mirror of
https://github.com/thelsing/knx.git
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269 lines
9.5 KiB
Python
269 lines
9.5 KiB
Python
from __future__ import annotations
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import pytest
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from pybind11_tests import numpy_vectorize as m
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np = pytest.importorskip("numpy")
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def test_vectorize(capture):
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assert np.isclose(m.vectorized_func3(np.array(3 + 7j)), [6 + 14j])
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for f in [m.vectorized_func, m.vectorized_func2]:
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with capture:
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assert np.isclose(f(1, 2, 3), 6)
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assert capture == "my_func(x:int=1, y:float=2, z:float=3)"
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with capture:
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assert np.isclose(f(np.array(1), np.array(2), 3), 6)
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assert capture == "my_func(x:int=1, y:float=2, z:float=3)"
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with capture:
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assert np.allclose(f(np.array([1, 3]), np.array([2, 4]), 3), [6, 36])
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assert (
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capture
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== """
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my_func(x:int=1, y:float=2, z:float=3)
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my_func(x:int=3, y:float=4, z:float=3)
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"""
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)
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with capture:
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a = np.array([[1, 2], [3, 4]], order="F")
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b = np.array([[10, 20], [30, 40]], order="F")
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c = 3
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result = f(a, b, c)
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assert np.allclose(result, a * b * c)
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assert result.flags.f_contiguous
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# All inputs are F order and full or singletons, so we the result is in col-major order:
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assert (
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capture
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== """
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my_func(x:int=1, y:float=10, z:float=3)
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my_func(x:int=3, y:float=30, z:float=3)
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my_func(x:int=2, y:float=20, z:float=3)
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my_func(x:int=4, y:float=40, z:float=3)
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"""
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)
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with capture:
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a, b, c = (
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np.array([[1, 3, 5], [7, 9, 11]]),
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np.array([[2, 4, 6], [8, 10, 12]]),
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3,
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)
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assert np.allclose(f(a, b, c), a * b * c)
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assert (
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capture
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== """
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my_func(x:int=1, y:float=2, z:float=3)
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my_func(x:int=3, y:float=4, z:float=3)
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my_func(x:int=5, y:float=6, z:float=3)
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my_func(x:int=7, y:float=8, z:float=3)
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my_func(x:int=9, y:float=10, z:float=3)
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my_func(x:int=11, y:float=12, z:float=3)
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"""
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)
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with capture:
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a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2
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assert np.allclose(f(a, b, c), a * b * c)
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assert (
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capture
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== """
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my_func(x:int=1, y:float=2, z:float=2)
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my_func(x:int=2, y:float=3, z:float=2)
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my_func(x:int=3, y:float=4, z:float=2)
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my_func(x:int=4, y:float=2, z:float=2)
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my_func(x:int=5, y:float=3, z:float=2)
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my_func(x:int=6, y:float=4, z:float=2)
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"""
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)
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with capture:
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a, b, c = np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2
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assert np.allclose(f(a, b, c), a * b * c)
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assert (
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capture
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== """
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my_func(x:int=1, y:float=2, z:float=2)
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my_func(x:int=2, y:float=2, z:float=2)
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my_func(x:int=3, y:float=2, z:float=2)
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my_func(x:int=4, y:float=3, z:float=2)
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my_func(x:int=5, y:float=3, z:float=2)
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my_func(x:int=6, y:float=3, z:float=2)
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"""
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)
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with capture:
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a, b, c = (
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np.array([[1, 2, 3], [4, 5, 6]], order="F"),
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np.array([[2], [3]]),
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2,
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)
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assert np.allclose(f(a, b, c), a * b * c)
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assert (
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capture
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== """
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my_func(x:int=1, y:float=2, z:float=2)
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my_func(x:int=2, y:float=2, z:float=2)
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my_func(x:int=3, y:float=2, z:float=2)
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my_func(x:int=4, y:float=3, z:float=2)
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my_func(x:int=5, y:float=3, z:float=2)
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my_func(x:int=6, y:float=3, z:float=2)
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"""
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)
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with capture:
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a, b, c = np.array([[1, 2, 3], [4, 5, 6]])[::, ::2], np.array([[2], [3]]), 2
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assert np.allclose(f(a, b, c), a * b * c)
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assert (
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capture
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== """
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my_func(x:int=1, y:float=2, z:float=2)
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my_func(x:int=3, y:float=2, z:float=2)
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my_func(x:int=4, y:float=3, z:float=2)
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my_func(x:int=6, y:float=3, z:float=2)
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"""
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)
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with capture:
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a, b, c = (
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np.array([[1, 2, 3], [4, 5, 6]], order="F")[::, ::2],
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np.array([[2], [3]]),
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2,
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)
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assert np.allclose(f(a, b, c), a * b * c)
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assert (
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capture
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== """
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my_func(x:int=1, y:float=2, z:float=2)
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my_func(x:int=3, y:float=2, z:float=2)
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my_func(x:int=4, y:float=3, z:float=2)
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my_func(x:int=6, y:float=3, z:float=2)
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"""
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)
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def test_type_selection():
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assert m.selective_func(np.array([1], dtype=np.int32)) == "Int branch taken."
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assert m.selective_func(np.array([1.0], dtype=np.float32)) == "Float branch taken."
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assert (
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m.selective_func(np.array([1.0j], dtype=np.complex64))
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== "Complex float branch taken."
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)
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def test_docs(doc):
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assert (
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doc(m.vectorized_func)
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== """
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vectorized_func(arg0: numpy.ndarray[numpy.int32], arg1: numpy.ndarray[numpy.float32], arg2: numpy.ndarray[numpy.float64]) -> object
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"""
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)
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def test_trivial_broadcasting():
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trivial, vectorized_is_trivial = m.trivial, m.vectorized_is_trivial
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assert vectorized_is_trivial(1, 2, 3) == trivial.c_trivial
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assert vectorized_is_trivial(np.array(1), np.array(2), 3) == trivial.c_trivial
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assert (
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vectorized_is_trivial(np.array([1, 3]), np.array([2, 4]), 3)
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== trivial.c_trivial
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)
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assert trivial.c_trivial == vectorized_is_trivial(
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np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3
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)
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assert (
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vectorized_is_trivial(np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2)
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== trivial.non_trivial
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)
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assert (
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vectorized_is_trivial(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2)
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== trivial.non_trivial
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)
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z1 = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype="int32")
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z2 = np.array(z1, dtype="float32")
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z3 = np.array(z1, dtype="float64")
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assert vectorized_is_trivial(z1, z2, z3) == trivial.c_trivial
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assert vectorized_is_trivial(1, z2, z3) == trivial.c_trivial
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assert vectorized_is_trivial(z1, 1, z3) == trivial.c_trivial
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assert vectorized_is_trivial(z1, z2, 1) == trivial.c_trivial
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assert vectorized_is_trivial(z1[::2, ::2], 1, 1) == trivial.non_trivial
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assert vectorized_is_trivial(1, 1, z1[::2, ::2]) == trivial.c_trivial
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assert vectorized_is_trivial(1, 1, z3[::2, ::2]) == trivial.non_trivial
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assert vectorized_is_trivial(z1, 1, z3[1::4, 1::4]) == trivial.c_trivial
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y1 = np.array(z1, order="F")
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y2 = np.array(y1)
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y3 = np.array(y1)
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assert vectorized_is_trivial(y1, y2, y3) == trivial.f_trivial
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assert vectorized_is_trivial(y1, 1, 1) == trivial.f_trivial
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assert vectorized_is_trivial(1, y2, 1) == trivial.f_trivial
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assert vectorized_is_trivial(1, 1, y3) == trivial.f_trivial
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assert vectorized_is_trivial(y1, z2, 1) == trivial.non_trivial
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assert vectorized_is_trivial(z1[1::4, 1::4], y2, 1) == trivial.f_trivial
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assert vectorized_is_trivial(y1[1::4, 1::4], z2, 1) == trivial.c_trivial
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assert m.vectorized_func(z1, z2, z3).flags.c_contiguous
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assert m.vectorized_func(y1, y2, y3).flags.f_contiguous
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assert m.vectorized_func(z1, 1, 1).flags.c_contiguous
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assert m.vectorized_func(1, y2, 1).flags.f_contiguous
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assert m.vectorized_func(z1[1::4, 1::4], y2, 1).flags.f_contiguous
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assert m.vectorized_func(y1[1::4, 1::4], z2, 1).flags.c_contiguous
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def test_passthrough_arguments(doc):
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assert doc(m.vec_passthrough) == (
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"vec_passthrough("
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+ ", ".join(
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[
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"arg0: float",
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"arg1: numpy.ndarray[numpy.float64]",
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"arg2: numpy.ndarray[numpy.float64]",
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"arg3: numpy.ndarray[numpy.int32]",
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"arg4: int",
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"arg5: m.numpy_vectorize.NonPODClass",
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"arg6: numpy.ndarray[numpy.float64]",
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]
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)
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+ ") -> object"
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)
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b = np.array([[10, 20, 30]], dtype="float64")
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c = np.array([100, 200]) # NOT a vectorized argument
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d = np.array([[1000], [2000], [3000]], dtype="int")
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g = np.array([[1000000, 2000000, 3000000]], dtype="int") # requires casting
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assert np.all(
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m.vec_passthrough(1, b, c, d, 10000, m.NonPODClass(100000), g)
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== np.array(
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[
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[1111111, 2111121, 3111131],
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[1112111, 2112121, 3112131],
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[1113111, 2113121, 3113131],
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]
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)
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)
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def test_method_vectorization():
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o = m.VectorizeTestClass(3)
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x = np.array([1, 2], dtype="int")
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y = np.array([[10], [20]], dtype="float32")
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assert np.all(o.method(x, y) == [[14, 15], [24, 25]])
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def test_array_collapse():
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assert not isinstance(m.vectorized_func(1, 2, 3), np.ndarray)
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assert not isinstance(m.vectorized_func(np.array(1), 2, 3), np.ndarray)
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z = m.vectorized_func([1], 2, 3)
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assert isinstance(z, np.ndarray)
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assert z.shape == (1,)
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z = m.vectorized_func(1, [[[2]]], 3)
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assert isinstance(z, np.ndarray)
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assert z.shape == (1, 1, 1)
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def test_vectorized_noreturn():
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x = m.NonPODClass(0)
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assert x.value == 0
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m.add_to(x, [1, 2, 3, 4])
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assert x.value == 10
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m.add_to(x, 1)
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assert x.value == 11
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m.add_to(x, [[1, 1], [2, 3]])
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assert x.value == 18
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