#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Windows.Forms;
using System.Windows.Forms.DataVisualization.Charting;
using HeuristicLab.Common;
using HeuristicLab.Visualization.ChartControlsExtensions;

namespace HeuristicLab.Problems.DataAnalysis.Views {
  public partial class GradientChart : EnhancedChart {
    private ModifiableDataset internalDataset;

    private bool useMedianValues;
    public bool UseMedianValues {
      get { return useMedianValues; }
      set {
        if (value == useMedianValues) return;
        useMedianValues = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private int row;
    public int Row {
      get { return row; }
      set {
        if (row == value) return;
        row = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private double min;
    public double Min {
      get { return min; }
      set {
        if (value.IsAlmost(min)) return;
        min = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private double max;
    public double Max {
      get { return max; }
      set {
        if (value.IsAlmost(max)) return;
        max = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private int points;
    public int Points {
      get { return points; }
      set {
        if (value == points) return;
        points = value;
        OnChartPropertyChanged(this, EventArgs.Empty);
        UpdateChart();
      }
    }

    private IRegressionProblemData problemData;
    public IRegressionProblemData ProblemData {
      get { return problemData; }
      set {
        if (!SolutionsCompatibleWithProblemData(value, solutionList))
          throw new ArgumentException("The problem data provided does not contain all the variables required by the solutions.");
        problemData = value;
        UpdateInternalDataset();
        UpdateChart();
      }
    }

    public string Target {
      get { return Solutions.First().ProblemData.TargetVariable; }
    }

    private string variable;
    public string Variable {
      get { return variable; }
      set {
        if (variable == value) return;
        if (!ProblemData.Dataset.DoubleVariables.Contains(value))
          throw new ArgumentException("The variable must be present in the problem dataset.");
        OnChartPropertyChanged(this, EventArgs.Empty);
        variable = value;
        var values = ProblemData.Dataset.GetReadOnlyDoubleValues(variable);
        min = values.Min();
        max = values.Max();
        UpdateChart();
      }
    }

    private List<IRegressionSolution> solutionList;
    public IEnumerable<IRegressionSolution> Solutions {
      get { return solutionList; }
      set {
        if (!value.Any())
          throw new ArgumentException("At least one solution must be provided.");
        if (SolutionsCompatibleWithProblemData(problemData, value))
          solutionList = new List<IRegressionSolution>(value);
        else
          throw new ArgumentException("The provided solution collection is not compatible with the existing problem data.");
        UpdateChart();
      }
    }

    public VerticalLineAnnotation VerticalLineAnnotation {
      get { return (VerticalLineAnnotation)Annotations.SingleOrDefault(x => x is VerticalLineAnnotation); }
    }

    public GradientChart() {
      InitializeComponent();
      RegisterEvents();
    }

    public void AddSolution(IRegressionSolution solution) {
      if (!SolutionsCompatibleWithProblemData(problemData, new[] { solution })) {
        throw new ArgumentException("The solution is not compatible with the problem data.");
      }
      solutionList.Add(solution);
      UpdateChart();
    }

    public void RemoveSolution(IRegressionSolution solution) {
      var removed = solutionList.RemoveAll(x => x == solution);
      if (removed > 0)
        UpdateChart();
    }

    private static bool SolutionsCompatibleWithProblemData(IRegressionProblemData pd, IEnumerable<IRegressionSolution> solutions) {
      if (pd == null || !solutions.Any()) return true;
      if (solutions.Any(x => x.ProblemData.TargetVariable != pd.TargetVariable)) return false;
      var variables = new HashSet<string>(pd.Dataset.DoubleVariables);
      return solutions.SelectMany(x => x.ProblemData.Dataset.DoubleVariables).All(variables.Contains);
    }


    public void Configure(IEnumerable<IRegressionSolution> solutions, IRegressionProblemData pd, double min, double max, int points) {
      if (!SolutionsCompatibleWithProblemData(pd, solutions))
        throw new ArgumentException("Solutions are not compatible with the problem data.");
      this.solutionList = new List<IRegressionSolution>(solutions);
      this.problemData = pd;
      this.variable = pd.Dataset.DoubleVariables.First();
      this.min = min;
      this.max = max;
      this.points = points;

      UpdateInternalDataset();
      UpdateChart();
    }

    private void UpdateChart() {
      // throw exceptions?
      if (internalDataset == null || solutionList == null || !solutionList.Any())
        return;
      if (min.IsAlmost(max) || min > max)
        return;
      if (points == 0)
        return;
      CalculateAxisInterval();
      Series.Clear();
      var vla = VerticalLineAnnotation;
      vla.Visible = true;
      Annotations.Clear();
      Annotations.Add(vla);
      for (int i = 0; i < solutionList.Count; ++i) {
        var solution = solutionList[i];
        var series = PlotSeries(solution);
        series.Name = Target + " " + i;
        Series.Add(series);
        var p = series.Points.Last();
        vla.X = p.XValue;
        var ta = new TextAnnotation {
          Text = string.Format("X: {0:0.000}, Y: {1:0.000}", p.XValue, p.YValues[0]),
          Visible = true,
          X = p.XValue,
          Y = p.YValues[0],
          AxisX = ChartAreas[0].AxisX,
          AxisY = ChartAreas[0].AxisY
        };
        ta.ClipToChartArea = "ChartArea1";
        ta.Name = series.Name;
        Annotations.Add(ta);
      }
      AddStripLines();
      AddLegends();
    }

    private void UpdateInternalDataset() {
      var variables = ProblemData.Dataset.DoubleVariables.ToList();
      var variableValues = new List<double>[variables.Count];

      if (UseMedianValues) {
        for (int i = 0; i < variables.Count; ++i) {
          var median = ProblemData.Dataset.GetDoubleValues(variables[i], ProblemData.TrainingIndices).Median();
          variableValues[i] = new List<double> { median };
        }
      } else {
        for (int i = 0; i < variables.Count; ++i) {
          var variableValue = ProblemData.Dataset.GetDoubleValue(variables[i], Row);
          variableValues[i] = new List<double> { variableValue };
        }
      }
      internalDataset = new ModifiableDataset(variables, variableValues);
    }

    private double GetEstimatedValue(IRegressionSolution solution, double x) {
      var v = internalDataset.GetDoubleValue(Variable, 0);
      internalDataset.SetVariableValue(x, Variable, 0);
      var y = solution.Model.GetEstimatedValues(internalDataset, new[] { 0 }).Single();
      internalDataset.SetVariableValue(v, Variable, 0);
      return y;
    }

    private Series PlotSeries(IRegressionSolution solution) {
      var v = internalDataset.GetDoubleValue(variable, 0);
      var series = new Series { ChartType = SeriesChartType.Point };

      var step = (max - min) / points;
      var axisX = ChartAreas[0].AxisX;
      axisX.Title = Variable;
      var axisY = ChartAreas[0].AxisY;
      axisY.Title = Target;
      double y;
      // lefthand section outside of the training range
      for (double x = axisX.Minimum; x < min; x += step) {
        y = GetEstimatedValue(solution, x);
        series.Points.Add(new DataPoint(x, y) { MarkerSize = 2, MarkerColor = Color.Orange });
      }
      // points in the trainig range
      for (double x = min; x < max; x += step) {
        y = GetEstimatedValue(solution, x);
        series.Points.Add(new DataPoint(x, y) { MarkerSize = 2 });
      }
      // righthand section outside of the training range
      for (double x = max; x < axisX.Maximum; x += step) {
        y = GetEstimatedValue(solution, x);
        series.Points.Add(new DataPoint(x, y) { MarkerSize = 2, MarkerColor = Color.Orange });
      }

      y = GetEstimatedValue(solution, v);
      series.Points.Add(new DataPoint(v, y) { MarkerSize = 5, MarkerColor = Color.Red });
      series.IsVisibleInLegend = true;

      return series;
    }

    private void AddLegends() {
      Legends.Clear();
      var legend = new Legend();
      //      legend.Name = s.Name;
      legend.Alignment = StringAlignment.Center;
      legend.LegendStyle = LegendStyle.Row;
      legend.Docking = Docking.Top;
      Legends.Add(legend);
      foreach (var s in Series) {
        s.Legend = legend.Name;
      }
    }

    private void AddStripLines() {
      var axisX = ChartAreas[0].AxisX;
      axisX.Title = Variable;
      var axisY = ChartAreas[0].AxisY;
      axisY.Title = ProblemData.TargetVariable;
      axisX.StripLines.Clear();
      axisX.StripLines.Add(new StripLine { BackColor = Color.FromArgb(30, Color.Green), IntervalOffset = axisX.Minimum, StripWidth = min - axisX.Minimum });
      axisX.StripLines.Add(new StripLine { BackColor = Color.FromArgb(30, Color.Green), IntervalOffset = max, StripWidth = axisX.Maximum - max });
    }

    private void CalculateAxisInterval() {
      double axisMin, axisMax, axisInterval;
      ChartUtil.CalculateAxisInterval(min, max, 5, out axisMin, out axisMax, out axisInterval);
      var axis = ChartAreas[0].AxisX;
      axis.Minimum = axisMin;
      axis.Maximum = axisMax;
      axis.Interval = axisInterval;
    }

    private void RegisterEvents() {
      AnnotationPositionChanging += chart_AnnotationPositionChanging;
      MouseMove += chart_MouseMove;
      FormatNumber += chart_FormatNumber;
    }

    #region events
    public event EventHandler ChartPropertyChanged;
    public void OnChartPropertyChanged(object sender, EventArgs args) {
      var changed = ChartPropertyChanged;
      if (changed == null) return;
      changed(sender, args);
    }

    //    private void chart_AnnotationPositionChanged(object sender, EventArgs e) {
    //      var a = Annotations[0];
    //      var x = a.X;
    //      var s = Series[0];
    //      var n = s.Points.Count;
    //      var y = GetEstimatedValue(x);
    //      s.Points[n - 1] = new DataPoint(x, y) { MarkerColor = Color.Red, MarkerSize = 5 };
    //      a.TextStyle = TextStyle.Default;
    //      Refresh();
    //    }

    private void chart_AnnotationPositionChanging(object sender, AnnotationPositionChangingEventArgs e) {
      var step = (max - min) / points;
      e.NewLocationX = step * (long)Math.Round(e.NewLocationX / step);
      var axisX = ChartAreas[0].AxisX;
      if (e.NewLocationX > axisX.Maximum)
        e.NewLocationX = axisX.Maximum;
      if (e.NewLocationX < axisX.Minimum)
        e.NewLocationX = axisX.Minimum;
      var x = e.NewLocationX;

      //      var va = VerticalLineAnnotation;
      //      Annotations.Clear();
      //      Annotations.Add(va);
      for (int i = 0; i < solutionList.Count; ++i) {
        var y = GetEstimatedValue(solutionList[i], x);
        var s = Series[i];
        var n = s.Points.Count;
        s.Points[n - 1] = new DataPoint(x, y) { MarkerColor = Color.Red, MarkerSize = 5 };
      }
      foreach (var annotation in Annotations.OfType<TextAnnotation>()) {
        var p = Series[annotation.Name].Points.Last();
        annotation.Text = string.Format("X: {0:0.000}, Y: {1:0.000}", p.XValue, p.YValues[0]);
        annotation.X = p.XValue;
        annotation.Y = p.YValues[0];
      }
      Update();
    }

    private void chart_MouseMove(object sender, MouseEventArgs e) {
      this.Cursor = HitTest(e.X, e.Y).ChartElementType == ChartElementType.Annotation ? Cursors.VSplit : Cursors.Default;
    }

    private void chart_FormatNumber(object sender, FormatNumberEventArgs e) {
      if (e.ElementType == ChartElementType.AxisLabels) {
        switch (e.Format) {
          case "CustomAxisXFormat":
            break;
          case "CustomAxisYFormat":
            var v = e.Value;
            e.LocalizedValue = string.Format("{0,5}", v);
            break;
          default:
            break;
        }
      }
    }
    #endregion

    private void GradientChart_DragDrop(object sender, DragEventArgs e) {
      var data = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat);
      if (data != null) {
        var solution = data as IRegressionSolution;
        if (!Solutions.Contains(solution))
          AddSolution(solution);
      }
    }

    private void GradientChart_DragEnter(object sender, DragEventArgs e) {
      if (!e.Data.GetDataPresent(HeuristicLab.Common.Constants.DragDropDataFormat)) return;
      e.Effect = DragDropEffects.None;

      var data = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat);
      var regressionSolution = data as IRegressionSolution;
      if (regressionSolution != null) {
        e.Effect = DragDropEffects.Copy;
      }
    }
  }
}