Solar Data Tools Submission

[pluflou]

Submitting Author: Sara Miskovich (@pluflou)
All current maintainers: (@pluflou, @bmeyers)
Package Name: Solar Data Tools
One-Line Description of Package: Library of tools for analyzing photovoltaic power time-series data.
Repository Link: https://github.com/slacgismo/solar-data-tools
Version submitted: 1.6.4
EiC: @cmarmo
Editor: @shirubana
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

• Include a brief paragraph describing what your package does:
  Solar Data Tools is an open-source Python library for analyzing PV power (and irradiance) time-series data. It provides methods for data I/O, cleaning, filtering, plotting, and analysis. These methods are largely automated and require little to no input from the user regardless of system type—from utility tracking systems to multi-pitch rooftop systems. Solar Data Tools was developed to enable analysis of unlabeled PV data, i.e. with no model, no meteorological data, and no performance index required, by taking a statistical signal processing approach in the algorithms used in the package’s main data processing pipeline.

Scope

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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
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  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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  • Who is the target audience and what are scientific applications of this package?
    This package is for anyone dealing with photovoltaic data, especially data with no meteorological information (unlabeled). This includes photovoltaic professionals (in private solar industry or utility companies for example), researchers and students in the solar power domain, community solar owners, and anyone with a rooftop system. The scientific goal of the package is to facilitate analysis of photovoltaic data for any system, even those that are difficult to model, and the package uses signal decomposition to achieve that.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?
    There are two other packages that are similar in that they offer data analysis tools for solar applications: PVAnalytics and RdTools. They are both model driven, and require the user to define their own analysis. PVAnalytics focuses on preprocessing and QA, while RdTools focuses on loss factor analysis. Solar Data Tools provides both data quality and loss factor analysis, runs automatically with little to no setup, and is model-free and does not require any weather or other information. Solar Data Tools is most suited for when users want a pre-defined pipeline to get information on complex systems/sites that can't be modeled easily and that no meteorological data. A recent tutorial that was part of a virtual tutorial series on open-source tools and open-access solar data held by DOE’s Solar Technology Office in March 2024 goes over the differences in these packages and when each tool is appropriate to use. You can find the recording here and the slide deck here (see slide 16 for a summary).

  • If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:
    Solar Data Tools pre-submission inquiry #204 (@cmarmo)

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[cmarmo]

Editor in Chief checks

Hi @pluflou ! Thank you for submitting your package for pyOpenSci review.
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• Initial onboarding survey was filled out
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Editor comments

Solar Data Tools is in excellent condition! Congratulation for all your work! 🚀

My only comment is related to the test coverage, which could be improved.
We don't set a minimal threshold for test coverage in order to start the review process, so, if you don't mind, just keep my comment somewhere as a reminder for future developments, and I'm going to look right away for an editor.

[cmarmo]

I saw a new 1.6.2 version was released while waiting for my feedback: I took the liberty to update the version submitted so the reviewers would deal with the latest version available.

[pluflou]

I saw a new 1.6.2 version was released while waiting for my feedback: I took the liberty to update the version submitted so the reviewers would deal with the latest version available.

Thank you!!

[cmarmo]

Hi @pluflou , I am glad to announce that we have an editor for Solar Data Tools review.

@shirubana kindly accepted to take care of your submission. I am letting her introduce herself here and wishing a nice review process to all people involved.

[pluflou]

Thank you @shirubana for volunteering to review our package! We are excited to work with you on this. In the meantime, please let us know if you have any questions!

[shirubana]

Hi, starting on this and navigating the various resources to do this properly.

[shirubana]

Ok, I think I am acquainted now with the steps/my job after perusing the guide and slack. I have started to look for reviewers.

[bmeyers]

Thank you @shirubana! Looking forward to working with you on this.

[bmeyers]

Hi @shirubana! I just wanted to check in and see if there were any updates, and if there was anything you needed from us. Thank you!

[pluflou]

@cmarmo @shirubana is it possible to update to the latest version (1.6.4)? This version is now available on conda-forge (previous versions were on a private channel).

[cmarmo]

@pluflou , since the review has not started yet, I have updated the information about the submitted version in the description.

@shirubana, please let us know if you need any help to find reviewers.... unfortunately, onboarding reviewers can be tricky....