ruff format

Author: FynnBe

example/dataset_statistics_demo.ipynb

@@ -329,12 +329,14 @@
    "source": [
     "# compute dataset statistics on all samples\n",
     "# (in this case we should really use the non-overlapping tiles as samples in dataset_for_initial_statistics)\n",
-    "with create_prediction_pipeline(\n",
-    "    bioimageio_model=model_resource,\n",
-    "    dataset_for_initial_statistics=dataset,\n",
-    "    update_dataset_stats_for_n_samples=0,  # if you call the prediciton pipeline more then len(dataset)\n",
-    "    # times you might want to set this to zero to avoid further updates to the dataset statistics\n",
-    ") as pp:\n",
+    "with (\n",
+    "    create_prediction_pipeline(\n",
+    "        bioimageio_model=model_resource,\n",
+    "        dataset_for_initial_statistics=dataset,\n",
+    "        update_dataset_stats_for_n_samples=0,  # if you call the prediciton pipeline more then len(dataset)\n",
+    "        # times you might want to set this to zero to avoid further updates to the dataset statistics\n",
+    "    ) as pp\n",
+    "):\n",
     "    only_init_dataset_stats = process_dataset(pp, dataset)"
    ]
   },

example/model_usage.ipynb

@@ -111,7 +111,6 @@
     "        Mapping[str, NDArray[Any]], Mapping[TensorId, Union[Tensor, NDArray[Any]]]\n",
     "    ],\n",
     ") -> None:\n",
-    "\n",
     "    for title, image in images.items():\n",
     "        if isinstance(image, Tensor):\n",
     "            input_array = image.data.data\n",

src/bioimageio/core/_magic_tensor_ops.py

@@ -71,12 +71,14 @@ def __ge__(self, other: _Compatible) -> Self:
 
     def __eq__(self, other: _Compatible) -> Self:  # type: ignore[override]
         return self._binary_op(
-            other, nputils.array_eq  # pyright: ignore[reportUnknownArgumentType]
+            other,
+            nputils.array_eq,  # pyright: ignore[reportUnknownArgumentType]
         )
 
     def __ne__(self, other: _Compatible) -> Self:  # type: ignore[override]
         return self._binary_op(
-            other, nputils.array_ne  # pyright: ignore[reportUnknownArgumentType]
+            other,
+            nputils.array_ne,  # pyright: ignore[reportUnknownArgumentType]
         )
 
     # When __eq__ is defined but __hash__ is not, then an object is unhashable,
@@ -171,22 +173,30 @@ def __invert__(self) -> Self:
 
     def round(self, *args: Any, **kwargs: Any) -> Self:
         return self._unary_op(
-            ops.round_, *args, **kwargs  # pyright: ignore[reportUnknownArgumentType]
+            ops.round_,
+            *args,
+            **kwargs,  # pyright: ignore[reportUnknownArgumentType]
         )
 
     def argsort(self, *args: Any, **kwargs: Any) -> Self:
         return self._unary_op(
-            ops.argsort, *args, **kwargs  # pyright: ignore[reportUnknownArgumentType]
+            ops.argsort,
+            *args,
+            **kwargs,  # pyright: ignore[reportUnknownArgumentType]
         )
 
     def conj(self, *args: Any, **kwargs: Any) -> Self:
         return self._unary_op(
-            ops.conj, *args, **kwargs  # pyright: ignore[reportUnknownArgumentType]
+            ops.conj,
+            *args,
+            **kwargs,  # pyright: ignore[reportUnknownArgumentType]
         )
 
     def conjugate(self, *args: Any, **kwargs: Any) -> Self:
         return self._unary_op(
-            ops.conjugate, *args, **kwargs  # pyright: ignore[reportUnknownArgumentType]
+            ops.conjugate,
+            *args,
+            **kwargs,  # pyright: ignore[reportUnknownArgumentType]
         )
 
     __add__.__doc__ = operator.add.__doc__

src/bioimageio/core/backends/torchscript_backend.py

@@ -44,7 +44,6 @@ def __init__(
     def _forward_impl(
         self, input_arrays: Sequence[Optional[NDArray[Any]]]
     ) -> List[Optional[NDArray[Any]]]:
-
         with torch.no_grad():
             torch_tensor = [
                 None if a is None else torch.from_numpy(a).to(self.devices[0])
@@ -60,7 +59,9 @@ def _forward_impl(
                 (
                     None
                     if r is None
-                    else r.cpu().numpy() if isinstance(r, torch.Tensor) else r
+                    else r.cpu().numpy()
+                    if isinstance(r, torch.Tensor)
+                    else r
                 )
                 for r in output_seq
             ]

src/bioimageio/core/block_meta.py

@@ -198,13 +198,13 @@ def __post_init__(self):
         if not isinstance(self.halo, Frozen):
             object.__setattr__(self, "halo", Frozen(self.halo))
 
-        assert all(
-            a in self.sample_shape for a in self.inner_slice
-        ), "block has axes not present in sample"
+        assert all(a in self.sample_shape for a in self.inner_slice), (
+            "block has axes not present in sample"
+        )
 
-        assert all(
-            a in self.inner_slice for a in self.halo
-        ), "halo has axes not present in block"
+        assert all(a in self.inner_slice for a in self.halo), (
+            "halo has axes not present in block"
+        )
 
         if any(s > self.sample_shape[a] for a, s in self.shape.items()):
             logger.warning(
@@ -343,9 +343,9 @@ def split_multiple_shapes_into_blocks(
     if strides is None:
         strides = {}
 
-    assert not (
-        unknown_block := [t for t in strides if t not in block_shapes]
-    ), f"`stride` specified for tensors without block shape: {unknown_block}"
+    assert not (unknown_block := [t for t in strides if t not in block_shapes]), (
+        f"`stride` specified for tensors without block shape: {unknown_block}"
+    )
 
     blocks: Dict[MemberId, Iterable[BlockMeta]] = {}
     n_blocks: Dict[MemberId, TotalNumberOfBlocks] = {}

src/bioimageio/core/proc_ops.py

@@ -237,9 +237,9 @@ class Clip(_SimpleOperator):
 
     def __post_init__(self):
         assert self.min is not None or self.max is not None, "missing min or max value"
-        assert (
-            self.min is None or self.max is None or self.min < self.max
-        ), f"expected min < max, but {self.min} !< {self.max}"
+        assert self.min is None or self.max is None or self.min < self.max, (
+            f"expected min < max, but {self.min} !< {self.max}"
+        )
 
     def _apply(self, x: Tensor, stat: Stat) -> Tensor:
         return x.clip(self.min, self.max)
@@ -321,9 +321,9 @@ def from_proc_descr(
             gain = xr.DataArray(np.atleast_1d(kwargs.gain), dims=axis)
             offset = xr.DataArray(np.atleast_1d(kwargs.offset), dims=axis)
         else:
-            assert (
-                isinstance(kwargs.gain, (float, int)) or len(kwargs.gain) == 1
-            ), kwargs.gain
+            assert isinstance(kwargs.gain, (float, int)) or len(kwargs.gain) == 1, (
+                kwargs.gain
+            )
             gain = (
                 kwargs.gain if isinstance(kwargs.gain, (float, int)) else kwargs.gain[0]
             )

src/bioimageio/core/sample.py

@@ -76,12 +76,12 @@ def split_into_blocks(
         pad_mode: PadMode,
         broadcast: bool = False,
     ) -> Tuple[TotalNumberOfBlocks, Iterable[SampleBlockWithOrigin]]:
-        assert not (
-            missing := [m for m in block_shapes if m not in self.members]
-        ), f"`block_shapes` specified for unknown members: {missing}"
-        assert not (
-            missing := [m for m in halo if m not in block_shapes]
-        ), f"`halo` specified for members without `block_shape`: {missing}"
+        assert not (missing := [m for m in block_shapes if m not in self.members]), (
+            f"`block_shapes` specified for unknown members: {missing}"
+        )
+        assert not (missing := [m for m in halo if m not in block_shapes]), (
+            f"`halo` specified for members without `block_shape`: {missing}"
+        )
 
         n_blocks, blocks = split_multiple_shapes_into_blocks(
             shapes=self.shape,

tests/conftest.py

@@ -129,7 +129,9 @@
 KERAS_MODELS = (
     []
     if keras is None
-    else ["unet2d_keras"] if tf_major_version == 1 else ["unet2d_keras_tf2"]
+    else ["unet2d_keras"]
+    if tf_major_version == 1
+    else ["unet2d_keras_tf2"]
 )
 TENSORFLOW_JS_MODELS: List[str] = []  # TODO: add a tensorflow_js example model
 
@@ -230,7 +232,9 @@ def convert_to_onnx(request: FixtureRequest):
     params=(
         []
         if tf_major_version is None
-        else ["unet2d_keras"] if tf_major_version == 1 else ["unet2d_keras_tf2"]
+        else ["unet2d_keras"]
+        if tf_major_version == 1
+        else ["unet2d_keras_tf2"]
     )
 )
 def unet2d_keras(request: FixtureRequest):

tests/test_prediction_pipeline.py

@@ -18,9 +18,9 @@ def _test_prediction_pipeline(
     )
 
     bio_model = load_description(model_package)
-    assert isinstance(
-        bio_model, (ModelDescr, ModelDescr04)
-    ), bio_model.validation_summary.format()
+    assert isinstance(bio_model, (ModelDescr, ModelDescr04)), (
+        bio_model.validation_summary.format()
+    )
     pp = create_prediction_pipeline(
         bioimageio_model=bio_model, weight_format=weights_format
     )

tests/test_tensor.py

@@ -23,7 +23,6 @@
     ],
 )
 def test_transpose_tensor_2d(axes: Sequence[str]):
-
     tensor = Tensor.from_numpy(np.random.rand(256, 256), dims=None)
     transposed = tensor.transpose([AxisId(a) for a in axes])
     assert transposed.ndim == len(axes)