uv: set minimum numpy version to 2.0 (#810)#899
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Running the suite on numpy 1.x breaks tests, so the floor should be numpy 2.x. The existing dependency set already implies a 2.x-compatible window: numba caps numpy at <2.4 and pandas>=2.3.3 needs numpy>=1.26 on py3.12+, but nothing pinned a 2.x floor explicitly. Set 'numpy>=2.0'. Verified in an isolated env pinned to the floor (numpy==2.0.2, which also pulled pandas 3.0.3 / sparse 0.18 / numba 0.65) that uv solves the environment and the full suite passes: 716 passed, 1 skipped, 12 xfailed. Closes casact#810.
SaguaroDev
requested review from
genedan,
henrydingliu,
jbogaardt and
kennethshsu
as code owners
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henrydingliu
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Jun 1, 2026
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Closes #810.
Running the suite on numpy 1.x breaks the unit tests, so the floor should be numpy 2.x. The existing dependency set already implies a 2.x-compatible window —
numbacaps numpy at<2.4, andpandas>=2.3.3needsnumpy>=1.26on py3.12+ — but nothing pinned a 2.x floor explicitly. This sets it.Change:
"numpy"→"numpy>=2.0"in[project.dependencies].Why 2.0 and not higher: 2.0 is the lowest numpy 2.x, it satisfies every transitive constraint in the dep tree (pandas, scikit-learn, sparse, numba, matplotlib all resolve against it), and
requires-python>=3.10is compatible with numpy 2.0+. Going higher would over-constrain without cause.Verification (the "uv solves AND passes tests" check from the issue): I built an isolated env and pinned numpy to the floor (
numpy==2.0.2). uv resolved the full environment cleanly, and at that floor the suite passes:(At the floor the resolver also pulled pandas 3.0.3 / sparse 0.18 / numba 0.65.1, so the floor holds even against the newest pandas — not just the currently-pinned 2.3.3.)
I left
uv.lockout of the diff so it regenerates in CI; happy to commit a refreshed lock if you'd prefer it in the PR.Note
Low Risk
Packaging-only constraint change; no runtime or library code is modified.
Overview
Pins NumPy 2.x as the minimum runtime dependency by changing
numpytonumpy>=2.0in[project.dependencies]inpyproject.toml.This aligns declared requirements with the stack the project already expects (tests fail on NumPy 1.x) without changing application code.
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