예제 #1
0
        public ProcessingReturnValues FitAndPlotFastFlyby(
            Dictionary <int, SingleMultiFrameMeasurement> measurements,
            FlybyMeasurementContext meaContext,
            FittingContext fittingContext,
            FittingValue fittingValue,
            GetFrameStateDataCallback getFrameStateDataCallback,
            Graphics g, FlybyPlottingContext plottingContext, float xScale, float yScale, int imageWidth, int imageHeight,
            out double motionRate)
        {
            // Do linear regression, use residual based exclusion rules
            // Two possible modes: (A) non trailed and (B) trailed images
            // A) Non Trailed Images
            // Report interpolated times for video normal position [How to use the MPC page for this?]
            // Don't forget to add the video normal position flag in the OBS file
            // Expect elongated images and apply instrumental delay corrections (in both integrated and non integrated modes)
            // B) Trailed Images
            // TODO: R&D Required

            if (fittingContext.ObjectExposureQuality == ObjectExposureQuality.GoodSignal)
            {
                return(FitAndPlotSlowFlyby(
                           measurements, meaContext, fittingContext, fittingValue, getFrameStateDataCallback,
                           g, plottingContext, xScale, yScale, imageWidth, imageHeight, out motionRate));
            }
            else
            {
                MessageBox.Show("This operation is currently not suppored.");
                motionRate = double.NaN;
                return(new ProcessingReturnValues());
            }
        }
예제 #2
0
        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>();
            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);
                        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);

                // TODO: Use weighted median
                double median = 0;
                medianList.Sort();
                if (numFramesUser % 2 == 1)
                    median = medianList[numFramesUser / 2];
                else
                    median = (medianList[numFramesUser / 2] + medianList[(numFramesUser / 2) - 1]) / 2;

                Trace.WriteLine(string.Format("{0}; Included: {1}; Average: {2}; Median: {3}",
                    meaContext.UserMidValue.ToString("0.00000"),
                    numFramesUser, AstroConvert.ToStringValue(average, "+HH MM SS.T"),
                    AstroConvert.ToStringValue(median, "+HH MM SS.T")));

                rv.FittedValue = median;
                // TODO: Use StdDev/SQRT(N)
                rv.FittedValueUncertaintyArcSec = TangraConfig.Settings.Astrometry.AssumedPositionUncertaintyPixels * meaContext.ArsSecsInPixel;
                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;
        }
예제 #3
0
        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;
            }
        }
예제 #4
0
        public ProcessingReturnValues FitAndPlotFastFlyby(
            Dictionary<int, SingleMultiFrameMeasurement> measurements,
            FlybyMeasurementContext meaContext,
            FittingContext fittingContext,
            FittingValue fittingValue,
            GetFrameStateDataCallback getFrameStateDataCallback,
            Graphics g, FlybyPlottingContext plottingContext, float xScale, float yScale, int imageWidth, int imageHeight,
            out double motionRate)
        {
            // Do linear regression, use residual based exclusion rules
            // Two possible modes: (A) non trailed and (B) trailed images
            // A) Non Trailed Images
            // Report interpolated times for video normal position [How to use the MPC page for this?]
            // Don't forget to add the video normal position flag in the OBS file
            // Expect elongated images and apply instrumental delay corrections (in both integrated and non integrated modes)
            // B) Trailed Images
            // TODO: R&D Required

            if (fittingContext.ObjectExposureQuality == ObjectExposureQuality.GoodSignal)
            {
                return FitAndPlotSlowFlyby(
                    measurements, meaContext, fittingContext, fittingValue, getFrameStateDataCallback,
                    g, plottingContext, xScale, yScale, imageWidth, imageHeight, out motionRate);
            }
            else
            {
                MessageBox.Show("This operation is currently not suppored.");
                motionRate = double.NaN;
                return new ProcessingReturnValues();
            }
        }
예제 #5
0
        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);
        }
예제 #6
0
        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);
            }
        }
예제 #7
0
        private ProcessingReturnValues DrawRAPanel()
        {
            ProcessingReturnValues retVal = null;

            using (Graphics g = Graphics.FromImage(m_RAImage))
            {
                g.Clear(Color.Gray);

                int midY = m_RAImage.Height / 2;
                g.DrawLine(Pens.DarkGray, 5, midY, m_RAImage.Width - 5, midY);

                int minFrame = m_AllMeasurements.Keys.Min();
                int maxFrame = m_AllMeasurements.Keys.Max();
                int frames = 1 +  maxFrame - minFrame;

                if (m_AllMeasurements.Count > 0)
                {
                    float xScale = m_RAImage.Width * 1.0f / frames;
                    float yScale = (m_RAImage.Height - 10) / (float)(m_MaxRA - m_MinRA);

                    double from = m_MinRA - (6 / 3600.0);
                    double to = m_MaxRA + (6 / 3600.0);
                    double curr = Math.Truncate((from * 3600.0) / 5) * 5 / (3600.0);
                    while (curr < to)
                    {
                        float y = (float)(curr - m_MinRA) * yScale + 5;
                        g.DrawLine(s_SecondLinesPen, 5, y, m_RAImage.Width - 5, y);
                        curr += (1 / 3600.0);
                    }

                    var meaContext = new FlybyMeasurementContext
                    {
                        UserMidValue = m_UserRAMid,
                        UserMidFrame = m_UserFrame,
                        MaxStdDev = m_MeasurementContext.MaxStdDev/3600.0,
                        FirstVideoFrame = m_VideoController.VideoFirstFrame,
                        ArsSecsInPixel = m_AstrometryController.GetCurrentAstroPlate().GetDistanceInArcSec(0, 0, 1, 1),
                        MinPositionUncertaintyPixels = TangraConfig.Settings.Astrometry.AssumedPositionUncertaintyPixels,
                        MinFrameNo = minFrame,
                        MaxFrameNo = maxFrame
                    };

                    var fittingContext = m_MeasurementContext.ToFittingContext();
                    fittingContext.Weighting = TangraConfig.Settings.Astrometry.MotionFitWeightingMode;

                    var plottingContext = new FlybyPlottingContext
                    {
                        MinValue = m_MinRA,
                        IncludedPen = Pens.SkyBlue,
                        ExcludedPen = Pens.Tomato,
                        AveragePen = s_RAAveragePen,
                    };

                    #region Compute the RA or DE value at the middle of the interval

                    GetProcessingValueCallback processingValueCallback =
                        delegate(SingleMultiFrameMeasurement measurement)
                        {
                            return new ProcessingValues
                            {
                                StdDev = measurement.StdDevRAArcSec / 3600.0,
                                Value = measurement.RADeg
                            };
                        };

                    switch (m_MeasurementContext.MovementExpectation)
                    {
                        case MovementExpectation.Slow:
                            retVal = m_FlyByMotionFitter.FitAndPlotSlowMotion(
                                        m_AllMeasurements, meaContext, processingValueCallback,
                                        g, plottingContext, xScale, yScale, m_RAImage.Width);
                            break;

                        case MovementExpectation.SlowFlyby:
                            double motionRate;
                            retVal = m_FlyByMotionFitter.FitAndPlotSlowFlyby(
                                        m_AllMeasurements, meaContext, fittingContext, FittingValue.RA, (frameId) => m_AstrometryController.GetFrameTimeInfo(frameId),
                                        g, plottingContext, xScale, yScale, m_RAImage.Width, m_RAImage.Height, out motionRate);
                            m_MotionRate = motionRate;
                            break;

                        case MovementExpectation.FastFlyby:
                            retVal = m_FlyByMotionFitter.FitAndPlotFastFlyby(
                                        m_AllMeasurements, meaContext, fittingContext, FittingValue.RA, (frameId) => m_AstrometryController.GetFrameTimeInfo(frameId),
                                        g, plottingContext, xScale, yScale, m_RAImage.Width, m_RAImage.Height, out motionRate);

                            break;

                        default:
                            throw new IndexOutOfRangeException();
                    }

                    #endregion

                    if (retVal != null)
                    {
                        m_EarliestDEFrame = retVal.EarliestFrame;
                        m_LatestDEFrame = retVal.LatestFrame;

                        if (!double.IsNaN(retVal.FittedValue))
                        {
                            lblAstRA.Text = string.Format("{0}", AstroConvert.ToStringValue(retVal.FittedValue / 15, "HH MM SS.TT"));
                            lblAlpha.Visible = true;
                            m_RADeg = retVal.FittedValue;
                            m_RAUncertaintyArcSec = retVal.FittedValueUncertaintyArcSec;
                            if (TangraConfig.Settings.Astrometry.ExportUncertainties) m_MPCRAUncertainty = retVal.FittedValueUncertaintyArcSec;
                            lblAstUncertainty.Text = string.Format("({0:0.00}, {1:0.00})\"", m_RAUncertaintyArcSec * Math.Cos(m_DEDeg * Math.PI / 180), m_DEUncertaintyArcSec);
                            lblUncert.Visible = true;
                            m_MPCRAHours = m_RADeg / 15;
                            m_MPCTime = retVal.FittedValueTime;
                            m_MPCTimePrecission = TimeSpan.MinValue;
                            m_MPCIsVideoNormalPosition = retVal.IsVideoNormalPosition;
                        }

                        CheckAndSetTimeAndMPCAdd();
                    }
                }

                g.DrawString("a", s_SymbolFont12, Brushes.Yellow, 5, 5);
                g.Save();
            }

            pnlRASeries.Image = m_RAImage;
            if (m_AstrometricState.MeasuringState != AstrometryInFramesState.RunningMeasurements)
                pnlRASeries.Refresh();

            return retVal;
        }