int main()
{
// create a peak map containing 4 dummy spectra and peaks
MSExperiment exp;
// The following examples creates a MSExperiment containing four MSSpectrum instances.
for (Size i = 0; i < 4; ++i)
{
MSSpectrum spectrum;
spectrum.setRT(i);
spectrum.setMSLevel(1);
for (float mz = 500.0; mz <= 900; mz += 100.0)
{
Peak1D peak;
peak.setMZ(mz + i);
spectrum.push_back(peak);
}
exp.addSpectrum(spectrum);
}
// Iteration over the RT range (2,3) and the m/z range (603,802) and print the peak positions.
for (auto it = exp.areaBegin(2.0, 3.0, 603.0, 802.0); it != exp.areaEnd(); ++it)
{
cout << it.getRT() << " - " << it->getMZ() << endl;
}
// Iteration over all peaks in the experiment.
// Output: RT, m/z, and intensity
// Note that the retention time is stored in the spectrum (not in the peak object)
for (auto s_it = exp.begin(); s_it != exp.end(); ++s_it)
{
for (auto p_it = s_it->begin(); p_it != s_it->end(); ++p_it)
{
cout << s_it->getRT() << " - " << p_it->getMZ() << " " << p_it->getIntensity() << endl;
}
}
// We could store the spectra to a mzML file with:
// MzMLFile mzml;
// mzml.store(filename, exp);
// And load it with
// mzml.load(filename, exp);
// If we wanted to load only the MS2 spectra we could speed up reading by setting:
// mzml.getOptions().addMSLevel(2);
// before executing: mzml.load(filename, exp);
return 0;
} //end of main
void ElutionPeakDetection::smoothData(MassTrace& mt, int win_size) const
{
// alternative smoothing using SavitzkyGolay
// looking at the unit test, this method gives better fits than lowess smoothing
// reference paper uses lowess smoothing
MSSpectrum<PeakType> spectrum;
spectrum.insert(spectrum.begin(), mt.begin(), mt.end());
SavitzkyGolayFilter sg;
Param param;
param.setValue("polynomial_order", 2);
param.setValue("frame_length", std::max(3, win_size)); // frame length must be at least polynomial_order+1, otherwise SG will fail
sg.setParameters(param);
sg.filter(spectrum);
MSSpectrum<PeakType>::iterator iter = spectrum.begin();
std::vector<double> smoothed_intensities;
for (; iter != spectrum.end(); ++iter)
{
smoothed_intensities.push_back(iter->getIntensity());
}
mt.setSmoothedIntensities(smoothed_intensities);
//alternative end
// std::cout << "win_size elution: " << scan_time << " " << win_size << std::endl;
// if there is no previous FWHM estimation... do it now
// if (win_size == 0)
// {
// mt.estimateFWHM(false); // estimate FWHM
// win_size = mt.getFWHMScansNum();
// }
// use one global window size for all mass traces to smooth
// std::vector<double> rts, ints;
//
// for (MassTrace::const_iterator c_it = mt.begin(); c_it != mt.end(); ++c_it)
// {
// rts.push_back(c_it->getRT());
// ints.push_back(c_it->getIntensity());
// }
// LowessSmoothing lowess_smooth;
// Param lowess_params;
// lowess_params.setValue("window_size", win_size);
// lowess_smooth.setParameters(lowess_params);
// std::vector<double> smoothed_data;
// lowess_smooth.smoothData(rts, ints, smoothed_data);
// mt.setSmoothedIntensities(smoothed_data);
}
bool IDEvaluationBase::addSearchFile(const String& file_name)
{
MSSpectrum<> points;
if (!loadCurve(file_name, points)) return false;
data_.addSpectrum(points);
MSExperiment<>* exp = new MSExperiment<>();
exp->addSpectrum(points);
spec_1d_->canvas()->addLayer(SpectrumCanvas::ExperimentSharedPtrType(exp));
spec_1d_->canvas()->setLayerName(spec_1d_->canvas()->getLayerCount() - 1, points.getMetaValue("search_engine"));
// set intensity mode (after spectrum has been added!)
setIntensityMode((int) SpectrumCanvas::IM_SNAP);
return true;
}
bool IDEvaluationBase::getPoints(std::vector<PeptideIdentification>& peptides /* cannot be const, to avoid copy */,
const std::vector<double>& q_value_thresholds, MSSpectrum<>& points)
{
points.clear(true);
FalseDiscoveryRate fdr;
fdr.setParameters(param_.copy("fdr:", true));
try
{
fdr.apply(peptides); // computes a q-value (if its params are correct)
}
catch (Exception::MissingInformation)
{
LOG_FATAL_ERROR << "Tool failed due to missing information (see above)." << std::endl;
return false;
}
// get list of q-values and sort them
std::vector<double> q_values;
q_values.reserve(peptides.size());
for (vector<PeptideIdentification>::iterator it = peptides.begin(); it != peptides.end(); ++it)
{
it->assignRanks();
if (it->getHits().size() > 0)
q_values.push_back(it->getHits()[0].getScore());
}
std::sort(q_values.begin(), q_values.end());
for (Size i = 0; i < q_value_thresholds.size(); ++i)
{
// get position in sorted q-values where cutoff is reached
std::vector<double>::iterator pos = std::upper_bound(q_values.begin(), q_values.end(), q_value_thresholds[i]);
Peak1D p;
p.setMZ(q_value_thresholds[i] * 100);
p.setIntensity(std::distance(q_values.begin(), pos));
points.push_back(p);
}
return true;
}
int main(int argc, const char** argv)
{
if (argc < 2) return 1;
// the path to the data should be given on the command line
String tutorial_data_path(argv[1]);
IndexedMzMLFileLoader imzml;
// load data from an indexed MzML file
OnDiscMSExperiment<> map;
imzml.load(tutorial_data_path + "/data/Tutorial_FileIO_indexed.mzML", map);
// Get the first spectrum in memory, do some constant (non-changing) data processing
MSSpectrum<> s = map.getSpectrum(0);
std::cout << "There are " << map.getNrSpectra() << " spectra in the input file." << std::endl;
std::cout << "The first spectrum has " << s.size() << " peaks." << std::endl;
// store the (unmodified) data in a different file
imzml.store("Tutorial_FileIO_output.mzML", map);
return 0;
} //end of main
Int main()
{
MSSpectrum<> spectrum;
Peak1D peak;
for (float mz = 1500.0; mz >= 500; mz -= 100.0)
{
peak.setMZ(mz);
spectrum.push_back(peak);
}
spectrum.sortByPosition();
MSSpectrum<>::Iterator it;
for (it = spectrum.MZBegin(800.0); it != spectrum.MZEnd(1000.0); ++it)
{
cout << it->getMZ() << endl;
}
return 0;
} //end of main
MSChromatogram<> toChromatogram(const MSSpectrum<>& in)
{
MSChromatogram<> out;
for (Size ic = 0; ic < in.size(); ++ic)
{
ChromatogramPeak peak;
peak.setMZ(in[ic].getMZ());
peak.setIntensity(in[ic].getIntensity());
out.push_back(peak);
}
out.setChromatogramType(ChromatogramSettings::SELECTED_ION_CURRENT_CHROMATOGRAM);
return out;
}
bool IDEvaluationBase::loadCurve(const String& file_name, MSSpectrum<>& points)
{
if (FileHandler::getType(file_name) != FileTypes::IDXML)
{
LOG_ERROR << "The file '" << file_name << "' is not an .idXML file" << std::endl;
return false;
}
std::vector<ProteinIdentification> prot_ids;
std::vector<PeptideIdentification> pep_ids;
IdXMLFile().load(file_name, prot_ids, pep_ids);
String ln = pep_ids[0].getScoreType(); // grab name here, since FDR-calculation will overwrite it with "q-value"
bool ret = getPoints(pep_ids, q_value_thresholds_, points); // FDR calculation failed?
points.setMetaValue("search_engine", ln);
return ret;
}
void getSwathFile(PeakMap& exp, int nr_swathes=32, bool ms1=true)
{
if (ms1)
{
MSSpectrum s;
s.setMSLevel(1);
Peak1D p; p.setMZ(100); p.setIntensity(200);
s.push_back(p);
exp.addSpectrum(s);
}
for (int i = 0; i< nr_swathes; i++)
{
MSSpectrum s;
s.setMSLevel(2);
std::vector<Precursor> prec(1);
prec[0].setIsolationWindowLowerOffset(12.5);
prec[0].setIsolationWindowUpperOffset(12.5);
prec[0].setMZ(400 + i*25 + 12.5);
s.setPrecursors(prec);
Peak1D p; p.setMZ(101 + i); p.setIntensity(201 + i);
s.push_back(p);
exp.addSpectrum(s);
}
}
START_TEST(SignalToNoiseEstimatorMedian, "$Id$") ///////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////// SignalToNoiseEstimatorMedian< >* ptr = nullptr; SignalToNoiseEstimatorMedian< >* nullPointer = nullptr; START_SECTION((SignalToNoiseEstimatorMedian())) ptr = new SignalToNoiseEstimatorMedian<>; TEST_NOT_EQUAL(ptr, nullPointer) SignalToNoiseEstimatorMedian<> sne; END_SECTION START_SECTION((SignalToNoiseEstimatorMedian& operator=(const SignalToNoiseEstimatorMedian &source))) MSSpectrum raw_data; SignalToNoiseEstimatorMedian<> sne; sne.init(raw_data); SignalToNoiseEstimatorMedian<> sne2 = sne; NOT_TESTABLE END_SECTION START_SECTION((SignalToNoiseEstimatorMedian(const SignalToNoiseEstimatorMedian &source))) MSSpectrum raw_data; SignalToNoiseEstimatorMedian<> sne; sne.init(raw_data); SignalToNoiseEstimatorMedian<> sne2(sne); NOT_TESTABLE END_SECTION START_SECTION((virtual ~SignalToNoiseEstimatorMedian()))
// Wrong assignment of the mono-isotopic mass for precursors are assumed:
// - if precursor_mz matches the mz of a non-monoisotopic feature mass trace
// - and in the case that believe_charge is true: if feature_charge matches the precursor_charge
// In the case of wrong mono-isotopic assignment several options for correction are available:
// keep_original will create a copy of the precursor and tandem spectrum for the new mono-isotopic mass trace and retain the original one
// all_matching_features does this not for only the closest feature but all features in a question
set<Size> correctToNearestFeature(const FeatureMap& features, PeakMap & exp, double rt_tolerance_s = 0.0, double mz_tolerance = 0.0, bool ppm = true, bool believe_charge = false, bool keep_original = false, bool all_matching_features = false, int max_trace = 2)
{
set<Size> corrected_precursors;
// for each precursor/MS2 find all features that are in the given tolerance window (bounding box + rt tolerances)
// if believe_charge is set, only add features that match the precursor charge
map<Size, set<Size> > scan_idx_to_feature_idx;
for (Size scan = 0; scan != exp.size(); ++scan)
{
// skip non-tandem mass spectra
if (exp[scan].getMSLevel() != 2 || exp[scan].getPrecursors().empty()) continue;
// extract precusor / MS2 information
const double pc_mz = exp[scan].getPrecursors()[0].getMZ();
const double rt = exp[scan].getRT();
const int pc_charge = exp[scan].getPrecursors()[0].getCharge();
for (Size f = 0; f != features.size(); ++f)
{
// feature is incompatible if believe_charge is set and charges don't match
if (believe_charge && features[f].getCharge() != pc_charge) continue;
// check if precursor/MS2 position overlap with feature
if (overlaps_(features[f], rt, pc_mz, rt_tolerance_s))
{
scan_idx_to_feature_idx[scan].insert(f);
}
}
}
// filter sets to retain compatible features:
// if precursor_mz = feature_mz + n * feature_charge (+/- mz_tolerance) a feature is compatible, others are removed from the set
for (map<Size, set<Size> >::iterator it = scan_idx_to_feature_idx.begin(); it != scan_idx_to_feature_idx.end(); ++it)
{
const Size scan = it->first;
const double pc_mz = exp[scan].getPrecursors()[0].getMZ();
const double mz_tolerance_da = ppm ? pc_mz * mz_tolerance * 1e-6 : mz_tolerance;
// Note: This is the "delete while iterating" pattern so mind the pre- and postincrement
for (set<Size>::iterator sit = it->second.begin(); sit != it->second.end(); )
{
if (!compatible_(features[*sit], pc_mz, mz_tolerance_da, max_trace))
{
it->second.erase(sit++);
}
else
{
++sit;
}
}
}
// remove entries with no compatible features (empty sets).
// Note: This is the "delete while iterating" pattern so mind the pre- and postincrement
for (map<Size, set<Size> >::iterator it = scan_idx_to_feature_idx.begin(); it != scan_idx_to_feature_idx.end(); )
{
if (it->second.empty())
{
scan_idx_to_feature_idx.erase(it++);
}
else
{
++it;
}
}
if (debug_level_ > 0)
{
LOG_INFO << "Number of precursors with compatible features: " << scan_idx_to_feature_idx.size() << endl;
}
if (!all_matching_features)
{
// keep only nearest features in set
for (map<Size, set<Size> >::iterator it = scan_idx_to_feature_idx.begin(); it != scan_idx_to_feature_idx.end(); ++it)
{
const Size scan = it->first;
const double pc_rt = exp[scan].getRT();
double min_distance = 1e16;
set<Size>::iterator best_feature = it->second.begin();
// determine nearest/best feature
for (set<Size>::iterator sit = it->second.begin(); sit != it->second.end(); ++sit)
{
const double current_distance = fabs(pc_rt - features[*sit].getRT());
if (current_distance < min_distance)
{
min_distance = current_distance;
best_feature = sit;
}
}
// delete all except the nearest/best feature
// Note: This is the "delete while iterating" pattern so mind the pre- and postincrement
for (set<Size>::iterator sit = it->second.begin(); sit != it->second.end(); )
{
if (sit != best_feature)
{
it->second.erase(sit++);
}
else
{
++sit;
}
}
}
}
// depending on all_matching_features option, only the nearest or all features are contained in the sets
// depending on options: move/copy corrected precursor and tandem spectrum
if (keep_original)
{
// duplicate spectra for each feature in set and adapt precursor_mz and precursor_charge to feature_mz and feature_charge
for (map<Size, set<Size> >::iterator it = scan_idx_to_feature_idx.begin(); it != scan_idx_to_feature_idx.end(); ++it)
{
const Size scan = it->first;
MSSpectrum<> spectrum = exp[scan];
corrected_precursors.insert(scan);
for (set<Size>::iterator f_it = it->second.begin(); f_it != it->second.end(); ++f_it)
{
spectrum.getPrecursors()[0].setMZ(features[*f_it].getMZ());
spectrum.getPrecursors()[0].setCharge(features[*f_it].getCharge());
exp.addSpectrum(spectrum);
}
}
}
else
{
// set precursor_mz and _charge to the feature_mz and _charge
for (map<Size, set<Size> >::iterator it = scan_idx_to_feature_idx.begin(); it != scan_idx_to_feature_idx.end(); ++it)
{
const Size scan = it->first;
exp[scan].getPrecursors()[0].setMZ(features[*it->second.begin()].getMZ());
exp[scan].getPrecursors()[0].setCharge(features[*it->second.begin()].getCharge());
corrected_precursors.insert(scan);
}
}
return corrected_precursors;
}
void PeakPickerHiRes::pick(const MSSpectrum& input, MSSpectrum& output, std::vector<PeakBoundary>& boundaries, bool check_spacings) const
{
// copy meta data of the input spectrum
output.clear(true);
output.SpectrumSettings::operator=(input);
output.MetaInfoInterface::operator=(input);
output.setRT(input.getRT());
output.setMSLevel(input.getMSLevel());
output.setName(input.getName());
output.setType(SpectrumSettings::CENTROID);
if (report_FWHM_)
{
output.getFloatDataArrays().resize(1);
output.getFloatDataArrays()[0].setName( report_FWHM_as_ppm_ ? "FWHM_ppm" : "FWHM");
}
// don't pick a spectrum with less than 5 data points
if (input.size() < 5) return;
// if both spacing constraints are disabled, don't check spacings at all:
if ((spacing_difference_ == std::numeric_limits<double>::infinity()) &&
(spacing_difference_gap_ == std::numeric_limits<double>::infinity()))
{
check_spacings = false;
}
// signal-to-noise estimation
SignalToNoiseEstimatorMedian<MSSpectrum > snt;
snt.setParameters(param_.copy("SignalToNoise:", true));
if (signal_to_noise_ > 0.0)
{
snt.init(input);
}
// find local maxima in profile data
for (Size i = 2; i < input.size() - 2; ++i)
{
double central_peak_mz = input[i].getMZ(), central_peak_int = input[i].getIntensity();
double left_neighbor_mz = input[i - 1].getMZ(), left_neighbor_int = input[i - 1].getIntensity();
double right_neighbor_mz = input[i + 1].getMZ(), right_neighbor_int = input[i + 1].getIntensity();
// do not interpolate when the left or right support is a zero-data-point
if (std::fabs(left_neighbor_int) < std::numeric_limits<double>::epsilon()) continue;
if (std::fabs(right_neighbor_int) < std::numeric_limits<double>::epsilon()) continue;
// MZ spacing sanity checks
double left_to_central = 0.0, central_to_right = 0.0, min_spacing = 0.0;
if (check_spacings)
{
left_to_central = central_peak_mz - left_neighbor_mz;
central_to_right = right_neighbor_mz - central_peak_mz;
min_spacing = (left_to_central < central_to_right) ? left_to_central : central_to_right;
}
double act_snt = 0.0, act_snt_l1 = 0.0, act_snt_r1 = 0.0;
if (signal_to_noise_ > 0.0)
{
act_snt = snt.getSignalToNoise(input[i]);
act_snt_l1 = snt.getSignalToNoise(input[i - 1]);
act_snt_r1 = snt.getSignalToNoise(input[i + 1]);
}
// look for peak cores meeting MZ and intensity/SNT criteria
if ((central_peak_int > left_neighbor_int) &&
(central_peak_int > right_neighbor_int) &&
(act_snt >= signal_to_noise_) &&
(act_snt_l1 >= signal_to_noise_) &&
(act_snt_r1 >= signal_to_noise_) &&
(!check_spacings ||
((left_to_central < spacing_difference_ * min_spacing) &&
(central_to_right < spacing_difference_ * min_spacing))))
{
// special case: if a peak core is surrounded by more intense
// satellite peaks (indicates oscillation rather than
// real peaks) -> remove
double act_snt_l2 = 0.0, act_snt_r2 = 0.0;
if (signal_to_noise_ > 0.0)
{
act_snt_l2 = snt.getSignalToNoise(input[i - 2]);
act_snt_r2 = snt.getSignalToNoise(input[i + 2]);
}
// checking signal-to-noise?
if ((i > 1) &&
(i + 2 < input.size()) &&
(left_neighbor_int < input[i - 2].getIntensity()) &&
(right_neighbor_int < input[i + 2].getIntensity()) &&
(act_snt_l2 >= signal_to_noise_) &&
(act_snt_r2 >= signal_to_noise_) &&
(!check_spacings ||
((left_neighbor_mz - input[i - 2].getMZ() < spacing_difference_ * min_spacing) &&
(input[i + 2].getMZ() - right_neighbor_mz < spacing_difference_ * min_spacing))))
{
++i;
continue;
}
std::map<double, double> peak_raw_data;
peak_raw_data[central_peak_mz] = central_peak_int;
peak_raw_data[left_neighbor_mz] = left_neighbor_int;
peak_raw_data[right_neighbor_mz] = right_neighbor_int;
// peak core found, now extend it
// to the left
Size k = 2;
bool previous_zero_left(false); // no need to extend peak if previous intensity was zero
Size missing_left(0);
Size left_boundary(i - 1); // index of the left boundary for the spline interpolation
while ((k <= i) && // prevent underflow
(i - k + 1 > 0) &&
!previous_zero_left &&
(missing_left <= missing_) &&
(input[i - k].getIntensity() <= peak_raw_data.begin()->second) &&
(!check_spacings ||
(peak_raw_data.begin()->first - input[i - k].getMZ() < spacing_difference_gap_ * min_spacing)))
{
double act_snt_lk = 0.0;
if (signal_to_noise_ > 0.0)
{
act_snt_lk = snt.getSignalToNoise(input[i - k]);
}
if ((act_snt_lk >= signal_to_noise_) &&
(!check_spacings ||
(peak_raw_data.begin()->first - input[i - k].getMZ() < spacing_difference_ * min_spacing)))
{
peak_raw_data[input[i - k].getMZ()] = input[i - k].getIntensity();
}
else
{
++missing_left;
if (missing_left <= missing_)
{
peak_raw_data[input[i - k].getMZ()] = input[i - k].getIntensity();
}
}
previous_zero_left = (input[i - k].getIntensity() == 0);
left_boundary = i - k;
++k;
}
// to the right
k = 2;
bool previous_zero_right(false); // no need to extend peak if previous intensity was zero
Size missing_right(0);
Size right_boundary(i+1); // index of the right boundary for the spline interpolation
while ((i + k < input.size()) &&
!previous_zero_right &&
(missing_right <= missing_) &&
(input[i + k].getIntensity() <= peak_raw_data.rbegin()->second) &&
(!check_spacings ||
(input[i + k].getMZ() - peak_raw_data.rbegin()->first < spacing_difference_gap_ * min_spacing)))
{
double act_snt_rk = 0.0;
if (signal_to_noise_ > 0.0)
{
act_snt_rk = snt.getSignalToNoise(input[i + k]);
}
if ((act_snt_rk >= signal_to_noise_) &&
(!check_spacings ||
(input[i + k].getMZ() - peak_raw_data.rbegin()->first < spacing_difference_ * min_spacing)))
{
peak_raw_data[input[i + k].getMZ()] = input[i + k].getIntensity();
}
else
{
++missing_right;
if (missing_right <= missing_)
{
peak_raw_data[input[i + k].getMZ()] = input[i + k].getIntensity();
}
}
previous_zero_right = (input[i + k].getIntensity() == 0);
right_boundary = i + k;
++k;
}
// skip if the minimal number of 3 points for fitting is not reached
if (peak_raw_data.size() < 3) continue;
CubicSpline2d peak_spline (peak_raw_data);
// calculate maximum by evaluating the spline's 1st derivative
// (bisection method)
double max_peak_mz = central_peak_mz;
double max_peak_int = central_peak_int;
double threshold = 1e-6;
OpenMS::Math::spline_bisection(peak_spline, left_neighbor_mz, right_neighbor_mz, max_peak_mz, max_peak_int, threshold);
//
// compute FWHM
//
if (report_FWHM_)
{
double fwhm_int = max_peak_int / 2.0;
threshold = 0.01 * fwhm_int;
double mz_mid, int_mid;
// left:
double mz_left = peak_raw_data.begin()->first;
double mz_center = max_peak_mz;
if (peak_spline.eval(mz_left) > fwhm_int)
{ // the spline ends before half max is reached -- take the leftmost point (probably an underestimation)
mz_mid = mz_left;
}
else
{
do
{
mz_mid = mz_left / 2 + mz_center / 2;
int_mid = peak_spline.eval(mz_mid);
if (int_mid < fwhm_int)
{
mz_left = mz_mid;
}
else
{
mz_center = mz_mid;
}
} while (fabs(int_mid - fwhm_int) > threshold);
}
const double fwhm_left_mz = mz_mid;
// right ...
double mz_right = peak_raw_data.rbegin()->first;
mz_center = max_peak_mz;
if (peak_spline.eval(mz_right) > fwhm_int)
{ // the spline ends before half max is reached -- take the rightmost point (probably an underestimation)
mz_mid = mz_right;
}
else
{
do
{
mz_mid = mz_right / 2 + mz_center / 2;
int_mid = peak_spline.eval(mz_mid);
if (int_mid < fwhm_int)
{
mz_right = mz_mid;
}
else
{
mz_center = mz_mid;
}
} while (fabs(int_mid - fwhm_int) > threshold);
}
const double fwhm_right_mz = mz_mid;
const double fwhm_absolute = fwhm_right_mz - fwhm_left_mz;
output.getFloatDataArrays()[0].push_back( report_FWHM_as_ppm_ ? fwhm_absolute / max_peak_mz * 1e6 : fwhm_absolute);
} // FWHM
// save picked peak into output spectrum
Peak1D peak;
PeakBoundary peak_boundary;
peak.setMZ(max_peak_mz);
peak.setIntensity(max_peak_int);
peak_boundary.mz_min = input[left_boundary].getMZ();
peak_boundary.mz_max = input[right_boundary].getMZ();
output.push_back(peak);
boundaries.push_back(peak_boundary);
// jump over profile data points that have been considered already
i = i + k - 1;
}
}
return;
}
SavitzkyGolayFilter* dsg_nullPointer = 0;
START_SECTION((SavitzkyGolayFilter()))
dsg_ptr = new SavitzkyGolayFilter;
TEST_NOT_EQUAL(dsg_ptr, dsg_nullPointer)
END_SECTION
START_SECTION((virtual ~SavitzkyGolayFilter()))
delete dsg_ptr;
END_SECTION
Param param;
param.setValue("polynomial_order",2);
param.setValue("frame_length",3);
START_SECTION((template < typename PeakType > void filter(MSSpectrum< PeakType > &spectrum)))
MSSpectrum<Peak1D> spectrum;
spectrum.resize(5);
MSSpectrum<Peak1D>::Iterator it = spectrum.begin();
for (int i=0; i<5; ++i, ++it)
{
it->setIntensity(0.0f);
if (i==2)
{
it->setIntensity(1.0f);
}
}
SavitzkyGolayFilter sgolay;
sgolay.setParameters(param);
sgolay.filter(spectrum);
c_feature_1.setIntensity(1000.00f);
c_feature_1.setCharge(4);
c_feature_1.setQuality((QualityType) 31.3334);
ConsensusFeature c_feature_2;
c_feature_2.setIntensity(122.01f);
c_feature_2.setCharge(3);
c_feature_2.setQuality((QualityType) 0.002);
ConsensusFeature c_feature_3;
c_feature_3.setIntensity(55.0f);
c_feature_3.setCharge(4);
c_feature_3.setQuality((QualityType) 1.);
///construct some test peaks
MSSpectrum spec;
Peak1D peak;
peak.setIntensity(201.334f);
spec.push_back(peak);
peak.setIntensity(2008.2f);
spec.push_back(peak);
peak.setIntensity(0.001f);
spec.push_back(peak);
MSSpectrum::FloatDataArrays& mdas = spec.getFloatDataArrays();
mdas.resize(3);
mdas[0].setName("test_int");
mdas[0].resize(3);
mdas[0][0] = 5;
mdas[0][1] = 10;
/**
@brief Applies the peak-picking algorithm to a map (MSExperiment). This
method picks peaks for each scan in the map consecutively. The resulting
picked peaks are written to the output map.
Currently we have to give up const-correctness but we know that everything on disc is constant
*/
void PeakPickerHiRes::pickExperiment(/* const */ OnDiscMSExperiment& input, PeakMap& output, const bool check_spectrum_type) const
{
// make sure that output is clear
output.clear(true);
// copy experimental settings
static_cast<ExperimentalSettings &>(output) = *input.getExperimentalSettings();
Size progress = 0;
startProgress(0, input.size() + input.getNrChromatograms(), "picking peaks");
// resize output with respect to input
output.resize(input.size());
if (input.getNrSpectra() > 0)
{
for (Size scan_idx = 0; scan_idx != input.size(); ++scan_idx)
{
if (ms_levels_.empty()) //auto mode
{
MSSpectrum s = input[scan_idx];
s.sortByPosition();
// determine type of spectral data (profile or centroided)
SpectrumSettings::SpectrumType spectrumType = s.getType();
if (spectrumType == SpectrumSettings::CENTROID)
{
output[scan_idx] = input[scan_idx];
}
else
{
pick(s, output[scan_idx]);
}
}
else if (!ListUtils::contains(ms_levels_, input[scan_idx].getMSLevel())) // manual mode
{
output[scan_idx] = input[scan_idx];
}
else
{
MSSpectrum s = input[scan_idx];
s.sortByPosition();
// determine type of spectral data (profile or centroided)
SpectrumSettings::SpectrumType spectrum_type = s.getType();
if (spectrum_type == SpectrumSettings::CENTROID && check_spectrum_type)
{
throw OpenMS::Exception::IllegalArgument(__FILE__, __LINE__, __FUNCTION__, "Error: Centroided data provided but profile spectra expected.");
}
pick(s, output[scan_idx]);
}
setProgress(++progress);
}
}
for (Size i = 0; i < input.getNrChromatograms(); ++i)
{
MSChromatogram chromatogram;
pick(input.getChromatogram(i), chromatogram);
output.addChromatogram(chromatogram);
setProgress(++progress);
}
endProgress();
return;
}
ExitCodes main_(int, const char**)
{
//----------------------------------------------------------------
// load data
//----------------------------------------------------------------
StringList in_list = getStringList_("in");
String out = getStringOption_("out");
String out_csv = getStringOption_("out_csv");
String format = getStringOption_("out_type");
if (out.empty() && out_csv.empty())
{
LOG_ERROR << "Neither 'out' nor 'out_csv' were provided. Please assign at least one of them." << std::endl;
return ILLEGAL_PARAMETERS;
}
if (!out.empty() && format == "") // get from filename
{
try
{
format = out.suffix('.');
}
catch (Exception::ElementNotFound& /*e*/)
{
format = "nosuffix";
}
// check if format is valid:
if (!ListUtils::contains(out_formats_, format.toLower()))
{
LOG_ERROR << "No explicit image output format was provided via 'out_type', and the suffix ('" << format << "') does not resemble a valid type. Please fix one of them." << std::endl;
return ILLEGAL_PARAMETERS;
}
}
double q_min = getDoubleOption_("q_min");
double q_max = getDoubleOption_("q_max");
if (q_min >= q_max)
{
LOG_ERROR << "The parameter 'q_min' must be smaller than 'q_max'. Quitting..." << std::endl;
return ILLEGAL_PARAMETERS;
}
IDEvaluationBase* mw = new IDEvaluationBase();
Param alg_param = mw->getParameters();
alg_param.insert("", getParam_().copy("algorithm:", true));
mw->setParameters(alg_param);
if (!mw->loadFiles(in_list))
{
LOG_ERROR << "Tool failed. See above." << std::endl;
return INCOMPATIBLE_INPUT_DATA;
}
mw->setVisibleArea(q_min, q_max);
if (!out.empty()) // save as image and exit
{
String error;
bool r = mw->exportAsImage(out.toQString(), error, format.toQString());
if (r) return EXECUTION_OK;
else
{
LOG_ERROR << error << std::endl;
return ILLEGAL_PARAMETERS;
}
}
if (!out_csv.empty())
{
TextFile tf;
for (Size i = 0; i < mw->getPoints().size(); ++i)
{
MSSpectrum s = mw->getPoints()[i];
StringList sl1;
StringList sl2;
for (Size j = 0; j < s.size(); ++j)
{
sl1.push_back(s[j].getMZ());
sl2.push_back(s[j].getIntensity());
}
tf.addLine(String("# ") + String(s.getMetaValue("search_engine")));
tf.addLine(ListUtils::concatenate(sl1, ","));
tf.addLine(ListUtils::concatenate(sl2, ","));
}
tf.store(out_csv);
}
delete(mw);
return EXECUTION_OK;
}
using namespace OpenMS;
GaussFilter* dgauss_ptr = nullptr;
GaussFilter* dgauss_nullPointer = nullptr;
START_SECTION((GaussFilter()))
dgauss_ptr = new GaussFilter;
TEST_NOT_EQUAL(dgauss_ptr, dgauss_nullPointer)
END_SECTION
START_SECTION((virtual ~GaussFilter()))
delete dgauss_ptr;
END_SECTION
START_SECTION((template <typename PeakType> void filter(MSSpectrum& spectrum)))
MSSpectrum spectrum;
spectrum.resize(5);
MSSpectrum::Iterator it = spectrum.begin();
for (Size i=0; i<5; ++i, ++it)
{
it->setIntensity(1.0f);
it->setMZ(500.0+0.2*i);
}
GaussFilter gauss;
Param param;
param.setValue( "gaussian_width", 1.0);
gauss.setParameters(param);
gauss.filter(spectrum);
it=spectrum.begin();
///////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////// MSSpectrum* ptr = 0; MSSpectrum* nullPointer = 0; START_SECTION((MSSpectrum())) ptr = new MSSpectrum(); TEST_NOT_EQUAL(ptr, nullPointer) END_SECTION START_SECTION((~MSSpectrum())) delete ptr; END_SECTION START_SECTION(([EXTRA] MSSpectrum())) MSSpectrum tmp; Peak1D peak; peak.getPosition()[0] = 47.11; tmp.push_back(peak); TEST_EQUAL(tmp.size(),1); TEST_REAL_SIMILAR(tmp[0].getMZ(),47.11); END_SECTION ///////////////////////////////////////////////////////////// // Member accessors START_SECTION((UInt getMSLevel() const)) MSSpectrum spec; TEST_EQUAL(spec.getMSLevel(),1) END_SECTION
PeakTypeEstimator* ptr = nullptr;
PeakTypeEstimator* nullPointer = nullptr;
START_SECTION(([EXTRA]PeakTypeEstimator()))
ptr = new PeakTypeEstimator();
TEST_NOT_EQUAL(ptr, nullPointer)
END_SECTION
START_SECTION(([EXTRA] ~PeakTypeEstimator()))
delete ptr;
END_SECTION
START_SECTION((template<typename PeakConstIterator> SpectrumSettings::SpectrumType estimateType(const PeakConstIterator& begin, const PeakConstIterator& end) const))
DTAFile file;
MSSpectrum spec;
// raw data (with zeros)
file.load(OPENMS_GET_TEST_DATA_PATH("PeakTypeEstimator_raw.dta"), spec);
TEST_EQUAL(PeakTypeEstimator::estimateType(spec.begin(), spec.end()), SpectrumSettings::PROFILE);
// TOF raw data (without zeros)
file.load(OPENMS_GET_TEST_DATA_PATH("PeakTypeEstimator_rawTOF.dta"), spec);
TEST_EQUAL(PeakTypeEstimator::estimateType(spec.begin(), spec.end()), SpectrumSettings::PROFILE);
// peak data
file.load(OPENMS_GET_TEST_DATA_PATH("PeakTypeEstimator_peak.dta"), spec);
TEST_EQUAL(PeakTypeEstimator::estimateType(spec.begin(), spec.end()), SpectrumSettings::CENTROID);
// too few data points
spec.resize(4);
TEST_EQUAL(PeakTypeEstimator::estimateType(spec.begin(), spec.end()), SpectrumSettings::UNKNOWN);
END_SECTION
/////////////////////////////////////////////////////////////
bool MSSpectrum::RTLess::operator()(const MSSpectrum &a, const MSSpectrum &b) const {
return a.getRT() < b.getRT();
}
