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The code under https://github.com/ome/ome-model/tree/v6.3.6/ome_model has had little maintenance/development over the last 4 years.
Functionally, its content is also very different from the rest of this code base which contains:
1- the source XSD files for the OME data model
2- sample XML files for the different schema versions
3- the code-generated Java implementation of the OME model objects
Following the extraction to a separate repository, a decision should likely be made with regard to maintaining it vs archiving it in favor of other community Python projects like https://pypi.org/project/ome-types/.
Agreed, as a proof of concept, I worked on a minimal example upgrading a IDR script generating an HCS companion file using this package to ome-types.
The code difference is very reasonable - see IDR/idr0092-ostrop-organoid#2 and the generated OME-XML is consistent with the original one with the only differences being related to attribute ordering and UUIDs.
Based on this minimal investigation, I am further inclined to remove this project and deprecate it in favor of ome-types. At the API level, I cannot see anything that I would care about maintaining. If any was raised as valuable, I would propose to discuss how to open it as a contribution against ome-types directly. Any valuable experience gained from this project could be also be converted into documentation https://ome-types.readthedocs.io/.
The code under https://github.com/ome/ome-model/tree/v6.3.6/ome_model has had little maintenance/development over the last 4 years.
Functionally, its content is also very different from the rest of this code base which contains:
1- the source XSD files for the OME data model
2- sample XML files for the different schema versions
3- the code-generated Java implementation of the OME model objects
Following the extraction to a separate repository, a decision should likely be made with regard to maintaining it vs archiving it in favor of other community Python projects like https://pypi.org/project/ome-types/.
Originally posted by @sbesson in #193 (review)
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