private elMatrix calculateElementMatrix(Difference data1, Difference data2, scattDifference[] firstStep)
{
elMatrix finalResult = new elMatrix();
finalResult.pureRes = 0.0;
finalResult.errorRes = 0.0;
int i;
int k;
int c1 = -1;
int c2 = -2;
if (data1.first > data2.second || data2.first > data1.second)
{
finalResult.pureRes = 0.0;
finalResult.errorRes = 0.0;
}
else if (data1.first == data2.second || data2.first == data1.second)
{
finalResult.pureRes = 0.0;
finalResult.errorRes = -C.SlopeError * C.SlopeError;
}
else if (data1.first == data2.first && data1.second == data2.second)
{
for (i = 0; i < firstStep.Length; i++)
{
if (firstStep[i].first == data1.first)
{
c1 = i;
}
if (firstStep[i].second == data1.second)
{
c2 = i;
}
}
if (c1 == c2) //vuol dire che lo step è uno solo
{
finalResult.pureRes = firstStep[c1].value;
finalResult.errorRes = 2.0 * C.SlopeError * C.SlopeError;
}
else //vuol dire che c'è più di 1 cella ed io sommo i sigma delle celle intermedie
{
for (k = c1; k <= c2; k++)
{
finalResult.pureRes = finalResult.pureRes + firstStep[k].value;
}
finalResult.errorRes = 2 * C.SlopeError * C.SlopeError;
}
}
else if (data1.first == data2.first)
{
c1 = data1.first;
c2 = Math.Min(data1.second, data2.second);
for (k = c1; k < c2; k++)
{
finalResult.pureRes = finalResult.pureRes + firstStep[k].value;
}
finalResult.errorRes = C.SlopeError * C.SlopeError;
}
else if (data1.second == data2.second)
{
c1 = Math.Max(data1.first, data1.first);
c2 = data1.second;
for (k = c1; k < c2; k++)
{
finalResult.pureRes = finalResult.pureRes + firstStep[k].value;
}
finalResult.errorRes = C.SlopeError * C.SlopeError;
}
else
{
c2 = Math.Min(data1.second, data2.second);
c1 = Math.Max(data1.first, data2.first);
for (k = c1; k < c2; k++)
{
finalResult.pureRes = finalResult.pureRes + firstStep[k].value;
}
finalResult.errorRes = 0;
}
return(finalResult);
}
private ArrayList angularDifferenceL(SySal.Tracking.MIPEmulsionTrackInfo[] data, scattDifference[] firstStep, double minRL)
{
ArrayList dataDiff = new ArrayList();
double den = 0.0;
#if _USE_MEAN_
double msx = (data[0].Slope.Y + data[data.Length - 1].Slope.Y) * 0.5;
double msy = -(data[0].Slope.X + data[data.Length - 1].Slope.X) * 0.5;
den = msx * msx + msy * msy;
if (den <= 0.0)
{
msx = 0.1;
msy = 0.1;
den = msx * msx + msy * msy;
}
den = 1.0 / Math.Sqrt(den);
#endif
//calcoliamo le differenze
int i;
if (minRL <= 0.0)
{
double sum = 0.0;
for (i = 0; i < firstStep.Length; i++)
{
sum += firstStep[i].value;
}
minRL = 0.9 * sum * 0.5;
}
for (i = 0; i < data.Length; i++)
{
double sum = 0.0;
int j;
for (j = i; j < firstStep.Length && sum < minRL; j++)
{
sum += firstStep[j].value;
}
// j--;
// if (i + j >= data.Length) break;
//if (j >= firstStep.Length) break;
if (sum < minRL)
{
break;
}
Difference info = new Difference();
double zDiffTest = Math.Abs(data[i].Intercept.Z - data[j].Intercept.Z);
if (zDiffTest > 1.0) //per essere sicuri di non prendere deltaZ = 0
{
#if _USE_X_
info.slopeDiff = data[i].Slope.X - data[j].Slope.X;
#elif _USE_Y_
info.slopeDiff = data[i].Slope.Y - data[j].Slope.Y;
#elif _USE_THETA_
const double Theta = Math.PI * 0.25;
info.slopeDiff = Math.Cos(Theta) * (data[i].Slope.X - data[j].Slope.X) - Math.Sin(Theta) * (data[i].Slope.Y - data[j].Slope.Y);
#elif _USE_S0_
den = Math.Sqrt(data[0].Slope.X * data[0].Slope.X + data[0].Slope.Y * data[0].Slope.Y);
info.slopeDiff = (((data[i].Slope.X - data[j].Slope.X) * (data[0].Slope.Y) - (data[i].Slope.Y - data[j].Slope.Y) * (data[0].Slope.X)) / den);
//Console.WriteLine(" " + info.slopeDiff);
#elif _USE_MEAN_
info.slopeDiff = (((data[i].Slope.X - data[j].Slope.X) * msy - (data[i].Slope.Y - data[j].Slope.Y) * msx) * den);
#else
den = Math.Sqrt(data[i].Slope.X * data[i].Slope.X + data[i].Slope.Y * data[i].Slope.Y);
info.slopeDiff = Math.Abs(((data[i].Slope.X - data[j].Slope.X) * (data[i].Slope.Y) - (data[i].Slope.Y - data[j].Slope.Y) * (data[i].Slope.X)) / den);
#endif
info.first = i;
info.second = j; //calcolo valore[i]-valore[(i+j)]]
info.dZeta = zDiffTest;
dataDiff.Add(info);
}
}
return(dataDiff);
}
