public bool DiscoverPeak(List <double> mzList, List <double> intensityList, double startMz, double stopMz,
out clsPeak peak, bool findFwhm = false, bool findSignalToNoise = false, bool fitPeak = false)
{
peak = new clsPeak();
var startIndex = PeakIndex.GetNearest(mzList, startMz, 0);
var stopIndex = PeakIndex.GetNearest(mzList, stopMz, startIndex);
peak.Mz = 0;
peak.Intensity = 0;
peak.DataIndex = -1;
peak.FWHM = 0;
peak.SignalToNoise = 0;
double maxIntensity = 0;
var found = false;
for (var i = startIndex; i < stopIndex; i++)
{
var intensity = intensityList[i];
if (intensity > maxIntensity)
{
maxIntensity = intensity;
peak.Mz = mzList[i];
peak.Intensity = intensity;
peak.DataIndex = i;
found = true;
}
}
if (found)
{
if (findFwhm)
{
peak.FWHM = PeakStatistician.FindFwhm(mzList, intensityList, peak.DataIndex);
}
if (findSignalToNoise)
{
peak.SignalToNoise = PeakStatistician.FindSignalToNoise(peak.Intensity, intensityList,
peak.DataIndex);
}
if (fitPeak)
{
peak.Mz = _peakFit.Fit(peak.DataIndex, mzList, intensityList);
}
}
return(found);
}
/// <summary>
/// Gets the peak that fits the point at a given index with a Lorentzian fit.
/// </summary>
/// <param name="index">index of the point in the m/z vectors which is the apex of the peak.</param>
/// <param name="mzs">List of raw data of m\zs.</param>
/// <param name="intensities">List of raw data of intensities.</param>
/// <param name="fwhm"></param>
/// <returns>returns the m/z of the peak.</returns>
private double LorentzianFit(List <double> mzs, List <double> intensities, int index, double fwhm)
{
var a = intensities[index];
var vo = mzs[index];
var e = Math.Abs((vo - mzs[index + 1]) / 100);
if (index < 1)
{
return(mzs[index]);
}
if (index == mzs.Count)
{
return(mzs[index]);
}
var lstart = PeakIndex.GetNearest(mzs, vo + fwhm, index) + 1;
var lstop = PeakIndex.GetNearest(mzs, vo - fwhm, index) - 1;
var currentE = LorentzianLS(mzs, intensities, a, fwhm, vo, lstart, lstop);
for (var i = 0; i < 50; i++)
{
var lastE = currentE;
vo = vo + e;
currentE = LorentzianLS(mzs, intensities, a, fwhm, vo, lstart, lstop);
if (currentE > lastE)
{
break;
}
}
vo = vo - e;
currentE = LorentzianLS(mzs, intensities, a, fwhm, vo, lstart, lstop);
for (var i = 0; i < 50; i++)
{
var lastE = currentE;
vo = vo - e;
currentE = LorentzianLS(mzs, intensities, a, fwhm, vo, lstart, lstop);
if (currentE > lastE)
{
break;
}
}
vo = vo + e;
return(vo);
}
public double GetSignalToNoise(List <double> mzList, List <double> intensityList, double peak)
{
var index = PeakIndex.GetNearest(mzList, peak, 0);
return(PeakStatistician.FindSignalToNoise(intensityList[index], intensityList, index));
}
public double GetFWHM(List <double> mzList, List <double> intensityList, double peak)
{
var index = PeakIndex.GetNearest(mzList, peak, 0);
return(PeakStatistician.FindFwhm(mzList, intensityList, index));
}