public static PlotModel CreateExponentialDistributionCustomBins(double mean = 1, int n = 50000)
{
var model = new PlotModel {
Title = "Exponential Distribution", Subtitle = "Custom bins (" + n + " samples)"
};
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = "Frequency"
});
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "x"
});
Random rnd = new Random();
HistogramSeries chs = new HistogramSeries();
var binningOptions = new BinningOptions(BinningOutlierMode.CountOutliers, BinningIntervalType.InclusiveLowerBound, BinningExtremeValueMode.ExcludeExtremeValues);
chs.Items.AddRange(HistogramHelpers.Collect(SampleExps(rnd, 1.0, n), new double[] { 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.75, 1.0, 2.0, 3.0, 4.0, 5.0 }, binningOptions));
chs.StrokeThickness = 1;
chs.FillColor = OxyColors.Purple;
model.Series.Add(chs);
return(model);
}
public static PlotModel CreateExponentialDistribution(double mean = 1, int n = 10000)
{
var model = new PlotModel {
Title = "Exponential Distribution", Subtitle = "Uniformly distributed bins (" + n + " samples)"
};
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = "Frequency"
});
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "x"
});
Random rnd = new Random(1);
HistogramSeries chs = new HistogramSeries();
var binningOptions = new BinningOptions(BinningOutlierMode.CountOutliers, BinningIntervalType.InclusiveLowerBound, BinningExtremeValueMode.ExcludeExtremeValues);
var binBreaks = HistogramHelpers.CreateUniformBins(0, 5, 15);
chs.Items.AddRange(HistogramHelpers.Collect(SampleExps(rnd, 1.0, n), binBreaks, binningOptions));
chs.StrokeThickness = 1;
model.Series.Add(chs);
return(model);
}
public static PlotModel CreateIndividualBinColors(double mean = 1, int n = 10000)
{
var model = new PlotModel {
Title = "Individual Bin Colors", Subtitle = "Minimum is Red, Maximum is Green"
};
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = "Frequency"
});
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "Observation"
});
Random rnd = new Random(1);
HistogramSeries chs = new HistogramSeries()
{
FillColor = OxyColors.Gray, RenderInLegend = true, Title = "Measurements"
};
var binningOptions = new BinningOptions(BinningOutlierMode.CountOutliers, BinningIntervalType.InclusiveLowerBound, BinningExtremeValueMode.ExcludeExtremeValues);
var binBreaks = HistogramHelpers.CreateUniformBins(0, 10, 20);
var bins = HistogramHelpers.Collect(SampleUniform(rnd, 0, 10, 1000), binBreaks, binningOptions).OrderBy(b => b.Count).ToArray();
bins.First().Color = OxyColors.Red;
bins.Last().Color = OxyColors.Green;
chs.Items.AddRange(bins);
chs.StrokeThickness = 1;
model.Series.Add(chs);
return(model);
}
public static PlotModel HistogramLabelPlacement()
{
var model = new PlotModel {
Title = "Label Placement"
};
var s1 = new HistogramSeries {
LabelPlacement = LabelPlacement.Base, LabelFormatString = "Base", StrokeThickness = 1, LabelMargin = 5
};
var s2 = new HistogramSeries {
LabelPlacement = LabelPlacement.Inside, LabelFormatString = "Inside", StrokeThickness = 1, LabelMargin = 5
};
var s3 = new HistogramSeries {
LabelPlacement = LabelPlacement.Middle, LabelFormatString = "Middle", StrokeThickness = 1, LabelMargin = 5
};
var s4 = new HistogramSeries {
LabelPlacement = LabelPlacement.Outside, LabelFormatString = "Outside", StrokeThickness = 1, LabelMargin = 5
};
s1.Items.Add(new HistogramItem(1, 2, 4, 4));
s1.Items.Add(new HistogramItem(2, 3, -4, 4));
s2.Items.Add(new HistogramItem(3, 4, 2, 2));
s2.Items.Add(new HistogramItem(4, 5, -2, 2));
s3.Items.Add(new HistogramItem(5, 6, 3, 3));
s3.Items.Add(new HistogramItem(6, 7, -3, 3));
s4.Items.Add(new HistogramItem(7, 8, 1, 1));
s4.Items.Add(new HistogramItem(8, 9, -1, -1));
model.Series.Add(s1);
model.Series.Add(s2);
model.Series.Add(s3);
model.Series.Add(s4);
return(model);
}
public Histogram()
{
InitializeComponent();
Syncfusion.Licensing.SyncfusionLicenseProvider.RegisterLicense("NDU4Nzc4QDMxMzkyZTMxMmUzMFFadmVERkJ4OFpEZ2J6TGpOKzU4d0tvenR3SFZJcHpQNzgzd3VGeVJtRTg9");
StackLayout stackLayout = new StackLayout();
Serializer serializer = new Serializer();
Profile profile = serializer.Deserialize("Test_user.xml");
ExpenseStatisticsBL esBL = new ExpenseStatisticsBL(profile.Spends, profile.Categories);
SfChart chart = new SfChart();
HistogramSeries histogramSeries = new HistogramSeries()
{
ItemsSource = esBL.TestData(),
XBindingPath = "XValue",
YBindingPath = "YValue",
Interval = 20
};
chart.Series.Add(histogramSeries);
stackLayout.Children.Add(chart);
Content = stackLayout;
}
public void Add(ChartControl chart, Pane ownerPane)
{
owner = chart;
seriesDeltas = new HistogramSeries("Accelerator");
SeriesResult = new List <Series.Series> {
seriesDeltas
};
EntitleIndicator();
}
public PlotModel GetPlotModel(string title = "", double titleFontSize = 12)
{
var distPlotModel = new PlotModel {
Title = title, IsLegendVisible = true, TitleFontSize = titleFontSize
};
distPlotModel.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Minimum = 0.0
});
var histoSeries = new HistogramSeries()
{
FillColor = OxyColors.Blue, StrokeThickness = 1, StrokeColor = OxyColors.Black, RenderInLegend = false
};
double peakDist = double.NegativeInfinity;
foreach (var bin in Bins)
{
double pct = bin.Value.Count / (double)Values.Count;
if (pct > peakDist)
{
peakDist = pct;
}
double start = bin.Key;
double end = bin.Key + BinLength - 10e-7;
histoSeries.Items.Add(new HistogramItem(start, end, pct, bin.Value.Count));
}
distPlotModel.Series.Add(histoSeries);
// plot average
ScatterSeries averageSeries = new ScatterSeries()
{
MarkerType = MarkerType.Diamond, MarkerFill = OxyColors.Red, MarkerSize = 5, Title = "Average"
};
averageSeries.Points.Add(new ScatterPoint(Average, peakDist / 2.0));
distPlotModel.Series.Add(averageSeries);
if (!double.IsNaN(Mode))
{
// plot mode
ScatterSeries modeSeries = new ScatterSeries()
{
MarkerType = MarkerType.Circle, MarkerFill = OxyColors.Orange, MarkerSize = 5, Title = "Mode"
};
modeSeries.Points.Add(new ScatterPoint(Mode, peakDist / 2.0));
distPlotModel.Series.Add(modeSeries);
}
return(distPlotModel);
}
public FrequencyHistogramViewModel(FrequencyDomain frequencyDomain)
{
Title = Properties.Resources.PLOT_FREQUENCY_HISTOGRAM_TITLE;
Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = Properties.Resources.PLOT_FREQUENCY_HISTOGRAM_LEFT_AXIS
});
Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = Properties.Resources.PLOT_FREQUENCY_HISTOGRAM_BOTTOM_AXIS
});
if (frequencyDomain != null)
{
HistogramSeries chs = new HistogramSeries();
chs.Items.AddRange(GetFrequencyBins(frequencyDomain));
Series.Add(chs);
}
}
public static PlotModel CustomItemMapping()
{
var model = new PlotModel {
Title = "Custom Item Mapping"
};
var s = new HistogramSeries {
Mapping = obj => (HistogramItem)obj, TrackerFormatString = "{Description}"
};
s.Items.Add(new CustomHistogramItem(1, 2, 4, 4, "Item 1"));
s.Items.Add(new CustomHistogramItem(2, 3, -4, 4, "Item 2"));
s.Items.Add(new CustomHistogramItem(3, 4, 2, 2, "Item 3"));
s.Items.Add(new CustomHistogramItem(4, 5, -2, 2, "Item 4"));
model.Series.Add(s);
return(model);
}
public void TestHistogram()
{
// dummy test for debug
var data = new List <double>()
{
4.7, 4.5, 4.9, 4, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4, 4.9, 4.7, 4.3, 4.4, 4.8, 5, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4, 4.4, 4.6, 4, 3.3, 4.2, 4.2, 4.2, 4.3, 3, 4.1
};
var hist = new Histogram();
var series = new HistogramSeries(hist);
series.RawValues = data;
series.ProcessRawValues();
Assert.AreEqual(0, series.BinCount);
}
public override View GetSampleContent(Context context)
{
SfChart chart = new SfChart(context);
chart.Title.Text = "Examination Result";
chart.Title.TextSize = 15;
chart.SetBackgroundColor(Color.White);
chart.ColorModel.ColorPalette = ChartColorPalette.Natural;
NumericalAxis categoryaxis = new NumericalAxis();
categoryaxis.Title.Text = "Score of Final Examination";
categoryaxis.EdgeLabelsDrawingMode = EdgeLabelsDrawingMode.Shift;
categoryaxis.ShowMajorGridLines = false;
chart.PrimaryAxis = categoryaxis;
NumericalAxis numericalaxis = new NumericalAxis();
numericalaxis.Title.Text = "Number of Students";
numericalaxis.ShowMajorGridLines = true;
numericalaxis.LineStyle.StrokeWidth = 0;
numericalaxis.MajorTickStyle.TickSize = 0;
chart.SecondaryAxis = numericalaxis;
HistogramSeries histogramSeries = new HistogramSeries();
histogramSeries.ItemsSource = MainPage.GetHistogramData();
histogramSeries.XBindingPath = "YValue";
histogramSeries.YBindingPath = "XValue";
histogramSeries.Interval = 20;
histogramSeries.TooltipEnabled = true;
histogramSeries.EnableAnimation = true;
histogramSeries.StrokeColor = Color.White;
histogramSeries.StrokeWidth = 1;
histogramSeries.DataMarker.ShowLabel = true;
histogramSeries.DataMarker.LabelStyle.LabelPosition = DataMarkerLabelPosition.Inner;
histogramSeries.DataMarker.LabelStyle.BackgroundColor = Color.Transparent;
histogramSeries.DataMarker.LabelStyle.TextColor = Color.White;
chart.Series.Add(histogramSeries);
return(chart);
}
public static PlotModel CreateNormalDistribution(double mean = 0, double std = 1, int n = 1000000)
{
var model = new PlotModel {
Title = $"Normal Distribution (μ={mean}, σ={std})", Subtitle = "95% of the distribution (" + n + " samples)"
};
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = "Frequency"
});
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "x"
});
Random rnd = new Random();
HistogramSeries chs = new HistogramSeries();
var binningOptions = new BinningOptions(BinningOutlierMode.CountOutliers, BinningIntervalType.InclusiveLowerBound, BinningExtremeValueMode.ExcludeExtremeValues);
var binBreaks = HistogramHelpers.CreateUniformBins(-std * 4, std * 4, 100);
chs.Items.AddRange(HistogramHelpers.Collect(SampleNormal(rnd, mean, std, n), binBreaks, binningOptions));
chs.StrokeThickness = 1;
double LimitHi = mean + 1.96 * std;
double LimitLo = mean - 1.96 * std;
OxyColor ColorHi = OxyColors.DarkRed;
OxyColor ColorLo = OxyColors.DarkRed;
chs.ColorMapping = (item) =>
{
if (item.RangeCenter > LimitHi)
{
return(ColorHi);
}
else if (item.RangeCenter < LimitLo)
{
return(ColorLo);
}
return(chs.ActualFillColor);
};
model.Series.Add(chs);
return(model);
}
public static PlotModel HistogramSeriesLabelPlacement()
{
// prepare the model
var model = new PlotModel()
{
Title = "Histogram Label Position",
LegendPlacement = LegendPlacement.Outside,
LegendPosition = LegendPosition.TopCenter,
LegendOrientation = LegendOrientation.Horizontal
};
// add two linear axes
model.Axes.Add(new LinearAxis()
{
Title = "Observation", Position = AxisPosition.Bottom
});
model.Axes.Add(new LinearAxis()
{
Title = "Frequency", Position = AxisPosition.Left, Maximum = 1.5
});
// add a histogram for each type of label placement
double x = 0.0;
foreach (var placement in new[] { LabelPlacement.Base, LabelPlacement.Middle, LabelPlacement.Inside, LabelPlacement.Outside })
{
var histogramSeries = new HistogramSeries()
{
Title = placement.ToString(),
LabelFormatString = "{0}",
LabelPlacement = placement,
StrokeThickness = 2,
};
histogramSeries.Items.Add(new HistogramItem(x + 0.0, x + 0.5, 0.4, 4));
histogramSeries.Items.Add(new HistogramItem(x + 0.5, x + 1.0, 0.6, 6));
model.Series.Add(histogramSeries);
x += 2.0;
}
return(model);
}
public static PlotModel CreateDisconnectedBins()
{
var model = new PlotModel {
Title = "Disconnected Bins"
};
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = "Representation"
});
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "x"
});
HistogramSeries chs = new HistogramSeries();
chs.Items.AddRange(new[] { new HistogramItem(0, 0.5, 10, 7), new HistogramItem(0.75, 1.0, 10, 7) });
chs.LabelFormatString = "{0:0.00}";
chs.LabelPlacement = LabelPlacement.Middle;
model.Series.Add(chs);
return(model);
}
public static PlotModel CreateExponentialDistribution(double mean = 1, int n = 10000)
{
var model = new PlotModel {
Title = "Exponential Distribution", Subtitle = "Uniformly distributed bins (" + n + " samples)"
};
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = "Frequency"
});
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "x"
});
Random rnd = new Random();
HistogramSeries chs = new HistogramSeries();
chs.Items.AddRange(HistogramHelpers.Collect(SampleExps(rnd, 1.0, n), 0, 5, 15, true));
chs.StrokeThickness = 1;
model.Series.Add(chs);
return(model);
}
public static PlotModel CreateExponentialDistributionCustomBins(double mean = 1, int n = 50000)
{
var model = new PlotModel {
Title = "Exponential Distribution", Subtitle = "Custom bins (" + n + " samples)"
};
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Title = "Frequency"
});
model.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "x"
});
Random rnd = new Random();
HistogramSeries chs = new HistogramSeries();
chs.Items.AddRange(HistogramHelpers.Collect(SampleExps(rnd, 1.0, n), new double[] { 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.75, 1.0, 2.0, 3.0, 4.0, 5.0 }, true));
chs.StrokeThickness = 1;
chs.FillColor = OxyColors.Purple;
model.Series.Add(chs);
return(model);
}
private void updateLayer(int index)
{
txtLayerIndex.Text = this.spotMaps[index].LayerIndex.ToString();
txtLayerEn.Text = this.spotMaps[index].NominalEnergy.ToString() + "MeV";
txtLayerSpots.Text = this.spotMaps[index].ScanSpotNumber.ToString();
txtLayerMaxMW.Text = this.spotMaps[index].MeterWeights.Max().ToString();
txtLayerMaxMU.Text = (this.spotMaps[index].MeterWeights.Max() * this.MUMWratio).ToString();
txtLayerMinMW.Text = this.spotMaps[index].MeterWeights.Min().ToString();
txtLayerMinMU.Text = (this.spotMaps[index].MeterWeights.Min() * this.MUMWratio).ToString();
//spotmap
var pmSM = new PlotModel {
Title = string.Format("{0:0.000}MeV", spotMaps[index].NominalEnergy)
};
var coloraxis = new LinearColorAxis {
Key = "ColorAxis", Position = AxisPosition.None, Minimum = spotMaps.Min(sm => sm.MeterWeights.Min()), Maximum = spotMaps.Max(sm => sm.MeterWeights.Max())
};
pmSM.Axes.Add(coloraxis);
var s = new ScatterSeries {
MarkerType = MarkerType.Circle, MarkerSize = 4, MarkerStrokeThickness = 0, ColorAxisKey = "ColorAxis", TrackerFormatString = "\nX: {2:0.###}\nY: {4:0.###}\nMU: {Tag}"
};
for (int i = 0; i < spotMaps[index].ScanSpotNumber; i++)
{
s.Points.Add(new ScatterPoint(spotMaps[index].X[i], spotMaps[index].Y[i], double.NaN, spotMaps[index].MeterWeights[i] * this.MUMWratio, (spotMaps[index].MeterWeights[i] * this.MUMWratio).ToString("0.###")));
}
pmSM.Series.Add(s);
pmSM.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Minimum = -200, Maximum = 200
});
pmSM.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Minimum = -200, Maximum = 200
});
plotView1.Model = pmSM;
this.painted = true;
//histogram
var pm = new PlotModel {
Title = "Spots MU Histogram"
};
float mwMax = this.spotMaps[index].MeterWeights.Max();
float mwMin = this.spotMaps[index].MeterWeights.Min();
float step = (mwMax - mwMin) / this.stepNumHistogram;
var histo = new Dictionary <double, int>();
HistogramSeries histogram = new HistogramSeries();
if (step > 0)
{
for (float curr = mwMin; curr <= mwMax; curr += step)
{
var count = this.spotMaps[index].MeterWeights.Where(x => x >= curr && x < curr + step).Count();
histo.Add(curr * this.MUMWratio, count);
}
foreach (var pair in histo.OrderBy(x => x.Key))
{
histogram.Items.Add(new HistogramItem(pair.Key, pair.Key + step * this.MUMWratio, pair.Value * step * this.MUMWratio, stepNumHistogram));
}
}
else
{
histogram.Items.Add(new HistogramItem(mwMin, mwMin + 5, 5, 1));
}
histogram.TrackerFormatString = "MU Spots Count:\nFrom{5:#.00} to {6:#.00}\nMU counts: {7}";
pm.Series.Add(histogram);
plotViewHistogram.Model = pm;
}
public static PlotModel HistogramSeries()
{
// prepare the model
var model = new PlotModel()
{
Title = "Cubic Distribution",
LegendPlacement = LegendPlacement.Outside,
LegendPosition = LegendPosition.TopCenter,
LegendOrientation = LegendOrientation.Horizontal
};
// add two linear axes
model.Axes.Add(new LinearAxis()
{
Title = "Observation", Position = AxisPosition.Bottom
});
model.Axes.Add(new LinearAxis()
{
Title = "Frequency", Position = AxisPosition.Left
});
// generate random samples from a polynomial distribution
double power = 3;
double max = 10.0;
int sampleCount = 1000;
double integral = Math.Pow(max, power + 1) / (power + 1);
var rnd = new Random(0);
List <double> samples = new List <double>();
for (int i = 0; i < sampleCount; i++)
{
samples.Add(Math.Pow(rnd.NextDouble() * (power + 1) * integral, 1.0 / (power + 1)));
}
// plot histogram of samples
var histogramSeries = new HistogramSeries()
{
Title = $"Empirical Distribution",
FillColor = OxyColors.Green,
StrokeColor = OxyColors.Black,
StrokeThickness = 2
};
var bins = HistogramHelpers.CreateUniformBins(0, max, 20);
var binningOptions = new BinningOptions(BinningOutlierMode.RejectOutliers, BinningIntervalType.InclusiveLowerBound, BinningExtremeValueMode.IncludeExtremeValues);
var items = HistogramHelpers.Collect(samples, bins, binningOptions);
histogramSeries.Items.AddRange(items);
model.Series.Add(histogramSeries);
// plot ideal line for comparison
var functionSeries = new FunctionSeries(x => Math.Pow(x, power) / integral, 0, max, 1000)
{
Title = "Ideal Distribution",
Color = OxyColors.Red
};
model.Series.Add(functionSeries);
return(model);
}
/// <summary>
/// 追加成交量
/// </summary>
/// <param name="field"></param>
/// <param name="hisColor"></param>
/// <returns></returns>
public HistogramSeries AddHistogram(string field, string displayName, string relateCandleName, int panelID)
{
if (field == null || this.dtAllMsg.Columns.Contains(field))
{
return null;
}
else
{
if (this.dicChartPanel.ContainsKey(panelID))
{
ChartPanel chartPanel = this.dicChartPanel[panelID];
HistogramSeries histogramSeries = new HistogramSeries();
histogramSeries.Up_LineColor = Color.Lime;
histogramSeries.RelateCandleName = relateCandleName;
histogramSeries.Down_lineColor = Color.Lime;
chartPanel.YScaleField.Add(field);
histogramSeries.Field = field;
histogramSeries.DisplayName = displayName;
chartPanel.HistoramSeriesList.Add(histogramSeries);
DataColumn dc = new DataColumn(field);
this.dtAllMsg.Columns.Add(dc);
return histogramSeries;
}
return null;
}
}
