public void TestCriticalPointCalculation()
{
foreach (int key in s_T_Coeff_95.Keys)
{
var coeff = TDistribution.CalculateCriticalValue(key, (1 - 0.95), 0.0001);
Assert.AreEqual(s_T_Coeff_95[key], coeff, 0.002);
}
}
public ProcessingReturnValues FitAndPlotSlowMotion(
Dictionary <int, SingleMultiFrameMeasurement> measurements,
FlybyMeasurementContext meaContext,
GetProcessingValueCallback getValueCallback,
Graphics g, FlybyPlottingContext plottingContext, float xScale, float yScale, int imageWidth)
{
// Compute median, use median based exclusion rules
// Report the median position for the time at the middle of the measured interval
// Do not expect elongated image (no corrections from the exposure)
// May apply instrumental delay corrections for the frame time
var rv = new ProcessingReturnValues();
double sum = 0;
double userSum = 0;
double stdDevUserSum = 0;
int numFramesUser = 0;
double userMidFrom = meaContext.UserMidValue - meaContext.MaxStdDev;
double userMidTo = meaContext.UserMidValue + meaContext.MaxStdDev;
rv.EarliestFrame = int.MaxValue;
rv.LatestFrame = int.MinValue;
List <double> medianList = new List <double>();
List <double> medianWeightsList = new List <double>();
var minPosUncertaintyArcSec = TangraConfig.Settings.Astrometry.AssumedPositionUncertaintyPixels * meaContext.ArsSecsInPixel;
foreach (SingleMultiFrameMeasurement measurement in measurements.Values)
{
float x = (measurement.FrameNo - meaContext.MinFrameNo) * xScale + 5;
ProcessingValues val = getValueCallback(measurement);
double valueFrom = val.Value - val.StdDev;
double valueTo = val.Value + val.StdDev;
float yFrom = (float)(valueFrom - plottingContext.MinValue) * yScale + 5;
float yTo = (float)(valueTo - plottingContext.MinValue) * yScale + 5;
sum += val.Value;
Pen mPen = plottingContext.IncludedPen;
if (!double.IsNaN(meaContext.UserMidValue))
{
if ((valueFrom >= userMidFrom && valueFrom <= userMidTo) ||
(valueTo >= userMidFrom && valueTo <= userMidTo))
{
numFramesUser++;
userSum += val.Value;
medianList.Add(val.Value);
medianWeightsList.Add(ComputePositionWeight(val.StdDev, measurement, minPosUncertaintyArcSec, WeightingMode.SNR));
stdDevUserSum += val.StdDev * val.StdDev;
if (rv.EarliestFrame > measurement.FrameNo)
{
rv.EarliestFrame = measurement.FrameNo;
}
if (rv.LatestFrame < measurement.FrameNo)
{
rv.LatestFrame = measurement.FrameNo;
}
}
else
{
mPen = plottingContext.ExcludedPen;
}
}
g.DrawLine(mPen, x, yFrom, x, yTo);
g.DrawLine(mPen, x - 1, yFrom, x + 1, yFrom);
g.DrawLine(mPen, x - 1, yTo, x + 1, yTo);
}
if (!double.IsNaN(meaContext.UserMidValue) && numFramesUser > 0)
{
double average = userSum / numFramesUser;
double err = Math.Sqrt(stdDevUserSum) / (numFramesUser - 1);
float yAve = (float)(average - plottingContext.MinValue) * yScale + 5;
g.DrawLine(plottingContext.AveragePen, 5, yAve - 1, imageWidth - 5, yAve - 1);
g.DrawLine(plottingContext.AveragePen, 5, yAve, imageWidth - 5, yAve);
g.DrawLine(plottingContext.AveragePen, 5, yAve + 1, imageWidth - 5, yAve + 1);
float yMin = (float)(userMidFrom - plottingContext.MinValue) * yScale + 5;
float yMax = (float)(userMidTo - plottingContext.MinValue) * yScale + 5;
g.DrawLine(plottingContext.AveragePen, 5, yMin, imageWidth - 5, yMin);
g.DrawLine(plottingContext.AveragePen, 5, yMax, imageWidth - 5, yMax);
double median;
double medianWeight;
WeightedMedian(Tuple.Create(medianList, medianWeightsList), out median, out medianWeight);
double standardMedian = medianList.Median();
Trace.WriteLine(string.Format("{0}; Included: {1}; Average: {2}; Wighted Median: {3}; Standard Median: {4}",
meaContext.UserMidValue.ToString("0.00000"),
numFramesUser, AstroConvert.ToStringValue(average, "+HH MM SS.TTT"),
AstroConvert.ToStringValue(median, "+HH MM SS.TTT"),
AstroConvert.ToStringValue(standardMedian, "+HH MM SS.TTT")));
rv.FittedValue = median;
var stdDevArcSec = 3600 * Math.Sqrt(medianList.Sum(x => (x - median) * (x - median)) / (medianList.Count - 1));
var tCoeff95 = TDistribution.CalculateCriticalValue(medianList.Count, (1 - 0.95), 0.0001);
var error95 = 1.253 * tCoeff95 * stdDevArcSec / Math.Sqrt(medianList.Count);
rv.FittedValueUncertaintyArcSec = error95;
rv.IsVideoNormalPosition = false;
}
else
{
double average = sum / measurements.Count;
float yAve = (float)(average - plottingContext.MinValue) * yScale + 5;
g.DrawLine(Pens.WhiteSmoke, 5, yAve, imageWidth - 5, yAve);
rv.FittedValue = double.NaN;
}
return(rv);
}