Tjcampdx Spectrometer::getBlackBodySpec(double max, double T){
QVector<double> lambda = pixToWavelength(pixNum);
QVector<double> intens;
intens.resize(lambda.size());
for(int i=0;i<lambda.size();i++){
intens[i]=blackBodyIntensNorm(lambda[i]*1e-9,T)*max;
}
Tjcampdx spectrum;
spectrum.LoadData(lambda.toStdVector(),intens.toStdVector());
return spectrum;
}
void IqtFit::plotGuess(QtProperty *) {
// Do nothing if there is no sample data curve
if (!m_uiForm.ppPlot->hasCurve("Sample"))
return;
CompositeFunction_sptr function = createFunction(true);
// Create the double* array from the input workspace
const size_t binIndxLow =
m_ffInputWS->binIndexOf(m_ffRangeManager->value(m_properties["StartX"]));
const size_t binIndxHigh =
m_ffInputWS->binIndexOf(m_ffRangeManager->value(m_properties["EndX"]));
const size_t nData = binIndxHigh - binIndxLow;
std::vector<double> inputXData(nData);
const Mantid::MantidVec &XValues = m_ffInputWS->readX(0);
const bool isHistogram = m_ffInputWS->isHistogramData();
for (size_t i = 0; i < nData; i++) {
if (isHistogram)
inputXData[i] =
0.5 * (XValues[binIndxLow + i] + XValues[binIndxLow + i + 1]);
else
inputXData[i] = XValues[binIndxLow + i];
}
FunctionDomain1DVector domain(inputXData);
FunctionValues outputData(domain);
function->function(domain, outputData);
QVector<double> dataX;
QVector<double> dataY;
for (size_t i = 0; i < nData; i++) {
dataX.append(inputXData[i]);
dataY.append(outputData.getCalculated(i));
}
IAlgorithm_sptr createWsAlg =
AlgorithmManager::Instance().create("CreateWorkspace");
createWsAlg->initialize();
createWsAlg->setChild(true);
createWsAlg->setLogging(false);
createWsAlg->setProperty("OutputWorkspace", "__GuessAnon");
createWsAlg->setProperty("NSpec", 1);
createWsAlg->setProperty("DataX", dataX.toStdVector());
createWsAlg->setProperty("DataY", dataY.toStdVector());
createWsAlg->execute();
MatrixWorkspace_sptr guessWs = createWsAlg->getProperty("OutputWorkspace");
m_uiForm.ppPlot->addSpectrum("Guess", guessWs, 0, Qt::green);
}
void Model::setSegmentDefinition(int id, const QVector<int>& markerIds, const QVector<Pair>& links, const QVector<Triad>& faces)
{
QMap<int,Segment*>::iterator it = this->m_Segments.find(id);
if (it != this->m_Segments.end())
{
(*it)->markerIds = markerIds;
(*it)->links = links;
(*it)->faces = faces;
(*it)->mesh->SetDefinition(markerIds.toStdVector(),links.toStdVector(),faces.toStdVector());
emit segmentDefinitionChanged(id);
}
};
void Viewport::setAppliedIlluminant(QVector<multi_img_base::Value> illum)
{
//bool change = (applied != illuminant_apply);
illuminantAppl = illum.toStdVector();
/* if (change) TODO: I assume this is already triggered by invalidated ROI
rebuild();*/
}
bool QvernoteStorage::listNotebooks(std::vector<Notebook>& notebookList)
{
QVector<Notebook> qNotebooklist;
QNotebook::loadList(getDB(), qNotebooklist);
notebookList = qNotebooklist.toStdVector();
return true;
}
bool QvernoteStorage::listTagsByNotebook(std::vector<Tag>& tagList, Guid notebookGuid)
{
QVector<Tag> qTaglist;
QTag::loadForNotebook(getDB(), notebookGuid, qTaglist);
tagList = qTaglist.toStdVector();
return true;
}
bool
EditableDenseThreeDimensionalModel::shouldUseLogValueScale() const
{
QReadLocker locker(&m_lock);
QVector<float> sample;
QVector<int> n;
for (int i = 0; i < 10; ++i) {
size_t index = i * 10;
if (index < m_data.size()) {
const Column &c = m_data.at(index);
while (c.size() > sample.size()) {
sample.push_back(0.f);
n.push_back(0);
}
for (int j = 0; j < c.size(); ++j) {
sample[j] += c.at(j);
++n[j];
}
}
}
if (sample.empty()) return false;
for (int j = 0; j < sample.size(); ++j) {
if (n[j]) sample[j] /= n[j];
}
return LogRange::useLogScale(sample.toStdVector());
}
bool QvernoteStorage::getFavoriteNotes(vector<Note>& noteList)
{
QVector<Note> qNoteList;
QNote::getFavorites(getDB(), qNoteList);
noteList = qNoteList.toStdVector();
return true;
}
vector<QAction*> ToolBarImp::getItems() const
{
QList<QAction*> actionList = actions();
QVector<QAction*> actionVector = actionList.toVector();
vector<QAction*> items = actionVector.toStdVector();
return items;
}
bool QvernoteStorage::listTags(vector<Tag>& tagList)
{
QVector<Tag> qTagList;
QTag::loadList(getDB(), qTagList);
tagList = qTagList.toStdVector();
return true;
}
// This test case is based on the example presented on page 725 of the
// paper: "Three dimensional triganulations from local transformations"
// by Barry Joe (Siam J. Sci. Stat. Comput. Vol 10, No 4, 1989).
void DelaunayTriangulationTest::joe89()
{
std::vector<chemkit::Point3> points;
points.push_back(chemkit::Point3(0.054f, 0.099f, 0.993f));
points.push_back(chemkit::Point3(0.066f, 0.756f, 0.910f));
points.push_back(chemkit::Point3(0.076f, 0.578f, 0.408f));
points.push_back(chemkit::Point3(0.081f, 0.036f, 0.954f));
points.push_back(chemkit::Point3(0.082f, 0.600f, 0.726f));
points.push_back(chemkit::Point3(0.085f, 0.327f, 0.731f));
points.push_back(chemkit::Point3(0.123f, 0.666f, 0.842f));
points.push_back(chemkit::Point3(0.161f, 0.303f, 0.975f));
chemkit::DelaunayTriangulation triangulation(points);
QCOMPARE(triangulation.vertexCount(), 8);
QCOMPARE(triangulation.tetrahedronCount(), 13);
QList<QVector<int> > expectedTetrahedra;
expectedTetrahedra.append(QVector<int>() << 0 << 1 << 2 << 4);
expectedTetrahedra.append(QVector<int>() << 0 << 1 << 4 << 5);
expectedTetrahedra.append(QVector<int>() << 0 << 1 << 5 << 7);
expectedTetrahedra.append(QVector<int>() << 0 << 2 << 3 << 5);
expectedTetrahedra.append(QVector<int>() << 0 << 2 << 4 << 5);
expectedTetrahedra.append(QVector<int>() << 0 << 3 << 5 << 7);
expectedTetrahedra.append(QVector<int>() << 1 << 2 << 4 << 6);
expectedTetrahedra.append(QVector<int>() << 1 << 4 << 5 << 7);
expectedTetrahedra.append(QVector<int>() << 1 << 4 << 6 << 7);
expectedTetrahedra.append(QVector<int>() << 2 << 3 << 5 << 7);
expectedTetrahedra.append(QVector<int>() << 2 << 4 << 5 << 6);
expectedTetrahedra.append(QVector<int>() << 2 << 5 << 6 << 7);
expectedTetrahedra.append(QVector<int>() << 4 << 5 << 6 << 7);
std::vector<std::vector<int> > tetrahedra = triangulation.tetrahedra();
QCOMPARE(tetrahedra.size(), size_t(expectedTetrahedra.size()));
for(unsigned int i = 0; i < tetrahedra.size(); i++){
std::vector<int> tetrahedron = tetrahedra[i];
std::sort(tetrahedron.begin(), tetrahedron.end());
int foundCount = 0;
for(int j = 0; j < expectedTetrahedra.size(); j++){
QVector<int> expectedTetrahedron = expectedTetrahedra[j];
if(tetrahedron == expectedTetrahedron.toStdVector()){
foundCount++;
}
}
if(foundCount != 1){
qDebug() << "tetrahedron (index " << i << ") was not found. verticies: " << QVector<int>::fromStdVector(tetrahedron);
}
QCOMPARE(foundCount, 1);
}
}
/********************************************//**
\brief Discrete Wavelet Transform including Daubechies and Biorthogonal
\param array : input array (1D)
\param filter : name of the filter to use (from TransformationType enum)
\param dir : direction (1:forward, -1:backward)
\param transformedArray : resulting transformed array
\param stop : flag indicating if we need to stop the process or not
\param origLength : original Length useful when dir is -1 to cut result
\return boolean true if the execution is finished,
false if the process has been stopped during the execution
***********************************************/
bool FWT::getDWT(const QVector<float>& array,
const TransformationType filter,
const int dir,
QVector<float>& transformedArray,
bool* stop,
const int origLength)
{
std::vector<float> result;
if(filter == DB4_TRANSFORMATION || filter == DB6_TRANSFORMATION || filter == DB8_TRANSFORMATION)
{
if(dir == 1) return fastWaveletTransf(array.toStdVector(), filter, result, stop);
else return iFastWaveletTransf(array.toStdVector(), filter, origLength, result, stop);
}
else if(filter == BIOR_5_3_TRANSFORMATION || filter == BIOR_9_7_TRANSFORMATION)
{
return perform_wavelet_transform(array.toStdVector(), filter, dir, origLength, result, stop);
}
transformedArray.fromStdVector(result);
return true;
}
void NScriptEngine::execute_line( const QString & line , int fragment_number, QVector<int> break_points){
if (connected){
const common::URI script_engine_path("//Tools/Python/ScriptEngine", common::URI::Scheme::CPATH);
SignalOptions options;
QString repl=QString(line);
repl.replace(QString("\t"),QString(";"));
repl.replace(QString("\n"),QString("?"));
options.add("script", repl.toStdString());
options.add("fragment",fragment_number);
options.add("breakpoints",break_points.toStdVector());
SignalFrame frame = options.create_frame("execute_script", uri(), script_engine_path);
NetworkQueue::global()->send( frame, NetworkQueue::IMMEDIATE );
}
}
bool DefectsModel::setData(const QModelIndex& index, const QVariant& value, int role)
{
if (index.isValid() && role == Qt::EditRole) {
switch (index.column()) {
case (0):
defectsList[index.row()].centre.x = value.toDouble();
break;
case (1):
defectsList[index.row()].centre.y = value.toDouble();
break;
case (2):
defectsList[index.row()].radius = value.toDouble();
break;
case (3): {
QString s = value.toString().replace("[", "").replace("]", "").trimmed();
QStringList sl = s.split(",");
QVector<double> vec;
for (int i = 0; i < sl.size(); ++i) {
vec << sl.at(i).toDouble();
}
GeneralComponentSystem g = GeneralComponentSystem(vec.toStdVector());
defectsList[index.row()].value.resize(g.components.size());
defectsList[index.row()].value = g;
break;
}
case (4):
defectsList[index.row()].boundaryCondition
= static_cast<BoundaryCondition>(value.toInt());
break;
}
emit dataChanged(index, index);
return true;
}
return false;
}
Aquila::SignalSource * procHighpassFilter(Aquila::SignalSource * source)
{
QVector<Aquila::SampleType> data;
const int step = 16;
int size = source->getSamplesCount();
for (int i=0; i<size; i++)
{
double res = 0.0;
for(int j=0; j<step; j++){
if(i-j < 0) res += source->sample(0);
else res += source->sample(i-j);
if(i+j >= size) res += source->sample(size-1);
else res += source->sample(i+j);
}
Aquila::SampleType val = source->sample(i) - (res / (step * 2));
if(val > 32767.0)
val = 32767.0;
if(val< -32767.0)
val =-32767.0;
data.append(val);
}
return new Aquila::SignalSource(data.toStdVector(), source->getSampleFrequency());
}
/**
* Main function: runs plugin based on its ID
* @param plugin ID
* @param image to be processed
**/
QSharedPointer<nmc::DkImageContainer> WriterIdentificationPlugin::runPlugin(
const QString &runID,
QSharedPointer<nmc::DkImageContainer> imgC,
const nmc::DkSaveInfo& saveInfo,
QSharedPointer<nmc::DkBatchInfo>& info) const {
if (!imgC)
return imgC;
if(runID == mRunIDs[id_calcuate_features]) {
qInfo() << "calculating features for writer identification";
WriterIdentification wi = WriterIdentification();
cv::Mat imgCv = nmc::DkImage::qImage2Mat(imgC->image());
wi.setImage(imgCv);
wi.calculateFeatures();
cv::cvtColor(imgCv, imgCv, CV_RGB2GRAY);
QVector<cv::KeyPoint> kp = wi.keyPoints();
//QVector<cv::KeyPoint>::iterator kpItr = kp.begin();
//cv::Mat desc = wi.descriptors();
//cv::Mat newDesc = cv::Mat(0, desc.cols, desc.type());
//int r = 0;
//rdf::Image::imageInfo(desc, "desc");
//for(auto kpItr = kp.begin(); kpItr != kp.end(); r++) {
// kpItr->size *= 1.5 * 4;
// if(kpItr->size > 70) {
// kpItr = kp.erase(kpItr);
// } else if(kpItr->size < 20) {
// kpItr = kp.erase(kpItr);
// } else {
// kpItr++;
// newDesc.push_back(desc.row(r).clone());
// }
//}
//rdf::Image::imageInfo(newDesc, "newDesc");
//wi.setDescriptors(newDesc);
//wi.setKeyPoints(kp);
cv::drawKeypoints(imgCv, kp.toStdVector(), imgCv, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
////cv::drawKeypoints(imgCv, wi.getKeyPoints().toStdVector(), imgCv, cv::Scalar::all(-1));
//
////QString fFilePath = featureFilePath(imgC->filePath(), true);
wi.saveFeatures(featureFilePath(imgC->filePath(), true));
//QImage img = nmc::DkImage::mat2QImage(imgCv);
//img = img.convertToFormat(QImage::Format_ARGB32);
//imgC->setImage(img, tr("SIFT keypoints"));
}
else if(runID == mRunIDs[id_generate_vocabulary]) {
qInfo() << "collecting files for vocabulary generation";
QString ffPath = featureFilePath(imgC->filePath());
int idxOfMinus = QFileInfo(imgC->filePath()).baseName().indexOf("-");
int idxOfUScore = QFileInfo(imgC->filePath()).baseName().indexOf("_");
int idx = -1;
if(idxOfMinus == -1 && idxOfUScore > 0)
idx = idxOfUScore;
else if(idxOfUScore == -1 && idxOfMinus > 0)
idx = idxOfMinus;
else if(idxOfMinus > 0 && idxOfUScore > 0)
idx = idxOfMinus > idxOfUScore ? idxOfMinus : idxOfUScore;
QString label = QFileInfo(imgC->filePath()).baseName().left(idx);
QSharedPointer<WIInfo> wInfo(new WIInfo(runID, imgC->filePath()));
wInfo->setWriter(label);
wInfo->setFeatureFilePath(ffPath);
info = wInfo;
}
else if(runID == mRunIDs[id_identify_writer]) {
qInfo() << "identifying writer";
}
else if(runID == mRunIDs[id_evaluate_database]) {
qInfo() << "collecting files evaluation";
if(mVocabulary.isEmpty()) {
qWarning() << "batchProcess: vocabulary is empty ... not evaluating";
return imgC;
}
QString fFilePath = featureFilePath(imgC->filePath());
if(QFileInfo(fFilePath).exists()) {
int idxOfMinus = QFileInfo(imgC->filePath()).baseName().indexOf("-");
int idxOfUScore = QFileInfo(imgC->filePath()).baseName().indexOf("_");
int idx = -1;
if(idxOfMinus == -1 && idxOfUScore > 0)
idx = idxOfUScore;
else if(idxOfUScore == -1 && idxOfMinus > 0)
idx = idxOfMinus;
else if(idxOfMinus > 0 && idxOfUScore > 0)
idx = idxOfMinus < idxOfUScore ? idxOfMinus : idxOfUScore;
QString label = QFileInfo(imgC->filePath()).baseName().left(idx);
qDebug() << "label: " << label << "\t\tbaseName:" << QFileInfo(imgC->filePath()).baseName();
cv::FileStorage fs(fFilePath.toStdString(), cv::FileStorage::READ);
if(!fs.isOpened()) {
qWarning() << " unable to read file " << fFilePath;
return imgC;
}
std::vector<cv::KeyPoint> kp;
fs["keypoints"] >> kp;
cv::Mat descriptors;
fs["descriptors"] >> descriptors;
fs.release();
if(mVocabulary.minimumSIFTSize() > 0 || mVocabulary.maximumSIFTSize() > 0) {
cv::Mat filteredDesc = cv::Mat(0, descriptors.cols, descriptors.type());
int r = 0;
for(auto kpItr = kp.begin(); kpItr != kp.end(); r++) {
if(kpItr->size*1.5 * 4 > mVocabulary.maximumSIFTSize() && mVocabulary.maximumSIFTSize() > 0) {
kpItr = kp.erase(kpItr);
}
else if(kpItr->size * 1.5 * 4 < mVocabulary.minimumSIFTSize()) {
kpItr = kp.erase(kpItr);
}
else {
kpItr++;
filteredDesc.push_back(descriptors.row(r).clone());
}
}
qDebug() << "filtered " << descriptors.rows - filteredDesc.rows << " SIFT features (maxSize:" << mVocabulary.maximumSIFTSize() << " minSize:" << mVocabulary.minimumSIFTSize() << ")";
descriptors = filteredDesc;
}
else
qDebug() << "not filtering SIFT features, min or max size not set";
cv::Mat feature = mVocabulary.generateHist(descriptors);
rdf::Image::imageInfo(descriptors, "descriptors");
rdf::Image::imageInfo(feature, "feature");
QSharedPointer<WIInfo> wInfo(new WIInfo(runID, imgC->filePath()));
wInfo->setWriter(label);
wInfo->setFeatureFilePath(fFilePath);
wInfo->setFeatureVector(feature);
info = wInfo;
} else {
void DistViewModel::setIlluminant(QVector<multi_img::Value> illum)
{
// no recalculation as is triggered elsewhere
illuminant = illum.toStdVector();
}
for (set <int>::const_iterator it = s.begin(); it != s.end(); it++) {
_l.push_back(v[*it - 1]);
}
}
QString Paper::getTitle() const {
return _title;
}
vector <Problem> &Paper::getProb() {
return _l;
}
const std::vector <Problem> &Paper::getProb() const {
return _l;
}
QDataStream & operator << (QDataStream &stream, const Paper &p) {
stream << p._title << QVector <Problem>::fromStdVector(p._l);
return stream;
}
QDataStream & operator >> (QDataStream &stream, Paper &p) {
QString title;
QVector <Problem> prob;
stream >> title;
stream >> prob;
p = Paper(title, prob.toStdVector());
return stream;
}
void MainWindow::on_startSimulationPushButton_clicked()
{
QTime timer;
timer.start();
qDebug() << "loading parameters...";
double alpha = ui->alpha_lineedit->text().toDouble();
int minNumberOfBusStopsInRegion = ui->minbusstopsinregion->text().toInt();
int maxNumberOfBusStopsInRegion = ui->maxbusstopsinregion->text().toInt();
int minBuslineLength = ui->minlinelength->text().toInt();
int maxBuslineLength = ui->maxlinelength->text().toInt();
int populationMultiplier = ui->multiplier_lineedit->text().toInt();
int percentageOfCrossedSolution = ui->crossed_lineedit->text().toInt();
int iterationsForDifferentBaseSolutions = ui->iterations_for_diff->text().toInt();
QVector<Parameters::MutationType> mutations;
if ( ui->mutationType_simple->isChecked() )
mutations.push_front(Parameters::SIMPLE_MUTATION);
if ( ui->mutationType_random->isChecked() )
mutations.push_front(Parameters::RANDOM_MUTATION);
if ( ui->mutationType_moreRandom->isChecked() )
mutations.push_front(Parameters::MORE_RANDOM_MUTATION);
if ( ui->mutationType_evenMoreRandom->isChecked() )
mutations.push_front(Parameters::EVEN_MORE_RANDOM_MUTATION);
long numberOfIterations = ui->numofiterations->text().toInt();
qDebug() << "Parameters loaded";
qDebug() << "alpha" << alpha << endl
<< "minNumberOfBusStopsInRegion" << minNumberOfBusStopsInRegion << endl
<< "maxNumberOfBusStopsInRegion" << maxNumberOfBusStopsInRegion << endl
<< "minBuslineLength" << minBuslineLength << endl
<< "maxBuslineLength" << maxBuslineLength << endl
<< "numberOfIterations" << numberOfIterations << endl
<< "populationSizeMultiplier" << populationMultiplier << endl;
Parameters::setAlpha(alpha);
Parameters::setMinNumberOfBusStopsInRegion(minNumberOfBusStopsInRegion);
Parameters::setMaxNumberOfBusStopsInRegion(maxNumberOfBusStopsInRegion);
Parameters::setMinNumberOfBusStopsInBusline(minBuslineLength);
Parameters::setMaxNumberOfBusStopsInBusLine(maxBuslineLength);
Parameters::setPopulationSizeMultiplier(populationMultiplier);
Parameters::setPercentageOfCrossedSolutions(percentageOfCrossedSolution);
Parameters::setMutationOperators(mutations.toStdVector());
// TODO : set the lambda parameter
algorithm->setNumberOfIterations(numberOfIterations);
if ( ! dataLoaded )
{
loadBusStops();
}
if ( ! dataLoaded ) return;
Solution optimalSolution;
Solution tempSolution;
QVector<double> vectorOfValuesFromDifferentBaseSolutions;
try
{
model->loadBusStopsFromFile(dataFileName.toStdString());
qDebug() << "Before optimization";
optimalSolution = algorithm->performOptimization();
vectorOfValuesFromDifferentBaseSolutions.push_back(model->objectiveFunction(optimalSolution));
for(int i = 1; i < iterationsForDifferentBaseSolutions; i++)
{
tempSolution = algorithm->performOptimization();
if(model->objectiveFunction(tempSolution) > model->objectiveFunction(optimalSolution))
optimalSolution = tempSolution;
vectorOfValuesFromDifferentBaseSolutions.push_back(model->objectiveFunction(tempSolution));
}
} catch ( InvalidBoundsException * exc )
{
QMessageBox::warning(NULL, "Error", "Ograniczenia są nieprawidłowe. Przerwano optymalizację", QMessageBox::Ok);
qDebug() << QString::fromStdString(exc->getMessage());
qDebug() << "Skipping optimization due to invalid bounds" << endl;
return;
}
saveResultsAndInvokeGnuplotIfRequested(QVector<double>::fromStdVector(algorithm->getValuesOfObjectiveFunction()),
QVector<int>::fromStdVector(optimalSolution.toStdVector()));
drawSolution(optimalSolution);
ui->objectiveValueLabel->setText(QString::number(model->objectiveFunction(optimalSolution)));
saveResultsFromDifferentBaseSolutions(vectorOfValuesFromDifferentBaseSolutions);
qDebug() << "Przeprowadzenie optymalizacji zajęło " << timer.elapsed() << " ms." << endl;
QApplication::beep();
QThread::sleep(1);
QApplication::beep();
QThread::sleep(1);
QApplication::beep();
QThread::sleep(1);
QApplication::beep();
}
void QmlFeatureBattery::handleEventRecv(const QVector<uint8_t> data) {
if (notification::is_hidpp20_notification(data.toStdVector(), (Hidpp20Device*)_device->backendDevice(), 0x00, feature::FEATURE_BATTERY)) {
feature::BatteryLevel level = notification::get_battery_notification(data.toStdVector());
updateBatteryLevel(level);
}
}
void core::DotMatrix::setInputs(const QVector<double> &inputs)
{
setInputs(inputs.toStdVector());
}
void core::DotMatrix::setInputs(const QVector<double> &inputs, int rows, int cols)
{
setInputs(inputs.toStdVector(), rows, cols);
}
void core::DotMatrix::setInputs(const QVector<int> &inputs, int rows, int cols)
{
vector<int> intInputs = inputs.toStdVector();
setInputs(vector<double>(intInputs.begin(), intInputs.end()), rows, cols);
}
qreal MonoChannelFrame::compressChannel(const QVector<qreal>& aValues)
{
Mean mean(aValues.size());
std::vector<qreal> values = aValues.toStdVector();
return accumulate(values.begin(), values.end(), 0.0, mean);
}
