrices().get(method); |
| 321 | 0 | ourClusterer.cluster(distanceMatrx); |
| 322 | 0 | groups = ourClusterer.createGroups(seqs); |
| 323 | } | |
| 324 | ||
| 325 | } |