private void SetupChart()
{
model = new PlotModel()
{
};
var linearAxis = new OxyPlot.Axes.LinearAxis()
{
MinimumPadding = 0,
MaximumPadding = 0.1,
};
var axis = new OxyPlot.Axes.CategoryAxis()
{
};
axis.Labels.AddRange(new[] { "Video A", "Video B" });
model.Axes.Add(linearAxis);
model.Axes.Add(axis);
series = new ErrorColumnSeries()
{
StrokeThickness = 1,
};
model.Series.Add(series);
chart.Children.Add(new PlotView()
{
Model = model,
Width = chart.Width,
Height = chart.Height,
});
}
/// <summary>
/// This function is called every frame to update the graph's values
/// </summary>
public override void Draw()
{
base.Draw();
PlotModel.Title = "Memory Usage";
CategoryAxis X = new CategoryAxis();
CategoryAxis Y = new CategoryAxis(); // Create the X and Y axes
X.Title = "Usage";
PlotModel.Axes.Add(Y);
PlotModel.Axes.Add(X); // Add them to the model
ColumnSeries alloc = new ColumnSeries();
alloc.Items.Add(new ColumnItem(256)); // create a column and set its value
alloc.ColumnWidth = 25;
alloc.FillColor = Colors.CornflowerBlue.ToOxyColor();
alloc.Title = "Allocated";
PlotModel.Series.Add(alloc);
ColumnSeries free = new ColumnSeries();
free.Items.Add(new ColumnItem(512));
free.ColumnWidth = 25;
free.FillColor = Colors.GreenYellow.ToOxyColor();
free.Title = "Free";
PlotModel.Series.Add(free);
ColumnSeries swap = new ColumnSeries();
swap.Items.Add(new ColumnItem(768));
swap.ColumnWidth = 25;
swap.FillColor = Colors.Red.ToOxyColor();
swap.Title = "Swap";
PlotModel.Series.Add(swap); // add the columns to the plot model
}
public void CategoryAxis()
{
var s1 = new OxyPlot.Axes.CategoryAxis();
var s2 = new CategoryAxis();
OxyAssert.PropertiesAreEqual(s1, s2);
}
private void CreateTradeRetsByDayAndHourChartModel()
{
var distinctDays = Data.tradeRetsByDayAndHour.Select(x => x.weekDay).Distinct();
var distinctHours = Data.tradeRetsByDayAndHour.Select(x => x.hour).OrderByDescending(x => x).Distinct();
var model = new PlotModel();
var xAxis = new OxyPlot.Axes.CategoryAxis
{
Key = "dayAxis",
Position = OxyPlot.Axes.AxisPosition.Bottom,
ItemsSource = distinctDays.Select(x => x.ToString())
};
var yAxis = new OxyPlot.Axes.CategoryAxis
{
Key = "hourAxis",
Position = OxyPlot.Axes.AxisPosition.Left,
ItemsSource = distinctHours.Select(x => x.ToString())
};
model.Axes.Add(xAxis);
model.Axes.Add(yAxis);
model.Axes.Add(new OxyPlot.Axes.LinearColorAxis
{
Palette = OxyPalettes.BlueWhiteRed31
});
//take avg trade ret by day/hour and turn it into an array
int dayCount = distinctDays.Count();
int hourCount = distinctHours.Count();
var data = new double[dayCount, hourCount];
var weekDayTrans = distinctDays.Select((x, a) => new { x, a }).ToDictionary(x => x.x, x => x.a);
var hourTrans = distinctHours.Select((x, a) => new { x, a }).OrderByDescending(x => x.x).ToDictionary(x => x.x, x => x.a);
foreach (var ret in Data.tradeRetsByDayAndHour)
{
data[weekDayTrans[ret.weekDay], hourTrans[ret.hour]] = ret.avgRet;
}
var heatMapSeries = new OxyPlot.Series.HeatMapSeries
{
X0 = 0,
X1 = dayCount - 1,
Y0 = 0,
Y1 = hourCount - 1,
XAxisKey = "dayAxis",
YAxisKey = "hourAxis",
RenderMethod = HeatMapRenderMethod.Rectangles,
LabelFontSize = 0.2, // neccessary to display the label
LabelFormatString = "p2",
Data = data
};
model.Series.Add(heatMapSeries);
TradeRetsByDayAndHourModel = model;
}
/// <summary>
/// This function is called every frame to update the graph's values
/// </summary>
public override void Draw()
{
base.Draw();
PlotModel.Title = "%CPU Utilisation";
CategoryAxis X = new CategoryAxis();
CategoryAxis Y = new CategoryAxis(); // Create the X and Y axes
X.Title = "% Utilised";
Y.IntervalLength = 1.0;
PlotModel.Axes.Add(Y);
PlotModel.Axes.Add(X); // add them to the model
ColumnSeries col = new ColumnSeries(); // create a column
col.Items.Add(new ColumnItem(100)); // set the value of the column
col.ColumnWidth = 25;
col.FillColor = Colors.CornflowerBlue.ToOxyColor();
PlotModel.Series.Add(col); // add the column to the graph
}
static PlotModel CreateChartOnlyBars()
{
var splotModel = new PlotModel();
splotModel.IsLegendVisible = false;
splotModel.Background = OxyColor.FromArgb(0, 0, 0, 255);
splotModel.TextColor = OxyColor.FromArgb(0, 0, 0, 255);
splotModel.PlotAreaBorderColor = OxyColor.FromArgb(0, 0, 0, 255);
var axis = new OxyPlot.Axes.LinearAxis();
axis.IsPanEnabled = axis.IsAxisVisible = false;
splotModel.Axes.Add(axis);
var dateAxis = new OxyPlot.Axes.CategoryAxis();
dateAxis.IsPanEnabled = dateAxis.IsAxisVisible = false;
splotModel.Axes.Add(dateAxis);
return(splotModel);
}
private static void MakeBarPlot([JetBrains.Annotations.NotNull] string outputPath, [ItemNotNull][JetBrains.Annotations.NotNull] List <Column> columns, int position, int day)
{
var plotModel2 = new PlotModel();
// filter significant columns
var allColumns = new List <Tuple <string, double> >();
for (var i = 1; i < columns.Count; i++)
{
allColumns.Add(new Tuple <string, double>(columns[i].Name, columns[i].MakeSum(position, 1440)));
}
allColumns.Sort((x, y) => y.Item2.CompareTo(x.Item2));
var topCols = allColumns.Select(x => x.Item1).Take(20).ToList();
var p = OxyPalettes.HueDistinct(topCols.Count);
var oxc = OxyColor.FromArgb(255, 50, 50, 50);
plotModel2.LegendPosition = LegendPosition.BottomCenter;
plotModel2.LegendPlacement = LegendPlacement.Outside;
plotModel2.LegendOrientation = LegendOrientation.Horizontal;
plotModel2.Title = "Day " + day;
// axes
var cate = new CategoryAxis
{
AbsoluteMinimum = 0,
MinimumPadding = 0,
GapWidth = 0,
MajorStep = 60,
Title = "Activities"
};
plotModel2.Axes.Add(cate);
var linearAxis2 = new LinearAxis
{
AbsoluteMinimum = 0,
MaximumPadding = 0.06,
MinimumPadding = 0,
Title = "Minutes"
};
plotModel2.Axes.Add(linearAxis2);
const int minutesToSum = 15;
for (var i = 1; i < columns.Count; i++)
{
if (columns[i].MakeSum(position, 1440) > 0)
{
var columnSeries2 = new ColumnSeries
{
IsStacked = true,
StrokeThickness = 0,
Title = columns[i].Name
};
for (var j = position; j < position + 1440; j += minutesToSum)
{
columnSeries2.Items.Add(new ColumnItem(columns[i].MakeSum(j, minutesToSum) / minutesToSum));
}
if (topCols.Contains(columns[i].Name))
{
var coloridx = topCols.IndexOf(columns[i].Name);
columnSeries2.FillColor = p.Colors[coloridx];
}
else
{
columnSeries2.FillColor = oxc;
}
plotModel2.Series.Add(columnSeries2);
}
}
var path2 = Path.Combine(outputPath, "ActivityPlot." + day + ".bar.png");
PngExporter.Export(plotModel2, path2, 3200, 1600, OxyColor.FromRgb(255, 255, 255), 100);
}
public void CategoryAxis()
{
var s1 = new OxyPlot.Axes.CategoryAxis();
var s2 = new CategoryAxis();
OxyAssert.PropertiesAreEqual(s1, s2);
}
public WeatherObservationPage ()
{
var weatherSeries = new LineSeries ();
var m = new PlotModel ("Weather");
var magnitudeAxis = new OxyPlot.Axes.LinearAxis ();
magnitudeAxis.Minimum = 0;
magnitudeAxis.Maximum = 40;
magnitudeAxis.Title = "Temperature";
m.Axes.Add (magnitudeAxis);
var dateAxis = new OxyPlot.Axes.CategoryAxis ();
dateAxis.Labels.Add("Station");
m.Axes.Add (dateAxis);
weatherSeries.ItemsSource = new List<DataPoint> (); // empty to start
m.Series.Add (weatherSeries);
var opv = new OxyPlotView {
WidthRequest = 300, HeightRequest = 300,
BackgroundColor = Color.Aqua
};
opv.Model = m;
Insights.Track ("SHOWGRAPH");
var l = new Label {
Text = "Hello, Oxyplot!",
VerticalOptions = LayoutOptions.CenterAndExpand,
HorizontalOptions = LayoutOptions.CenterAndExpand,
};
var b = new Button { Text = "Get Weather Data" };
b.Clicked += async (sender, e) => {
var sv = new GeoNamesWebService();
var we = await sv.GetWeatherObservationsAsync();
Xamarin.Forms.Device.BeginInvokeOnMainThread( () => {
Debug.WriteLine("found " + we.Length + " weather observations");
l.Text = we.Length + " weather observations";
var eqlist = new List<WeatherObservation>(we);
// eqlist.Sort((x, y) => string.Compare(x.datetime, y.datetime));
var columSeries = new ColumnSeries();
foreach (var eq in eqlist) {
double t = 0.0;
Double.TryParse(eq.temperature, out t);
columSeries.Items.Add(new ColumnItem(t, 0));
//pts.Add(new DataPoint(OxyPlot.Axes.DateTimeAxis.ToDouble(DateTime.Parse(eq.datetime)), Double.Parse(eq.temperature)));
}
opv.Model.Series.Clear();
opv.Model.Series.Add(columSeries);
Device.BeginInvokeOnMainThread(() => {
opv.InvalidateDisplay ();
});
});
};
Padding = new Thickness (0, 20, 0, 0);
Content = new StackLayout {
Children = {
opv,
b,
l
}
};
}
static PlotModel CreateChartOnlyBars ()
{
var splotModel = new PlotModel ();
splotModel.IsLegendVisible = false;
splotModel.Background = OxyColor.FromArgb (0, 0, 0, 255);
splotModel.TextColor = OxyColor.FromArgb (0, 0, 0, 255);
splotModel.PlotAreaBorderColor = OxyColor.FromArgb (0, 0, 0, 255);
var axis = new OxyPlot.Axes.LinearAxis ();
axis.IsPanEnabled = axis.IsAxisVisible = false;
splotModel.Axes.Add (axis);
var dateAxis = new OxyPlot.Axes.CategoryAxis ();
dateAxis.IsPanEnabled = dateAxis.IsAxisVisible = false;
splotModel.Axes.Add (dateAxis);
return splotModel;
}
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");
}
}
protected override void OnCreate(Bundle bundle)
{
base.OnCreate (bundle);
//somehow an orientation change changes the language. Therefore we check and reset the language here depending on the stored preferences
//check language preferences, if they are set apply them otherwise stay with the current language
ISharedPreferences sharedPref = GetSharedPreferences("com.FSoft.are_u_ok.PREFERENCES",FileCreationMode.Private);
String savedLanguage = sharedPref.GetString ("Language", "");
//if there is a saved language (length > 0) and the current language is different from the saved one, then change
Android.Content.Res.Configuration conf = Resources.Configuration;
if ((savedLanguage.Length > 0) & (conf.Locale.Language != savedLanguage)){
//set language and restart activity to see the effect
conf.Locale = new Java.Util.Locale(savedLanguage);
Android.Util.DisplayMetrics dm = this.Resources.DisplayMetrics;
this.Resources.UpdateConfiguration (conf, dm);
}
// Create your application here
SetContentView(Resource.Layout.PlotDoubleScreen);
db = new MoodDatabase(this);
Button Back = FindViewById<Button> (Resource.Id.button1);
Back.Click += delegate {
//create an intent to go back to the history screen
// Intent intent = new Intent(this, typeof(History));
// intent.SetFlags(ActivityFlags.ClearTop); //remove the history and go back to home screen
// StartActivity(intent);
OnBackPressed();
};
//CREATE HISTOGRAM FOR PEOPLE CONTEXT ON THE LEFT
plotViewModelLeft = FindViewById<PlotView>(Resource.Id.plotViewModelLeft);
//select all mood values for which people was 0 (alone)
cursor = db.ReadableDatabase.RawQuery("SELECT mood FROM MoodData WHERE people = 0 AND NOT mood = -1", null); // cursor query
if (cursor.Count > 0) {
//initialize with 9 zero entries
int[] histArray = new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0 };
//go through each entry and create the histogram count
for (int ii = 0; ii < cursor.Count; ii++) {
cursor.MoveToPosition (ii);
int mood_temp = cursor.GetInt (0); //get mood from database
histArray [mood_temp] += 1; //increase histogram frequency by one
//System.Console.WriteLine("Mood: " + mood_temp.ToString() + " Freq: " + histArray [mood_temp].ToString());
}
PlotModel temp = new PlotModel ();
//determine font size, either keep default or for small screens set it to a smaller size
double dFontSize = temp.DefaultFontSize;
if (Resources.DisplayMetrics.HeightPixels <= 320)
dFontSize = 5;
//define axes
//we need 9 categories for the histogram since we score the mood between 0 and 8
var categoryAxis1 = new OxyPlot.Axes.CategoryAxis ();
// categoryAxis1.GapWidth = 0;
categoryAxis1.LabelField = "Label";
// categoryAxis1.MinorStep = 1;
categoryAxis1.Position = OxyPlot.Axes.AxisPosition.Bottom;
categoryAxis1.ActualLabels.Add ("0");
categoryAxis1.ActualLabels.Add ("1");
categoryAxis1.ActualLabels.Add ("2");
categoryAxis1.ActualLabels.Add ("3");
categoryAxis1.ActualLabels.Add ("4");
categoryAxis1.ActualLabels.Add ("5");
categoryAxis1.ActualLabels.Add ("6");
categoryAxis1.ActualLabels.Add ("7");
categoryAxis1.ActualLabels.Add ("8");
// categoryAxis1.AbsoluteMaximum = 10;
// categoryAxis1.Maximum = 10;
categoryAxis1.StringFormat = "0";
categoryAxis1.IsPanEnabled = false;
categoryAxis1.IsZoomEnabled = false;
categoryAxis1.FontSize = dFontSize;
categoryAxis1.Title = Resources.GetString (Resource.String.Mood);
temp.Axes.Add (categoryAxis1);
var linearAxis1 = new OxyPlot.Axes.LinearAxis ();
linearAxis1.AbsoluteMinimum = 0;
linearAxis1.AbsoluteMaximum = histArray.Max () * 1.2; //this has to be a bit higher than the highest frequency of the histogram
linearAxis1.Minimum = 0;
linearAxis1.Maximum = histArray.Max () * 1.2;
// linearAxis1.MaximumPadding = 0.1;
// linearAxis1.MinimumPadding = 0;
linearAxis1.Position = OxyPlot.Axes.AxisPosition.Left;
linearAxis1.FontSize = dFontSize;
linearAxis1.IsZoomEnabled = false;
linearAxis1.StringFormat = "0.0";
linearAxis1.Title = Resources.GetString (Resource.String.Frequency);
temp.Axes.Add (linearAxis1);
var columnSeries1 = new ColumnSeries ();
//http://forums.xamarin.com/discussion/20809/is-there-no-plotview-which-is-in-oxyplot-compent-in-xamarin-android
//add data
foreach (int i in histArray) {
columnSeries1.Items.Add (new ColumnItem (i, -1));
}
columnSeries1.LabelFormatString = "{0}";
columnSeries1.FontSize = dFontSize;
temp.Series.Add (columnSeries1);
temp.Title = Resources.GetString (Resource.String.Alone);
temp.TitleFontSize = dFontSize;
MyModelLeft = temp;
plotViewModelLeft.Model = MyModelLeft;
}
//CREATE HISTOGRAM FOR PEOPLE CONTEXT ON THE RIGHT
plotViewModelRight = FindViewById<PlotView>(Resource.Id.plotViewModelRight);
//select all mood values for which people was 0 (alone)
cursor = db.ReadableDatabase.RawQuery("SELECT mood FROM MoodData WHERE NOT people = 0 AND NOT mood = -1", null); // cursor query
//only continue if there is data, otherwise there will be an error
if (cursor.Count > 0) {
//initialize with 9 zero entries
int[] histArrayRight = new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0 };
//go through each entry and create the histogram count
for (int ii = 0; ii < cursor.Count; ii++) {
cursor.MoveToPosition (ii);
int mood_temp = cursor.GetInt (0); //get mood from database
histArrayRight [mood_temp] += 1; //increase histogram frequency by one
//System.Console.WriteLine("Mood: " + mood_temp.ToString() + " Freq: " + histArray [mood_temp].ToString());
}
PlotModel tempRight = new PlotModel ();
double dFontSize = tempRight.DefaultFontSize;
if (Resources.DisplayMetrics.HeightPixels <= 320)
dFontSize = 5;
//define axes
//we need 9 categories for the histogram since we score the mood between 0 and 8
var categoryAxisRight = new OxyPlot.Axes.CategoryAxis ();
//categoryAxisRight.LabelField = "Label";
categoryAxisRight.Position = OxyPlot.Axes.AxisPosition.Bottom;
categoryAxisRight.ActualLabels.Add ("0");
categoryAxisRight.ActualLabels.Add ("1");
categoryAxisRight.ActualLabels.Add ("2");
categoryAxisRight.ActualLabels.Add ("3");
categoryAxisRight.ActualLabels.Add ("4");
categoryAxisRight.ActualLabels.Add ("5");
categoryAxisRight.ActualLabels.Add ("6");
categoryAxisRight.ActualLabels.Add ("7");
categoryAxisRight.ActualLabels.Add ("8");
categoryAxisRight.StringFormat = "0";
categoryAxisRight.IsPanEnabled = false;
categoryAxisRight.IsZoomEnabled = false;
categoryAxisRight.FontSize = dFontSize;
categoryAxisRight.Title = Resources.GetString (Resource.String.Mood);
tempRight.Axes.Add (categoryAxisRight);
var linearAxisRight = new OxyPlot.Axes.LinearAxis ();
linearAxisRight.AbsoluteMinimum = 0;
linearAxisRight.AbsoluteMaximum = histArrayRight.Max () * 1.2; //this has to be a bit higher than the highest frequency of the histogram
linearAxisRight.Minimum = 0;
linearAxisRight.Maximum = histArrayRight.Max () * 1.2;
linearAxisRight.Position = OxyPlot.Axes.AxisPosition.Left;
linearAxisRight.FontSize = dFontSize;
linearAxisRight.IsZoomEnabled = false;
linearAxisRight.StringFormat = "0.0";
linearAxisRight.Title = Resources.GetString (Resource.String.Frequency);
tempRight.Axes.Add (linearAxisRight);
var columnSeriesRight = new ColumnSeries ();
//http://forums.xamarin.com/discussion/20809/is-there-no-plotview-which-is-in-oxyplot-compent-in-xamarin-android
//add data
foreach (int i in histArrayRight) {
columnSeriesRight.Items.Add (new ColumnItem (i, -1));
}
columnSeriesRight.LabelFormatString = "{0}";
columnSeriesRight.FontSize = dFontSize;
tempRight.Series.Add (columnSeriesRight);
tempRight.Title = Resources.GetString (Resource.String.WithPeople);
tempRight.TitleFontSize = dFontSize;
MyModelRight = tempRight;
plotViewModelRight.Model = MyModelRight;
}
}
private void CreateTradeRetsByDayAndHourChartModel()
{
var distinctDays = Data.tradeRetsByDayAndHour.Select(x => x.weekDay).Distinct();
var distinctHours = Data.tradeRetsByDayAndHour.Select(x => x.hour).OrderByDescending(x => x).Distinct();
var model = new PlotModel();
var xAxis = new OxyPlot.Axes.CategoryAxis
{
Key = "dayAxis",
Position = OxyPlot.Axes.AxisPosition.Bottom,
ItemsSource = distinctDays.Select(x => x.ToString())
};
var yAxis = new OxyPlot.Axes.CategoryAxis
{
Key = "hourAxis",
Position = OxyPlot.Axes.AxisPosition.Left,
ItemsSource = distinctHours.Select(x => x.ToString())
};
model.Axes.Add(xAxis);
model.Axes.Add(yAxis);
model.Axes.Add(new OxyPlot.Axes.LinearColorAxis
{
Palette = OxyPalettes.BlueWhiteRed31
});
//take avg trade ret by day/hour and turn it into an array
int dayCount = distinctDays.Count();
int hourCount = distinctHours.Count();
var data = new double[dayCount, hourCount];
var weekDayTrans = distinctDays.Select((x, a) => new { x, a }).ToDictionary(x => x.x, x => x.a);
var hourTrans = distinctHours.Select((x, a) => new { x, a }).OrderByDescending(x => x.x).ToDictionary(x => x.x, x => x.a);
foreach (var ret in Data.tradeRetsByDayAndHour)
{
data[weekDayTrans[ret.weekDay], hourTrans[ret.hour]] = ret.avgRet;
}
var heatMapSeries = new OxyPlot.Series.HeatMapSeries
{
X0 = 0,
X1 = dayCount - 1,
Y0 = 0,
Y1 = hourCount - 1,
XAxisKey = "dayAxis",
YAxisKey = "hourAxis",
RenderMethod = HeatMapRenderMethod.Rectangles,
LabelFontSize = 0.2, // neccessary to display the label
LabelFormatString = "p2",
Data = data
};
model.Series.Add(heatMapSeries);
TradeRetsByDayAndHourModel = model;
}
public WeatherObservationPage()
{
var weatherSeries = new LineSeries();
var m = new PlotModel("Weather");
var magnitudeAxis = new OxyPlot.Axes.LinearAxis();
magnitudeAxis.Minimum = 0;
magnitudeAxis.Maximum = 40;
magnitudeAxis.Title = "Temperature";
m.Axes.Add(magnitudeAxis);
var dateAxis = new OxyPlot.Axes.CategoryAxis();
dateAxis.Labels.Add("Station");
m.Axes.Add(dateAxis);
weatherSeries.ItemsSource = new List <DataPoint> (); // empty to start
m.Series.Add(weatherSeries);
var opv = new OxyPlotView {
WidthRequest = 300, HeightRequest = 300,
BackgroundColor = Color.Aqua
};
opv.Model = m;
Insights.Track("SHOWGRAPH");
var l = new Label {
Text = "Hello, Oxyplot!",
VerticalOptions = LayoutOptions.CenterAndExpand,
HorizontalOptions = LayoutOptions.CenterAndExpand,
};
var b = new Button {
Text = "Get Weather Data"
};
b.Clicked += async(sender, e) => {
var sv = new GeoNamesWebService();
var we = await sv.GetWeatherObservationsAsync();
Xamarin.Forms.Device.BeginInvokeOnMainThread(() => {
Debug.WriteLine("found " + we.Length + " weather observations");
l.Text = we.Length + " weather observations";
var eqlist = new List <WeatherObservation>(we);
// eqlist.Sort((x, y) => string.Compare(x.datetime, y.datetime));
var columSeries = new ColumnSeries();
foreach (var eq in eqlist)
{
double t = 0.0;
Double.TryParse(eq.temperature, out t);
columSeries.Items.Add(new ColumnItem(t, 0));
//pts.Add(new DataPoint(OxyPlot.Axes.DateTimeAxis.ToDouble(DateTime.Parse(eq.datetime)), Double.Parse(eq.temperature)));
}
opv.Model.Series.Clear();
opv.Model.Series.Add(columSeries);
Device.BeginInvokeOnMainThread(() => {
opv.InvalidateDisplay();
});
});
};
Padding = new Thickness(0, 20, 0, 0);
Content = new StackLayout {
Children =
{
opv,
b,
l
}
};
}
