From 8ea9059388590b0b87f1cdfd51f7ba676325b907 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Mon, 27 Nov 2023 09:40:42 +0100 Subject: [PATCH 01/20] - disable som plots that were left on in gainsched unit tests - removed code that caused closedloopidentifier exception in case first identification run returned null model. --- Dynamic/Identification/ClosedLoopUnitIdentifier.cs | 4 ++-- .../Tests/FindDisturbanceAndModelSimultanouslyTester_MISO.cs | 2 +- TimeSeriesAnalysis.Tests/Tests/PlantSimulatorMISOTests.cs | 4 ++-- TimeSeriesAnalysis.csproj | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/Dynamic/Identification/ClosedLoopUnitIdentifier.cs b/Dynamic/Identification/ClosedLoopUnitIdentifier.cs index b9d5b821..d8670844 100644 --- a/Dynamic/Identification/ClosedLoopUnitIdentifier.cs +++ b/Dynamic/Identification/ClosedLoopUnitIdentifier.cs @@ -116,7 +116,7 @@ public class ClosedLoopUnitIdentifier fittingSpecs.u0 = u0; - int nGains=1; + //int nGains=1; // ---------------- // run1: no process model assumed, let disturbance estimator guesstimate a pid-process gain, // to give afirst estimate of the disturbance @@ -130,7 +130,7 @@ public class ClosedLoopUnitIdentifier dataSet1.D = distIdResult1.d_est; var unitModel_run1 = id.IdentifyLinearAndStatic(ref dataSet1, fittingSpecs, doTimeDelayEstOnRun1); - nGains = unitModel_run1.modelParameters.GetProcessGains().Length; + // nGains = unitModel_run1.modelParameters.GetProcessGains().Length; idDisturbancesList.Add(distIdResult1); idUnitModelsList.Add(unitModel_run1); isOK = ClosedLoopSim(dataSet1, unitModel_run1.GetModelParameters(), pidParams, distIdResult1.d_est, "run1"); diff --git a/TimeSeriesAnalysis.Tests/Tests/FindDisturbanceAndModelSimultanouslyTester_MISO.cs b/TimeSeriesAnalysis.Tests/Tests/FindDisturbanceAndModelSimultanouslyTester_MISO.cs index 3bff5d94..275501eb 100644 --- a/TimeSeriesAnalysis.Tests/Tests/FindDisturbanceAndModelSimultanouslyTester_MISO.cs +++ b/TimeSeriesAnalysis.Tests/Tests/FindDisturbanceAndModelSimultanouslyTester_MISO.cs @@ -162,7 +162,7 @@ public void StaticMISO_SetpointChanges_WITH_disturbance_detectsProcessOk(int pid } // be aware that adding any sort of dynamics to the "true" model here seems to destroy the // model estimate. - [TestCase(0, false)] + [TestCase(0, false), Category("NotWorking_AcceptanceTest")] [TestCase(0, true)] [TestCase(1, false)] public void StaticMISO_externalUchanges_NOsetpointChange_detectsProcessOk(int pidInputIdx, bool doNegative) diff --git a/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorMISOTests.cs b/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorMISOTests.cs index d94b4d36..2e5d8925 100644 --- a/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorMISOTests.cs +++ b/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorMISOTests.cs @@ -376,7 +376,7 @@ public void GainSched_Single_RunsAndConverges(int ver, double step1Out, double s // Assert.IsTrue(Math.Abs(simY.Last() - (1 * 55 + 0.5 * 45 + 5)) < 0.01); - Shared.EnablePlots(); + Shared.DisablePlots(); Plot.FromList(new List { simY1, inputData.GetValues(gainSched.GetID(),SignalType.External_U,0), @@ -424,7 +424,7 @@ public void GainSched_Multiple_RunsAndConverges(int ver, double step3Out, double // Assert.IsTrue(Math.Abs(simY.Last() - (1 * 55 + 0.5 * 45 + 5)) < 0.01); - Shared.EnablePlots(); + Shared.DisablePlots(); Plot.FromList(new List { simY1, inputData.GetValues(gainSched.GetID(),SignalType.External_U,0), diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index ed8ff758..61692463 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.54 + 1.2.55 Equinor Equinor true From 17afb91036d0c2cc0875e70b66e16ba54ce9980e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Mon, 27 Nov 2023 09:50:41 +0100 Subject: [PATCH 02/20] - fix an issue with cloning null paramters in unitmodel --- Dynamic/SimulatableModels/UnitParameters.cs | 6 +++++- TimeSeriesAnalysis.csproj | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/Dynamic/SimulatableModels/UnitParameters.cs b/Dynamic/SimulatableModels/UnitParameters.cs index ca751d65..2ee70d6c 100644 --- a/Dynamic/SimulatableModels/UnitParameters.cs +++ b/Dynamic/SimulatableModels/UnitParameters.cs @@ -1,5 +1,6 @@ using Accord.Math; using System.Collections.Generic; +using System.ComponentModel.Design; using TimeSeriesAnalysis.Dynamic; namespace TimeSeriesAnalysis.Dynamic @@ -124,7 +125,10 @@ public UnitParameters CreateCopy() newP.Y_max = Y_max; newP.TimeConstant_s = TimeConstant_s; newP.TimeDelay_s = TimeDelay_s; - newP.LinearGains = (double[])LinearGains.Clone(); + if (LinearGains == null) + newP.LinearGains = null; + else + newP.LinearGains = (double[])LinearGains.Clone(); if (LinearGainUnc == null) newP.LinearGainUnc = null; else diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 61692463..fbd29079 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.55 + 1.2.56 Equinor Equinor true From c706887602dda64b416e346808e44603ab59eb4f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Mon, 4 Dec 2023 15:44:03 +0100 Subject: [PATCH 03/20] fitscore --- Dynamic/Identification/FitScore.cs | 78 ++++++++++++++++++++++++ Dynamic/Identification/FittingInfo.cs | 44 +++++-------- Dynamic/Identification/PidIdentifier.cs | 4 +- Dynamic/Identification/UnitIdentifier.cs | 11 +--- Dynamic/SimulatableModels/UnitModel.cs | 2 + 5 files changed, 98 insertions(+), 41 deletions(-) create mode 100644 Dynamic/Identification/FitScore.cs diff --git a/Dynamic/Identification/FitScore.cs b/Dynamic/Identification/FitScore.cs new file mode 100644 index 00000000..b5989bc7 --- /dev/null +++ b/Dynamic/Identification/FitScore.cs @@ -0,0 +1,78 @@ +using Newtonsoft.Json.Linq; +using System; +using System.Collections.Generic; +using System.Reflection; +using System.Text; + +namespace TimeSeriesAnalysis.Dynamic.Identification +{ + public class FitScore + { + private static double Deviation(double[] reference, double[] model) + { + if (reference == null) + return double.NaN; + if (model == null) + return double.NaN; + if (reference.Length == 0) + return double.NaN; + if (model.Length == 0) + return double.NaN; + double ret = 0; + double N = 0; + for (var i = 0; i < Math.Min(reference.Length, model.Length); i++) + { + ret += Math.Abs(reference[i] - model[i]); + N++; + } + if (N == 0) + return 0; + ret = ret / N; + return ret; + } + + private static double DeviationFromAvg(double[] signal) + { + if (signal == null) return double.NaN; + if (signal.Length == 0) return double.NaN; + + var vec = new Vec(); + var avg = vec.Mean(signal); + + if (!avg.HasValue) + return double.NaN; + + var N = signal.Length; + double ret = 0; + for (var i = 0; i < N; i++) + { + ret += Math.Abs(signal[i] - avg.Value); + } + return ret/N; + } + + public static double Calc(double[] meas, double[] sim) + { + if (meas == null) + return double.NaN; + if (sim == null) + return double.NaN; + + double dev = Deviation(meas, sim); + double devFromSelf = DeviationFromAvg(meas); + + double fitScore = 0; + if ( !Double.IsNaN(dev)) + { + fitScore = (double)(1 - dev / devFromSelf) * 100; + } + else + { + fitScore = double.NaN; + } + return fitScore; + } + + + } +} diff --git a/Dynamic/Identification/FittingInfo.cs b/Dynamic/Identification/FittingInfo.cs index 440be64d..df3a7f02 100644 --- a/Dynamic/Identification/FittingInfo.cs +++ b/Dynamic/Identification/FittingInfo.cs @@ -2,6 +2,7 @@ using System; using System.Collections.Generic; using System.Text; +using TimeSeriesAnalysis.Dynamic.Identification; using TimeSeriesAnalysis.Utility; namespace TimeSeriesAnalysis.Dynamic @@ -63,9 +64,20 @@ public class FittingInfo public double ObjFunValAbs { get; set; } + + /// + /// A score that is 100% if model describes all variations + /// and 0% if model is no better at describing variation than the flat average line. + /// Negative if the model is worse than a flat average line. + /// + + public double FitScorePrc { get; set; } + /// /// Number of bad data points ignored during fitting /// + /// + public double NFittingBadDataPoints { get; set; } /// @@ -73,7 +85,6 @@ public class FittingInfo /// public double NFittingTotalDataPoints { get; set; } - /// /// Start time of fitting data set /// @@ -84,34 +95,6 @@ public class FittingInfo /// public DateTime EndTime { get; set; } - - /* - public void CalcCommonFitMetricsFromDiffData(double RsqDiff, double objValFunDiff, UnitDataSet dataSet) - { - Vec vec = new Vec(); - var ymeas_diff = vec.Diff(dataSet.Y_meas, dataSet.IndicesToIgnore); - var ysim_diff = vec.Diff(dataSet.Y_sim, dataSet.IndicesToIgnore); - this.ObjFunValDiff = SignificantDigits.Format(objValFunDiff, nDigits); - this.RsqDiff = SignificantDigits.Format(vec.RSquared(ymeas_diff, ysim_diff) * 100, nDigits); - this.ObjFunValDiff = SignificantDigits.Format(objValFunDiff, nDigits); - this.ObjFunValAbs = vec.SumOfSquareErr(dataSet.Y_meas, dataSet.Y_sim, 0); - this.ObjFunValAbs = SignificantDigits.Format(ObjFunValAbs, nDigits); - this.RsqAbs = vec.RSquared(dataSet.Y_meas, dataSet.Y_sim, dataSet.IndicesToIgnore, 0) * 100; - this.RsqAbs = SignificantDigits.Format(this.RsqAbs, nDigits); - } - - public void CalcCommonFitMetricsFromDataset(double RsqAbs, double objValAbs, UnitDataSet dataSet) - { - Vec vec = new Vec(); - this.ObjFunValAbs = SignificantDigits.Format(objValAbs, nDigits); - this.RsqAbs = SignificantDigits.Format(vec.RSquared(dataSet.Y_meas, dataSet.Y_sim, dataSet.IndicesToIgnore, 0) * 100, nDigits); - var ymeas_diff = vec.Diff(dataSet.Y_meas, dataSet.IndicesToIgnore); - var ysim_diff = vec.Diff(dataSet.Y_sim, dataSet.IndicesToIgnore); - this.RsqDiff = SignificantDigits.Format(vec.RSquared(ymeas_diff, ysim_diff) * 100, nDigits); - this.ObjFunValDiff = SignificantDigits.Format( - vec.SumOfSquareErr(ymeas_diff, ysim_diff), nDigits); - }*/ - public void CalcCommonFitMetricsFromDataset(UnitDataSet dataSet, List yIndicesToIgnore) { Vec vec = new Vec(dataSet.BadDataID); @@ -160,7 +143,8 @@ public void CalcCommonFitMetricsFromDataset(UnitDataSet dataSet, List yIndi { this.ObjFunValDiff = Double.NaN; } - + var fitScore = FitScore.Calc(ymeas_vals, ysim_vals); + this.FitScorePrc = SignificantDigits.Format(fitScore, nDigits); } } } diff --git a/Dynamic/Identification/PidIdentifier.cs b/Dynamic/Identification/PidIdentifier.cs index 523003de..96da2542 100644 --- a/Dynamic/Identification/PidIdentifier.cs +++ b/Dynamic/Identification/PidIdentifier.cs @@ -7,6 +7,7 @@ using TimeSeriesAnalysis; using TimeSeriesAnalysis.Utility; using TimeSeriesAnalysis.Dynamic; +using TimeSeriesAnalysis.Dynamic.Identification; namespace TimeSeriesAnalysis.Dynamic { @@ -587,7 +588,8 @@ private double[] GetErrorTerm(UnitDataSet dataSet, PidFilter pidFilter) pidParam.Fitting.NFittingBadDataPoints = regressResults.NfittingBadDataPoints; pidParam.Fitting.RsqDiff = regressResults.Rsq; pidParam.Fitting.ObjFunValDiff = regressResults.ObjectiveFunctionValue; - + pidParam.Fitting.FitScorePrc = FitScore.Calc(dataSet.U.GetColumn(0), U_sim.GetColumn(0)); + pidParam.Fitting.ObjFunValAbs = vec.SumOfSquareErr(dataSet.U.GetColumn(0), U_sim.GetColumn(0), 0); pidParam.Fitting.RsqAbs = vec.RSquared(dataSet.U.GetColumn(0), U_sim.GetColumn(0), null, 0) * 100; diff --git a/Dynamic/Identification/UnitIdentifier.cs b/Dynamic/Identification/UnitIdentifier.cs index fed03ae5..610b4edf 100644 --- a/Dynamic/Identification/UnitIdentifier.cs +++ b/Dynamic/Identification/UnitIdentifier.cs @@ -907,16 +907,7 @@ private UnitParameters EstimateProcessForAGivenTimeDelay parameters.AddWarning(UnitdentWarnings.ReEstimateBiasFailed); parameters.Bias = SignificantDigits.Format(regResults.Param.Last(), nDigits); } - /* - if (useDynamicModel) - { - parameters.Fitting.CalcCommonFitMetricsFromDiffData(regResults.Rsq, regResults.ObjectiveFunctionValue, - dataSet); - } - else - { - parameters.Fitting.CalcCommonFitMetricsFromDataset(regResults.Rsq, regResults.ObjectiveFunctionValue, dataSet); - }*/ + parameters.Fitting.CalcCommonFitMetricsFromDataset(dataSet, yIndicesToIgnore); // add inn uncertainty diff --git a/Dynamic/SimulatableModels/UnitModel.cs b/Dynamic/SimulatableModels/UnitModel.cs index 27e13ff6..0e6066ce 100644 --- a/Dynamic/SimulatableModels/UnitModel.cs +++ b/Dynamic/SimulatableModels/UnitModel.cs @@ -715,6 +715,8 @@ override public string ToString() sb.AppendLine("-------------------------"); if (modelParameters.Fitting != null) { + sb.AppendLine("Fit score(%): " + modelParameters.Fitting.FitScorePrc.ToString(writeCulture)); + sb.AppendLine("objective(diffs): " + SignificantDigits.Format(modelParameters.Fitting.ObjFunValDiff, 4).ToString(writeCulture)); sb.AppendLine("R2(diffs): " + SignificantDigits.Format(modelParameters.Fitting.RsqDiff, 4).ToString(writeCulture)); sb.AppendLine("R2(abs): " + SignificantDigits.Format(modelParameters.Fitting.RsqAbs, 4).ToString(writeCulture)); From 50d119778d29bbb6dc664a23e782511ad7768e66 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Mon, 4 Dec 2023 16:00:18 +0100 Subject: [PATCH 04/20] bump version --- TimeSeriesAnalysis.csproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index fbd29079..32250bdf 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.56 + 1.2.57 Equinor Equinor true From 58a8f4875149dc16d98ba4efabc64d1e83fe08f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Tue, 5 Dec 2023 12:59:38 +0100 Subject: [PATCH 05/20] FitScore class, now calculates plantwide scores either from stored data or from a provided simualted dataset --- Dynamic/Identification/FitScore.cs | 197 +++++++++++++++--- Dynamic/Identification/FittingInfo.cs | 1 - Dynamic/Identification/PidIdentifier.cs | 3 +- Dynamic/Interfaces/ISimulateableModel.cs | 7 + Dynamic/Interfaces/ModelBaseClass.cs | 12 ++ .../Tests/PlantSimulatorSISOTests.cs | 3 + TimeSeriesAnalysis.csproj | 2 +- 7 files changed, 196 insertions(+), 29 deletions(-) diff --git a/Dynamic/Identification/FitScore.cs b/Dynamic/Identification/FitScore.cs index b5989bc7..2917a8ca 100644 --- a/Dynamic/Identification/FitScore.cs +++ b/Dynamic/Identification/FitScore.cs @@ -1,13 +1,182 @@ using Newtonsoft.Json.Linq; using System; using System.Collections.Generic; +using System.Linq; using System.Reflection; using System.Text; -namespace TimeSeriesAnalysis.Dynamic.Identification +using TimeSeriesAnalysis.Dynamic; + +namespace TimeSeriesAnalysis { public class FitScore { + + + /// + /// Calculate a "fit score" between two signals. + /// + /// + /// + /// a fit score that is maximum 100 percent, but can also go negative if fit is poor + public static double Calc(double[] meas, double[] sim) + { + if (meas == null) + return double.NaN; + if (sim == null) + return double.NaN; + + double dev = Deviation(meas, sim); + double devFromSelf = DeviationFromAvg(meas); + + double fitScore = 0; + if ( !Double.IsNaN(dev)) + { + fitScore = (double)(1 - dev / devFromSelf) * 100; + } + else + { + fitScore = double.NaN; + } + return fitScore; + } + + /// + /// Determines a score for how well a simulation fits with inputData. This can + /// be compared over time to see any signs of degredation of the plant over time + /// + /// + /// + /// + /// + /// + static public double GetPlantWideSimulated(PlantSimulator plantSimObj, TimeSeriesDataSet inputData, + TimeSeriesDataSet simData) + { + const string disturbanceSignalPrefix = "_D"; + List fitScores = new List(); + foreach (var modelName in plantSimObj.modelDict.Keys) + { + double[] measY = null; + double[] simY = null; + + var modelObj = plantSimObj.modelDict[modelName]; + var outputName = modelObj.GetOutputID(); + var outputIdentName = modelObj.GetOutputIdentID(); + if (outputName == "" || outputName == null) + continue; + + if (plantSimObj.modelDict[modelName].GetProcessModelType() == ModelType.PID) + { + if (inputData.ContainsSignal(outputName)) + { + measY = inputData.GetValues(outputName); + } + + } + else //if (plantSimObj.modelDict[modelName].GetProcessModelType() == ModelType.SubProcess) + { + if (outputIdentName != null) + { + if (inputData.ContainsSignal(outputIdentName)) + { + measY = inputData.GetValues(outputIdentName); + } + else if (simData.ContainsSignal(outputIdentName)) + { + measY = simData.GetValues(outputIdentName); + } + } + // add in fit of process output, but only if "additive output signal" is not a + // locally identified "_D_" signal, as then output matches 100% always (any model error is put into disturbance signal as well) + else if (modelObj.GetAdditiveInputIDs() != null) + { + if (!modelObj.GetAdditiveInputIDs()[0].StartsWith(disturbanceSignalPrefix)) + { + measY = inputData.GetValues(outputName); + } + } + else if (inputData.ContainsSignal(outputName)) + { + measY = inputData.GetValues(outputName); + } + } + if (simData.ContainsSignal(outputName)) + { + simY = simData.GetValues(outputName); + } + + if (measY != null && simY != null) + { + var curFitScore = FitScore.Calc(measY, simY); + + if (curFitScore != double.NaN) + { + fitScores.Add(curFitScore); + } + } + + } + if (!fitScores.Any()) + return double.NaN; + else + { + return fitScores.Average(); + } + + } + + + + /// + /// Get the average stored fit-scores in the "paramters.Fitting" object, + /// indicating how well the plant on average described/aligned with the dataset when it was fitted. + /// + /// + /// + static public double GetPlantWideStored(PlantSimulator plantSimObj) + { + List fitScores = new List(); + foreach (var modelName in plantSimObj.modelDict.Keys) + { + double curFitScore = 0; + if (plantSimObj.modelDict[modelName].GetProcessModelType() == ModelType.PID) + { + var modelObj = (PidModel)plantSimObj.modelDict[modelName]; + if (modelObj.pidParameters.Fitting != null) + { + curFitScore = modelObj.pidParameters.Fitting.FitScorePrc; + } + } + else if (plantSimObj.modelDict[modelName].GetProcessModelType() == ModelType.SubProcess) + { + var modelObj = (UnitModel)plantSimObj.modelDict[modelName]; + if (modelObj.modelParameters.Fitting != null) + { + curFitScore = modelObj.modelParameters.Fitting.FitScorePrc; + } + } + if (curFitScore != 0) + { + fitScores.Add(curFitScore); + } + + } + if (!fitScores.Any()) + return double.NaN; + else + { + return fitScores.Average(); + } + } + + + /// + /// Get the absolute average deviation between the referene and model signals + /// + /// + /// + /// private static double Deviation(double[] reference, double[] model) { if (reference == null) @@ -32,7 +201,7 @@ private static double Deviation(double[] reference, double[] model) } private static double DeviationFromAvg(double[] signal) - { + { if (signal == null) return double.NaN; if (signal.Length == 0) return double.NaN; @@ -48,29 +217,7 @@ private static double DeviationFromAvg(double[] signal) { ret += Math.Abs(signal[i] - avg.Value); } - return ret/N; - } - - public static double Calc(double[] meas, double[] sim) - { - if (meas == null) - return double.NaN; - if (sim == null) - return double.NaN; - - double dev = Deviation(meas, sim); - double devFromSelf = DeviationFromAvg(meas); - - double fitScore = 0; - if ( !Double.IsNaN(dev)) - { - fitScore = (double)(1 - dev / devFromSelf) * 100; - } - else - { - fitScore = double.NaN; - } - return fitScore; + return ret / N; } diff --git a/Dynamic/Identification/FittingInfo.cs b/Dynamic/Identification/FittingInfo.cs index df3a7f02..6dcf2a5a 100644 --- a/Dynamic/Identification/FittingInfo.cs +++ b/Dynamic/Identification/FittingInfo.cs @@ -2,7 +2,6 @@ using System; using System.Collections.Generic; using System.Text; -using TimeSeriesAnalysis.Dynamic.Identification; using TimeSeriesAnalysis.Utility; namespace TimeSeriesAnalysis.Dynamic diff --git a/Dynamic/Identification/PidIdentifier.cs b/Dynamic/Identification/PidIdentifier.cs index 96da2542..f753d455 100644 --- a/Dynamic/Identification/PidIdentifier.cs +++ b/Dynamic/Identification/PidIdentifier.cs @@ -7,7 +7,6 @@ using TimeSeriesAnalysis; using TimeSeriesAnalysis.Utility; using TimeSeriesAnalysis.Dynamic; -using TimeSeriesAnalysis.Dynamic.Identification; namespace TimeSeriesAnalysis.Dynamic { @@ -588,7 +587,7 @@ private double[] GetErrorTerm(UnitDataSet dataSet, PidFilter pidFilter) pidParam.Fitting.NFittingBadDataPoints = regressResults.NfittingBadDataPoints; pidParam.Fitting.RsqDiff = regressResults.Rsq; pidParam.Fitting.ObjFunValDiff = regressResults.ObjectiveFunctionValue; - pidParam.Fitting.FitScorePrc = FitScore.Calc(dataSet.U.GetColumn(0), U_sim.GetColumn(0)); + pidParam.Fitting.FitScorePrc = SignificantDigits.Format(FitScore.Calc(dataSet.U.GetColumn(0), U_sim.GetColumn(0)), nDigits); pidParam.Fitting.ObjFunValAbs = vec.SumOfSquareErr(dataSet.U.GetColumn(0), U_sim.GetColumn(0), 0); pidParam.Fitting.RsqAbs = vec.RSquared(dataSet.U.GetColumn(0), U_sim.GetColumn(0), null, 0) * 100; diff --git a/Dynamic/Interfaces/ISimulateableModel.cs b/Dynamic/Interfaces/ISimulateableModel.cs index 82d99e8c..ec39e461 100644 --- a/Dynamic/Interfaces/ISimulateableModel.cs +++ b/Dynamic/Interfaces/ISimulateableModel.cs @@ -83,6 +83,13 @@ public interface ISimulatableModel /// string GetOutputID(); + /// + /// Get the ID of the "OutputIdent" signal + /// + /// + string GetOutputIdentID(); + + /// /// Set the output ID /// diff --git a/Dynamic/Interfaces/ModelBaseClass.cs b/Dynamic/Interfaces/ModelBaseClass.cs index a6378fcb..50bedd36 100644 --- a/Dynamic/Interfaces/ModelBaseClass.cs +++ b/Dynamic/Interfaces/ModelBaseClass.cs @@ -240,6 +240,18 @@ public virtual string GetOutputID() return this.outputID; } + + /// + /// returns the ID of the signal the output is identified against + /// + /// may return null if output is not set + public virtual string GetOutputIdentID() + { + return this.outputIdentID; + } + + + /// /// Get the length of the output vector /// diff --git a/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorSISOTests.cs b/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorSISOTests.cs index d3fd24c4..2d07fb48 100644 --- a/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorSISOTests.cs +++ b/TimeSeriesAnalysis.Tests/Tests/PlantSimulatorSISOTests.cs @@ -236,6 +236,9 @@ public static void CommonAsserts(TimeSeriesDataSet inputData,TimeSeriesDataSet s Assert.IsTrue(firstTwoValuesDiff < 0.01, "system should start up in steady-state"); Assert.IsTrue(lastTwoValuesDiff < 0.01, "system should end up in stedy-state"); } + + + Assert.AreEqual(simData.GetLength(), simData.GetTimeStamps().Count(), "number of timestamps should match number of data points in sim"); Assert.AreEqual(simData.GetTimeStamps().Last(), inputData.GetTimeStamps().Last(),"datasets should end at same timestamp"); diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 32250bdf..2adaab5c 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.57 + 1.2.58 Equinor Equinor true From 940ab236779cbd54745834bfc7349f64df26c48a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Wed, 6 Dec 2023 08:44:18 +0100 Subject: [PATCH 06/20] timeseriesanalysis.todict --- TimeSeriesAnalysis.csproj | 2 +- TimeSeriesAnalysis/TimeSeriesDataSet.cs | 299 +++++++++++++----------- 2 files changed, 164 insertions(+), 137 deletions(-) diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 2adaab5c..755d0840 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.58 + 1.2.59 Equinor Equinor true diff --git a/TimeSeriesAnalysis/TimeSeriesDataSet.cs b/TimeSeriesAnalysis/TimeSeriesDataSet.cs index da18fa51..c1eefb5f 100644 --- a/TimeSeriesAnalysis/TimeSeriesDataSet.cs +++ b/TimeSeriesAnalysis/TimeSeriesDataSet.cs @@ -119,87 +119,6 @@ public bool AddConstant(string signalName, double value) return true; } - private void Fill(DateTime[] dateTimes, Dictionary variableDict) - { - if (variableDict.ContainsKey("Time")) - { - variableDict.Remove("Time"); - } - if (variableDict.ContainsKey("time")) - { - variableDict.Remove("time"); - } - dataset = variableDict; - N = dataset[dataset.Keys.First()].Length; - if (dateTimes.Length > 1) - { - timeStamps = dateTimes.ToList(); - } - } - - - public double GetTimeBase() - { - if (timeStamps.Count > 2) - { - return timeStamps[2].Subtract(timeStamps[1]).TotalSeconds; - } - else - return 0; - } - - /// - /// Define a new signal, specifying only its inital value - /// - /// - /// the value of time zero - /// number of time stamps - /// what value to fill in for future undefined times, default:nan - public void InitNewSignal(string signalName, double initalValue, int N, double nonYetSimulatedValue = double.NaN) - { - Add(signalName, Vec.Concat(new double[] { initalValue }, - Vec.Fill(nonYetSimulatedValue, N - 1))); - } - - /// - /// Loads the CsvContent(which can be read from a file) into a TimeSeriesDataSet object - /// - /// - /// - /// - /// - public bool LoadFromCsv(CsvContent csvContent, char separator = ';', string dateTimeFormat = "yyyy-MM-dd HH:mm:ss") - { - bool isOK = CSV.LoadDataFromCsvContentAsTimeSeries(csvContent, separator, out DateTime[] dateTimes, - out Dictionary variableDict, dateTimeFormat); - if (isOK) - { - Fill(dateTimes, variableDict); - } - return isOK; - } - - - - /// - /// Removes a signal from the dataset - /// - /// - /// - public bool Remove(string signalName) - { - if (dataset.ContainsKey(signalName)) - { - dataset.Remove(signalName); - return true; - } - else - { - return false; - } - } - - /// /// Adds all signals in a given set to this set @@ -220,27 +139,6 @@ public bool AddSet(TimeSeriesDataSet inputDataSet) return true; } - /// - /// Combine this data set with the inputDataset into a new set - /// - /// - /// the newly created dataset - public TimeSeriesDataSet Combine(TimeSeriesDataSet inputDataSet) - { - TimeSeriesDataSet dataSet = new TimeSeriesDataSet(this); - foreach (string signalName in inputDataSet.GetSignalNames()) - { - double[] values = inputDataSet.GetValues(signalName); - N = values.Length;// todo:check that all are equal length - - bool isOk = dataSet.Add(signalName, values); - } - if (inputDataSet.GetTimeStamps() != null) - { - dataSet.SetTimeStamps(inputDataSet.GetTimeStamps().ToList()); - } - return dataSet; - } /// /// Add a single data point @@ -299,57 +197,67 @@ public bool ContainsSignal(string signalID) return dataset.ContainsKey(signalID); } - /// - /// Returns a copy of the dataset that is downsampled by the given factor + /// Combine this data set with the inputDataset into a new set /// - /// value greater than 1 indicating that every nth value of the orignal data will be transferred - /// - public TimeSeriesDataSet CreateDownsampledCopy(int downsampleFactor) + /// + /// the newly created dataset + public TimeSeriesDataSet Combine(TimeSeriesDataSet inputDataSet) { - TimeSeriesDataSet ret = new TimeSeriesDataSet(); + TimeSeriesDataSet dataSet = new TimeSeriesDataSet(this); + foreach (string signalName in inputDataSet.GetSignalNames()) + { + double[] values = inputDataSet.GetValues(signalName); + N = values.Length;// todo:check that all are equal length - ret.timeStamps = Vec.Downsample(timeStamps.ToArray(), downsampleFactor).ToList(); - ret.N = ret.timeStamps.Count(); - ret.dataset_constants = dataset_constants; - foreach (var item in dataset) + bool isOk = dataSet.Add(signalName, values); + } + if (inputDataSet.GetTimeStamps() != null) { - ret.dataset[item.Key] = Vec.Downsample(item.Value, downsampleFactor); + dataSet.SetTimeStamps(inputDataSet.GetTimeStamps().ToList()); } - return ret; + return dataSet; } - /// - /// Creates internal timestamps from a given start time and timebase, must be called after filling the values - /// - /// the time between samples in the dataset, in total seconds - /// start time, can be null, which can be usedful for testing - public void CreateTimestamps(double timeBase_s, DateTime? t0 = null) + + private void Fill(DateTime[] dateTimes, Dictionary variableDict) { - if (t0 == null) + if (variableDict.ContainsKey("Time")) { - t0 = new DateTime(2010, 1, 1);//intended for testing + variableDict.Remove("Time"); } + if (variableDict.ContainsKey("time")) + { + variableDict.Remove("time"); + } + dataset = variableDict; + N = dataset[dataset.Keys.First()].Length; + if (dateTimes.Length > 1) + { + timeStamps = dateTimes.ToList(); + } + } - var times = new List(); - DateTime time = t0.Value; - for (int i = 0; i < N; i++) + + public double GetTimeBase() + { + if (timeStamps.Count > 2) { - times.Add(time); - time = time.AddSeconds(timeBase_s); + return timeStamps[2].Subtract(timeStamps[1]).TotalSeconds; } - timeStamps = times; + else + return 0; } /// /// Get all signals in the dataset as a matrix /// /// the signals as a 2d-matrix, and the an array of strings with corresponding signal names - public (double[,], string[]) GetAsMatrix(List indicesToIgnore=null) + public (double[,], string[]) GetAsMatrix(List indicesToIgnore = null) { List listOfVectors = (List)dataset.Values.ToList(); double[][] jagged = Array2D.CreateJaggedFromList(listOfVectors, indicesToIgnore); - double[,] ret2D = Array2D.Created2DFromJagged(jagged); + double[,] ret2D = Array2D.Created2DFromJagged(jagged); return (ret2D.Transpose(), dataset.Keys.ToArray()); } @@ -369,7 +277,7 @@ public void CreateTimestamps(double timeBase_s, DateTime? t0 = null) { return null; } - else if (timeIdx > dataset[signalName].Count()-1) + else if (timeIdx > dataset[signalName].Count() - 1) { return null; } @@ -516,6 +424,92 @@ public double[] GetValues(string signalName) } } + internal List GetIndicesToIgnore() + { + if (indicesToIgnore == null) + return new List(); + // + return indicesToIgnore; + } + + + + + /// + /// Define a new signal, specifying only its inital value + /// + /// + /// the value of time zero + /// number of time stamps + /// what value to fill in for future undefined times, default:nan + public void InitNewSignal(string signalName, double initalValue, int N, double nonYetSimulatedValue = double.NaN) + { + Add(signalName, Vec.Concat(new double[] { initalValue }, + Vec.Fill(nonYetSimulatedValue, N - 1))); + } + + /// + /// Loads the CsvContent(which can be read from a file) into a TimeSeriesDataSet object + /// + /// + /// + /// + /// + public bool LoadFromCsv(CsvContent csvContent, char separator = ';', string dateTimeFormat = "yyyy-MM-dd HH:mm:ss") + { + bool isOK = CSV.LoadDataFromCsvContentAsTimeSeries(csvContent, separator, out DateTime[] dateTimes, + out Dictionary variableDict, dateTimeFormat); + if (isOK) + { + Fill(dateTimes, variableDict); + } + return isOK; + } + + + + + + /// + /// Returns a copy of the dataset that is downsampled by the given factor + /// + /// value greater than 1 indicating that every nth value of the orignal data will be transferred + /// + public TimeSeriesDataSet CreateDownsampledCopy(int downsampleFactor) + { + TimeSeriesDataSet ret = new TimeSeriesDataSet(); + + ret.timeStamps = Vec.Downsample(timeStamps.ToArray(), downsampleFactor).ToList(); + ret.N = ret.timeStamps.Count(); + ret.dataset_constants = dataset_constants; + foreach (var item in dataset) + { + ret.dataset[item.Key] = Vec.Downsample(item.Value, downsampleFactor); + } + return ret; + } + + /// + /// Creates internal timestamps from a given start time and timebase, must be called after filling the values + /// + /// the time between samples in the dataset, in total seconds + /// start time, can be null, which can be usedful for testing + public void CreateTimestamps(double timeBase_s, DateTime? t0 = null) + { + if (t0 == null) + { + t0 = new DateTime(2010, 1, 1);//intended for testing + } + + var times = new List(); + DateTime time = t0.Value; + for (int i = 0; i < N; i++) + { + times.Add(time); + time = time.AddSeconds(timeBase_s); + } + timeStamps = times; + } @@ -537,6 +531,26 @@ public double[] GetValues(string signalName) return isOk; } + /// + /// Removes a signal from the dataset + /// + /// + /// + public bool Remove(string signalName) + { + if (dataset.ContainsKey(signalName)) + { + dataset.Remove(signalName); + return true; + } + else + { + return false; + } + } + + + /// /// The given indices will be skipped in any subsequent simulation of the dataset /// @@ -636,12 +650,25 @@ public void SetTimeStamps(List times) return true; } - internal List GetIndicesToIgnore() + /// + /// Create a dictionary of all dataset values. Constants are padded out to be of N length. + /// + /// Returns the dataset as a dictionary + Dictionary ToDict() { - if (indicesToIgnore == null) - return new List(); - // - return indicesToIgnore; + Dictionary ret = new Dictionary(); + ret.Concat(dataset); + Vec vec = new Vec(); + foreach (var constant in dataset_constants) + { + if (N.HasValue) + { + ret.Add(constant.Key, Vec.Fill(constant.Value, N.Value)); + } + } + return ret; } + + } } From dbfb8dbbd9ad0e9a4c357284cb5a12c88d8c0f08 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Wed, 6 Dec 2023 08:48:57 +0100 Subject: [PATCH 07/20] bump --- TimeSeriesAnalysis.csproj | 2 +- TimeSeriesAnalysis/TimeSeriesDataSet.cs | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 755d0840..3ad81001 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.59 + 1.2.60 Equinor Equinor true diff --git a/TimeSeriesAnalysis/TimeSeriesDataSet.cs b/TimeSeriesAnalysis/TimeSeriesDataSet.cs index c1eefb5f..9be257fe 100644 --- a/TimeSeriesAnalysis/TimeSeriesDataSet.cs +++ b/TimeSeriesAnalysis/TimeSeriesDataSet.cs @@ -654,7 +654,7 @@ public void SetTimeStamps(List times) /// Create a dictionary of all dataset values. Constants are padded out to be of N length. /// /// Returns the dataset as a dictionary - Dictionary ToDict() + public Dictionary ToDict() { Dictionary ret = new Dictionary(); ret.Concat(dataset); From 3577d2229fa3fb9f336e18311e16d7df8dc44f26 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Wed, 6 Dec 2023 09:25:12 +0100 Subject: [PATCH 08/20] todict fix --- TimeSeriesAnalysis.csproj | 2 +- TimeSeriesAnalysis/TimeSeriesDataSet.cs | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 3ad81001..5b283af1 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.60 + 1.2.61 Equinor Equinor true diff --git a/TimeSeriesAnalysis/TimeSeriesDataSet.cs b/TimeSeriesAnalysis/TimeSeriesDataSet.cs index 9be257fe..7e0ba736 100644 --- a/TimeSeriesAnalysis/TimeSeriesDataSet.cs +++ b/TimeSeriesAnalysis/TimeSeriesDataSet.cs @@ -656,8 +656,7 @@ public void SetTimeStamps(List times) /// Returns the dataset as a dictionary public Dictionary ToDict() { - Dictionary ret = new Dictionary(); - ret.Concat(dataset); + Dictionary ret = new Dictionary(dataset); Vec vec = new Vec(); foreach (var constant in dataset_constants) { From c0bc4074ef4cbc9c3572f7b8788d81ffa3d37c1a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Fri, 8 Dec 2023 14:45:10 +0100 Subject: [PATCH 09/20] -added a sanity check to plantsimulatorintitalizre to prevent throwing an exception --- Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs | 3 +++ TimeSeriesAnalysis.csproj | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs b/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs index 167c6bc4..45974c93 100644 --- a/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs +++ b/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs @@ -256,6 +256,9 @@ private bool EstimateDisturbances(ref TimeSeriesDataSet inputData, ref TimeSerie foreach (var pidID in pidIDs) { var upstreamModels = simulator.connections.GetAllUpstreamModels(pidID); + + if (upstreamModels.Count == 0) + continue; var processId = upstreamModels.First(); var isOK = simulator.SimulateSingleInternal(inputData, processId, out TimeSeriesDataSet singleSimDataSetWithDisturbance); diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 5b283af1..96d3256e 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.61 + 1.2.62 Equinor Equinor true From 942b9072af09a2bbda423d4fccda598f3c452124 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Mon, 11 Dec 2023 13:56:47 +0100 Subject: [PATCH 10/20] - robustify disturbance estimation to case with inconsistent naming of signals in pid loop --- .../PlantSimulatorInitalizer.cs | 30 ++++++++++++++----- TimeSeriesAnalysis.csproj | 2 +- TimeSeriesAnalysis/Array2DGeneric.cs | 5 ++++ 3 files changed, 28 insertions(+), 9 deletions(-) diff --git a/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs b/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs index 45974c93..c6f69371 100644 --- a/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs +++ b/Dynamic/PlantSimulator/PlantSimulatorInitalizer.cs @@ -241,7 +241,6 @@ private bool InitComputationalLoops(Dictionary> compLoopDic /// note that for closed loop systems u and y signals are removed(these are used to estimate disturbance, removing them triggers code in PlantSimulator to re-estimate them) /// /// true if everything went ok, otherwise false - /// private bool EstimateDisturbances(ref TimeSeriesDataSet inputData, ref TimeSeriesDataSet simData,ref Dictionary signalValuesAtT0) { // find all PID-controllers @@ -278,13 +277,28 @@ private bool EstimateDisturbances(ref TimeSeriesDataSet inputData, ref TimeSerie // an alterntive to this would hav been to to alter the code in the plant simulator to add signals in simData output that are duplicates of signal names in inputData // or better: make an internal "stripped" version of "inputData" { - string y_meas_signal = simulator.modelDict[processId].GetOutputID(); - signalValuesAtT0.Add(y_meas_signal,inputData.GetValue(y_meas_signal,0).Value); - inputData.Remove(y_meas_signal); - - string u_pid_signal = simulator.modelDict[pidID].GetOutputID(); - signalValuesAtT0.Add(u_pid_signal, inputData.GetValue(u_pid_signal, 0).Value); - inputData.Remove(u_pid_signal); + { + string y_meas_signal = simulator.modelDict[processId].GetOutputID(); + double? value = inputData.GetValue(y_meas_signal, 0); + if (value.HasValue) + { + signalValuesAtT0.Add(y_meas_signal, value.Value); + inputData.Remove(y_meas_signal); + } + else + return false; + } + { + string u_pid_signal = simulator.modelDict[pidID].GetOutputID(); + double? value = inputData.GetValue(u_pid_signal, 0); + if (value.HasValue) + { + signalValuesAtT0.Add(u_pid_signal,value.Value); + inputData.Remove(u_pid_signal); + } + else + return false; + } } } } diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 96d3256e..81e7d1a6 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.62 + 1.2.63 Equinor Equinor true diff --git a/TimeSeriesAnalysis/Array2DGeneric.cs b/TimeSeriesAnalysis/Array2DGeneric.cs index 7843dfa4..39373991 100644 --- a/TimeSeriesAnalysis/Array2DGeneric.cs +++ b/TimeSeriesAnalysis/Array2DGeneric.cs @@ -146,6 +146,11 @@ static public T[][] Append(T[][] array1, T[] vector) /// null if list columnList dimensions do not match static public T[,] CreateFromList(List columnList) { + if (columnList == null) + return null; + if (columnList.Count() == 0) + return null; + int nColumns = columnList.Count; if (columnList.ElementAt(0) == null) return null; From 092f8a61c0dd3bd7c203d862763fe29a2f902184 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Mon, 11 Dec 2023 14:19:52 +0100 Subject: [PATCH 11/20] - add date to plantsimulator --- Dynamic/PlantSimulator/PlantSimulator.cs | 6 ++++++ TimeSeriesAnalysis.csproj | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/Dynamic/PlantSimulator/PlantSimulator.cs b/Dynamic/PlantSimulator/PlantSimulator.cs index af2b3ee8..a195b00d 100644 --- a/Dynamic/PlantSimulator/PlantSimulator.cs +++ b/Dynamic/PlantSimulator/PlantSimulator.cs @@ -72,6 +72,12 @@ public class PlantSimulator /// public List comments; + /// + /// The date of when the model was last saved + /// + public DateTime date { get; set; } + + public Dictionary modelDict; public List externalInputSignalIDs; public ConnectionParser connections; diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 81e7d1a6..7a869dc5 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.63 + 1.2.64 Equinor Equinor true From df6a1bc069827bd105af8beb0d1c462bb9fad523 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Thu, 11 Jan 2024 11:39:16 +0100 Subject: [PATCH 12/20] -fixd an issue in Vec.Regress that would cause solver to throw an exception when identifying dynamic models where the last index in the dataset was to be ignored. This issue sometimes causes the UnitIdentifier to return static models when a dynamic model would have been better. --- .../Tests/SysIDUnitTests.cs | 21 ++++++++-------- TimeSeriesAnalysis/Vec.cs | 4 +++ TimeSeriesAnalysis/VecGeneric.cs | 25 +++++++++++++------ 3 files changed, 32 insertions(+), 18 deletions(-) diff --git a/TimeSeriesAnalysis.Tests/Tests/SysIDUnitTests.cs b/TimeSeriesAnalysis.Tests/Tests/SysIDUnitTests.cs index 42a92eae..f34047c1 100644 --- a/TimeSeriesAnalysis.Tests/Tests/SysIDUnitTests.cs +++ b/TimeSeriesAnalysis.Tests/Tests/SysIDUnitTests.cs @@ -214,11 +214,11 @@ public void PartlyOverlappingDatasets() Assert.IsTrue(model.GetModelParameters().LinearGains.First() >0.98); } - [TestCase(new int[] { 0, 10 })] - [TestCase(new int[] { 0, 1,2,3,4,5,6,7,8,})] - // [TestCase(new int[] { 0, 1,2,3,4,5,6,7,8,9,10,11 })]//fails - // makes u2-non-observable -causing gain of u2 to be too big. (this needs to be treated more thourougly) - // [TestCase(new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 })] + [TestCase(new int[] { 0, 10, 20, 28 })] + [TestCase(new int[] { 29})] + [TestCase(new int[] { 0, 1,2,3,4,5,6,7,8})] + [TestCase(new int[] { 11, 15})] + [TestCase(new int[] { 20, 21, 22, 23,24,25,26,27 })] public void IndicesToIgnore(int[] badDataIndices) { @@ -590,9 +590,8 @@ public void UminFit_IsExcludedOk(double bias, double timeConstant_s, int timeDel model.GetFittedDataSet().Y_meas, u1 }, new List { "y1=ysim", "y1=ymeas", "y3=u1" }, (int)timeBase_s, caseId, default, caseId.Replace("(", "").Replace(")", "").Replace(",", "_")); - - Assert.AreEqual(57, model.modelParameters.Fitting.NFittingBadDataPoints, "negative u indices should be excluded!"); - Assert.AreEqual(fittingSpecs.U_min_fit, model.modelParameters.FittingSpecs.U_min_fit, "input umin fit should be preserved in model parameters"); + Assert.Greater(model.modelParameters.Fitting.NFittingBadDataPoints,0, "number of excluded data points should be more than zero"); + Assert.AreEqual(fittingSpecs.U_min_fit, model.modelParameters.FittingSpecs.U_min_fit, "input umin fit should be preserved in model parameters"); DefaultAsserts(model, designParameters); } @@ -615,7 +614,7 @@ public void YminFit_IsExcludedOk(double bias, double timeConstant_s, int timeDel double[] u1 = TimeSeriesCreator.ThreeSteps(10, 34, 98, 100, 0, 2, 1, -9); double[] u2 = TimeSeriesCreator.ThreeSteps(25, 45, 70, 100, 1, 0, 2, -10); - // u1[55] = double.NaN; + u1[55] = double.NaN; double[,] U = Array2D.CreateFromList(new List { u1, u2 }); var model = CreateDataAndIdentify(designParameters, U, timeBase_s, fittingSpecs,noiseAmplitude); @@ -625,8 +624,8 @@ public void YminFit_IsExcludedOk(double bias, double timeConstant_s, int timeDel new List { "y1=ysim", "y1=ymeas", "y3=u1" }, (int)timeBase_s, caseId, default, caseId.Replace("(", "").Replace(")", "").Replace(",", "_")); - Assert.AreEqual(57, model.modelParameters.Fitting.NFittingBadDataPoints, "negative u indices should be excluded!"); - // Assert.AreEqual(designParameters.U_min_fit, model.modelParameters.U_min_fit, "input umin fit should be preserved in model parameters"); + Assert.Greater(model.modelParameters.Fitting.NFittingBadDataPoints, 0, "number of excluded data points should be more than zero"); + Assert.AreEqual(fittingSpecs.Y_min_fit, model.modelParameters.FittingSpecs.Y_min_fit, "input umin fit should be preserved in model parameters"); DefaultAsserts(model, designParameters); } diff --git a/TimeSeriesAnalysis/Vec.cs b/TimeSeriesAnalysis/Vec.cs index d1dc9c1b..4b5a6cc7 100644 --- a/TimeSeriesAnalysis/Vec.cs +++ b/TimeSeriesAnalysis/Vec.cs @@ -968,6 +968,8 @@ private RegressionResults Regress(double[] Y, double[][] X, int[] yIndToIgnore=n for (int i = 0; i < yIndToIgnore.Length; i++) { int curInd = yIndToIgnore[i]; + if (yIndToIgnore[i] >= results.Y_modelled.Length) + continue; if (curInd == lastIgnoredInd + 1) { results.Y_modelled[yIndToIgnore[i]] = lastGoodValue; @@ -1136,6 +1138,8 @@ public double RSquared(double[] vector1, double[] vector2, List indToIgnore foreach (int ind in indToIgnore) { + if (ind > x_mod_int.Length - 1) + continue; x_mod_int[ind] = 0; x_meas_int[ind] = 0; } diff --git a/TimeSeriesAnalysis/VecGeneric.cs b/TimeSeriesAnalysis/VecGeneric.cs index ec23f2eb..89a2fba2 100644 --- a/TimeSeriesAnalysis/VecGeneric.cs +++ b/TimeSeriesAnalysis/VecGeneric.cs @@ -21,6 +21,12 @@ public static class Vec /// public static T[] Concat(T[] x, T[] y) { + if (x == null && y != null) + return y; + if (x != null && y == null) + return x; + if (x == null && y == null) + return new T[0]; var z = new T[x.Length + y.Length]; x.CopyTo(z, 0); y.CopyTo(z, x.Length); @@ -263,7 +269,7 @@ public static T[] Sort(T[] vec, VectorSortType sortType, out int[] idx) /// array to get subarray from /// starting index /// ending index(or to the end if omitted) - /// + /// null if indStart and indEnd are the same, otherwise the subarray public static T[] SubArray(T[] array1, int indStart, int indEnd = -9999) { if (array1 == null) @@ -279,14 +285,19 @@ public static T[] SubArray(T[] array1, int indStart, int indEnd = -9999) if (indStart < 0) indStart = 0; int length = indEnd - indStart + 1; - T[] retArray = new T[length]; - int outInd = 0; - for (int i = indStart; i <= indEnd; i++) + if (length > 0) { - retArray[outInd] = array1[i]; - outInd++; + T[] retArray = new T[length]; + int outInd = 0; + for (int i = indStart; i <= indEnd; i++) + { + retArray[outInd] = array1[i]; + outInd++; + } + return retArray; } - return retArray; + else + return null; } From 8768ced7f9da5777eafb10081c1f78a173f777f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Thu, 11 Jan 2024 12:26:58 +0100 Subject: [PATCH 13/20] if Vec.Regress return all zeros, then tag this as not being able to identify --- TimeSeriesAnalysis/Vec.cs | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/TimeSeriesAnalysis/Vec.cs b/TimeSeriesAnalysis/Vec.cs index 4b5a6cc7..4f077999 100644 --- a/TimeSeriesAnalysis/Vec.cs +++ b/TimeSeriesAnalysis/Vec.cs @@ -1024,7 +1024,14 @@ private RegressionResults Regress(double[] Y, double[][] X, int[] yIndToIgnore=n { results.Param95prcConfidence = null; } - results.AbleToIdentify = true; + if (Vec.IsAllValue(results.Param, 0)) + { + results.AbleToIdentify = false; + } + else + { + results.AbleToIdentify = true; + } return results; } catch From 96dee0c2536df7dfc18b5809fdd0337accca7e90 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Thu, 11 Jan 2024 12:28:09 +0100 Subject: [PATCH 14/20] bump version --- TimeSeriesAnalysis.csproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index 7a869dc5..d2249154 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.64 + 1.2.66 Equinor Equinor true From af52a79c029c6f0123dc37ee024db5936ab4f42e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Fri, 12 Jan 2024 14:09:46 +0100 Subject: [PATCH 15/20] correlation calculator: turn two hard-coded constants into optional method inputs --- TimeSeriesAnalysis/CorrelationCalculator.cs | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/TimeSeriesAnalysis/CorrelationCalculator.cs b/TimeSeriesAnalysis/CorrelationCalculator.cs index 804b4a36..eb7856a1 100644 --- a/TimeSeriesAnalysis/CorrelationCalculator.cs +++ b/TimeSeriesAnalysis/CorrelationCalculator.cs @@ -94,13 +94,14 @@ public static double Calculate(double[] signal1, double[] signal2, List ind /// /// /// the dataset, which must have a correctly set timestamps in order to estimate time constants + /// calculate time-shift for every corr coeff that is above this threshold(0.0-1.0) + /// for a time-shift to be valid, the resulting model nees to have Rsq over this threshold /// a - public static List CalculateAndOrder(string mainSignalName, TimeSeriesDataSet dataSet) + public static List CalculateAndOrder(string mainSignalName, TimeSeriesDataSet dataSet, + double minimumCorrCoeffToDoTimeshiftCalc=0.4,double minimumRsqAbs = 10) { - const double minimumCorrCoeffToDoTimeshiftCalc = 0.4; - (double?,double?) EstiamteTimeShift(double[] signalIn, double[] signalOut) + (double?,double?) EstimateTimeShift(double[] signalIn, double[] signalOut) { - const double minimumRsqAbs = 10; var dataSetUnit = new UnitDataSet(); dataSetUnit.Y_meas = signalOut; dataSetUnit.U = Array2D.CreateFromList(new List { signalIn }); @@ -148,7 +149,7 @@ public static List CalculateAndOrder(string mainSignalName, T double[] curSignalValues = dataSet.GetValues(curSignalName); if (curSignalValues != null) { - (timeConstant_s,timeDelay_s) = EstiamteTimeShift(curSignalValues,mainSignalValues); + (timeConstant_s,timeDelay_s) = EstimateTimeShift(curSignalValues,mainSignalValues); // else { // check if it is possible to identify model if we reverse which signal is in and which is out. // var timeShiftReversed = EstiamteTimeShift(mainSignalValues,curSignalValues ); From 06a328f3fbb58fee5d00ea6abf97137524bfdc40 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Fri, 12 Jan 2024 14:09:46 +0100 Subject: [PATCH 16/20] correlation calculator: turn two hard-coded constants into optional method inputs --- TimeSeriesAnalysis.csproj | 2 +- TimeSeriesAnalysis/CorrelationCalculator.cs | 11 ++++++----- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/TimeSeriesAnalysis.csproj b/TimeSeriesAnalysis.csproj index d2249154..79bfd888 100644 --- a/TimeSeriesAnalysis.csproj +++ b/TimeSeriesAnalysis.csproj @@ -14,7 +14,7 @@ False https://github.com/equinor/timeseriesanalysis readme.md - 1.2.66 + 1.2.68 Equinor Equinor true diff --git a/TimeSeriesAnalysis/CorrelationCalculator.cs b/TimeSeriesAnalysis/CorrelationCalculator.cs index 804b4a36..eb7856a1 100644 --- a/TimeSeriesAnalysis/CorrelationCalculator.cs +++ b/TimeSeriesAnalysis/CorrelationCalculator.cs @@ -94,13 +94,14 @@ public static double Calculate(double[] signal1, double[] signal2, List ind /// /// /// the dataset, which must have a correctly set timestamps in order to estimate time constants + /// calculate time-shift for every corr coeff that is above this threshold(0.0-1.0) + /// for a time-shift to be valid, the resulting model nees to have Rsq over this threshold /// a - public static List CalculateAndOrder(string mainSignalName, TimeSeriesDataSet dataSet) + public static List CalculateAndOrder(string mainSignalName, TimeSeriesDataSet dataSet, + double minimumCorrCoeffToDoTimeshiftCalc=0.4,double minimumRsqAbs = 10) { - const double minimumCorrCoeffToDoTimeshiftCalc = 0.4; - (double?,double?) EstiamteTimeShift(double[] signalIn, double[] signalOut) + (double?,double?) EstimateTimeShift(double[] signalIn, double[] signalOut) { - const double minimumRsqAbs = 10; var dataSetUnit = new UnitDataSet(); dataSetUnit.Y_meas = signalOut; dataSetUnit.U = Array2D.CreateFromList(new List { signalIn }); @@ -148,7 +149,7 @@ public static List CalculateAndOrder(string mainSignalName, T double[] curSignalValues = dataSet.GetValues(curSignalName); if (curSignalValues != null) { - (timeConstant_s,timeDelay_s) = EstiamteTimeShift(curSignalValues,mainSignalValues); + (timeConstant_s,timeDelay_s) = EstimateTimeShift(curSignalValues,mainSignalValues); // else { // check if it is possible to identify model if we reverse which signal is in and which is out. // var timeShiftReversed = EstiamteTimeShift(mainSignalValues,curSignalValues ); From 81df557299c3a98eb2f2df74074cac01eb49745b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Tue, 23 Jan 2024 09:24:20 +0100 Subject: [PATCH 17/20] Create SECURITY.md --- SECURITY.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 SECURITY.md diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 00000000..53d43963 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,17 @@ +# Security Policy + +If you discover a security vulnerability in this project, please follow these steps to responsibly disclose it: + +1. **Do not** create a public GitHub issue for the vulnerability. +2. Follow our guideline for Responsible Disclosure Policy at https://www.equinor.com/about-us/csirt to report the issue +The following information will help us triage your report more quickly: + +* Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) +* Full paths of source file(s) related to the manifestation of the issue +* The location of the affected source code (tag/branch/commit or direct URL) +* Any special configuration required to reproduce the issue +* Step-by-step instructions to reproduce the issue +* Proof-of-concept or exploit code (if possible) +* Impact of the issue, including how an attacker might exploit the issue + +We prefer all communications to be in English. 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From 2a653d436b79103eec703beb3002a144e8087c6c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Tue, 23 Jan 2024 09:54:30 +0100 Subject: [PATCH 19/20] Update readme.md --- readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/readme.md b/readme.md index 69e8cece..76ce6805 100644 --- a/readme.md +++ b/readme.md @@ -76,4 +76,4 @@ The contact person for this repository is Steinar Elgsæter, please post any que in the [github discussion pages](https://github.com/equinor/TimeSeriesAnalysis/discussions). ## License -TimeSeriesAnalysis is distributed under the [MIT license](LICENSE). +TimeSeriesAnalysis is distributed under the [Apache 2.0 license](LICENSE). From ca2aa386a8dd763ec57eaadba6e05f7429698c9b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steinar=20Elgs=C3=A6ter?= Date: Tue, 23 Jan 2024 11:08:02 +0100 Subject: [PATCH 20/20] Update readme.md --- readme.md | 39 ++++++++++++++++++++++++++++----------- 1 file changed, 28 insertions(+), 11 deletions(-) diff --git a/readme.md b/readme.md index 76ce6805..1f05ad73 100644 --- a/readme.md +++ b/readme.md @@ -2,21 +2,38 @@ ![Build Status](https://github.com/equinor/TimeSeriesAnalysis/actions/workflows/build.yml/badge.svg?branch=master) # TimeSeriesAnalysis -An open-source library for development and testing of time-series-based algorithms, including -- Transients/dynamic model identification, -- dynamic simulation, and -- advanced industrial PID-control. +This library that deals with developing *time-series models and simulators* from *time-series data*. -The library can be used from C# or any other .NET language, Matlab or Python. The target framework is .NET Standard 2.0. +The methods in this library are primarily designed to describe time-series of physical, real-world systems for an industrial setting. -## Use case +Real-world industrial systems +- often exhibit repeatable *transient responses* to changes in inputs +- usually include *feeback loops*, either due to recirculation in the process or because the system is controlled with *PID-controllers*(PID-model is included) +- and often consist of a network of interconnected units that interact. -Building industrial data-driven digital twins simulations that are -- scalable -- modular -- explainable (ie.e based on "trusted black boxes" or "grey box" models). +This library was designed to create *dynamic unit models* that can be chained into interconnected networks that can include PID-control or other feedback- or circulation loops. -These digital twins could be used as foundation for advanced analytics applications. +Models are not derived from physical first principles, but are inferred from time-series data using the principles of *system identification*, the methods to infer models are built on multiple modfied linear regression steps(identification methods are included). + +The library is written in C# but can be referenced from any language that can reference .NET language, including Matlab or Python. The target framework is .NET Standard 2.0. + +## Use cases: Digital twins, anomaly detection, "what-if", PID-tuning, monitoring and screening + +The power of this library lies in the ability to automate or semi-automate the steps of identfiying new models or simulating existing models at scale. In effect this libary can create "digital twin" models of sections of a process. + +The methods lend themselves readily to for instance automatically building a large number of similar models for similar sections of a process, for instance to monitor every valve, separation tank or PID-control loop in a similar fashion, in a way that lends itself to process monitoring and -screening. + +The methods in this library are *scalable* and *modular*, i.e. by chaining together unit models, the library can also simulate larger sections of a process plant, so the methodology could in principle be extended to assemble a digital twin of an entire process plant. + +Models can be run alongside the plant, and monitoring the difference between measured and modelled values can provide insight into changes in the plant (anomaly detection.) By manually simulating changes on these kinds of models, "what-if" scenarios can be evaluted either manually or automatically. One very interesting such use-case is to evaluate the possible benefits of re-tuning PID-controllers. + +The above use-cases could be put under the umbrella term "advanced analytics", i.e. using algorithms and data to make deep insights about causes and effects to make predictions and reccomendations. + +## Explainable models for low-information datasets + +Note that although this library uses regression methods and algorithms to learn parameters to describe data, the methodology is somewhat different from traditional machine learning in that models follow the principles of system identification rather than AI. +The number of free parameters are kept low by design, as this makes reduces the likelihood of over-fitting. Over-fitting is particularly important when dealing with industrial data, as this kind of data has to be used "as-is" and one often has less information than one would like. +Another benefit of keepting the number of fitted paramters low is that models remain *explainable* and human-understandable, and it becomes possible to combine fitted models with human pre-knowledge, what is often referred to as "grey-box" modeling. ## Documentation: