Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
PCAModel	40	74	38	0.3934426339.3%	Show methods

Class PCAModel

Class PCAModel	Line # 40	Total Statements 74	Complexity 38	TOTAL Coverage 0.3934426339.3%
PCAModel(AlignmentView,SequenceI[],boolean,ScoreModelI,SimilarityParamsI)   PCAModel(AlignmentView,SequenceI[],boolean,ScoreModelI,SimilarityParamsI)	7878	5.05	1.01	1.0 1.0100%
calculate() : void   calculate() : void	9292	16.016	5.05	0.90909094 0.9090909490.9%
applyAnnotationsToPoints(SequencePoint,int) : void   applyAnnotationsToPoints(SequencePoint,int) : void	135135	9.09	8.08	0.0 0.00%
updateRc(RotatableCanvasI) : void   updateRc(RotatableCanvasI) : void	157157	1.01	1.01	1.0 1.0100%
isNucleotide() : boolean   isNucleotide() : boolean	162162	1.01	1.01	0.0 0.00%
setNucleotide(boolean) : void   setNucleotide(boolean) : void	167167	1.01	1.01	0.0 0.00%
getTop() : int   getTop() : int	177177	1.01	1.01	1.0 1.0100%
setTop(int) : void   setTop(int) : void	182182	1.01	1.01	1.0 1.0100%
updateRcView(int,int,int) : void   updateRcView(int,int,int) : void	197197	3.03	2.02	0.0 0.00%
getDetails() : String   getDetails() : String	208208	1.01	1.01	0.0 0.00%
getInputData() : AlignmentView   getInputData() : AlignmentView	213213	1.01	1.01	0.0 0.00%
setInputData(AlignmentView) : void   setInputData(AlignmentView) : void	218218	1.01	1.01	1.0 1.0100%
getPointsasCsv(boolean,int,int,int) : String   getPointsasCsv(boolean,int,int,int) : String	223223	26.026	6.06	0.0 0.00%
getScoreModelName() : String   getScoreModelName() : String	271271	1.01	2.02	0.6666667 0.666666766.7%
setScoreModel(ScoreModelI) : void   setScoreModel(ScoreModelI) : void	276276	1.01	1.01	0.0 0.00%
getSimilarityParameters() : SimilarityParamsI   getSimilarityParameters() : SimilarityParamsI	286286	1.01	1.01	1.0 1.0100%
getSequencePoints() : List<SequencePoint>   getSequencePoints() : List<SequencePoint>	291291	1.01	1.01	1.0 1.0100%
setSequencePoints(List<SequencePoint>) : void   setSequencePoints(List<SequencePoint>) : void	296296	1.01	1.01	1.0 1.0100%
getPcaData() : PCA   getPcaData() : PCA	306306	1.01	1.01	1.0 1.0100%
setPCA(PCA) : void   setPCA(PCA) : void	311311	1.01	1.01	1.0 1.0100%

 

Contributing tests

Contributing tests

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	Test contribution	Test	Result	Actions
	0.39344263	jalview.project.Jalview2xmlTests.testPcaViewAssociationjalview.project.Jalview2xmlTests.testPcaViewAssociation	1PASS

 

Source view

1		/*
2		* Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
3		* Copyright (C) $$Year-Rel$$ The Jalview Authors
4		*
5		* This file is part of Jalview.
6		*
7		* Jalview is free software: you can redistribute it and/or
8		* modify it under the terms of the GNU General Public License
9		* as published by the Free Software Foundation, either version 3
10		* of the License, or (at your option) any later version.
11		*
12		* Jalview is distributed in the hope that it will be useful, but
13		* WITHOUT ANY WARRANTY; without even the implied warranty
14		* of MERCHANTABILITY or FITNESS FOR A PARTICULAR
15		* PURPOSE. See the GNU General Public License for more details.
16		*
17		* You should have received a copy of the GNU General Public License
18		* along with Jalview. If not, see <http://www.gnu.org/licenses/>.
19		* The Jalview Authors are detailed in the 'AUTHORS' file.
20		*/
21		package jalview.viewmodel;
22
23		import jalview.analysis.AnalysisModel;
24		import jalview.analysis.PCA;
25		import jalview.api.RotatableCanvasI;
26		import jalview.api.analysis.ScoreModelI;
27		import jalview.api.analysis.SimilarityParamsI;
28		import jalview.datamodel.AlignmentAnnotation;
29		import jalview.datamodel.AlignmentView;
30		import jalview.datamodel.Point;
31		import jalview.datamodel.SequenceI;
32		import jalview.datamodel.SequenceNode;
33		import jalview.datamodel.SequencePoint;
34
35		import java.util.ArrayList;
36		import java.util.HashMap;
37		import java.util.List;
38		import java.util.Vector;
39
		39.3% Uncovered Elements: 74 (122) Complexity: 38 Complexity Density: 0.51
40		public class PCAModel extends AnalysisModel
41		{
42		/*
43		* inputs
44		*/
45		private AlignmentView inputData;
46
47		private SequenceI[] seqs;
48
49		private final SimilarityParamsI similarityParams;
50
51		/*
52		* options - score model, nucleotide / protein
53		*/
54		private ScoreModelI scoreModel;
55
56		private boolean nucleotide = false;
57
58		/*
59		* outputs
60		*/
61		private PCA pca;
62
63		int top;
64
65		private List<SequencePoint> points;
66
67
68		/**
69		* Constructor given sequence data, score model and score calculation
70		* parameter options.
71		*
72		* @param seqData
73		* @param sqs
74		* @param nuc
75		* @param modelName
76		* @param params
77		*/
		100% Uncovered Elements: 0 (5) Complexity: 1 Complexity Density: 0.2
78	2	public PCAModel(AlignmentView seqData, SequenceI[] sqs, boolean nuc,...
79		ScoreModelI modelName, SimilarityParamsI params)
80		{
81	2	inputData = seqData;
82	2	seqs = sqs;
83	2	nucleotide = nuc;
84	2	scoreModel = modelName;
85	2	similarityParams = params;
86		}
87
88		/**
89		* Performs the PCA calculation (in the same thread) and extracts result data
90		* needed for visualisation by PCAPanel
91		*/
		90.9% Uncovered Elements: 2 (22) Complexity: 5 Complexity Density: 0.31
92	1	public void calculate()...
93		{
94	1	pca = new PCA(inputData, scoreModel, similarityParams);
95
96	1	initAnnotationData();
97
98	1	seqs = scoreModel.expandSeqData(seqs, inputData, similarityParams,
99		labels, ssAnnotationForSeqs, annotationDetails);
100
101
102	1	pca.run(); // executes in same thread, wait for completion
103
104		// Now find the component coordinates
105	1	int ii = 0;
106
107	16	while ((ii < seqs.length) && (seqs[ii] != null))
108		{
109	15	ii++;
110		}
111
112	1	int height = pca.getHeight();
113		// top = pca.getM().height() - 1;
114	1	top = height - 1;
115
116	1	points = new Vector<>();
117	1	Point[] scores = pca.getComponents(top - 1, top - 2, top - 3, 1);
118
119	16	for (int i = 0; i < height; i++)
120		{
121	15	SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
122	15	if (scoreModel.isSecondaryStructure())
123		{
124	0	applyAnnotationsToPoints(sp, i);
125		}
126	15	points.add(sp);
127		}
128		}
129
130		/**
131		* Attaches annotation and metadata to a specific tree node.
132		* @param sp The SequenceNode (leaf) being processed
133		* @param index The index of the sequence in the alignment
134		*/
		0% Uncovered Elements: 17 (17) Complexity: 8 Complexity Density: 0.89
135	0	private void applyAnnotationsToPoints(SequencePoint sp, int index)...
136		{
137	0	if (labels != null && index < labels.size())
138		{
139	0	sp.setLabel(labels.get(index));
140		}
141	0	if (ssAnnotationForSeqs != null && index < ssAnnotationForSeqs.size())
142		{
143	0	AlignmentAnnotation aa = ssAnnotationForSeqs.get(index);
144	0	if (aa != null)
145		{
146	0	sp.setAlignmentAnnotation(aa);
147
148	0	String detail = annotationDetails.get(index);
149	0	if (detail != null && !detail.isEmpty())
150		{
151	0	sp.setAnnotationDetails(detail);
152		}
153		}
154		}
155		}
156
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
157	1	public void updateRc(RotatableCanvasI rc)...
158		{
159	1	rc.setPoints(points, pca.getHeight());
160		}
161
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
162	0	public boolean isNucleotide()...
163		{
164	0	return nucleotide;
165		}
166
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
167	0	public void setNucleotide(boolean nucleotide)...
168		{
169	0	this.nucleotide = nucleotide;
170		}
171
172		/**
173		* Answers the index of the principal dimension of the PCA
174		*
175		* @return
176		*/
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
177	1	public int getTop()...
178		{
179	1	return top;
180		}
181
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
182	1	public void setTop(int t)...
183		{
184	1	top = t;
185		}
186
187		/**
188		* Updates the 3D coordinates for the list of points to the given dimensions.
189		* Principal dimension is getTop(). Next greatest eigenvector is getTop()-1.
190		* Note - pca.getComponents starts counting the spectrum from rank-2 to zero,
191		* rather than rank-1, so getComponents(dimN ...) == updateRcView(dimN+1 ..)
192		*
193		* @param dim1
194		* @param dim2
195		* @param dim3
196		*/
		0% Uncovered Elements: 5 (5) Complexity: 2 Complexity Density: 0.67
197	0	public void updateRcView(int dim1, int dim2, int dim3)...
198		{
199		// note: actual indices for components are dim1-1, etc (patch for JAL-1123)
200	0	Point[] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 1);
201
202	0	for (int i = 0; i < pca.getHeight(); i++)
203		{
204	0	points.get(i).coord = scores[i];
205		}
206		}
207
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
208	0	public String getDetails()...
209		{
210	0	return pca.getDetails();
211		}
212
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
213	0	public AlignmentView getInputData()...
214		{
215	0	return inputData;
216		}
217
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
218	1	public void setInputData(AlignmentView data)...
219		{
220	1	inputData = data;
221		}
222
		0% Uncovered Elements: 36 (36) Complexity: 6 Complexity Density: 0.23
223	0	public String getPointsasCsv(boolean transformed, int xdim, int ydim,...
224		int zdim)
225		{
226	0	StringBuffer csv = new StringBuffer();
227	0	csv.append("\"Sequence\"");
228	0	if (transformed)
229		{
230	0	csv.append(",");
231	0	csv.append(xdim);
232	0	csv.append(",");
233	0	csv.append(ydim);
234	0	csv.append(",");
235	0	csv.append(zdim);
236		}
237		else
238		{
239	0	for (int d = 1, dmax = pca.component(1).length; d <= dmax; d++)
240		{
241	0	csv.append("," + d);
242		}
243		}
244	0	csv.append("\n");
245	0	for (int s = 0; s < seqs.length; s++)
246		{
247	0	csv.append("\"" + seqs[s].getName() + "\"");
248	0	double fl[];
249	0	if (!transformed)
250		{
251		// output pca in correct order
252	0	fl = pca.component(s);
253	0	for (int d = fl.length - 1; d >= 0; d--)
254		{
255	0	csv.append(",");
256	0	csv.append(fl[d]);
257		}
258		}
259		else
260		{
261	0	Point p = points.get(s).coord;
262	0	csv.append(",").append(p.x);
263	0	csv.append(",").append(p.y);
264	0	csv.append(",").append(p.z);
265		}
266	0	csv.append("\n");
267		}
268	0	return csv.toString();
269		}
270
		66.7% Uncovered Elements: 1 (3) Complexity: 2 Complexity Density: 2
271	3	public String getScoreModelName()...
272		{
273	3	return scoreModel == null ? "" : scoreModel.getName();
274		}
275
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
276	0	public void setScoreModel(ScoreModelI sm)...
277		{
278	0	this.scoreModel = sm;
279		}
280
281		/**
282		* Answers the parameters configured for pairwise similarity calculations
283		*
284		* @return
285		*/
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
286	1	public SimilarityParamsI getSimilarityParameters()...
287		{
288	1	return similarityParams;
289		}
290
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
291	1	public List<SequencePoint> getSequencePoints()...
292		{
293	1	return points;
294		}
295
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
296	1	public void setSequencePoints(List<SequencePoint> sp)...
297		{
298	1	points = sp;
299		}
300
301		/**
302		* Answers the object holding the values of the computed PCA
303		*
304		* @return
305		*/
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
306	1	public PCA getPcaData()...
307		{
308	1	return pca;
309		}
310
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
311	1	public void setPCA(PCA data)...
312		{
313	1	pca = data;
314		}
315		}