PolicyEngine / PolicyEngine/nj-ctc-increase
Migrate NJ analyses off split state/district H5 datasets
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Description
PolicyEngine/populace#237 removed release generation for one-file-per-state and one-file-per-district artifacts. The replacement path is the single certified Populace national H5 plus the standard region/scoping machinery in policyengine.py, not direct hf://policyengine/policyengine-us-data/states/... or districts/... files.
GitHub code search still finds split-H5 references in this repo:
nj_credit_calc/microsimulation.pyscripts/modal_pipeline.pyscripts/modal_district_pipeline.py
Migration checklist:
- Stop constructing or documenting
policyengine-us-data/states/*.h5andpolicyengine-us-data/districts/*.h5dataset URIs. - Use the certified Populace default dataset through policyengine.py, with region filters/scoping for state and congressional-district analyses.
- Add or update a regression test so future state/CD runs do not depend on split regional H5 files.
- Keep historical/generated output unchanged unless it is actively used by the app or docs.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with nj_credit_calc/microsimulation.py, scripts/modal_pipeline.py, and scripts/modal_district_pipeline.py, then inspect policyengine.py and the existing analysis tests. Trace every split state or district H5 reference and identify how regional scoping is currently applied. Done means state and congressional-district runs use the certified national dataset, with regression coverage and no active split-H5 dependency.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-engineering
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 58/100