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BlackMarblePy Submission #207
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Hi @g4brielvs , thank you for submitting BlackMarblePy to pyOpenSci. |
Hi Chira (@cmarmo)! Thank you for your message and for taking the time to review BlackMarblePy. I appreciate the update and look forward to any feedback you may have. |
Editor in Chief checksHi @g4brielvs ! Thank you for submitting your package for pyOpenSci review. Below are the basic checks that your package needs to pass to begin our review. Please check our Python packaging guide for more information on the elements below.
Editor commentsBelow my comments about the unchecked boxes up there, and some others related to more general questions.
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Thank you @g4brielvs ! I believe we are ready to seek for an editor. |
@cmarmo Thanks so much! And absolutely. I just created the v2024.08.1 release. Please let us know if there is anything we can do to help. |
Hello @g4brielvs, I am glad to announce that @yeelauren kindly accepted to serve as editor for the BlackMarblePy submission. Thank you so much Lauren! I'm letting Lauren introduce herself here and wishing to you all a happy review process! 🎉 |
Thank you for the update @cmarmo @lwasser! Welcome, @yeelauren, and thank you for taking on the role of editor for the BlackMarblePy submission. I’m looking forward to working with you throughout the review process. If there’s anything you need from me or any way I can assist, please don’t hesitate to reach out. |
Hey @g4brielvs, I've reached out a few potential reviewers and waiting to hear back :) |
Editor response to review:Editor comments👋 Hi @gadomski and @ehinman! Thank you for volunteering to review Please fill out our pre-review surveyBefore beginning your review, please fill out our pre-review survey. This helps us improve all aspects of our review and better understand our community. No personal data will be shared from this survey - it will only be used in an aggregated format by our Executive Director to improve our processes and programs.
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Please get in touch with any questions or concerns! Your review is due: |
Thanks for the poke! I'm planning on tackling this week 🙇🏼 ... I'll update if that slips. |
I've started my review! I'm hoping to hit the Wednesday deadline still. |
I am having the same issue as mentioned here: worldbank/blackmarblepy#96. I'm not sure how to get around it. I tried using my own gdf shape, as well as the example given in Using |
Package Review
DocumentationThe package includes all the following forms of documentation:
Readme file requirements
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
UsabilityReviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole.
Functionality
Final approval (post-review)
Estimated hours spent reviewing:
Review notesI've included my comments to the authors as footnotes. For the editors:
Footnotes
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These arose while doing my pyOpenSci review: pyOpenSci/software-submission#207 (comment)
These arose while doing my pyOpenSci review: pyOpenSci/software-submission#207 (comment)
Thank you for the opportunity to review! This was my first time reviewing a package for pyOpenSci, and I am VERY excited about this package's capabilities and use cases. I hope you find my review helpful and would love to learn how to properly run the package so I can successfully download nighttime light data. Package ReviewPlease check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
DocumentationThe package includes all the following forms of documentation:
Readme file requirements
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
UsabilityReviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole.
Functionality
Final approval (post-review)
Estimated hours spent reviewing: 5 Review Comments
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Submitting Author: Gabriel Stefanini Vicente (@g4brielvs)
All current maintainers: @g4brielvs, @ramarty
Package Name: BlackMarblePy
One-Line Description of Package: Georeferenced Rasters and Statistics of Nightlights from NASA Black Marble
Repository Link: https://github.com/worldbank/blackmarblepy
Version submitted: v2024.8.1
EiC: @cmarmo
Editor: @yeelauren
Reviewer 1: @gadomski
Reviewer 2: @ehinman!
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD
Code of Conduct & Commitment to Maintain Package
Description
BlackMarblePy is a Python package that provides a simple way to use nighttime lights data from NASA’s Black Marble project. Black Marble is a NASA Earth Science Data Systems (ESDS) project that provides a product suite of daily, monthly and yearly global nighttime lights. This package automates the process of downloading all relevant tiles from the NASA LAADS DAAC to cover a region of interest, converting the raw files (in HDF5 format), to georeferenced rasters, and mosaicking rasters together when needed.
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Who is the target audience and what are scientific applications of this package?
The target audience for BlackMarblePy includes researchers, scientists, and analysts working in the fields of urban studies, environmental science, and socio-economic research. The package facilitates access to NASA's Black Marble nighttime lights data, enabling applications such as monitoring urban growth, assessing the impact of natural disasters, and studying human activities' influence on the environment.
Are there other Python packages that accomplish the same thing? If so, how does yours differ?
While there are other Python packages that provide access to satellite imagery and remote sensing data, BlackMarblePy is specifically tailored for NASA's Black Marble nighttime lights data. It offers a more streamlined and efficient way to retrieve, process, and analyze this particular dataset, providing functionalities and tools optimized for nighttime lights research.
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