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Coverage Report

  1. Project Clover database Thu Aug 13 2020 12:04:21 BST
  2. Package jalview.analysis

File AAFrequency.java

 

Coverage histogram

../../img/srcFileCovDistChart9.png
12% of files have more coverage

Code metrics

104
227
13
1
752
441
77
0.34
17.46
13
5.92

Classes

Class Line # Actions
AAFrequency 54 227 77
0.87587.5%
 

Contributing tests

This file is covered by 102 tests. .

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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.datamodel.AlignedCodonFrame;
24    import jalview.datamodel.AlignmentAnnotation;
25    import jalview.datamodel.AlignmentI;
26    import jalview.datamodel.Annotation;
27    import jalview.datamodel.Profile;
28    import jalview.datamodel.ProfileI;
29    import jalview.datamodel.Profiles;
30    import jalview.datamodel.ProfilesI;
31    import jalview.datamodel.ResidueCount;
32    import jalview.datamodel.ResidueCount.SymbolCounts;
33    import jalview.datamodel.SequenceI;
34    import jalview.ext.android.SparseIntArray;
35    import jalview.util.Comparison;
36    import jalview.util.Format;
37    import jalview.util.MappingUtils;
38    import jalview.util.QuickSort;
39   
40    import java.awt.Color;
41    import java.util.Arrays;
42    import java.util.Hashtable;
43    import java.util.List;
44   
45    /**
46    * Takes in a vector or array of sequences and column start and column end and
47    * returns a new Hashtable[] of size maxSeqLength, if Hashtable not supplied.
48    * This class is used extensively in calculating alignment colourschemes that
49    * depend on the amount of conservation in each alignment column.
50    *
51    * @author $author$
52    * @version $Revision$
53    */
 
54    public class AAFrequency
55    {
56    public static final String PROFILE = "P";
57   
58    /*
59    * Quick look-up of String value of char 'A' to 'Z'
60    */
61    private static final String[] CHARS = new String['Z' - 'A' + 1];
62   
 
63  18 toggle static
64    {
65  486 for (char c = 'A'; c <= 'Z'; c++)
66    {
67  468 CHARS[c - 'A'] = String.valueOf(c);
68    }
69    }
70   
 
71  3 toggle public static final ProfilesI calculate(List<SequenceI> list, int start,
72    int end)
73    {
74  3 return calculate(list, start, end, false);
75    }
76   
 
77  357 toggle public static final ProfilesI calculate(List<SequenceI> sequences,
78    int start, int end, boolean profile)
79    {
80  357 SequenceI[] seqs = new SequenceI[sequences.size()];
81  357 int width = 0;
82  357 synchronized (sequences)
83    {
84  3031 for (int i = 0; i < sequences.size(); i++)
85    {
86  2674 seqs[i] = sequences.get(i);
87  2674 int length = seqs[i].getLength();
88  2674 if (length > width)
89    {
90  356 width = length;
91    }
92    }
93   
94  357 if (end >= width)
95    {
96  209 end = width;
97    }
98   
99  357 ProfilesI reply = calculate(seqs, width, start, end, profile);
100  357 return reply;
101    }
102    }
103   
104    /**
105    * Calculate the consensus symbol(s) for each column in the given range.
106    *
107    * @param sequences
108    * @param width
109    * the full width of the alignment
110    * @param start
111    * start column (inclusive, base zero)
112    * @param end
113    * end column (exclusive)
114    * @param saveFullProfile
115    * if true, store all symbol counts
116    */
 
117  820 toggle public static final ProfilesI calculate(final SequenceI[] sequences,
118    int width, int start, int end, boolean saveFullProfile)
119    {
120    // long now = System.currentTimeMillis();
121  820 int seqCount = sequences.length;
122  820 boolean nucleotide = false;
123  820 int nucleotideCount = 0;
124  820 int peptideCount = 0;
125   
126  820 ProfileI[] result = new ProfileI[width];
127   
128  519296 for (int column = start; column < end; column++)
129    {
130    /*
131    * Apply a heuristic to detect nucleotide data (which can
132    * be counted in more compact arrays); here we test for
133    * more than 90% nucleotide; recheck every 10 columns in case
134    * of misleading data e.g. highly conserved Alanine in peptide!
135    * Mistakenly guessing nucleotide has a small performance cost,
136    * as it will result in counting in sparse arrays.
137    * Mistakenly guessing peptide has a small space cost,
138    * as it will use a larger than necessary array to hold counts.
139    */
140  518527 if (nucleotideCount > 100 && column % 10 == 0)
141    {
142  46962 nucleotide = (9 * peptideCount < nucleotideCount);
143    }
144  518529 ResidueCount residueCounts = new ResidueCount(nucleotide);
145   
146  9887476 for (int row = 0; row < seqCount; row++)
147    {
148  9339777 if (sequences[row] == null)
149    {
150  0 System.err.println(
151    "WARNING: Consensus skipping null sequence - possible race condition.");
152  0 continue;
153    }
154  9201147 if (sequences[row].getLength() > column)
155    {
156  9258157 char c = sequences[row].getCharAt(column);
157  9267848 residueCounts.add(c);
158  9507282 if (Comparison.isNucleotide(c))
159    {
160  801724 nucleotideCount++;
161    }
162  8647552 else if (!Comparison.isGap(c))
163    {
164  592450 peptideCount++;
165    }
166    }
167    else
168    {
169    /*
170    * count a gap if the sequence doesn't reach this column
171    */
172  29635 residueCounts.addGap();
173    }
174    }
175   
176  514252 int maxCount = residueCounts.getModalCount();
177  514231 String maxResidue = residueCounts.getResiduesForCount(maxCount);
178  518407 int gapCount = residueCounts.getGapCount();
179  518408 ProfileI profile = new Profile(seqCount, gapCount, maxCount,
180    maxResidue);
181   
182  517849 if (saveFullProfile)
183    {
184  500166 profile.setCounts(residueCounts);
185    }
186   
187  517570 result[column] = profile;
188    }
189  820 return new Profiles(result);
190    // long elapsed = System.currentTimeMillis() - now;
191    // System.out.println(elapsed);
192    }
193   
194    /**
195    * Make an estimate of the profile size we are going to compute i.e. how many
196    * different characters may be present in it. Overestimating has a cost of
197    * using more memory than necessary. Underestimating has a cost of needing to
198    * extend the SparseIntArray holding the profile counts.
199    *
200    * @param profileSizes
201    * counts of sizes of profiles so far encountered
202    * @return
203    */
 
204  0 toggle static int estimateProfileSize(SparseIntArray profileSizes)
205    {
206  0 if (profileSizes.size() == 0)
207    {
208  0 return 4;
209    }
210   
211    /*
212    * could do a statistical heuristic here e.g. 75%ile
213    * for now just return the largest value
214    */
215  0 return profileSizes.keyAt(profileSizes.size() - 1);
216    }
217   
218    /**
219    * Derive the consensus annotations to be added to the alignment for display.
220    * This does not recompute the raw data, but may be called on a change in
221    * display options, such as 'ignore gaps', which may in turn result in a
222    * change in the derived values.
223    *
224    * @param consensus
225    * the annotation row to add annotations to
226    * @param profiles
227    * the source consensus data
228    * @param startCol
229    * start column (inclusive)
230    * @param endCol
231    * end column (exclusive)
232    * @param ignoreGaps
233    * if true, normalise residue percentages ignoring gaps
234    * @param showSequenceLogo
235    * if true include all consensus symbols, else just show modal
236    * residue
237    * @param nseq
238    * number of sequences
239    */
 
240  595 toggle public static void completeConsensus(AlignmentAnnotation consensus,
241    ProfilesI profiles, int startCol, int endCol, boolean ignoreGaps,
242    boolean showSequenceLogo, long nseq)
243    {
244    // long now = System.currentTimeMillis();
245  595 if (consensus == null || consensus.annotations == null
246    || consensus.annotations.length < endCol)
247    {
248    /*
249    * called with a bad alignment annotation row
250    * wait for it to be initialised properly
251    */
252  0 return;
253    }
254   
255  500773 for (int i = startCol; i < endCol; i++)
256    {
257  500087 ProfileI profile = profiles.get(i);
258  499877 if (profile == null)
259    {
260    /*
261    * happens if sequences calculated over were
262    * shorter than alignment width
263    */
264  0 consensus.annotations[i] = null;
265  0 return;
266    }
267   
268  499756 final int dp = getPercentageDp(nseq);
269   
270  498782 float value = profile.getPercentageIdentity(ignoreGaps);
271   
272  498881 String description = getTooltip(profile, value, showSequenceLogo,
273    ignoreGaps, dp);
274   
275  500158 String modalResidue = profile.getModalResidue();
276  499632 if ("".equals(modalResidue))
277    {
278  1107 modalResidue = "-";
279    }
280  498522 else if (modalResidue.length() > 1)
281    {
282  5335 modalResidue = "+";
283    }
284  499551 consensus.annotations[i] = new Annotation(modalResidue, description,
285    ' ', value);
286    }
287    // long elapsed = System.currentTimeMillis() - now;
288    // System.out.println(-elapsed);
289    }
290   
291    /**
292    * Derive the gap count annotation row.
293    *
294    * @param gaprow
295    * the annotation row to add annotations to
296    * @param profiles
297    * the source consensus data
298    * @param startCol
299    * start column (inclusive)
300    * @param endCol
301    * end column (exclusive)
302    */
 
303  471 toggle public static void completeGapAnnot(AlignmentAnnotation gaprow,
304    ProfilesI profiles, int startCol, int endCol, long nseq)
305    {
306  471 if (gaprow == null || gaprow.annotations == null
307    || gaprow.annotations.length < endCol)
308    {
309    /*
310    * called with a bad alignment annotation row
311    * wait for it to be initialised properly
312    */
313  0 return;
314    }
315    // always set ranges again
316  471 gaprow.graphMax = nseq;
317  471 gaprow.graphMin = 0;
318  471 double scale = 0.8 / nseq;
319  481511 for (int i = startCol; i < endCol; i++)
320    {
321  481040 ProfileI profile = profiles.get(i);
322  481040 if (profile == null)
323    {
324    /*
325    * happens if sequences calculated over were
326    * shorter than alignment width
327    */
328  0 gaprow.annotations[i] = null;
329  0 return;
330    }
331   
332  481040 final int gapped = profile.getNonGapped();
333   
334  481040 String description = "" + gapped;
335   
336  481040 gaprow.annotations[i] = new Annotation("", description, '\0', gapped,
337    jalview.util.ColorUtils.bleachColour(Color.DARK_GRAY,
338    (float) scale * gapped));
339    }
340    }
341   
342    /**
343    * Returns a tooltip showing either
344    * <ul>
345    * <li>the full profile (percentages of all residues present), if
346    * showSequenceLogo is true, or</li>
347    * <li>just the modal (most common) residue(s), if showSequenceLogo is
348    * false</li>
349    * </ul>
350    * Percentages are as a fraction of all sequence, or only ungapped sequences
351    * if ignoreGaps is true.
352    *
353    * @param profile
354    * @param pid
355    * @param showSequenceLogo
356    * @param ignoreGaps
357    * @param dp
358    * the number of decimal places to format percentages to
359    * @return
360    */
 
361  498855 toggle static String getTooltip(ProfileI profile, float pid,
362    boolean showSequenceLogo, boolean ignoreGaps, int dp)
363    {
364  498855 ResidueCount counts = profile.getCounts();
365   
366  498763 String description = null;
367  498857 if (counts != null && showSequenceLogo)
368    {
369  33760 int normaliseBy = ignoreGaps ? profile.getNonGapped()
370    : profile.getHeight();
371  33760 description = counts.getTooltip(normaliseBy, dp);
372    }
373    else
374    {
375  465121 StringBuilder sb = new StringBuilder(64);
376  466173 String maxRes = profile.getModalResidue();
377  465469 if (maxRes.length() > 1)
378    {
379  2346 sb.append("[").append(maxRes).append("]");
380    }
381    else
382    {
383  463386 sb.append(maxRes);
384    }
385  465831 if (maxRes.length() > 0)
386    {
387  465296 sb.append(" ");
388  465559 Format.appendPercentage(sb, pid, dp);
389  465795 sb.append("%");
390    }
391  466302 description = sb.toString();
392    }
393  500180 return description;
394    }
395   
396    /**
397    * Returns the sorted profile for the given consensus data. The returned array
398    * contains
399    *
400    * <pre>
401    * [profileType, numberOfValues, totalPercent, charValue1, percentage1, charValue2, percentage2, ...]
402    * in descending order of percentage value
403    * </pre>
404    *
405    * @param profile
406    * the data object from which to extract and sort values
407    * @param ignoreGaps
408    * if true, only non-gapped values are included in percentage
409    * calculations
410    * @return
411    */
 
412  32922 toggle public static int[] extractProfile(ProfileI profile, boolean ignoreGaps)
413    {
414  32922 ResidueCount counts = profile.getCounts();
415  32922 if (counts == null)
416    {
417  0 return null;
418    }
419   
420  32922 SymbolCounts symbolCounts = counts.getSymbolCounts();
421  32922 char[] symbols = symbolCounts.symbols;
422  32922 int[] values = symbolCounts.values;
423  32922 QuickSort.sort(values, symbols);
424  32922 int totalPercentage = 0;
425  32922 final int divisor = ignoreGaps ? profile.getNonGapped()
426    : profile.getHeight();
427   
428    /*
429    * traverse the arrays in reverse order (highest counts first)
430    */
431  32922 int[] result = new int[3 + 2 * symbols.length];
432  32922 int nextArrayPos = 3;
433  32922 int nonZeroCount = 0;
434   
435  84303 for (int i = symbols.length - 1; i >= 0; i--)
436    {
437  51383 int theChar = symbols[i];
438  51383 int charCount = values[i];
439  51383 final int percentage = (charCount * 100) / divisor;
440  51383 if (percentage == 0)
441    {
442    /*
443    * this count (and any remaining) round down to 0% - discard
444    */
445  2 break;
446    }
447  51381 nonZeroCount++;
448  51381 result[nextArrayPos++] = theChar;
449  51381 result[nextArrayPos++] = percentage;
450  51381 totalPercentage += percentage;
451    }
452   
453    /*
454    * truncate array if any zero values were discarded
455    */
456  32922 if (nonZeroCount < symbols.length)
457    {
458  2 int[] tmp = new int[3 + 2 * nonZeroCount];
459  2 System.arraycopy(result, 0, tmp, 0, tmp.length);
460  2 result = tmp;
461    }
462   
463    /*
464    * fill in 'header' values
465    */
466  32922 result[0] = AlignmentAnnotation.SEQUENCE_PROFILE;
467  32922 result[1] = nonZeroCount;
468  32922 result[2] = totalPercentage;
469   
470  32922 return result;
471    }
472   
473    /**
474    * Extract a sorted extract of cDNA codon profile data. The returned array
475    * contains
476    *
477    * <pre>
478    * [profileType, numberOfValues, totalPercentage, charValue1, percentage1, charValue2, percentage2, ...]
479    * in descending order of percentage value, where the character values encode codon triplets
480    * </pre>
481    *
482    * @param hashtable
483    * @return
484    */
 
485  2 toggle public static int[] extractCdnaProfile(
486    Hashtable<String, Object> hashtable,
487    boolean ignoreGaps)
488    {
489    // this holds #seqs, #ungapped, and then codon count, indexed by encoded
490    // codon triplet
491  2 int[] codonCounts = (int[]) hashtable.get(PROFILE);
492  2 int[] sortedCounts = new int[codonCounts.length - 2];
493  2 System.arraycopy(codonCounts, 2, sortedCounts, 0,
494    codonCounts.length - 2);
495   
496  2 int[] result = new int[3 + 2 * sortedCounts.length];
497    // first value is just the type of profile data
498  2 result[0] = AlignmentAnnotation.CDNA_PROFILE;
499   
500  2 char[] codons = new char[sortedCounts.length];
501  130 for (int i = 0; i < codons.length; i++)
502    {
503  128 codons[i] = (char) i;
504    }
505  2 QuickSort.sort(sortedCounts, codons);
506  2 int totalPercentage = 0;
507  2 int distinctValuesCount = 0;
508  2 int j = 3;
509  2 int divisor = ignoreGaps ? codonCounts[1] : codonCounts[0];
510  8 for (int i = codons.length - 1; i >= 0; i--)
511    {
512  8 final int codonCount = sortedCounts[i];
513  8 if (codonCount == 0)
514    {
515  0 break; // nothing else of interest here
516    }
517  8 final int percentage = codonCount * 100 / divisor;
518  8 if (percentage == 0)
519    {
520    /*
521    * this (and any remaining) values rounded down to 0 - discard
522    */
523  2 break;
524    }
525  6 distinctValuesCount++;
526  6 result[j++] = codons[i];
527  6 result[j++] = percentage;
528  6 totalPercentage += percentage;
529    }
530  2 result[2] = totalPercentage;
531   
532    /*
533    * Just return the non-zero values
534    */
535    // todo next value is redundant if we limit the array to non-zero counts
536  2 result[1] = distinctValuesCount;
537  2 return Arrays.copyOfRange(result, 0, j);
538    }
539   
540    /**
541    * Compute a consensus for the cDNA coding for a protein alignment.
542    *
543    * @param alignment
544    * the protein alignment (which should hold mappings to cDNA
545    * sequences)
546    * @param hconsensus
547    * the consensus data stores to be populated (one per column)
548    */
 
549  4 toggle public static void calculateCdna(AlignmentI alignment,
550    Hashtable<String, Object>[] hconsensus)
551    {
552  4 final char gapCharacter = alignment.getGapCharacter();
553  4 List<AlignedCodonFrame> mappings = alignment.getCodonFrames();
554  4 if (mappings == null || mappings.isEmpty())
555    {
556  0 return;
557    }
558   
559  4 int cols = alignment.getWidth();
560  1928 for (int col = 0; col < cols; col++)
561    {
562    // todo would prefer a Java bean for consensus data
563  1924 Hashtable<String, Object> columnHash = new Hashtable<>();
564    // #seqs, #ungapped seqs, counts indexed by (codon encoded + 1)
565  1924 int[] codonCounts = new int[66];
566  1924 codonCounts[0] = alignment.getSequences().size();
567  1924 int ungappedCount = 0;
568  1924 for (SequenceI seq : alignment.getSequences())
569    {
570  20870 if (seq.getCharAt(col) == gapCharacter)
571    {
572  10166 continue;
573    }
574  10704 List<char[]> codons = MappingUtils.findCodonsFor(seq, col,
575    mappings);
576  10704 for (char[] codon : codons)
577    {
578  10657 int codonEncoded = CodingUtils.encodeCodon(codon);
579  10657 if (codonEncoded >= 0)
580    {
581  10657 codonCounts[codonEncoded + 2]++;
582  10657 ungappedCount++;
583  10657 break;
584    }
585    }
586    }
587  1924 codonCounts[1] = ungappedCount;
588    // todo: sort values here, save counts and codons?
589  1924 columnHash.put(PROFILE, codonCounts);
590  1924 hconsensus[col] = columnHash;
591    }
592    }
593   
594    /**
595    * Derive displayable cDNA consensus annotation from computed consensus data.
596    *
597    * @param consensusAnnotation
598    * the annotation row to be populated for display
599    * @param consensusData
600    * the computed consensus data
601    * @param showProfileLogo
602    * if true show all symbols present at each position, else only the
603    * modal value
604    * @param nseqs
605    * the number of sequences in the alignment
606    */
 
607  4 toggle public static void completeCdnaConsensus(
608    AlignmentAnnotation consensusAnnotation,
609    Hashtable<String, Object>[] consensusData, boolean showProfileLogo,
610    int nseqs)
611    {
612  4 if (consensusAnnotation == null
613    || consensusAnnotation.annotations == null
614    || consensusAnnotation.annotations.length < consensusData.length)
615    {
616    // called with a bad alignment annotation row - wait for it to be
617    // initialised properly
618  0 return;
619    }
620   
621    // ensure codon triplet scales with font size
622  4 consensusAnnotation.scaleColLabel = true;
623  1928 for (int col = 0; col < consensusData.length; col++)
624    {
625  1924 Hashtable<String, Object> hci = consensusData[col];
626  1924 if (hci == null)
627    {
628    // gapped protein column?
629  0 continue;
630    }
631    // array holds #seqs, #ungapped, then codon counts indexed by codon
632  1924 final int[] codonCounts = (int[]) hci.get(PROFILE);
633  1924 int totalCount = 0;
634   
635    /*
636    * First pass - get total count and find the highest
637    */
638  1924 final char[] codons = new char[codonCounts.length - 2];
639  125060 for (int j = 2; j < codonCounts.length; j++)
640    {
641  123136 final int codonCount = codonCounts[j];
642  123136 codons[j - 2] = (char) (j - 2);
643  123136 totalCount += codonCount;
644    }
645   
646    /*
647    * Sort array of encoded codons by count ascending - so the modal value
648    * goes to the end; start by copying the count (dropping the first value)
649    */
650  1924 int[] sortedCodonCounts = new int[codonCounts.length - 2];
651  1924 System.arraycopy(codonCounts, 2, sortedCodonCounts, 0,
652    codonCounts.length - 2);
653  1924 QuickSort.sort(sortedCodonCounts, codons);
654   
655  1924 int modalCodonEncoded = codons[codons.length - 1];
656  1924 int modalCodonCount = sortedCodonCounts[codons.length - 1];
657  1924 String modalCodon = String
658    .valueOf(CodingUtils.decodeCodon(modalCodonEncoded));
659  1924 if (sortedCodonCounts.length > 1 && sortedCodonCounts[codons.length
660    - 2] == sortedCodonCounts[codons.length - 1])
661    {
662    /*
663    * two or more codons share the modal count
664    */
665  25 modalCodon = "+";
666    }
667  1924 float pid = sortedCodonCounts[sortedCodonCounts.length - 1] * 100
668    / (float) totalCount;
669   
670    /*
671    * todo ? Replace consensus hashtable with sorted arrays of codons and
672    * counts (non-zero only). Include total count in count array [0].
673    */
674   
675    /*
676    * Scan sorted array backwards for most frequent values first. Show
677    * repeated values compactly.
678    */
679  1924 StringBuilder mouseOver = new StringBuilder(32);
680  1924 StringBuilder samePercent = new StringBuilder();
681  1924 String percent = null;
682  1924 String lastPercent = null;
683  1924 int percentDecPl = getPercentageDp(nseqs);
684   
685  3823 for (int j = codons.length - 1; j >= 0; j--)
686    {
687  3823 int codonCount = sortedCodonCounts[j];
688  3823 if (codonCount == 0)
689    {
690    /*
691    * remaining codons are 0% - ignore, but finish off the last one if
692    * necessary
693    */
694  1924 if (samePercent.length() > 0)
695    {
696  1899 mouseOver.append(samePercent).append(": ").append(percent)
697    .append("% ");
698    }
699  1924 break;
700    }
701  1899 int codonEncoded = codons[j];
702  1899 final int pct = codonCount * 100 / totalCount;
703  1899 String codon = String
704    .valueOf(CodingUtils.decodeCodon(codonEncoded));
705  1899 StringBuilder sb = new StringBuilder();
706  1899 Format.appendPercentage(sb, pct, percentDecPl);
707  1899 percent = sb.toString();
708  1899 if (showProfileLogo || codonCount == modalCodonCount)
709    {
710  1899 if (percent.equals(lastPercent) && j > 0)
711    {
712  0 samePercent.append(samePercent.length() == 0 ? "" : ", ");
713  0 samePercent.append(codon);
714    }
715    else
716    {
717  1899 if (samePercent.length() > 0)
718    {
719  0 mouseOver.append(samePercent).append(": ").append(lastPercent)
720    .append("% ");
721    }
722  1899 samePercent.setLength(0);
723  1899 samePercent.append(codon);
724    }
725  1899 lastPercent = percent;
726    }
727    }
728   
729  1924 consensusAnnotation.annotations[col] = new Annotation(modalCodon,
730    mouseOver.toString(), ' ', pid);
731    }
732    }
733   
734    /**
735    * Returns the number of decimal places to show for profile percentages. For
736    * less than 100 sequences, returns zero (the integer percentage value will be
737    * displayed). For 100-999 sequences, returns 1, for 1000-9999 returns 2, etc.
738    *
739    * @param nseq
740    * @return
741    */
 
742  501661 toggle protected static int getPercentageDp(long nseq)
743    {
744  501664 int scale = 0;
745  501689 while (nseq >= 100)
746    {
747  0 scale++;
748  0 nseq /= 10;
749    }
750  501702 return scale;
751    }
752    }
  • Report generated by OpenClover v 4.4.1 on Thu Aug 13 2020 12:06:48 BST using coverage data from Thu Aug 13 2020 12:06:40 BST.
  • OpenClover is free and open-source software.