private (int, int) MedianHistogram(int buckets)
{
//Converts fitsArray to vector for histogram purposes
List <double> fsVector = new List <double>();
for (int i = 0; i < Xaxis; i++)
{
for (int j = 0; j < Yaxis; j++)
{
fsVector.Add(FITSArray[i, j]);
}
}
MathNet.Numerics.Statistics.Histogram fsHistBuckets = new MathNet.Numerics.Statistics.Histogram(fsVector, buckets);
//Create an list that can be statistically analyzed
List <double> fsHistList = new List <double>();
for (int i = 0; i < fsHistBuckets.BucketCount; i++)
{
fsHistList.Add(fsHistBuckets[i].Count);
}
//Determine the gaussian distribution of the histogram
//double medianBucketValue = MathNet.Numerics.Statistics.Statistics.Mean(fsHistList);
double maxBucketValue = fsHistList.Max();
//int medianBucketIndex = FindNearestBucketSize(fsHistBuckets,medianBucketValue);
int maxBucketIndex = FindNearestBucketSize(fsHistBuckets, 0, fsHistBuckets.BucketCount, maxBucketValue);
//double stdDev = MathNet.Numerics.Statistics.Statistics.StandardDeviation(fsHistList);
int lowerBucketIndex = FindNearestBucketSize(fsHistBuckets, 0, maxBucketIndex, maxBucketValue / 4);
int upperBucketIndex = FindNearestBucketSize(fsHistBuckets, maxBucketIndex, fsHistBuckets.BucketCount, maxBucketValue / 4);
int upperBucketValue = (int)fsHistBuckets[upperBucketIndex].UpperBound;
int lowerBucketValue = (int)fsHistBuckets[lowerBucketIndex].LowerBound;
return(lowerBucketValue, upperBucketValue);
}
public static string GetDerivHistogram(CylData cylData)
{
try
{
var derivs = new List <double>();
double max = double.MinValue;
double min = double.MaxValue;
for (int i = 0; i < cylData.Count - 1; i++)
{
var p1 = cylData[i + 1];
var p0 = cylData[i];
var d = Math.Abs((p1.R - p0.R) / (p1.ThetaRad - p0.ThetaRad));
if (d < min)
{
min = d;
}
if (d > max)
{
max = d;
}
derivs.Add(d);
}
//double deltaD = (max - min) / 20;
var h = new MathNet.Numerics.Statistics.Histogram(derivs, 10, min, max);
var bmax = 100 * h.GetBucketOf(max).Count / h.DataCount;
var bmin = 100 * h.GetBucketOf(min).Count / h.DataCount;
return(String.Concat("%min, %max Derivatives : ", bmin.ToString("f4"), " , ", bmax.ToString("f4")));
}
catch (Exception)
{
throw;
}
}
private int FindNearestBucketSize(MathNet.Numerics.Statistics.Histogram hg, int startLookingIndex, int stopLookingIndex, double level)
{
//find the bucket whose contents is closest to the value level
int min = int.MaxValue;
int thisone = 0;
for (int i = startLookingIndex; i < stopLookingIndex; i++)
{
if (Math.Abs(hg[i].Count - level) < min)
{
min = (int)Math.Abs(hg[i].Count - level);
thisone = i;
}
}
return(thisone);
}
public static List <HistogramBucket> GetDerivHistogram(List <double> data, int bucketCount)
{
try
{
var derivs = new List <double>();
double max = double.MinValue;
double min = double.MaxValue;
for (int i = 0; i < data.Count - 1; i++)
{
var p1 = data[i + 1];
var p0 = data[i];
var d = Math.Abs(p1 - p0);
if (d < min)
{
min = d;
}
if (d > max)
{
max = d;
}
derivs.Add(d);
}
double deltaD = (max - min) / bucketCount;
var h = new MathNet.Numerics.Statistics.Histogram(derivs, bucketCount, min, max);
var bucketV = min + (deltaD / 2.0);
var histogram = new List <HistogramBucket>();
while (bucketV <= max)
{
var b = h.GetBucketOf(bucketV);
histogram.Add(new HistogramBucket(bucketV, b.Count));
bucketV += deltaD;
}
return(histogram);
//return String.Concat("%min, %max Derivatives : ", bmin.ToString("f4"), " , ", bmax.ToString("f4"));
}
catch (Exception)
{
throw;
}
}
private void PlotNormalDistribution(double[] data, Histogram histogram, string distributionName, Color color)
{
if (data == null || histogram == null)
return;
var points = 50;
var normalDistribution = new NormalDistribution(data.Average(), Math.Variance(data), points, histogram.LowerBound, histogram.UpperBound);
//var normalDistribution = new NormalDistribution(histogram.Mean(), histogram.Variance(), points, histogram.LowerBound, histogram.UpperBound);
var densityCurve = normalDistribution.DensityCurve;
var xValues = new double[points];
var yValues = new double[points];
for (var index = 0; index < points; index++)
{
xValues[index] = densityCurve[index, 0];
yValues[index] = densityCurve[index, 1];
}
// Add data sources.
var yDataSource = new EnumerableDataSource<double>(yValues);
yDataSource.SetYMapping(Y => Y);
yDataSource.AddMapping(ShapeElementPointMarker.ToolTipTextProperty, Y => string.Format("Normal Value \n\n{0}", Y));
var xDataSource = new EnumerableDataSource<double>(xValues);
xDataSource.SetXMapping(X => X);
var compositeDataSource = new CompositeDataSource(xDataSource, yDataSource);
// MatchValuePlotter.Viewport.Restrictions.Add(new PhysicalProportionsRestriction { ProportionRatio = 500000 });
var graph = ChartPlotter.AddLineGraph(compositeDataSource, color, 0.5, distributionName);
// Cache for later usage (e.g. change visibility).
if (graph != null)
_histogramGraphs.Add(graph);
}
private void PlotHistogram(Histogram histogram, string histogramName, Color color)
{
if (histogram == null)
return;
// Prepare data in arrays.
var pointIndex = 0;
var dataPointCount = 4 * histogram.BucketCount;
var x = new double[dataPointCount];
var y = new double[dataPointCount];
foreach (var bucket in histogram.Buckets())
{
//var yValue = bucket.Count;
var yValue = bucket.RelativeCount(histogram);
// lower left point of the histogram bar
x[pointIndex] = bucket.LowerBound;
y[pointIndex] = 0;
// upper left point of the histogram bar
x[pointIndex + 1] = bucket.LowerBound;
y[pointIndex + 1] = yValue;
// upper right point of the histogram bar
x[pointIndex + 2] = bucket.UpperBound;
y[pointIndex + 2] = yValue;
// lower right point of the histogram bar
x[pointIndex + 3] = bucket.UpperBound;
y[pointIndex + 3] = 0;
pointIndex += 4;
}
// Add data sources.
var yDataSource = new EnumerableDataSource<double>(y);
yDataSource.SetYMapping(Y => Y);
yDataSource.AddMapping(ShapeElementPointMarker.ToolTipTextProperty, Y => string.Format("Match Value \n\n{0}", Y));
var xDataSource = new EnumerableDataSource<double>(x);
xDataSource.SetXMapping(X => X);
var compositeDataSource = new CompositeDataSource(xDataSource, yDataSource);
// MatchValuePlotter.Viewport.Restrictions.Add(new PhysicalProportionsRestriction { ProportionRatio = 500000 });
var graph = ChartPlotter.AddLineGraph(compositeDataSource, color, 1, histogramName);
// Cache for later usage (e.g. change visibility).
if (graph != null)
_histogramGraphs.Add(graph);
}
private void GetSTAResponseHistogramPlotData(out Histogram rawStimuliSTAResponseHistogram, out double[] rawStimuliSTAMatchValues, out Histogram spikeTriggeredStimuliSTAResponseDiagram, out double[] spikeTriggeredStimuliSTAMatchValues, out Nonlinearity nonlinearity, bool recalcData = true)
{
rawStimuliSTAResponseHistogram = null;
rawStimuliSTAMatchValues = null;
spikeTriggeredStimuliSTAResponseDiagram = null;
spikeTriggeredStimuliSTAMatchValues = null;
nonlinearity = null;
if (_spikes == null)
return;
// caching
if (!recalcData)
{
rawStimuliSTAResponseHistogram = _rawStimuliSTAResponseHistogram;
spikeTriggeredStimuliSTAResponseDiagram = _spikeTriggeredStimuliSTAResponseDiagram;
nonlinearity = _nonlinearity;
return;
}
var spikes = new List<double[][]>();
if (Cell1CheckBox.IsChecked != null && Cell1CheckBox.IsChecked.Value)
spikes.Add(_spikes[0]);
if (Cell2CheckBox.IsChecked != null && Cell2CheckBox.IsChecked.Value)
spikes.Add(_spikes[1]);
if (Cell3CheckBox.IsChecked != null && Cell3CheckBox.IsChecked.Value)
spikes.Add(_spikes[2]);
if (Cell4CheckBox.IsChecked != null && Cell4CheckBox.IsChecked.Value)
spikes.Add(_spikes[3]);
if (spikes.Count == 0)
return;
int offset;
if (!int.TryParse(OffsetTextbox.Text, out offset))
offset = 13;
int maxTime;
if (!int.TryParse(TimeTextbox.Text, out maxTime))
maxTime = 13;
var rawStimuliHistogramBuckets = MatchValuesForRawStimuliBucketsUpDown.Value != null ? MatchValuesForRawStimuliBucketsUpDown.Value.Value : 200;
var spikeTriggeredStimuliHistogramBuckets = MatchValuesForSpikeTriggeredStimuliBucketsUpDown.Value != null ? MatchValuesForSpikeTriggeredStimuliBucketsUpDown.Value.Value : 200;
double[,] smoothKernel = null;
bool useDynamicDivisorForEdges = false;
GetSmoothKernel(out smoothKernel, out useDynamicDivisorForEdges);
SpikeTriggeredAnalysis.CalculateSTAResponseHistogram(_stimuli, spikes.ToArray(), false, offset, maxTime, smoothKernel, useDynamicDivisorForEdges, rawStimuliHistogramBuckets, out rawStimuliSTAMatchValues, out rawStimuliSTAResponseHistogram);
SpikeTriggeredAnalysis.CalculateSTAResponseHistogram(_stimuli, spikes.ToArray(), true, offset, maxTime, smoothKernel, useDynamicDivisorForEdges, spikeTriggeredStimuliHistogramBuckets, out spikeTriggeredStimuliSTAMatchValues, out spikeTriggeredStimuliSTAResponseDiagram);
_rawStimuliSTAResponseHistogram = rawStimuliSTAResponseHistogram;
_rawStimuliSTAMatchValues = rawStimuliSTAMatchValues;
_spikeTriggeredStimuliSTAResponseDiagram = spikeTriggeredStimuliSTAResponseDiagram;
_spikeTriggeredStimuliSTAMatchValues = spikeTriggeredStimuliSTAMatchValues;
var frequencyRawStimuli = new NormalDistribution(rawStimuliSTAMatchValues.Average(), Math.Variance(rawStimuliSTAMatchValues), 10, rawStimuliSTAResponseHistogram.LowerBound, rawStimuliSTAResponseHistogram.UpperBound);
var frequencySpikeTriggeredStimuli = new NormalDistribution(spikeTriggeredStimuliSTAMatchValues.Average(), Math.Variance(spikeTriggeredStimuliSTAMatchValues), 10, spikeTriggeredStimuliSTAResponseDiagram.LowerBound, spikeTriggeredStimuliSTAResponseDiagram.UpperBound);
nonlinearity =
// caching
_nonlinearity = new Nonlinearity(frequencyRawStimuli, frequencySpikeTriggeredStimuli, 100);
}