diff --git a/doc/user_manual/PostProcessors/Validation.tex b/doc/user_manual/PostProcessors/Validation.tex index 122a7fb40b..96825d30c1 100644 --- a/doc/user_manual/PostProcessors/Validation.tex +++ b/doc/user_manual/PostProcessors/Validation.tex @@ -89,10 +89,12 @@ \subsubsection{Validation PostProcessors} % \begin{itemize} - \item \xmlNode{Features}, \xmlDesc{comma separated string, required field}, specifies the names of the features. - \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. + \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, + 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 @@ -105,7 +107,18 @@ \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{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{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 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{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 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{optional string attribute}, options are `ratio' or `raw\_values'. The default is `ratio'. + \end{itemize} \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. @@ -115,13 +128,27 @@ \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} + +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} \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}] @@ -133,18 +160,71 @@ \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 + 0.0|0.5 DataSynthesis - 1,1 - 1,1 + 1 + 1 + + + outMC1|x1 + outMC2|x2 + dss + time1 + time2 + 0.2475|0.495 + 0.3475|0.695 + DataSynthesis + 1 + 1 ... ... + + ... + + + 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} diff --git a/ravenframework/Models/PostProcessors/Validations/PPDSS.py b/ravenframework/Models/PostProcessors/Validations/PPDSS.py index e21a30afdb..e5c395c5af 100644 --- a/ravenframework/Models/PostProcessors/Validations/PPDSS.py +++ b/ravenframework/Models/PostProcessors/Validations/PPDSS.py @@ -57,14 +57,30 @@ class cls. pivotParameterTargetInput = InputData.parameterInputFactory("pivotParameterTarget", contentType=InputTypes.StringType, descr="""Pivot parameter for target inputs""") inputSpecification.addSub(pivotParameterTargetInput) + 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 + If 'None' is provided, the DSS postprocessing will be applied to the full interval.""") + 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=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, 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 +104,10 @@ def __init__(self): self.pivotValuesFeature = [] # Feature pivot parameter values self.pivotParameterTarget = None # Target pivot parameter variable self.pivotValuesTarget = [] # Target pivot parameter values + 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 @@ -114,6 +134,14 @@ def _handleInput(self, paramInput): self.pivotParameterFeature = child.value elif child.getName() == 'pivotParameterTarget': self.pivotParameterTarget = child.value + elif child.getName() == 'separateFeatureData': + if 'type' in child.parameterValues.keys(): + self.separateFeatureType = child.parameterValues["type"] + self.separateFeatureData = child.value + elif child.getName() == 'separateTargetData': + if 'type' in child.parameterValues.keys(): + self.separateTargetType = child.parameterValues["type"] + self.separateTargetData = child.value elif child.getName() == 'scale': self.scaleType = child.value elif child.getName() == 'scaleBeta': @@ -150,14 +178,9 @@ 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.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): @@ -169,192 +192,266 @@ 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 + 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.separateFeatureData != None: + separateTime = self.separateFeatureData + separateTime = separateTime.split("|") + startTime = separateTime[0] + endTime = separateTime[1] + if startTime >= 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] >= featureStartTime and featureStartCount == 0: + startFeatureLocation = i + featureStartCount += 1 + if pivotFeatureData[i] > featureEndTime: + endFeatureLocation = i-1 + break + 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: - pivotSize = pivotFeatureSize + 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] >= targetStartTime and targetStartCount == 0: + startTargetLocation = j + targetStartCount += 1 + if pivotTargetData[j] > targetEndTime: + endTargetLocation = j-1 + break + elif pivotTargetData[j] == targetEndTime: + 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_error_'+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/pp1_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp1_print_0.csv new file mode 100644 index 0000000000..5b92801824 --- /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_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 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a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv index f93b5c916f..03e5befbab 100644 --- a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv @@ -1,101 +1,50 @@ -pivot_parameter,dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1,dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_deviation_y2_y1 -0.0,0.0756901816043,24.887493332,-0.000351283270388,2.59700011532,0.0356012101676,53.1999350236,0.206070488345,4.78635605162 -0.005,0.0510745233206,24.887493332,-0.000385934668242,2.59700011532,0.0448169166406,53.1999350236,0.210768607572,4.78635605162 -0.01,0.0256194405153,24.887493332,-0.000406159113474,2.59700011532,0.052224344465,53.1999350236,0.204272609211,4.78635605162 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-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/gold/DSS/pp3_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv new file mode 100644 index 0000000000..1e23b4a160 --- /dev/null +++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp3_print_0.csv @@ -0,0 +1,49 @@ +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 +0.2650000000000001,5.6474025521e-09,45.9392389813,0.0117282303925,11.6635977182 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+0.4550000000000003,0.844114615642,45.9392389813,-0.0133001156101,11.6635977182 +0.4600000000000003,1.78958540519,45.9392389813,-0.0134524801745,11.6635977182 +0.4650000000000003,5.5599518202,45.9392389813,-0.0136865706534,11.6635977182 +0.4700000000000003,73.9861119393,45.9392389813,-0.0140151125411,11.6635977182 +0.4750000000000003,31.6158515968,45.9392389813,-0.014456569239,11.6635977182 +0.4800000000000003,4.13269491253,45.9392389813,-0.0150375025086,11.6635977182 +0.4850000000000003,2.02013643514,45.9392389813,-0.0156040042252,11.6635977182 diff --git a/tests/framework/PostProcessors/Validation/test_validation_dss.xml b/tests/framework/PostProcessors/Validation/test_validation_dss.xml index 10f1440a35..eedbef4a3d 100644 --- a/tests/framework/PostProcessors/Validation/test_validation_dss.xml +++ b/tests/framework/PostProcessors/Validation/test_validation_dss.xml @@ -3,19 +3,21 @@ framework/PostProcessors/Validation/test_validation_dss yoshrk - 2021-03-16 + 2022-05-13 PostProcessors.Validation 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 DSS - mcRun1, mcRun2, PP2 + mcRun1, mcRun2, PP1, PP2, PP3 1 @@ -26,15 +28,39 @@ 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 + 0.5|1.0 + 0.5|1.0 + DataSynthesis + 1 + 1 + + + outMC1|y1 + outMC2|y2 dss time1 time2 + 0.2475|0.495 + 0.3475|0.695 DataSynthesis - 1,1 - 1,1 + 1 + 1 @@ -48,10 +74,25 @@ x0,y0,z0 OutputPlaceHolder + + + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_error_x2_x1 + + + pivot_parameter + + - dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_deviation_x2_x1, - dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_deviation_y2_y1 + dss_x2_x1,total_distance_x2_x1,process_time_x2_x1,standard_error_x2_x1 + + + pivot_parameter + + + + + dss_y2_y1,total_distance_y2_y1,process_time_y2_y1,standard_error_y2_y1 pivot_parameter @@ -74,10 +115,18 @@ + + csv + pp1_out + csv pp2_out + + csv + pp3_out + @@ -127,6 +176,13 @@ MC_external outMC2 + + outMC1 + outMC2 + pp1 + pp1_out + pp1_print + outMC1 outMC2 @@ -134,6 +190,13 @@ 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 [../]