diff --git a/doc/theory_manual/Makefile b/doc/theory_manual/Makefile
index a1c9479474..f393f5a98c 100644
--- a/doc/theory_manual/Makefile
+++ b/doc/theory_manual/Makefile
@@ -1,6 +1,6 @@
SRCFILE = raven_theory_manual
LATEX_FLAGS=-shell-escape
-GUIDE_FILES = raven_theory_manual.tex statisticalAnalysis.tex forwardSampling.tex adaptiveSampling.tex dataMining.tex reducedOrderModeling.tex ravenStructure.tex introduction.tex ../version.tex
+GUIDE_FILES = raven_theory_manual.tex statisticalAnalysis.tex forwardSampling.tex adaptiveSampling.tex dataMining.tex reducedOrderModeling.tex ravenStructure.tex introduction.tex dssPostProcessor.tex ../version.tex
MAKE_DIR = $(shell pwd)
#all: raven_theory_manual.pdf
diff --git a/doc/theory_manual/dssPostProcessor.tex b/doc/theory_manual/dssPostProcessor.tex
new file mode 100644
index 0000000000..c55994f192
--- /dev/null
+++ b/doc/theory_manual/dssPostProcessor.tex
@@ -0,0 +1,113 @@
+\section{Dynamical System Scaling}
+\label{sec:dssdoc}
+
+The DSS approach to system scaling is based on transforming the typical view of processes to a special coordinate system in terms of the parameter of interest and its agents of change \cite{DSS2015}.
+By parameterizing using a time term that will be introduced later in this section, data reproduced can be converted to the special three coordinate system (also called the phase space)
+and form a geometry with curves along the surface containing invariant and intrinsic properties. The remainder of this section is a review of DSS theory introduced in publications
+by Reyes \cite{DSS2015,Reyes2015,Martin2019} and is used in this analysis for FR scaling. The parameter of interest is defined to be a conserved quantity within a control volume:
+\begin{equation}
+ \label{eq_1}
+ \beta(t)=\frac{1}{\Psi_{0}}\iiint_{V}{\psi\left(\vec{x},t\right)}dV
+\end{equation}
+$\beta$ is defined as the volume integral of the time and space dependent conserved quantity $\psi$ normalized by a time-independent value, $\Psi_{0}$, that characterizes the process. The agents of change are defined as the first derivative of the normalized parameter of interest:
+\begin{equation}
+ \label{eq_2}
+ \omega=\frac{1}{\Psi_{0}}\frac{d}{dt}\iiint_{V}{\psi\left(\vec{x},t\right)}dV=\iiint_{V}{\left(\phi_{v}+\phi_{f}\right)}dV+\iint_{A}{\left(\vec{j}\cdot\vec{n}\right)}dA-\iint_{A}{\psi\left(\vec{v}-\vec{v}_{s}\cdot\vec{n}dA\right)}dA
+\end{equation}
+The change is categorized into three components; volumetric, surface, and quantity transport. The agents of change is also the sum of the individual agent of change:
+\begin{equation}
+ \omega=\frac{1}{\Psi_{0}}\frac{d}{dt}\iiint_{V}{\psi\left(\vec{x},t\right)}dV=\sum^{n}_{i=1}{\omega_{i}}
+\end{equation}
+The relation of $\omega$ and $\beta$ is the following:
+\begin{equation}
+ \label{eq_3}
+ \omega(t)=\left.\frac{d\beta}{dt}\right|_{t}=\sum^{n}_{i=1}{\omega_{i}}
+\end{equation}
+Where $\omega$ is the first derivative of reference time. As defined in Einstein and Infeld, time is a value stepping in constant increments \cite{Einstein1966}. The process dependent term in DSS is called process time:
+\begin{equation}
+ \label{eq_4}
+ \tau(t)=\frac{\beta(t)}{\omega(t)}
+\end{equation}
+To measure the progression difference between reference time and process time in respect to reference time, the idea of temporal displacement rate (D) is adopted:
+\begin{equation}
+ \label{eq_5}
+ D=\frac{d\tau-dt}{dt}=-\frac{\beta}{\omega^{2}}\frac{d\omega}{dt}
+\end{equation}
+The interval of process time is:
+\begin{equation}
+ \label{eq_8}
+ d\tau=\tau_{s}=\left(1+D\right)dt
+\end{equation}
+Applying the process action to normalize the phase space coordinates gives the following normalized terms:
+\begin{equation}
+ \label{eq_10}
+ \tilde{\Omega}=\omega\tau_{s},\qquad \tilde{\beta}=\beta,\qquad \tilde{t}=\frac{t}{\tau_{s}},\qquad \tilde{\tau}=\frac{\tau}{\tau_{s}},\qquad
+ \tilde{D}=D
+\end{equation}
+The scaling relation between the prototype and model can be defined both for $\beta$ and $\omega$ and represents the scaling of the parameter of interest and the corresponding agents of change (or frequency given from the units of per time):
+\begin{equation}
+ \label{eq_11}
+ \lambda_{A}=\frac{\beta_{M}}{\beta_{P}},\qquad \lambda_{B}=\frac{\omega_{M}}{\omega_{P}}
+\end{equation}
+The subscripts $M$ and $P$ stand for the model and prototype. Applying these scaling ratios to equations (\ref{eq_4}), (\ref{eq_5}), and (\ref{eq_10}) provides the scaling ratios for other parameters as well:
+\begin{equation}
+ \label{eq_12}
+ \frac{t_{M}}{t_{P}}=\frac{\lambda_{A}}{\lambda_{B}},\qquad \frac{\tau_{M}}{\tau_{P}}=\frac{\lambda_{A}}{\lambda_{B}},\qquad \frac{\tilde{\beta}_{M}}{\tilde{\beta}_{P}}=\lambda_{A},\qquad \frac{\tilde{\Omega}_{M}}{\tilde{\Omega}_{P}}=\lambda_{A},\qquad \frac{\tilde{\tau}_{M}}{\tilde{\tau}_{P}}=1,\qquad \frac{D_{M}}{D_{P}}=1
+\end{equation}
+Normalized agents of change is the sum in the same respect:
+\begin{equation}
+ \label{eq_18}
+ \Omega=\sum^{k}_{i=1}{\Omega_{i}}
+\end{equation}
+The ratio of $\Omega$ is expressed in the following alternate form:
+\begin{equation}
+ \label{eq_19}
+ \Omega_{R}=\frac{\Omega_{M}}{\Omega_{P}}=\frac{\sum^{k}_{i=1}{\Omega_{M,i}}}{\sum^{k}_{i=1}{\Omega_{P,i}}}=\frac{\Omega_{M,1}+\Omega_{M,2}+...+\Omega_{M,k}}{\Omega_{P,1}+\Omega_{P,2}+...+\Omega_{P,k}}
+\end{equation}
+By the law of scaling ratios, The following must be true:
+\begin{equation}
+ \label{eq_13}
+ \lambda_{A}=\frac{\Omega_{M,1}}{\Omega_{P,1}},\lambda_{A}=\frac{\Omega_{M,2}}{\Omega_{P,2}},...,\lambda_{A}=\frac{\Omega_{M,k}}{\Omega_{P,k}}
+\end{equation}
+Depending on the scaling ratio values, From Reyes, the scaling methods and similarity criteria is subdivided into five categories; 2-2 affine, dilation, $\beta$-strain, $\omega$-strain, and identity \cite{DSS2015}.
+Table \ref{DSS:table_1} summarizes the similarity criteria. Despite the five categories, in essence, all are 2-2 affine with exceptions of partial scaling ratios values being 1.
+\begin{table}[H]
+\centering
+\begin{tabular}{c|c|c|c|c}
+\hline
+%\rowcolor{lightgray}
+\multicolumn{5}{c}{Basis for Process Space-time Coordinate Scaling}\\
+\hline
+Metric & \multirow{2}{*}{$d\tilde{\tau}_{P}=d\tilde{\tau}_{P}$} & \multirow{2}{*}{And} & Covariance & \multirow{2}{*}{$\frac{1}{\omega_{P}}\frac{d\beta_{P}}{dt_{P}}=\frac{1}{\omega_{M}}\frac{d\beta_{M}}{dt_{M}}$} \\
+Invariance & & & Principle & \\
+\hline
+\multicolumn{5}{c}{$\beta-\omega$ Coordinate Transformations}\\
+\hline
+2-2 Affine & Dilation & $\beta$-Strain & $\omega$-Strain & Identity \\
+$\beta_{R}=\lambda_{A}$ & $\beta_{R}=\lambda$ & $\beta_{R}=\lambda_{A}$ & $\beta_{R}=1=\lambda_{B}$ & $\beta_{R}=1$ \\
+$\omega_{R}=\lambda_{B}$ & $\omega_{R}=\lambda$ & $\omega_{R}=1$ & $\omega_{R}=\lambda_{B}$ & $\omega_{R}=1$ \\
+\hline
+\multicolumn{5}{c}{Similarity Criteria}\\
+\hline
+$\tilde{\Omega}_{R}=\lambda_{A}$ & $\tilde{\Omega}_{R}=\lambda$ & $\tilde{\Omega}_{R}=\lambda_{A}$ & $\tilde{\Omega}_{R}=1$ & $\tilde{\Omega}_{R}=1$ \\
+$\tau_{R}=t_{R}=\frac{\lambda_{A}}{\lambda_{B}}$ & $\tau_{R}=t_{R}=1$ & $\tau_{R}=t_{R}=\lambda_{A}$ & $\tau_{R}=t_{R}=\frac{1}{\lambda_{B}}$ & $\tau_{R}=t_{R}=1$ \\
+\hline
+\end{tabular}
+\caption{Scaling Methods and Similarity Criteria Resulting from Two-Parameter Transformations \cite{DSS2015}}\label{DSS:table_1}
+\end{table}
+The separation between both process curves along the constant normalized process time is the local distortion \cite{Martin2019}:
+\begin{equation}
+ \label{eq_15}
+ \tilde{\eta}_{k}=\beta_{P_{k}}\sqrt{\varepsilon D_{P_{k}}}\left[\frac{1}{\Omega_{P_{k}}}-\frac{\lambda_{A}}{\Omega_{M_{k}}}\right]
+\end{equation}
+Where $\epsilon$ is a sign adjuster ensuring positive values within the square root. The total distortion is:
+\begin{equation}
+ \label{eq_16}
+ \tilde{\eta}_{T}=\sum^{N}_{k=1}{\left|\tilde{\eta}_{k}\right|}
+\end{equation}
+And, the equivalent standard deviation is:
+\begin{equation}
+ \label{eq_17}
+ \sigma_{est}=\sqrt{\frac{1}{N}\sum^{N}_{k=1}{\tilde{\eta}^{2}_{k}}}
+\end{equation}
+
diff --git a/doc/theory_manual/raven_theory_manual.bib b/doc/theory_manual/raven_theory_manual.bib
index 5838b9914a..5808168e27 100644
--- a/doc/theory_manual/raven_theory_manual.bib
+++ b/doc/theory_manual/raven_theory_manual.bib
@@ -2,10 +2,35 @@
%% http://bibdesk.sourceforge.net/
-%% Created for Andrea Alfonsi at 2015-01-21 08:58:35 -0700
+%% Created for Andrea Alfonsi at 2015-01-21 08:58:35 -0700
-%% Saved with string encoding Unicode (UTF-8)
+%% Saved with string encoding Unicode (UTF-8)
+
+@book{Einstein1966,
+ author = {A. Einstein and L. Infeld},
+ title = {{The Evolution of Physics from early concepts to relativity and quanta}},
+ year = {1966},
+ publisher = {Simon and Schuster Publisher},
+ address = {New York, NY}}}
+
+@article{Reyes2015,
+ author = {J.N. Reyes and Cesear Frepoli and J.P. Yurko},
+ title = {{The Dynamical System Scaling Methodology: Comparing Dimensionless Governing Equations with the H2TS and FSA Methodologies}},
+ journal = {The 16th International Topical Meeting on Nuclear Thermal Hydraulics (NURETH-16)},
+ year = {2015}}
+
+@book{Martin2019,
+ author = {R.P. Martin and C. Frepoli},
+ title = {{Design-Basis Accident Analysis Methods for Light-Water Nuclear Power Plants}},
+ year = {2019},
+ publisher = {World Scientific}}
+
+@article{DSS2015,
+ author = {J.N. Reyes},
+ title = {{T}he {D}ynamical {S}ystem {S}caling {M}ethodology},
+ journal = {The 16th International Topical Meeting on Nuclear Thermal Hydraulics (NURETH-16)},
+ year = {2015}}
@Book{StochasticMethods,
author = {C. Gardiner},
@@ -216,53 +241,53 @@ @article{benchmark
year={1968}
}
-@article{SCLagrange,
-auTHor = "Babuska and Nobile and Tempone",
-Title = "{A stochastic collocation method for elliptic partial differential equations with random input data}",
+@article{SCLagrange,
+auTHor = "Babuska and Nobile and Tempone",
+Title = "{A stochastic collocation method for elliptic partial differential equations with random input data}",
journal = "SIAM Journal on Numerical Analysis",
volume = 45,
-YEAR = 2007,
-}
+YEAR = 2007,
+}
-@article{hdmr,
-auTHor = "Li and Rosenthal and Rabitz",
-Title = "{High dimensional model representations}",
+@article{hdmr,
+auTHor = "Li and Rosenthal and Rabitz",
+Title = "{High dimensional model representations}",
journal = "J. Phys. Chem. A",
volume = 105,
-YEAR = 2001,
-}
+YEAR = 2001,
+}
-@article{hdmr_neutron,
-auTHor = "Hu and Smith and Willert and Kelley",
-Title = "{High dimensional model representations for the neutron transport equation}",
+@article{hdmr_neutron,
+auTHor = "Hu and Smith and Willert and Kelley",
+Title = "{High dimensional model representations for the neutron transport equation}",
journal = "NS\&E",
volume = 177,
-YEAR = 2014,
-}
+YEAR = 2014,
+}
#no TD or HC, just SC
-@article{sparseSC,
-auTHor = "Nobile and Tempone and Webster",
-Title = "{A sparse grid stochastic collocation method for partial differential equations with random input data}",
+@article{sparseSC,
+auTHor = "Nobile and Tempone and Webster",
+Title = "{A sparse grid stochastic collocation method for partial differential equations with random input data}",
journal = "SIAM Journal on Numerical Analysis",
volume = 46,
-YEAR = 2008,
-}
+YEAR = 2008,
+}
-@article{sparse1,
-auTHor = "Barthelmann and Novak and Ritter",
-Title = "{High dimensional polynomial interpolation on sparse grids}",
+@article{sparse1,
+auTHor = "Barthelmann and Novak and Ritter",
+Title = "{High dimensional polynomial interpolation on sparse grids}",
journal = "Advances in Computational Mathematics",
volume = 12,
-YEAR = 2000,
-}
+YEAR = 2000,
+}
-@article{sparse2,
-auTHor = "Bungartz and Griebel",
-Title = "{Sparse grids}",
+@article{sparse2,
+auTHor = "Bungartz and Griebel",
+Title = "{Sparse grids}",
journal = "Acta Numerica",
volume = 13,
-YEAR = 2004,
+YEAR = 2004,
}
@book{textbook,
@@ -274,20 +299,20 @@ @book{textbook
Title = {Spectral methods for uncertainty quantification with applications to computational fluid dynamics},
Year = {2010}}
-@article{erin01,
-auTHor = "Fichtl and Prinja",
-Title = "{The stochastic collocation method for radiation transport in random media}",
+@article{erin01,
+auTHor = "Fichtl and Prinja",
+Title = "{The stochastic collocation method for radiation transport in random media}",
journal = "J. Quantitative Spectroscopy \& Radiative Transfer",
volume= 12,
-YEAR = 2011,
+YEAR = 2011,
}
-@article{mike01,
-auTHor = "Rising and Prinja and Talou",
-Title = "{Prompt fission neutron spectrum uncertainty propagation using polynomial chaos expansion}",
+@article{mike01,
+auTHor = "Rising and Prinja and Talou",
+Title = "{Prompt fission neutron spectrum uncertainty propagation using polynomial chaos expansion}",
journal = "Nucl. Sci. Eng.",
volume= 175,
-YEAR = 2013,
+YEAR = 2013,
}
@TechReport{RAVENuserManual,
@@ -497,11 +522,11 @@ @article{SVM_Burges
issn = {1384-5810},
pages = {121--167},
publisher = {Kluwer Academic Publishers},
-}
+}
@article{MD_spline,
title = {Multidimensional Spline Interpolation: Theory and Applications},
- author = {Habermann, Christian and Kindermann, Fabian},
+ author = {Habermann, Christian and Kindermann, Fabian},
year = {2007},
journal = {Computational Economics},
volume = {30},
@@ -537,7 +562,7 @@ @inproceedings{Shepard
pages = {517--524},
publisher = {ACM},
address = {New York, NY, USA}
-}
+}
@misc{PBS,
Date-Added = {2015-01-20 19:25:37 +0000},
@@ -772,7 +797,7 @@ @inproceedings{ANS2014alf
Date-Added = {2013-01-29 22:01:50 +0000},
Title = {Performing Probabilist Risk Assessment Through RAVEN},
Year = {2014}}
-
+
@inproceedings{ANS2014alfADET,
Author = {A. Alfonsi and C. Rabiti and D. Mandelli and J. Cogliati and B. Kinoshita},
Booktitle = {Proceedings American Nuclear Society 2014 Winter Meeting Nuclear-The Foundation of Clean Energy, Anaheim, CA, (2014)},
@@ -793,7 +818,7 @@ @TechReport{RELAP5userManual
OPTnote = {•},
annote = {rev.7}
}
-
+
@article{Bailey2018,
Author = {Paul Bailey and Ahmad Emad and Ting Zhang and Qingshu Xie and Emmanuel Sikali},
Date-Added = {2021-10-29 22:01:18 +0000},
diff --git a/doc/theory_manual/raven_theory_manual.tex b/doc/theory_manual/raven_theory_manual.tex
index a611e77b96..5eb138a645 100644
--- a/doc/theory_manual/raven_theory_manual.tex
+++ b/doc/theory_manual/raven_theory_manual.tex
@@ -37,6 +37,9 @@
\usepackage{lscape}
\usepackage[toc,page]{appendix}
\usepackage{RAVEN}
+\usepackage{tabls}
+\usepackage{multirow}
+\usepackage{float}
\newtheorem{mydef}{Definition}
\newcommand{\norm}[1]{\lVert#1\rVert}
@@ -404,6 +407,7 @@
\input{reducedOrderModeling.tex}
\input{statisticalAnalysis.tex}
\input{dataMining.tex}
+\input{dssPostProcessor.tex}
\clearpage
\begin{appendices}
\section{Document Version Information}
diff --git a/doc/user_manual/metrics.tex b/doc/user_manual/metrics.tex
index edf3c4082b..5df36ad6fa 100644
--- a/doc/user_manual/metrics.tex
+++ b/doc/user_manual/metrics.tex
@@ -70,8 +70,7 @@ \section{Metrics}
\item \textbf{Other metric}, such as \xmlString{DTW}.
\end{itemize}
-The valid \textbf{MetricID}s are: \xmlAttr{SKL}, \xmlAttr{ScipyMetric}, \xmlAttr{DTW}, \xmlAttr{CDFAreaDifference},
-and \xmlAttr{PDFCommonArea}. This XML node requires the following attributes:
+The valid \textbf{MetricID}s are: \xmlAttr{SKL}, \xmlAttr{ScipyMetric}, \xmlAttr{DTW}, \xmlAttr{CDFAreaDifference}, \xmlAttr{PDFCommonArea}, and \xmlAttr{DSS}. This XML node requires the following attributes:
\begin{itemize}
\item \xmlAttr{name}, \xmlDesc{required string attribute}, user-defined name of this metric. \nb As with other
objects, this name can be used to refer to this specific entity from other input blocks in the XML.
@@ -599,3 +598,25 @@ \subsubsection{Distance Based Metric}
In addition to this XML subnode, the users can also specify the corresponding parameters for each `metric' according to
previous sections.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\subsection{Dynamical System Scaling}
+\label{subsection:DSS}
+The Dynamical System Scaling (DSS) is a distance metrics that is used to measure the separation
+between two time-dependent data sets.
+
+The specifications of a DSS metric is defined within the \xmlNode{Metric} XML block. The XML node \xmlAttr{subType} must be \textbf{PPDSS} (see \ref{subsubsec:Validation}) in the \xmlNode{PostProcessor} for the outputs of the post-processor to be in the right format for DSS metric inputs.
+
+An example of DSS defined in RAVEN is provided below:
+\begin{lstlisting}[style=XML]
+
+ ...
+
+ ...
+
+
+ ...
+
+ ...
+
+\end{lstlisting}
diff --git a/doc/user_manual/postprocessor.tex b/doc/user_manual/postprocessor.tex
index 72a39131ae..8b6232af3f 100644
--- a/doc/user_manual/postprocessor.tex
+++ b/doc/user_manual/postprocessor.tex
@@ -1622,16 +1622,16 @@ \subsubsection{Validation}
dataset and/or models (e.g. Distributions).
The post-processor is in charge of deploying a common infrastructure
for the user of \textbf{Validation} problems.
-Several algorithms are avaialable within this post-processor:
+Several algorithms are available within this post-processor:
\begin{itemize}
\item \textbf{Probabilistic}, for Static and Time-dependent data
- % \item \textbf{DSS}
+ \item \textbf{PPDSS}, for Time-dependent data
% \item \textbf{Representativity}
% \item \textbf{PCM}
\end{itemize}
%
-The \textbf{Validation} post-processor makes use of the \textbf{Metric} system (See Chapter \ref{sec:Metrics}) to, in conjucntion with the specific algorithm chosen from the list above,
+The \textbf{Validation} post-processor makes use of the \textbf{Metric} system (See Chapter \ref{sec:Metrics}) to, in conjunction with the specific algorithm chosen from the list above,
to report validation scores for both static and time-dependent data.
Indeed, Both \textbf{PointSet} and \textbf{HistorySet} can be accepted by this post-processor (depending on which algorithm is chosen).
If the name of given variable to be compared is unique, it can be used directly, otherwise the variable can be specified
@@ -1654,13 +1654,36 @@ \subsubsection{Validation}
The choice of the available metrics depends on the specific validation algorithm that is chosen (see table \ref{tab:ValidationAlgorithms})
\end{itemize}
-In addition to the nodes above, the user must choose a validation algorithm:
+In addition to the nodes above, the user must use the sub-nodes specific to the chosen validation algorithm:
\begin{itemize}
- \item \xmlNode{Probabilistic}, \xmlDesc{XML node, optional field}, specify that the validation needs to be performed
+ \item Probabilistic: specify that the validation needs to be performed
using the Probabilistic metrics: \textbf{CDFAreaDifference} (see \ref{subsubsec:metric_CDFAreaDifference}) or \textbf{PDFCommonArea} (see \ref{subsubsec:metric_PDFCommonArea})
- This xml-node accepts the following attribute:
+ This validation algorithm must have the following sub-nodes for HistorySet:
\begin{itemize}
- \item \xmlAttr{ name}, \xmlDesc{required string attribute}, the user defined name of the validation algorithm used as prefix for the output results.
+ \item \xmlNode{pivotParameter}, \xmlDesc{string, required field}, specifies the pivotParameter for a . The pivot parameter is the shared index of the output variables in the data object.
+ \end{itemize}
+ \item PPDSS: specify that the validation needs to be performed
+ using the PPDSS metrics: \textbf{DSS} (\ref{subsection:DSS})
+ This validation algorithm must have the following sub-nodes:
+ \begin{itemize}
+ \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{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.
+ \item \textbf{2\_2\_affine}, calculating the distortion for two data sets with scaling ratios for parameter of interest and agent of changes.
+ \item \textbf{dilation}, calculating the distortion for two data sets with scaling ratios for parameter of interest and agent of changes.
+ \item \textbf{beta\_strain}, calculating the distortion for two data sets with scaling ratio for parameter of interest.
+ \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.
\end{itemize}
%\item \xmlNode{DSS}, \xmlDesc{XML node, optional field}, specify that the validation needs to be performed via DSS.
%This xml-node accepts the following attribute:
@@ -1680,7 +1703,7 @@ \subsubsection{Validation}
\hline
\textbf{Validation Algorithm} & \textbf{DataObject} & \textbf{Available Metrics} \\ \hline
Probabilistic & \begin{tabular}[c]{@{}c@{}}PointSet \\ HistorySet\end{tabular} & \begin{tabular}[c]{@{}c@{}}CDFAreaDifference\\ \\ PDFCommonArea\end{tabular} \\ \hline
-DSS & HistorySet & Not Available Yet \\ \hline
+PPDSS & HistorySet & DSS \\ \hline
\end{tabular}
\end{table}
diff --git a/doc/user_manual/raven_user_manual.tex b/doc/user_manual/raven_user_manual.tex
index 4bf52be6b6..7286ec41d9 100644
--- a/doc/user_manual/raven_user_manual.tex
+++ b/doc/user_manual/raven_user_manual.tex
@@ -270,6 +270,7 @@
\\Matteo Donorio (new external code interface)
\\Fabio Giannetti (new external code interface)
\\Jia Zhou (conjugate gradient optimizer)
+\\Ramon K. Yoshiura (DSS validation code)
}
% \\James B. Tompkins} Just people who actually ``developed'' a significant capability in the code should be placed here. Andrea
%\author{\textbf{\textit{Main Developers:}} \\Andrea Alfonsi}
diff --git a/framework/MetricDistributor.py b/framework/MetricDistributor.py
index f6a0215f61..fff1a3463b 100644
--- a/framework/MetricDistributor.py
+++ b/framework/MetricDistributor.py
@@ -138,6 +138,16 @@ def evaluate(self,pairedData, weights = None, multiOutput='mean',**kwargs):
targIn = targVals[:,hist]
out = self.estimator.evaluate(featIn, targIn)
dynamicOutput.append(out)
+ elif self.estimator.isInstanceString(['DSS']):
+ featVals = np.asarray(feat)
+ targVals = np.asarray(targ)
+ assert(featVals.shape[0] == targVals.shape[0])
+ assert(featVals.shape[1] == targVals.shape[1])
+ assert(featVals.shape[2] == targVals.shape[2])
+ if self.canHandleDynamicData:
+ dynamicOutput = self.estimator.evaluate(featVals, targVals)
+ else:
+ self.raiseAnError(IOError, "Must Handle Dynamic Data!")
else:
self.raiseAMessage('Using non-PDF/CDF metrics ...')
featVals = np.asarray(feat[0])
diff --git a/framework/Metrics/metrics/DSS.py b/framework/Metrics/metrics/DSS.py
new file mode 100644
index 0000000000..a7d877a7e4
--- /dev/null
+++ b/framework/Metrics/metrics/DSS.py
@@ -0,0 +1,112 @@
+# Copyright 2017 Battelle Energy Alliance, LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Created on December 20 2020
+
+@author: yoshrk
+"""
+#External Modules------------------------------------------------------------------------------------
+import numpy as np
+import copy
+#External Modules End--------------------------------------------------------------------------------
+
+#Internal Modules------------------------------------------------------------------------------------
+from .MetricInterface import MetricInterface
+from utils import InputData, InputTypes
+#Internal Modules End--------------------------------------------------------------------------------
+
+class DSS(MetricInterface):
+ """
+ Dynamical System Scaling Metric
+ Class for measuring the metric between two instantaneous data points at the same (or approximate) process time
+ @ Data Synthesis or Engineering Scaling.
+ @ Data Synthesis is the act to measure the metric between data sets that are presumed to be equivalent in phenomena,
+ in reference time, and initial/boundary conditions. Essentially, both data sets are expected to be equivalent.
+ For the sake of data comparison with no intentions to match data but only to measure the metric, this model may be
+ used as well.
+ @ For engineering scaling, although the phenomena are equivalent, the reference time and initial/boundary conditions
+ are different. Both sets are tied by process time only.
+ @ In either case, if the to be compared data sets are of little relevance, it is most likely DSS will fail to
+ measure the metric distance accurately.
+ """
+
+ @classmethod
+ def getInputSpecification(cls):
+ """
+ Method to get a reference to a class that specifies the input data for
+ class cls.
+ @ In, cls, the class for which we are retrieving the specification
+ @ Out, inputSpecification, InputData.ParameterInput, class to use for
+ specifying input of cls.
+ """
+ inputSpecification = super().getInputSpecification()
+ return inputSpecification
+
+ def __init__(self):
+ """
+ Constructor
+ @ In, None
+ @ Out, None
+ """
+ super().__init__()
+ # True indicates the metric needs to be able to handle dynamic data
+ self._dynamicHandling = True
+ # True indicates the metric needs to be able to handle pairwise data
+ self._pairwiseHandling = False
+
+ def run(self, x, y, weights=None, axis=0, **kwargs):
+ """
+ This method computes DSS distance between two inputs x and y based on given metric
+ @ In, x, numpy.ndarray, array containing data of x, 3D array provided.
+ Includes [scaled feature omega normalized,scaled normalized feature process time,feature beta] for
+ all samples and time-steps.
+ @ In, y, numpy.ndarray, array containing data of y, 3D array provided.
+ Includes [target omega normalized,target temporal displacement rate,target beta] for
+ all samples and time-steps.
+ @ In, weights, array_like (numpy.array or list), optional, weights associated
+ with input, shape (n_samples) if axis = 0, otherwise shape (n_time_steps)
+ @ In, axis, integer, optional, axis along which a metric is performed, default is 0,
+ i.e. the metric will performed along the first dimension (the "rows").
+ If metric postprocessor is used, the first dimension is the RAVEN_sample_ID,
+ and the second dimension is the pivotParameter if HistorySet is provided.
+ @ In, kwargs, dict, dictionary of parameters characteristic of each metric
+ @ Out, value, float, metric result
+ """
+ assert (isinstance(x, np.ndarray))
+ assert (isinstance(y, np.ndarray))
+ tempX = x
+ tempY = y
+ omegaNormTarget = tempX[0]
+ omegaNormScaledFeature = tempY[0]
+ pTime = tempX[1]
+ D = tempY[1]
+ betaFeature = tempX[2]
+ betaTarget = tempX[2]
+ distance = np.zeros((pTime.shape))
+ distanceSum = np.zeros((pTime.shape[0]))
+ sigma = np.zeros((pTime.shape[0]))
+ for cnt in range(len(pTime)):
+ distanceSquaredSum = 0
+ for cnt2 in range(len(pTime[cnt])):
+ if D[cnt][cnt2] == 0 or omegaNormTarget[cnt][cnt2] == 0 or omegaNormScaledFeature[cnt][cnt2] == 0:
+ distance[cnt][cnt2] = 0
+ else:
+ distance[cnt][cnt2] = betaTarget[cnt][cnt2]*abs(D[cnt][cnt2])**0.5*(1/omegaNormTarget[cnt][cnt2]-1/omegaNormScaledFeature[cnt][cnt2])
+ if np.isnan(distance[cnt][cnt2]) == True:
+ distance[cnt][cnt2] = 0
+ distanceSum[cnt] += abs(distance[cnt][cnt2])
+ distanceSquaredSum += distance[cnt][cnt2]**2
+ sigma[cnt] = (1/len(sigma)*distanceSquaredSum)**0.5
+ value = distance
+ return value
diff --git a/framework/Metrics/metrics/Factory.py b/framework/Metrics/metrics/Factory.py
index 2e705f735d..6c1b6a0984 100644
--- a/framework/Metrics/metrics/Factory.py
+++ b/framework/Metrics/metrics/Factory.py
@@ -25,6 +25,7 @@
from .CDFAreaDifference import CDFAreaDifference
from .PDFCommonArea import PDFCommonArea
from .ScipyMetric import ScipyMetric
+from .DSS import DSS
factory = EntityFactory('Metrics')
factory.registerAllSubtypes(MetricInterface)
diff --git a/framework/Models/PostProcessors/Factory.py b/framework/Models/PostProcessors/Factory.py
index 0c99ac797f..2e45d3640c 100644
--- a/framework/Models/PostProcessors/Factory.py
+++ b/framework/Models/PostProcessors/Factory.py
@@ -37,6 +37,7 @@
from .EconomicRatio import EconomicRatio
from .ValidationBase import ValidationBase
from .Validations import Probabilistic
+from .Validations import PPDSS
from .TSACharacterizer import TSACharacterizer
### PostProcessorFunctions (orig: InterfacedPostProcessor)
diff --git a/framework/Models/PostProcessors/Validations/PPDSS.py b/framework/Models/PostProcessors/Validations/PPDSS.py
new file mode 100644
index 0000000000..32d93200e5
--- /dev/null
+++ b/framework/Models/PostProcessors/Validations/PPDSS.py
@@ -0,0 +1,410 @@
+# Copyright 2017 Battelle Energy Alliance, LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Created on January XX, 2021
+
+@author: yoshrk
+"""
+#External Modules------------------------------------------------------------------------------------
+import numpy as np
+from scipy.interpolate import interp1d
+from scipy.integrate import simps
+import xarray as xr
+import os
+from collections import OrderedDict
+import itertools
+import copy
+#External Modules End--------------------------------------------------------------------------------
+
+#Internal Modules------------------------------------------------------------------------------------
+from utils import utils
+from utils import InputData, InputTypes
+from utils import xmlUtils
+import Files
+import DataObjects
+from ..ValidationBase import ValidationBase
+#Internal Modules End--------------------------------------------------------------------------------
+
+class PPDSS(ValidationBase):
+ """
+ DSS Scaling class.
+ """
+ @classmethod
+ def getInputSpecification(cls):
+ """
+ Method to get a reference to a class that specifies the input data for
+ class cls.
+ @ In, cls, the class for which we are retrieving the specification
+ @ Out, inputSpecification, InputData.ParameterInput, class to use for
+ specifying input of cls.
+ """
+ inputSpecification = super(PPDSS, cls).getInputSpecification()
+ # Have added the new pivotParameters for feature and target. The original has been commented out.
+ pivotParameterFeatureInput = InputData.parameterInputFactory("pivotParameterFeature", contentType=InputTypes.StringType,
+ descr="""Pivot parameter for feature inputs""")
+ inputSpecification.addSub(pivotParameterFeatureInput)
+ pivotParameterTargetInput = InputData.parameterInputFactory("pivotParameterTarget", contentType=InputTypes.StringType,
+ descr="""Pivot parameter for target inputs""")
+ inputSpecification.addSub(pivotParameterTargetInput)
+ 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,
+ descr="""Scaling ratio for the parameter of interest""")
+ inputSpecification.addSub(scaleRatioBetaInput)
+ scaleRatioOmegaInput = InputData.parameterInputFactory("scaleOmega", contentType=InputTypes.FloatListType,
+ descr="""Scaling ratio for the agents of change""")
+ inputSpecification.addSub(scaleRatioOmegaInput)
+ return inputSpecification
+
+ def __init__(self):
+ """
+ Constructor
+ @ In, None
+ @ Out, None
+ """
+ super().__init__()
+ self.printTag = 'POSTPROCESSOR DSS Scaling and Metrics' # Naming
+ self.name = 'PPDSS' # Postprocessor name
+ self.dynamic = True # Must be time-dependent?
+ self.dynamicType = ['dynamic'] # Specification of dynamic type
+ self.features = None # list of feature variables
+ self.targets = None # list of target variables
+ self.multiOutput = 'raw_values' # defines aggregating of multiple outputs for HistorySet
+ # currently allow raw_values
+ self.pivotParameterFeature = None # Feature pivot parameter variable
+ self.pivotValuesFeature = [] # Feature pivot parameter values
+ self.pivotParameterTarget = None # Target pivot parameter variable
+ self.pivotValuesTarget = [] # Target pivot parameter values
+ self.scaleType = None # Scaling type
+ # assembler objects to be requested
+ self.scaleRatioBeta = [] # Scaling ratio for the parameter of interest
+ self.scaleRatioOmega = [] # Scaling ratio for the agents of change
+
+ def _handleInput(self, paramInput):
+ """
+ Function to handle the parsed paramInput for this class.
+ @ In, paramInput, ParameterInput, the already parsed input.
+ @ Out, None
+ """
+ super()._handleInput(paramInput)
+ for child in paramInput.subparts:
+ if child.getName() == 'Metric':
+ if 'type' not in child.parameterValues.keys() or 'class' not in child.parameterValues.keys():
+ self.raiseAnError(IOError, 'Tag Metric must have attributes "class" and "type"')
+ elif child.getName() == 'Features':
+ self.features = child.value
+ elif child.getName() == 'Targets':
+ self.targets = child.value
+ elif child.getName() == 'multiOutput':
+ self.multiOutput = child.value
+ elif child.getName() == 'pivotParameterFeature':
+ self.pivotParameterFeature = child.value
+ elif child.getName() == 'pivotParameterTarget':
+ self.pivotParameterTarget = child.value
+ elif child.getName() == 'scale':
+ self.scaleType = child.value
+ elif child.getName() == 'scaleBeta':
+ self.scaleRatioBeta = child.value
+ elif child.getName() == 'scaleOmega':
+ self.scaleRatioOmega = child.value
+ else:
+ self.raiseAnError(IOError, "Unknown xml node ", child.getName(), " is provided for metric system")
+
+ def run(self, inputIn):
+ """
+ This method executes the postprocessor action. In this case it loads the
+ results to specified dataObject
+ @ In, inputIn, list, dictionary of data to process
+ @ Out, outputDict, dict, dictionary containing the post-processed results
+ """
+ # assert
+ assert(isinstance(inputIn["Data"], list))
+ assert(isinstance(inputIn["Data"][0][-1], xr.Dataset) and isinstance(inputIn["Data"][1][-1], xr.Dataset))
+ # the input can be either be a list of dataobjects or a list of datasets (xarray)
+ datasets = [data for _, _, data in inputIn['Data']]
+ names = []
+ pivotParameterTarget = self.pivotParameterTarget
+ pivotParameterFeature = self.pivotParameterFeature
+ names = [self.getDataSetName(inp[-1]) for inp in inputIn['Data']]
+ if len(inputIn['Data'][0][-1].indexes) and (self.pivotParameterTarget is None or self.pivotParameterFeature is None):
+ if 'dynamic' not in self.dynamicType: #self.model.dataType:
+ self.raiseAnError(IOError, "The validation algorithm '{}' is not a dynamic model but time-dependent data has been inputted in object {}".format(self._type, inputIn['Data'][0][-1].name))
+ else:
+ pivotParameterTarget = inputIn['Data'][1][2]
+ pivotParameterFeature = inputIn['Data'][0][2]
+ # check if pivotParameter
+ evaluation = self._evaluate(datasets)
+ 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]
+ return evaluation
+
+ def _evaluate(self, datasets, **kwargs):
+ """
+ Main method to "do what you do".
+ @ In, datasets, list, list of datasets (data1,data2,etc.) to used.
+ @ In, kwargs, dict, keyword arguments
+ @ Out, outputDict, dict, dictionary containing the results {"feat"_"target"_"metric_name":value}
+ """
+ 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")
+ 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
+
+ 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:
+ 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
+ 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
+ #
+ 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:
+ 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
+ #
+ 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 = {}
+ 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 = {}
+ for cnt2 in range(len(realizationArray)):
+ for key, val in realizationArray[cnt2][cnt].items():
+ out[key] = val
+ realizations.append(out)
+ return realizations
+
+ def _getDataFromDatasets(self, datasets, var, names=None):
+ """
+ Utility function to retrieve the data from dataDict
+ @ In, datasets, list, list of datasets (data1,data2,etc.) to search from.
+ @ In, names, list, optional, list of datasets names (data1,data2,etc.). If not present, the search will be done on the full list.
+ @ In, var, str, the variable to find (either in fromat dataobject|var or simply var)
+ @ Out, data, tuple(numpy.ndarray, xarray.DataArray or None), the retrived data (data, probability weights (None if not present))
+ """
+ data = None
+ pw = None
+ dat = None
+ if "|" in var and names is not None:
+ do, feat = var.split("|")
+ doindex = names.index(do)
+ dat = datasets[doindex][feat]
+ else:
+ for doindex, ds in enumerate(datasets):
+ if var in ds:
+ dat = ds[var]
+ break
+ if 'ProbabilityWeight-{}'.format(feat) in datasets[names.index(do)]:
+ pw = datasets[doindex]['ProbabilityWeight-{}'.format(feat)].values
+ elif 'ProbabilityWeight' in datasets[names.index(do)]:
+ pw = datasets[doindex]['ProbabilityWeight'].values
+ dim = len(dat.shape)
+ # (numRealizations, numHistorySteps) for MetricDistributor
+ dat = dat.values
+ if dim == 1:
+ # the following reshaping does not require a copy
+ dat.shape = (dat.shape[0], 1)
+ data = dat, pw
+ return data
+
+ def collectOutput(self, finishedJob, output):
+ """
+ Function to place all of the computed data into the output object
+ @ In, finishedJob, JobHandler External or Internal instance, A JobHandler object that is in charge of running this post-processor
+ @ In, output, DataObject.DataObject, The object where we want to place our computed results
+ @ Out, None
+ """
+ evaluation = finishedJob.getEvaluation()
+ realizations = evaluation[1]
+ for rlz in realizations:
+ output.addRealization(rlz)
diff --git a/framework/Models/PostProcessors/Validations/__init__.py b/framework/Models/PostProcessors/Validations/__init__.py
index 1a05f0befa..88d55b68bf 100644
--- a/framework/Models/PostProcessors/Validations/__init__.py
+++ b/framework/Models/PostProcessors/Validations/__init__.py
@@ -19,3 +19,4 @@
@author: wangc
"""
from .Probabilistic import Probabilistic
+from .PPDSS import PPDSS
diff --git a/tests/framework/PostProcessors/Validation/DSS/lorentzAttractor_timeScale_I.py b/tests/framework/PostProcessors/Validation/DSS/lorentzAttractor_timeScale_I.py
new file mode 100644
index 0000000000..c0ff5d0959
--- /dev/null
+++ b/tests/framework/PostProcessors/Validation/DSS/lorentzAttractor_timeScale_I.py
@@ -0,0 +1,65 @@
+# Copyright 2017 Battelle Energy Alliance, LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# from wikipedia: dx/dt = sigma*(y-x) ; dy/dt = x*(rho-z)-y dz/dt = x*y-beta*z
+
+import numpy as np
+
+def initialize(self,runInfoDict,inputFiles):
+ """
+ Constructor
+ @ In, runInfoDict, dict, dictionary of input file names, file location, and other parameters
+ RAVEN run.
+ @ In, inputFiles, list, data objects required for external model initialization.
+ @ Out, None
+ """
+ self.sigma = 10.0
+ self.rho = 28.0
+ self.beta = 8.0/3.0
+ return
+
+def run(self,Input):
+ """
+ Constructor
+ @ In, Input, dict, dictionary of input values for each feature.
+ @ Out, None
+ """
+ disc = 2.0
+ maxTime = 0.5
+ tStep = 0.005
+ self.sigma = 10.0
+ self.rho = 28.0
+ self.beta = 8.0/3.0
+
+ numberTimeSteps = int(maxTime/tStep)
+
+ self.x1 = np.zeros(numberTimeSteps)
+ self.y1 = np.zeros(numberTimeSteps)
+ self.z1 = np.zeros(numberTimeSteps)
+ self.time1 = np.zeros(numberTimeSteps)
+
+ self.x0 = Input['x0']
+ self.y0 = Input['y0']
+ self.z0 = Input['z0']
+
+ self.x1[0] = Input['x0']
+ self.y1[0] = Input['y0']
+ self.z1[0] = Input['z0']
+ self.time1[0]= 0.0
+
+ for t in range (numberTimeSteps-1):
+ self.time1[t+1] = self.time1[t] + tStep
+ self.x1[t+1] = self.x1[t] + disc*self.sigma*(self.y1[t]-self.x1[t]) * tStep
+ self.y1[t+1] = self.y1[t] + disc*(self.x1[t]*(self.rho-self.z1[t])-self.y1[t]) * tStep
+ self.z1[t+1] = self.z1[t] + disc*(self.x1[t]*self.y1[t]-self.beta*self.z1[t]) * tStep
diff --git a/tests/framework/PostProcessors/Validation/DSS/lorentzAttractor_timeScale_II.py b/tests/framework/PostProcessors/Validation/DSS/lorentzAttractor_timeScale_II.py
new file mode 100644
index 0000000000..592fda8977
--- /dev/null
+++ b/tests/framework/PostProcessors/Validation/DSS/lorentzAttractor_timeScale_II.py
@@ -0,0 +1,65 @@
+# Copyright 2017 Battelle Energy Alliance, LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# from wikipedia: dx/dt = sigma*(y-x) ; dy/dt = x*(rho-z)-y dz/dt = x*y-beta*z
+
+import numpy as np
+
+def initialize(self,runInfoDict,inputFiles):
+ """
+ Constructor
+ @ In, runInfoDict, dict, dictionary of input file names, file location, and other parameters
+ RAVEN run.
+ @ In, inputFiles, list, data objects required for external model initialization.
+ @ Out, None
+ """
+ self.sigma = 10.0
+ self.rho = 28.0
+ self.beta = 8.0/3.0
+ return
+
+def run(self,Input):
+ """
+ Constructor
+ @ In, Input, dict, dictionary of input values for each feature.
+ @ Out, None
+ """
+ maxTime = 0.7
+ tStep = 0.005
+ disc = 1.0
+ self.sigma = 10.0
+ self.rho = -28.0
+ self.beta = 8.0/3.0
+
+ numberTimeSteps = int(maxTime/tStep)
+
+ self.x2 = np.zeros(numberTimeSteps)
+ self.y2 = np.zeros(numberTimeSteps)
+ self.z2 = np.zeros(numberTimeSteps)
+ self.time2 = np.zeros(numberTimeSteps)
+
+ self.x0 = Input['x0']
+ self.y0 = Input['y0']
+ self.z0 = Input['z0']
+
+ self.x2[0] = Input['x0']
+ self.y2[0] = Input['y0']
+ self.z2[0] = Input['z0']
+ self.time2[0]= 0.0
+
+ for t in range (numberTimeSteps-1):
+ self.time2[t+1] = self.time2[t] + tStep
+ self.x2[t+1] = self.x2[t] + disc*self.sigma*(self.y2[t]-self.x2[t]) * tStep
+ self.y2[t+1] = self.y2[t] + disc*(self.x2[t]*(self.rho-self.z2[t])-self.y2[t]) * tStep
+ self.z2[t+1] = self.z2[t] + disc*(self.x2[t]*self.y2[t]-self.beta*self.z2[t]) * tStep
diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv
new file mode 100644
index 0000000000..f93b5c916f
--- /dev/null
+++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_0.csv
@@ -0,0 +1,101 @@
+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
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diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_1.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_1.csv
new file mode 100644
index 0000000000..a704d0ef68
--- /dev/null
+++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_1.csv
@@ -0,0 +1,101 @@
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diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_2.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_2.csv
new file mode 100644
index 0000000000..c063dfa2e5
--- /dev/null
+++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_2.csv
@@ -0,0 +1,101 @@
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diff --git a/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_3.csv b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_3.csv
new file mode 100644
index 0000000000..9e2bd88dd9
--- /dev/null
+++ b/tests/framework/PostProcessors/Validation/gold/DSS/pp2_print_3.csv
@@ -0,0 +1,101 @@
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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/test_validation_dss.xml b/tests/framework/PostProcessors/Validation/test_validation_dss.xml
new file mode 100644
index 0000000000..10f1440a35
--- /dev/null
+++ b/tests/framework/PostProcessors/Validation/test_validation_dss.xml
@@ -0,0 +1,139 @@
+
+
+
+ framework/PostProcessors/Validation/test_validation_dss
+ yoshrk
+ 2021-03-16
+ PostProcessors.Validation
+
+ This test checks the DSS PostProcessor with DSS metric
+
+
+ no writing directly to files from postprocessors
+
+
+
+
+ DSS
+ mcRun1, mcRun2, PP2
+ 1
+
+
+
+
+ sigma,rho,beta,x1,y1,z1,time1,x0,y0,z0
+
+
+ sigma,rho,beta,x2,y2,z2,time2,x0,y0,z0
+
+
+ outMC1|x1,outMC1|y1
+ outMC2|x2,outMC2|y2
+ dss
+ time1
+ time2
+ DataSynthesis
+ 1,1
+ 1,1
+
+
+
+
+
+
+
+
+
+
+ x0,y0,z0
+
+
+
+
+
+ pivot_parameter
+
+
+
+ x0,y0,z0
+
+
+ time1
+
+
+
+ x0,y0,z0
+
+
+ time2
+
+
+
+
+
+
+ csv
+ pp2_out
+
+
+
+
+
+
+ 4
+ 1
+
+
+ 4
+ 1
+
+
+ 4
+ 1
+
+
+
+
+
+
+ 10
+ 1
+
+
+ x0_distrib
+
+
+ y0_distrib
+
+
+ z0_distrib
+
+
+
+
+
+
+ inputPlaceHolder
+ PythonModule1
+ MC_external
+
+
+
+ inputPlaceHolder
+ PythonModule2
+ MC_external
+
+
+
+ outMC1
+ outMC2
+ pp2
+
+
+
+
+
+
diff --git a/tests/framework/PostProcessors/Validation/tests b/tests/framework/PostProcessors/Validation/tests
index 42e5079e63..0420a95157 100644
--- a/tests/framework/PostProcessors/Validation/tests
+++ b/tests/framework/PostProcessors/Validation/tests
@@ -13,4 +13,11 @@
rel_err = 0.00001
zero_threshold = 1e-9
[../]
+ [./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'
+ rel_err = 0.00001
+ zero_threshold = 1e-9
+ [../]
[]