public void Test4()
{
// Based on https://onlinecourses.science.psu.edu/stat501/node/397
double[] x_values = new double[] { 16, 14, 22, 10, 14, 17, 10, 13, 19, 12, 18, 11 };
double[] y_values = new double[] { 77, 70, 85, 50, 62, 70, 55, 63, 88, 57, 81, 51 };
var reg_ols = new LinearRegression();
for (int i = 0; i < x_values.Length; i++)
{
reg_ols.AddDataPoint(x_values[i], y_values[i]);
}
reg_ols.Solve();
Assert.AreEqual(3.269, reg_ols.A, 0.001);
Assert.AreEqual(19.47, reg_ols.B, 0.01);
Assert.AreEqual(4.5983, reg_ols.StdDev, 0.0001);
Assert.AreEqual(0.365, reg_ols.Uncertainty_A, 0.001);
Assert.AreEqual(5.52, reg_ols.Uncertainty_B, 0.01);
var residuals = reg_ols.Residuals.ToArray();
var reg2 = new LinearRegression();
for (int i = 0; i < x_values.Length; i++)
{
reg2.AddDataPoint(x_values[i], Math.Abs(residuals[i]));
}
reg2.Solve();
var predictedValues = new double[x_values.Length];
for (int i = 0; i < x_values.Length; i++)
{
predictedValues[i] = reg2.ComputeY(x_values[i]);
}
var reg_wls = new LinearRegression();
var factor = 1;
for (int i = 0; i < x_values.Length; i++)
{
double weight = factor / (predictedValues[i] * predictedValues[i]);
reg_wls.AddDataPoint(x_values[i], y_values[i], weight);
}
reg_wls.Solve();
Assert.AreEqual(3.421, reg_wls.A, 0.001);
Assert.AreEqual(17.30, reg_wls.B, 0.01);
Assert.AreEqual(1.15935, reg_wls.StdDevUnscaled, 0.00001);
}
public void Test2()
{
var reg = new LinearRegression();
reg.AddDataPoint(1, 6);
reg.AddDataPoint(2, 5);
reg.AddDataPoint(3, 7);
reg.AddDataPoint(4, 10);
reg.Solve();
Assert.AreEqual(1.4, reg.A, 0.001);
Assert.AreEqual(3.5, reg.B, 0.001);
}
private void DoLinearFit()
{
m_LinearFitX.Reset();
m_LinearFitY.Reset();
// Look for linear movement:
// x = a + i * b
// y = a + i * b
int firstFrameId = m_PreviousPositionFrameIds[0];
for (int i = 0; i < m_PreviousPositions.Count; i++)
{
int deltaFrames = m_PreviousPositionFrameIds[i] - firstFrameId;
m_LinearFitX.AddDataPoint(deltaFrames, m_PreviousPositions[i].XDouble);
m_LinearFitY.AddDataPoint(deltaFrames, m_PreviousPositions[i].YDouble);
}
m_LinearFitX.Solve();
m_LinearFitY.Solve();
}
public void Test3()
{
// Based on https://onlinecourses.science.psu.edu/stat501/node/352
double[] x_values = new double[] { 0.21, 0.20, 0.19, 0.18, 0.17, 0.16, 0.15 };
double[] y_values = new double[] { 0.1726, 0.1707, 0.1637, 0.1640, 0.1613, 0.1617, 0.1598 };
double[] sd_values = new double[] { 0.01988, 0.01938, 0.01896, 0.02037, 0.01654, 0.01594, 0.01763 };
var reg_ols = new LinearRegression();
var reg_wls = new LinearRegression();
var reg_wls2 = new LinearRegression();
for (int i = 0; i < x_values.Length; i++)
{
reg_ols.AddDataPoint(x_values[i], y_values[i]);
reg_wls.AddDataPoint(x_values[i], y_values[i], 1 / (sd_values[i] * sd_values[i]));
reg_wls2.AddDataPoint(x_values[i], y_values[i], 2 / (sd_values[i] * sd_values[i]));
}
reg_ols.Solve();
reg_wls.Solve();
reg_wls2.Solve();
Assert.AreEqual(0.2048, reg_wls.A, 0.0001);
Assert.AreEqual(0.12796, reg_wls.B, 0.0001);
Assert.AreEqual(0.2048, reg_wls2.A, 0.0001);
Assert.AreEqual(0.12796, reg_wls2.B, 0.0001);
if (Math.Abs(reg_wls.StdDevUnscaled - reg_wls2.StdDevUnscaled) < 0.0001)
{
Assert.Fail("Proportional weights result in different StdDevs in the weighted population");
}
;
Assert.AreEqual(reg_wls.StdDev, reg_wls2.StdDev, 0.0001, "Proportional weights result in the same scaled StdDev");
Assert.AreEqual(0.2100, reg_ols.A, 0.0001);
Assert.AreEqual(0.12703, reg_ols.B, 0.0001);
}
public int AstroAnalogueVideoNormaliseNtpDataIfNeeded(Action <int> progressCallback, out float oneSigmaError)
{
int ntpError = -1;
oneSigmaError = float.NaN;
if (NtpDataAvailable && !OcrDataAvailable && !m_UseNtpTimeAsCentralExposureTime)
{
if (m_CountFrames > 1 /* No Timestamp for first frame */ + 1 /* No Timestamp for last frame*/ + 3 /* Minimum timestamped frames for a FIT */)
{
try
{
double frameDurationMilliseconds = 1000 / m_FrameRate;
var lr = new LinearRegression();
long zeroPointTicks = -1;
int percentDone = 0;
int percentDoneCalled = 0;
if (progressCallback != null)
{
progressCallback(percentDone);
}
long ntpTimestampErrorSum = 0;
int ntpTimestampErrorDatapoints = 0;
for (int i = m_FirstFrame; i < m_FirstFrame + m_CountFrames; i++)
{
FrameStateData stateChannel = GetFrameStatusChannel(i);
if (stateChannel.HasValidNtpTimeStamp)
{
long centralTicks = stateChannel.EndFrameNtpTime.AddMilliseconds(-0.5 * frameDurationMilliseconds).Ticks;
if (zeroPointTicks == -1)
{
zeroPointTicks = centralTicks;
}
lr.AddDataPoint(i, new TimeSpan(centralTicks - zeroPointTicks).TotalMilliseconds);
ntpTimestampErrorSum += stateChannel.NtpTimeStampError;
ntpTimestampErrorDatapoints++;
}
percentDone = 100 * (i - m_FirstFrame) / m_CountFrames;
if (progressCallback != null && percentDone - percentDoneCalled > 5)
{
progressCallback(percentDone);
percentDoneCalled = percentDone;
}
}
if (lr.NumberOfDataPoints > 3)
{
lr.Solve();
m_CalibratedNtpTimeZeroPoint = zeroPointTicks;
m_CalibratedNtpTimeSource = lr;
m_UseNtpTimeAsCentralExposureTime = true;
m_NtpTimeFitSigma = lr.StdDev;
m_NtpTimeAverageNetworkError = (ntpTimestampErrorSum * 1.0 / ntpTimestampErrorDatapoints);
ntpError = (int)Math.Round(m_NtpTimeFitSigma + m_NtpTimeAverageNetworkError);
Trace.WriteLine(string.Format("NTP Timebase Established. 1-Sigma = {0} ms", lr.StdDev.ToString("0.00")));
oneSigmaError = (float)m_NtpTimeFitSigma;
}
progressCallback(100);
}
catch (Exception ex)
{
Trace.WriteLine(ex.GetFullStackTrace());
}
}
}
return(ntpError);
}
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);
}
}
private ImagePixel GetExpectedPosition(int frameNo)
{
ImagePixel rv = null;
var intervalValues = new Dictionary <int, List <ImagePixel> >();
var intervalMedians = new Dictionary <int, ImagePixel>();
int earliestFrame = m_PastFrameNos[0];
for (int i = 0; i < m_PastFrameNos.Count; i++)
{
int integrationInterval = (m_PastFrameNos[i] - earliestFrame) / m_MeasurementContext.IntegratedFramesCount;
List <ImagePixel> intPoints;
if (!intervalValues.TryGetValue(integrationInterval, out intPoints))
{
intPoints = new List <ImagePixel>();
intervalValues.Add(integrationInterval, intPoints);
}
intPoints.Add(new ImagePixel(m_PastFramePosX[i], m_PastFramePosY[i]));
}
var calcBucketX = new List <double>();
var calcBucketY = new List <double>();
foreach (int key in intervalValues.Keys)
{
calcBucketX.Clear();
calcBucketY.Clear();
intervalValues[key].ForEach(v =>
{
calcBucketX.Add(v.XDouble);
calcBucketY.Add(v.YDouble);
});
double xMed = calcBucketX.Median();
double yMed = calcBucketY.Median();
intervalMedians.Add(key, new ImagePixel(xMed, yMed));
}
var xMotion = new LinearRegression();
var yMotion = new LinearRegression();
foreach (int intInt in intervalMedians.Keys)
{
long t = intInt;
double x = intervalMedians[intInt].XDouble;
double y = intervalMedians[intInt].YDouble;
if (x > 0 && y > 0)
{
xMotion.AddDataPoint(t, x);
yMotion.AddDataPoint(t, y);
}
}
try
{
xMotion.Solve();
yMotion.Solve();
int currIntInterval = (frameNo - earliestFrame) / m_MeasurementContext.IntegratedFramesCount;
rv = new ImagePixel(xMotion.ComputeY(currIntInterval), yMotion.ComputeY(currIntInterval));
}
catch (Exception ex)
{
Trace.WriteLine(ex.GetFullStackTrace());
}
return(rv);
}
public void Test1()
{
// Based on https://www.medcalc.org/manual/weighted-regression-worked-example.php
var reg = new LinearRegression();
double[] x_values = new double[]
{
27, 21, 22, 24, 25, 23, 20, 20, 29, 24, 25, 28, 26, 38, 32, 33, 31, 34, 37, 38, 33, 35, 30, 31, 37, 39, 46,
49, 40, 42, 43, 46, 43, 44, 46, 47, 45, 49, 48, 40, 42, 55, 54, 57, 52, 53, 56, 52, 50, 59, 50, 52, 58,
57
};
double[] y_values = new double[]
{
73, 66, 63, 75, 71, 70, 65, 70, 79, 72, 68, 67, 79, 91, 76, 69, 66, 73, 78, 87, 76, 79, 73, 80, 68, 75, 89,
101, 70, 72, 80, 83, 75, 71, 80, 96, 92, 80, 70, 90, 85, 76, 71, 99, 86, 79, 92, 85, 71, 90, 91, 100, 80,
109
};
for (int i = 0; i < x_values.Length; i++)
{
reg.AddDataPoint(x_values[i], y_values[i]);
}
reg.Solve();
Assert.AreEqual(0.5800, reg.A, 0.0001);
Assert.AreEqual(56.1569, reg.B, 0.0001);
Assert.AreEqual(8.1457, reg.StdDev, 0.0001);
Assert.AreEqual(0.09695, reg.Uncertainty_A, 0.0001);
Assert.AreEqual(3.9937, reg.Uncertainty_B, 0.0001);
var residuals = reg.Residuals.ToArray();
var reg2 = new LinearRegression();
for (int i = 0; i < x_values.Length; i++)
{
reg2.AddDataPoint(x_values[i], Math.Abs(residuals[i]));
}
reg2.Solve();
Assert.AreEqual(0.1982, reg2.A, 0.0001);
Assert.AreEqual(-1.5495, reg2.B, 0.0001);
Assert.AreEqual(4.4606, reg2.StdDev, 0.0001);
Assert.AreEqual(0.05309, reg2.Uncertainty_A, 0.0001);
Assert.AreEqual(2.1869, reg2.Uncertainty_B, 0.0001);
var predictedValues = new double[x_values.Length];
for (int i = 0; i < x_values.Length; i++)
{
predictedValues[i] = reg2.ComputeY(x_values[i]);
}
var reg3 = new LinearRegression();
var factor = 1;
for (int i = 0; i < x_values.Length; i++)
{
double weight = factor / (predictedValues[i] * predictedValues[i]);
reg3.AddDataPoint(x_values[i], y_values[i], weight);
}
reg3.Solve();
Assert.AreEqual(0.5963, reg3.A, 0.0001);
Assert.AreEqual(55.5658, reg3.B, 0.0001);
Assert.AreEqual(1.2130, reg3.StdDevUnscaled, 0.0001);
}
public void TestRAUncertainty()
{
var regressionRA = new LinearRegression();
var regressionRACosDE = new LinearRegression();
var regressionDE = new LinearRegression();
#region DATA
var data3 = new double[100, 2]
{
{ 107.739483057629, 23.1727860863356 },
{ 107.739758423225, 23.1725858289442 },
{ 107.739270316112, 23.1727265176296 },
{ 107.740138616241, 23.1726236358575 },
{ 107.739635635855, 23.1720172219518 },
{ 107.739770516897, 23.1729142307849 },
{ 107.73978035157, 23.1724868171702 },
{ 107.7402464908, 23.1722211800014 },
{ 107.740153319844, 23.17248806658 },
{ 107.739638162317, 23.1721297891023 },
{ 107.740152592624, 23.1724840894266 },
{ 107.73958378293, 23.1723613227751 },
{ 107.740253757515, 23.172044928013 },
{ 107.739869281082, 23.1718262300426 },
{ 107.739789179397, 23.1719229687229 },
{ 107.740109562638, 23.1722060231074 },
{ 107.740283891099, 23.1718843884705 },
{ 107.739908616613, 23.1721244581715 },
{ 107.740456941346, 23.171955354053 },
{ 107.739775905586, 23.1718647897894 },
{ 107.739910899659, 23.1717039889273 },
{ 107.740289075343, 23.1716186337573 },
{ 107.739949394724, 23.171506240555 },
{ 107.739932221015, 23.1716027243754 },
{ 107.740295228809, 23.1716475314679 },
{ 107.740110388037, 23.1713426960706 },
{ 107.740000979798, 23.1716693560547 },
{ 107.740683952325, 23.1709681704731 },
{ 107.740203119785, 23.1714519510736 },
{ 107.740273707513, 23.1713702195282 },
{ 107.740715664145, 23.1708006096487 },
{ 107.740242143812, 23.1714449185373 },
{ 107.740687548443, 23.1714370988362 },
{ 107.740393828828, 23.1711679651411 },
{ 107.740547330181, 23.1707733940985 },
{ 107.740304974691, 23.1709983145174 },
{ 107.74034540204, 23.1710469626238 },
{ 107.740904365499, 23.1712337543625 },
{ 107.740271422439, 23.1707876401664 },
{ 107.740473226403, 23.1707728540074 },
{ 107.740636055235, 23.1704881107108 },
{ 107.740425649522, 23.1706972672815 },
{ 107.741075014634, 23.1711886612524 },
{ 107.740786717375, 23.1703917179741 },
{ 107.740612480474, 23.1707997640991 },
{ 107.740513888292, 23.1702033095878 },
{ 107.740861057518, 23.1707917436653 },
{ 107.740431450664, 23.1704962321596 },
{ 107.740894364108, 23.1706460451468 },
{ 107.740737653969, 23.1699454114361 },
{ 107.741167338868, 23.1704390641158 },
{ 107.7413536072, 23.1702117449598 },
{ 107.741086391515, 23.1708025027409 },
{ 107.740817260861, 23.1707126761071 },
{ 107.741221711022, 23.1703529560508 },
{ 107.740861537438, 23.1703420196572 },
{ 107.74133696574, 23.1699266624604 },
{ 107.741008974082, 23.1698332023904 },
{ 107.741330374957, 23.1699747581779 },
{ 107.740956577025, 23.1699971703065 },
{ 107.741383593614, 23.1700614545797 },
{ 107.741111473117, 23.1693429455465 },
{ 107.741121545844, 23.1698626383014 },
{ 107.740820518692, 23.1696821611261 },
{ 107.740916798005, 23.1696481452449 },
{ 107.741407098196, 23.1697794065368 },
{ 107.741287855641, 23.1694896025056 },
{ 107.741231209856, 23.1697278715938 },
{ 107.741571326879, 23.1696485314836 },
{ 107.741256422563, 23.1692796164232 },
{ 107.741083941569, 23.1692085591873 },
{ 107.742137813992, 23.1692501809322 },
{ 107.741681536193, 23.1691250721346 },
{ 107.741303539868, 23.1692497992003 },
{ 107.741635549928, 23.1696783769589 },
{ 107.741659390834, 23.1692018221502 },
{ 107.741519314827, 23.1689582724524 },
{ 107.741584255568, 23.1690406639767 },
{ 107.741457448448, 23.1689571870363 },
{ 107.741598496613, 23.1690963575179 },
{ 107.741640939783, 23.1689548869089 },
{ 107.741369971733, 23.1688034552239 },
{ 107.741859029521, 23.1688553893107 },
{ 107.741963565581, 23.1683666431612 },
{ 107.741666822527, 23.1688987338326 },
{ 107.741641792823, 23.1689634685279 },
{ 107.742023968239, 23.1690510611327 },
{ 107.741962846523, 23.1688076089897 },
{ 107.741683243803, 23.1683235937605 },
{ 107.742121706882, 23.1689619396987 },
{ 107.741538062532, 23.168283426697 },
{ 107.742422013432, 23.1687801621234 },
{ 107.741896618583, 23.1686362544736 },
{ 107.741972513575, 23.1686551496664 },
{ 107.74206679923, 23.168500337937 },
{ 107.742405073144, 23.1684770104653 },
{ 107.742032294448, 23.1683475533958 },
{ 107.742230287337, 23.1682284222132 },
{ 107.742201384546, 23.1682279605208 },
{ 107.741884032454, 23.1682565185304 }
};
var data2 = new double[100, 2]
{
{ 107.739477771965, 69.1728347555165 },
{ 107.73961196667, 69.1726830767986 },
{ 107.739409817125, 69.1725933008645 },
{ 107.74019077671, 69.172494940147 },
{ 107.740585586026, 69.1726077103564 },
{ 107.74059838218, 69.1722819118463 },
{ 107.740208274081, 69.1723120729806 },
{ 107.740712281929, 69.1720791829606 },
{ 107.74033612626, 69.1723780587458 },
{ 107.741242715081, 69.1721192003436 },
{ 107.741266002392, 69.1722343854433 },
{ 107.741105006249, 69.1721003309252 },
{ 107.741363419742, 69.172088671089 },
{ 107.741690798727, 69.1722568414364 },
{ 107.741666861184, 69.17205417308 },
{ 107.741361837266, 69.1720675384819 },
{ 107.742084712903, 69.1718874998462 },
{ 107.742042010619, 69.1719175816475 },
{ 107.742280817921, 69.1719094396275 },
{ 107.742577304285, 69.1717986068223 },
{ 107.741966244595, 69.1718798130166 },
{ 107.742684141694, 69.1718510228511 },
{ 107.742976630078, 69.17157595174 },
{ 107.743352294911, 69.1715857376448 },
{ 107.743061556848, 69.1714241326127 },
{ 107.743090113926, 69.1714458284253 },
{ 107.743275509384, 69.1715522559223 },
{ 107.743176399757, 69.1714823812197 },
{ 107.743814300524, 69.1713901151672 },
{ 107.743767969983, 69.1714428793583 },
{ 107.743858065548, 69.1713035534992 },
{ 107.743822885639, 69.1712307918348 },
{ 107.744334295083, 69.1713055626603 },
{ 107.744427875787, 69.1712388751409 },
{ 107.744340783728, 69.1710928940912 },
{ 107.744805413896, 69.1710055991679 },
{ 107.74502108349, 69.1709137441353 },
{ 107.744828974649, 69.1709496406011 },
{ 107.74512401154, 69.1708573778863 },
{ 107.745281281021, 69.1708161629737 },
{ 107.745576469803, 69.1710203029433 },
{ 107.745740147716, 69.1707075932863 },
{ 107.745502577032, 69.1708074622807 },
{ 107.745623997879, 69.1706684821245 },
{ 107.746509028511, 69.1708362938784 },
{ 107.74602549245, 69.1706568551002 },
{ 107.745897609677, 69.170596921634 },
{ 107.746469521491, 69.1704027875192 },
{ 107.746277468908, 69.1703449453588 },
{ 107.74672857027, 69.1705498632184 },
{ 107.746946720371, 69.1702725215642 },
{ 107.746884661238, 69.1704713141467 },
{ 107.746860604767, 69.1702156222331 },
{ 107.747294089089, 69.1701998357532 },
{ 107.747620144745, 69.1702161529364 },
{ 107.747858455982, 69.170059426359 },
{ 107.747632580795, 69.1700516486805 },
{ 107.747755094573, 69.1698765265442 },
{ 107.747994751954, 69.1701514424405 },
{ 107.748121399878, 69.1700667592208 },
{ 107.748172662863, 69.1698849007696 },
{ 107.748228803142, 69.1699923304849 },
{ 107.749014214424, 69.1697868133965 },
{ 107.748792915115, 69.1700161340914 },
{ 107.748638559821, 69.1698409328545 },
{ 107.748636809009, 69.1697153039492 },
{ 107.749136438823, 69.1697074633957 },
{ 107.749338377121, 69.1697654496001 },
{ 107.749569612206, 69.1697500026688 },
{ 107.749595422398, 69.169419529115 },
{ 107.750383523525, 69.1696656542994 },
{ 107.75048831811, 69.1692035028989 },
{ 107.750352623105, 69.1693815488115 },
{ 107.750761537512, 69.1692866838914 },
{ 107.750685403332, 69.1693024139098 },
{ 107.750705748086, 69.1692717484141 },
{ 107.750843059166, 69.1692580533525 },
{ 107.750716179597, 69.1691529497331 },
{ 107.75072202975, 69.1691168107163 },
{ 107.75108559747, 69.1691834005496 },
{ 107.751493043153, 69.1691522809292 },
{ 107.751596829163, 69.169012841963 },
{ 107.75131727043, 69.1688012714158 },
{ 107.751666922544, 69.1689359714136 },
{ 107.751578388338, 69.1687399323656 },
{ 107.751905351653, 69.1688376229061 },
{ 107.752250104552, 69.1687619831483 },
{ 107.752678677296, 69.1687244251449 },
{ 107.752525957028, 69.1686536798413 },
{ 107.752373714866, 69.1686287601216 },
{ 107.753019411842, 69.1686380478501 },
{ 107.752741905599, 69.1684961094107 },
{ 107.753018243388, 69.1683971436757 },
{ 107.752942371889, 69.1684595315802 },
{ 107.753259742823, 69.1684806579804 },
{ 107.753511849838, 69.168268224757 },
{ 107.75342673874, 69.1685388792275 },
{ 107.753991832751, 69.1681999371544 },
{ 107.753606583523, 69.1682240722345 },
{ 107.753806764198, 69.1682937040626 },
};
var data = new double[100, 2]
{
{ 107.73935730677, 69.172711754819 },
{ 107.739941719805, 69.1725607327368 },
{ 107.739969545763, 69.1728020832412 },
{ 107.739725613496, 69.1724948444009 },
{ 107.739031353708, 69.1726159366499 },
{ 107.739747958109, 69.1721826902977 },
{ 107.74074663056, 69.1722130865804 },
{ 107.739619292955, 69.1724872676585 },
{ 107.739526386824, 69.1723097829692 },
{ 107.7410771233, 69.1725703918204 },
{ 107.740739660076, 69.1724144742981 },
{ 107.740697636644, 69.1721550394849 },
{ 107.741880544558, 69.1719738870443 },
{ 107.741833247397, 69.1721940884685 },
{ 107.742314186337, 69.17193450189 },
{ 107.740926254247, 69.1721384298611 },
{ 107.741970497859, 69.1721455511604 },
{ 107.742119450987, 69.1721636360282 },
{ 107.742132805385, 69.1719603779096 },
{ 107.742777129314, 69.1714210932301 },
{ 107.743075397391, 69.1714527431053 },
{ 107.743768675633, 69.1715943552255 },
{ 107.742542542626, 69.1716645234002 },
{ 107.743524460336, 69.1716088168889 },
{ 107.743350115709, 69.1717882604853 },
{ 107.743489485345, 69.1721692852173 },
{ 107.74401344733, 69.1717430018303 },
{ 107.743001358569, 69.171745750306 },
{ 107.744101155658, 69.1715995718388 },
{ 107.743357827113, 69.1709192593056 },
{ 107.744693317033, 69.1716866797303 },
{ 107.74508962137, 69.1713006067015 },
{ 107.743997983147, 69.1708910615953 },
{ 107.743996491802, 69.1715212096561 },
{ 107.74373222623, 69.1709626863127 },
{ 107.744993875976, 69.1714069871322 },
{ 107.744573043011, 69.1710885783853 },
{ 107.744610440986, 69.1709524781463 },
{ 107.744515880438, 69.171012647055 },
{ 107.745021853434, 69.1706795177817 },
{ 107.745928469586, 69.1709513201446 },
{ 107.746274328526, 69.1709990525173 },
{ 107.746003621776, 69.1703636654999 },
{ 107.746860479622, 69.1707788370646 },
{ 107.746015994963, 69.170532070676 },
{ 107.745308266138, 69.1703833282827 },
{ 107.747434171025, 69.1707583505672 },
{ 107.746380716695, 69.1707477283657 },
{ 107.746322773734, 69.1705759472448 },
{ 107.747195770778, 69.1704783387962 },
{ 107.746726832268, 69.1705445287354 },
{ 107.746479807916, 69.170295182694 },
{ 107.746858852632, 69.170151842435 },
{ 107.748005552167, 69.1701324143026 },
{ 107.746440725163, 69.1702997117708 },
{ 107.747632752596, 69.1699714950714 },
{ 107.746288060495, 69.170247112624 },
{ 107.748643753256, 69.1698662806951 },
{ 107.74881046717, 69.1699979516342 },
{ 107.747535292112, 69.170078645581 },
{ 107.747766333879, 69.1699744718271 },
{ 107.748809227233, 69.1700193921839 },
{ 107.749697498078, 69.1695793718616 },
{ 107.74893998829, 69.1698713857366 },
{ 107.748878487794, 69.1695228880432 },
{ 107.74904534252, 69.1698472549382 },
{ 107.749463140037, 69.1696628177742 },
{ 107.749830174792, 69.1698519550262 },
{ 107.749699649195, 69.1695956041761 },
{ 107.750502343619, 69.1693099555417 },
{ 107.750326261356, 69.1691415029809 },
{ 107.748673873243, 69.1692620977489 },
{ 107.75106313072, 69.1690631812159 },
{ 107.75052688368, 69.1691938573698 },
{ 107.750543324985, 69.1695068538202 },
{ 107.751031016666, 69.1692150774484 },
{ 107.751096804392, 69.1688797213772 },
{ 107.75042386222, 69.1690032179007 },
{ 107.750153005185, 69.169176739356 },
{ 107.751104742713, 69.1687864249361 },
{ 107.751084417858, 69.1690641095538 },
{ 107.751285471877, 69.1690996992436 },
{ 107.751597211541, 69.169157134543 },
{ 107.751909360988, 69.1690350795147 },
{ 107.752462310445, 69.1684796654783 },
{ 107.751635885797, 69.1688208914861 },
{ 107.752428595531, 69.1685312843568 },
{ 107.753169064017, 69.1685892661358 },
{ 107.752228822227, 69.1685323946141 },
{ 107.753263916127, 69.168469632772 },
{ 107.753442528401, 69.1684911064022 },
{ 107.753490171167, 69.168976339076 },
{ 107.753304279553, 69.1685787342008 },
{ 107.75333647205, 69.1683355355218 },
{ 107.753820078749, 69.1683194672346 },
{ 107.753790511111, 69.1683609672304 },
{ 107.754442898298, 69.1681319406902 },
{ 107.753649542414, 69.1683447688382 },
{ 107.754436235939, 69.1684819745314 },
{ 107.754808387808, 69.1680718218884 },
};
#endregion
double SEC_TO_DAY = 1.0 / (24 * 3600);
for (int i = 0; i < 100; i++)
{
var ra = data[i, 0];
var de = data[i, 1];
regressionRA.AddDataPoint(i * SEC_TO_DAY, ra);
regressionDE.AddDataPoint(i * SEC_TO_DAY, de);
regressionRACosDE.AddDataPoint(i * SEC_TO_DAY, ra * Math.Cos(de * Math.PI / 180.0));
}
regressionRA.Solve();
regressionDE.Solve();
regressionRACosDE.Solve();
double timeOfDay = 50 * SEC_TO_DAY;
ErrorMethod errMethod = ErrorMethod.HalfStdDev;
double errRAArcSec;
double errDEArcSec;
var raHours = regressionRA.ComputeYWithError(timeOfDay, out errRAArcSec, errMethod) / 15.0;
var deDeg = regressionDE.ComputeYWithError(timeOfDay, out errDEArcSec, errMethod);
double cosDEFactor = Math.Cos(deDeg * Math.PI / 180);
double errRACosDEArcSec = errRAArcSec * cosDEFactor * 3600;
errDEArcSec *= 3600;
Trace.WriteLine(string.Format("RA = {0} +/- {1:0.00}\" StdDev={4:0.000}; DE={2} +/- {3:0.00}\"",
AstroConvert.ToStringValue(raHours, "HH MM SS.TT"), errRACosDEArcSec, AstroConvert.ToStringValue(deDeg, "+DD MM SS.T"), errDEArcSec, regressionRA.StdDevUnscaled * cosDEFactor * 3600));
double errRA2;
var raHoursCosDE = regressionRACosDE.ComputeYWithError(timeOfDay, out errRA2, errMethod) / 15.0;
raHours = raHoursCosDE / cosDEFactor;
errRA2 *= 3600;
Trace.WriteLine(string.Format("RA = {0} +/- {1:0.00}\" StdDev={4:0.000}; DE={2} +/- {3:0.00}\"",
AstroConvert.ToStringValue(raHours, "HH MM SS.TT"), errRA2, AstroConvert.ToStringValue(deDeg, "+DD MM SS.T"), errDEArcSec, regressionRACosDE.StdDevUnscaled * 3600));
Assert.IsTrue(regressionRA.StdDevUnscaled * cosDEFactor < regressionRACosDE.StdDevUnscaled);
// NOTE: StdDev in the RA fit appears to be smaller in RA.CosDE terms when the fitting is done in RA, compared to when it is done in RA.CosDE.
// Therefore we continue to use the RA fitting, rather than RA.CosDE fitting.
// TODO: This needs to be looked through and proper mathematical evaluation needs to be done for the uncertainties before the RA.CosDE fitting can be used directly
}
public void Calculate(
MeasurementPositionEntry[] entries, WeightingMode weighting, bool removeOutliers, double outlierSigmaCoeff,
double instDelayTimeOfDay, double minUncertainty,
bool includePositionalUncertainties, ErrorMethod errorMethod, double smallestReportedUncertaintyArcSec)
{
m_InstDelayTimeOfDay = instDelayTimeOfDay;
m_Weighting = weighting;
m_ErrorMethod = errorMethod;
m_SmallestReportedUncertaintyArcSec = smallestReportedUncertaintyArcSec;
m_MinSinglePositionUncertainty = minUncertainty;
var regRA = new LinearRegression();
var regDE = new LinearRegression();
foreach (var entry in entries)
{
var midFrameTime = entry.TimeOfDayUTC - instDelayTimeOfDay;
if (weighting == WeightingMode.None)
{
regRA.AddDataPoint(midFrameTime, entry.RADeg);
regDE.AddDataPoint(midFrameTime, entry.DEDeg);
}
else
{
var weightRA = CalulateWeight(entry, entry.SolutionUncertaintyRACosDEArcSec);
var weightDE = CalulateWeight(entry, entry.SolutionUncertaintyDEArcSec);
regRA.AddDataPoint(midFrameTime, entry.RADeg, weightRA);
regDE.AddDataPoint(midFrameTime, entry.DEDeg, weightDE);
}
}
m_Entries = new List <MeasurementPositionEntry>();
regRA.Solve();
regDE.Solve();
RemovedOutliers = 0;
if (removeOutliers)
{
var outlierLimitRA = regRA.StdDev * outlierSigmaCoeff;
var residualsRA = regRA.Residuals.ToArray();
var outlierLimitDE = regDE.StdDev * outlierSigmaCoeff;
var residualsDE = regDE.Residuals.ToArray();
for (int i = 0; i < entries.Length; i++)
{
if (Math.Abs(residualsRA[i]) <= outlierLimitRA && Math.Abs(residualsDE[i]) <= outlierLimitDE)
{
m_Entries.Add(entries[i]);
}
else
{
RemovedOutliers++;
}
}
m_RegressionRA = new LinearRegression();
m_RegressionDE = new LinearRegression();
foreach (var entry in m_Entries)
{
var midFrameTime = entry.TimeOfDayUTC - instDelayTimeOfDay;
if (weighting == WeightingMode.None)
{
m_RegressionRA.AddDataPoint(midFrameTime, entry.RADeg);
m_RegressionDE.AddDataPoint(midFrameTime, entry.DEDeg);
}
else
{
var weightRA = CalulateWeight(entry, entry.SolutionUncertaintyRACosDEArcSec);
m_RegressionRA.AddDataPoint(midFrameTime, entry.RADeg, weightRA);
var weightDE = CalulateWeight(entry, entry.SolutionUncertaintyDEArcSec);
m_RegressionDE.AddDataPoint(midFrameTime, entry.DEDeg, weightDE);
}
}
m_RegressionRA.Solve();
m_RegressionDE.Solve();
}
else
{
m_RegressionRA = regRA;
m_RegressionDE = regDE;
m_Entries = entries.ToList();
}
if (includePositionalUncertainties)
{
var posUncertaintyAveLst = new List <double>();
foreach (var entry in m_Entries)
{
var posUncertainty = entry.FWHMArcSec / (2.355 * entry.SNR);
if (posUncertainty < m_MinSinglePositionUncertainty)
{
posUncertainty = m_MinSinglePositionUncertainty;
}
posUncertaintyAveLst.Add(posUncertainty);
}
var posUncertaintyMedian = posUncertaintyAveLst.Median();
m_PosUncertaintyMedArcSec = posUncertaintyMedian / Math.Sqrt(posUncertaintyAveLst.Count);
}
else
{
m_PosUncertaintyMedArcSec = null;
}
}
internal void Plot(Graphics g, Pen pen,
int fullWidth, int fullHeight,
double minY, double maxY,
string motionName,
double aveIncl,
Func <CalculatedEntry, double> getVal,
Func <CalculatedEntry, double> getWeight,
Func <CalculatedEntry, double> getCalcVal,
Func <FastMotionChunkPositionExtractor, double> getMidPointPos,
Func <CalculatedEntry, double> rateCalcFactor)
{
g.Clear(SystemColors.ControlDarkDark);
if (m_Chunks.Count == 0)
{
return;
}
float clientAreaWidth = fullWidth - PADDING_L - PADDING_R - 2 * BORDER;
float clientAreaHeight = fullHeight - PADDING_L - PADDING_R - 2 * BORDER;
g.DrawRectangle(SystemPens.ControlDark, PADDING_L, PADDING_R, clientAreaWidth + 2 * BORDER, clientAreaHeight + 2 * BORDER);
double minX = m_Chunks.Min(x => x.MinTimeOfDayUTCInstrDelayApplied);
double maxX = m_Chunks.Max(x => x.MaxTimeOfDayUTCInstrDelayApplied);
float scaleX = (float)(clientAreaWidth / (maxX - minX));
float scaleY = (float)(clientAreaHeight / (maxY - minY));
var repX = new List <double>();
var repY = new List <double>();
double?motionRate = null;
Pen constraintPen = new Pen(Color.FromArgb(100, pen.Color));
var allFrameNos = m_AllEntries.Select(x => x.FrameNo).ToList();
foreach (var chunk in m_Chunks)
{
double? startX = null;
double? startYCalc = null;
CalculatedEntry lastEntry = null;
foreach (var entry in chunk.Entries)
{
if (!entry.IsConstraintPoint)
{
lastEntry = entry;
}
float x = (float)Math.Round(PADDING_L + BORDER + (scaleX * (entry.TimeOfDayUTCInstrDelayApplied - minX)));
float y = fullHeight - PADDING_L - BORDER - (float)(scaleY * (getVal(entry) - minY));
if (!startX.HasValue && !entry.IsConstraintPoint)
{
startX = entry.TimeOfDayUTCInstrDelayApplied;
startYCalc = getCalcVal(entry);
}
var entryPen = entry.IsConstraintPoint ? constraintPen : pen;
g.DrawEllipse(entryPen, x - 1, y - 1, 2, 2);
float yErr = (float)(scaleY * getWeight(entry));
if (yErr > 0)
{
g.DrawLine(entryPen, x, y - yErr, x, y + yErr);
g.DrawLine(entryPen, x - 1, y - yErr, x + 1, y - yErr);
g.DrawLine(entryPen, x - 1, y + yErr, x + 1, y + yErr);
}
if (allFrameNos.IndexOf(entry.FrameNo) == -1)
{
Trace.WriteLine(string.Format("ERROR: Frame number {0} has been already used in another chunk!", entry.FrameNo));
}
else
{
allFrameNos.Remove(entry.FrameNo);
}
}
// Plot fitted line
if (lastEntry != null)
{
double endX = lastEntry.TimeOfDayUTCInstrDelayApplied;
double endYCalc = getCalcVal(lastEntry);
float x1 = (float)Math.Round(PADDING_L + BORDER + (scaleX * (startX.Value - minX)));
float y1 = fullHeight - PADDING_L - BORDER - (float)(scaleY * (startYCalc.Value - minY));
float x2 = (float)Math.Round(PADDING_L + BORDER + (scaleX * (endX - minX)));
float y2 = fullHeight - PADDING_L - BORDER - (float)(scaleY * (endYCalc - minY));
g.DrawLine(Pens.Azure, x1, y1, x2, y2);
g.DrawLine(Pens.Azure, x1, y1 + 1, x2, y2 + 1);
double fittedY = getMidPointPos(chunk);
double midX = chunk.GetMidPointDelayCorrectedTimeOfDay();
float xf = (float)Math.Round(PADDING_L + BORDER + (scaleX * (midX - minX)));
float yf = fullHeight - PADDING_L - BORDER - (float)(scaleY * (fittedY - minY));
g.FillEllipse(Brushes.Azure, xf - 3, yf - 3, 7, 7);
repX.Add(midX);
repY.Add(fittedY);
var rate = Math.Abs((endYCalc - startYCalc.Value) * 3600.0 / ((endX - startX.Value) * SECONDS_IN_A_DAY));
if (!motionRate.HasValue || motionRate.Value < rate)
{
motionRate = rate * rateCalcFactor(lastEntry);
}
}
}
int allRemovedOutliers = m_Chunks.Sum(x => x.RemovedOutliers);
// Plot total included points and outliers
string statsText = m_RemoveOutliers
? string.Format("Included points: {0}, Excluded outliers: {1}", m_AllEntries.Count, allRemovedOutliers)
: string.Format("Included points: {0}", m_AllEntries.Count);
var szf = g.MeasureString(statsText, s_LegendFont);
string rateText = null;
SizeF rszf = SizeF.Empty;
if (motionRate.HasValue)
{
rateText = string.Format("Rate({0}): {1:0.00}\"/sec", motionName, motionRate.Value);
rszf = g.MeasureString(rateText, s_LegendFont);
}
bool topLeft = aveIncl > 0;
if (topLeft)
{
g.FillRectangle(SystemBrushes.ControlDarkDark, PADDING_L + BORDER, PADDING_R + BORDER, szf.Width, szf.Height);
g.DrawString(statsText, s_LegendFont, Brushes.Azure, PADDING_L + BORDER, PADDING_R + BORDER);
g.FillRectangle(SystemBrushes.ControlDarkDark, fullWidth - PADDING_R - BORDER - rszf.Width, fullHeight - PADDING_L - BORDER - rszf.Height - 2, rszf.Width, rszf.Height);
g.DrawString(rateText, s_LegendFont, Brushes.Azure, fullWidth - PADDING_R - BORDER - rszf.Width, fullHeight - PADDING_L - BORDER - rszf.Height - 2);
}
else
{
g.FillRectangle(SystemBrushes.ControlDarkDark, fullWidth - PADDING_R - BORDER - szf.Width, PADDING_R + BORDER, szf.Width, szf.Height);
g.DrawString(statsText, s_LegendFont, Brushes.Azure, fullWidth - PADDING_R - BORDER - szf.Width, PADDING_R + BORDER);
g.FillRectangle(SystemBrushes.ControlDarkDark, PADDING_L + BORDER, fullHeight - PADDING_L - BORDER - rszf.Height - 2, rszf.Width, rszf.Height);
g.DrawString(rateText, s_LegendFont, Brushes.Azure, PADDING_L + BORDER, fullHeight - PADDING_L - BORDER - rszf.Height - 2);
}
// Calculate the StdDev of linear motion from all reported points and draw it on the plot
if (repX.Count > 2)
{
var repReg = new LinearRegression();
for (int i = 0; i < repX.Count; i++)
{
repReg.AddDataPoint(repX[i], repY[i]);
}
repReg.Solve();
double stdDevArcSec = repReg.StdDev * 3600.0;
string text = string.Format("StdDev from {0} measurements: {1:0.00} arcsec", repX.Count, stdDevArcSec);
szf = g.MeasureString(text, s_LegendFont);
if (topLeft)
{
g.FillRectangle(SystemBrushes.ControlDarkDark, PADDING_L + BORDER, PADDING_R + BORDER + szf.Height + 2, szf.Width, szf.Height);
g.DrawString(text, s_LegendFont, Brushes.Azure, PADDING_L + BORDER, PADDING_R + BORDER + szf.Height + 2);
}
else
{
g.FillRectangle(SystemBrushes.ControlDarkDark, fullWidth - PADDING_R - BORDER - szf.Width, PADDING_R + BORDER + szf.Height + 2, szf.Width, szf.Height);
g.DrawString(text, s_LegendFont, Brushes.Azure, fullWidth - PADDING_R - BORDER - szf.Width, PADDING_R + BORDER + szf.Height + 2);
}
}
// Plot Axis Marks
var minYArcSec = (long)Math.Ceiling(minY * 3600);
var maxYArcSec = (long)Math.Floor(maxY * 3600);
for (long wholeArcSec = minYArcSec; wholeArcSec <= maxYArcSec; wholeArcSec++)
{
int len = 1;
if (wholeArcSec % 60 == 0)
{
len = 5;
}
else if (wholeArcSec % 10 == 0)
{
len = 3;
}
float y = fullHeight - PADDING_L - BORDER - (float)(scaleY * (wholeArcSec / 3600.0 - minY));
g.DrawLine(SystemPens.ControlDark, PADDING_L + 1, y, PADDING_L + 1 + len, y);
g.DrawLine(SystemPens.ControlDark, fullWidth - PADDING_R - 1, y, fullWidth - PADDING_R - 1 - len, y);
}
var minXSeconds = (long)Math.Ceiling(minX * SECONDS_IN_A_DAY);
var maxXSeconds = (long)Math.Floor(maxX * SECONDS_IN_A_DAY);
for (long wholeSeconds = minXSeconds; wholeSeconds <= maxXSeconds; wholeSeconds++)
{
int len = 1;
if (wholeSeconds % 60 == 0)
{
len = 5;
}
else if (wholeSeconds % 10 == 0)
{
len = 3;
}
float x = (float)Math.Round(PADDING_L + BORDER + (scaleX * (wholeSeconds / SECONDS_IN_A_DAY - minX)));
g.DrawLine(SystemPens.ControlDark, x, fullHeight - PADDING_L - 1, x, fullHeight - PADDING_L - 1 - len);
g.DrawLine(SystemPens.ControlDark, x, PADDING_R + 1, x, PADDING_R + 1 + len);
}
string axisText = "Time (sec)";
var axf = g.MeasureString(axisText, s_LegendFont);
g.DrawString(axisText, s_LegendFont, Brushes.Azure, new PointF(PADDING_L + (clientAreaWidth - axf.Width) / 2, fullHeight - axf.Height));
axisText = string.Format("Motion {0} (arcsec)", motionName);
axf = g.MeasureString(axisText, s_LegendFont);
s_VerticalDrawFormat.Alignment = StringAlignment.Far;
g.TranslateTransform(fullWidth, fullHeight);
g.RotateTransform(180);
g.DrawString(axisText, s_LegendFont, Brushes.Azure, new PointF(TITLE_PADDING + clientAreaWidth + axf.Height, (fullHeight + axf.Width) / 2), s_VerticalDrawFormat);
}
