mirror of
https://github.com/pybind/pybind11.git
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391c75447d
This udpates all the remaining tests to the new test suite code and comment styles started in #898. For the most part, the test coverage here is unchanged, with a few minor exceptions as noted below. - test_constants_and_functions: this adds more overload tests with overloads with different number of arguments for more comprehensive overload_cast testing. The test style conversion broke the overload tests under MSVC 2015, prompting the additional tests while looking for a workaround. - test_eigen: this dropped the unused functions `get_cm_corners` and `get_cm_corners_const`--these same tests were duplicates of the same things provided (and used) via ReturnTester methods. - test_opaque_types: this test had a hidden dependence on ExampleMandA which is now fixed by using the global UserType which suffices for the relevant test. - test_methods_and_attributes: this required some additions to UserType to make it usable as a replacement for the test's previous SimpleType: UserType gained a value mutator, and the `value` property is not mutable (it was previously readonly). Some overload tests were also added to better test overload_cast (as described above). - test_numpy_array: removed the untemplated mutate_data/mutate_data_t: the templated versions with an empty parameter pack expand to the same thing. - test_stl: this was already mostly in the new style; this just tweaks things a bit, localizing a class, and adding some missing `// test_whatever` comments. - test_virtual_functions: like `test_stl`, this was mostly in the new test style already, but needed some `// test_whatever` comments. This commit also moves the inherited virtual example code to the end of the file, after the main set of tests (since it is less important than the other tests, and rather length); it also got renamed to `test_inherited_virtuals` (from `test_inheriting_repeat`) because it tests both inherited virtual approaches, not just the repeat approach.
197 lines
8.5 KiB
Python
197 lines
8.5 KiB
Python
import pytest
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from pybind11_tests import numpy_vectorize as m
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pytestmark = pytest.requires_numpy
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with pytest.suppress(ImportError):
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import numpy as np
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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 capture == """
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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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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 capture == """
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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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with capture:
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a, b, c = np.array([[1, 3, 5], [7, 9, 11]]), np.array([[2, 4, 6], [8, 10, 12]]), 3
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assert np.allclose(f(a, b, c), a * b * c)
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assert capture == """
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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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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 capture == """
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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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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 capture == """
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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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with capture:
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a, b, c = np.array([[1, 2, 3], [4, 5, 6]], order='F'), np.array([[2], [3]]), 2
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assert np.allclose(f(a, b, c), a * b * c)
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assert capture == """
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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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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 capture == """
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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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with capture:
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a, b, c = np.array([[1, 2, 3], [4, 5, 6]], order='F')[::, ::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 capture == """
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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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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 m.selective_func(np.array([1.0j], dtype=np.complex64)) == "Complex float branch taken."
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def test_docs(doc):
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assert doc(m.vectorized_func) == """
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vectorized_func(arg0: numpy.ndarray[int32], arg1: numpy.ndarray[float32], arg2: numpy.ndarray[float64]) -> object
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""" # noqa: E501 line too long
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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 vectorized_is_trivial(np.array([1, 3]), np.array([2, 4]), 3) == trivial.c_trivial
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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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assert vectorized_is_trivial(
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np.array([[1, 2, 3], [4, 5, 6]]), np.array([2, 3, 4]), 2) == trivial.non_trivial
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assert vectorized_is_trivial(
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np.array([[1, 2, 3], [4, 5, 6]]), np.array([[2], [3]]), 2) == trivial.non_trivial
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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(" + ", ".join([
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"arg0: float",
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"arg1: numpy.ndarray[float64]",
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"arg2: numpy.ndarray[float64]",
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"arg3: numpy.ndarray[int32]",
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"arg4: int",
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"arg5: m.numpy_vectorize.NonPODClass",
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"arg6: numpy.ndarray[float64]"]) + ") -> object")
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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([[1111111, 2111121, 3111131],
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[1112111, 2112121, 3112131],
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[1113111, 2113121, 3113131]]))
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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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