Write Results to Indigo-DB

CLASS: DateTimeResults

    /// <summary>
    /// Date time.
    /// </summary>
    public DateTime Datetime { get; set; }
    
    /// <summary>
    /// Warning (depending on the threshold and probability).
    /// </summary>
    public bool Warning { get; set; }
    
    /// <summary>
    /// Probability (depending on the estimated and actual value(s)).
    /// </summary>
    public double Probability { get; set; }
    
    /// <summary>
    /// Multi-dimensional estimated output vector.
    /// </summary>
    public double[] OutputPointsEstimate { get; set; }
    
    /// <summary>
    /// Multi-dimensional output vector.
    /// </summary>
    public double[] OutputPoints { get; set; }

METHOD: Tranfer

  public static void SaveResults(List<DateTimeResult> listDateTimeResults, TrainingPreferences preferences, Asset asset)
    {
        var token = GetToken();
        List<string> channels = preferences.ParameterSettings.Where(x => x.Category == SignalCatgory.Output).Select(x => x.ParameterCode).ToList();


        string timeContentName = preferences.Methodology.ToString() + "-" + preferences.Name;
        var content = new TimedataContent(timeContentName);
        foreach (var channel in channels)
            content.Measurements.Add(new Measurement(channel + "-estimate", "-"));
        foreach (var channel in channels)
            content.Measurements.Add(new Measurement(channel + "-actual", "-"));
        content.Measurements.Add(new Measurement("Probability", "%"));
        content.Measurements.Add(new Measurement("Warning", "-"));

        foreach (var result in listDateTimeResults)
        {
            double[] resultrecord = new double[result.OutputPointsEstimate.Length + result.OutputPoints.Length + 2];

            for (int i = 0; i < result.OutputPointsEstimate.Length; i++)
                resultrecord[i] = result.OutputPointsEstimate[i];
            for (int i = 0; i < result.OutputPoints.Length; i++)
                resultrecord[result.OutputPointsEstimate.Length + i] = result.OutputPoints[i];

            resultrecord[result.OutputPointsEstimate.Length + result.OutputPoints.Length] = result.Probability;
            resultrecord[result.OutputPointsEstimate.Length + result.OutputPoints.Length + 1] = result.Warning ? 1 : 0;

            content.AppendDataset(result.Datetime, resultrecord);
        }

        var handler = new tk_MinCloudDatapublish.tkMinDatapublish(_applicationaccesskey);
        handler.UploadData(token, asset.Name, content).Wait();
    }