/// <summary>
/// Sort XicPoints and select one peak per scan
/// </summary>
/// <param name="xic"></param>
/// <returns></returns>
public static Xic GetSelectedXic(Xic xic)
{
if (xic.Count == 0)
{
return(xic);
}
xic.Sort(); // Need to guarantee a sorted Xic
// select one best peak for each scan
var newXic = new Xic();
var prevScanNum = xic[0].ScanNum;
var bestPeak = xic[0];
for (var i = 1; i < xic.Count; i++)
{
var xicPeak = xic[i];
if (xicPeak.ScanNum > prevScanNum)
{
newXic.Add(bestPeak);
bestPeak = xicPeak;
}
else
{
if (xicPeak.Intensity > bestPeak.Intensity)
{
bestPeak = xicPeak;
}
}
prevScanNum = xicPeak.ScanNum;
}
newXic.Add(bestPeak);
return(newXic);
}
/// <summary>
/// Check 2 Xics for equality
/// </summary>
/// <param name="other"></param>
/// <returns></returns>
protected bool Equals(Xic other)
{
for (var i = 0; i < Count; i++)
{
if (!this[i].Equals(other[i]))
{
return(false);
}
}
return(true);
}
/// <summary>
/// Get the Pearson correlation of 2 XICs
/// </summary>
/// <param name="other"></param>
/// <returns></returns>
public double GetCorrelation(Xic other)
{
if (Count == 0 || other == null || other.Count == 0)
{
return(0);
}
var count1 = Count;
var count2 = other.Count;
var index1 = 0;
var index2 = 0;
var intList1 = new List <double>();
var intList2 = new List <double>();
while (index1 < count1 && index2 < count2)
{
var comp = this[index1].ScanNum - other[index2].ScanNum;
if (comp < 0)
{
++index1;
}
else if (comp > 0)
{
++index2;
}
else
{
intList1.Add(this[index1].Intensity);
intList2.Add(other[index2].Intensity);
++index1;
++index2;
}
}
var correlation = FitScoreCalculator.GetPearsonCorrelation(intList1.ToArray(), intList2.ToArray());
return(correlation);
}
/// <summary>
/// Get the Cosine score of 2 XICs
/// </summary>
/// <param name="other"></param>
/// <returns></returns>
public double GetCosine(Xic other)
{
if (Count == 0 || other == null || other.Count == 0)
{
return(0);
}
//var minScanNum = Math.Min(this[0].ScanNum, other[0].ScanNum);
//var maxScanNum = Math.Max(this[Count-1].ScanNum, other[other.Count-1].ScanNum);
var minScanNum = this[0].ScanNum;
var maxScanNum = this[Count - 1].ScanNum;
var intArr1 = new double[maxScanNum - minScanNum + 1];
foreach (var p in this)
{
intArr1[p.ScanNum - minScanNum] = p.Intensity;
}
var intArr2 = new double[maxScanNum - minScanNum + 1];
foreach (var p in other)
{
var index = p.ScanNum - minScanNum;
if (index < 0 || index >= intArr2.Length)
{
continue;
}
intArr2[p.ScanNum - minScanNum] = p.Intensity;
}
var correlation = FitScoreCalculator.GetCosine(intArr1, intArr2);
return(correlation);
}
