Light edits in JOSS paper

Mostly adds missing commas

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 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. 
+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 (e.g., timeseries and text records) and quality (e.g., 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. 
 
@@ -60,7 +60,7 @@ timeseries | Production data | *site*, *timestamp*, *power production*, *irradia
  | | | 
 text2time | O&M records and production data | see entries for `text` and  `timeseries` modules above | analyze overlaps between O&M and production (timeseries) records, visualize overlaps between O&M records and production data
  | | | 
-iv | IV records | *current*, *voltage*, *irradiance*, *temperature*  | *simulate* IV curves with physical faults, extract diode parameters from IV curves, classify faults using IV curves
+iv | IV records | *current*, *voltage*, *irradiance*, *temperature*  | simulate IV curves with physical faults, extract diode parameters from IV curves, classify faults using IV curves
 
 The functions within each module can be used to build pipelines that integrate relevant data processing, fusion, and visualization capabilities to support user endgoals. For example, a user with IV curve data could build a pipeline that leverages functions within the `iv` module to process and extract diode parameters within IV curves as well as train models to support classifications based on fault type. A pipeline could be also be built that leverages functions across modules if a user has access to multiple types of data (e.g., both O&M and production records). A sample end-to-end workflow using `pvOps` modules could be: