removing redundant reference to openpvtools

paper.md

@@ -43,7 +43,7 @@ The purpose of `pvOps` is to support empirical evaluations of data collected in
 
 # Statement of Need
 
-Continued interest in PV deployment across the world has resulted in increased awareness of needs associated with managing reliability and performance of these systems during operation. Current open-source packages for PV analysis (see [openpvtools](https://openpvtools.readthedocs.io/en/latest/) for a list) focus on theoretical evaluations of solar power simulations (e.g., `pvlib`; [@holmgren2018pvlib]), data cleaning and feature development for production data (e.g., `pvanalytics`; [@perry2022pvanalytics]), specific use cases of empirical evaluations (e.g., `RdTools`; [@deceglie2018rdtools] and `Pecos`; [@klise2016performance] for degradation analysis), or analysis of electroluminescene images (e.g., `PVimage`; [@pierce2020identifying]); see XX for a list of open source PV packages that are currently available in PV. However, a general package that can support data-driven, exploratory evaluations of diverse field collected information is currently lacking. For example, a maintenance log that describes an inverter failure may be temporally correlated to a dip in production levels. Identifying such relationships across different types of field data can improve understanding of the impacts of certain types of failures on a PV plant. To address this gap, we present `pvOps`, an open-source, Python package that can be used by  researchers and industry analysts alike to evaluate and extract insights from different types of data routinely collected during PV field operations. 
+Continued interest in PV deployment across the world has resulted in increased awareness of needs associated with managing reliability and performance of these systems during operation. Current open-source packages for PV analysis focus on theoretical evaluations of solar power simulations (e.g., `pvlib`; [@holmgren2018pvlib]), data cleaning and feature development for production data (e.g., `pvanalytics`; [@perry2022pvanalytics]), specific use cases of empirical evaluations (e.g., `RdTools`; [@deceglie2018rdtools] and `Pecos`; [@klise2016performance] for degradation analysis), or analysis of electroluminescene images (e.g., `PVimage`; [@pierce2020identifying]); see [openpvtools](https://openpvtools.readthedocs.io/en/latest/) for a list of additional open source PV packages. However, a general package that can support data-driven, exploratory evaluations of diverse field collected information is currently lacking. For example, a maintenance log that describes an inverter failure may be temporally correlated to a dip in production levels. Identifying such relationships across different types of field data can improve understanding of the impacts of certain types of failures on a PV plant. To address this gap, we present `pvOps`, an open-source, Python package that can be used by  researchers and industry analysts alike to evaluate and extract insights from different types of data routinely collected during PV field operations. 
 
 PV data collected in the field varies greatly in structure (i.e., timeseries and text records) and quality (i.e., completeness and consistency). The data available for analysis is frequently semi-structured. Furthermore, the level of detail collected between different owners/operators might vary. For example, some may capture a general start and end time for an associated event whereas others might include additional time details for different resolution activities. This diversity in data types and structures often leads to data being under-utilized due to the amount of manual processing required. To address these issues, `pvOps` provides a suite of data processing, cleaning, and visualization methods to leverage insights across a broad range of data types, including operations and maintenance records,  production timeseries, and IV curves. The functions within `pvOps` enable users to better parse available data to understand patterns in outages and production losses. 
 
@@ -79,4 +79,4 @@ This material is supported by the U.S. Department of Energy, Office of Energy Ef
 
 # References
 
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