perf: accelerate crop raster aggregation - #34
Merged
koen-vg merged 4 commits intoJul 22, 2026
Conversation
koen-vg
marked this pull request as ready for review
July 22, 2026 19:31
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
Performance
On the full-resolution central wheat-irrigated case, pinned to one core:
This is about 9x faster with 64% less peak memory for crop yields, and about 4x faster with 37% less peak memory for harvested area. The mapping cost is amortized across all crop and water-supply jobs in a configuration.
Correctness
Optimized CSVs are byte-identical to the baseline for:
The implementation retains exact fractional polygon coverage. A center-cell region mapping was rejected because it materially changes boundary allocation.
Batching crops into fewer Snakemake jobs was also rejected: after removing the repeated spatial work, independent jobs are faster in aggregate under normal parallel execution and retain finer failure, scheduling, and invalidation granularity.
Validation
pytest -q tests/test_region_class_aggregation.py(5 passed)pytest -q tests/test_integration.py::test_workflow_dryrun