static List <GUI> Init(ref ICurveFitting Fitter, ISpectrumReader reader)
{
double ppm = 5;
object resultLock = new object();
List <double> Retention = new List <double>();
List <double> Guis = new List <double>();
IGUIFinder finder = new BinarySearchFinder(ppm);
IProcess picking = new LocalNeighborPicking();
Dictionary <int, List <GUI> > pointMaps = new Dictionary <int, List <GUI> >();
int start = reader.GetFirstScan();
int end = reader.GetLastScan();
Parallel.For(start, end, (i) =>
{
if (reader.GetMSnOrder(i) < 2)
{
ISpectrum spectrum = picking.Process(reader.GetSpectrum(i));
lock (resultLock)
{
pointMaps[i] = finder.FindGlucoseUnits(spectrum);
}
}
});
List <List <GUI> > points =
pointMaps.OrderBy(p => p.Key).Select(p => p.Value).ToList();
IGUISequencer sequencer = new DynamicProgrammingSequencer();
List <GUI> GuiPoints = sequencer.Choose(points);
Fitter = new PolynomialFitting();
Dictionary <int, GUI> guiSelected = new Dictionary <int, GUI>();
foreach (GUI gui in GuiPoints)
{
if (guiSelected.ContainsKey(gui.Unit))
{
if (guiSelected[gui.Unit].Peak.GetIntensity() < gui.Peak.GetIntensity())
{
guiSelected[gui.Unit] = gui;
}
}
else
{
guiSelected[gui.Unit] = gui;
}
}
Retention.Clear();
Guis.Clear();
List <GUI> guiChoice = guiSelected.Values.OrderBy(g => g.Scan).ToList();
foreach (GUI gui in guiChoice)
{
int scan = gui.Scan;
double time = reader.GetRetentionTime(scan);
Retention.Add(time);
Guis.Add(gui.Unit);
}
Fitter.Fit(Retention, Guis);
return(GuiPoints);
}
public void Run(string path, Counter counter)
{
ISpectrumReader reader = new ThermoRawSpectrumReader();
reader.Init(path);
IGUIFinder finder = new BinarySearchFinder(PPM);
IProcess picking = new LocalNeighborPicking();
Dictionary <int, List <GUI> > pointMaps = new Dictionary <int, List <GUI> >();
int start = reader.GetFirstScan();
int end = reader.GetLastScan();
Parallel.For(start, end, (i) =>
{
if (reader.GetMSnOrder(i) < 2)
{
ISpectrum spectrum = picking.Process(reader.GetSpectrum(i));
lock (resultLock)
{
pointMaps[i] = finder.FindGlucoseUnits(spectrum);
}
}
counter.Add(end - start);
});
List <List <GUI> > points =
pointMaps.OrderBy(p => p.Key).Select(p => p.Value).ToList();
IGUISequencer sequencer = new DynamicProgrammingSequencer();
List <GUI> guiPoints = sequencer.Choose(points);
Dictionary <int, GUI> guiSelected = new Dictionary <int, GUI>();
foreach (GUI gui in guiPoints)
{
if (guiSelected.ContainsKey(gui.Unit))
{
if (guiSelected[gui.Unit].Peak.GetIntensity() < gui.Peak.GetIntensity())
{
guiSelected[gui.Unit] = gui;
}
}
else
{
guiSelected[gui.Unit] = gui;
}
}
Retention.Clear();
Guis.Clear();
List <GUI> looped = guiSelected.Values.OrderBy(g => g.Scan).ToList();
string output = Path.Combine(Path.GetDirectoryName(path),
Path.GetFileNameWithoutExtension(path) + ".csv");
using (FileStream ostrm = new FileStream(output, FileMode.OpenOrCreate, FileAccess.Write))
{
using (StreamWriter writer = new StreamWriter(ostrm))
{
writer.WriteLine("scan,time,gui,peak,intensity");
foreach (GUI gui in looped)
{
int scan = gui.Scan;
double time = reader.GetRetentionTime(scan);
Retention.Add(time);
Guis.Add(gui.Unit);
writer.WriteLine(scan.ToString() + "," +
time.ToString() + "," +
gui.Unit.ToString() + "," +
gui.Peak.GetMZ().ToString() + "," +
gui.Peak.GetIntensity().ToString());
}
writer.Flush();
}
}
Fitter.Fit(Retention, Guis);
}