From 2e9a3fd29c67873a18d495acf2101558df604630 Mon Sep 17 00:00:00 2001
From: "Joshua J. Cogliati" <joshua.cogliati@inl.gov>
Date: Thu, 15 Jun 2023 14:01:14 -0600
Subject: [PATCH] Fixing things found in review.

---
 doc/user_manual/PostProcessors/SparseSensing.tex              | 4 ++--
 ravenframework/Models/PostProcessors/SparseSensing.py         | 2 +-
 .../PostProcessors/SparseSensing/testSPSLOptiTwist.xml        | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/user_manual/PostProcessors/SparseSensing.tex b/doc/user_manual/PostProcessors/SparseSensing.tex
index d865b3b9dc..5b8367097b 100644
--- a/doc/user_manual/PostProcessors/SparseSensing.tex
+++ b/doc/user_manual/PostProcessors/SparseSensing.tex
@@ -17,7 +17,7 @@ \subsubsection{SparseSensing}
 \item \xmlNode{Goal}, \xmlDesc{string, required field}, the goal of the sparse sensor optimization.
 User has to provide \xmlAttr{subType} which is a \xmlDesc{string, required field} representing the goal of the sparse sensing optimization; i.e., which goal function is used in the optimization? Examples for such goal functions are:
 \begin{itemize}
-    \item \textbf{Reconstruction} deals with predicting the values of a quantity of interest at different locations other than those where sensors are located. For example, one might predict the temperature at a point point in the middle of a fuel rod based on readings taken at various other positions.
+    \item \textbf{Reconstruction} deals with predicting the values of a quantity of interest at different locations other than those where sensors are located. For example, one might predict the temperature at a point in the middle of a fuel rod based on readings taken at various other positions.
 
     \item \textbf{Classification} is the problem of predicting which category an example belongs to, given a set of training data (e.g. determining whether digital photos are of dogs or cats).
 \end{itemize}
@@ -26,7 +26,7 @@ \subsubsection{SparseSensing}
 \begin{itemize}
 	\item \xmlNode{features}, \xmlDesc{comma separated strings, required field}, features/inputs of the data model, i.e., locations or time stamps we should sense
 	\item \xmlNode{target}, \xmlDesc{comma separated strings, required field}, target of data model
-	\item \xmlNode{basis} , \xmlDesc{string, optional field}, the type of basis onto which the data are projected: \xmlString{Identity},\xmlString{ SVD}, \xmlString{Random}. \default{SVD}
+	\item \xmlNode{basis} , \xmlDesc{string, optional field}, the type of basis onto which the data are projected: \xmlString{Identity}, \xmlString{SVD}, \xmlString{Random}. \default{SVD}
 	\item \xmlNode{nModes},  \xmlDesc{integer, required field}, the number of modes  used to project the data.
 	\item \xmlNode{nSensors},  \xmlDesc{integer, required field}, the number of sensors used
 	\item \xmlNode{optimizer},  \xmlDesc{string, optional field}, the optimizer used to find the sensors: \xmlString{QR}, \xmlString{CCQR} for unconstrained and cost constrained respectively.
diff --git a/ravenframework/Models/PostProcessors/SparseSensing.py b/ravenframework/Models/PostProcessors/SparseSensing.py
index 4b0b6c7b0c..bf6bb45fb6 100644
--- a/ravenframework/Models/PostProcessors/SparseSensing.py
+++ b/ravenframework/Models/PostProcessors/SparseSensing.py
@@ -135,7 +135,7 @@ def _handleInput(self, paramInput):
         self.raiseAnError(IOError, '{} is not a recognized option, allowed options are {}'.format(child.getName(),self.goalsDict.keys()))
     _, notFound = paramInput.subparts[0].findNodesAndExtractValues(['nModes','nSensors','features','target'])
     # notFound must be empty
-    assert(not notFound)
+    assert not notFound, "Unexpected nodes in _handleInput"
 
   def run(self,inputIn):
     """
diff --git a/tests/framework/PostProcessors/SparseSensing/testSPSLOptiTwist.xml b/tests/framework/PostProcessors/SparseSensing/testSPSLOptiTwist.xml
index bb20f62aa9..a988c56190 100644
--- a/tests/framework/PostProcessors/SparseSensing/testSPSLOptiTwist.xml
+++ b/tests/framework/PostProcessors/SparseSensing/testSPSLOptiTwist.xml
@@ -4,7 +4,7 @@
     <name>tests/framework/Postprocessors/SPSL</name>
     <author>Mohammad Abdo (@Jimmy-INL)</author>
     <created>2022-07-18</created>
-    <classesTested>Postprocessors.SPSL</classesTested>
+    <classesTested>Models.Postprocessors.SparseSensing</classesTested>
     <description>This test aims to check the ability of the Sparse Sensing Postprocessor
      to locate and plot the selected sensors to reconstruct the temperature field of the OPTI-TWIST prototype
      when perturbing the heater power.