ncompare

[danielfromearth]

Submitting Author: Daniel Kaufman (@danielfromearth)
All current maintainers: (@danielfromearth)
Package Name: ncompare
One-Line Description of Package: ncompare compares two netCDF files at the command line, by generating a report of the matching and non-matching groups, variables, and attributes.
Repository Link: https://github.com/nasa/ncompare
Version submitted: 1.4.0
EiC: @NickleDave
Editor: @tomalrussell
Reviewer 1: @cmarmo
Reviewer 2: @cmtso
Archive: 10.5281/zenodo.10625407
JOSS DOI: 10.21105/joss.06490
Version accepted: 1.7.2
Date accepted (month/day/year): 02/06/2024

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Description

This tool ("ncompare") compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid, human-readable, multi-file evaluation by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing. As an option, ncompare can save comparison reports in text and/or comma-separated value (CSV) formats.

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  • Data retrieval
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- [ ] Geospatial
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The target audience is anyone who manages the generation, manipulation, or validation of netCDF files. This package can be applied to to these netCDF file tasks in any scientific discipline; although it would be most relevant to applications with large multidimensional datasets, e.g., for comparing climate models, for Earth science data reanalyses, and for remote sensing data.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

The ncdiff function in the nco (netCDF Operators) library, as well as ncmpidiff and nccmp, compute value
differences, but --- as far as we are aware --- do not have a dedicated function to show structural differences between netCDF4 datasets. Our package, ncompare provides a light-weight Python-based tool for rapid visual comparisons of group & variable structures, attributes, and chunking.

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[NickleDave]

Thanks @danielfromearth for making the full submission!

Here are the initial editor-in-chief checks.

Almost everything we need to start the review is there, but there's one I don't see.

• User-facing documentation that overviews how to install and start using the package.

I think you can add this at the top of the single page docs you have now? Basically repeating what you have in the README.

Please let me know if you can add that.
We're ready to find an editor and reviewers once you do.

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Editor comments

• The package imports but there is nothing in the ncompare namespace because nothing is imported in ncompare/init.py. I understand the package to be designed principally for use through the command-line, so this doesn't affect use

[danielfromearth]

👋 Hi @NickleDave, thanks for the info!
I've now updated the GitHub Pages to include the installation and basic usage section from the README.md.

[NickleDave]

Great, thank you @danielfromearth -- sorry I didn't reply sooner.

Just letting you know that @tomalrussell has graciously volunteered to be the editor for this review. We are now seeking reviewers and @tomalrussell will reply here with next steps once we have found them.

[tomalrussell]

Hi @danielfromearth, thanks for the package submission - we've now identified reviewers and can move on to the next steps..

👋🏻 welcome, @cmarmo and @cmtso! Thank you for volunteering to review
for pyOpenSci ☺️ The review process is outlined in the guides below,
and I'll aim to check in occasionally over the next weeks as we go.

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Reviewers: @cmarmo @cmtso
Due date: 13 December 2023

[tomalrussell]

Hi @cmarmo , @cmtso just checking in - thanks for filling out the pre-review surveys! Are you both on track to post reviews next week? Do raise any questions here or drop me a line.