Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
TreeBuilder	36	44	14	0.00%	Show methods

Class TreeBuilder

Class TreeBuilder	Line # 36	Total Statements 44	Complexity 14	TOTAL Coverage 0.00%
TreeBuilder(AlignmentViewport,ScoreModelI,SimilarityParamsI)   TreeBuilder(AlignmentViewport,ScoreModelI,SimilarityParamsI)	5555	12.012	2.02	0.0 0.00%
getSequences() : SequenceI[]   getSequences() : SequenceI[]	8181	1.01	1.01	0.0 0.00%
hasDistances() : boolean   hasDistances() : boolean	9090	1.01	1.01	0.0 0.00%
hasBootstrap() : boolean   hasBootstrap() : boolean	9999	1.01	1.01	0.0 0.00%
hasRootDistance() : boolean   hasRootDistance() : boolean	104104	1.01	1.01	0.0 0.00%
computeTree(ScoreModelI,SimilarityParamsI) : void   computeTree(ScoreModelI,SimilarityParamsI) : void	122122	4.04	1.01	0.0 0.00%
init(AlignmentView,int,int) : void   init(AlignmentView,int,int) : void	133133	14.014	4.04	0.0 0.00%
makeLeaves() : void   makeLeaves() : void	171171	9.09	2.02	0.0 0.00%
getOriginalData() : AlignmentView   getOriginalData() : AlignmentView	188188	1.01	1.01	0.0 0.00%

 

Contributing tests

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;
22
23		import jalview.api.analysis.ScoreModelI;
24		import jalview.api.analysis.SimilarityParamsI;
25		import jalview.datamodel.AlignmentView;
26		import jalview.datamodel.BinaryNode;
27		import jalview.datamodel.CigarArray;
28		import jalview.datamodel.SeqCigar;
29		import jalview.datamodel.SequenceI;
30		import jalview.datamodel.SequenceNode;
31		import jalview.viewmodel.AlignmentViewport;
32
33		import java.util.BitSet;
34		import java.util.Vector;
35
		0% Uncovered Elements: 63 (63) Complexity: 14 Complexity Density: 0.32
36		public abstract class TreeBuilder extends TreeEngine
37		{
38		public static final String AVERAGE_DISTANCE = "AV";
39
40		public static final String NEIGHBOUR_JOINING = "NJ";
41
42		protected SequenceI[] sequences;
43
44		public AlignmentView seqData;
45
46		private AlignmentView seqStrings;
47
48		/**
49		* Constructor
50		*
51		* @param av
52		* @param sm
53		* @param scoreParameters
54		*/
		0% Uncovered Elements: 14 (14) Complexity: 2 Complexity Density: 0.17
55	0	public TreeBuilder(AlignmentViewport av, ScoreModelI sm,...
56		SimilarityParamsI scoreParameters)
57		{
58	0	int start, end;
59	0	boolean selview = av.getSelectionGroup() != null
60		&& av.getSelectionGroup().getSize() > 1;
61	0	seqStrings = av.getAlignmentView(selview);
62	0	if (!selview)
63		{
64	0	start = 0;
65	0	end = av.getAlignment().getWidth();
66	0	this.sequences = av.getAlignment().getSequencesArray();
67		}
68		else
69		{
70	0	start = av.getSelectionGroup().getStartRes();
71	0	end = av.getSelectionGroup().getEndRes() + 1;
72	0	this.sequences = av.getSelectionGroup()
73		.getSequencesInOrder(av.getAlignment());
74		}
75
76	0	init(seqStrings, start, end);
77
78	0	computeTree(sm, scoreParameters);
79		}
80
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
81	0	public SequenceI[] getSequences()...
82		{
83	0	return sequences;
84		}
85
86		/**
87		*
88		* @return true if tree has real distances
89		*/
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
90	0	public boolean hasDistances()...
91		{
92	0	return true;
93		}
94
95		/**
96		*
97		* @return true if tree has real bootstrap values
98		*/
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
99	0	public boolean hasBootstrap()...
100		{
101	0	return false;
102		}
103
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
104	0	public boolean hasRootDistance()...
105		{
106	0	return true;
107		}
108
109		/**
110		* Calculates the tree using the given score model and parameters, and the
111		* configured tree type
112		* <p>
113		* If the score model computes pairwise distance scores, then these are used
114		* directly to derive the tree
115		* <p>
116		* If the score model computes similarity scores, then the range of the scores
117		* is reversed to give a distance measure, and this is used to derive the tree
118		*
119		* @param sm
120		* @param scoreOptions
121		*/
		0% Uncovered Elements: 4 (4) Complexity: 1 Complexity Density: 0.25
122	0	protected void computeTree(ScoreModelI sm, SimilarityParamsI scoreOptions)...
123		{
124	0	distances = sm.findDistances(seqData, scoreOptions);
125
126	0	makeLeaves();
127
128	0	noClus = clusters.size();
129
130	0	cluster();
131		}
132
		0% Uncovered Elements: 20 (20) Complexity: 4 Complexity Density: 0.29
133	0	protected void init(AlignmentView seqView, int start, int end)...
134		{
135	0	this.node = new Vector<BinaryNode>();
136	0	if (seqView != null)
137		{
138	0	this.seqData = seqView;
139		}
140		else
141		{
142	0	SeqCigar[] seqs = new SeqCigar[sequences.length];
143	0	for (int i = 0; i < sequences.length; i++)
144		{
145	0	seqs[i] = new SeqCigar(sequences[i], start, end);
146		}
147	0	CigarArray sdata = new CigarArray(seqs);
148	0	sdata.addOperation(CigarArray.M, end - start + 1);
149	0	this.seqData = new AlignmentView(sdata, start);
150		}
151
152		/*
153		* count the non-null sequences
154		*/
155	0	noseqs = 0;
156
157	0	done = new BitSet();
158
159	0	for (SequenceI seq : sequences)
160		{
161	0	if (seq != null)
162		{
163	0	noseqs++;
164		}
165		}
166		}
167
168		/**
169		* Start by making a cluster for each individual sequence
170		*/
		0% Uncovered Elements: 11 (11) Complexity: 2 Complexity Density: 0.22
171	0	void makeLeaves()...
172		{
173	0	clusters = new Vector<BitSet>();
174
175	0	for (int i = 0; i < noseqs; i++)
176		{
177	0	SequenceNode sn = new SequenceNode();
178
179	0	sn.setElement(sequences[i]);
180	0	sn.setName(sequences[i].getName());
181	0	node.addElement(sn);
182	0	BitSet bs = new BitSet();
183	0	bs.set(i);
184	0	clusters.addElement(bs);
185		}
186		}
187
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
188	0	public AlignmentView getOriginalData()...
189		{
190	0	return seqStrings;
191		}
192
193		}