public void ExecuteRecipe(Plot plt)
{
// generate sample heights are based on https://ourworldindata.org/human-height
Random rand = new(0);
double[] heights = ScottPlot.DataGen.RandomNormal(rand, pointCount: 1234, mean: 178.4, stdDev: 7.6);
// create a histogram
var hist = new ScottPlot.Statistics.Histogram(heights, min: 140, max: 220, binSize: 1);
// display histogram probabability as a bar plot
var bar = plt.AddBar(values: hist.countsFrac, positions: hist.bins);
bar.FillColor = ColorTranslator.FromHtml("#9bc3eb");
bar.BorderColor = ColorTranslator.FromHtml("#82add9");
bar.BarWidth = hist.binSize;
// display histogram distribution curve as a line plot
plt.AddScatterLines(
xs: hist.bins,
ys: hist.probability,
color: Color.Black,
lineWidth: 2,
lineStyle: LineStyle.Dash);
// customize the plot style
plt.Title("Adult Male Height");
plt.YAxis.Label("Probability");
plt.XAxis.Label("Height (cm)");
plt.SetAxisLimits(yMin: 0);
}
public void ExecuteRecipe(Plot plt)
{
// create sample data for two datasets
Random rand = new Random(0);
double[] values1 = DataGen.RandomNormal(rand, pointCount: 1000, mean: 50, stdDev: 20);
double[] values2 = DataGen.RandomNormal(rand, pointCount: 1000, mean: 45, stdDev: 25);
var hist1 = new ScottPlot.Statistics.Histogram(values1, min: 0, max: 100);
var hist2 = new ScottPlot.Statistics.Histogram(values2, min: 0, max: 100);
// display datasets as step plots
var sp1 = plt.AddScatter(hist1.bins, hist1.cumulativeFrac);
sp1.Label = "Sample A";
sp1.StepDisplay = true;
sp1.MarkerSize = 0;
var sp2 = plt.AddScatter(hist2.bins, hist2.cumulativeFrac);
sp2.Label = "Sample B";
sp2.StepDisplay = true;
sp2.MarkerSize = 0;
// decorate the plot
plt.Legend();
plt.SetAxisLimits(yMin: 0, yMax: 1);
plt.Grid(lineStyle: LineStyle.Dot);
plt.Title("Cumulative Probability Histogram");
plt.XAxis.Label("Probability (fraction)");
plt.YAxis.Label("Value (units)");
}
public void ExecuteRecipe(Plot plt)
{
Random rand = new Random(0);
double[] values = DataGen.RandomNormal(rand, pointCount: 1000, mean: 50, stdDev: 20);
var hist = new ScottPlot.Statistics.Histogram(values, min: 0, max: 100);
// plot the bins as a bar graph (on the primary Y axis)
var bar = plt.AddBar(hist.counts, hist.bins);
bar.BarWidth = hist.binSize * 1.2; // oversize to reduce render artifacts
bar.BorderLineWidth = 0;
bar.YAxisIndex = 0;
plt.YAxis.Label("Count (#)");
plt.YAxis.Color(bar.FillColor);
// plot the mean curve as a scatter plot (on the secondary Y axis)
var sp = plt.AddScatter(hist.bins, hist.countsFracCurve);
sp.MarkerSize = 0;
sp.LineWidth = 2;
sp.YAxisIndex = 1;
plt.YAxis2.Label("Fraction");
plt.YAxis2.Color(sp.Color);
plt.YAxis2.Ticks(true);
// decorate the plot
plt.XAxis2.Label("Normal Random Data", bold: true);
plt.XAxis.Label("Value (units)");
plt.SetAxisLimits(yMin: 0);
plt.Grid(lineStyle: LineStyle.Dot);
}
void PlotHistogram()
{
double[] values = { 0, 20, 25, 28, 39, 59 };
var his = new ScottPlot.Statistics.Histogram(values, min: 0, max: 70);
double barWidth = his.binSize * 1.2;
plt.PlotBar(his.bins, his.countsFracCurve, barWidth: barWidth, outlineWidth: 0);
plt.PlotScatter(his.bins, his.countsFracCurve, markerSize: 0, lineWidth: 2, color: Color.Black);
plt.Axis(null, null, 0, null);
}
public void ExecuteRecipe(Plot plt)
{
// male and female heights are based on https://ourworldindata.org/human-height
Random rand = new(0);
double[] heightsMale = ScottPlot.DataGen.RandomNormal(rand, pointCount: 2345, mean: 178.4, stdDev: 7.6);
double[] heightsFemale = ScottPlot.DataGen.RandomNormal(rand, pointCount: 1234, mean: 164.7, stdDev: 7.1);
// calculate histograms for male and female datasets
var histMale = new ScottPlot.Statistics.Histogram(heightsMale, min: 140, max: 210, binSize: 1);
var histFemale = new ScottPlot.Statistics.Histogram(heightsFemale, min: 140, max: 210, binSize: 1);
// plot histograms
var barMale = plt.AddBar(values: histMale.countsFrac, positions: histMale.bins);
barMale.BarWidth = histMale.binSize;
barMale.FillColor = Color.FromArgb(50, Color.Blue);
barMale.BorderLineWidth = 0;
var barFemale = plt.AddBar(values: histFemale.countsFrac, positions: histFemale.bins);
barFemale.BarWidth = histFemale.binSize;
barFemale.FillColor = Color.FromArgb(50, Color.Red);
barFemale.BorderLineWidth = 0;
// plot probability function curves
plt.AddScatterLines(
xs: histMale.bins,
ys: histMale.probability,
color: Color.FromArgb(150, Color.Blue),
lineWidth: 3,
label: $"Male (n={heightsMale.Length:N0})");
plt.AddScatterLines(
xs: histFemale.bins,
ys: histFemale.probability,
color: Color.FromArgb(150, Color.Red),
lineWidth: 3,
label: $"Female (n={heightsFemale.Length:N0})");
// customize styling
plt.Title("Human Height by Sex");
plt.YLabel("Probability");
plt.XLabel("Height (cm)");
plt.Legend(location: ScottPlot.Alignment.UpperLeft);
plt.SetAxisLimits(yMin: 0);
}
public void Render(Plot plt)
{
Random rand = new Random(0);
double[] values = DataGen.RandomNormal(rand, pointCount: 1000, mean: 50, stdDev: 20);
var hist = new ScottPlot.Statistics.Histogram(values, min: 0, max: 100);
double barWidth = hist.binSize * 1.2; // slightly over-side to reduce anti-alias rendering artifacts
plt.PlotBar(hist.bins, hist.countsFrac, barWidth: barWidth, outlineWidth: 0);
plt.PlotScatter(hist.bins, hist.countsFracCurve, markerSize: 0, lineWidth: 2, color: Color.Black);
plt.Title("Normal Random Data");
plt.YLabel("Frequency (fraction)");
plt.XLabel("Value (units)");
plt.Axis(null, null, 0, null);
plt.Grid(lineStyle: LineStyle.Dot);
}
public void Render(Plot plt)
{
// create sample data for two datasets
Random rand = new Random(0);
double[] values1 = DataGen.RandomNormal(rand, pointCount: 1000, mean: 50, stdDev: 20);
double[] values2 = DataGen.RandomNormal(rand, pointCount: 1000, mean: 45, stdDev: 25);
var hist1 = new ScottPlot.Statistics.Histogram(values1, min: 0, max: 100);
var hist2 = new ScottPlot.Statistics.Histogram(values2, min: 0, max: 100);
// display datasets as step plots
plt.Title("Cumulative Probability Histogram");
plt.YLabel("Probability (fraction)");
plt.XLabel("Value (units)");
plt.PlotStep(hist1.bins, hist1.cumulativeFrac, lineWidth: 1.5, label: "sample A");
plt.PlotStep(hist2.bins, hist2.cumulativeFrac, lineWidth: 1.5, label: "sample B");
plt.Legend();
plt.Axis(null, null, 0, 1);
}
public void ExecuteRecipe(Plot plt)
{
// generate sample heights are based on https://ourworldindata.org/human-height
Random rand = new(0);
double[] heights = ScottPlot.DataGen.RandomNormal(rand, pointCount: 1234, mean: 178.4, stdDev: 7.6);
// create a histogram
var hist = new ScottPlot.Statistics.Histogram(heights, min: 140, max: 220, binSize: 1);
// display the histogram counts as a bar plot
var bar = plt.AddBar(hist.counts, hist.bins);
bar.BarWidth = hist.binSize;
// customize the plot style
plt.YAxis.Label("Count (#)");
plt.XAxis.Label("Height (cm)");
plt.SetAxisLimits(yMin: 0);
}
private void PlotResults(double[] timesMsec)
{
double mean = timesMsec.Sum() / timesMsec.Length;
Array.Sort(timesMsec);
double median = timesMsec[(int)(timesMsec.Length / 2)];
var hist = new ScottPlot.Statistics.Histogram(timesMsec);
formsPlot2.plt.Clear();
formsPlot2.plt.PlotBar(hist.bins, hist.counts);
formsPlot2.plt.PlotVLine(mean, lineWidth: 3, label: "mean");
formsPlot2.plt.PlotVLine(median, lineWidth: 3, label: "median", lineStyle: ScottPlot.LineStyle.Dot);
formsPlot2.plt.Legend();
formsPlot2.plt.AxisAuto();
formsPlot2.plt.Axis(y1: 0);
formsPlot2.plt.YLabel("Count");
formsPlot2.plt.XLabel("Render Time (ms)");
formsPlot2.plt.Title($"Median: {median} ms");
formsPlot2.Render();
}
private WpfPlot DisplayNormalDistribution(DataColumn column, int columnIndex, int columnsCount)
{
var wpfPlot = new WpfPlot
{
Margin = new System.Windows.Thickness(columnIndex * (1800 / columnsCount), 0, 1800 - (columnIndex + 1) * (1800 / columnsCount), 500)
};
(var minimum, var maximum) = DistributionProcessor.GetColumnBoundaries(column);
var normalDistributionEmpirical = new ScottPlot.Statistics.Histogram(column.Data.ToArray(), binCount: 10, min: minimum, max: maximum);
double barWidth = normalDistributionEmpirical.binSize * 1.2;
wpfPlot.plt.PlotBar(normalDistributionEmpirical.bins, normalDistributionEmpirical.countsFrac.Select(v => v / normalDistributionEmpirical.binSize).ToArray(), barWidth: barWidth, outlineWidth: 0, fillColor: Color.Gray);
wpfPlot.plt.PlotScatter(column.NormalDistribution.Select(nd => nd.x).ToArray(), column.NormalDistribution.Select(nd => nd.y).ToArray(), markerSize: 0, lineWidth: 2);
wpfPlot.plt.AxisAutoY(margin: 0);
wpfPlot.plt.Axis(x1: minimum);
wpfPlot.plt.Ticks(numericFormatStringY: "0.00");
wpfPlot.plt.Title(column.Name);
return(wpfPlot);
}
public static Mat MakeHistogram(Mat mat)
{
double[] array;
unsafe
{
var span = new ReadOnlySpan <byte>(mat.Data.ToPointer(), (int)mat.Total() * mat.Channels());
array = new double[span.Length];
for (int i = 0; i < span.Length; i++)
{
array[i] = span[i];
}
}
var plt = new Plot(640, 480);
var hist = new ScottPlot.Statistics.Histogram(array, min: 0, max: 255, binSize: 1);
plt.PlotBar(hist.bins, hist.counts, barWidth: hist.binSize, outlineWidth: 0);
plt.Axis(null, null, 0, null);
plt.Grid(lineStyle: LineStyle.Dot);
var bitmap = plt.GetBitmap();
return(bitmap.ToMat());
}
public void RenderPlot()
{
plotFrame.plt.Clear();
RefreshTitleAndAxis(false);
bool containsDateAxis = false;
foreach (PlotParameters curr in ((App)App.Current).GetSeries())
{
if (curr.drawSettings.label == "")
{
curr.drawSettings.label = null;//Prevents it from showing up in the legend
}
if (curr.drawSettings.dateXAxis)
{
containsDateAxis = true;
object timeUnit;
object numTimeUnitsObj;
curr.metaData.TryGetValue("timeUnit", out timeUnit);
curr.metaData.TryGetValue("numTimeUnits", out numTimeUnitsObj);
ScottPlot.Config.DateTimeUnit dateTimeUnit = (ScottPlot.Config.DateTimeUnit)timeUnit;
int numTimeUnits = (int)numTimeUnitsObj;
dateUnit = dateTimeUnit;
dateUnitSpacing = numTimeUnits;
if (dateTimeUnit == ScottPlot.Config.DateTimeUnit.Year)//Grid spacing of one year is currently unsupported -_-
{
plotFrame.plt.Grid(xSpacing: 12, xSpacingDateTimeUnit: ScottPlot.Config.DateTimeUnit.Month);
}
else
{
plotFrame.plt.Grid(xSpacing: numTimeUnits, xSpacingDateTimeUnit: dateTimeUnit);
}
plotFrame.plt.Ticks(dateTimeX: true, xTickRotation: 30, fontSize: 10); // Default fontSize is 12
}
switch (curr.drawSettings.type)
{
case PlotType.scatter:
double[] xsScatter = ((double[][])curr.data)[0];
double[] ysScatter = ((double[][])curr.data)[1];
if (logAxis)
{
ysScatter = ScottPlot.Tools.Log10(ysScatter);
}
if (!curr.hasErrorData)
{
plotFrame.plt.PlotScatterHighlight(xsScatter, ysScatter, curr.drawSettings.colour, curr.drawSettings.drawLine ? 1 : 0, label: curr.drawSettings.label, markerShape: curr.drawSettings.markerShape, highlightedColor: curr.drawSettings.colour);
}
else
{
double[] errorX = ((double[][])curr.errorData)[0];
double[] errorY = ((double[][])curr.errorData)[1];
plotFrame.plt.PlotScatterHighlight(xsScatter, ysScatter, curr.drawSettings.colour, curr.drawSettings.drawLine ? 1 : 0, label: curr.drawSettings.label, markerShape: curr.drawSettings.markerShape, errorX: errorX, errorY: errorY, highlightedColor: curr.drawSettings.colour);
}
if (curr.drawSettings.drawLinearRegression)
{
var model = new ScottPlot.Statistics.LinearRegressionLine(xsScatter, ysScatter);
double x1 = xsScatter[0];
double x2 = xsScatter[xsScatter.Length - 1];
double y1 = model.GetValueAt(x1);
double y2 = model.GetValueAt(x2);
plotFrame.plt.PlotLine(x1, y1, x2, y2, lineWidth: 3, label: $"ŷ = {model.offset:f9} + {model.slope:f9}x");
}
break;
case PlotType.signal:
object sampleRate = 100;
curr.metaData.TryGetValue("sampleRate", out sampleRate);
object xOffset = 0;
curr.metaData.TryGetValue("xOffset", out xOffset);
double[] data = (double[])curr.data;
if (logAxis)
{
data = ScottPlot.Tools.Log10(data);
}
plotFrame.plt.PlotSignalConst(data, (double)sampleRate, (double)xOffset, color: curr.drawSettings.colour, lineWidth: curr.drawSettings.drawLine ? 1 : 0, label: curr.drawSettings.label, markerSize: 0);
break;
case PlotType.bar:
double[] xsBar = ((double[][])curr.data)[0];
double[] ysBar = ((double[][])curr.data)[1];
if (logAxis)
{
ysBar = ScottPlot.Tools.Log10(ysBar);
}
if (!curr.hasErrorData)
{
plotFrame.plt.PlotBar(xsBar, ysBar, fillColor: curr.drawSettings.colour, outlineColor: curr.drawSettings.colour, errorColor: curr.drawSettings.colour, label: curr.drawSettings.label);
}
else
{
double[] errorY = ((double[])curr.errorData);
plotFrame.plt.PlotBar(xsBar, ysBar, fillColor: curr.drawSettings.colour, outlineColor: curr.drawSettings.colour, errorColor: curr.drawSettings.colour, label: curr.drawSettings.label, errorY: errorY);
}
break;
case PlotType.histogram:
ScottPlot.Statistics.Histogram histogram = new ScottPlot.Statistics.Histogram((double[])curr.data);
double[] yData = histogram.counts;
switch (curr.drawSettings.histogramType)
{
case HistogramType.fraction | HistogramType.density:
yData = histogram.countsFrac;
break;
case HistogramType.fraction | HistogramType.cumulative:
yData = histogram.cumulativeFrac;
break;
case HistogramType.count | HistogramType.cumulative:
yData = histogram.cumulativeCounts;
break;
}
plotFrame.plt.PlotBar(histogram.bins, yData, fillColor: curr.drawSettings.colour, outlineColor: curr.drawSettings.colour, errorColor: curr.drawSettings.colour, label: curr.drawSettings.label);
break;
case PlotType.horizontal_line:
double hLineData = (double)curr.data;
plotFrame.plt.PlotHLine(hLineData, color: curr.drawSettings.colour, label: curr.drawSettings.label);
break;
case PlotType.vertical_line:
double vLineData = (double)curr.data;
plotFrame.plt.PlotVLine(vLineData, color: curr.drawSettings.colour, label: curr.drawSettings.label);
break;
case PlotType.horizontal_span:
(double hSpanMin, double hSpanMax) = (ValueTuple <double, double>)curr.data;
plotFrame.plt.PlotHSpan(hSpanMin, hSpanMax, color: curr.drawSettings.colour, label: curr.drawSettings.label);
break;
case PlotType.vertical_span:
(double vSpanMin, double vSpanMax) = (ValueTuple <double, double>)curr.data;
plotFrame.plt.PlotVSpan(vSpanMin, vSpanMax, color: curr.drawSettings.colour, label: curr.drawSettings.label);
break;
case PlotType.box_whisker:
var plotObj = plotFrame.plt.PlotPopulations((ScottPlot.Statistics.Population)curr.data, label: curr.drawSettings.label);
plotObj.boxStyle = PlottablePopulations.BoxStyle.BoxMedianQuartileOutlier;
plotObj.displayDistributionCurve = false;
break;
case PlotType.function:
var f = (Func <double, double?>)curr.data;
plotFrame.plt.PlotFunction(f, label: curr.drawSettings.label, markerShape: MarkerShape.none, lineStyle: LineStyle.Dash);
break;
case PlotType.bar_grouped:
var plotData = ((double[][])curr.data);
string[] groupLabels = (string[])curr.metaData.GetValueOrDefault("group_names");
string[] seriesLabels = (string[])curr.metaData.GetValueOrDefault("series_names");
if (!curr.hasErrorData)
{
plotFrame.plt.PlotBarGroups(groupLabels, seriesLabels, plotData);
}
else
{
plotFrame.plt.PlotBarGroups(groupLabels, seriesLabels, plotData, (double[][])curr.errorData);
}
break;
}
}
isDateAxis = containsDateAxis;
plotFrame.plt.Legend();
(double highlightX, double highlightY) = (plotFrame.plt.Axis()[0], plotFrame.plt.Axis()[3]);
foreach (var curr in rawPlottables)
{
if (curr != snappedCoordinates || showCoordinates)
{
plotFrame.plt.Add(curr);//Got axed when we cleared everything
}
}
RenderFrame();
}
private void PlotHistogram()
{
double?min = (double)nudMin.Value;
double?max = (double)nudMax.Value;
if (cbMinAuto.Checked)
{
min = null;
}
if (cbMaxAuto.Checked)
{
max = null;
}
double?binSize = (double)nudBinSize.Value;
double?binCount = (double)nudBinCount.Value;
if (cbBinSizeAuto.Checked)
{
binSize = null;
}
if (cbBinCountAuto.Checked)
{
binCount = null;
}
// ignore binCount if both binSize and binCount are given
if ((binSize != null) && (binCount != null))
{
binCount = null;
}
bool ignoreOutOfBounds = cbIgnoreOutOfBounds.Checked;
var hist = new ScottPlot.Statistics.Histogram(values, min, max, binSize, binCount, ignoreOutOfBounds);
binSize = hist.bins[1] - hist.bins[0];
if (nudMin.Enabled == false)
{
nudMin.Value = (decimal)hist.bins[0];
}
if (nudMax.Enabled == false)
{
nudMax.Value = (decimal)(hist.bins[hist.bins.Length - 1] + binSize);
}
if (nudBinSize.Enabled == false)
{
nudBinSize.Value = (decimal)binSize;
}
if (nudBinCount.Enabled == false)
{
nudBinCount.Value = (decimal)hist.bins.Length;
}
lbBins.Items.Clear();
foreach (double bin in hist.bins)
{
lbBins.Items.Add(bin.ToString());
}
if (cbCount.Checked)
{
PlotHistogramCount(hist.bins, hist.counts);
}
else if (cbNorm.Checked)
{
PlotHistogramFrac(hist.bins, hist.countsFrac);
}
else if (cbCph.Checked)
{
PlotHistogramCumulative(hist.bins, hist.cumulativeFrac);
}
}
