public void DrawPoints() { if (Opt.Points == null) throw new NullReferenceException("No any points!"); foreach (var poinInfo in Opt.Points) { if (poinInfo.IsPointVisible) { var pointLine = new LineSeries { StrokeThickness = 4, Smooth = true, Color = OxyColor.FromRgb(0, 0, 0), Points = new List<IDataPoint>() }; pointLine.Points.Add(new DataPoint(poinInfo.Point.X, poinInfo.Point.Y)); _mainPlot.Series.Add(pointLine); } if (poinInfo.Label != null) { var stringLabel = new OxyPlot.Annotations.TextAnnotation { Text = poinInfo.Label, Position = new DataPoint(poinInfo.Point.X + 2, poinInfo.Point.Y + 2), TextColor = OxyColor.FromArgb(0, 0, 0, 0), StrokeThickness = 0, Stroke = OxyColor.FromArgb(0, 255, 255, 255), FontSize = 25 }; _mainPlot.Annotations.Add(stringLabel); } } }
public PlotModel InitPlot(GraphOptions opt) { var plot = new PlotModel { PlotAreaBorderThickness = 0, Title = opt.GraphTitle, TitleFontSize = 10, LegendFontSize = 10, LegendBackground = OxyColor.FromArgb(0, 0, 0, 0), LegendSymbolLength = 20, LegendOrientation = LegendOrientation.Horizontal, LegendPlacement = LegendPlacement.Inside, LegendPosition = LegendPosition.BottomRight, LegendBorder = OxyColor.FromArgb(0, 0, 0, 0), PlotAreaBorderColor = OxyColor.FromArgb(0, 0, 0, 0) }; var linearAxisX = new LinearAxis { Maximum = opt.Diapason.ToX, Minimum = opt.Diapason.FromX, Position = AxisPosition.Bottom, PositionAtZeroCrossing = true, TickStyle = TickStyle.Crossing, MajorStep = 30, MinorStep = 30, }; var linearAxisY = new LinearAxis { Maximum = opt.Diapason.ToY, Minimum = opt.Diapason.FromY, PositionAtZeroCrossing = true, MajorStep = 10, MinorStep = 10, TickStyle = TickStyle.Crossing, }; var labelX = new OxyPlot.Annotations.TextAnnotation { Text = "φ, °", FontSize = 25, Position = new DataPoint(opt.Diapason.ToX - 1, 5), TextColor = OxyColor.FromArgb(0, 0, 0, 0), StrokeThickness = 0, Stroke = OxyColor.FromArgb(0, 255, 255, 255), FontWeight = FontWeights.Bold }; var labelY = new OxyPlot.Annotations.TextAnnotation { Text = "Т, МПа", FontSize = 25, Position = new DataPoint(5, opt.Diapason.ToY - 1), TextColor = OxyColor.FromArgb(0, 0, 0, 0), StrokeThickness = 0, Stroke = OxyColor.FromArgb(0, 255, 255, 255), FontWeight = FontWeights.Bold }; plot.Annotations.Add(labelX); plot.Annotations.Add(labelY); plot.Axes.Add(linearAxisX); plot.Axes.Add(linearAxisY); return plot; }
private void CreateMdsChartModel() { var model = new PlotModel(); var xAxis = new OxyPlot.Axes.LinearAxis { Position = OxyPlot.Axes.AxisPosition.Bottom, MajorGridlineStyle = LineStyle.None }; model.Axes.Add(xAxis); var yAxis = new OxyPlot.Axes.LinearAxis { Position = OxyPlot.Axes.AxisPosition.Left, MajorGridlineStyle = LineStyle.None }; model.Axes.Add(yAxis); var series = new OxyPlot.Series.ScatterSeries { ItemsSource = (Data.MdsCoords.Rows.Cast <filterReportDS.MdsCoordsRow>() .Select(dr => new DataPoint(dr.X, dr.Y))), DataFieldX = "X", DataFieldY = "Y", MarkerType = MarkerType.Circle, MarkerSize = 2, MarkerFill = OxyColor.FromRgb(79, 129, 189) }; model.Series.Add(series); foreach (filterReportDS.MdsCoordsRow dr in Data.MdsCoords.Rows) { var annotation = new OxyPlot.Annotations.TextAnnotation { Text = dr.StrategyName, TextPosition = new DataPoint(dr.X, dr.Y), TextHorizontalAlignment = OxyPlot.HorizontalAlignment.Center, TextVerticalAlignment = OxyPlot.VerticalAlignment.Top, Font = "Segoe UI", TextColor = OxyColor.FromRgb(0, 0, 0), StrokeThickness = 0 }; model.Annotations.Add(annotation); } MdsChartModel = model; }
private void btn_CreateResultTable_Click(object sender, EventArgs e) { OxyColor EEGColor = OxyColors.Cyan; OxyColor EDAColor = OxyColors.LightGreen; OxyColor HRColor = OxyColors.Salmon; //var tester = new List<double> //{ // 0.01 //}; //var tester2 = new List<double> //{ // 0.01, 0.01 //}; //var tester3 = new List<double> //{ // 0.01, 0.01, 0.01 //}; //MessageBox.Show(tester.FisherCombineP().ToString("0.00000000") + "\n" + tester2.FisherCombineP().ToString("0.00000000") + "\n" + tester3.FisherCombineP().ToString("0.00000000")); //var res1 = FisherCompare1(0.4, 10, 0.3, 12); //var res2 = FisherCompare2(0.4, 10, 0.3, 12); //MessageBox.Show("1)\n" + res1.Item1 + "\n" + res1.Item2 + "\n" + res1.Item3 + "\n\n2)\n" + res2.Item1 + "\n" + res2.Item2 + "\n" + res2.Item3); string corrType = "Pearson"; //string corrType = "Kendall"; //string corrType = "Spearman"; double minMilliseconds = 10000; FolderBrowserDialog fbd = new FolderBrowserDialog(); if (fbd.ShowDialog() == DialogResult.OK) { //sensor is first string var timeTable = new Dictionary<string, Dictionary<int, List<Tuple<double, double>>>>(); var stimuliTable = new Dictionary<string, Dictionary<string, List<Tuple<double, double>>>>(); var totalList = new Dictionary<string, List<Tuple<double, double>>>(); var big5List = new Dictionary<string, List<Dictionary<Big5, int>>>(); List<string> sensors = new List<string>(); List<int> times = new List<int>(); List<string> stimulis = new List<string>(); List<string> resultFiles = new List<string>(); foreach (var folder in Directory.GetDirectories(fbd.SelectedPath)) { if (folder.Contains("Stimuli high") || folder.Contains("Stimuli low") || folder.Contains("Time 0") || folder.Contains("Time 1") || folder.Contains("Time 2") || folder.Contains(".git") || folder.Split('\\').Last() == "3" || folder.Split('\\').Last() == "6" || folder.Split('\\').Last() == "13") { continue; } string subject = folder.Split('\\').Last(); var metaLines = File.ReadAllLines($"{folder}/meta.txt"); var big5 = GetBig5(metaLines); int time = int.Parse(metaLines[0].Split('=').Last()); string stimuli = metaLines[1].Split('=').Last(); stimuli = stimuli == "neu" ? "low" : "high"; if (!times.Contains(time)) times.Add(time); if (!stimulis.Contains(stimuli)) stimulis.Add(stimuli); List<string> foldersToExamine = new List<string>(); foldersToExamine.Add(fbd.SelectedPath + "\\Time " + time); if (time > 0) { foldersToExamine.Add(fbd.SelectedPath + "\\Stimuli " + stimuli); } if (!big5List.ContainsKey("time" + time)) { big5List.Add("time" + time, new List<Dictionary<Big5, int>>()); } if (!big5List.ContainsKey("stim" + stimuli)) { big5List.Add("stim" + stimuli, new List<Dictionary<Big5, int>>()); } if (!big5List.ContainsKey("total")) { big5List.Add("total", new List<Dictionary<Big5, int>>()); } if (!big5List.ContainsKey("corr")) { big5List.Add("corr", new List<Dictionary<Big5, int>>()); } if (!big5List.ContainsKey("revCorr")) { big5List.Add("revCorr", new List<Dictionary<Big5, int>>()); } big5List["time" + time].Add(big5); if (time != 0) { big5List["stim" + stimuli].Add(big5); } big5List["total"].Add(big5); foreach (var folderToExamine in foldersToExamine) { foreach (var resultFile in Directory.GetFiles(folderToExamine).Where(f => f.Split('\\').Last().StartsWith(subject) && f.Split('\\').Last().EndsWith(".txt"))) { if (resultFiles.Contains(resultFile.Split('\\').Last()) || !folderToExamine.Contains("Time") && !foldersToExamine.Contains("Stimuli") || resultFile.Contains("dtw")) { continue; } resultFiles.Add(resultFile.Split('\\').Last()); string sensor = new String(resultFile.Split('.').First().SkipWhile(x => x != '_').Skip(1).SkipWhile(x => x != '_').Skip(1).ToArray()); if (!sensors.Contains(sensor)) sensors.Add(sensor); var resultLines = File.ReadAllLines(resultFile); string correlationLine = resultLines.First(x => x.Contains("|" + corrType)); int corrId = resultLines.ToList().IndexOf(correlationLine); int sigId = corrId + 2; string significanceLine = resultLines[sigId]; string N = resultLines[sigId + 2]; double highPassThreshold = minMilliseconds / 1000; if (sensor.Contains("EEG")) { highPassThreshold *= 128; } else if (sensor.Contains("GSR")) { highPassThreshold *= 20; } else if (sensor.Contains("HR")) { highPassThreshold *= 1; } string[] Nsplit = N.Split(new char[] { '|', ' ' }, StringSplitOptions.RemoveEmptyEntries); if (correlationLine.Contains(".a") || significanceLine.Contains(".a") || int.Parse(Nsplit[2]) < highPassThreshold) { if (int.Parse(Nsplit[2]) < highPassThreshold) { Log.LogMessage("Removing - " + Nsplit[2] + ": " + resultFile); } continue; } int splitIndex = (corrType == "Pearson") ? 3 : 4; double pearsCorrelation = double.Parse(correlationLine.Split(new char[] { '|', '*' }, StringSplitOptions.RemoveEmptyEntries)[splitIndex].Replace(',', '.'), System.Globalization.CultureInfo.InvariantCulture); double pearsSignificance = double.Parse(significanceLine.Split(new char[] { '|', '*' }, StringSplitOptions.RemoveEmptyEntries)[splitIndex].Replace(',', '.'), System.Globalization.CultureInfo.InvariantCulture); var result = Tuple.Create(pearsCorrelation, pearsSignificance); if (!timeTable.ContainsKey(sensor)) { timeTable.Add(sensor, new Dictionary<int, List<Tuple<double, double>>>()); stimuliTable.Add(sensor, new Dictionary<string, List<Tuple<double, double>>>()); totalList.Add(sensor, new List<Tuple<double, double>>()); } if (!timeTable[sensor].ContainsKey(time)) { timeTable[sensor].Add(time, new List<Tuple<double, double>>()); } if (!stimuliTable[sensor].ContainsKey(stimuli)) { stimuliTable[sensor].Add(stimuli, new List<Tuple<double, double>>()); } timeTable[sensor][time].Add(result); totalList[sensor].Add(result); if (time != 0) { stimuliTable[sensor][stimuli].Add(result); } if (pearsCorrelation > 0) { big5List["corr"].Add(big5); } else { big5List["revCorr"].Add(big5); } } } } //done gathering results List<string> totalToWrite = new List<string>(); totalToWrite.Add("Sensor&Avg Corr&Avg Sig. \\\\"); foreach (var sensor in sensors) { double avgCorrelation = totalList[sensor].Average(x => x.Item1); double stdevCorrelation = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(totalList[sensor].Select(x => x.Item1).ToArray()); double avgSignificance = totalList[sensor].Average(x => x.Item2); double stdevSignificance = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(totalList[sensor].Select(x => x.Item2).ToArray()); totalToWrite.Add($"{sensor}&{avgCorrelation.ToString("0.000")}({stdevCorrelation.ToString("0.000")})&{avgSignificance.ToString("0.000")}({stdevSignificance.ToString("0.000")}) \\\\"); } Dictionary<Big5, List<string>> big5Anova = new Dictionary<Big5, List<string>>(); foreach (Big5 item in Enum.GetValues(typeof(Big5))) { big5Anova.Add(item, new List<string>()); totalToWrite.Add(item + " Mean: " + big5List["total"].Average(x => x[item]).ToString("0.00") + ", SD: " + MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(big5List["total"].Select(x => x[item]).ToArray()).ToString("0.00") + "."); big5List["time0"].ForEach(x => big5Anova[item].Add("0;" + x[item])); big5List["time1"].ForEach(x => big5Anova[item].Add("1;" + x[item])); big5List["time2"].ForEach(x => big5Anova[item].Add("2;" + x[item])); big5List["stimlow"].ForEach(x => big5Anova[item].Add("3;" + x[item])); big5List["stimhigh"].ForEach(x => big5Anova[item].Add("4;" + x[item])); } foreach (var big5group in big5Anova) { File.WriteAllLines(fbd.SelectedPath + "/" + big5group.Key + "_anova.csv", big5group.Value); } File.WriteAllLines(fbd.SelectedPath + "/" + corrType + "_totals.txt", totalToWrite); double width = 1 / (sensors.Count * 1.4); double widthTime = 0.3; var timeModel = new PlotModel() { Title = $"Time Groups Box Plot" }; var avgLineSeries = new OxyPlot.Series.LineSeries() { }; List<OxyColor> colors = new List<OxyColor>() { }; int small = sensors.Count / 3; int mid = small * 2; int stepsize = 255 / small; for (int i = 0; i < sensors.Count; i++) { byte increaser = (byte)((i % small) * stepsize); byte decreaser = (byte)(255 - increaser); if (i < small) { colors.Add(OxyColor.FromRgb(increaser, decreaser, decreaser)); } else if (i < mid) { colors.Add(OxyColor.FromRgb(decreaser, increaser, decreaser)); } else { colors.Add(OxyColor.FromRgb(decreaser, decreaser, increaser)); } } List<string> sensorsAdded = new List<string>(); foreach (var sensor in sensors) { List<string> timeAnova = new List<string>(); foreach (var time in times) { timeTable[sensor][time].ForEach(x => timeAnova.Add(time + ";" + x.Item1)); } File.WriteAllLines(fbd.SelectedPath + "/" + sensor + ".csv", timeAnova); } Dictionary<string, int[]> significantAmount = new Dictionary<string, int[]>(); Dictionary<string, int[]> significantAmountMax = new Dictionary<string, int[]>(); var significantCorr = new Dictionary<string, Tuple<double, double, double, double>[]>(); foreach (var sensor in sensors) { significantAmount.Add(sensor, new int[5]); significantAmountMax.Add(sensor, new int[5]); //significantCorr.Add(sensor, new Tuple<double, double>[5]); } significantCorr.Add("EEG", new Tuple<double, double, double, double>[5]); significantCorr.Add("EDA", new Tuple<double, double, double, double>[5]); significantCorr.Add("HR", new Tuple<double, double, double, double>[5]); //significantCorr.Add("AVG", new Tuple<double, double, double, double>[5]); Action<string, int, List<Tuple<double, double>>> AddCorrelation = (sens, id, correl) => { //old average + sd //significantCorr[sens][id] = Tuple.Create(correl.Average(x => x.Item1), correl.Select(x => x.Item1).STDEV(), correl.Average(x => x.Item2), correl.Select(x => x.Item2).STDEV()); //new fisher algorithms significantCorr[sens][id] = Tuple.Create(FisherInverse(correl.Average(x => Fisher(x.Item1))), Math.Round((double)correl.Count), correl.Select(x => x.Item2).FisherCombineP(), Math.Round((double)correl.Count)); }; List<string> amountTimeSignificant = new List<string>(); List<double> timeSignificantPoints = new List<double>(); var TimeErrorModel = new PlotModel() { Title = $"Time Error Model" }; var timeErrorSeries = new OxyPlot.Series.ErrorColumnSeries() { }; TimeErrorModel.Series.Add(timeErrorSeries); TimeErrorModel.Axes.Add(new OxyPlot.Axes.LinearAxis { Position = OxyPlot.Axes.AxisPosition.Left }); var axis = new OxyPlot.Axes.CategoryAxis { Position = OxyPlot.Axes.AxisPosition.Bottom }; axis.Labels.Add("Time 0"); axis.Labels.Add("Time 1"); axis.Labels.Add("Time 2"); TimeErrorModel.Axes.Add(axis); TimeErrorModel.Annotations.Add(new OxyPlot.Annotations.LineAnnotation() { Y = 0, Type = OxyPlot.Annotations.LineAnnotationType.Horizontal }); var TimeErrorModel2 = new PlotModel() { Title = $"Time Error Model" }; TimeErrorModel2.Axes.Add(new OxyPlot.Axes.LinearAxis { Position = OxyPlot.Axes.AxisPosition.Left }); var axis2 = new OxyPlot.Axes.CategoryAxis { Position = OxyPlot.Axes.AxisPosition.Bottom }; axis2.Labels.Add("Time 0"); axis2.Labels.Add("Time 1"); axis2.Labels.Add("Time 2"); TimeErrorModel2.Axes.Add(axis2); List<string> AnovaIndividual = new List<string>(); List<string> AnovaAvg = new List<string>(); int anovaIndividualId = 0; int anovaAvgId = 0; List<string> AnovaIndividualLegend = new List<string>(); List<string> AnovaAvgLegend = new List<string>(); foreach (var time in times) { int sensorId = 0; List<string> timeToWrite = new List<string>(); timeToWrite.Add("Sensor&Avg Corr&Avg Sig. \\\\"); List<double> avgs = new List<double>(); List<double> sigPoints = new List<double>(); var errorSeries = new OxyPlot.Series.ErrorColumnSeries(); TimeErrorModel2.Series.Add(errorSeries); var EEGAllCorrelations = new List<Tuple<double, double>>(); var GSRAllCorrelations = timeTable["GSR"][time]; var HRAllCorrelations = timeTable["HR"][time]; foreach (var sensor in sensors) { if (sensor.Contains("EEG")) { EEGAllCorrelations.AddRange(timeTable[sensor][time]); } double avgCorrelation = timeTable[sensor][time].Average(x => x.Item1); double stdevCorrelation = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Select(x => x.Item1).ToArray()); double avgSignificance = timeTable[sensor][time].Average(x => x.Item2); double stdevSignificance = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Select(x => x.Item2).ToArray()); var orderedAll = timeTable[sensor][time].OrderBy(x => x.Item1).ToList();//timeTable[sensor][time].Where(x => x.Item2 * 100 < (int)5).OrderBy(x => x.Item1).ToList(); amountTimeSignificant.Add(time + " & " + sensor + " & " + orderedAll.Count); significantAmount[sensor][time] = orderedAll.Count; significantAmountMax[sensor][time] = timeTable[sensor][time].Count; //significantCorr[sensor][time] = Tuple.Create(orderedAll.Average(x => x.Item1), MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(orderedAll.Select(x => x.Item1).ToArray())); //var boxItem = new OxyPlot.Series.BoxPlotItem(time + sensorId * widthTime - (0.5 * widthTime * sensors.Count), orderedAll[0].Item1, orderedAll[(int)(orderedAll.Count * 0.25)].Item1, orderedAll[orderedAll.Count / 2].Item1, orderedAll[(int)(orderedAll.Count * 0.75)].Item1, orderedAll.Last().Item1); //var boxItem = new OxyPlot.Series.BoxPlotItem(sensorId + time * widthTime - (0.5 * widthTime * (times.Count - 1)), orderedAll[0].Item1, orderedAll[(int)(orderedAll.Count * 0.25)].Item1, orderedAll[orderedAll.Count / 2].Item1, orderedAll[(int)(orderedAll.Count * 0.75)].Item1, orderedAll.Last().Item1); //var boxSeries = new OxyPlot.Series.BoxPlotSeries() { }; //boxSeries.BoxWidth = widthTime; //boxSeries.WhiskerWidth = widthTime; //boxSeries.Items.Add(boxItem); //boxSeries.Fill = colors[sensorId]; //timeModel.Series.Add(boxSeries); errorSeries.Items.Add(new OxyPlot.Series.ErrorColumnItem(orderedAll.Average(x => x.Item1), MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(orderedAll.Select(x => x.Item1).ToArray()), time) { Color = colors[sensorId] }); //if (!sensorsAdded.Contains(sensor)) //{ // sensorsAdded.Add(sensor); // boxSeries.Title = sensor; //} avgs.Add(orderedAll.Average(x => x.Item1)); sigPoints.AddRange(orderedAll.Select(x => x.Item1)); if (avgSignificance * 100 < (int)5) { timeToWrite.Add($"\\textbf{{{sensor}}}&\\textbf{{{avgCorrelation.ToString("0.000")}({stdevCorrelation.ToString("0.000")})}}&\\textbf{{{avgSignificance.ToString("0.000")}({stdevSignificance.ToString("0.000")})}} \\\\"); } else { timeToWrite.Add($"{sensor}&{avgCorrelation.ToString("0.000")}({stdevCorrelation.ToString("0.000")})&{avgSignificance.ToString("0.000")}({stdevSignificance.ToString("0.000")}) \\\\"); } sensorId++; } double boxWidth = 0.3; //eeg EEGAllCorrelations = EEGAllCorrelations.OrderBy(x => x.Item1).ToList(); var EEGSeries = new OxyPlot.Series.BoxPlotSeries() { Fill = EEGColor, BoxWidth = boxWidth, WhiskerWidth = boxWidth }; if (time == 0) EEGSeries.Title = "EEG"; var EEGItem = CreateBoxItem(EEGAllCorrelations); EEGItem.X = time - EEGSeries.BoxWidth * 1; EEGSeries.Items.Add(EEGItem); timeModel.Series.Add(EEGSeries); AddCorrelation("EEG", time, EEGAllCorrelations); foreach (var cor in EEGAllCorrelations) { AnovaIndividual.Add(anovaIndividualId + ";" + cor.Item1); } AnovaIndividualLegend.Add(anovaIndividualId++ + "=time_" + time + "_EEG"); //gsr GSRAllCorrelations = GSRAllCorrelations.OrderBy(x => x.Item1).ToList(); var GSRSeries = new OxyPlot.Series.BoxPlotSeries() { Fill = EDAColor, BoxWidth = boxWidth, WhiskerWidth = boxWidth }; if (time == 0) GSRSeries.Title = "EDA"; var GSRItem = CreateBoxItem(GSRAllCorrelations); GSRItem.X = time; GSRSeries.Items.Add(GSRItem); timeModel.Series.Add(GSRSeries); AddCorrelation("EDA", time, GSRAllCorrelations); foreach (var cor in GSRAllCorrelations) { AnovaIndividual.Add(anovaIndividualId + ";" + cor.Item1); } AnovaIndividualLegend.Add(anovaIndividualId++ + "=time_" + time + "_GSR"); //hr HRAllCorrelations = HRAllCorrelations.OrderBy(x => x.Item1).ToList(); var HRSeries = new OxyPlot.Series.BoxPlotSeries() { Fill = HRColor, BoxWidth = boxWidth, WhiskerWidth = boxWidth }; if (time == 0) HRSeries.Title = "HR"; var HRItem = CreateBoxItem(HRAllCorrelations); HRItem.X = time + HRSeries.BoxWidth * 1; HRSeries.Items.Add(HRItem); timeModel.Series.Add(HRSeries); AddCorrelation("HR", time, HRAllCorrelations); foreach (var cor in HRAllCorrelations) { AnovaIndividual.Add(anovaIndividualId + ";" + cor.Item1); } AnovaIndividualLegend.Add(anovaIndividualId++ + "=time_" + time + "_HR"); //avg var AVGAllCorrelations = EEGAllCorrelations.Concat(GSRAllCorrelations.Concat(HRAllCorrelations)).ToList(); //AddCorrelation("AVG", time, AVGAllCorrelations); foreach (var cor in AVGAllCorrelations) { AnovaAvg.Add(anovaAvgId + ";" + cor.Item1); } AnovaAvgLegend.Add(anovaAvgId++ + "=time_" + time); double totalAvg = AVGAllCorrelations.Average(x => x.Item1); var txtAvg = new OxyPlot.Annotations.TextAnnotation() { TextPosition = new OxyPlot.DataPoint(time, -1), Text = "Avg " + totalAvg.ToString(".000").Replace(",", "."), Stroke = OxyColors.White }; timeModel.Annotations.Add(txtAvg); timeErrorSeries.Items.Add(new OxyPlot.Series.ErrorColumnItem(sigPoints.Average(), MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(sigPoints.ToArray()), time)); avgLineSeries.Points.Add(new OxyPlot.DataPoint(time, avgs.Average())); File.WriteAllLines(fbd.SelectedPath + "/" + corrType + "_time" + time + ".txt", timeToWrite); } File.WriteAllLines(fbd.SelectedPath + "/significantTime.tex", amountTimeSignificant); timeModel.LegendPlacement = LegendPlacement.Outside; timeModel.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Left, Maximum = 1, Minimum = -1, Title = "Pearson's r" }); timeModel.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Bottom, Maximum = 2.5, Minimum = -0.5, MajorStep = 1, Title = "Time", MinorTickSize = 0 }); //timeModel.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Bottom, Maximum = sensors.Count - 0.5, Minimum = -0.5, MajorStep = 1, Title = "Sensors", MinorTickSize = 0 }); //boxModel.Series.Add(avgLineSeries); PngExporter pnger = new PngExporter(); pnger.ExportToFile(timeModel, fbd.SelectedPath + "/timeBox.png"); pnger.ExportToFile(TimeErrorModel, fbd.SelectedPath + "/errorPlotTest.png"); pnger.ExportToFile(TimeErrorModel2, fbd.SelectedPath + "/errorPlotTest2.png"); /* //correlation and reverse correlation foreach (var time in times) { //Correlation List<string> correlationTimeToWrite = new List<string>(); correlationTimeToWrite.Add("Sensor&Avg Corr&Avg Sig. \\\\"); //Reverse correlation List<string> reverseCorrelationTimeToWrite = new List<string>(); reverseCorrelationTimeToWrite.Add("Sensor & Avg Corr & Avg Sig. \\\\"); foreach (var sensor in sensors) { double correlationAvgCorrelation = timeTable[sensor][time].Where(x => x.Item1 >= 0).Average(x => x.Item1); double correlationStdevCorrelation = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Where(x => x.Item1 >= 0).Select(x => x.Item1).ToArray()); double correlationAvgSignificance = timeTable[sensor][time].Where(x => x.Item1 >= 0).Average(x => x.Item2); double correlationStdevSignificance = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Where(x => x.Item1 >= 0).Select(x => x.Item2).ToArray()); double reverseCorrelationAvgCorrelation = timeTable[sensor][time].Where(x => x.Item1 < 0).Average(x => x.Item1); double reverseCorrelationStdevCorrelation = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Where(x => x.Item1 < 0).Select(x => x.Item1).ToArray()); double reverseCorrelationAvgSignificance = timeTable[sensor][time].Where(x => x.Item1 < 0).Average(x => x.Item2); double reverseCorrelationStdevSignificance = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Where(x => x.Item1 < 0).Select(x => x.Item2).ToArray()); correlationTimeToWrite.Add($"{sensor}&{correlationAvgCorrelation.ToString("0.000")}({correlationStdevCorrelation.ToString("0.000")})&{correlationAvgSignificance.ToString("0.000")}({correlationStdevSignificance.ToString("0.000")}) \\\\"); reverseCorrelationTimeToWrite.Add($"{sensor}&{reverseCorrelationAvgCorrelation.ToString("0.000")}({reverseCorrelationStdevCorrelation.ToString("0.000")})&{reverseCorrelationAvgSignificance.ToString("0.000")}({reverseCorrelationStdevSignificance.ToString("0.000")}) \\\\"); } foreach (Big5 item in Enum.GetValues(typeof(Big5))) { correlationTimeToWrite.Add(item + " Mean: " + big5List["corr"].Average(x => x[item]).ToString("0.00") + ", SD: " + MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(big5List["corr"].Select(x => x[item]).ToArray()).ToString("0.00") + "."); reverseCorrelationTimeToWrite.Add(item + " Mean: " + big5List["revCorr"].Average(x => x[item]).ToString("0.00") + ", SD: " + MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(big5List["revCorr"].Select(x => x[item]).ToArray()).ToString("0.00") + "."); } File.WriteAllLines(fbd.SelectedPath + "/correlationTime" + time + ".txt", correlationTimeToWrite); File.WriteAllLines(fbd.SelectedPath + "/reverseCorrelationTime" + time + ".txt", reverseCorrelationTimeToWrite); } */ var Big5timeBox = new PlotModel() { Title = "Big5 Time Box Plots", LegendPlacement = LegendPlacement.Outside }; Dictionary<Big5, OxyPlot.Series.BoxPlotSeries> big5timeSeries = new Dictionary<Big5, OxyPlot.Series.BoxPlotSeries>(); foreach (Big5 item in Enum.GetValues(typeof(Big5))) { big5timeSeries.Add(item, new OxyPlot.Series.BoxPlotSeries() { Fill = colors[(int)item * 2], Title = item.ToString(), BoxWidth = 0.1, WhiskerWidth = 0.1 }); Big5timeBox.Series.Add(big5timeSeries[item]); } Big5timeBox.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Left, Maximum = 50, Minimum = 10, Title = "Score" }); Big5timeBox.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Bottom, Maximum = 2.5, Minimum = -0.5, MajorStep = 1, Title = "Time", MinorTickSize = 0 }); foreach (var time in times) { foreach (Big5 item in Enum.GetValues(typeof(Big5))) { var orderino = big5List["time" + time].Select(x => x[item]).OrderBy(x => x).ToList(); big5timeSeries[item].Items.Add(new OxyPlot.Series.BoxPlotItem(time - 0.25 + (int)item * 0.1, orderino[0], orderino[(int)(orderino.Count * 0.25)], orderino[orderino.Count / 2], orderino[(int)(orderino.Count * 0.75)], orderino.Last())); } } pnger.ExportToFile(Big5timeBox, fbd.SelectedPath + "/timeBoxBig5.png"); foreach (var time in times) { List<string> timeToWrite = new List<string>(); timeToWrite.Add("\\begin{table}"); timeToWrite.Add("\\centering"); timeToWrite.Add("{\\large \\textbf{Time " + time + "}}\\vspace{1pt}"); timeToWrite.Add("\\begin{tabular}{ccc}"); timeToWrite.Add("\\toprule"); timeToWrite.Add("Sensor&Avg Corr&Avg Sig. \\\\"); timeToWrite.Add("\\midrule"); foreach (var sensor in sensors) { double avgCorrelation = timeTable[sensor][time].Average(x => x.Item1); double stdevCorrelation = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Select(x => x.Item1).ToArray()); double avgSignificance = timeTable[sensor][time].Average(x => x.Item2); double stdevSignificance = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(timeTable[sensor][time].Select(x => x.Item2).ToArray()); if (avgSignificance < 0.05) { timeToWrite.Add($"\\textbf{{{sensor}}}&\\textbf{{{avgCorrelation.ToString("0.000")}({stdevCorrelation.ToString("0.000")})}}&\\textbf{{{avgSignificance.ToString("0.000")}({stdevSignificance.ToString("0.000")})}} \\\\"); } else { timeToWrite.Add($"{sensor}&{avgCorrelation.ToString("0.000")}({stdevCorrelation.ToString("0.000")})&{avgSignificance.ToString("0.000")}({stdevSignificance.ToString("0.000")}) \\\\"); } } timeToWrite.Add("\\bottomrule"); timeToWrite.Add("\\end{tabular}"); timeToWrite.Add("\\caption{Results from time " + time + "."); foreach (Big5 item in Enum.GetValues(typeof(Big5))) { timeToWrite.Add(item + " Mean: " + big5List["time" + time].Average(x => x[item]).ToString("0.00") + ", SD: " + MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(big5List["time" + time].Select(x => x[item]).ToArray()).ToString("0.00") + "."); } timeToWrite.Add("}"); timeToWrite.Add("\\label{[TABLE] res time" + time + "}"); timeToWrite.Add("\\end{table}"); File.WriteAllLines(fbd.SelectedPath + "/" + corrType + "_time" + time + ".txt", timeToWrite); } var stimModel = new PlotModel() { Title = "Stimuli Groups Box Plot" }; int stimId = 0; sensorsAdded.Clear(); avgLineSeries.Points.Clear(); var Big5StimBox = new PlotModel() { Title = "Big5 Stimuli Box Plots", LegendPlacement = LegendPlacement.Outside }; Dictionary<Big5, OxyPlot.Series.BoxPlotSeries> big5Series = new Dictionary<Big5, OxyPlot.Series.BoxPlotSeries>(); foreach (Big5 item in Enum.GetValues(typeof(Big5))) { big5Series.Add(item, new OxyPlot.Series.BoxPlotSeries() { Fill = colors[(int)item * 2], Title = item.ToString(), BoxWidth = 0.1, WhiskerWidth = 0.1 }); Big5StimBox.Series.Add(big5Series[item]); } Big5StimBox.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Left, Maximum = 50, Minimum = 10, Title = "Score" }); Big5StimBox.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Bottom, Maximum = 1.5, Minimum = -0.5, MajorStep = 1, Title = "Category", MinorTickSize = 0 }); List<string> amountStimSignificant = new List<string>(); foreach (var stimuli in stimulis) { List<string> stimuliToWrite = new List<string>(); stimuliToWrite.Add("\\begin{table}"); stimuliToWrite.Add("\\centering"); stimuliToWrite.Add("{\\large \\textbf{Stimuli " + stimuli + "}}\\vspace{1pt}"); stimuliToWrite.Add("\\begin{tabular}{ccc}"); stimuliToWrite.Add("\\toprule"); stimuliToWrite.Add("Sensor&Avg Corr&Avg Sig. \\\\"); stimuliToWrite.Add("\\midrule"); List<double> avgs = new List<double>(); int sensorId = 0; var EEGAllCorrelations = new List<Tuple<double, double>>(); var GSRAllCorrelations = stimuliTable["GSR"][stimuli]; var HRAllCorrelations = stimuliTable["HR"][stimuli]; foreach (var sensor in sensors) { if (sensor.Contains("EEG")) { EEGAllCorrelations.AddRange(stimuliTable[sensor][stimuli]); } double avgCorrelation = stimuliTable[sensor][stimuli].Average(x => x.Item1); double stdevCorrelation = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(stimuliTable[sensor][stimuli].Select(x => x.Item1).ToArray()); double avgSignificance = stimuliTable[sensor][stimuli].Average(x => x.Item2); double stdevSignificance = MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(stimuliTable[sensor][stimuli].Select(x => x.Item2).ToArray()); var orderedAll = stimuliTable[sensor][stimuli].Where(x => x.Item2 * 100 < (int)5).OrderBy(x => x.Item1).ToList(); amountStimSignificant.Add(stimuli + " & " + sensor + " & " + orderedAll.Count); significantAmount[sensor][stimuli == "low" ? 3 : 4] = orderedAll.Count; significantAmountMax[sensor][stimuli == "low" ? 3 : 4] = stimuliTable[sensor][stimuli].Count; //significantCorr[sensor][stimuli == "low" ? 3 : 4] = Tuple.Create(orderedAll.Average(x => x.Item1), MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(orderedAll.Select(x => x.Item1).ToArray())); var boxItem = new OxyPlot.Series.BoxPlotItem(((1 + stimId) % 2) + sensorId * width - (0.5 * width * sensors.Count), orderedAll[0].Item1, orderedAll[(int)(orderedAll.Count * 0.25)].Item1, orderedAll[orderedAll.Count / 2].Item1, orderedAll[(int)(orderedAll.Count * 0.75)].Item1, orderedAll.Last().Item1); //var boxItem = new OxyPlot.Series.BoxPlotItem(sensorId + ((1 + stimId) % 2) * widthTime - (0.5 * widthTime), orderedAll[0].Item1, orderedAll[(int)(orderedAll.Count * 0.25)].Item1, orderedAll[orderedAll.Count / 2].Item1, orderedAll[(int)(orderedAll.Count * 0.75)].Item1, orderedAll.Last().Item1); var boxSeries = new OxyPlot.Series.BoxPlotSeries() { }; boxSeries.BoxWidth = width; boxSeries.WhiskerWidth = width; //boxSeries.BoxWidth = widthTime; //boxSeries.WhiskerWidth = widthTime; boxSeries.Items.Add(boxItem); boxSeries.Fill = colors[sensorId]; //stimModel.Series.Add(boxSeries); avgs.Add(orderedAll.Average(x => x.Item1)); if (!sensorsAdded.Contains(sensor)) { sensorsAdded.Add(sensor); boxSeries.Title = sensor; } if (avgSignificance < 0.05) { stimuliToWrite.Add($"\\textbf{{{sensor}}}&\\textbf{{{avgCorrelation.ToString("0.000")}({stdevCorrelation.ToString("0.000")})}}&\\textbf{{{avgSignificance.ToString("0.000")}({stdevSignificance.ToString("0.000")})}} \\\\"); } else { stimuliToWrite.Add($"{sensor}&{avgCorrelation.ToString("0.000")}({stdevCorrelation.ToString("0.000")})&{avgSignificance.ToString("0.000")}({stdevSignificance.ToString("0.000")}) \\\\"); } sensorId++; } double boxWidth = 0.3; //eeg EEGAllCorrelations = EEGAllCorrelations.OrderBy(x => x.Item1).ToList(); var EEGSeries = new OxyPlot.Series.BoxPlotSeries() { Fill = EEGColor, BoxWidth = boxWidth, WhiskerWidth = boxWidth }; if (stimuli == "low") EEGSeries.Title = "EEG"; var EEGItem = CreateBoxItem(EEGAllCorrelations); EEGItem.X = (stimuli == "low" ? 0 : 1) - EEGSeries.BoxWidth * 1; EEGSeries.Items.Add(EEGItem); stimModel.Series.Add(EEGSeries); AddCorrelation("EEG", stimuli == "low" ? 3 : 4, EEGAllCorrelations); foreach (var cor in EEGAllCorrelations) { AnovaIndividual.Add(anovaIndividualId + ";" + cor.Item1); } AnovaIndividualLegend.Add(anovaIndividualId++ + "=stimuli_" + stimuli + "_EEG"); //gsr GSRAllCorrelations = GSRAllCorrelations.OrderBy(x => x.Item1).ToList(); var GSRSeries = new OxyPlot.Series.BoxPlotSeries() { Fill = EDAColor, BoxWidth = boxWidth, WhiskerWidth = boxWidth }; if (stimuli == "low") GSRSeries.Title = "EDA"; var GSRItem = CreateBoxItem(GSRAllCorrelations); GSRItem.X = (stimuli == "low" ? 0 : 1); GSRSeries.Items.Add(GSRItem); stimModel.Series.Add(GSRSeries); AddCorrelation("EDA", stimuli == "low" ? 3 : 4, GSRAllCorrelations); foreach (var cor in GSRAllCorrelations) { AnovaIndividual.Add(anovaIndividualId + ";" + cor.Item1); } AnovaIndividualLegend.Add(anovaIndividualId++ + "=stimuli_" + stimuli + "_GSR"); //hr HRAllCorrelations = HRAllCorrelations.OrderBy(x => x.Item1).ToList(); var HRSeries = new OxyPlot.Series.BoxPlotSeries() { Fill = HRColor, BoxWidth = boxWidth, WhiskerWidth = boxWidth }; if (stimuli == "low") HRSeries.Title = "HR"; var HRItem = CreateBoxItem(HRAllCorrelations); HRItem.X = (stimuli == "low" ? 0 : 1) + HRSeries.BoxWidth * 1; HRSeries.Items.Add(HRItem); stimModel.Series.Add(HRSeries); AddCorrelation("HR", stimuli == "low" ? 3 : 4, HRAllCorrelations); foreach (var cor in HRAllCorrelations) { AnovaIndividual.Add(anovaIndividualId + ";" + cor.Item1); } AnovaIndividualLegend.Add(anovaIndividualId++ + "=stimuli_" + stimuli + "_HR"); //avg var AVGAllCorrelations = EEGAllCorrelations.Concat(GSRAllCorrelations.Concat(HRAllCorrelations)).ToList(); //AddCorrelation("AVG", stimuli == "low" ? 3 : 4, AVGAllCorrelations); foreach (var cor in AVGAllCorrelations) { AnovaAvg.Add(anovaAvgId + ";" + cor.Item1); } AnovaAvgLegend.Add(anovaAvgId++ + "=stimuli_" + stimuli); avgLineSeries.Points.Add(new OxyPlot.DataPoint(0, avgs.Average())); stimuliToWrite.Add("\\bottomrule"); stimuliToWrite.Add("\\end{tabular}"); stimuliToWrite.Add("\\caption{Results from stimuli " + stimuli + "."); foreach (Big5 item in Enum.GetValues(typeof(Big5))) { stimuliToWrite.Add(item + " Mean: " + big5List["stim" + stimuli].Average(x => x[item]).ToString("0.00") + ", SD: " + MathNet.Numerics.Statistics.ArrayStatistics.PopulationStandardDeviation(big5List["stim" + stimuli].Select(x => x[item]).ToArray()).ToString("0.00") + "."); var orderino = big5List["stim" + stimuli].Select(x => x[item]).OrderBy(x => x).ToList(); big5Series[item].Items.Add(new OxyPlot.Series.BoxPlotItem(((1 + stimId) % 2) - 0.25 + (int)item * 0.1, orderino[0], orderino[(int)(orderino.Count * 0.25)], orderino[orderino.Count / 2], orderino[(int)(orderino.Count * 0.75)], orderino.Last())); } stimuliToWrite.Add("}"); stimuliToWrite.Add("\\label{[TABLE] res stimuli" + stimuli + "}"); stimuliToWrite.Add("\\end{table}"); File.WriteAllLines(fbd.SelectedPath + "/" + corrType + "_stimuli_" + stimuli + ".txt", stimuliToWrite); stimId++; } File.WriteAllLines(fbd.SelectedPath + "/significantStim.tex", amountStimSignificant); List<string> sigAmountLines = new List<string>(); foreach (var sensor in sensors) { string linerino = sensor; for (int i = 0; i < 5; i++) { linerino += $" & {significantAmount[sensor][i]}/{significantAmountMax[sensor][i]}"; } sigAmountLines.Add(linerino + "\\\\"); } File.WriteAllLines(fbd.SelectedPath + "/significantTable.tex", sigAmountLines); //File.WriteAllLines(fbd.SelectedPath + "/significantTable.tex", significantAmount.Select(x => $"{x.Key} & {x.Value[0]} & {x.Value[1]} & {x.Value[2]} & {x.Value[3]} & {x.Value[4]}").ToList()); File.WriteAllLines(fbd.SelectedPath + "/significantCorrTable.tex", significantCorr.Select(x => $"{x.Key} & {x.Value[0].Item1.ToString(".000")}({x.Value[0].Item2.ToString(".000")}) & {x.Value[1].Item1.ToString(".000")}({x.Value[1].Item2.ToString(".000")}) & {x.Value[2].Item1.ToString(".000")}({x.Value[2].Item2.ToString(".000")}) & {x.Value[3].Item1.ToString(".000")}({x.Value[3].Item2.ToString(".000")}) & {x.Value[4].Item1.ToString(".000")}({x.Value[4].Item2.ToString(".000")}) \\\\")); File.WriteAllLines(fbd.SelectedPath + "/significantCorrTableTime.tex", significantCorr.Select(x => $"{x.Key} & {x.Value[0].Item1.ToString(".000")} (SD={x.Value[0].Item2.ToString(".000")}, p={x.Value[0].Item3.ToString(".000000")}) & {x.Value[1].Item1.ToString(".000")} (SD={x.Value[1].Item2.ToString(".000")}, p={x.Value[1].Item3.ToString(".000000")}) & {x.Value[2].Item1.ToString(".000")} (SD={x.Value[2].Item2.ToString(".000")}, p={x.Value[2].Item3.ToString(".000000")}) \\\\")); File.WriteAllLines(fbd.SelectedPath + "/significantCorrTableStimuli.tex", significantCorr.Select(x => $"{x.Key} & {x.Value[3].Item1.ToString(".000")} (SD={x.Value[3].Item2.ToString(".000")}, p={x.Value[3].Item3.ToString(".000")}) & {x.Value[4].Item1.ToString(".000")} (SD={x.Value[4].Item2.ToString(".000")}, p={x.Value[4].Item3.ToString(".000")}) \\\\")); List<string> timeLines = new List<string>() { "sensor & 0 vs 1 & 1 vs 2 & 0 vs 2" }; List<string> stimLines = new List<string>() { "sensor & 0 vs Low & Low vs High & 0 vs High" }; foreach (var item in significantCorr) { var z01 = ZCalc(item.Value[0].Item1, Convert.ToInt32(item.Value[0].Item2), item.Value[1].Item1, Convert.ToInt32(item.Value[1].Item2)); var z12 = ZCalc(item.Value[1].Item1, Convert.ToInt32(item.Value[1].Item2), item.Value[2].Item1, Convert.ToInt32(item.Value[2].Item2)); var z02 = ZCalc(item.Value[0].Item1, Convert.ToInt32(item.Value[0].Item2), item.Value[2].Item1, Convert.ToInt32(item.Value[2].Item2)); var p01 = ZtoP(z01); var p12 = ZtoP(z12); var p02 = ZtoP(z02); timeLines.Add($"{item.Key} & z: {z01} | p: {p01} & z: {z12} | p: {p12} & z: {z02} | p: {p02}"); var z0Low = ZCalc(item.Value[0].Item1, Convert.ToInt32(item.Value[0].Item2), item.Value[3].Item1, Convert.ToInt32(item.Value[3].Item2)); var zLowHigh = ZCalc(item.Value[3].Item1, Convert.ToInt32(item.Value[3].Item2), item.Value[4].Item1, Convert.ToInt32(item.Value[4].Item2)); var z0High = ZCalc(item.Value[0].Item1, Convert.ToInt32(item.Value[0].Item2), item.Value[4].Item1, Convert.ToInt32(item.Value[4].Item2)); var p0Low = ZtoP(z0Low); var pLowHigh = ZtoP(zLowHigh); var p0High = ZtoP(z0High); stimLines.Add($"{item.Key} & z: {z0Low} | p: {p0Low} & z: {zLowHigh} | p: {pLowHigh} & z: {z0High} | p: {p0High}"); } File.WriteAllLines(fbd.SelectedPath + "/significantCorrCompareTime.tex", timeLines); File.WriteAllLines(fbd.SelectedPath + "/significantCorrCompareStimuli.tex", stimLines); pnger.ExportToFile(Big5StimBox, fbd.SelectedPath + "/stimBoxBig5.png"); stimModel.LegendPlacement = LegendPlacement.Outside; //index 1 = low var stimTxt0 = new OxyPlot.Annotations.TextAnnotation() { TextPosition = new OxyPlot.DataPoint(0, -1), Text = "Avg " + avgLineSeries.Points[1].Y.ToString(".000").Replace(",", "."), Stroke = OxyColors.White }; //index 0 = high var stimTxt1 = new OxyPlot.Annotations.TextAnnotation() { TextPosition = new OxyPlot.DataPoint(1, -1), Text = "Avg " + avgLineSeries.Points[0].Y.ToString(".000").Replace(",", "."), Stroke = OxyColors.White }; stimModel.Annotations.Add(stimTxt0); stimModel.Annotations.Add(stimTxt1); stimModel.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Left, Maximum = 1, Minimum = -1, Title = "Pearson's r" }); stimModel.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Bottom, Maximum = 1.5, Minimum = -0.5, MajorStep = 1, Title = "Stimuli", MinorTickSize = 0 }); //stimModel.Axes.Add(new OxyPlot.Axes.LinearAxis() { Position = OxyPlot.Axes.AxisPosition.Bottom, Maximum = sensors.Count - 0.5, Minimum = -0.5, MajorStep = 1, Title = "Sensors", MinorTickSize = 0 }); pnger.ExportToFile(stimModel, fbd.SelectedPath + "/stimBox.png"); File.WriteAllLines(fbd.SelectedPath + "/anovaIndividual.csv", AnovaIndividual); File.WriteAllLines(fbd.SelectedPath + "/anovaIndividualLegend.csv", AnovaIndividualLegend); File.WriteAllLines(fbd.SelectedPath + "/anovaAvg.csv", AnovaAvg); File.WriteAllLines(fbd.SelectedPath + "/anovaAvgLegend.csv", AnovaAvgLegend); Log.LogMessage("DonnoDK"); } }
private void CreateMdsChartModel() { var model = new PlotModel(); var xAxis = new OxyPlot.Axes.LinearAxis { Position = OxyPlot.Axes.AxisPosition.Bottom, MajorGridlineStyle = LineStyle.None }; model.Axes.Add(xAxis); var yAxis = new OxyPlot.Axes.LinearAxis { Position = OxyPlot.Axes.AxisPosition.Left, MajorGridlineStyle = LineStyle.None }; model.Axes.Add(yAxis); var series = new OxyPlot.Series.ScatterSeries { ItemsSource = (Data.MdsCoords.Rows.Cast<filterReportDS.MdsCoordsRow>() .Select(dr => new DataPoint(dr.X, dr.Y))), DataFieldX = "X", DataFieldY = "Y", MarkerType = MarkerType.Circle, MarkerSize = 2, MarkerFill = OxyColor.FromRgb(79, 129, 189) }; model.Series.Add(series); foreach(filterReportDS.MdsCoordsRow dr in Data.MdsCoords.Rows) { var annotation = new OxyPlot.Annotations.TextAnnotation { Text = dr.StrategyName, TextPosition = new DataPoint(dr.X, dr.Y), TextHorizontalAlignment = OxyPlot.HorizontalAlignment.Center, TextVerticalAlignment = OxyPlot.VerticalAlignment.Top, Font = "Segoe UI", TextColor = OxyColor.FromRgb(0, 0, 0), StrokeThickness = 0 }; model.Annotations.Add(annotation); } MdsChartModel = model; }
public void MakeIntervalBars([JetBrains.Annotations.NotNull] ResultFileEntry srcResultFileEntry, [JetBrains.Annotations.NotNull] string plotName, [JetBrains.Annotations.NotNull] DirectoryInfo basisPath, [ItemNotNull][JetBrains.Annotations.NotNull] List <Tuple <string, double> > consumption, [JetBrains.Annotations.NotNull] ChartTaggingSet taggingSet, [JetBrains.Annotations.NotNull] string newFileNameSuffix, bool showTitle, [JetBrains.Annotations.NotNull] GenericChartBase gcb, CalcOption sourceOption) { var fontsize = 48; if (consumption.Count <= 20) { fontsize = 22; } if (consumption.Count > 20 && consumption.Count <= 30) { fontsize = 16; } if (consumption.Count > 30 && consumption.Count <= 40) { fontsize = 14; } if (consumption.Count > 40 && consumption.Count <= 50) { fontsize = 12; } if (consumption.Count > 50) { fontsize = 10; } if (!Config.MakePDFCharts) { fontsize = (int)(fontsize * 0.8); } var unit = "min"; var xaxislabel = "Time Consumption in Percent"; if (srcResultFileEntry.LoadTypeInformation != null) { var lti = srcResultFileEntry.LoadTypeInformation; if (!lti.ShowInCharts) { return; } unit = lti.UnitOfSum; xaxislabel = lti.Name + " in Percent"; } consumption.Sort((x, y) => y.Item2.CompareTo(x.Item2)); OxyPalette p; if (consumption.Count > 1) { if (taggingSet.Categories.Count > 1) { p = OxyPalettes.HueDistinct(taggingSet.Categories.Count); } else { p = OxyPalettes.Hue64; } } else { p = OxyPalettes.Hue64; } var plotModel1 = new PlotModel { LegendBorderThickness = 0, LegendOrientation = LegendOrientation.Vertical, LegendPlacement = LegendPlacement.Inside, LegendPosition = LegendPosition.TopLeft, PlotAreaBorderColor = OxyColors.White, LegendFontSize = fontsize, LegendSymbolMargin = 25 }; if (showTitle) { plotModel1.Title = plotName; } if (Config.MakePDFCharts) { plotModel1.DefaultFontSize = fontsize; } var ca = new CategoryAxis { Position = AxisPosition.Left, GapWidth = 0, MaximumPadding = 0.03, MajorTickSize = 0 }; plotModel1.Axes.Add(ca); var la = new LinearAxis { Minimum = 0, MinimumPadding = 0, Title = ChartLocalizer.Get().GetTranslation(xaxislabel), Position = AxisPosition.Bottom, MinorTickSize = 0 }; plotModel1.Axes.Add(la); var caSub = new CategoryAxis { StartPosition = 0.5, EndPosition = 1, Position = AxisPosition.Left, Key = "Sub", GapWidth = 0.3, MajorTickSize = 0, MinorTickSize = 0 }; plotModel1.Axes.Add(caSub); double runningSum = 0; var row = 0; var sum = consumption.Select(x => x.Item2).Sum(); var allBarSeries = new Dictionary <string, IntervalBarSeries>(); var ba = new BarSeries { YAxisKey = "Sub", LabelFormatString = "{0:N1} %" }; foreach (var s in taggingSet.Categories) { caSub.Labels.Add(ChartLocalizer.Get().GetTranslation(s)); var ibs = new IntervalBarSeries(); // ibs.Title = var coloridx = taggingSet.GetCategoryIndexOfCategory(s); ibs.FillColor = p.Colors[coloridx]; ibs.StrokeThickness = 0; ibs.FontSize = fontsize; allBarSeries.Add(s, ibs); double categorysum = 0; foreach (var tuple in consumption) { if (taggingSet.AffordanceToCategories[tuple.Item1] == s) { categorysum += tuple.Item2; } } var percent = categorysum / sum * 100; var bai = new BarItem(percent) { Color = p.Colors[coloridx] }; ba.Items.Add(bai); } plotModel1.Series.Add(ba); foreach (var tuple in consumption) { var percentage = tuple.Item2 / sum * 100; var name = ChartLocalizer.Get().GetTranslation(tuple.Item1.Trim()); if (name.Length > 100) { name = name.Substring(0, 97) + "..."; } var textAnnotation1 = new TextAnnotation { StrokeThickness = 0, FontSize = fontsize, Padding = new OxyThickness(10, 0, 10, 0) }; var txtValue = tuple.Item2.ToString("N1", CultureInfo.CurrentCulture); if (srcResultFileEntry.LoadTypeInformation == null) { var ts = TimeSpan.FromMinutes(tuple.Item2); txtValue = ts.ToString(); } textAnnotation1.Text = " " + name + " (" + txtValue + " " + unit + ", " + (tuple.Item2 / sum * 100).ToString("N1", CultureInfo.CurrentCulture) + " %) "; if (runningSum < 50) { textAnnotation1.TextHorizontalAlignment = HorizontalAlignment.Left; textAnnotation1.TextPosition = new DataPoint(runningSum + percentage, row - 0.6); } else { textAnnotation1.TextPosition = new DataPoint(runningSum, row - 0.5); textAnnotation1.TextHorizontalAlignment = HorizontalAlignment.Right; } plotModel1.Annotations.Add(textAnnotation1); var item = new IntervalBarItem(runningSum, runningSum + percentage); var category = taggingSet.AffordanceToCategories[tuple.Item1]; allBarSeries[category].Items.Add(item); foreach (var pair in allBarSeries) { if (pair.Key != category) { pair.Value.Items.Add(new IntervalBarItem(0, 0)); } } ca.Labels.Add(string.Empty); runningSum += percentage; row++; } foreach (var pair in allBarSeries) { plotModel1.Series.Add(pair.Value); } gcb.Save(plotModel1, plotName, srcResultFileEntry.FullFileName + newFileNameSuffix, basisPath, sourceOption); // ".interval" }
private void AddBars([JetBrains.Annotations.NotNull] CalculationProfiler.ProgramPart part, int row, double offset, int fontsize, [JetBrains.Annotations.NotNull] Dictionary <int, IntervalBarSeries> itemsByLevel, [JetBrains.Annotations.NotNull] OxyPalette palette, [JetBrains.Annotations.NotNull] PlotModel pm) { var runningsum = offset; for (var i = 0; i < part.Children.Count; i++) { var programPart = part.Children[i]; AddBars(programPart, row + 1, runningsum, fontsize, itemsByLevel, palette, pm); runningsum += programPart.Duration2; } //bar var item = new IntervalBarItem(offset, offset + part.Duration2) { Color = palette.Colors[_parts.IndexOf(part)] }; if (!itemsByLevel.ContainsKey(1)) { var series = new IntervalBarSeries { FontSize = fontsize }; itemsByLevel.Add(1, series); } var ibs = new IntervalBarSeries(); for (var i = 0; i < row; i++) { ibs.Items.Add(new IntervalBarItem(0, 0, "")); } ibs.StrokeThickness = 0.1; ibs.Items.Add(item); pm.Series.Add(ibs); // item.Title = name; //annotation if (string.IsNullOrWhiteSpace(part.Key)) { throw new LPGException("Empty profiler key"); } var name = part.Key; if (name.Length > 100) { name = name.Substring(0, 97) + "..."; } var textAnnotation1 = new TextAnnotation { StrokeThickness = 0, FontSize = 6, Padding = new OxyThickness(10, 0, 10, 0) }; var txtValue = name + " - " + part.Duration2.ToString("N1", CultureInfo.InvariantCulture) + "s"; textAnnotation1.Text = txtValue; textAnnotation1.TextHorizontalAlignment = HorizontalAlignment.Left; textAnnotation1.TextPosition = new DataPoint(offset, row + GetOffset(row)); pm.Annotations.Add(textAnnotation1); }