From b2d51423965d074371bfa20efebb2396141a9333 Mon Sep 17 00:00:00 2001 From: yoshiurr-INL Date: Fri, 13 May 2022 13:14:20 -0600 Subject: [PATCH 1/8] Added the capability to specify time interval and removed function to postprocess more than one feature and target set per step --- doc/user_manual/PostProcessors/Validation.tex | 38 +- .../PostProcessors/Validations/PPDSS.py | 425 ++++++++++-------- .../Validation/gold/DSS/pp2_print_0.csv | 151 +++---- .../Validation/gold/DSS/pp2_print_1.csv | 101 ----- .../Validation/gold/DSS/pp2_print_2.csv | 101 ----- .../Validation/gold/DSS/pp2_print_3.csv | 101 ----- .../Validation/test_validation_dss.xml | 73 ++- .../framework/PostProcessors/Validation/tests | 2 +- 8 files changed, 383 insertions(+), 609 deletions(-) delete mode 100644 tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_1.csv delete mode 100644 tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_2.csv delete mode 100644 tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_3.csv diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index 122a7fb40b..d40898a939 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -89,10 +89,13 @@ \subsubsection{Validation PostProcessors} % \begin{itemize} - \item \xmlNode{Features}, \xmlDesc{comma separated string, required field}, specifies the names of the features. + \item \xmlNode{Features}, \xmlDesc{comma separated string, required field}, specifies the names of the features. To enable user defined + time interval selection, this postprocessor will only consider the first feature name provided. If user provides more than one, + it will ignore the other inputs. \item \xmlNode{Targets}, \xmlDesc{comma separated string, required field}, contains a comma separated list of - targets. \nb Each target is paired with a feature listed in xml node \xmlNode{Features}. In this case, the - number of targets should be equal to the number of features. + targets. \nb Each target is paired with a feature listed in xml node \xmlNode{Features}. To enable user defined + time interval selection, this postprocessor will only consider the first feature name provided. If user provides more than one, + it will ignore the other inputs. \item \xmlNode{pivotParameter}, \xmlDesc{string, required field if HistorySet is used}, specifies the pivotParameter for a . The pivot parameter is the shared index of the output variables in the data object. \item \xmlNode{Metric}, \xmlDesc{string, required field}, specifies the \textbf{Metric} name that is defined via @@ -105,6 +108,8 @@ \subsubsection{Validation PostProcessors} refer to \ref{sec:Metrics} for detailed descriptions about this metric. \item \xmlNode{pivotParameterFeature}, \xmlDesc{string, required field}, specifies the pivotParameter for a feature . The feature pivot parameter is the shared index of the output variables in the data object. \item \xmlNode{pivotParameterTarget}, \xmlDesc{string, required field}, specifies the pivotParameter for a target . The target pivot parameter is the shared index of the output variables in the data object. + \item \xmlNode{separateData}, \xmlDesc{string, optional field}, specifies the custom interval to apply DSS postprocessing. The string should contain three parts; start time, `|', and end time all in one. For example, 0.0|0.5. + The start and end time should be in ratios of the full interval. In this case 0.5 would be the midpoint time. This is node is not required and if not provided, the default is the full time interval. \item \xmlNode{multiOutput}, \xmlDesc{string, required field}, to extract raw values for the HistorySet. The user must use ‘raw values’ for the full set of metrics’ calculations to be dumped. \item \xmlNode{scale}, \xmlDesc{string, required field}, specifies the type of time scaling. The following are the options for scaling (specific definitions for each scaling type is provided in \ref{sec:dssdoc}): \begin{itemize} @@ -115,12 +120,8 @@ \subsubsection{Validation PostProcessors} \item \textbf{omega\_strain}, calculating the distortion for two data sets with scaling ratios for agent of changes. \item \textbf{identity}, calculating the distortion for two data sets with scaling ratios of 1. \end{itemize} - \item \xmlNode{scaleBeta}, \xmlDesc{float or comma separated list of floats, required field}, specifies the parameter of interest scaling ratio between the feature and target. - To provide more than one scaling factor, separate by adding a comma in between each number. Providing more than one scaling factor presumes there are more than one parameter to be post-processed. - If so, \xmlNode{Features}, \xmlNode{Targets}, and \xmlNode{scaleOmega} must have the same number scaling factors. - \item \xmlNode{scaleOmega}, \xmlDesc{float or comma separated list of floats, required field}, specifies the agents of change scaling ratio between the feature and target. - To provide more than one scaling factor, separate by adding a comma in between each number. Providing more than one scaling factor presumes there are more than one parameter to be post-processed. - If so, \xmlNode{Features}, \xmlNode{Targets}, and \xmlNode{scaleBeta} must have the same number scaling factors. + \item \xmlNode{scaleBeta}, \xmlDesc{float, required field}, specifies the parameter of interest scaling ratio between the feature and target. + \item \xmlNode{scaleOmega}, \xmlDesc{float, required field}, specifies the agents of change scaling ratio between the feature and target. \end{itemize} \textbf{Example:} @@ -133,15 +134,26 @@ \subsubsection{Validation PostProcessors} ... ... + + outMC1|x1 + outMC2|x2 + dss + time1 + time2 + DataSynthesis + 1 + 1 + - outMC1|x1,outMC1|y1 - outMC2|x2,outMC2|y2 + outMC1|x1 + outMC2|x2 dss time1 time2 + 0.0|0.5 DataSynthesis - 1,1 - 1,1 + 1 + 1 ... diff --git a/ravenframework/Models/PostProcessors/Validations/PPDSS.py b/ravenframework/Models/PostProcessors/Validations/PPDSS.py index e21a30afdb..179483f22e 100644 --- a/ravenframework/Models/PostProcessors/Validations/PPDSS.py +++ b/ravenframework/Models/PostProcessors/Validations/PPDSS.py @@ -57,14 +57,22 @@ class cls. pivotParameterTargetInput = InputData.parameterInputFactory("pivotParameterTarget", contentType=InputTypes.StringType, descr="""Pivot parameter for target inputs""") inputSpecification.addSub(pivotParameterTargetInput) + separateDataInput = InputData.parameterInputFactory("separateData", contentType=InputTypes.StringType, + descr="""Time points to separate data and conduct DSS postprocessing + on selected time interval. Values must be fractions of the full time interval. + To distinguish strart and end, '|' should be placed between both values. Example: 0.0|0.4 + To provide more than one selection for the same data set, + '+' should be placed at the end of each interval to start defining the new interval. Example: 0.0|0.4+0.4|1.0 + If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") + inputSpecification.addSub(separateDataInput) scaleTypeInput = InputData.parameterInputFactory("scale", contentType=InputTypes.makeEnumType("scale","scaleType",['DataSynthesis','2_2_affine','dilation','beta_strain','omega_strain','identity']), descr="""Scaling type for the time transformation. Available types are DataSynthesis, 2_2_affine, dilation, beta_strain, omega_strain, and identity""") inputSpecification.addSub(scaleTypeInput) - scaleRatioBetaInput = InputData.parameterInputFactory("scaleBeta", contentType=InputTypes.FloatListType, + scaleRatioBetaInput = InputData.parameterInputFactory("scaleBeta", contentType=InputTypes.FloatType, descr="""Scaling ratio for the parameter of interest""") inputSpecification.addSub(scaleRatioBetaInput) - scaleRatioOmegaInput = InputData.parameterInputFactory("scaleOmega", contentType=InputTypes.FloatListType, + scaleRatioOmegaInput = InputData.parameterInputFactory("scaleOmega", contentType=InputTypes.FloatType, descr="""Scaling ratio for the agents of change""") inputSpecification.addSub(scaleRatioOmegaInput) return inputSpecification @@ -88,6 +96,7 @@ def __init__(self): self.pivotValuesFeature = [] # Feature pivot parameter values self.pivotParameterTarget = None # Target pivot parameter variable self.pivotValuesTarget = [] # Target pivot parameter values + self.separateData = None # String of data separation points. Default is None. self.scaleType = None # Scaling type # assembler objects to be requested self.scaleRatioBeta = [] # Scaling ratio for the parameter of interest @@ -114,6 +123,8 @@ def _handleInput(self, paramInput): self.pivotParameterFeature = child.value elif child.getName() == 'pivotParameterTarget': self.pivotParameterTarget = child.value + elif child.getName() == 'separateData': + self.separateData = child.value elif child.getName() == 'scale': self.scaleType = child.value elif child.getName() == 'scaleBeta': @@ -150,14 +161,9 @@ def run(self, inputIn): if not isinstance(evaluation, list): self.raiseAnError(IOError,"The data type in evaluation is not list") if pivotParameterFeature and pivotParameterTarget: - if len(datasets[0][pivotParameterFeature]) != len(list(evaluation[0].values())[0]) and len(datasets[1][pivotParameterTarget]) != len(list(evaluation[1].values())[0]): - self.raiseAnError(RuntimeError, "The pivotParameterFeature value '{}' has size '{}' and validation output has size '{}' The pivotParameterTarget value '{}' has size '{}' and validation output has size '{}'.".format( len(datasets[0][self.pivotParameterFeature]), len(evaluation.values()[0]))) - if pivotParameterFeature not in evaluation and pivotParameterTarget not in evaluation: - for i in range(len(evaluation)): - if len(datasets[0][pivotParameterFeature]) < len(datasets[1][pivotParameterTarget]): - evaluation[i]['pivot_parameter'] = datasets[0][pivotParameterFeature] - else: - evaluation[i]['pivot_parameter'] = datasets[1][pivotParameterTarget] + if self.separateData == None: + if len(datasets[0][pivotParameterFeature]) != len(list(evaluation[0].values())[0]) and len(datasets[1][pivotParameterTarget]) != len(list(evaluation[1].values())[0]): + self.raiseAnError(RuntimeError, "The pivotParameterFeature value '{}' has size '{}' and validation output has size '{}' The pivotParameterTarget value '{}' has size '{}' and validation output has size '{}'.".format( len(datasets[0][self.pivotParameterFeature]), len(evaluation.values()[0]))) return evaluation def _evaluate(self, datasets, **kwargs): @@ -169,192 +175,243 @@ def _evaluate(self, datasets, **kwargs): """ realizations = [] realizationArray = [] - for feat, targ, scaleRatioBeta, scaleRatioOmega in zip(self.features, self.targets, self.scaleRatioBeta, self.scaleRatioOmega): - nameFeat = feat.split("|") - nameTarg = targ.split("|") - names = [nameFeat[0],nameTarg[0]] - featData = self._getDataFromDatasets(datasets, feat, names)[0] - targData = self._getDataFromDatasets(datasets, targ, names)[0] - if (isinstance(scaleRatioBeta,int) or isinstance(scaleRatioBeta,float)) and (isinstance(scaleRatioOmega,int) or isinstance(scaleRatioOmega,float)) is True: - if self.scaleType == 'DataSynthesis': - timeScalingRatio = 1 - if abs(1-scaleRatioBeta) > 10**(-4) or abs(1-scaleRatioOmega) > 10**(-4): - self.raiseAnError(IOError, "Either beta or omega scaling ratio are not 1. Both must be 1") - elif self.scaleType == '2_2_affine': - timeScalingRatio = scaleRatioBeta/scaleRatioOmega - elif self.scaleType == 'dilation': - timeScalingRatio = 1 - if abs(1-scaleRatioBeta/scaleRatioOmega) > 10**(-4): - self.raiseAnError(IOError, "Beta scaling ratio:",scaleRatioBeta,"and Omega scaling ratio:",scaleRatioOmega,"are not nearly equivalent") - elif self.scaleType == 'beta_strain': - timeScalingRatio = scaleRatioBeta - if abs(1-scaleRatioOmega) > 10**(-4): - self.raiseAnError(IOError, "Omega scaling ratio:",scaleRatioOmega,"must be 1") - elif self.scaleType == 'omega_strain': - timeScalingRatio = 1/scaleRatioOmega - if abs(1-scaleRatioBeta) > 10**(-4): - self.raiseAnError(IOError, "Beta scaling ratio:",scaleRatioBeta,"must be 1") - elif self.scaleType == 'identity': - timeScalingRatio = 1 - if abs(1-scaleRatioBeta) > 10**(-4) or abs(1-scaleRatioOmega) > 10**(-4): - self.raiseAnError(IOError, "Either beta or omega scaling ratio are not 1. Both must be 1") - else: - self.raiseAnError(IOError, "Scaling Type",self.scaleType, "is not provided") + if len(self.features) > 1 or len(self.targets) > 1: + self.raiseAnError(IOError, "The number of inputs for features or targets is greater than 1. Please restrict to one set per step.") + feat = self.features[0] + targ = self.targets[0] + scaleRatioBeta = self.scaleRatioBeta + scaleRatioOmega = self.scaleRatioOmega + nameFeat = feat.split("|") + nameTarg = targ.split("|") + names = [nameFeat[0],nameTarg[0]] + featDataSet = self._getDataFromDatasets(datasets, feat, names)[0] + targDataSet = self._getDataFromDatasets(datasets, targ, names)[0] + if (isinstance(scaleRatioBeta,int) or isinstance(scaleRatioBeta,float)) and (isinstance(scaleRatioOmega,int) or isinstance(scaleRatioOmega,float)) is True: + if self.scaleType == 'DataSynthesis': + timeScalingRatio = 1 + if abs(1-scaleRatioBeta) > 10**(-4) or abs(1-scaleRatioOmega) > 10**(-4): + self.raiseAnError(IOError, "Either beta or omega scaling ratio are not 1. Both must be 1") + elif self.scaleType == '2_2_affine': + timeScalingRatio = scaleRatioBeta/scaleRatioOmega + elif self.scaleType == 'dilation': + timeScalingRatio = 1 + if abs(1-scaleRatioBeta/scaleRatioOmega) > 10**(-4): + self.raiseAnError(IOError, "Beta scaling ratio:",scaleRatioBeta,"and Omega scaling ratio:",scaleRatioOmega,"are not nearly equivalent") + elif self.scaleType == 'beta_strain': + timeScalingRatio = scaleRatioBeta + if abs(1-scaleRatioOmega) > 10**(-4): + self.raiseAnError(IOError, "Omega scaling ratio:",scaleRatioOmega,"must be 1") + elif self.scaleType == 'omega_strain': + timeScalingRatio = 1/scaleRatioOmega + if abs(1-scaleRatioBeta) > 10**(-4): + self.raiseAnError(IOError, "Beta scaling ratio:",scaleRatioBeta,"must be 1") + elif self.scaleType == 'identity': + timeScalingRatio = 1 + if abs(1-scaleRatioBeta) > 10**(-4) or abs(1-scaleRatioOmega) > 10**(-4): + self.raiseAnError(IOError, "Either beta or omega scaling ratio are not 1. Both must be 1") else: - self.raiseAnError(IOError, scaleRatioBeta,"or",scaleRatioOmega,"is not a numerical number") + self.raiseAnError(IOError, "Scaling Type",self.scaleType, "is not provided") + else: + self.raiseAnError(IOError, scaleRatioBeta,"or",scaleRatioOmega,"is not a numerical number") - pivotFeature = self._getDataFromDatasets(datasets, names[0]+"|"+self.pivotParameterFeature, names)[0] - pivotFeature = np.transpose(pivotFeature)[0] - pivotTarget = self._getDataFromDatasets(datasets, names[1]+"|"+self.pivotParameterTarget, names)[0] - pivotTarget = np.transpose(pivotTarget)[0] - pivotFeatureSize = pivotFeature.shape[0] - pivotTargetSize = pivotTarget.shape[0] - if pivotFeatureSize >= pivotTargetSize: - pivotSize = pivotTargetSize - else: - pivotSize = pivotFeatureSize + pivotFeatureData = self._getDataFromDatasets(datasets, names[0]+"|"+self.pivotParameterFeature, names)[0] + pivotFeatureData = np.transpose(pivotFeatureData)[0] + pivotTargetData = self._getDataFromDatasets(datasets, names[1]+"|"+self.pivotParameterTarget, names)[0] + pivotTargetData = np.transpose(pivotTargetData)[0] + if self.separateData != None: + separateTime = self.separateData + separateTime = separateTime.split("|") + startTime = separateTime[0] + endTime = separateTime[1] + if startTime >= endTime: + self.raiseAnError(IOError, "Start time", startTime, "is equal or larger than end time", endTime) + featureStartCount = 0 + startFeatureLocation = None + endFeatureLocation = None + for i in range(len(pivotFeatureData)): + if pivotFeatureData[i] >= pivotFeatureData[0]+float(startTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]) and featureStartCount == 0: + startFeatureLocation = i + featureStartCount += 1 + if pivotFeatureData[i] > pivotFeatureData[0]+float(endTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]): + endFeatureLocation = i-1 + break + elif pivotFeatureData[i] == pivotFeatureData[0]+float(endTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]): + endFeatureLocation = i + break + pivotFeature = pivotFeatureData[startFeatureLocation:endFeatureLocation] + featData = np.zeros((np.shape(featDataSet)[0],endFeatureLocation-startFeatureLocation)) + for i in range(len(featDataSet)): + featData[i] = featDataSet[i][startFeatureLocation:endFeatureLocation] + targetStartCount = 0 + startTargetLocation = None + endTargetLocation = None + for j in range(len(pivotTargetData)): + if pivotTargetData[j] >= pivotTargetData[0]+float(startTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]) and targetStartCount == 0: + startTargetLocation = j + targetStartCount += 1 + if pivotTargetData[j] > pivotTargetData[0]+float(endTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]): + endTargetLocation = j-1 + break + elif pivotTargetData[j] == pivotTargetData[0]+float(endTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]): + endTargetLocation = j + pivotTarget = pivotTargetData[startTargetLocation:endTargetLocation] + targData = np.zeros((np.shape(targDataSet)[0],endTargetLocation-startTargetLocation)) + for i in range(len(targDataSet)): + targData[i] = targDataSet[i][startTargetLocation:endTargetLocation] + else: + pivotFeature = pivotFeatureData + pivotTarget = pivotTargetData + featData = featDataSet + targData = targDataSet + + pivotFeatureSize = pivotFeature.shape[0] + pivotTargetSize = pivotTarget.shape[0] + if pivotFeatureSize >= pivotTargetSize: + pivotSize = pivotTargetSize + else: + pivotSize = pivotFeatureSize + if pivotFeatureSize == pivotSize: + yCount = featData.shape[0] + zCount = featData.shape[1] + else: + yCount = targData.shape[0] + zCount = targData.shape[1] + featureD = np.zeros((yCount,zCount)) + featureProcessTimeNorm = np.zeros((yCount,zCount)) + featureOmegaNorm = np.zeros((yCount,zCount)) + featureBeta = np.zeros((yCount,zCount)) + naNCount = np.zeros((yCount,zCount)) + # + feature = nameFeat[1] + for cnt2 in range(yCount): if pivotFeatureSize == pivotSize: - yCount = featData.shape[0] - zCount = featData.shape[1] + featureBeta[cnt2] = featData[cnt2] + interpGrid = pivotFeature else: - yCount = targData.shape[0] - zCount = targData.shape[1] - featureD = np.zeros((yCount,zCount)) - featureProcessTimeNorm = np.zeros((yCount,zCount)) - featureOmegaNorm = np.zeros((yCount,zCount)) - featureBeta = np.zeros((yCount,zCount)) - naNCount = np.zeros((yCount,zCount)) - # - feature = nameFeat[1] - for cnt2 in range(yCount): - if pivotFeatureSize == pivotSize: - featureBeta[cnt2] = featData[cnt2] - interpGrid = pivotFeature - else: - interpFunction = interp1d(pivotFeature,featData[cnt2],kind='linear',fill_value='extrapolate') - interpGrid = timeScalingRatio*pivotTarget - featureBeta[cnt2] = interpFunction(interpGrid) - featureOmega = np.gradient(featureBeta[cnt2],interpGrid) - featureProcessTime = featureBeta[cnt2]/featureOmega - featureDiffOmega = np.gradient(featureOmega,interpGrid) - featureD[cnt2] = -featureBeta[cnt2]/featureOmega**2*featureDiffOmega - for cnt3 in range(zCount): - if np.isnan(featureD[cnt2][cnt3]) == True: - naNCount[cnt2][cnt3] = 1 - elif np.isinf(featureD[cnt2][cnt3]) == True: - naNCount[cnt2][cnt3] = 1 - featureInt = featureD[cnt2]+1 - # Excluding NaN type data and exclude corresponding time in grid in - # preperation for numpy simpson integration - count=0 + interpFunction = interp1d(pivotFeature,featData[cnt2],kind='linear',fill_value='extrapolate') + interpGrid = timeScalingRatio*pivotTarget + featureBeta[cnt2] = interpFunction(interpGrid) + featureOmega = np.gradient(featureBeta[cnt2],interpGrid) + featureProcessTime = featureBeta[cnt2]/featureOmega + featureDiffOmega = np.gradient(featureOmega,interpGrid) + featureD[cnt2] = -featureBeta[cnt2]/featureOmega**2*featureDiffOmega + for cnt3 in range(zCount): + if np.isnan(featureD[cnt2][cnt3]) == True: + naNCount[cnt2][cnt3] = 1 + elif np.isinf(featureD[cnt2][cnt3]) == True: + naNCount[cnt2][cnt3] = 1 + featureInt = featureD[cnt2]+1 + # Excluding NaN type data and exclude corresponding time in grid in + # preperation for numpy simpson integration + count=0 + for i in range(len(featureD[cnt2])): + if np.isnan(featureD[cnt2][i])==False and np.isinf(featureD[cnt2][i])==False: + count += 1 + if count > 0: + featureIntNew = np.zeros(count) + interpGridNew = np.zeros(count) + trackCount = 0 for i in range(len(featureD[cnt2])): if np.isnan(featureD[cnt2][i])==False and np.isinf(featureD[cnt2][i])==False: - count += 1 - if count > 0: - featureIntNew = np.zeros(count) - interpGridNew = np.zeros(count) - trackCount = 0 - for i in range(len(featureD[cnt2])): - if np.isnan(featureD[cnt2][i])==False and np.isinf(featureD[cnt2][i])==False: - interpGridNew[trackCount] = interpGrid[i] - featureIntNew[trackCount] = featureInt[i] - trackCount += 1 - else: - featureD[cnt2][i] = 0 - # - featureProcessAction = simps(featureIntNew, interpGridNew) - featureProcessTimeNorm[cnt2] = featureProcessTime/featureProcessAction - featureOmegaNorm[cnt2] = featureProcessAction*featureOmega + interpGridNew[trackCount] = interpGrid[i] + featureIntNew[trackCount] = featureInt[i] + trackCount += 1 + else: + featureD[cnt2][i] = 0 # - targetD = np.zeros((yCount,zCount)) - targetProcessTimeNorm = np.zeros((yCount,zCount)) - targetOmegaNorm = np.zeros((yCount,zCount)) - targetBeta = np.zeros((yCount,zCount)) - target = nameTarg[1] - for cnt2 in range(yCount): - if pivotTargetSize == pivotSize: - targetBeta[cnt2] = targData[cnt2] - interpGrid = pivotTarget - else: - interpFunction = interp1d(pivotTarget,targData[cnt2],kind='linear',fill_value='extrapolate') - interpGrid = 1/timeScalingRatio*pivotFeature - targetBeta[cnt2] = interpFunction(interpGrid) - targetOmega = np.gradient(targetBeta[cnt2],interpGrid) - #print("targetOmega:",targetOmega) - targetProcessTime = targetBeta[cnt2]/targetOmega - targetDiffOmega = np.gradient(targetOmega,interpGrid) - targetD[cnt2] = -targetBeta[cnt2]/targetOmega**2*targetDiffOmega - for cnt3 in range(zCount): - if np.isnan(targetD[cnt2][cnt3]) == True: - naNCount[cnt2][cnt3] = 1 - elif np.isinf(targetD[cnt2][cnt3]) == True: - naNCount[cnt2][cnt3] = 1 - targetInt = targetD[cnt2]+1 - # Excluding NaN type data and exclude corresponding time in grid in - # preperation for numpy simpson integration - count=0 + featureProcessAction = simps(featureIntNew, interpGridNew) + featureProcessTimeNorm[cnt2] = featureProcessTime/featureProcessAction + featureOmegaNorm[cnt2] = featureProcessAction*featureOmega + # + targetD = np.zeros((yCount,zCount)) + targetProcessTimeNorm = np.zeros((yCount,zCount)) + targetOmegaNorm = np.zeros((yCount,zCount)) + targetBeta = np.zeros((yCount,zCount)) + target = nameTarg[1] + for cnt2 in range(yCount): + if pivotTargetSize == pivotSize: + targetBeta[cnt2] = targData[cnt2] + interpGrid = pivotTarget + else: + interpFunction = interp1d(pivotTarget,targData[cnt2],kind='linear',fill_value='extrapolate') + interpGrid = 1/timeScalingRatio*pivotFeature + targetBeta[cnt2] = interpFunction(interpGrid) + targetOmega = np.gradient(targetBeta[cnt2],interpGrid) + #print("targetOmega:",targetOmega) + targetProcessTime = targetBeta[cnt2]/targetOmega + targetDiffOmega = np.gradient(targetOmega,interpGrid) + targetD[cnt2] = -targetBeta[cnt2]/targetOmega**2*targetDiffOmega + for cnt3 in range(zCount): + if np.isnan(targetD[cnt2][cnt3]) == True: + naNCount[cnt2][cnt3] = 1 + elif np.isinf(targetD[cnt2][cnt3]) == True: + naNCount[cnt2][cnt3] = 1 + targetInt = targetD[cnt2]+1 + # Excluding NaN type data and exclude corresponding time in grid in + # preperation for numpy simpson integration + count=0 + for i in range(len(targetD[cnt2])): + if np.isnan(targetD[cnt2][i])==False and np.isinf(targetD[cnt2][i])==False: + count += 1 + if count > 0: + targetIntNew = np.zeros(count) + interpGridNew = np.zeros(count) + trackCount = 0 for i in range(len(targetD[cnt2])): if np.isnan(targetD[cnt2][i])==False and np.isinf(targetD[cnt2][i])==False: - count += 1 - if count > 0: - targetIntNew = np.zeros(count) - interpGridNew = np.zeros(count) - trackCount = 0 - for i in range(len(targetD[cnt2])): - if np.isnan(targetD[cnt2][i])==False and np.isinf(targetD[cnt2][i])==False: - interpGridNew[trackCount] = interpGrid[i] - targetIntNew[trackCount] = targetInt[i] - trackCount += 1 - else: - targetD[cnt2][i] = 0 - # - targetProcessAction = simps(targetIntNew, interpGridNew) - targetProcessTimeNorm[cnt2] = targetProcessTime/targetProcessAction - targetOmegaNorm[cnt2] = targetProcessAction*targetOmega + interpGridNew[trackCount] = interpGrid[i] + targetIntNew[trackCount] = targetInt[i] + trackCount += 1 + else: + targetD[cnt2][i] = 0 # - featureProcessTimeNormScaled = np.zeros((yCount,zCount)) - featureOmegaNormScaled = np.zeros((yCount,zCount)) - for cnt3 in range(yCount): - featureProcessTimeNormScaled[cnt3] = featureProcessTimeNorm[cnt3]/timeScalingRatio - featureOmegaNormScaled[cnt3] = featureOmegaNorm[cnt3]/scaleRatioBeta - newfeatureData = np.asarray([featureOmegaNormScaled,featureProcessTimeNormScaled,featureBeta]) - newtargetData = np.asarray([targetOmegaNorm,targetD,targetBeta]) - #------------------------------------------------------------------------------------------ - if pivotTargetSize == pivotSize: - timeParameter = pivotTarget - else: - timeParameter = pivotFeature + targetProcessAction = simps(targetIntNew, interpGridNew) + targetProcessTimeNorm[cnt2] = targetProcessTime/targetProcessAction + targetOmegaNorm[cnt2] = targetProcessAction*targetOmega + # + featureProcessTimeNormScaled = np.zeros((yCount,zCount)) + featureOmegaNormScaled = np.zeros((yCount,zCount)) + for cnt3 in range(yCount): + featureProcessTimeNormScaled[cnt3] = featureProcessTimeNorm[cnt3]/timeScalingRatio + featureOmegaNormScaled[cnt3] = featureOmegaNorm[cnt3]/scaleRatioBeta + newfeatureData = np.asarray([featureOmegaNormScaled,featureProcessTimeNormScaled,featureBeta]) + newtargetData = np.asarray([targetOmegaNorm,targetD,targetBeta]) + #------------------------------------------------------------------------------------------ + if pivotTargetSize == pivotSize: + timeParameter = pivotTarget + else: + timeParameter = pivotFeature + outputDict = {} + distanceTotal = np.zeros((yCount,zCount)) + sigma = np.zeros((yCount,zCount)) + for metric in self.metrics: + name = "{}_{}_{}".format(metric.estimator.name, targ.split("|")[-1], feat.split("|")[-1]) + output = metric.evaluate((newfeatureData,newtargetData), multiOutput='raw_values') + for cnt2 in range(yCount): + distanceSum = abs(np.sum(output[cnt2])) + sigmaSum = 0 + for cnt3 in range(zCount): + distanceTotal[cnt2][cnt3] = distanceSum + sigmaSum += output[cnt2][cnt3]**2 + for cnt3 in range(zCount): + sigma[cnt2][cnt3] = (1/(zCount-np.sum(naNCount[cnt2]))*sigmaSum)**0.5 + rlz = [] + for cnt in range(yCount): outputDict = {} - distanceTotal = np.zeros((yCount,zCount)) - sigma = np.zeros((yCount,zCount)) - for metric in self.metrics: - name = "{}_{}_{}".format(metric.estimator.name, targ.split("|")[-1], feat.split("|")[-1]) - output = metric.evaluate((newfeatureData,newtargetData), multiOutput='raw_values') - for cnt2 in range(yCount): - distanceSum = abs(np.sum(output[cnt2])) - sigmaSum = 0 - for cnt3 in range(zCount): - distanceTotal[cnt2][cnt3] = distanceSum - sigmaSum += output[cnt2][cnt3]**2 - for cnt3 in range(zCount): - sigma[cnt2][cnt3] = (1/(zCount-np.sum(naNCount[cnt2]))*sigmaSum)**0.5 - rlz = [] - for cnt in range(yCount): - outputDict = {} - outputDict[name] = abs(np.atleast_1d(output[cnt])) - outputDict['pivot_parameter'] = timeParameter - outputDict['total_distance_'+nameTarg[1]+'_'+nameFeat[1]] = distanceTotal[cnt] - outputDict['feature_beta_'+nameTarg[1]+'_'+nameFeat[1]] = featureBeta[cnt] - outputDict['target_beta_'+nameTarg[1]+'_'+nameFeat[1]] = targetBeta[cnt] - outputDict['feature_omega_'+nameTarg[1]+'_'+nameFeat[1]] = featureOmegaNormScaled[cnt] - outputDict['target_omega_'+nameTarg[1]+'_'+nameFeat[1]] = targetOmegaNorm[cnt] - outputDict['feature_D_'+nameTarg[1]+'_'+nameFeat[1]] = featureD[cnt] - outputDict['target_D_'+nameTarg[1]+'_'+nameFeat[1]] = targetD[cnt] - outputDict['process_time_'+nameTarg[1]+'_'+nameFeat[1]] = newfeatureData[1][cnt] - outputDict['standard_deviation_'+nameTarg[1]+'_'+nameFeat[1]] = sigma[cnt] - rlz.append(outputDict) - realizationArray.append(rlz) + outputDict[name] = abs(np.atleast_1d(output[cnt])) + outputDict['pivot_parameter'] = timeParameter + outputDict['total_distance_'+nameTarg[1]+'_'+nameFeat[1]] = distanceTotal[cnt] + outputDict['feature_beta_'+nameTarg[1]+'_'+nameFeat[1]] = featureBeta[cnt] + outputDict['target_beta_'+nameTarg[1]+'_'+nameFeat[1]] = targetBeta[cnt] + outputDict['feature_omega_'+nameTarg[1]+'_'+nameFeat[1]] = featureOmegaNormScaled[cnt] + outputDict['target_omega_'+nameTarg[1]+'_'+nameFeat[1]] = targetOmegaNorm[cnt] + outputDict['feature_D_'+nameTarg[1]+'_'+nameFeat[1]] = featureD[cnt] + outputDict['target_D_'+nameTarg[1]+'_'+nameFeat[1]] = targetD[cnt] + outputDict['process_time_'+nameTarg[1]+'_'+nameFeat[1]] = newfeatureData[1][cnt] + outputDict['standard_deviation_'+nameTarg[1]+'_'+nameFeat[1]] = sigma[cnt] + rlz.append(outputDict) + realizationArray.append(rlz) #--------------- for cnt in range(len(realizationArray[0])): out = {} diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv index f93b5c916f..c4d2ba4232 100644 --- a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv @@ -1,101 +1,50 @@ 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-0.4750000000000003,0.00120658782902,44.0357167442,-8.96022798672e-05,4.26023895104,0.248409416677,89.5779339625,-0.00277267748798,6.37493074725 -0.4800000000000003,0.00200560785636,44.0357167442,-8.86964203642e-05,4.26023895104,0.0696616165828,89.5779339625,-0.00292440131514,6.37493074725 -0.4850000000000003,0.00293042744372,44.0357167442,-8.85123865531e-05,4.26023895104,0.030694566771,89.5779339625,-0.00312384969114,6.37493074725 -0.4900000000000003,0.00351792539219,44.0357167442,-8.90557076899e-05,4.26023895104,0.0142117199018,89.5779339625,-0.00338938462777,6.37493074725 -0.49500000000000033,0.00355039112854,44.0357167442,-9.06229094189e-05,4.26023895104,0.00873543121424,89.5779339625,-0.00358403899994,6.37493074725 diff --git a/tests/framework/PostProcessors/Validation/test_validation_dss.xml b/tests/framework/PostProcessors/Validation/test_validation_dss.xml index 10f1440a35..7f89056225 100644 --- a/tests/framework/PostProcessors/Validation/test_validation_dss.xml +++ b/tests/framework/PostProcessors/Validation/test_validation_dss.xml @@ -3,7 +3,7 @@ framework/PostProcessors/Validation/test_validation_dss yoshrk - 2021-03-16 + 2022-05-13 PostProcessors.Validation This test checks the DSS PostProcessor with DSS metric @@ -15,7 +15,7 @@ DSS - mcRun1, mcRun2, PP2 + mcRun1, mcRun2, PP1, PP2, PP3 1 @@ -26,15 +26,37 @@ sigma,rho,beta,x2,y2,z2,time2,x0,y0,z0 + + outMC1|x1 + outMC2|x2 + dss + time1 + time2 + DataSynthesis + 1 + 1 + - outMC1|x1,outMC1|y1 - outMC2|x2,outMC2|y2 + outMC1|x1 + outMC2|x2 + dss + time1 + time2 + 0.0|0.5 + DataSynthesis + 1 + 1 + + + outMC1|y1 + outMC2|y2 dss time1 time2 + 0.5|1.0 DataSynthesis - 1,1 - 1,1 + 1 + 1 @@ -48,9 +70,24 @@ x0,y0,z0 OutputPlaceHolder + + + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 + + + pivot_parameter + + - dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1, + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 + + + pivot_parameter + + + + dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_deviation_y2_y1 @@ -74,10 +111,18 @@ + + csv + pp1_out + csv pp2_out + + csv + pp3_out + @@ -127,6 +172,13 @@ MC_external outMC2 + + outMC1 + outMC2 + pp1 + pp1_out + pp1_print + outMC1 outMC2 @@ -134,6 +186,13 @@ pp2_out pp2_print + + outMC1 + outMC2 + pp3 + pp3_out + pp3_print + diff --git a/tests/framework/PostProcessors/Validation/tests b/tests/framework/PostProcessors/Validation/tests index 0420a95157..5f664c9799 100644 --- a/tests/framework/PostProcessors/Validation/tests +++ b/tests/framework/PostProcessors/Validation/tests @@ -16,7 +16,7 @@ [./test_validation_dss] type = 'RavenFramework' input = 'test_validation_dss.xml' - csv = 'DSS/pp2_print_0.csv DSS/pp2_print_1.csv DSS/pp2_print_2.csv DSS/pp2_print_3.csv' + csv = 'DSS/pp1_print_0.csv DSS/pp2_print_0.csv DSS/pp3_print_0.csv' rel_err = 0.00001 zero_threshold = 1e-9 [../] From 95a2587806a084fa0980e9fa8105988c9a71fd11 Mon Sep 17 00:00:00 2001 From: yoshiurr-INL <78757932+yoshiurr-INL@users.noreply.github.com> Date: Fri, 13 May 2022 14:37:30 -0600 Subject: [PATCH 2/8] Add files via upload Uploading required test files that were not addable during local branch merging. --- .../Validation/gold/DSS/pp1_print_0.csv | 101 ++++++++++++++++++ .../Validation/gold/DSS/pp3_print_0.csv | 50 +++++++++ 2 files changed, 151 insertions(+) create mode 100644 tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv create mode 100644 tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv new file mode 100644 index 0000000000..6febf5adfc --- /dev/null +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv @@ -0,0 +1,101 @@ +pivot_parameter,dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 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+0.4550000000000003,0.933131516996,49.6436564943,-0.0128939722793,12.8456212641 +0.4600000000000003,1.98243419705,49.6436564943,-0.0130416841134,12.8456212641 +0.4650000000000003,6.17126535233,49.6436564943,-0.0132686262118,12.8456212641 +0.4700000000000003,82.2755374374,49.6436564943,-0.0135871354727,12.8456212641 +0.4750000000000003,35.222031831,49.6436564943,-0.0140151114837,12.8456212641 +0.4800000000000003,5.27029691584,49.6436564943,-0.0145783048945,12.8456212641 +0.4850000000000003,1.82409685136,49.6436564943,-0.015314125834,12.8456212641 +0.4900000000000003,1.07694611971,49.6436564943,-0.0159632992582,12.8456212641 From 18b0467dba40a2268e343f58df13c79d601acd82 Mon Sep 17 00:00:00 2001 From: yoshiurr-INL Date: Tue, 17 May 2022 14:36:58 -0600 Subject: [PATCH 3/8] Added capability to specify in ratios and raw values. --- doc/user_manual/PostProcessors/Validation.tex | 29 +++++- .../PostProcessors/Validations/PPDSS.py | 82 ++++++++++++---- .../Validation/gold/DSS/pp2_print_0.csv | 98 +++++++++---------- .../Validation/gold/DSS/pp3_print_0.csv | 97 +++++++++--------- .../Validation/test_validation_dss.xml | 8 +- 5 files changed, 192 insertions(+), 122 deletions(-) diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index d40898a939..f642aa6a70 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -108,8 +108,18 @@ \subsubsection{Validation PostProcessors} refer to \ref{sec:Metrics} for detailed descriptions about this metric. \item \xmlNode{pivotParameterFeature}, \xmlDesc{string, required field}, specifies the pivotParameter for a feature . The feature pivot parameter is the shared index of the output variables in the data object. \item \xmlNode{pivotParameterTarget}, \xmlDesc{string, required field}, specifies the pivotParameter for a target . The target pivot parameter is the shared index of the output variables in the data object. - \item \xmlNode{separateData}, \xmlDesc{string, optional field}, specifies the custom interval to apply DSS postprocessing. The string should contain three parts; start time, `|', and end time all in one. For example, 0.0|0.5. - The start and end time should be in ratios of the full interval. In this case 0.5 would be the midpoint time. This is node is not required and if not provided, the default is the full time interval. + \item \xmlNode{separateFeatureData}, \xmlDesc{string, optional field}, specifies the custom feature interval to apply DSS postprocessing. The string should contain three parts; start time, `|', and end time all in one. For example, 0.0|0.5. + The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This is node is not required and if not provided, the default is the full time interval. + the following attributes need to be specified: + \begin{itemize} + \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw_values'. The default is `ratio'. + \end{itemize} + \item \xmlNode{separateTargetData}, \xmlDesc{string, optional field}, specifies the custom target interval to apply DSS postprocessing. The string should contain three parts; start time, `|', and end time all in one. For example, 0.0|0.5. + The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This is node is not required and if not provided, the default is the full time interval. + the following attributes need to be specified: + \begin{itemize} + \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw_values'. The default is `ratio'. + \end{itemize} \item \xmlNode{multiOutput}, \xmlDesc{string, required field}, to extract raw values for the HistorySet. The user must use ‘raw values’ for the full set of metrics’ calculations to be dumped. \item \xmlNode{scale}, \xmlDesc{string, required field}, specifies the type of time scaling. The following are the options for scaling (specific definitions for each scaling type is provided in \ref{sec:dssdoc}): \begin{itemize} @@ -150,7 +160,20 @@ \subsubsection{Validation PostProcessors} dss time1 time2 - 0.0|0.5 + 0.0|0.5 + 0.0|0.5 + DataSynthesis + 1 + 1 + + + outMC1|x1 + outMC2|x2 + dss + time1 + time2 + 0.2475|0.495 + 0.3475|0.695 DataSynthesis 1 1 diff --git a/ravenframework/Models/PostProcessors/Validations/PPDSS.py b/ravenframework/Models/PostProcessors/Validations/PPDSS.py index 179483f22e..4fe1e53db4 100644 --- a/ravenframework/Models/PostProcessors/Validations/PPDSS.py +++ b/ravenframework/Models/PostProcessors/Validations/PPDSS.py @@ -57,14 +57,24 @@ class cls. pivotParameterTargetInput = InputData.parameterInputFactory("pivotParameterTarget", contentType=InputTypes.StringType, descr="""Pivot parameter for target inputs""") inputSpecification.addSub(pivotParameterTargetInput) - separateDataInput = InputData.parameterInputFactory("separateData", contentType=InputTypes.StringType, - descr="""Time points to separate data and conduct DSS postprocessing + separateFeatureDataInput = InputData.parameterInputFactory("separateFeatureData", contentType=InputTypes.StringType, + descr="""Time points to separate feature data and conduct DSS postprocessing on selected time interval. Values must be fractions of the full time interval. To distinguish strart and end, '|' should be placed between both values. Example: 0.0|0.4 To provide more than one selection for the same data set, '+' should be placed at the end of each interval to start defining the new interval. Example: 0.0|0.4+0.4|1.0 If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") - inputSpecification.addSub(separateDataInput) + separateFeatureDataInput.addParam("type", InputTypes.StringType) + inputSpecification.addSub(separateFeatureDataInput) + separateTargetDataInput = InputData.parameterInputFactory("separateTargetData", contentType=InputTypes.StringType, + descr="""Time points to separate target data and conduct DSS postprocessing + on selected time interval. Values must be fractions of the full time interval. + To distinguish strart and end, '|' should be placed between both values. Example: 0.0|0.4 + To provide more than one selection for the same data set, + '+' should be placed at the end of each interval to start defining the new interval. Example: 0.0|0.4+0.4|1.0 + If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") + separateTargetDataInput.addParam("type", InputTypes.StringType) + inputSpecification.addSub(separateTargetDataInput) scaleTypeInput = InputData.parameterInputFactory("scale", contentType=InputTypes.makeEnumType("scale","scaleType",['DataSynthesis','2_2_affine','dilation','beta_strain','omega_strain','identity']), descr="""Scaling type for the time transformation. Available types are DataSynthesis, 2_2_affine, dilation, beta_strain, omega_strain, and identity""") @@ -96,7 +106,10 @@ def __init__(self): self.pivotValuesFeature = [] # Feature pivot parameter values self.pivotParameterTarget = None # Target pivot parameter variable self.pivotValuesTarget = [] # Target pivot parameter values - self.separateData = None # String of data separation points. Default is None. + self.separateFeatureData = None # String of data separation points. Default is None. + self.separateFeatureType = 'ratio' # defines the type of feature time separation to apply. Options are ratio and raw_values + self.separateTargetData = None # String of data separation points. Default is None. + self.separateTargetType = 'ratio' # defines the type of target time separation to apply. Options are ratio and raw_values self.scaleType = None # Scaling type # assembler objects to be requested self.scaleRatioBeta = [] # Scaling ratio for the parameter of interest @@ -123,8 +136,18 @@ def _handleInput(self, paramInput): self.pivotParameterFeature = child.value elif child.getName() == 'pivotParameterTarget': self.pivotParameterTarget = child.value - elif child.getName() == 'separateData': - self.separateData = child.value + elif child.getName() == 'separateFeatureData': + if 'type' not in child.parameterValues.keys(): + self.raiseAnError(IOError, 'Tag separateFeatureData must have attribute "type"') + else: + self.separateFeatureType = child.parameterValues["type"] + self.separateFeatureData = child.value + elif child.getName() == 'separateTargetData': + if 'type' not in child.parameterValues.keys(): + self.raiseAnError(IOError, 'Tag separateTargetData must have attribute "type"') + else: + self.separateTargetType = child.parameterValues["type"] + self.separateTargetData = child.value elif child.getName() == 'scale': self.scaleType = child.value elif child.getName() == 'scaleBeta': @@ -161,9 +184,9 @@ def run(self, inputIn): if not isinstance(evaluation, list): self.raiseAnError(IOError,"The data type in evaluation is not list") if pivotParameterFeature and pivotParameterTarget: - if self.separateData == None: - if len(datasets[0][pivotParameterFeature]) != len(list(evaluation[0].values())[0]) and len(datasets[1][pivotParameterTarget]) != len(list(evaluation[1].values())[0]): - self.raiseAnError(RuntimeError, "The pivotParameterFeature value '{}' has size '{}' and validation output has size '{}' The pivotParameterTarget value '{}' has size '{}' and validation output has size '{}'.".format( len(datasets[0][self.pivotParameterFeature]), len(evaluation.values()[0]))) + if self.separateFeatureData == None and self.separateTargetData: + if len(datasets[0][pivotParameterFeature]) != len(list(evaluation[0].values())[0]) and len(datasets[1][pivotParameterTarget]) != len(list(evaluation[0].values())[0]): + self.raiseAnError(RuntimeError, "The pivotParameterFeature values has size '{}' and pivotParameterTarget values has size '{}'. The validation output has size '{}' and must match either pivot parameters.".format(len(datasets[0][self.pivotParameterFeature]), len(datasets[0][self.pivotParameterTarget]), len(evaluation[0].values()[0]))) return evaluation def _evaluate(self, datasets, **kwargs): @@ -218,41 +241,64 @@ def _evaluate(self, datasets, **kwargs): pivotFeatureData = np.transpose(pivotFeatureData)[0] pivotTargetData = self._getDataFromDatasets(datasets, names[1]+"|"+self.pivotParameterTarget, names)[0] pivotTargetData = np.transpose(pivotTargetData)[0] - if self.separateData != None: - separateTime = self.separateData + if self.separateFeatureData != None: + separateTime = self.separateFeatureData separateTime = separateTime.split("|") startTime = separateTime[0] endTime = separateTime[1] if startTime >= endTime: - self.raiseAnError(IOError, "Start time", startTime, "is equal or larger than end time", endTime) + self.raiseAnError(IOError, "Feature start time", startTime, "is equal or larger than end time", endTime) + if self.separateFeatureType == "ratio": + featureStartTime = pivotFeatureData[0]+float(startTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]) + featureEndTime = pivotFeatureData[0]+float(endTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]) + elif self.separateFeatureType == "raw_values": + featureStartTime = float(startTime) + featureEndTime = float(endTime) + else: + self.raiseAnError(IOError, 'separateFeatureData attribute "type" must be either "ratio" or "raw_values"') featureStartCount = 0 startFeatureLocation = None endFeatureLocation = None for i in range(len(pivotFeatureData)): - if pivotFeatureData[i] >= pivotFeatureData[0]+float(startTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]) and featureStartCount == 0: + if pivotFeatureData[i] >= featureStartTime and featureStartCount == 0: startFeatureLocation = i featureStartCount += 1 - if pivotFeatureData[i] > pivotFeatureData[0]+float(endTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]): + if pivotFeatureData[i] > featureEndTime: endFeatureLocation = i-1 break - elif pivotFeatureData[i] == pivotFeatureData[0]+float(endTime)*(pivotFeatureData[len(pivotFeatureData)-1]-pivotFeatureData[0]): + elif pivotFeatureData[i] == featureEndTime: endFeatureLocation = i break pivotFeature = pivotFeatureData[startFeatureLocation:endFeatureLocation] featData = np.zeros((np.shape(featDataSet)[0],endFeatureLocation-startFeatureLocation)) for i in range(len(featDataSet)): featData[i] = featDataSet[i][startFeatureLocation:endFeatureLocation] + if self.separateTargetData != None: + separateTime = self.separateTargetData + separateTime = separateTime.split("|") + startTime = separateTime[0] + endTime = separateTime[1] + if startTime >= endTime: + self.raiseAnError(IOError, "Target start time", startTime, "is equal or larger than end time", endTime) + if self.separateTargetType == "ratio": + targetStartTime = pivotTargetData[0]+float(startTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]) + targetEndTime = pivotTargetData[0]+float(endTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]) + elif self.separateTargetType == "raw_values": + targetStartTime = float(startTime) + targetEndTime = float(endTime) + else: + self.raiseAnError(IOError, 'separateTargetData attribute "type" must be either "ratio" or "raw_values"') targetStartCount = 0 startTargetLocation = None endTargetLocation = None for j in range(len(pivotTargetData)): - if pivotTargetData[j] >= pivotTargetData[0]+float(startTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]) and targetStartCount == 0: + if pivotTargetData[j] >= targetStartTime and targetStartCount == 0: startTargetLocation = j targetStartCount += 1 - if pivotTargetData[j] > pivotTargetData[0]+float(endTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]): + if pivotTargetData[j] > targetEndTime: endTargetLocation = j-1 break - elif pivotTargetData[j] == pivotTargetData[0]+float(endTime)*(pivotTargetData[len(pivotTargetData)-1]-pivotTargetData[0]): + elif pivotTargetData[j] == targetEndTime: endTargetLocation = j pivotTarget = pivotTargetData[startTargetLocation:endTargetLocation] targData = np.zeros((np.shape(targDataSet)[0],endTargetLocation-startTargetLocation)) diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv index c4d2ba4232..be3fdc1c97 100644 --- a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv @@ -1,50 +1,50 @@ pivot_parameter,dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 -0.0,0.249793446773,26.5220630729,0.0130858808348,4.94237366266 -0.005,0.193209781996,26.5220630729,0.0143767025201,4.94237366266 -0.01,0.118937337698,26.5220630729,0.0151300964405,4.94237366266 -0.015,0.0797424135222,26.5220630729,0.0156975902671,4.94237366266 -0.02,0.0594772161934,26.5220630729,0.0161218912386,4.94237366266 -0.025,0.0469669492784,26.5220630729,0.0164409763216,4.94237366266 -0.030000000000000002,0.0383464315575,26.5220630729,0.0166866460123,4.94237366266 -0.035,0.0319342466539,26.5220630729,0.0168847622387,4.94237366266 -0.04,0.0268750851535,26.5220630729,0.0170562815674,4.94237366266 -0.045,0.0226764696692,26.5220630729,0.0172185596209,4.94237366266 -0.049999999999999996,0.0190188046729,26.5220630729,0.0173866783976,4.94237366266 -0.05499999999999999,0.0156610629855,26.5220630729,0.01757472279,4.94237366266 -0.05999999999999999,0.0123726693993,26.5220630729,0.0177970365363,4.94237366266 -0.06499999999999999,0.00881922743606,26.5220630729,0.0180695568904,4.94237366266 -0.06999999999999999,0.00384516964443,26.5220630729,0.018411393471,4.94237366266 -0.075,0.00619725096002,26.5220630729,0.0188469129669,4.94237366266 -0.08,0.00907820498199,26.5220630729,0.0194087650247,4.94237366266 -0.085,0.01075250319,26.5220630729,0.0201426263117,4.94237366266 -0.09000000000000001,0.0115374262683,26.5220630729,0.021115151441,4.94237366266 -0.09500000000000001,0.0111588855706,26.5220630729,0.0224281984515,4.94237366266 -0.10000000000000002,0.00833570515033,26.5220630729,0.0242461878855,4.94237366266 -0.10500000000000002,0.00848545141676,26.5220630729,0.0268535214762,4.94237366266 -0.11000000000000003,0.0195649264562,26.5220630729,0.0307894546457,4.94237366266 -0.11500000000000003,0.0324814900327,26.5220630729,0.0372179556658,4.94237366266 -0.12000000000000004,0.051414228881,26.5220630729,0.0492061458702,4.94237366266 -0.12500000000000003,0.0875006389452,26.5220630729,0.0783219658949,4.94237366266 -0.13000000000000003,0.246431167025,26.5220630729,0.240798767035,4.94237366266 -0.13500000000000004,0.118378694733,26.5220630729,-0.180259400428,4.94237366266 -0.14000000000000004,0.00936497328804,26.5220630729,-0.0612226817548,4.94237366266 -0.14500000000000005,0.0599352640357,26.5220630729,-0.0354697039019,4.94237366266 -0.15000000000000005,0.108773255052,26.5220630729,-0.0243147967168,4.94237366266 -0.15500000000000005,0.173682419789,26.5220630729,-0.0181145876917,4.94237366266 -0.16000000000000006,0.279024924942,26.5220630729,-0.0141557614671,4.94237366266 -0.16500000000000006,0.491384179865,26.5220630729,-0.0113697310779,4.94237366266 -0.17000000000000007,1.09821009967,26.5220630729,-0.00924315713495,4.94237366266 -0.17500000000000007,5.21911843116,26.5220630729,-0.00749242719014,4.94237366266 -0.18000000000000008,34.1561115738,26.5220630729,-0.00594260234102,4.94237366266 -0.18500000000000008,1.0945438435,26.5220630729,-0.00447518089902,4.94237366266 -0.19000000000000009,0.222907562006,26.5220630729,-0.003002824792,4.94237366266 -0.1950000000000001,0.0499002371896,26.5220630729,-0.00145577069112,4.94237366266 -0.2000000000000001,0.00431277757541,26.5220630729,0.000226428227803,4.94237366266 -0.2050000000000001,0.0246533322276,26.5220630729,0.00210081823081,4.94237366266 -0.2100000000000001,0.0326028403985,26.5220630729,0.00422627107619,4.94237366266 -0.2150000000000001,0.035309975401,26.5220630729,0.00666920205121,4.94237366266 -0.2200000000000001,0.0355672105924,26.5220630729,0.00951096793374,4.94237366266 -0.22500000000000012,0.0345658819548,26.5220630729,0.0128585625854,4.94237366266 -0.23000000000000012,0.0328157503949,26.5220630729,0.0168612917199,4.94237366266 -0.23500000000000013,0.0275795844202,26.5220630729,0.021738436959,4.94237366266 -0.24000000000000013,0.0222637461126,26.5220630729,0.0254877628256,4.94237366266 +0.2500000000000001,0.0,31.7398719204,-0.0020455233141,6.91668255155 +0.2550000000000001,0.0,31.7398719204,-0.00239971845471,6.91668255155 +0.2600000000000001,0.0,31.7398719204,-0.00331677952163,6.91668255155 +0.2650000000000001,0.0,31.7398719204,-0.0049465215992,6.91668255155 +0.27000000000000013,0.0,31.7398719204,-0.00866656133261,6.91668255155 +0.27500000000000013,0.0,31.7398719204,-0.0257386944523,6.91668255155 +0.28000000000000014,0.0,31.7398719204,0.0344198285928,6.91668255155 +0.28500000000000014,0.0,31.7398719204,0.0111804199039,6.91668255155 +0.29000000000000015,0.0,31.7398719204,0.00698967049847,6.91668255155 +0.29500000000000015,0.0,31.7398719204,0.00525140953615,6.91668255155 +0.30000000000000016,0.0,31.7398719204,0.00431368613198,6.91668255155 +0.30500000000000016,0.0,31.7398719204,0.00373926813816,6.91668255155 +0.31000000000000016,0.0,31.7398719204,0.00336301900639,6.91668255155 +0.31500000000000017,0.0,31.7398719204,0.00310926463252,6.91668255155 +0.3200000000000002,0.0,31.7398719204,0.00293901691928,6.91668255155 +0.3250000000000002,0.0,31.7398719204,0.00283080844019,6.91668255155 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+0.39500000000000024,0.127763495946,31.7398719204,0.0215026884574,6.91668255155 +0.40000000000000024,0.134277864168,31.7398719204,0.194781855873,6.91668255155 +0.40500000000000025,0.122180961232,31.7398719204,-0.0276724826571,6.91668255155 +0.41000000000000025,0.0838427756195,31.7398719204,-0.0130139894463,6.91668255155 +0.41500000000000026,0.0633996654987,31.7398719204,-0.00857973241051,6.91668255155 +0.42000000000000026,0.0489384999525,31.7398719204,-0.00645567612071,6.91668255155 +0.42500000000000027,0.037438182394,31.7398719204,-0.00522026981231,6.91668255155 +0.43000000000000027,0.0273442529376,31.7398719204,-0.00441985090751,6.91668255155 +0.4350000000000003,0.0171428145705,31.7398719204,-0.00386483657795,6.91668255155 +0.4400000000000003,0.0057463020243,31.7398719204,-0.00346208979338,6.91668255155 +0.4450000000000003,0.0164943134485,31.7398719204,-0.00316060876391,6.91668255155 +0.4500000000000003,0.0210192392535,31.7398719204,-0.00293021811989,6.91668255155 +0.4550000000000003,0.0233569794738,31.7398719204,-0.00275202251782,6.91668255155 +0.4600000000000003,0.0239098840743,31.7398719204,-0.00261369133406,6.91668255155 +0.4650000000000003,0.0221323360034,31.7398719204,-0.00250695035519,6.91668255155 +0.4700000000000003,0.0155416881526,31.7398719204,-0.00242617022149,6.91668255155 +0.4750000000000003,0.0166729829524,31.7398719204,-0.0023675397721,6.91668255155 +0.4800000000000003,0.0354140749232,31.7398719204,-0.00232857223888,6.91668255155 +0.4850000000000003,0.047103942208,31.7398719204,-0.00230781444373,6.91668255155 +0.4900000000000003,0.0501634552771,31.7398719204,-0.00233655040779,6.91668255155 diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv index 7fb65f36ad..615773b64c 100644 --- a/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv @@ -1,50 +1,49 @@ pivot_parameter,dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_deviation_y2_y1 -0.2500000000000001,1.12351233783e-08,49.6436564943,0.0132310015451,12.8456212641 -0.2550000000000001,7.55650415433e-09,49.6436564943,0.0126373015391,12.8456212641 -0.2600000000000001,0.0,49.6436564943,0.0119568146027,12.8456212641 -0.2650000000000001,6.52656287335e-09,49.6436564943,0.0113700874488,12.8456212641 -0.27000000000000013,0.0,49.6436564943,0.0108821587873,12.8456212641 -0.27500000000000013,8.30895535352e-09,49.6436564943,0.0104962596247,12.8456212641 -0.28000000000000014,0.0,49.6436564943,0.0102151963764,12.8456212641 -0.28500000000000014,7.18558134706e-09,49.6436564943,0.0100427603628,12.8456212641 -0.29000000000000015,0.0,49.6436564943,0.00998514155388,12.8456212641 -0.29500000000000015,6.08694526288e-09,49.6436564943,0.0100524837798,12.8456212641 -0.30000000000000016,0.0,49.6436564943,0.0102608474897,12.8456212641 -0.30500000000000016,5.03122983711e-09,49.6436564943,0.0106350191477,12.8456212641 -0.31000000000000016,0.0,49.6436564943,0.0112129264525,12.8456212641 -0.31500000000000017,3.39724011817e-09,49.6436564943,0.0120530847218,12.8456212641 -0.3200000000000002,0.0,49.6436564943,0.0132479831366,12.8456212641 -0.3250000000000002,1.43570043546e-09,49.6436564943,0.0149498825133,12.8456212641 -0.3300000000000002,0.0,49.6436564943,0.0174249365187,12.8456212641 -0.3350000000000002,0.0,49.6436564943,0.0211800089223,12.8456212641 -0.3400000000000002,0.0,49.6436564943,0.0273088504369,12.8456212641 -0.3450000000000002,0.0,49.6436564943,0.038679334438,12.8456212641 -0.3500000000000002,0.00555137225846,49.6436564943,0.0659670780311,12.8456212641 -0.3550000000000002,0.0405597419707,49.6436564943,0.210731239572,12.8456212641 -0.3600000000000002,0.0331910215341,49.6436564943,-0.191811341029,12.8456212641 -0.3650000000000002,0.0158705969549,49.6436564943,-0.0685646579914,12.8456212641 -0.3700000000000002,0.0177079679127,49.6436564943,-0.0429596776802,12.8456212641 -0.3750000000000002,0.0174287060661,49.6436564943,-0.0320105156903,12.8456212641 -0.3800000000000002,0.0165048677827,49.6436564943,-0.0260065976579,12.8456212641 -0.38500000000000023,0.0148661109296,49.6436564943,-0.0222614274324,12.8456212641 -0.39000000000000024,0.0118561789072,49.6436564943,-0.0197344855044,12.8456212641 -0.39500000000000024,0.00372757285987,49.6436564943,-0.0179384465539,12.8456212641 -0.40000000000000024,0.0137290191215,49.6436564943,-0.0166150785905,12.8456212641 -0.40500000000000025,0.0227429535783,49.6436564943,-0.0156152119137,12.8456212641 -0.41000000000000025,0.032585684904,49.6436564943,-0.0148471181433,12.8456212641 -0.41500000000000026,0.0445614186428,49.6436564943,-0.014251767338,12.8456212641 -0.42000000000000026,0.0600028819474,49.6436564943,-0.0137899506847,12.8456212641 -0.42500000000000027,0.0808034757661,49.6436564943,-0.0134351362462,12.8456212641 -0.43000000000000027,0.109991883168,49.6436564943,-0.0131693058852,12.8456212641 -0.4350000000000003,0.152792389188,49.6436564943,-0.0129804441206,12.8456212641 -0.4400000000000003,0.218912226193,49.6436564943,-0.0128609993941,12.8456212641 -0.4450000000000003,0.328078117985,49.6436564943,-0.0128069568681,12.8456212641 -0.4500000000000003,0.525417692487,49.6436564943,-0.0128173296754,12.8456212641 -0.4550000000000003,0.933131516996,49.6436564943,-0.0128939722793,12.8456212641 -0.4600000000000003,1.98243419705,49.6436564943,-0.0130416841134,12.8456212641 -0.4650000000000003,6.17126535233,49.6436564943,-0.0132686262118,12.8456212641 -0.4700000000000003,82.2755374374,49.6436564943,-0.0135871354727,12.8456212641 -0.4750000000000003,35.222031831,49.6436564943,-0.0140151114837,12.8456212641 -0.4800000000000003,5.27029691584,49.6436564943,-0.0145783048945,12.8456212641 -0.4850000000000003,1.82409685136,49.6436564943,-0.015314125834,12.8456212641 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b/tests/framework/PostProcessors/Validation/test_validation_dss.xml index 7f89056225..6fb924b4c4 100644 --- a/tests/framework/PostProcessors/Validation/test_validation_dss.xml +++ b/tests/framework/PostProcessors/Validation/test_validation_dss.xml @@ -9,7 +9,7 @@ This test checks the DSS PostProcessor with DSS metric - no writing directly to files from postprocessors + no writing directly to files from postprocessors @@ -42,7 +42,8 @@ dss time1 time2 - 0.0|0.5 + 0.5|1.0 + 0.5|1.0 DataSynthesis 1 1 @@ -53,7 +54,8 @@ dss time1 time2 - 0.5|1.0 + 0.2475|0.495 + 0.3475|0.695 DataSynthesis 1 1 From 7c94021e52d660ea0ad4e731c1e02a83ed133850 Mon Sep 17 00:00:00 2001 From: yoshiurr-INL Date: Tue, 17 May 2022 16:26:43 -0600 Subject: [PATCH 4/8] Fixed doc issues in user manual --- doc/user_manual/PostProcessors/Validation.tex | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index f642aa6a70..440d803302 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -112,15 +112,15 @@ \subsubsection{Validation PostProcessors} The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This is node is not required and if not provided, the default is the full time interval. the following attributes need to be specified: \begin{itemize} - \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw_values'. The default is `ratio'. + \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw\_values'. The default is `ratio'. \end{itemize} \item \xmlNode{separateTargetData}, \xmlDesc{string, optional field}, specifies the custom target interval to apply DSS postprocessing. The string should contain three parts; start time, `|', and end time all in one. For example, 0.0|0.5. The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This is node is not required and if not provided, the default is the full time interval. the following attributes need to be specified: \begin{itemize} - \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw_values'. The default is `ratio'. + \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw\_values'. The default is `ratio'. \end{itemize} - \item \xmlNode{multiOutput}, \xmlDesc{string, required field}, to extract raw values for the HistorySet. The user must use ‘raw values’ for the full set of metrics’ calculations to be dumped. + \item \xmlNode{multiOutput}, \xmlDesc{string, required field}, to extract raw values for the HistorySet. The user must use `raw values' for the full set of metrics’ calculations to be dumped. \item \xmlNode{scale}, \xmlDesc{string, required field}, specifies the type of time scaling. The following are the options for scaling (specific definitions for each scaling type is provided in \ref{sec:dssdoc}): \begin{itemize} \item \textbf{DataSynthesis}, calculating the distortion for two data sets without applying other scaling ratios. From c70a712939833a4273bbdd71ef800544af0b3c8a Mon Sep 17 00:00:00 2001 From: yoshiurr-INL Date: Wed, 18 May 2022 11:18:52 -0600 Subject: [PATCH 5/8] Added content to specify pivot paramter format for output DataObjects --- doc/user_manual/PostProcessors/Validation.tex | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index 440d803302..e3a7be9fe4 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -134,6 +134,8 @@ \subsubsection{Validation PostProcessors} \item \xmlNode{scaleOmega}, \xmlDesc{float, required field}, specifies the agents of change scaling ratio between the feature and target. \end{itemize} +The output \textbf{DataObjects} pivot parameter must be named `pivot\_parameter' and this array is assigned within the post-processor algorithm. + \textbf{Example:} \begin{lstlisting}[style=XML,morekeywords={subType}] @@ -181,5 +183,34 @@ \subsubsection{Validation PostProcessors} ... ... + + ... + + + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 + + + pivot_parameter + + + + + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 + + + pivot_parameter + + + + + dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_deviation_y2_y1 + + + pivot_parameter + + + ... + + ... \end{lstlisting} From d65bf9dcabea8c08c59d28719863d1ca5b8beb32 Mon Sep 17 00:00:00 2001 From: yoshiurr-INL Date: Wed, 25 May 2022 15:59:36 -0600 Subject: [PATCH 6/8] Changed files based on review comments. --- doc/user_manual/PostProcessors/Validation.tex | 44 +++++++++++++------ .../PostProcessors/Validations/PPDSS.py | 20 +++------ .../Validation/gold/DSS/pp1_print_0.csv | 2 +- .../Validation/gold/DSS/pp2_print_0.csv | 2 +- .../Validation/gold/DSS/pp3_print_0.csv | 2 +- .../Validation/test_validation_dss.xml | 10 +++-- 6 files changed, 47 insertions(+), 33 deletions(-) diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index e3a7be9fe4..d9f309830c 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -89,13 +89,12 @@ \subsubsection{Validation PostProcessors} % \begin{itemize} - \item \xmlNode{Features}, \xmlDesc{comma separated string, required field}, specifies the names of the features. To enable user defined - time interval selection, this postprocessor will only consider the first feature name provided. If user provides more than one, - it will ignore the other inputs. - \item \xmlNode{Targets}, \xmlDesc{comma separated string, required field}, contains a comma separated list of - targets. \nb Each target is paired with a feature listed in xml node \xmlNode{Features}. To enable user defined - time interval selection, this postprocessor will only consider the first feature name provided. If user provides more than one, - it will ignore the other inputs. + \item \xmlNode{Features}, \xmlDesc{comma separated string, required field}, specifies the names of the features. Make sure the feature data are normalized by a nominal value. + To enable user defined time interval selection, this postprocessor will only consider the first feature name provided. If user provides more than one, + it will output an error. + \item \xmlNode{Targets}, \xmlDesc{comma separated string, required field}, specifies the names of the targets. Make sure the feature data are normalized by a nominal value. \nb Each target is paired with a feature listed in xml node \xmlNode{Features}. + To enable user defined time interval selection, this postprocessor will only consider the first feature name provided. If user provides more than one, + itt will output an error. \item \xmlNode{pivotParameter}, \xmlDesc{string, required field if HistorySet is used}, specifies the pivotParameter for a . The pivot parameter is the shared index of the output variables in the data object. \item \xmlNode{Metric}, \xmlDesc{string, required field}, specifies the \textbf{Metric} name that is defined via @@ -109,18 +108,17 @@ \subsubsection{Validation PostProcessors} \item \xmlNode{pivotParameterFeature}, \xmlDesc{string, required field}, specifies the pivotParameter for a feature . The feature pivot parameter is the shared index of the output variables in the data object. \item \xmlNode{pivotParameterTarget}, \xmlDesc{string, required field}, specifies the pivotParameter for a target . The target pivot parameter is the shared index of the output variables in the data object. \item \xmlNode{separateFeatureData}, \xmlDesc{string, optional field}, specifies the custom feature interval to apply DSS postprocessing. The string should contain three parts; start time, `|', and end time all in one. For example, 0.0|0.5. - The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This is node is not required and if not provided, the default is the full time interval. + The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This node is not required and if not provided, the default is the full time interval. the following attributes need to be specified: \begin{itemize} - \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw\_values'. The default is `ratio'. + \item \xmlAttr{type}, \xmlDesc{optional string attribute}, options are `ratio' or `raw\_values'. The default is `ratio'. \end{itemize} \item \xmlNode{separateTargetData}, \xmlDesc{string, optional field}, specifies the custom target interval to apply DSS postprocessing. The string should contain three parts; start time, `|', and end time all in one. For example, 0.0|0.5. - The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This is node is not required and if not provided, the default is the full time interval. + The start and end time should be in ratios or raw values of the full interval. In this case 0.5 would be either the midpoint time or time 0.5 of the given time units. This node is not required and if not provided, the default is the full time interval. the following attributes need to be specified: \begin{itemize} - \item \xmlAttr{type}, \xmlDesc{required string attribute}, options are `ratio' or `raw\_values'. The default is `ratio'. + \item \xmlAttr{type}, \xmlDesc{optional string attribute}, options are `ratio' or `raw\_values'. The default is `ratio'. \end{itemize} - \item \xmlNode{multiOutput}, \xmlDesc{string, required field}, to extract raw values for the HistorySet. The user must use `raw values' for the full set of metrics’ calculations to be dumped. \item \xmlNode{scale}, \xmlDesc{string, required field}, specifies the type of time scaling. The following are the options for scaling (specific definitions for each scaling type is provided in \ref{sec:dssdoc}): \begin{itemize} \item \textbf{DataSynthesis}, calculating the distortion for two data sets without applying other scaling ratios. @@ -134,7 +132,27 @@ \subsubsection{Validation PostProcessors} \item \xmlNode{scaleOmega}, \xmlDesc{float, required field}, specifies the agents of change scaling ratio between the feature and target. \end{itemize} -The output \textbf{DataObjects} pivot parameter must be named `pivot\_parameter' and this array is assigned within the post-processor algorithm. +The output \textbf{DataObjects} has required and optional components to provide the user the flexibility to obtain desired postprocessed data. The following are information about DSS output \textbf{DataObjects}: +\begin{itemize} + \item \xmlNode{Output}, \xmlDesc{string, required field}, specifies the string of postprocessed results to output. The following is the list of DSS output names: + \begin{itemize} + \item \textbf{pivot_parameter}, provides the pivot parameter used to postprocess feature and target input data. + \item \textbf{total_distance_targetName_featureName}, provides the total metric distance of the whole time interval. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{feature_beta_targetName_featureName}, provides the normalized feature data provided from \textbf{DataObjects} input. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{target_beta_targetName_featureName}, provides the normalized target data provided from \textbf{DataObjects} input. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{feature_omega_targetName_featureName}, provides the normalized feature first order derivative data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{target_omega_targetName_featureName}, provides the normalized target first order derivative data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{feature_D_targetName_featureName}, provides the feature temporal displacement rate (second order term) data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{target_D_targetName_featureName}, provides the target temporal displacement rate (second order term) data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{process_time_targetName_featureName}, provides the shared process time data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{standard_error_targetName_featureName}, provides the standard error of the overall transient data. `targetName' and `featureName' are the string names of the input target and feature. + \end{itemize} + \item \xmlNode{Options} + \begin{itemize} + \item \xmlNode{pivotParameter} + \end{itemize} +\end{itemize} +pivot parameter must be named `pivot\_parameter' and this array is assigned within the post-processor algorithm. \textbf{Example:} \begin{lstlisting}[style=XML,morekeywords={subType}] diff --git a/ravenframework/Models/PostProcessors/Validations/PPDSS.py b/ravenframework/Models/PostProcessors/Validations/PPDSS.py index 4fe1e53db4..a8a222959f 100644 --- a/ravenframework/Models/PostProcessors/Validations/PPDSS.py +++ b/ravenframework/Models/PostProcessors/Validations/PPDSS.py @@ -61,19 +61,17 @@ class cls. descr="""Time points to separate feature data and conduct DSS postprocessing on selected time interval. Values must be fractions of the full time interval. To distinguish strart and end, '|' should be placed between both values. Example: 0.0|0.4 - To provide more than one selection for the same data set, - '+' should be placed at the end of each interval to start defining the new interval. Example: 0.0|0.4+0.4|1.0 If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") - separateFeatureDataInput.addParam("type", InputTypes.StringType) + separateFeatureDataInput.addParam("type", InputTypes.StringType, descr="""Specifies what type of user defined time intervals have been selected from + the feature pivot parameter. Options are currently `ratio' or `raw_values'""") inputSpecification.addSub(separateFeatureDataInput) separateTargetDataInput = InputData.parameterInputFactory("separateTargetData", contentType=InputTypes.StringType, descr="""Time points to separate target data and conduct DSS postprocessing on selected time interval. Values must be fractions of the full time interval. To distinguish strart and end, '|' should be placed between both values. Example: 0.0|0.4 - To provide more than one selection for the same data set, - '+' should be placed at the end of each interval to start defining the new interval. Example: 0.0|0.4+0.4|1.0 If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") - separateTargetDataInput.addParam("type", InputTypes.StringType) + separateTargetDataInput.addParam("type", InputTypes.StringType, descr="""Specifies what type of user defined time intervals have been selected from + the target pivot parameter. Options are currently `ratio' or `raw_values'""") inputSpecification.addSub(separateTargetDataInput) scaleTypeInput = InputData.parameterInputFactory("scale", contentType=InputTypes.makeEnumType("scale","scaleType",['DataSynthesis','2_2_affine','dilation','beta_strain','omega_strain','identity']), descr="""Scaling type for the time transformation. Available types are DataSynthesis, @@ -137,15 +135,11 @@ def _handleInput(self, paramInput): elif child.getName() == 'pivotParameterTarget': self.pivotParameterTarget = child.value elif child.getName() == 'separateFeatureData': - if 'type' not in child.parameterValues.keys(): - self.raiseAnError(IOError, 'Tag separateFeatureData must have attribute "type"') - else: + if 'type' in child.parameterValues.keys(): self.separateFeatureType = child.parameterValues["type"] self.separateFeatureData = child.value elif child.getName() == 'separateTargetData': - if 'type' not in child.parameterValues.keys(): - self.raiseAnError(IOError, 'Tag separateTargetData must have attribute "type"') - else: + if 'type' in child.parameterValues.keys(): self.separateTargetType = child.parameterValues["type"] self.separateTargetData = child.value elif child.getName() == 'scale': @@ -455,7 +449,7 @@ def _evaluate(self, datasets, **kwargs): outputDict['feature_D_'+nameTarg[1]+'_'+nameFeat[1]] = featureD[cnt] outputDict['target_D_'+nameTarg[1]+'_'+nameFeat[1]] = targetD[cnt] outputDict['process_time_'+nameTarg[1]+'_'+nameFeat[1]] = newfeatureData[1][cnt] - outputDict['standard_deviation_'+nameTarg[1]+'_'+nameFeat[1]] = sigma[cnt] + outputDict['standard_error_'+nameTarg[1]+'_'+nameFeat[1]] = sigma[cnt] rlz.append(outputDict) realizationArray.append(rlz) #--------------- diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv index 6febf5adfc..5b92801824 100644 --- a/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv @@ -1,4 +1,4 @@ -pivot_parameter,dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 +pivot_parameter,dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_error_x2_x1 0.0,0.0756901816043,24.887493332,-0.000351283270388,2.59700011532 0.005,0.0510745233206,24.887493332,-0.000385934668242,2.59700011532 0.01,0.0256194405153,24.887493332,-0.000406159113474,2.59700011532 diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv index be3fdc1c97..03e5befbab 100644 --- a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv @@ -1,4 +1,4 @@ -pivot_parameter,dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 +pivot_parameter,dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_error_x2_x1 0.2500000000000001,0.0,31.7398719204,-0.0020455233141,6.91668255155 0.2550000000000001,0.0,31.7398719204,-0.00239971845471,6.91668255155 0.2600000000000001,0.0,31.7398719204,-0.00331677952163,6.91668255155 diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv index 615773b64c..1e23b4a160 100644 --- a/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv @@ -1,4 +1,4 @@ -pivot_parameter,dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_deviation_y2_y1 +pivot_parameter,dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_error_y2_y1 0.2500000000000001,9.7025440085e-09,45.9392389813,0.0136477608587,11.6635977182 0.2550000000000001,6.52926849278e-09,45.9392389813,0.0130353600759,11.6635977182 0.2600000000000001,0.0,45.9392389813,0.0123334386874,11.6635977182 diff --git a/tests/framework/PostProcessors/Validation/test_validation_dss.xml b/tests/framework/PostProcessors/Validation/test_validation_dss.xml index 6fb924b4c4..eedbef4a3d 100644 --- a/tests/framework/PostProcessors/Validation/test_validation_dss.xml +++ b/tests/framework/PostProcessors/Validation/test_validation_dss.xml @@ -9,7 +9,9 @@ This test checks the DSS PostProcessor with DSS metric - no writing directly to files from postprocessors + no writing directly to files from postprocessors + accepted format to merge with RAVEN devel + added capability to postprocess based on user defined time segmentation @@ -74,7 +76,7 @@ - dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_error_x2_x1 pivot_parameter @@ -82,7 +84,7 @@ - dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1 + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_error_x2_x1 pivot_parameter @@ -90,7 +92,7 @@ - dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_deviation_y2_y1 + dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_error_y2_y1 pivot_parameter From d444b2f2f32a163c3230e70f6e5d0c0a8caf75da Mon Sep 17 00:00:00 2001 From: yoshiurr-INL Date: Wed, 25 May 2022 16:42:07 -0600 Subject: [PATCH 7/8] Forgot to add backslash to underscore text. --- doc/user_manual/PostProcessors/Validation.tex | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index d9f309830c..afc3a87ca1 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -136,16 +136,16 @@ \subsubsection{Validation PostProcessors} \begin{itemize} \item \xmlNode{Output}, \xmlDesc{string, required field}, specifies the string of postprocessed results to output. The following is the list of DSS output names: \begin{itemize} - \item \textbf{pivot_parameter}, provides the pivot parameter used to postprocess feature and target input data. - \item \textbf{total_distance_targetName_featureName}, provides the total metric distance of the whole time interval. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{feature_beta_targetName_featureName}, provides the normalized feature data provided from \textbf{DataObjects} input. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{target_beta_targetName_featureName}, provides the normalized target data provided from \textbf{DataObjects} input. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{feature_omega_targetName_featureName}, provides the normalized feature first order derivative data. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{target_omega_targetName_featureName}, provides the normalized target first order derivative data. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{feature_D_targetName_featureName}, provides the feature temporal displacement rate (second order term) data. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{target_D_targetName_featureName}, provides the target temporal displacement rate (second order term) data. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{process_time_targetName_featureName}, provides the shared process time data. `targetName' and `featureName' are the string names of the input target and feature. - \item \textbf{standard_error_targetName_featureName}, provides the standard error of the overall transient data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{pivot\_parameter}, provides the pivot parameter used to postprocess feature and target input data. + \item \textbf{total\_distance\_targetName\_featureName}, provides the total metric distance of the whole time interval. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{feature\_beta\_targetName\_featureName}, provides the normalized feature data provided from \textbf{DataObjects} input. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{target\_beta\_targetName\_featureName}, provides the normalized target data provided from \textbf{DataObjects} input. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{feature\_omega\_targetName\_featureName}, provides the normalized feature first order derivative data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{target\_omega\_targetName\_featureName}, provides the normalized target first order derivative data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{feature\_D\_targetName\_featureName}, provides the feature temporal displacement rate (second order term) data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{target\_D\_targetName\_featureName}, provides the target temporal displacement rate (second order term) data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{process\_time\_targetName\_featureName}, provides the shared process time data. `targetName' and `featureName' are the string names of the input target and feature. + \item \textbf{standard\_error\_targetName\_featureName}, provides the standard error of the overall transient data. `targetName' and `featureName' are the string names of the input target and feature. \end{itemize} \item \xmlNode{Options} \begin{itemize} From 08e843a79cff194026d3537a05dc37ef24f73d30 Mon Sep 17 00:00:00 2001 From: yoshiurr-INL Date: Fri, 10 Jun 2022 11:10:19 -0600 Subject: [PATCH 8/8] Made user manual edits and docstring edits according to review. --- doc/user_manual/PostProcessors/Validation.tex | 6 +----- ravenframework/Models/PostProcessors/Validations/PPDSS.py | 8 ++++---- 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index afc3a87ca1..96825d30c1 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -94,7 +94,7 @@ \subsubsection{Validation PostProcessors} it will output an error. \item \xmlNode{Targets}, \xmlDesc{comma separated string, required field}, specifies the names of the targets. Make sure the feature data are normalized by a nominal value. \nb Each target is paired with a feature listed in xml node \xmlNode{Features}. To enable user defined time interval selection, this postprocessor will only consider the first feature name provided. If user provides more than one, - itt will output an error. + it will output an error. \item \xmlNode{pivotParameter}, \xmlDesc{string, required field if HistorySet is used}, specifies the pivotParameter for a . The pivot parameter is the shared index of the output variables in the data object. \item \xmlNode{Metric}, \xmlDesc{string, required field}, specifies the \textbf{Metric} name that is defined via @@ -147,10 +147,6 @@ \subsubsection{Validation PostProcessors} \item \textbf{process\_time\_targetName\_featureName}, provides the shared process time data. `targetName' and `featureName' are the string names of the input target and feature. \item \textbf{standard\_error\_targetName\_featureName}, provides the standard error of the overall transient data. `targetName' and `featureName' are the string names of the input target and feature. \end{itemize} - \item \xmlNode{Options} - \begin{itemize} - \item \xmlNode{pivotParameter} - \end{itemize} \end{itemize} pivot parameter must be named `pivot\_parameter' and this array is assigned within the post-processor algorithm. diff --git a/ravenframework/Models/PostProcessors/Validations/PPDSS.py b/ravenframework/Models/PostProcessors/Validations/PPDSS.py index a8a222959f..e5c395c5af 100644 --- a/ravenframework/Models/PostProcessors/Validations/PPDSS.py +++ b/ravenframework/Models/PostProcessors/Validations/PPDSS.py @@ -62,16 +62,16 @@ class cls. on selected time interval. Values must be fractions of the full time interval. To distinguish strart and end, '|' should be placed between both values. Example: 0.0|0.4 If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") - separateFeatureDataInput.addParam("type", InputTypes.StringType, descr="""Specifies what type of user defined time intervals have been selected from - the feature pivot parameter. Options are currently `ratio' or `raw_values'""") + separateFeatureDataInput.addParam("type", InputTypes.StringType, descr=r"""Specifies what type of user defined time intervals have been selected from + the feature pivot parameter. Options are currently `ratio' or `raw\_values'""") inputSpecification.addSub(separateFeatureDataInput) separateTargetDataInput = InputData.parameterInputFactory("separateTargetData", contentType=InputTypes.StringType, descr="""Time points to separate target data and conduct DSS postprocessing on selected time interval. Values must be fractions of the full time interval. To distinguish strart and end, '|' should be placed between both values. Example: 0.0|0.4 If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") - separateTargetDataInput.addParam("type", InputTypes.StringType, descr="""Specifies what type of user defined time intervals have been selected from - the target pivot parameter. Options are currently `ratio' or `raw_values'""") + separateTargetDataInput.addParam("type", InputTypes.StringType, descr=r"""Specifies what type of user defined time intervals have been selected from + the target pivot parameter. Options are currently `ratio' or `raw\_values'""") inputSpecification.addSub(separateTargetDataInput) scaleTypeInput = InputData.parameterInputFactory("scale", contentType=InputTypes.makeEnumType("scale","scaleType",['DataSynthesis','2_2_affine','dilation','beta_strain','omega_strain','identity']), descr="""Scaling type for the time transformation. Available types are DataSynthesis,