linter-related test updates
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43f038d761
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@ -3,7 +3,8 @@
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Test fixtures
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"""
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from typing import Iterable, Any
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# ruff: noqa: ARG001
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from typing import Any
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import numpy
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from numpy.typing import NDArray
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import pytest # type: ignore
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@ -20,18 +21,18 @@ FixtureRequest = Any
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(5, 5, 5),
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# (7, 7, 7),
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])
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def shape(request: FixtureRequest) -> Iterable[tuple[int, ...]]:
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yield (3, *request.param)
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def shape(request: FixtureRequest) -> tuple[int, ...]:
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return (3, *request.param)
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@pytest.fixture(scope='module', params=[1.0, 1.5])
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def epsilon_bg(request: FixtureRequest) -> Iterable[float]:
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yield request.param
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def epsilon_bg(request: FixtureRequest) -> float:
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return request.param
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@pytest.fixture(scope='module', params=[1.0, 2.5])
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def epsilon_fg(request: FixtureRequest) -> Iterable[float]:
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yield request.param
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def epsilon_fg(request: FixtureRequest) -> float:
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return request.param
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@pytest.fixture(scope='module', params=['center', '000', 'random'])
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@ -40,7 +41,7 @@ def epsilon(
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shape: tuple[int, ...],
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epsilon_bg: float,
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epsilon_fg: float,
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) -> Iterable[NDArray[numpy.float64]]:
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) -> NDArray[numpy.float64]:
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is3d = (numpy.array(shape) == 1).sum() == 0
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if is3d:
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if request.param == '000':
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@ -60,17 +61,17 @@ def epsilon(
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high=max(epsilon_bg, epsilon_fg),
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size=shape)
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yield epsilon
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return epsilon
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@pytest.fixture(scope='module', params=[1.0]) # 1.5
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def j_mag(request: FixtureRequest) -> Iterable[float]:
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yield request.param
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def j_mag(request: FixtureRequest) -> float:
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return request.param
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@pytest.fixture(scope='module', params=[1.0, 1.5])
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def dx(request: FixtureRequest) -> Iterable[float]:
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yield request.param
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def dx(request: FixtureRequest) -> float:
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return request.param
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@pytest.fixture(scope='module', params=['uniform', 'centerbig'])
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@ -78,7 +79,7 @@ def dxes(
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request: FixtureRequest,
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shape: tuple[int, ...],
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dx: float,
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) -> Iterable[list[list[NDArray[numpy.float64]]]]:
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) -> list[list[NDArray[numpy.float64]]]:
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if request.param == 'uniform':
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dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
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elif request.param == 'centerbig':
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@ -90,5 +91,5 @@ def dxes(
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dxe = [PRNG.uniform(low=1.0 * dx, high=1.1 * dx, size=s) for s in shape[1:]]
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dxh = [(d + numpy.roll(d, -1)) / 2 for d in dxe]
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dxes = [dxe, dxh]
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yield dxes
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return dxes
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@ -1,4 +1,4 @@
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from typing import Iterable
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# ruff: noqa: ARG001
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import dataclasses
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import pytest # type: ignore
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import numpy
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@ -61,24 +61,24 @@ def test_poynting_planes(sim: 'FDResult') -> None:
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# Also see conftest.py
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@pytest.fixture(params=[1 / 1500])
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def omega(request: FixtureRequest) -> Iterable[float]:
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yield request.param
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def omega(request: FixtureRequest) -> float:
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return request.param
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@pytest.fixture(params=[None])
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def pec(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
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yield request.param
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def pec(request: FixtureRequest) -> NDArray[numpy.float64] | None:
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return request.param
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@pytest.fixture(params=[None])
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def pmc(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
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yield request.param
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def pmc(request: FixtureRequest) -> NDArray[numpy.float64] | None:
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return request.param
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#@pytest.fixture(scope='module',
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# params=[(25, 5, 5)])
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#def shape(request):
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# yield (3, *request.param)
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#def shape(request: FixtureRequest):
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# return (3, *request.param)
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@pytest.fixture(params=['diag']) # 'center'
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@ -86,7 +86,7 @@ def j_distribution(
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request: FixtureRequest,
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shape: tuple[int, ...],
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j_mag: float,
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) -> Iterable[NDArray[numpy.float64]]:
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) -> NDArray[numpy.float64]:
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j = numpy.zeros(shape, dtype=complex)
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center_mask = numpy.zeros(shape, dtype=bool)
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center_mask[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = True
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@ -96,7 +96,7 @@ def j_distribution(
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elif request.param == 'diag':
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j[numpy.roll(center_mask, [1, 1, 1], axis=(1, 2, 3))] = (1 + 1j) * j_mag
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j[numpy.roll(center_mask, [-1, -1, -1], axis=(1, 2, 3))] = (1 - 1j) * j_mag
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yield j
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return j
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@dataclasses.dataclass()
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@ -1,4 +1,4 @@
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from typing import Iterable
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# ruff: noqa: ARG001
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import pytest # type: ignore
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import numpy
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from numpy.typing import NDArray
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@ -44,30 +44,30 @@ def test_pml(sim: FDResult, src_polarity: int) -> None:
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# Also see conftest.py
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@pytest.fixture(params=[1 / 1500])
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def omega(request: FixtureRequest) -> Iterable[float]:
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yield request.param
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def omega(request: FixtureRequest) -> float:
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return request.param
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@pytest.fixture(params=[None])
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def pec(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
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yield request.param
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def pec(request: FixtureRequest) -> NDArray[numpy.float64] | None:
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return request.param
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@pytest.fixture(params=[None])
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def pmc(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
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yield request.param
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def pmc(request: FixtureRequest) -> NDArray[numpy.float64] | None:
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return request.param
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@pytest.fixture(params=[(30, 1, 1),
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(1, 30, 1),
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(1, 1, 30)])
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def shape(request: FixtureRequest) -> Iterable[tuple[int, ...]]:
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yield (3, *request.param)
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def shape(request: FixtureRequest) -> tuple[int, int, int]:
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return (3, *request.param)
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@pytest.fixture(params=[+1, -1])
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def src_polarity(request: FixtureRequest) -> Iterable[int]:
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yield request.param
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def src_polarity(request: FixtureRequest) -> int:
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return request.param
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@pytest.fixture()
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@ -78,7 +78,7 @@ def j_distribution(
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dxes: dx_lists_mut,
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omega: float,
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src_polarity: int,
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) -> Iterable[NDArray[numpy.complex128]]:
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) -> NDArray[numpy.complex128]:
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j = numpy.zeros(shape, dtype=complex)
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dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
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@ -106,7 +106,7 @@ def j_distribution(
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j = fdfd.waveguide_3d.compute_source(E=e, wavenumber=wavenumber_corrected, omega=omega, dxes=dxes,
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axis=dim, polarity=src_polarity, slices=slices, epsilon=epsilon)
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yield j
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return j
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@pytest.fixture()
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@ -115,9 +115,9 @@ def epsilon(
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shape: tuple[int, ...],
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epsilon_bg: float,
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epsilon_fg: float,
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) -> Iterable[NDArray[numpy.float64]]:
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) -> NDArray[numpy.float64]:
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epsilon = numpy.full(shape, epsilon_fg, dtype=float)
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yield epsilon
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return epsilon
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@pytest.fixture(params=['uniform'])
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@ -127,7 +127,7 @@ def dxes(
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dx: float,
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omega: float,
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epsilon_fg: float,
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) -> Iterable[list[list[NDArray[numpy.float64]]]]:
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) -> list[list[NDArray[numpy.float64]]]:
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if request.param == 'uniform':
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dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
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dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
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@ -141,7 +141,7 @@ def dxes(
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epsilon_effective=epsilon_fg,
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thickness=10,
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)
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yield dxes
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return dxes
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@pytest.fixture()
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from typing import Iterable, Any
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# ruff: noqa: ARG001
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from typing import Any
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import dataclasses
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import pytest # type: ignore
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import numpy
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@ -150,8 +151,8 @@ def test_poynting_planes(sim: 'TDResult') -> None:
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@pytest.fixture(params=[0.3])
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def dt(request: FixtureRequest) -> Iterable[float]:
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yield request.param
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def dt(request: FixtureRequest) -> float:
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return request.param
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@dataclasses.dataclass()
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@ -168,8 +169,8 @@ class TDResult:
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@pytest.fixture(params=[(0, 4, 8)]) # (0,)
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def j_steps(request: FixtureRequest) -> Iterable[tuple[int, ...]]:
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yield request.param
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def j_steps(request: FixtureRequest) -> tuple[int, ...]:
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return request.param
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@pytest.fixture(params=['center', 'random'])
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@ -177,7 +178,7 @@ def j_distribution(
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request: FixtureRequest,
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shape: tuple[int, ...],
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j_mag: float,
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) -> Iterable[NDArray[numpy.float64]]:
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) -> NDArray[numpy.float64]:
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j = numpy.zeros(shape)
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if request.param == 'center':
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j[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = j_mag
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@ -185,7 +186,7 @@ def j_distribution(
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j[:, 0, 0, 0] = j_mag
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elif request.param == 'random':
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j[:] = PRNG.uniform(low=-j_mag, high=j_mag, size=shape)
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yield j
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return j
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@pytest.fixture()
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@ -199,9 +200,8 @@ def sim(
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j_steps: tuple[int, ...],
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) -> TDResult:
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is3d = (numpy.array(shape) == 1).sum() == 0
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if is3d:
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if dt != 0.3:
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pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)')
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if is3d and dt != 0.3:
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pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)')
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sim = TDResult(
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shape=shape,
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from typing import Any
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import numpy
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from numpy.typing import NDArray
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@ -10,22 +8,25 @@ PRNG = numpy.random.RandomState(12345)
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def assert_fields_close(
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x: NDArray,
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y: NDArray,
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*args: Any,
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**kwargs: Any,
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) -> None:
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numpy.testing.assert_allclose(
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x, y, verbose=False, # type: ignore
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err_msg='Fields did not match:\n{}\n{}'.format(numpy.moveaxis(x, -1, 0),
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numpy.moveaxis(y, -1, 0)),
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*args,
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**kwargs,
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) -> None:
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x_disp = numpy.moveaxis(x, -1, 0)
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y_disp = numpy.moveaxis(y, -1, 0)
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numpy.testing.assert_allclose(
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x, # type: ignore
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y, # type: ignore
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*args,
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verbose=False,
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err_msg=f'Fields did not match:\n{x_disp}\n{y_disp}',
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**kwargs,
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)
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def assert_close(
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x: NDArray,
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y: NDArray,
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*args: Any,
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**kwargs: Any,
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*args,
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**kwargs,
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) -> None:
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numpy.testing.assert_allclose(x, y, *args, **kwargs)
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