Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
PCAModel	35	61	29	0.448979644.9%	Show methods

Class PCAModel

Class PCAModel	Line # 35	Total Statements 61	Complexity 29	TOTAL Coverage 0.448979644.9%
PCAModel(AlignmentView,SequenceI[],boolean,ScoreModelI,SimilarityParamsI)   PCAModel(AlignmentView,SequenceI[],boolean,ScoreModelI,SimilarityParamsI)	7272	5.05	1.01	1.0 1.0100%
calculate() : void   calculate() : void	8686	12.012	4.04	1.0 1.0100%
updateRc(RotatableCanvasI) : void   updateRc(RotatableCanvasI) : void	113113	1.01	1.01	1.0 1.0100%
isNucleotide() : boolean   isNucleotide() : boolean	118118	1.01	1.01	0.0 0.00%
setNucleotide(boolean) : void   setNucleotide(boolean) : void	123123	1.01	1.01	0.0 0.00%
getTop() : int   getTop() : int	133133	1.01	1.01	1.0 1.0100%
setTop(int) : void   setTop(int) : void	138138	1.01	1.01	1.0 1.0100%
updateRcView(int,int,int) : void   updateRcView(int,int,int) : void	153153	3.03	2.02	0.0 0.00%
getDetails() : String   getDetails() : String	164164	1.01	1.01	0.0 0.00%
getInputData() : AlignmentView   getInputData() : AlignmentView	169169	1.01	1.01	0.0 0.00%
setInputData(AlignmentView) : void   setInputData(AlignmentView) : void	174174	1.01	1.01	1.0 1.0100%
getPointsasCsv(boolean,int,int,int) : String   getPointsasCsv(boolean,int,int,int) : String	179179	26.026	6.06	0.0 0.00%
getScoreModelName() : String   getScoreModelName() : String	227227	1.01	2.02	0.6666667 0.666666766.7%
setScoreModel(ScoreModelI) : void   setScoreModel(ScoreModelI) : void	232232	1.01	1.01	0.0 0.00%
getSimilarityParameters() : SimilarityParamsI   getSimilarityParameters() : SimilarityParamsI	242242	1.01	1.01	1.0 1.0100%
getSequencePoints() : List<SequencePoint>   getSequencePoints() : List<SequencePoint>	247247	1.01	1.01	1.0 1.0100%
setSequencePoints(List<SequencePoint>) : void   setSequencePoints(List<SequencePoint>) : void	252252	1.01	1.01	1.0 1.0100%
getPcaData() : PCA   getPcaData() : PCA	262262	1.01	1.01	1.0 1.0100%
setPCA(PCA) : void   setPCA(PCA) : void	267267	1.01	1.01	1.0 1.0100%

 

Contributing tests

Contributing tests

Select all

	Test contribution	Test	Result	Actions
	0.4489796	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.PCA;
24		import jalview.api.RotatableCanvasI;
25		import jalview.api.analysis.ScoreModelI;
26		import jalview.api.analysis.SimilarityParamsI;
27		import jalview.datamodel.AlignmentView;
28		import jalview.datamodel.Point;
29		import jalview.datamodel.SequenceI;
30		import jalview.datamodel.SequencePoint;
31
32		import java.util.List;
33		import java.util.Vector;
34
		44.9% Uncovered Elements: 54 (98) Complexity: 29 Complexity Density: 0.48
35		public class PCAModel
36		{
37		/*
38		* inputs
39		*/
40		private AlignmentView inputData;
41
42		private final SequenceI[] seqs;
43
44		private final SimilarityParamsI similarityParams;
45
46		/*
47		* options - score model, nucleotide / protein
48		*/
49		private ScoreModelI scoreModel;
50
51		private boolean nucleotide = false;
52
53		/*
54		* outputs
55		*/
56		private PCA pca;
57
58		int top;
59
60		private List<SequencePoint> points;
61
62		/**
63		* Constructor given sequence data, score model and score calculation
64		* parameter options.
65		*
66		* @param seqData
67		* @param sqs
68		* @param nuc
69		* @param modelName
70		* @param params
71		*/
		100% Uncovered Elements: 0 (5) Complexity: 1 Complexity Density: 0.2
72	2	public PCAModel(AlignmentView seqData, SequenceI[] sqs, boolean nuc,...
73		ScoreModelI modelName, SimilarityParamsI params)
74		{
75	2	inputData = seqData;
76	2	seqs = sqs;
77	2	nucleotide = nuc;
78	2	scoreModel = modelName;
79	2	similarityParams = params;
80		}
81
82		/**
83		* Performs the PCA calculation (in the same thread) and extracts result data
84		* needed for visualisation by PCAPanel
85		*/
		100% Uncovered Elements: 0 (16) Complexity: 4 Complexity Density: 0.33
86	1	public void calculate()...
87		{
88	1	pca = new PCA(inputData, scoreModel, similarityParams);
89	1	pca.run(); // executes in same thread, wait for completion
90
91		// Now find the component coordinates
92	1	int ii = 0;
93
94	16	while ((ii < seqs.length) && (seqs[ii] != null))
95		{
96	15	ii++;
97		}
98
99	1	int height = pca.getHeight();
100		// top = pca.getM().height() - 1;
101	1	top = height - 1;
102
103	1	points = new Vector<>();
104	1	Point[] scores = pca.getComponents(top - 1, top - 2, top - 3, 1);
105
106	16	for (int i = 0; i < height; i++)
107		{
108	15	SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
109	15	points.add(sp);
110		}
111		}
112
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
113	1	public void updateRc(RotatableCanvasI rc)...
114		{
115	1	rc.setPoints(points, pca.getHeight());
116		}
117
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
118	0	public boolean isNucleotide()...
119		{
120	0	return nucleotide;
121		}
122
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
123	0	public void setNucleotide(boolean nucleotide)...
124		{
125	0	this.nucleotide = nucleotide;
126		}
127
128		/**
129		* Answers the index of the principal dimension of the PCA
130		*
131		* @return
132		*/
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
133	1	public int getTop()...
134		{
135	1	return top;
136		}
137
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
138	1	public void setTop(int t)...
139		{
140	1	top = t;
141		}
142
143		/**
144		* Updates the 3D coordinates for the list of points to the given dimensions.
145		* Principal dimension is getTop(). Next greatest eigenvector is getTop()-1.
146		* Note - pca.getComponents starts counting the spectrum from rank-2 to zero,
147		* rather than rank-1, so getComponents(dimN ...) == updateRcView(dimN+1 ..)
148		*
149		* @param dim1
150		* @param dim2
151		* @param dim3
152		*/
		0% Uncovered Elements: 5 (5) Complexity: 2 Complexity Density: 0.67
153	0	public void updateRcView(int dim1, int dim2, int dim3)...
154		{
155		// note: actual indices for components are dim1-1, etc (patch for JAL-1123)
156	0	Point[] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 1);
157
158	0	for (int i = 0; i < pca.getHeight(); i++)
159		{
160	0	points.get(i).coord = scores[i];
161		}
162		}
163
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
164	0	public String getDetails()...
165		{
166	0	return pca.getDetails();
167		}
168
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
169	0	public AlignmentView getInputData()...
170		{
171	0	return inputData;
172		}
173
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
174	1	public void setInputData(AlignmentView data)...
175		{
176	1	inputData = data;
177		}
178
		0% Uncovered Elements: 36 (36) Complexity: 6 Complexity Density: 0.23
179	0	public String getPointsasCsv(boolean transformed, int xdim, int ydim,...
180		int zdim)
181		{
182	0	StringBuffer csv = new StringBuffer();
183	0	csv.append("\"Sequence\"");
184	0	if (transformed)
185		{
186	0	csv.append(",");
187	0	csv.append(xdim);
188	0	csv.append(",");
189	0	csv.append(ydim);
190	0	csv.append(",");
191	0	csv.append(zdim);
192		}
193		else
194		{
195	0	for (int d = 1, dmax = pca.component(1).length; d <= dmax; d++)
196		{
197	0	csv.append("," + d);
198		}
199		}
200	0	csv.append("\n");
201	0	for (int s = 0; s < seqs.length; s++)
202		{
203	0	csv.append("\"" + seqs[s].getName() + "\"");
204	0	double fl[];
205	0	if (!transformed)
206		{
207		// output pca in correct order
208	0	fl = pca.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
		66.7% Uncovered Elements: 1 (3) Complexity: 2 Complexity Density: 2
227	3	public String getScoreModelName()...
228		{
229	3	return scoreModel == null ? "" : scoreModel.getName();
230		}
231
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
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		*/
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
242	1	public SimilarityParamsI getSimilarityParameters()...
243		{
244	1	return similarityParams;
245		}
246
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
247	1	public List<SequencePoint> getSequencePoints()...
248		{
249	1	return points;
250		}
251
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
252	1	public void setSequencePoints(List<SequencePoint> sp)...
253		{
254	1	points = sp;
255		}
256
257		/**
258		* Answers the object holding the values of the computed PCA
259		*
260		* @return
261		*/
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
262	1	public PCA getPcaData()...
263		{
264	1	return pca;
265		}
266
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
267	1	public void setPCA(PCA data)...
268		{
269	1	pca = data;
270		}
271		}