Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
FeatureDistanceModel	40	55	25	0.8823529588.2%	Show methods

Class FeatureDistanceModel

Class FeatureDistanceModel	Line # 40	Total Statements 55	Complexity 25	TOTAL Coverage 0.8823529588.2%
FeatureDistanceModel()   FeatureDistanceModel()	5151	0.00	1.01	-1.0 -1.0 -
getInstance(AlignmentViewPanel) : ScoreModelI   getInstance(AlignmentViewPanel) : ScoreModelI	5555	8.08	3.03	0.625 0.62562.5%
configureFromAlignmentView(AlignmentViewPanel) : boolean   configureFromAlignmentView(AlignmentViewPanel) : boolean	7575	2.02	1.01	1.0 1.0100%
findDistances(AlignmentView,SimilarityParamsI) : MatrixI   findDistances(AlignmentView,SimilarityParamsI) : MatrixI	9696	30.030	13.013	0.9375 0.937593.8%
findFeatureTypesAtColumn(SeqCigar[],int) : Map<SeqCigar, Set<String>>   findFeatureTypesAtColumn(SeqCigar[],int) : Map<SeqCigar, Set<String>>	191191	10.010	2.02	1.0 1.0100%
getName() : String   getName() : String	216216	1.01	1.01	1.0 1.0100%
getDescription() : String   getDescription() : String	222222	1.01	1.01	0.0 0.00%
isDNA() : boolean   isDNA() : boolean	228228	1.01	1.01	1.0 1.0100%
isProtein() : boolean   isProtein() : boolean	234234	1.01	1.01	1.0 1.0100%
toString() : String   toString() : String	240240	1.01	1.01	0.0 0.00%

 

Contributing tests

Contributing tests

Select all

	Test contribution	Test	Result	Actions
	0.8352941	jalview.analysis.scoremodels.FeatureDistanceModelTest.testFindDistances_withParamsjalview.analysis.scoremodels.FeatureDistanceModelTest.testFindDistances_withParams	1PASS
	0.8117647	jalview.analysis.scoremodels.FeatureDistanceModelTest.testFindDistancesjalview.analysis.scoremodels.FeatureDistanceModelTest.testFindDistances	1PASS
	0.8117647	jalview.analysis.scoremodels.FeatureDistanceModelTest.testFeatureScoreModel_hiddenFirstColumnjalview.analysis.scoremodels.FeatureDistanceModelTest.testFeatureScoreModel_hiddenFirstColumn	1PASS
	0.8117647	jalview.analysis.scoremodels.FeatureDistanceModelTest.testFeatureScoreModeljalview.analysis.scoremodels.FeatureDistanceModelTest.testFeatureScoreModel	1PASS
	0.8117647	jalview.analysis.scoremodels.FeatureDistanceModelTest.testFeatureScoreModel_HiddenColumnsjalview.analysis.scoremodels.FeatureDistanceModelTest.testFeatureScoreModel_HiddenColumns	1PASS
	0.07058824	jalview.gui.CalculationChooserTest.testGetApplicableScoreModelsjalview.gui.CalculationChooserTest.testGetApplicableScoreModels	1PASS
	0.023529412	jalview.analysis.scoremodels.ScoreModelsTest.testConstructorjalview.analysis.scoremodels.ScoreModelsTest.testConstructor	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.analysis.scoremodels;
22
23		import jalview.api.AlignmentViewPanel;
24		import jalview.api.FeatureRenderer;
25		import jalview.api.analysis.ScoreModelI;
26		import jalview.api.analysis.SimilarityParamsI;
27		import jalview.datamodel.AlignmentView;
28		import jalview.datamodel.SeqCigar;
29		import jalview.datamodel.SequenceFeature;
30		import jalview.math.Matrix;
31		import jalview.math.MatrixI;
32		import jalview.util.SetUtils;
33
34		import java.util.HashMap;
35		import java.util.HashSet;
36		import java.util.List;
37		import java.util.Map;
38		import java.util.Set;
39
		88.2% Uncovered Elements: 10 (85) Complexity: 25 Complexity Density: 0.45
40		public class FeatureDistanceModel extends DistanceScoreModel
41		{
42		private static final String NAME = "Sequence Feature Similarity";
43
44		private String description;
45
46		FeatureRenderer fr;
47
48		/**
49		* Constructor
50		*/
		- Uncovered Elements: 0 (0) Complexity: 1 Complexity Density: -
51	89	public FeatureDistanceModel()...
52		{
53		}
54
		62.5% Uncovered Elements: 3 (8) Complexity: 3 Complexity Density: 0.38
55	5	@Override...
56		public ScoreModelI getInstance(AlignmentViewPanel view)
57		{
58	5	FeatureDistanceModel instance;
59	5	try
60		{
61	5	instance = this.getClass().getDeclaredConstructor().newInstance();
62	5	instance.configureFromAlignmentView(view);
63	5	return instance;
64		} catch (InstantiationException | IllegalAccessException e)
65		{
66	0	jalview.bin.Console.errPrintln("Error in " + getClass().getName()
67		+ ".getInstance(): " + e.getMessage());
68	0	return null;
69		} catch (ReflectiveOperationException roe)
70		{
71	0	return null;
72		}
73		}
74
		100% Uncovered Elements: 0 (2) Complexity: 1 Complexity Density: 0.5
75	5	boolean configureFromAlignmentView(AlignmentViewPanel view)...
76
77		{
78	5	fr = view.cloneFeatureRenderer();
79	5	return true;
80		}
81
82		/**
83		* Calculates a distance measure [i][j] between each pair of sequences as the
84		* average number of features they have but do not share. That is, find the
85		* features each sequence pair has at each column, ignore feature types they
86		* have in common, and count the rest. The totals are normalised by the number
87		* of columns processed.
88		* <p>
89		* The parameters argument provides settings for treatment of gap-residue
90		* aligned positions, and whether the score is over the longer or shorter of
91		* each pair of sequences
92		*
93		* @param seqData
94		* @param params
95		*/
		93.8% Uncovered Elements: 3 (48) Complexity: 13 Complexity Density: 0.43
96	6	@Override...
97		public MatrixI findDistances(AlignmentView seqData,
98		SimilarityParamsI params)
99		{
100	6	SeqCigar[] seqs = seqData.getSequences();
101	6	int noseqs = seqs.length;
102	6	int cpwidth = 0;// = seqData.getWidth();
103	6	double[][] distances = new double[noseqs][noseqs];
104	6	List<String> dft = null;
105	6	if (fr != null)
106		{
107	6	dft = fr.getDisplayedFeatureTypes();
108		}
109	6	if (dft == null || dft.isEmpty())
110		{
111	0	return new Matrix(distances);
112		}
113
114		// need to get real position for view position
115	6	int[] viscont = seqData.getVisibleContigs();
116
117		/*
118		* scan each column, compute and add to each distance[i, j]
119		* the number of feature types that seqi and seqj do not share
120		*/
121	12	for (int vc = 0; vc < viscont.length; vc += 2)
122		{
123	32	for (int cpos = viscont[vc]; cpos <= viscont[vc + 1]; cpos++)
124		{
125	26	cpwidth++;
126
127		/*
128		* first record feature types in this column for each sequence
129		*/
130	26	Map<SeqCigar, Set<String>> sfap = findFeatureTypesAtColumn(seqs,
131		cpos);
132
133		/*
134		* count feature types on either i'th or j'th sequence but not both
135		* and add this 'distance' measure to the total for [i, j] for j > i
136		*/
137	70	for (int i = 0; i < (noseqs - 1); i++)
138		{
139	115	for (int j = i + 1; j < noseqs; j++)
140		{
141	71	SeqCigar sc1 = seqs[i];
142	71	SeqCigar sc2 = seqs[j];
143	71	Set<String> set1 = sfap.get(sc1);
144	71	Set<String> set2 = sfap.get(sc2);
145	71	boolean gap1 = set1 == null;
146	71	boolean gap2 = set2 == null;
147
148		/*
149		* gap-gap always scores zero
150		* residue-residue is always scored
151		* include gap-residue score if params say to do so
152		*/
153	71	if ((!gap1 && !gap2) || params.includeGaps())
154		{
155	67	int seqDistance = SetUtils.countDisjunction(set1, set2);
156	67	distances[i][j] += seqDistance;
157		}
158		}
159		}
160		}
161		}
162
163		/*
164		* normalise the distance scores (summed over columns) by the
165		* number of visible columns used in the calculation
166		* and fill in the bottom half of the matrix
167		*/
168		// TODO JAL-2424 cpwidth may be out by 1 - affects scores but not tree shape
169	24	for (int i = 0; i < noseqs; i++)
170		{
171	39	for (int j = i + 1; j < noseqs; j++)
172		{
173	21	distances[i][j] /= cpwidth;
174	21	distances[j][i] = distances[i][j];
175		}
176		}
177	6	return new Matrix(distances);
178		}
179
180		/**
181		* Builds and returns a map containing a (possibly empty) list (one per
182		* SeqCigar) of visible feature types at the given column position. The map
183		* does not include entries for features which straddle a gapped column
184		* positions.
185		*
186		* @param seqs
187		* @param columnPosition
188		* (0..)
189		* @return
190		*/
		100% Uncovered Elements: 0 (12) Complexity: 2 Complexity Density: 0.2
191	26	protected Map<SeqCigar, Set<String>> findFeatureTypesAtColumn(...
192		SeqCigar[] seqs, int columnPosition)
193		{
194	26	Map<SeqCigar, Set<String>> sfap = new HashMap<>();
195	26	for (SeqCigar seq : seqs)
196		{
197	70	int spos = seq.findPosition(columnPosition);
198	70	if (spos != -1)
199		{
200		/*
201		* position is not a gap
202		*/
203	60	Set<String> types = new HashSet<>();
204	60	List<SequenceFeature> sfs = fr
205		.findFeaturesAtResidue(seq.getRefSeq(), spos, spos);
206	60	for (SequenceFeature sf : sfs)
207		{
208	70	types.add(sf.getType());
209		}
210	60	sfap.put(seq, types);
211		}
212		}
213	26	return sfap;
214		}
215
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
216	170	@Override...
217		public String getName()
218		{
219	170	return NAME;
220		}
221
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
222	0	@Override...
223		public String getDescription()
224		{
225	0	return description;
226		}
227
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
228	4	@Override...
229		public boolean isDNA()
230		{
231	4	return true;
232		}
233
		100% Uncovered Elements: 0 (1) Complexity: 1 Complexity Density: 1
234	2	@Override...
235		public boolean isProtein()
236		{
237	2	return true;
238		}
239
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
240	0	@Override...
241		public String toString()
242		{
243	0	return "Score between sequences based on hamming distance between binary vectors marking features displayed at each column";
244		}
245		}