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);
}
public ProcessingReturnValues FitAndPlotSlowFlyby(
Dictionary<int, SingleMultiFrameMeasurement> measurements,
FlybyMeasurementContext meaContext,
FittingContext fittingContext,
FittingValue fittingValue,
GetFrameStateDataCallback getFrameStateDataCallback,
Graphics g, FlybyPlottingContext plottingContext, float xScale, float yScale, int imageWidth, int imageHight,
out double motionRate)
{
try
{
#region Building Test Cases
if (m_DumpTestCaseData)
{
var mvSer = new XmlSerializer(typeof(FlybyMeasurementContext));
var sb = new StringBuilder();
using (var wrt = new StringWriter(sb))
{
mvSer.Serialize(wrt, meaContext);
}
Trace.WriteLine(sb.ToString());
var fcSer = new XmlSerializer(typeof(FittingContext));
sb.Clear();
using (var wrt = new StringWriter(sb))
{
fcSer.Serialize(wrt, fittingContext);
}
Trace.WriteLine(sb.ToString());
var smfmSer = new XmlSerializer(typeof(SingleMultiFrameMeasurement));
foreach (int key in measurements.Keys)
{
sb.Clear();
using (var wrt2 = new StringWriter(sb))
{
smfmSer.Serialize(wrt2, measurements[key]);
if (measurements[key].FrameNo != key)
throw new InvalidOperationException();
Trace.WriteLine(sb.ToString());
}
}
}
#endregion
// Do linear regression, use residual based exclusion rules
// Report the interpolated position at the middle of the measured interva
// Don't forget to add the video normal position flag in the OBS file
// Expect elongated images and apply instrumental delay corrections
motionRate = double.NaN;
var rv = new ProcessingReturnValues();
int numFramesUser = 0;
rv.EarliestFrame = int.MaxValue;
rv.LatestFrame = int.MinValue;
var intervalValues = new Dictionary<int, Tuple<List<double>, List<double>>>();
var intervalMedians = new Dictionary<double, double>();
var intervalWeights = new Dictionary<double, double>();
LinearRegression regression = null;
if (measurements.Values.Count > 1)
{
rv.EarliestFrame = measurements.Values.Select(m => m.FrameNo).Min();
rv.LatestFrame = measurements.Values.Select(m => m.FrameNo).Max();
var minUncertainty = meaContext.MinPositionUncertaintyPixels * meaContext.ArsSecsInPixel;
foreach (SingleMultiFrameMeasurement measurement in measurements.Values)
{
int integrationInterval = (measurement.FrameNo - fittingContext.FirstFrameIdInIntegrationPeroid) / fittingContext.IntegratedFramesCount;
Tuple<List<double>,List<double>> intPoints;
if (!intervalValues.TryGetValue(integrationInterval, out intPoints))
{
intPoints = Tuple.Create(new List<double>(), new List<double>());
intervalValues.Add(integrationInterval, intPoints);
}
if (fittingValue == FittingValue.RA)
{
intPoints.Item1.Add(measurement.RADeg);
intPoints.Item2.Add(ComputePositionWeight(measurement.SolutionUncertaintyRACosDEArcSec, measurement, minUncertainty, fittingContext.Weighting));
}
else
{
intPoints.Item1.Add(measurement.DEDeg);
intPoints.Item2.Add(ComputePositionWeight(measurement.SolutionUncertaintyDEArcSec, measurement, minUncertainty, fittingContext.Weighting));
}
}
if (intervalValues.Count > 2)
{
regression = new LinearRegression();
foreach (int integratedFrameNo in intervalValues.Keys)
{
Tuple<List<double>, List<double>> data = intervalValues[integratedFrameNo];
double median;
double medianWeight;
WeightedMedian(data, out median, out medianWeight);
// Assign the data point to the middle of the integration interval (using frame numbers)
//
// |--|--|--|--|--|--|--|--|
// | | |
//
// Because the time associated with the first frame is the middle of the frame, but the
// time associated with the middle of the interval is the end of the field then the correction
// is (N / 2) - 0.5 frames
double dataPointFrameNo =
rv.EarliestFrame +
fittingContext.IntegratedFramesCount * integratedFrameNo
+ (fittingContext.IntegratedFramesCount / 2)
- 0.5;
intervalMedians.Add(dataPointFrameNo, median);
intervalWeights.Add(dataPointFrameNo, medianWeight);
if (fittingContext.Weighting != WeightingMode.None)
regression.AddDataPoint(dataPointFrameNo, median, medianWeight);
else
regression.AddDataPoint(dataPointFrameNo, median);
}
regression.Solve();
var firstPos = measurements[rv.EarliestFrame];
var lastPos = measurements[rv.LatestFrame];
double distanceArcSec = AngleUtility.Elongation(firstPos.RADeg, firstPos.DEDeg, lastPos.RADeg, lastPos.DEDeg) * 3600;
var firstTime = GetTimeForFrame(fittingContext, rv.EarliestFrame, meaContext.FirstVideoFrame, getFrameStateDataCallback, firstPos.OCRedTimeStamp);
var lastTime = GetTimeForFrame(fittingContext, rv.LatestFrame, meaContext.FirstVideoFrame, getFrameStateDataCallback, lastPos.OCRedTimeStamp);
double elapsedSec = new TimeSpan(lastTime.UT.Ticks - firstTime.UT.Ticks).TotalSeconds;
motionRate = distanceArcSec / elapsedSec;
}
}
FrameTime resolvedTime = null;
if (int.MinValue != meaContext.UserMidFrame)
{
// Find the closest video 'normal' MPC time and compute the frame number for it
// Now compute the RA/DE for the computed 'normal' frame
resolvedTime = GetTimeForFrame(fittingContext, meaContext.UserMidFrame, meaContext.FirstVideoFrame, getFrameStateDataCallback, measurements[meaContext.UserMidFrame].OCRedTimeStamp);
#region Plotting Code
if (g != null)
{
float xPosBeg = (float)(resolvedTime.ClosestNormalIntervalFirstFrameNo - rv.EarliestFrame) * xScale + 5;
float xPosEnd = (float)(resolvedTime.ClosestNormalIntervalLastFrameNo - rv.EarliestFrame) * xScale + 5;
g.FillRectangle(s_NormalTimeIntervalHighlightBrush, xPosBeg, 1, (xPosEnd - xPosBeg), imageHight - 2);
}
#endregion
}
Dictionary<double, double> secondPassData = new Dictionary<double, double>();
int minFrameId = measurements.Keys.Min();
#region Plotting Code
if (g != null)
{
foreach (SingleMultiFrameMeasurement measurement in measurements.Values)
{
float x = (measurement.FrameNo - minFrameId) * xScale + 5;
ProcessingValues val = new ProcessingValues()
{
Value = fittingValue == FittingValue.RA ? measurement.RADeg : measurement.DEDeg,
StdDev = fittingValue == FittingValue.RA ? measurement.StdDevRAArcSec / 3600.0 : measurement.StdDevDEArcSec / 3600.0
};
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;
g.DrawLine(plottingContext.IncludedPen, x, yFrom, x, yTo);
g.DrawLine(plottingContext.IncludedPen, x - 1, yFrom, x + 1, yFrom);
g.DrawLine(plottingContext.IncludedPen, x - 1, yTo, x + 1, yTo);
}
}
#endregion
foreach (double integrFrameNo in intervalMedians.Keys)
{
double val = intervalMedians[integrFrameNo];
double fittedValAtFrame = regression != null
? regression.ComputeY(integrFrameNo)
: double.NaN;
bool included = Math.Abs(fittedValAtFrame - val) < 3 * regression.StdDev;
#region Plotting Code
if (g != null)
{
if (fittingContext.IntegratedFramesCount > 1)
{
Pen mPen = included ? plottingContext.IncludedPen : plottingContext.ExcludedPen;
float x = (float)(integrFrameNo - minFrameId) * xScale + 5;
float y = (float)(val - plottingContext.MinValue) * yScale + 5;
g.DrawEllipse(mPen, x - 3, y - 3, 6, 6);
g.DrawLine(mPen, x - 5, y - 5, x + 5, y + 5);
g.DrawLine(mPen, x + 5, y - 5, x - 5, y + 5);
}
}
#endregion
if (included) secondPassData.Add(integrFrameNo, val);
}
#region Second Pass
regression = null;
if (secondPassData.Count > 2)
{
regression = new LinearRegression();
foreach (double frameNo in secondPassData.Keys)
{
if (fittingContext.Weighting != WeightingMode.None)
regression.AddDataPoint(frameNo, secondPassData[frameNo], intervalWeights[frameNo]);
else
regression.AddDataPoint(frameNo, secondPassData[frameNo]);
}
regression.Solve();
}
#endregion
if (regression != null)
{
#region Plotting Code
if (g != null)
{
double leftFittedVal = regression.ComputeY(rv.EarliestFrame);
double rightFittedVal = regression.ComputeY(rv.LatestFrame);
double err = 3 * regression.StdDev;
float leftAve = (float)(leftFittedVal - plottingContext.MinValue) * yScale + 5;
float rightAve = (float)(rightFittedVal - plottingContext.MinValue) * yScale + 5;
float leftX = 5 + (float)(rv.EarliestFrame - rv.EarliestFrame) * xScale;
float rightX = 5 + (float)(rv.LatestFrame - rv.EarliestFrame) * xScale;
g.DrawLine(plottingContext.AveragePen, leftX, leftAve - 1, rightX, rightAve - 1);
g.DrawLine(plottingContext.AveragePen, leftX, leftAve, rightX, rightAve);
g.DrawLine(plottingContext.AveragePen, leftX, leftAve + 1, rightX, rightAve + 1);
float leftMin = (float)(leftFittedVal - err - plottingContext.MinValue) * yScale + 5;
float leftMax = (float)(leftFittedVal + err - plottingContext.MinValue) * yScale + 5;
float rightMin = (float)(rightFittedVal - err - plottingContext.MinValue) * yScale + 5;
float rightMax = (float)(rightFittedVal + err - plottingContext.MinValue) * yScale + 5;
g.DrawLine(plottingContext.AveragePen, leftX, leftMin, rightX, rightMin);
g.DrawLine(plottingContext.AveragePen, leftX, leftMax, rightX, rightMax);
}
#endregion
if (int.MinValue != meaContext.UserMidFrame &&
resolvedTime != null)
{
// Find the closest video 'normal' MPC time and compute the frame number for it
// Now compute the RA/DE for the computed 'normal' frame
double fittedValueUncertainty;
double fittedValueAtMiddleFrame = regression.ComputeYWithError(resolvedTime.ClosestNormalFrameNo, out fittedValueUncertainty);
Trace.WriteLine(string.Format("{0}; Included: {1}; Normal Frame No: {2}; Fitted Val: {3} +/- {4:0.00}",
meaContext.UserMidValue.ToString("0.00000"),
numFramesUser, resolvedTime.ClosestNormalFrameNo,
AstroConvert.ToStringValue(fittedValueAtMiddleFrame, "+HH MM SS.T"),
regression.StdDev * 60 * 60));
// Report the interpolated position at the middle of the measured interval
// Don't forget to add the video normal position flag in the OBS file
// Expect elongated images and apply instrumental delay corrections
rv.FittedValue = fittedValueAtMiddleFrame;
rv.FittedValueTime = resolvedTime.ClosestNormalFrameTime;
rv.IsVideoNormalPosition = true;
rv.FittedNormalFrame = resolvedTime.ClosestNormalFrameNo;
rv.FittedValueUncertaintyArcSec = fittedValueUncertainty * 60 * 60;
#region Plotting Code
if (g != null)
{
// Plot the frame
float xPos = (float)(resolvedTime.ClosestNormalFrameNo - rv.EarliestFrame) * xScale + 5;
float yPos = (float)(rv.FittedValue - plottingContext.MinValue) * yScale + 5;
g.DrawLine(Pens.Yellow, xPos, 1, xPos, imageHight - 2);
g.FillEllipse(Brushes.Yellow, xPos - 3, yPos - 3, 6, 6);
}
#endregion
}
else
rv.FittedValue = double.NaN;
}
else
rv.FittedValue = double.NaN;
return rv;
}
catch (Exception ex)
{
Trace.WriteLine(ex.GetFullStackTrace());
motionRate = 0;
return null;
}
}
public ProcessingReturnValues FitAndPlotSlowFlyby(
Dictionary <int, SingleMultiFrameMeasurement> measurements,
FlybyMeasurementContext meaContext,
FittingContext fittingContext,
FittingValue fittingValue,
GetFrameStateDataCallback getFrameStateDataCallback,
Graphics g, FlybyPlottingContext plottingContext, float xScale, float yScale, int imageWidth, int imageHight,
out double motionRate)
{
try
{
#region Building Test Cases
if (m_DumpTestCaseData)
{
var mvSer = new XmlSerializer(typeof(FlybyMeasurementContext));
var sb = new StringBuilder();
using (var wrt = new StringWriter(sb))
{
mvSer.Serialize(wrt, meaContext);
}
Trace.WriteLine(sb.ToString());
var fcSer = new XmlSerializer(typeof(FittingContext));
sb.Clear();
using (var wrt = new StringWriter(sb))
{
fcSer.Serialize(wrt, fittingContext);
}
Trace.WriteLine(sb.ToString());
var smfmSer = new XmlSerializer(typeof(SingleMultiFrameMeasurement));
foreach (int key in measurements.Keys)
{
sb.Clear();
using (var wrt2 = new StringWriter(sb))
{
smfmSer.Serialize(wrt2, measurements[key]);
if (measurements[key].FrameNo != key)
{
throw new InvalidOperationException();
}
Trace.WriteLine(sb.ToString());
}
}
}
#endregion
// Do linear regression, use residual based exclusion rules
// Report the interpolated position at the middle of the measured interva
// Don't forget to add the video normal position flag in the OBS file
// Expect elongated images and apply instrumental delay corrections
motionRate = double.NaN;
var rv = new ProcessingReturnValues();
int numFramesUser = 0;
rv.EarliestFrame = int.MaxValue;
rv.LatestFrame = int.MinValue;
var intervalValues = new Dictionary <int, Tuple <List <double>, List <double> > >();
var intervalMedians = new Dictionary <double, double>();
var intervalWeights = new Dictionary <double, double>();
LinearRegression regression = null;
if (measurements.Values.Count > 1)
{
rv.EarliestFrame = measurements.Values.Select(m => m.FrameNo).Min();
rv.LatestFrame = measurements.Values.Select(m => m.FrameNo).Max();
var minUncertainty = meaContext.MinPositionUncertaintyPixels * meaContext.ArsSecsInPixel;
foreach (SingleMultiFrameMeasurement measurement in measurements.Values)
{
int integrationInterval = (measurement.FrameNo - fittingContext.FirstFrameIdInIntegrationPeroid) / fittingContext.IntegratedFramesCount;
Tuple <List <double>, List <double> > intPoints;
if (!intervalValues.TryGetValue(integrationInterval, out intPoints))
{
intPoints = Tuple.Create(new List <double>(), new List <double>());
intervalValues.Add(integrationInterval, intPoints);
}
if (fittingValue == FittingValue.RA)
{
intPoints.Item1.Add(measurement.RADeg);
intPoints.Item2.Add(ComputePositionWeight(measurement.SolutionUncertaintyRACosDEArcSec, measurement, minUncertainty, fittingContext.Weighting));
}
else
{
intPoints.Item1.Add(measurement.DEDeg);
intPoints.Item2.Add(ComputePositionWeight(measurement.SolutionUncertaintyDEArcSec, measurement, minUncertainty, fittingContext.Weighting));
}
}
if (intervalValues.Count > 2)
{
regression = new LinearRegression();
foreach (int integratedFrameNo in intervalValues.Keys)
{
Tuple <List <double>, List <double> > data = intervalValues[integratedFrameNo];
double median;
double medianWeight;
WeightedMedian(data, out median, out medianWeight);
// Assign the data point to the middle of the integration interval (using frame numbers)
//
// |--|--|--|--|--|--|--|--|
// | | |
//
// Because the time associated with the first frame is the middle of the frame, but the
// time associated with the middle of the interval is the end of the field then the correction
// is (N / 2) - 0.5 frames when integration is used or no correction when integration of x1 is used.
double dataPointFrameNo =
rv.EarliestFrame +
fittingContext.IntegratedFramesCount * integratedFrameNo
+ (fittingContext.IntegratedFramesCount / 2)
- (fittingContext.IntegratedFramesCount > 1 ? 0.5 : 0);
intervalMedians.Add(dataPointFrameNo, median);
intervalWeights.Add(dataPointFrameNo, medianWeight);
if (fittingContext.Weighting != WeightingMode.None)
{
regression.AddDataPoint(dataPointFrameNo, median, medianWeight);
}
else
{
regression.AddDataPoint(dataPointFrameNo, median);
}
}
regression.Solve();
var firstPos = measurements[rv.EarliestFrame];
var lastPos = measurements[rv.LatestFrame];
double distanceArcSec = AngleUtility.Elongation(firstPos.RADeg, firstPos.DEDeg, lastPos.RADeg, lastPos.DEDeg) * 3600;
var firstTime = GetTimeForFrame(fittingContext, rv.EarliestFrame, meaContext.FirstVideoFrame, getFrameStateDataCallback, firstPos.FrameTimeStamp);
var lastTime = GetTimeForFrame(fittingContext, rv.LatestFrame, meaContext.FirstVideoFrame, getFrameStateDataCallback, lastPos.FrameTimeStamp);
double elapsedSec = new TimeSpan(lastTime.UT.Ticks - firstTime.UT.Ticks).TotalSeconds;
motionRate = distanceArcSec / elapsedSec;
}
}
FrameTime resolvedTime = null;
if (int.MinValue != meaContext.UserMidFrame)
{
// Find the closest video 'normal' MPC time and compute the frame number for it
// Now compute the RA/DE for the computed 'normal' frame
resolvedTime = GetTimeForFrame(fittingContext, meaContext.UserMidFrame, meaContext.FirstVideoFrame, getFrameStateDataCallback, measurements[meaContext.UserMidFrame].FrameTimeStamp);
#region Plotting Code
if (g != null)
{
float xPosBeg = (float)(resolvedTime.ClosestNormalIntervalFirstFrameNo - rv.EarliestFrame) * xScale + 5;
float xPosEnd = (float)(resolvedTime.ClosestNormalIntervalLastFrameNo - rv.EarliestFrame) * xScale + 5;
g.FillRectangle(s_NormalTimeIntervalHighlightBrush, xPosBeg, 1, (xPosEnd - xPosBeg), imageHight - 2);
}
#endregion
}
Dictionary <double, double> secondPassData = new Dictionary <double, double>();
int minFrameId = measurements.Keys.Min();
#region Plotting Code
if (g != null)
{
foreach (SingleMultiFrameMeasurement measurement in measurements.Values)
{
float x = (measurement.FrameNo - minFrameId) * xScale + 5;
ProcessingValues val = new ProcessingValues()
{
Value = fittingValue == FittingValue.RA ? measurement.RADeg : measurement.DEDeg,
StdDev = fittingValue == FittingValue.RA ? measurement.StdDevRAArcSec / 3600.0 : measurement.StdDevDEArcSec / 3600.0
};
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;
g.DrawLine(plottingContext.IncludedPen, x, yFrom, x, yTo);
g.DrawLine(plottingContext.IncludedPen, x - 1, yFrom, x + 1, yFrom);
g.DrawLine(plottingContext.IncludedPen, x - 1, yTo, x + 1, yTo);
}
}
#endregion
foreach (double integrFrameNo in intervalMedians.Keys)
{
double val = intervalMedians[integrFrameNo];
double fittedValAtFrame = regression != null
? regression.ComputeY(integrFrameNo)
: double.NaN;
bool included = Math.Abs(fittedValAtFrame - val) < 3 * regression.StdDev;
#region Plotting Code
if (g != null)
{
if (fittingContext.IntegratedFramesCount > 1)
{
Pen mPen = included ? plottingContext.IncludedPen : plottingContext.ExcludedPen;
float x = (float)(integrFrameNo - minFrameId) * xScale + 5;
float y = (float)(val - plottingContext.MinValue) * yScale + 5;
g.DrawEllipse(mPen, x - 3, y - 3, 6, 6);
g.DrawLine(mPen, x - 5, y - 5, x + 5, y + 5);
g.DrawLine(mPen, x + 5, y - 5, x - 5, y + 5);
}
}
#endregion
if (included)
{
secondPassData.Add(integrFrameNo, val);
}
}
#region Second Pass
regression = null;
if (secondPassData.Count > 2)
{
regression = new LinearRegression();
foreach (double frameNo in secondPassData.Keys)
{
if (fittingContext.Weighting != WeightingMode.None)
{
regression.AddDataPoint(frameNo, secondPassData[frameNo], intervalWeights[frameNo]);
}
else
{
regression.AddDataPoint(frameNo, secondPassData[frameNo]);
}
}
regression.Solve();
}
#endregion
if (regression != null)
{
#region Plotting Code
if (g != null)
{
double leftFittedVal = regression.ComputeY(rv.EarliestFrame);
double rightFittedVal = regression.ComputeY(rv.LatestFrame);
double err = 3 * regression.StdDev;
float leftAve = (float)(leftFittedVal - plottingContext.MinValue) * yScale + 5;
float rightAve = (float)(rightFittedVal - plottingContext.MinValue) * yScale + 5;
float leftX = 5 + (float)(rv.EarliestFrame - rv.EarliestFrame) * xScale;
float rightX = 5 + (float)(rv.LatestFrame - rv.EarliestFrame) * xScale;
g.DrawLine(plottingContext.AveragePen, leftX, leftAve - 1, rightX, rightAve - 1);
g.DrawLine(plottingContext.AveragePen, leftX, leftAve, rightX, rightAve);
g.DrawLine(plottingContext.AveragePen, leftX, leftAve + 1, rightX, rightAve + 1);
float leftMin = (float)(leftFittedVal - err - plottingContext.MinValue) * yScale + 5;
float leftMax = (float)(leftFittedVal + err - plottingContext.MinValue) * yScale + 5;
float rightMin = (float)(rightFittedVal - err - plottingContext.MinValue) * yScale + 5;
float rightMax = (float)(rightFittedVal + err - plottingContext.MinValue) * yScale + 5;
g.DrawLine(plottingContext.AveragePen, leftX, leftMin, rightX, rightMin);
g.DrawLine(plottingContext.AveragePen, leftX, leftMax, rightX, rightMax);
}
#endregion
if (int.MinValue != meaContext.UserMidFrame &&
resolvedTime != null)
{
// Find the closest video 'normal' MPC time and compute the frame number for it
// Now compute the RA/DE for the computed 'normal' frame
double fittedValueUncertainty;
double fittedValueAtMiddleFrame = regression.ComputeYWithError(resolvedTime.ClosestNormalFrameNo, out fittedValueUncertainty);
Trace.WriteLine(string.Format("{0}; Included: {1}; Normal Frame No: {2}; Fitted Val: {3} +/- {4:0.00}",
meaContext.UserMidValue.ToString("0.00000"),
numFramesUser, resolvedTime.ClosestNormalFrameNo,
AstroConvert.ToStringValue(fittedValueAtMiddleFrame, "+HH MM SS.T"),
regression.StdDev * 60 * 60));
// Report the interpolated position at the middle of the measured interval
// Don't forget to add the video normal position flag in the OBS file
// Expect elongated images and apply instrumental delay corrections
rv.FittedValue = fittedValueAtMiddleFrame;
rv.FittedValueTime = resolvedTime.ClosestNormalFrameTime;
rv.IsVideoNormalPosition = true;
rv.FittedNormalFrame = resolvedTime.ClosestNormalFrameNo;
rv.FittedValueUncertaintyArcSec = fittedValueUncertainty * 60 * 60;
#region Plotting Code
if (g != null)
{
// Plot the frame
float xPos = (float)(resolvedTime.ClosestNormalFrameNo - rv.EarliestFrame) * xScale + 5;
float yPos = (float)(rv.FittedValue - plottingContext.MinValue) * yScale + 5;
g.DrawLine(Pens.Yellow, xPos, 1, xPos, imageHight - 2);
g.FillEllipse(Brushes.Yellow, xPos - 3, yPos - 3, 6, 6);
}
#endregion
}
else
{
rv.FittedValue = double.NaN;
}
}
else
{
rv.FittedValue = double.NaN;
}
return(rv);
}
catch (Exception ex)
{
Trace.WriteLine(ex.GetFullStackTrace());
motionRate = 0;
return(null);
}
}