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Submitting Author: Daniel Kaufman (@danielfromearth)
Package Name: ncompare
One-Line Description of Package: Compare the structure of two netCDF files at the command line
Repository Link (if existing): https://github.com/nasa/ncompare
Code of Conduct & Commitment to Maintain Package
I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
Include a brief paragraph describing what your package does:
This tool ("ncompare") compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons 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.
Community Partnerships
We partner with communities to support peer review with an additional layer of
checks that satisfy community requirements. If your package fits into an
existing community please check below:
Please indicate which category or categories this package falls under:
Check out our package scope page to learn more about our
scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):
Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:
When creating or modifying Network Common Data Form (netCDF) files, there is often a need to evaluate the differences between an original, unmodified, file and a new, modified file, especially for validation and regression testing. ncompare was developed to avoid the ineffective process of manually opening two netCDF files and inspecting their contents to determine whether there are differences in the structure and shapes of groups and variables
Who is the target audience and what are the scientific applications of this package?
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 similar things? 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.
Any other questions or issues we should be aware of:
P.S. Have feedback/comments about our review process? Leave a comment here
The text was updated successfully, but these errors were encountered:
hey there @danielfromearth great to see you here! 👋 i'm just dropping in to say hello given we have a brief interaction on the openscapes slack!! Someone from our team will be getting back to you in the next week or so about this submission!!
this looks like a really cool tool!! i sometimes miss working with netcdf and hdf files.
Welcome @danielfromearth! Thank you for this detailed presubmission inquiry.
ncompare is definitely in scope. Please proceed with a full submission and please reference this issue by number when you do. Once you confirm you will do so, I will close this issue
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Submitting Author: Daniel Kaufman (@danielfromearth)
Package Name: ncompare
One-Line Description of Package: Compare the structure of two netCDF files at the command line
Repository Link (if existing): https://github.com/nasa/ncompare
Code of Conduct & Commitment to Maintain Package
Description
This tool ("ncompare") compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons 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.Community Partnerships
We partner with communities to support peer review with an additional layer of
checks that satisfy community requirements. If your package fits into an
existing community please check below:
Scope
Please indicate which category or categories this package falls under:
Check out our package scope page to learn more about our
scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):
Domain Specific & Community Partnerships
When creating or modifying Network Common Data Form (netCDF) files, there is often a need to evaluate the differences between an original, unmodified, file and a new, modified file, especially for validation and regression testing.
ncompare
was developed to avoid the ineffective process of manually opening two netCDF files and inspecting their contents to determine whether there are differences in the structure and shapes of groups and variablesThe 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.
The
ncdiff
function in thenco
(netCDF Operators) library, as well asncmpidiff
andnccmp
, compute valuedifferences, 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.
P.S. Have feedback/comments about our review process? Leave a comment here
The text was updated successfully, but these errors were encountered: