numba 0.67.0rc1: vectorize

Clean-room public code sample — generation instructions

Write a brand-new, minimal, self-contained code sample in this clean-room directory.
Do not copy, paraphrase, or reference any existing project source. Work only from this spec.

A csx.json manifest scaffold already exists. Do not recreate it from memory. Preserve its case.goal, packages and symbols; fill its empty case.contract with exact assertions and correct its environment, commands and verifierAdapter for the files you generate.

Goal: verify numba.vectorize in pkg:pypi/numba@0.67.0rc1
Kind: HOW

Use EXACTLY these public packages and versions:
  - pkg:pypi/numba@0.67.0rc1
Demonstrate these symbols/APIs:
  - numba.vectorize
Constraints:
  - executionContext: node
Required runtime conditions:
  - ecosystem: npm
  - language: javascript
  - moduleSystem: cjs
  - packageManager: npm@10.9.8
  - runtime: node@22.23.2

Rules:
  - One focused purpose; the smallest project that proves the goal.
  - Include a contract test (test/contract.*) that runs OFFLINE and exits 0 exactly when the goal behavior works.
  - Pin every dependency with a lockfile so resolution is reproducible.
  - No secrets, credentials, or tokens. No real URLs (only example.com or localhost). No absolute paths.
  - No personal names, emails, company names, or project identifiers of any kind.
  - No binaries and no generated output (node_modules, dist, target, venv, .git, .env).
  - Keep it under 200 files and 256KB packed.

{"case":{"caseId":"case:sha256:14fc57c4238e293e2114a9c09b0ea81810c06b988bb877df1d2fc5f2c848e52b","contract":["assert vectorize compiles ufunc with explicit integer and float signatures on scalar and 1D array inputs","assert vectorize compiles dynamically without explicit signatures on invocation","assert vectorize performs multi-dimensional array broadcasting","assert vectorize generated ufunc supports reduce and accumulate methods","assert vectorize with explicit signature raises TypeError when called with incompatible argument types"],"goal":"verify numba.vectorize in pkg:pypi/numba@0.67.0rc1","kind":"HOW","packages":["pkg:pypi/numba@0.67.0rc1"],"schemaVersion":1,"symbols":["numba.vectorize"]},"contractCommand":["python","test/contract.py"],"environment":{"arch":"x64","distro":"ubuntu","ecosystem":"pypi","libc":"glibc","libcVersion":"2.39","os":"linux","osVersionBucket":"24","packageManager":"pip","schemaVersion":1},"license":"MIT-0","packages":["pkg:pypi/numba@0.67.0rc1"],"schemaVersion":1,"subject":"pkg:pypi/numba@0.67.0rc1","symbols":["numba.vectorize"],"verifierAdapter":"python@1"}

[project]
name = "verify-numba"
version = "0.1.0"
dependencies = [
    "llvmlite==0.49.0",
    "numba==0.67.0rc1",
    "numpy==2.5.2",
]

llvmlite==0.49.0
numba==0.67.0rc1
numpy==2.5.2

{
  "schemaVersion": 1,
  "goal": "verify numba.vectorize in pkg:pypi/numba@0.67.0rc1",
  "kind": "HOW",
  "packages": [
    "pkg:pypi/numba@0.67.0rc1"
  ],
  "symbols": [
    "numba.vectorize"
  ],
  "constraints": {
    "executionContext": "node"
  },
  "runtimeConditions": {
    "ecosystem": "npm",
    "language": "javascript",
    "moduleSystem": "cjs",
    "packageManager": "npm@10.9.8",
    "runtime": "node@22.23.2"
  }
}

import numpy as np
import numba
from numba import vectorize, int64, float64


def test_vectorize_explicit_signatures():
    @vectorize([int64(int64, int64), float64(float64, float64)])
    def vec_add(x, y):
        return x + y

    # Scalar inputs
    assert vec_add(10, 20) == 30
    assert vec_add(1.5, 2.5) == 4.0

    # 1D array inputs
    a = np.array([1, 2, 3], dtype=np.int64)
    b = np.array([4, 5, 6], dtype=np.int64)
    res = vec_add(a, b)
    assert isinstance(res, np.ndarray)
    assert np.array_equal(res, np.array([5, 7, 9], dtype=np.int64))

    # Broadcasting: 1D array + scalar
    res_bcast = vec_add(a, 10)
    assert np.array_equal(res_bcast, np.array([11, 12, 13], dtype=np.int64))


def test_vectorize_dynamic_compilation():
    @vectorize
    def vec_mul(x, y):
        return x * y

    # Compiles dynamically on invocation
    res_scalar = vec_mul(3, 4)
    assert res_scalar == 12

    arr1 = np.array([1.0, 2.0, 3.0], dtype=np.float64)
    arr2 = np.array([2.0, 3.0, 4.0], dtype=np.float64)
    res_arr = vec_mul(arr1, arr2)
    assert np.allclose(res_arr, np.array([2.0, 6.0, 12.0]))


def test_vectorize_multidimensional_broadcasting():
    @vectorize([float64(float64, float64)])
    def vec_sub(x, y):
        return x - y

    mat = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], dtype=np.float64)
    row = np.array([1.0, 2.0, 3.0], dtype=np.float64)
    res = vec_sub(mat, row)
    expected = np.array([[9.0, 18.0, 27.0], [39.0, 48.0, 57.0]], dtype=np.float64)
    assert res.shape == (2, 3)
    assert np.allclose(res, expected)


def test_vectorize_ufunc_methods():
    @vectorize([int64(int64, int64)])
    def vec_sum(x, y):
        return x + y

    arr = np.array([1, 2, 3, 4, 5], dtype=np.int64)

    # ufunc reduce method
    total = vec_sum.reduce(arr)
    assert total == 15

    # ufunc accumulate method
    accum = vec_sum.accumulate(arr)
    assert np.array_equal(accum, np.array([1, 3, 6, 10, 15], dtype=np.int64))


def test_vectorize_type_error():
    @vectorize([int64(int64, int64)])
    def vec_int_add(x, y):
        return x + y

    try:
        vec_int_add("invalid", "strings")
        assert False, "Should have raised TypeError"
    except TypeError as e:
        assert isinstance(e, TypeError)


def main():
    test_vectorize_explicit_signatures()
    test_vectorize_dynamic_compilation()
    test_vectorize_multidimensional_broadcasting()
    test_vectorize_ufunc_methods()
    test_vectorize_type_error()
    print("All vectorize contract assertions passed.")


if __name__ == "__main__":
    main()