Editorial format requests.

joss2024/paper.md

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 # Summary
 
-Solar energy is a form of renewable energy whose resource (i.e. sunlight) is available on the earth's surface with a relatively low energy density. This type of resource inherently requires spatial distribution of collection infrastructure in order to achieve increased generation scale. This is true both in the case of distributed (e.g. rooftop solar) and centralized generation. As international responses to climate change promote growing interest in solar energy, there is a corresponding growth of interest in tools for working with distributed solar energy data that possesses these characteristics. This package, `SolarSpatialTools`, aims to contribute to that need by providing research codes for spatial analyses of solar energy data and resources. 
+Solar energy is a form of renewable energy whose resource (i.e., sunlight) is available on the earth's surface with a relatively low energy density. This type of resource inherently requires spatial distribution of collection infrastructure in order to achieve increased generation scale. This is true both in the case of distributed (e.g., rooftop solar) and centralized generation. As international responses to climate change promote growing interest in solar energy, there is a corresponding growth of interest in tools for working with distributed solar energy data that possesses these characteristics. This package, `SolarSpatialTools`, aims to contribute to that need by providing research codes for spatial analyses of solar energy data and resources. 
 
 # Statement of need
 
-As mature packages already exist for supporting general analysis and modeling of solar energy systems, such as `pvlib-python` [@anderson_pvlib_2023] and `pvanalytics` [@perry_pvanalytics_2022], this package is not intended to serve as a replacement, a competitor, or to fragment those communities. Rather, `SolarSpatialTools` serves to collect codes for several tasks that are out-of-scope for `pvlib-python` and `pvanalytics`, but are still of general interest to the research community. Where appropriate, capabilities of `SolarSpatialTools` are contributed to `pvlib-python` or `pvanalytics`. For example, a python language port of the Wavelet Variability Model [@lave_cloud_2013] contained in the MATLAB `pvlib` package [@andrews_introduction_2014] was first developed within `SolarSpatialTools` but was contributed to `pvlib-python` in 2019. `SolarSpatialTools` primarily grew out of personal research codes developed by the lead author under the name `solartoolbox`, but as tools have reached a level of maturity that attracted interest of a broader audience, it has been prepared as a package for more general public use.
+As mature packages already exist for supporting general analysis and modeling of solar energy systems, such as `pvlib-python` [@anderson_pvlib_2023] and `pvanalytics` [@perry_pvanalytics_2022], this package is not intended to serve as a replacement, a competitor, or to fragment those communities. Rather, `SolarSpatialTools` serves to collect codes for several tasks that are out-of-scope for `pvlib-python` and `pvanalytics`, but are still of general interest to the research community. Where appropriate, capabilities of `SolarSpatialTools` are contributed to `pvlib-python` or `pvanalytics`. For example, a Python language port of the Wavelet Variability Model [@lave_cloud_2013] contained in the MATLAB `pvlib` package [@andrews_introduction_2014] was first developed within `SolarSpatialTools` but was contributed to `pvlib-python` in 2019. `SolarSpatialTools` primarily grew out of personal research codes developed by the lead author under the name `solartoolbox`, but as tools have reached a level of maturity that attracted interest of a broader audience, it has been prepared as a package for more general public use.
 
-To be more specific, a variety of analytical techniques related to solar energy are documented in literature, but are not already implemented by existing packages in part due to their relatively high complexity relative to those packages' intended scope. For example, techniques for processing cloud motion vectors (CMVs) from spatially distributed data sets are documented in the literature, such as the method by Jamaly and Kleissl [@jamaly_robust_2018] and that by Gagne et al. [@gagne_directional_2018]. Implementation of these techniques is laborious, requiring calculation of mutual correlation between all possible sensor pairs within a distributed data set. This fundamentally leads to a need to handle data types (i.e. simultaneous time series for each sensor) that are not aligned with the primary focus of the existing packages. Further, the number of calculation steps that are specialized for these CMV calculations makes them unattractive for inclusion in existing solar energy packages, without leading to an extreme broadening of scope to adapt to this singular use case. At the same time, the level of detail in those calculation steps makes them potentially difficult for other investigators to individually implement on a consistent and optimized basis. As they serve a common need within solar energy research, they are implemented in a well documented way by `SolarSpatialTools` to help alleviate this challenge.
+To be more specific, a variety of analytical techniques related to solar energy are documented in literature, but are not already implemented by existing packages in part due to their relatively high complexity relative to those packages' intended scope. For example, techniques for processing cloud motion vectors (CMVs) from spatially distributed data sets are documented in the literature, such as the method by @jamaly_robust_2018 and that by @gagne_directional_2018. Implementation of these techniques is laborious, requiring calculation of mutual correlation between all possible sensor pairs within a distributed data set. This fundamentally leads to a need to handle data types (i.e., simultaneous time series for each sensor) that are not aligned with the primary focus of the existing packages. Further, the number of calculation steps that are specialized for these CMV calculations makes them unattractive for inclusion in existing solar energy packages, without leading to an extreme broadening of scope to adapt to this singular use case. At the same time, the level of detail in those calculation steps makes them potentially difficult for other investigators to individually implement on a consistent and optimized basis. As they serve a common need within solar energy research, they are implemented in a well documented way by `SolarSpatialTools` to help alleviate this challenge.
 
 # Features
 
@@ -47,13 +47,13 @@ These three main capabilities are also supported by extended documentation and t
 - `demos`: demonstration codes and sample data to help users get started with the package
 
 ## Signal Processing
-The `signalproc` module was developed as part of efforts to analyze aggregation of irradiance by spatially distributed plants, but may also be applicable to other signal processing tasks. This approach is used by the Wavelet Variability Model [@lave_cloud_2013], the model of Marcos et al. [@marcos_power_2011] and the Cloud Advection Model [@ranalli_cloud_2021], which was developed by the lead author based on the physical intuition of Hoff and Perez [@hoff_quantifying_2010]. The module contains codes for implementing these types of models using a transfer function paradigm. Some wrappers are provided for `scipy` [@virtanen_scipy_2020] signal processing functions to simplify their application on the data type conventions used by this package. A demonstration of the signal processing capability as it pertains to comparing the different spatial aggregation models is provided in the `demos` directory of the package (`signalproc_demo.py`).
+The `signalproc` module was developed as part of efforts to analyze aggregation of irradiance by spatially distributed plants, but may also be applicable to other signal processing tasks. This approach is used by the Wavelet Variability Model [@lave_cloud_2013], the model of @marcos_power_2011 and the Cloud Advection Model [@ranalli_cloud_2021], which was developed by the lead author based on the physical intuition of @hoff_quantifying_2010. The module contains codes for implementing these types of models using a transfer function paradigm. Some wrappers are provided for `scipy` [@virtanen_scipy_2020] signal processing functions to simplify their application on the data type conventions used by this package. A demonstration of the signal processing capability as it pertains to comparing the different spatial aggregation models is provided in the `demos` directory of the package (`signalproc_demo.py`).
 
 ## Cloud Motion Vector Calculation
-The `cmv` module contains tools for calculating the cloud motion vector from a spatially distributed data set. Two methods from the literature are implemented, that of Jamaly and Kleissl [@jamaly_robust_2018] and that of Gagne et al. [@gagne_directional_2018]. These methods are both based upon computation of the relative time delay between individual sensors but utilize different techniques to process those into a global cloud motion vector. This module depends upon `signalproc` for some of its computations. A demonstration of the cloud motion vector calculation capability is provided in the `demos` directory of the package (`cmv_demo.py`) along with a Jupyter notebook with detailed explanations (`cmv_demo.ipynb`).
+The `cmv` module contains tools for calculating the cloud motion vector from a spatially distributed data set. Two methods from the literature are implemented, that of @jamaly_robust_2018 and that of @gagne_directional_2018. These methods are both based upon computation of the relative time delay between individual sensors but utilize different techniques to process those into a global cloud motion vector. This module depends upon `signalproc` for some of its computations. A demonstration of the cloud motion vector calculation capability is provided in the `demos` directory of the package (`cmv_demo.py`) along with a Jupyter notebook with detailed explanations (`cmv_demo.ipynb`).
 
 ## Field Analysis
-The `field` module contains an implementation of the method developed by Ranalli and Hobbs [@Ranalli2024_JPV], [@Ranalli2024_PVSC] for comparison of a plant's layout from its design plan with that inferred from relative cloud motion across the plant. The method produces a prediction of a single reference sensor's apparent position on the basis of the relative delay between it and other nearby sensors. The application relies on the availability of two distinct cloud motion vectors, which allow triangulation of the sensor's planar position. The implementation depends on both `signalproc` and `cmv`. It is demonstrated in several of the codes in the `demos` directory including `field_demo.ipynb`, and `field_demo_detailed.ipynb`. Aspects of automating the process [@Ranalli2024_PVSC] are demonstrated by `automate_cmv_demo`, `field_reassignment_demo` and `field_demo_full_process`. The last of these demonstrations also exemplifies parallelization of the implementation to speed up the processing for an entire plant.
+The `field` module contains an implementation of the method developed by the authors [@Ranalli2024_JPV; @Ranalli2024_PVSC] for comparison of a plant's layout from its design plan with that inferred from relative cloud motion across the plant. The method produces a prediction of a single reference sensor's apparent position on the basis of the relative delay between it and other nearby sensors. The application relies on the availability of two distinct cloud motion vectors, which allow triangulation of the sensor's planar position. The implementation depends on both `signalproc` and `cmv`. It is demonstrated in several of the codes in the `demos` directory including `field_demo.ipynb`, and `field_demo_detailed.ipynb`. Aspects of automating the process [@Ranalli2024_PVSC] are demonstrated by `automate_cmv_demo`, `field_reassignment_demo` and `field_demo_full_process`. The last of these demonstrations also exemplifies parallelization of the implementation to speed up the processing for an entire plant.
 
 ## Demos
 The `demos` directory includes a variety of demonstration codes and explanatory Jupyter notebooks for the tools in the package, as described in the preceding sections. These demonstrations make use of a few sample datasets that are included in `h5` files. Two samples are subsets of distributed irradiance network timeseries taken as a subset of the HOPE Melpitz campaign [@macke_hdcp2_2017]. One hour of sample data is available with the dataset's native sample rate of 1 s, while a longer four-day subset is available with 10 s resolution. Two additional sample data sets consist of combiner-level data from operational photovoltaic generation plants. Each is taken from a different plant and consists of five, distinct one hour periods of 10 s resolution time series of combiner current. These periods are chosen as those known to experience a high degree of variability due to cloud motion, making them suitable for use with the CMV and signal processing analyses. Data from these plants are anonymized to prevent identification of proprietary data; combiner locations are only given in relative east and north spatial coordinates and their generation magnitudes are scaled to an arbitrary value. As the analytial techniques contained in this package are primarily based on the variability of the signals, the anonymization process does not affect the utility of the data for the purposes of the demonstrations, and in particular, the plant data are used to demonstrate the `field` module.