void FeatureMap::updateRanges()
{
this->clearRanges();
updateRanges_(this->begin(), this->end());
//enlarge the range by the convex hull points
for (Size i = 0; i < this->size(); ++i)
{
DBoundingBox<2> box = this->operator[](i).getConvexHull().getBoundingBox();
if (!box.isEmpty())
{
//update RT
if (box.minPosition()[Peak2D::RT] < this->pos_range_.minPosition()[Peak2D::RT])
{
this->pos_range_.setMinX(box.minPosition()[Peak2D::RT]);
}
if (box.maxPosition()[Peak2D::RT] > this->pos_range_.maxPosition()[Peak2D::RT])
{
this->pos_range_.setMaxX(box.maxPosition()[Peak2D::RT]);
}
//update m/z
if (box.minPosition()[Peak2D::MZ] < this->pos_range_.minPosition()[Peak2D::MZ])
{
this->pos_range_.setMinY(box.minPosition()[Peak2D::MZ]);
}
if (box.maxPosition()[Peak2D::MZ] > this->pos_range_.maxPosition()[Peak2D::MZ])
{
this->pos_range_.setMaxY(box.maxPosition()[Peak2D::MZ]);
}
}
}
}
void markFeatureLocations_(FeatureMap & feature_map, MSExperiment<> & exp, QImage & image, bool transpose, QColor color)
{
double xcoef = image.width(), ycoef = image.height();
if (transpose)
{
xcoef /= exp.getMaxRT() - exp.getMinRT();
ycoef /= exp.getMaxMZ() - exp.getMinMZ();
}
else
{
xcoef /= exp.getMaxMZ() - exp.getMinMZ();
ycoef /= exp.getMaxRT() - exp.getMinRT();
}
for (FeatureMap::Iterator feat_iter = feature_map.begin();
feat_iter != feature_map.end(); ++feat_iter)
{
const ConvexHull2D convex_hull = feat_iter->getConvexHull();
DBoundingBox<2> box = convex_hull.getBoundingBox();
double rt = feat_iter->getRT();
double mz = feat_iter->getMZ();
double lower_mz = box.minY();
double lower_rt = box.minX();
double upper_mz = box.maxY();
double upper_rt = box.maxX();
int lx, ly, ux, uy, cx, cy;
if (transpose)
{
lx = int(xcoef * (lower_rt - exp.getMinRT()));
ly = int(ycoef * (exp.getMaxMZ() - lower_mz));
ux = int(xcoef * (upper_rt - exp.getMinRT()));
uy = int(ycoef * (exp.getMaxMZ() - upper_mz));
cx = int(xcoef * (rt - exp.getMinRT()));
cy = int(ycoef * (mz - lower_mz));
}
else
{
lx = int(xcoef * (lower_mz - exp.getMinMZ()));
ly = int(ycoef * (exp.getMaxRT() - lower_rt));
ux = int(xcoef * (upper_mz - exp.getMinMZ()));
uy = int(ycoef * (exp.getMaxRT() - upper_rt));
cx = int(xcoef * (mz - exp.getMinMZ()));
cy = int(ycoef * (exp.getMaxRT() - rt));
}
addFeatureBox_(ly, lx, uy, ux, image, color);
addPoint_(cx, cy, image, Qt::black); // mark center
}
}
void IDMapper::increaseBoundingBox_(DBoundingBox<2> & box)
{
DPosition<2> sub_min(rt_tolerance_,
getAbsoluteMZTolerance_(box.minPosition().getY())),
add_max(rt_tolerance_, getAbsoluteMZTolerance_(box.maxPosition().getY()));
box.setMin(box.minPosition() - sub_min);
box.setMax(box.maxPosition() + add_max);
}
ConvexHull2D& Feature::getConvexHull() const
{
//recalculate convex hull if necessary
if (convex_hulls_modified_)
{
//only one mass trace convex hull => use it as overall convex hull
if (convex_hulls_.size() == 1)
{
convex_hull_ = convex_hulls_[0];
}
else
{
convex_hull_.clear();
if (convex_hulls_.size() > 0)
{
/*
-- this does not work with our current approach of "non-convex"hull computation as the mass traces of features cannot be combined
-- meaningfully. We thus print only the bounding box of the traces (for now)
for (Size hull=0; hull<convex_hulls_.size(); ++hull)
{
convex_hull_.addPoints(convex_hulls_[hull].getHullPoints());
}
*/
DBoundingBox<2> box;
for (Size hull = 0; hull < convex_hulls_.size(); ++hull)
{
box.enlarge(convex_hulls_[hull].getBoundingBox().minPosition()[0], convex_hulls_[hull].getBoundingBox().minPosition()[1]);
box.enlarge(convex_hulls_[hull].getBoundingBox().maxPosition()[0], convex_hulls_[hull].getBoundingBox().maxPosition()[1]);
}
convex_hull_.addPoint(ConvexHull2D::PointType(box.minX(), box.minY()));
convex_hull_.addPoint(ConvexHull2D::PointType(box.maxX(), box.minY()));
convex_hull_.addPoint(ConvexHull2D::PointType(box.minX(), box.maxY()));
convex_hull_.addPoint(ConvexHull2D::PointType(box.maxX(), box.maxY()));
}
}
convex_hulls_modified_ = false;
}
return convex_hull_;
}
bool overlaps_(const Feature& feature, const double rt, const double pc_mz, const double rt_tolerance) const
{
if (feature.getConvexHulls().empty())
{
LOG_WARN << "HighResPrecursorMassCorrector warning: at least one feature has no convex hull - omitting feature for matching" << std::endl;
}
// get bounding box and extend by retention time tolerance
DBoundingBox<2> box = feature.getConvexHull().getBoundingBox();
DPosition<2> extend_rt(rt_tolerance, 0.01);
box.setMin(box.minPosition() - extend_rt);
box.setMax(box.maxPosition() + extend_rt);
DPosition<2> pc_pos(rt, pc_mz);
if (box.encloses(pc_pos))
{
return true;
}
else
{
return false;
}
}
//
// V_MarkRect
//
void V_MarkRect (int x, int y, int width, int height)
{
dirtybox.AddToBox (x, y);
dirtybox.AddToBox (x+width-1, y+height-1);
}
void Spectrum2DWidget::showGoToDialog()
{
Spectrum2DGoToDialog goto_dialog(this);
//set range
const DRange<2>& area = canvas()->getVisibleArea();
goto_dialog.setRange(area.minY(), area.maxY(), area.minX(), area.maxX());
goto_dialog.setMinMaxOfRange(canvas()->getDataRange().minY(), canvas()->getDataRange().maxY(), canvas()->getDataRange().minX(), canvas()->getDataRange().maxX());
// feature numbers only for consensus&feature maps
goto_dialog.enableFeatureNumber(canvas()->getCurrentLayer().type == LayerData::DT_FEATURE || canvas()->getCurrentLayer().type == LayerData::DT_CONSENSUS);
//execute
if (goto_dialog.exec())
{
if (goto_dialog.showRange())
{
goto_dialog.fixRange();
SpectrumCanvas::AreaType area(goto_dialog.getMinMZ(), goto_dialog.getMinRT(), goto_dialog.getMaxMZ(), goto_dialog.getMaxRT());
if (goto_dialog.clip_checkbox->checkState() == Qt::Checked)
correctAreaToObeyMinMaxRanges_(area);
canvas()->setVisibleArea(area);
}
else
{
String feature_id = goto_dialog.getFeatureNumber();
//try to convert to UInt64 id
UniqueIdInterface uid;
uid.setUniqueId(feature_id);
Size feature_index(-1); // TODO : not use -1
if (canvas()->getCurrentLayer().type == LayerData::DT_FEATURE)
feature_index = canvas()->getCurrentLayer().getFeatureMap()->uniqueIdToIndex(uid.getUniqueId());
else if (canvas()->getCurrentLayer().type == LayerData::DT_CONSENSUS)
feature_index = canvas()->getCurrentLayer().getConsensusMap()->uniqueIdToIndex(uid.getUniqueId());
if (feature_index == Size(-1)) // UID does not exist
{
try
{
feature_index = feature_id.toInt(); // normal feature index as stored in map
}
catch (...) // we might still deal with a UID, so toInt() will throw as the number is too big
{
feature_index = Size(-1);
}
}
//check if the feature index exists
if ((canvas()->getCurrentLayer().type == LayerData::DT_FEATURE && feature_index >= canvas()->getCurrentLayer().getFeatureMap()->size())
|| (canvas()->getCurrentLayer().type == LayerData::DT_CONSENSUS && feature_index >= canvas()->getCurrentLayer().getConsensusMap()->size()))
{
QMessageBox::warning(this, "Invalid feature number", "Feature number too large/UniqueID not found.\nPlease select a valid feature!");
return;
}
//display feature with a margin
if (canvas()->getCurrentLayer().type == LayerData::DT_FEATURE)
{
const FeatureMapType& map = *canvas()->getCurrentLayer().getFeatureMap();
DBoundingBox<2> bb = map[feature_index].getConvexHull().getBoundingBox();
double rt_margin = (bb.maxPosition()[0] - bb.minPosition()[0]) * 0.5;
double mz_margin = (bb.maxPosition()[1] - bb.minPosition()[1]) * 2;
SpectrumCanvas::AreaType narea(bb.minPosition()[1] - mz_margin, bb.minPosition()[0] - rt_margin, bb.maxPosition()[1] + mz_margin, bb.maxPosition()[0] + rt_margin);
canvas()->setVisibleArea(narea);
}
else // Consensus Feature
{
const ConsensusFeature& cf = (*canvas()->getCurrentLayer().getConsensusMap())[feature_index];
double rt_margin = 30;
double mz_margin = 5;
SpectrumCanvas::AreaType narea(cf.getMZ() - mz_margin, cf.getRT() - rt_margin, cf.getMZ() + mz_margin, cf.getRT() + rt_margin);
canvas()->setVisibleArea(narea);
}
}
}
}
TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[0][0],1.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[0][1],2.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[1][0],3.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[1][1],4.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[0][0],0.5) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[0][1],0.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[1][0],1.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[1][1],1.0) END_SECTION START_SECTION((ConvexHull2D& getConvexHull() const)) Feature tmp; tmp.setConvexHulls(hulls); //check if the bounding box is ok DBoundingBox<2> bb = tmp.getConvexHull().getBoundingBox(); TEST_REAL_SIMILAR(bb.minPosition()[0],0.5) TEST_REAL_SIMILAR(bb.minPosition()[1],0.0) TEST_REAL_SIMILAR(bb.maxPosition()[0],3.0) TEST_REAL_SIMILAR(bb.maxPosition()[1],4.0) //check the convex hull points TEST_EQUAL(tmp.getConvexHull().getHullPoints().size(),4) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[0][0],0.5) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[0][1],0.0) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[1][0],3.0) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[1][1],0.0) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[2][0],3.0) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[2][1],4.0) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[3][0],0.5) TEST_REAL_SIMILAR(tmp.getConvexHull().getHullPoints()[3][1],4.0)
