[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

tests/backends/ott/test_backend.py

@@ -29,7 +29,7 @@
 
 
 class TestSinkhorn:
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("jit", [False, True])
     @pytest.mark.parametrize("eps", [None, 1e-2, 1e-1])
     def test_matches_ott(self, x: Geom_t, eps: Optional[float], jit: bool):
@@ -212,7 +212,7 @@ def test_matches_ott(self, x: Geom_t, y: Geom_t, xy: Geom_t, eps: Optional[float
         assert isinstance(solver.xy, PointCloud)
         np.testing.assert_allclose(gt.matrix, pred.transport_matrix, rtol=RTOL, atol=ATOL)
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("alpha", [0.1, 0.9])
     def test_alpha(self, x: Geom_t, y: Geom_t, xy: Geom_t, alpha: float) -> None:
         thresh, eps = 5e-2, 1e-1

tests/conftest.py

@@ -39,7 +39,7 @@ def _close_figure():
     plt.close()
 
 
-@pytest.fixture()
+@pytest.fixture
 def x() -> Geom_t:
     rng = np.random.RandomState(0)
     n = 20  # number of points in the first distribution
@@ -51,7 +51,7 @@ def x() -> Geom_t:
     return jnp.asarray(xs)
 
 
-@pytest.fixture()
+@pytest.fixture
 def y() -> Geom_t:
     rng = np.random.RandomState(1)
     n2 = 30  # number of points in the second distribution
@@ -63,7 +63,7 @@ def y() -> Geom_t:
     return jnp.asarray(xt)
 
 
-@pytest.fixture()
+@pytest.fixture
 def xy() -> Tuple[Geom_t, Geom_t]:
     rng = np.random.RandomState(2)
     n = 20  # number of points in the first distribution
@@ -83,36 +83,36 @@ def xy() -> Tuple[Geom_t, Geom_t]:
     return jnp.asarray(ys), jnp.asarray(yt)
 
 
-@pytest.fixture()
+@pytest.fixture
 def ab() -> Tuple[np.ndarray, np.ndarray]:
     rng = np.random.RandomState(42)
     return rng.normal(size=(20, 2)), rng.normal(size=(30, 4))
 
 
-@pytest.fixture()
+@pytest.fixture
 def x_cost(x: Geom_t) -> jnp.ndarray:
     return ((x[:, None, :] - x[None, ...]) ** 2).sum(-1)
 
 
-@pytest.fixture()
+@pytest.fixture
 def y_cost(y: Geom_t) -> jnp.ndarray:
     return ((y[:, None, :] - y[None, ...]) ** 2).sum(-1)
 
 
-@pytest.fixture()
+@pytest.fixture
 def xy_cost(xy: Geom_t) -> jnp.ndarray:
     x, y = xy
     return ((x[:, None, :] - y[None, ...]) ** 2).sum(-1)
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_x(x: Geom_t) -> AnnData:
     rng = np.random.RandomState(43)
     pc = rng.normal(size=(len(x), 4))
     return AnnData(X=np.asarray(x, dtype=float), obsm={"X_pca": pc})
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_y(y: Geom_t) -> AnnData:
     rng = np.random.RandomState(44)
     pc = rng.normal(size=(len(y), 4))
@@ -126,7 +126,7 @@ def creat_prob(n: int, *, uniform: bool = False, seed: Optional[int] = None) ->
     return jnp.asarray(a)
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_time() -> AnnData:
     rng = np.random.RandomState(42)
 
@@ -156,7 +156,7 @@ def adata_time() -> AnnData:
     return adata
 
 
-@pytest.fixture()
+@pytest.fixture
 def gt_temporal_adata() -> AnnData:
     adata = _gt_temporal_adata.copy()
     # TODO(michalk8): remove both lines once data has been regenerated
@@ -165,7 +165,7 @@ def gt_temporal_adata() -> AnnData:
     return adata
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_space_rotate() -> AnnData:
     rng = np.random.RandomState(31)
     grid = _make_grid(10)
@@ -182,15 +182,15 @@ def adata_space_rotate() -> AnnData:
     return adata
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_mapping() -> AnnData:
     grid = _make_grid(10)
     adataref, adata1, adata2 = _make_adata(grid, n=3, seed=17, cat_key="covariate", num_categories=3)
     sc.pp.pca(adataref, n_comps=30)
     return ad.concat([adataref, adata1, adata2], label="batch", join="outer", index_unique="-")
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_translation() -> AnnData:
     rng = np.random.RandomState(31)
     adatas = [AnnData(X=csr_matrix(rng.normal(size=(100, 60)))) for _ in range(3)]
@@ -202,7 +202,7 @@ def adata_translation() -> AnnData:
     return adata
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_translation_split(adata_translation) -> Tuple[AnnData, AnnData]:
     rng = np.random.RandomState(15)
     adata_src = adata_translation[adata_translation.obs.batch != "0"].copy()
@@ -212,7 +212,7 @@ def adata_translation_split(adata_translation) -> Tuple[AnnData, AnnData]:
     return adata_src, adata_tgt
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_anno(
     problem_kind: Literal["temporal", "cross_modality", "alignment", "mapping"],
 ) -> Union[AnnData, Tuple[AnnData, AnnData]]:
@@ -258,7 +258,7 @@ def adata_anno(
     return adata
 
 
-@pytest.fixture()
+@pytest.fixture
 def gt_tm_annotation() -> np.ndarray:
     tm = np.zeros((10, 15))
     for i in range(10):

tests/datasets/test_dataset.py

@@ -9,15 +9,15 @@
 
 
 class TestSimulateData:
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("n_distributions", [2, 4])
     @pytest.mark.parametrize("key", ["batch", "day"])
     def test_n_distributions(self, n_distributions: int, key: str):
         adata = simulate_data(n_distributions=n_distributions, key=key)
         assert key in adata.obs.columns
         assert adata.obs[key].nunique() == n_distributions
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("obs_to_add", [{"celltype": 2}, {"celltype": 5, "cluster": 4}])
     def test_obs_to_add(self, obs_to_add: Mapping[str, int]):
         adata = simulate_data(obs_to_add=obs_to_add)
@@ -26,7 +26,7 @@ def test_obs_to_add(self, obs_to_add: Mapping[str, int]):
             assert colname in adata.obs.columns
             assert adata.obs[colname].nunique() == k
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("spatial_dim", [None, 2, 3])
     def test_quad_term_spatial(self, spatial_dim: Optional[int]):
         kwargs = {}
@@ -40,7 +40,7 @@ def test_quad_term_spatial(self, spatial_dim: Optional[int]):
         else:
             assert adata.obsm["spatial"].shape[1] == spatial_dim
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("n_intBCs", [None, 4, 7])
     @pytest.mark.parametrize("barcode_dim", [None, 5, 8])
     def test_quad_term_barcode(self, n_intBCs: Optional[int], barcode_dim: Optional[int]):
@@ -63,7 +63,7 @@ def test_quad_term_barcode(self, n_intBCs: Optional[int], barcode_dim: Optional[
         else:
             assert len(np.unique(adata.obsm["barcode"])) <= n_intBCs
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("n_initial_nodes", [None, 4, 7])
     @pytest.mark.parametrize("n_distributions", [3, 6])
     def test_quad_term_tree(self, n_initial_nodes: Optional[int], n_distributions: int):

tests/plotting/conftest.py

@@ -24,7 +24,7 @@
 DPI = 40
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_pl_cell_transition(gt_temporal_adata: AnnData) -> AnnData:
     plot_vars = {
         "transition_matrix": gt_temporal_adata.uns["cell_transition_10_105_forward"],
@@ -38,7 +38,7 @@ def adata_pl_cell_transition(gt_temporal_adata: AnnData) -> AnnData:
     return gt_temporal_adata
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_pl_push(adata_time: AnnData) -> AnnData:
     rng = np.random.RandomState(0)
     plot_vars = {"key": "time", "data": "celltype", "subset": "A", "source": 0, "target": 1}
@@ -57,7 +57,7 @@ def adata_pl_push(adata_time: AnnData) -> AnnData:
     return adata_time
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_pl_pull(adata_time: AnnData) -> AnnData:
     rng = np.random.RandomState(0)
     plot_vars = {"key": "time", "data": "celltype", "subset": "A", "source": 0, "target": 1}
@@ -75,7 +75,7 @@ def adata_pl_pull(adata_time: AnnData) -> AnnData:
     return adata_time
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_pl_sankey(adata_time: AnnData) -> AnnData:
     rng = np.random.RandomState(0)
     celltypes = ["A", "B", "C", "D", "E"]

tests/problems/base/test_compound_problem.py

@@ -68,7 +68,7 @@ def test_sc_pipeline(self, adata_time: AnnData):
             assert problem[key].solution is problem.solutions[key]
 
     @pytest.mark.parametrize("scale", [True, False])
-    @pytest.mark.fast()
+    @pytest.mark.fast
     def test_default_callback(self, adata_time: AnnData, mocker: MockerFixture, scale: bool):
         subproblem = OTProblem(adata_time, adata_tgt=adata_time.copy())
         xy_callback_kwargs = {"n_comps": 5, "scale": scale}
@@ -88,7 +88,7 @@ def test_default_callback(self, adata_time: AnnData, mocker: MockerFixture, scal
         assert isinstance(problem.problems, dict)
         spy.assert_called_with("xy", subproblem.adata_src, subproblem.adata_tgt, **xy_callback_kwargs)
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     def test_custom_callback_lin(self, adata_time: AnnData, mocker: MockerFixture):
         expected_keys = [(0, 1), (1, 2)]
         spy = mocker.spy(TestCompoundProblem, "xy_callback")
@@ -106,7 +106,7 @@ def test_custom_callback_lin(self, adata_time: AnnData, mocker: MockerFixture):
 
         assert spy.call_count == len(expected_keys)
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     def test_custom_callback_quad(self, adata_time: AnnData, mocker: MockerFixture):
         expected_keys = [(0, 1), (1, 2)]
         spy_x = mocker.spy(TestCompoundProblem, "x_callback")
@@ -164,7 +164,7 @@ def test_different_passings_linear(self, adata_with_cost_matrix: AnnData):
         np.testing.assert_allclose(gt.matrix, p1_tmap, rtol=RTOL, atol=ATOL)
         np.testing.assert_allclose(gt.matrix, p2_tmap, rtol=RTOL, atol=ATOL)
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("cost", [("sq_euclidean", SqEuclidean), ("euclidean", Euclidean), ("cosine", Cosine)])
     def test_prepare_cost(self, adata_time: AnnData, cost: Tuple[str, Any]):
         problem = Problem(adata=adata_time)
@@ -179,7 +179,7 @@ def test_prepare_cost(self, adata_time: AnnData, cost: Tuple[str, Any]):
         assert isinstance(problem[0, 1].x.cost, cost[1])
         assert isinstance(problem[0, 1].y.cost, cost[1])
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("cost", [("sq_euclidean", SqEuclidean), ("euclidean", Euclidean), ("cosine", Cosine)])
     def test_prepare_cost_with_callback(self, adata_time: AnnData, cost: Tuple[str, Any]):
         problem = Problem(adata=adata_time)
@@ -196,7 +196,7 @@ def test_prepare_cost_with_callback(self, adata_time: AnnData, cost: Tuple[str,
         assert isinstance(problem[0, 1].x.cost, cost[1])
         assert isinstance(problem[0, 1].y.cost, cost[1])
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     def test_prepare_different_costs(self, adata_time: AnnData):
         problem = Problem(adata=adata_time)
         problem = problem.prepare(

tests/problems/base/test_general_problem.py

@@ -29,7 +29,7 @@ def test_simple_run(self, adata_x: AnnData, adata_y: AnnData):
 
         assert isinstance(prob.solution, BaseDiscreteSolverOutput)
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     def test_output(self, adata_x: AnnData, x: Geom_t):
         problem = OTProblem(adata_x)
         problem._solution = MockSolverOutput(x * x.T)

tests/problems/conftest.py

@@ -10,7 +10,7 @@
 from tests._utils import Geom_t
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_with_cost_matrix(adata_x: Geom_t, adata_y: Geom_t) -> AnnData:
     adata = ad.concat([adata_x, adata_y], label="batch", index_unique="-")
     C = pairwise_distances(adata_x.obsm["X_pca"], adata_y.obsm["X_pca"]) ** 2
@@ -19,7 +19,7 @@ def adata_with_cost_matrix(adata_x: Geom_t, adata_y: Geom_t) -> AnnData:
     return adata
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_time_with_tmap(adata_time: AnnData) -> AnnData:
     adata = adata_time[adata_time.obs["time"].isin([0, 1])].copy()
     rng = np.random.RandomState(42)

tests/problems/cross_modality/test_mixins.py

@@ -65,7 +65,7 @@ def test_translate_alternative(
             trans_backward = tp.translate(source=src, target=tgt, forward=False, alternative_attr=alternative_attr)
             assert trans_backward.shape == adata_src[adata_src.obs["batch"] == "1"].obsm["X_pca"].shape
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("forward", [True, False])
     @pytest.mark.parametrize("normalize", [True, False])
     def test_cell_transition_pipeline(
@@ -107,7 +107,7 @@ def test_cell_transition_pipeline(
         with pytest.raises(AssertionError):
             pd.testing.assert_frame_equal(result1, result2)
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("forward", [True, False])
     @pytest.mark.parametrize("mapping_mode", ["max", "sum"])
     @pytest.mark.parametrize("batch_size", [3, 7, None])

tests/problems/cross_modality/test_translation_problem.py

@@ -25,7 +25,7 @@
 
 
 class TestTranslationProblem:
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("src_attr", ["emb_src", {"attr": "obsm", "key": "emb_src"}])
     @pytest.mark.parametrize("tgt_attr", ["emb_tgt", {"attr": "obsm", "key": "emb_tgt"}])
     @pytest.mark.parametrize("joint_attr", [None, "X_pca", {"attr": "obsm", "key": "X_pca"}])
@@ -50,7 +50,7 @@ def test_prepare_dummy_policy(
         assert tp[prob_key].shape == (2 * n_obs, n_obs)
         np.testing.assert_array_equal(tp._policy._cat, prob_key)
 
-    @pytest.mark.fast()
+    @pytest.mark.fast
     @pytest.mark.parametrize("src_attr", ["emb_src", {"attr": "obsm", "key": "emb_src"}])
     @pytest.mark.parametrize("tgt_attr", ["emb_tgt", {"attr": "obsm", "key": "emb_tgt"}])
     @pytest.mark.parametrize("joint_attr", [None, "X_pca", {"attr": "obsm", "key": "X_pca"}])

tests/problems/generic/conftest.py

@@ -6,7 +6,7 @@
 from anndata import AnnData
 
 
-@pytest.fixture()
+@pytest.fixture
 def adata_time_with_tmap(adata_time: AnnData) -> AnnData:
     adata = adata_time[adata_time.obs["time"].isin([0, 1])].copy()
     rng = np.random.RandomState(42)

tests/problems/generic/test_conditional_neural_problem.py

@@ -15,7 +15,7 @@
 
 
 class TestGENOTLinProblem:
-    @pytest.mark.fast()
+    @pytest.mark.fast
     def test_prepare(self, adata_time: ad.AnnData):
         problem = GENOTLinProblem(adata=adata_time)
         problem = problem.prepare(key="time", joint_attr="X_pca", conditional_attr={"attr": "obs", "key": "time"})