/**
* Set the selected profile. The profile must already be contained in this profile manager.
*
* @param profile The profile to select
*/
public void setSelected(Profile profile) {
final Profile newSelected = fProfiles.get(profile.getID());
if (newSelected != null && !newSelected.equals(fSelected)) {
fSelected = newSelected;
notifyObservers(SELECTION_CHANGED_EVENT);
}
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
Library library = (Library) o;
if (id != null ? !id.equals(library.id) : library.id != null) return false;
if (name != null ? !name.equals(library.name) : library.name != null) return false;
if (creationTime != null
? !creationTime.equals(library.creationTime)
: library.creationTime != null) return false;
if (profile != null ? !profile.equals(library.profile) : library.profile != null) return false;
return booksCount != null ? booksCount.equals(library.booksCount) : library.booksCount == null;
}
private int processSingle(
SCE_LFE scelfe, FilterBank filterBank, int channel, Profile profile, SampleFrequency sf)
throws AACException {
final ICStream ics = scelfe.getICStream();
final ICSInfo info = ics.getInfo();
final LTPrediction ltp = info.getLTPrediction1();
final int elementID = scelfe.getElementInstanceTag();
// inverse quantization
final float[] iqData = ics.getInvQuantData();
// prediction
if (profile.equals(Profile.AAC_MAIN) && info.isICPredictionPresent())
info.getICPrediction().process(ics, iqData, sf);
if (LTPrediction.isLTPProfile(profile) && info.isLTPrediction1Present())
ltp.process(ics, iqData, filterBank, sf);
// dependent coupling
processDependentCoupling(false, elementID, CCE.BEFORE_TNS, iqData, null);
// TNS
if (ics.isTNSDataPresent()) ics.getTNS().process(ics, iqData, sf, false);
// dependent coupling
processDependentCoupling(false, elementID, CCE.AFTER_TNS, iqData, null);
// filterbank
filterBank.process(
info.getWindowSequence(),
info.getWindowShape(ICSInfo.CURRENT),
info.getWindowShape(ICSInfo.PREVIOUS),
iqData,
data[channel],
channel);
if (LTPrediction.isLTPProfile(profile))
ltp.updateState(data[channel], filterBank.getOverlap(channel), profile);
// dependent coupling
processIndependentCoupling(false, elementID, data[channel], null);
// gain control
if (ics.isGainControlPresent())
ics.getGainControl()
.process(
iqData,
info.getWindowShape(ICSInfo.CURRENT),
info.getWindowShape(ICSInfo.PREVIOUS),
info.getWindowSequence());
// SBR
int chs = 1;
if (sbrPresent && config.isSBREnabled()) {
if (data[channel].length == config.getFrameLength())
LOGGER.log(Level.WARNING, "SBR data present, but buffer has normal size!");
final SBR sbr = scelfe.getSBR();
if (sbr.isPSUsed()) {
chs = 2;
scelfe.getSBR().process(data[channel], data[channel + 1]);
} else scelfe.getSBR().process(data[channel], null);
}
return chs;
}
private void processPair(
CPE cpe, FilterBank filterBank, int channel, Profile profile, SampleFrequency sf)
throws AACException {
final ICStream ics1 = cpe.getLeftChannel();
final ICStream ics2 = cpe.getRightChannel();
final ICSInfo info1 = ics1.getInfo();
final ICSInfo info2 = ics2.getInfo();
final LTPrediction ltp1 = info1.getLTPrediction1();
final LTPrediction ltp2 =
cpe.isCommonWindow() ? info1.getLTPrediction2() : info2.getLTPrediction1();
final int elementID = cpe.getElementInstanceTag();
// inverse quantization
final float[] iqData1 = ics1.getInvQuantData();
final float[] iqData2 = ics2.getInvQuantData();
// MS
if (cpe.isCommonWindow() && cpe.isMSMaskPresent()) MS.process(cpe, iqData1, iqData2);
// main prediction
if (profile.equals(Profile.AAC_MAIN)) {
if (info1.isICPredictionPresent()) info1.getICPrediction().process(ics1, iqData1, sf);
if (info2.isICPredictionPresent()) info2.getICPrediction().process(ics2, iqData2, sf);
}
// IS
IS.process(cpe, iqData1, iqData2);
// LTP
if (LTPrediction.isLTPProfile(profile)) {
if (info1.isLTPrediction1Present()) ltp1.process(ics1, iqData1, filterBank, sf);
if (cpe.isCommonWindow() && info1.isLTPrediction2Present())
ltp2.process(ics2, iqData2, filterBank, sf);
else if (info2.isLTPrediction1Present()) ltp2.process(ics2, iqData2, filterBank, sf);
}
// dependent coupling
processDependentCoupling(true, elementID, CCE.BEFORE_TNS, iqData1, iqData2);
// TNS
if (ics1.isTNSDataPresent()) ics1.getTNS().process(ics1, iqData1, sf, false);
if (ics2.isTNSDataPresent()) ics2.getTNS().process(ics2, iqData2, sf, false);
// dependent coupling
processDependentCoupling(true, elementID, CCE.AFTER_TNS, iqData1, iqData2);
// filterbank
filterBank.process(
info1.getWindowSequence(),
info1.getWindowShape(ICSInfo.CURRENT),
info1.getWindowShape(ICSInfo.PREVIOUS),
iqData1,
data[channel],
channel);
filterBank.process(
info2.getWindowSequence(),
info2.getWindowShape(ICSInfo.CURRENT),
info2.getWindowShape(ICSInfo.PREVIOUS),
iqData2,
data[channel + 1],
channel + 1);
if (LTPrediction.isLTPProfile(profile)) {
ltp1.updateState(data[channel], filterBank.getOverlap(channel), profile);
ltp2.updateState(data[channel + 1], filterBank.getOverlap(channel + 1), profile);
}
// independent coupling
processIndependentCoupling(true, elementID, data[channel], data[channel + 1]);
// gain control
if (ics1.isGainControlPresent())
ics1.getGainControl()
.process(
iqData1,
info1.getWindowShape(ICSInfo.CURRENT),
info1.getWindowShape(ICSInfo.PREVIOUS),
info1.getWindowSequence());
if (ics2.isGainControlPresent())
ics2.getGainControl()
.process(
iqData2,
info2.getWindowShape(ICSInfo.CURRENT),
info2.getWindowShape(ICSInfo.PREVIOUS),
info2.getWindowSequence());
// SBR
if (sbrPresent && config.isSBREnabled()) {
if (data[channel].length == config.getFrameLength())
LOGGER.log(Level.WARNING, "SBR data present, but buffer has normal size!");
cpe.getSBR().process(data[channel], data[channel + 1]);
}
}
private void computeCorrelations() {
log.info("Filling repository with most correlated profiles");
List<String> referenceProfile;
for (AssayType assayType : AssayType.values()) {
referenceProfile = getReferenceProfile(assayType);
List<Profile> profiles = profileRepository.findByAssayType(assayType);
String[] profileNames = new String[profiles.size()];
double[][] distanceMatrix = new double[profiles.size()][profiles.size()];
int i = 0;
for (Profile profileA : profiles) {
profileNames[i] = profileA.getId().toString();
Double maxPearson = Double.MIN_VALUE;
Profile maxProfile = profileA;
int j = 0;
for (Profile profileB : profiles) {
if (profileA.equals(profileB)) {
distanceMatrix[i][j] = 0;
j++;
continue;
}
double[] vectorA = profileA.getVector();
double[] vectorB = profileB.getVector();
PearsonsCorrelation pearson = new PearsonsCorrelation();
Double pearsonCorrelation = pearson.correlation(vectorA, vectorB);
if (pearsonCorrelation >= maxPearson) {
maxPearson = pearsonCorrelation;
maxProfile = profileB;
}
double[] profileAasDouble = UtilsTransform.intArrayToDouble(profileA.getColors());
double[] profileBasDouble = UtilsTransform.intArrayToDouble(profileB.getColors());
Double pearsonOfColors = pearson.correlation(profileAasDouble, profileBasDouble);
distanceMatrix[i][j] = pearsonOfColors;
j++;
}
profileA.setCorrelatedVector(maxProfile.getListWrapper());
SortedSet<StringDouble> positivePeptides =
UtilsStatistics.influentialPeptides(
profileA.getVector(), maxProfile.getVector(), referenceProfile, true);
profileA.setPositivePeptides(UtilsTransform.SortedSetToHTML(positivePeptides, false));
DecimalFormat df = new DecimalFormat("0.0000");
String peptideCorrelation = " <br/><br/><b style=\"color: #23527c;\">%s</b>";
profileA.setPositiveCorrelation(
maxProfile.toString() + String.format(peptideCorrelation, df.format(maxPearson)));
profileRepository.save(profileA);
i++;
}
}
}
