private void PlotLikelihood(string text, NumericalTools.Likelihood lk)
{
if (lk == null)
{
Image im = pbLkDisplay.Image;
Graphics g = Graphics.FromImage(im);
g.FillRectangle(new SolidBrush(Color.White), 0, 0, im.Width, im.Height);
g.DrawString("Likelihood not available - press Compute", new Font("Lucida", 10), new SolidBrush(Color.Black), 0, 0);
pbLkDisplay.Refresh();
}
else
{
Image im = pbLkDisplay.Image;
Graphics g = Graphics.FromImage(im);
g.FillRectangle(new SolidBrush(Color.White), 0, 0, im.Width, im.Height);
double[] p = new double[(int)Math.Floor((lk.MaxBound(0) - lk.MinBound(0)) / 0.05 + 1.0)];
double[] logl = new double[p.Length];
int i;
for (i = 0; i < p.Length; i++)
{
p[i] = lk.MinBound(0) + i * 0.05;
logl[i] = lk.LogValue(p[i]);
}
NumericalTools.Plot pl = new NumericalTools.Plot();
pl.VecX = p;
pl.VecY = logl;
double dummy = 0.0;
double miny = 0.0, maxy = 0.0;
NumericalTools.Fitting.FindStatistics(logl, ref maxy, ref miny, ref dummy, ref dummy);
maxy += 0.2 * (maxy - miny);
pl.JoinPenThickness = 2;
pl.MinX = lk.MinBound(0) - 0.05;
pl.MaxX = lk.MaxBound(0) + 0.05;
pl.SetXDefaultLimits = false;
pl.MinY = miny;
pl.MaxY = maxy;
pl.SetYDefaultLimits = false;
pl.XTitle = "P";
pl.YTitle = text;
pl.LabelFont = new Font("Lucida", 10);
pl.PanelFont = new Font("Lucida", 10);
pl.PanelY = 3;
pl.Scatter(g, im.Width, im.Height);
pbLkDisplay.Refresh();
}
}
private void btnCompute_Click(object sender, EventArgs e)
{
if (clSlopeSets.CheckedItems.Count < 1)
{
MessageBox.Show("At least one data set must be enabled.", "Input error", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
NumericalTools.Likelihood[] lk = new NumericalTools.Likelihood[clSlopeSets.CheckedItems.Count];
int i;
for (i = 0; i < clSlopeSets.CheckedItems.Count; i++)
{
FitSet fs = (FitSet)clSlopeSets.CheckedItems[i];
if (fs.Likelihood == null)
try
{
MCSLikelihood.ProcessData(fs.Tracks, out fs.Likelihood);
}
catch (Exception x)
{
MessageBox.Show("Error computing likelihood for set \"" + fs.ToString() + "\":\r\n" + x.Message, "Computation Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
lk[i] = fs.Likelihood;
}
Likelihood = null;
txtResults.Text = "";
try
{
Likelihood = new NumericalTools.OneParamLogLikelihood(0.05, lk);
string t = Likelihood.Best(0).ToString("F2", System.Globalization.CultureInfo.InvariantCulture) + ";";
double cl = ((SySal.Processing.MCSLikelihood.Configuration)MCSLikelihood.Config).ConfidenceLevel;
double[] bounds = Likelihood.ConfidenceRegions(0, cl);
for (i = 0; i < bounds.Length; i += 2)
t += " [" + bounds[i].ToString("F2", System.Globalization.CultureInfo.InvariantCulture) + "," + bounds[i + 1].ToString("F2", System.Globalization.CultureInfo.InvariantCulture) + "]";
t += "; " + cl.ToString("F2", System.Globalization.CultureInfo.InvariantCulture);
txtResults.Text = t;
}
catch (Exception x)
{
MessageBox.Show("Error computing combined likelihood:\r\n" + x.Message, "Computation Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
PlotLikelihood("LogL (combined)", Likelihood);
}
/// <summary>
/// Computes the momentum and confidence limits using positions and slopes provided.
/// </summary>
/// <param name="data">the position and slopes of the track (even Z-unordered).</param>
/// <param name="likelihood">the output variable that will contain the likelihood function.</param>
/// <returns>the momentum and confidence limits.</returns>
public MomentumResult ProcessData(SySal.Tracking.MIPEmulsionTrackInfo[] data, out NumericalTools.Likelihood likelihood)
{
MomentumResult myResult = new MomentumResult();
//ordiniamo gli elementi della geometria e dei dati inseriti per z
Geometry gm = C.Geometry;
data = orderElementData(data);
//Controllo che la traccia sia contenuta in tutto il brick
if (data[0].Intercept.Z <gm.Layers[0].ZMin || data[data.Length - 1].Intercept.Z> gm.Layers[gm.Layers.Length - 1].ZMin)
{
throw new Exception("Data span a Z interval that exceeds the geometry bounds.");
}
//Calcolo gli scattering ad ogni step e li scrivo nel file di dump se c'è!!
scattDifference[] scatDif = calcScattDiff(gm, data);
if (m_tkDumpFile != null)
{
writeInFileTK(scatDif);
}
//calcolo le differenze
ArrayList resultDiff = new ArrayList();
ArrayList resultDiffL = new ArrayList();
double myRadiationLength = C.MinimumRadiationLengths;
resultDiff = angularDifference(data, scatDif, myRadiationLength);
resultDiffL = angularDifferenceL(data, scatDif, myRadiationLength);
if (resultDiff.Count == 0)
{
throw new Exception("No slope difference available for computation.");
}
if (m_angularDiffDumpFile != null)
{
writeInFileAD(resultDiff);
}
//Calcolo elementi della matrice di covarianza e li scrivo nel file di dump se c'è!!
ArrayList matrixFinal = new ArrayList();
matrixFinal = covMatrix(resultDiff, scatDif);
if (m_cvDumpFile != null)
{
writeInFileCV(matrixFinal);
}
//Calcolo la probabilità totale e li scrivo nel file di dump se c'è!!
int maxIndex = 0;
ProbMomentum[] finalData = totalProbability(resultDiff, matrixFinal, C.MinimumMomentum, C.MaximumMomentum, C.MomentumStep, ref maxIndex);
if (m_lkDumpFile != null)
{
writeInFileLK(finalData);
}
//Calcolo i limiti di confidenza
double maxLike = finalData[maxIndex].probTot;
double myCutLike;
myCutLike = cutLike(maxLike);
limit myLimit;
myLimit = limitCalculation(finalData, myCutLike, maxIndex);
//Scrivo i risultati
myResult.Value = finalData[maxIndex].momValue;
myResult.LowerBound = myLimit.lwLimit;
myResult.UpperBound = myLimit.upLimit;
myResult.ConfidenceLevel = 0.90;
double[] lkval = new double[finalData.Length];
int i;
for (i = 0; i < lkval.Length; i++)
{
lkval[i] = finalData[i].probTot;
}
likelihood = new NumericalTools.OneParamLogLikelihood(finalData[0].momValue, finalData[finalData.Length - 1].momValue, lkval, "P");
return(myResult);
/*
* finalData = totalProbability(resultDiffL, matrixFinal, C.MinimumMomentum, C.MaximumMomentum, C.MomentumStep, ref maxIndex);
* for (i = 0; i < lkval.Length; i++)
* lkval[i] = finalData[i].probTot;
* likelihood = new NumericalTools.OneParamLogLikelihood(C.MomentumStep, likelihood, new NumericalTools.OneParamLogLikelihood(finalData[0].momValue, finalData[finalData.Length - 1].momValue, lkval, "P"));
* myResult.Value = likelihood.Best(0);
* double[] cr = likelihood.ConfidenceRegions(0, C.ConfidenceLevel);
* myResult.LowerBound = cr[0];
* myResult.UpperBound = cr[1];
* return myResult;
*/
}
public FitSet(SySal.Tracking.MIPEmulsionTrackInfo[] tks, TrackBrowser src)
{
Tracks = tks;
Source = src;
Likelihood = null;
}
/// <summary>
/// Computes the momentum and confidence limits using positions and slopes provided.
/// </summary>
/// <param name="data">the position and slopes of the track (even Z-unordered).</param>
/// <returns>the momentum and confidence limits.</returns>
public MomentumResult ProcessData(SySal.Tracking.MIPEmulsionTrackInfo[] data)
{
NumericalTools.Likelihood lk = null;
return(ProcessData(data, out lk));
}
