// --------------------------------------------------------------------------------------------------------------------
// <copyright file="HistogramSeriesExamples.cs" company="OxyPlot">
// Copyright (c) 2014 OxyPlot contributors
// </copyright>
// <summary>
// Creates example histograms
// </summary>
// --------------------------------------------------------------------------------------------------------------------

namespace ExampleLibrary
{
 using System;
 using System.Linq;

 using ExampleLibrary.Utilities;

 using OxyPlot;
 using OxyPlot.Axes;
 using OxyPlot.Series;
 using System.Collections.Generic;

 [Examples("HistogramSeries"), Tags("Series")]
 public class HistogramSeriesExamples
 {
 [Example("Exponential Distribution")]
 public static PlotModel ExponentialDistribution()
 {
 return CreateExponentialDistribution();
 }

 [Example("Exponential Distribution (transposed)")]
 public static PlotModel ExponentialDistributionTransposed()
 {
 return ExponentialDistribution().Transpose();
 }

 [Example("Label Placement")]
 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;
 }

 [Example("Label Placement (transposed)")]
 public static PlotModel LabelPlacementTransposed()
 {
 return HistogramLabelPlacement().Transpose();
 }

 [Example("Label Placement (reversed Y Axis)")]
 public static PlotModel LabelPlacementReversed()
 {
 return HistogramLabelPlacement().ReverseYAxis();
 }

 [Example("Label Placement (reversed Y Axis, transposed)")]
 public static PlotModel LabelPlacementReversedTransposed()
 {
 return LabelPlacementReversed().Transpose();
 }

 [Example("Custom Bins")]
 public static PlotModel CustomBins()
 {
 return CreateExponentialDistributionCustomBins();
 }

 [Example("Disconnected Bins")]
 public static PlotModel DisconnectedBins()
 {
 return CreateDisconnectedBins();
 }

 [Example("Normal Distribution Three Colors")]
 public static PlotModel NormalDistribution()
 {
 return CreateNormalDistribution();
 }

 [Example("Individual Bin Colors")]
 public static PlotModel IndividualBinColors()
 {
 return CreateIndividualBinColors();
 }

 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 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 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 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 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;
 }

 private static IEnumerable<double> SampleExps(Random rnd, double mean, int count)
 {
 for (int i = 0; i < count; i++)
 {
 yield return SampleExp(rnd, mean);
 }
 }

 private static double SampleExp(Random rnd, double mean)
 {
 return Math.Log(1.0 - rnd.NextDouble()) / -mean;
 }

 private static IEnumerable<double> SampleNormal(Random rnd, double mean, double std, int count)
 {
 for (int i = 0; i < count; i++)
 {
 yield return SampleNormal(rnd, mean, std);
 }
 }

 private static double SampleNormal(Random rnd, double mean, double std)
 {
 // http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
 var u1 = rnd.NextDouble();
 var u2 = rnd.NextDouble();
 return Math.Sqrt(-2 * Math.Log(u1)) * Math.Cos(2 * Math.PI * u2);
 }

 private static IEnumerable<double> SampleUniform(Random rnd, double min, double max, int count)
 {
 for (int i = 0; i < count; i++)
 {
 yield return rnd.NextDouble() * (max - min) + min;
 }
 }
 }
}