feat(ruff): enable flake8-pyi, flake8-pytest-style, flake8-quotes, fl…

…ake8-return, flake8-raise, flake8-self, flake8-slots (#22654)

.ruff.toml

@@ -182,15 +182,14 @@ select = [
   "C4",    # flake8-comprehensions
   "C90",   # mccabe
   "COM",   # flake8-commas
-  "CPY",   # flake8-copyright
   "D",     # pydocstyle
   "DTZ",   # flake8-datetimez
   "E",     # pycodestyle
   "EM",    # flake8-errmsg
   "EXE",   # flake8-executable
   "F",     # Pyflakes
-  "FBT",   # flake8-boolean-trap
   "FAST",  # FastAPI
+  "FBT",   # flake8-boolean-trap
   "FLY",   # flynt
   "FURB",  # Refurb
   "G",     # flake8-logging-format
@@ -201,17 +200,25 @@ select = [
   "N",     # pep8-naming
   "NPY",   # NumPy
   "PERF",  # Perflint
-  "PIE",   # flake8-pie
   "PGH",   # pygrep-hooks
+  "PIE",   # flake8-pie
   "PL",    # Pylint
+  "PYI",   # flake8-pyi
+  "PT",    # flake8-pytest-style
+  "Q",     # flake8-quotes
+  "RET",   # flake8-return
+  "RSE",   # flake8-raise
   "RUF",   # Ruff
   "S",     # flake8-bandit
+  "SLF",   # flake8-self
+  "SLOT",  # flake8-slots
   "T10",   # flake8-debugger
   "T20",   # flake8-print
   "TRY",   # tryceratops
   "UP",    # pyupgrade
   "W",     # pycodestyle
   "YTT",   # flake8-2020
+  # "CPY", # flake8-copyright
   # "DJ",  # flake8-django
   # "FA",  # flake8-future-annotations
   # "INP", # flake8-no-pep420

authorization/hm-opal-client/opal_fetcher_postgres/provider.py

@@ -110,10 +110,9 @@ async def _fetch_(self) -> list[asyncpg.Record]:
                 return []
             row = await self._connection.fetchrow(self._event.config.query)
             return [row]
-        else:
-            if self._connection is None:
-                return []
-            return await self._connection.fetch(self._event.config.query)
+        if self._connection is None:
+            return []
+        return await self._connection.fetch(self._event.config.query)
 
     async def _process_(
         self,
@@ -123,10 +122,9 @@ async def _process_(
             if records and len(records) > 0:
                 return dict(records[0])
             return {}
-        else:
-            if self._event.config.dict_key is None:
-                return [dict(record) for record in records]
-            return {
-                dict(record)[self._event.config.dict_key]: dict(record)
-                for record in records
-            }
+        if self._event.config.dict_key is None:
+            return [dict(record) for record in records]
+        return {
+            dict(record)[self._event.config.dict_key]: dict(record)
+            for record in records
+        }

computer-vision/hm-imagebind/src/main.py

@@ -57,9 +57,8 @@ def download_file(client: httpx.Client, url: str, is_audio: bool) -> Path:
                 file.write(response.content)
                 logger.info(f"Downloaded file: {local_file_path}")
             return local_file_path
-        else:
-            msg = f"Download failed: {response}"
-            raise RuntimeError(msg)
+        msg = f"Download failed: {response}"
+        raise RuntimeError(msg)
 
     @staticmethod
     def download_all_files() -> tuple[list[Path], list[Path]]:
@@ -156,11 +155,10 @@ def create_gradio_interface(
             examples=audio_paths,
             flagging_mode="never",
         )
-        gradio_interface = gr.TabbedInterface(
+        return gr.TabbedInterface(
             [image_to_text_audio, text_to_image_audio, audio_to_image_text],
             ["Image to Text/Audio", "Text to Image/Audio", "Audio to Image/Text"],
         )
-        return gradio_interface
 
 
 def main() -> None:

data-orchestration/hm-airflow/dags/s3_download.py

@@ -16,12 +16,11 @@
     @task
     def download_from_s3(key: str, bucket: str, local_path: str) -> str:
         hook = S3Hook("s3_connection")
-        file_path = hook.download_file(
+        return hook.download_file(
             key=key,
             bucket_name=bucket,
             local_path=local_path,
         )
-        return file_path
 
     @task
     def rename_file(file_path: str, new_file_name: str) -> None:

data-visualization/iads/iads-data-manager/iads-data-reader/pyproject.toml

@@ -5,7 +5,7 @@ requires-python = "~=3.13.0"
 dependencies = [
   "pandas==2.2.3",
   "pyarrow==18.1.0",
-  "pywin32==308",
+  "pywin32==308; sys_platform=='win32'",
 ]
 
 [tool.uv]

data-visualization/iads/iads-data-manager/iads-data-reader/src/utils/iads_util.py

@@ -10,8 +10,6 @@
 from zoneinfo import ZoneInfo
 
 import pandas as pd
-import pythoncom
-import win32com.client
 
 logger = logging.getLogger(__name__)
 
@@ -79,15 +77,15 @@ def get_irig_times(iads_metadata_path: Path) -> tuple[str, str, int]:
         ):
             msg = "Invalid time format in archive info file"
             raise ValueError(msg)
-        elif irig_start_time > max_irig_time:
+        if irig_start_time > max_irig_time:
             msg = (
                 f"Start time '{irig_start_time}' cannot be bigger than {max_irig_time}"
             )
             raise ValueError(msg)
-        elif irig_end_time == "000:00:00:00.000" or irig_end_time > max_irig_time:
+        if irig_end_time == "000:00:00:00.000" or irig_end_time > max_irig_time:
             msg = f"End time '{irig_end_time}' cannot be 000:00:00:00.000 or bigger than {max_irig_time}"
             raise ValueError(msg)
-        elif irig_end_time <= irig_start_time:
+        if irig_end_time <= irig_start_time:
             msg = f"End time '{irig_end_time}' must be greater than start time '{irig_start_time}'"
             raise ValueError(msg)
 
@@ -129,9 +127,8 @@ def export_to_parquet(
         result = subprocess.run(cmd, capture_output=True, text=True, check=False)  # noqa: S603
         if result.returncode == 0:
             return parquet_file_path
-        else:
-            msg = f"Error output: {result}"
-            raise ValueError(msg)
+        msg = f"Error output: {result}"
+        raise ValueError(msg)
 
     @staticmethod
     def convert_irig_to_unix_time_ns(
@@ -152,6 +149,9 @@ def get_iads_dataframe(
         iads_data_path: Path,
         timezone: str,
     ) -> pd.DataFrame:
+        import pythoncom
+        import win32com.client
+
         iads_config_path = iads_data_path / Path(IadsUtil.IADS_CONFIG_FILE_NAME)
         iads_metadata_path = iads_data_path / Path(IadsUtil.IADS_METADATA_FILE_NAME)
         iads_config: Any | None = None

data-visualization/iads/iads-data-manager/iads-data-reader/src/utils/iads_util_test.py

@@ -45,7 +45,10 @@ def test_get_irig_times_invalid_date_format(self, tmp_path: Path) -> None:
 FlightDate = 2025-01-01
 """
         test_file.write_text(test_content)
-        with pytest.raises(ValueError):
+        with pytest.raises(
+            ValueError,
+            match="time data '2025-01-01' does not match format '%m/%d/%y'",
+        ):
             IadsUtil.get_irig_times(test_file)
 
     def test_get_irig_times_invalid_time_range(self, tmp_path: Path) -> None:
@@ -113,7 +116,7 @@ def test_get_irig_times_invalid_end_time_too_big(self, tmp_path: Path) -> None:
             IadsUtil.get_irig_times(test_file)
 
     @pytest.mark.parametrize(
-        "start_time,end_time",
+        ("start_time", "end_time"),
         [
             ("---:--:--:--.---", "---:--:--:--.---"),
             ("abc:12:34:56.789", "def:12:34:56.789"),

machine-learning/convolutional-neural-network/src/model/net.py

@@ -19,5 +19,4 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = torch.flatten(x, 1)  # flatten all dimensions except batch
         x = F.relu(self.fc1(x))
         x = F.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
+        return self.fc3(x)

machine-learning/graph-neural-network/src/model/conv.py

@@ -22,11 +22,10 @@ def __init__(self, emb_dim: int) -> None:
 
     def forward(self, x: Tensor, edge_index: Tensor, edge_attr: Tensor) -> Tensor:
         edge_embedding = self.bond_encoder(edge_attr)
-        out = self.mlp(
+        return self.mlp(
             (1 + self.eps) * x
             + self.propagate(edge_index, x=x, edge_attr=edge_embedding),
         )
-        return out
 
     def message(self, x_j: Tensor, edge_attr: Tensor) -> Tensor:
         return F.relu(x_j + edge_attr)

machine-learning/hm-flax/src/main.py

@@ -22,8 +22,7 @@ def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
         x = x.reshape((x.shape[0], -1))  # flatten
         x = nn.Dense(features=256)(x)
         x = nn.relu(x)
-        x = nn.Dense(features=10)(x)
-        return x
+        return nn.Dense(features=10)(x)
 
 
 def create_train_state(

machine-learning/hm-gradio/applications/classify-image/src/main.py

@@ -17,8 +17,7 @@ def predict(image_tensor: torch.Tensor) -> dict[str, float]:
         image_tensor = transforms.ToTensor()(image_tensor).unsqueeze(0)
         with torch.no_grad():
             prediction = torch.nn.functional.softmax(model(image_tensor)[0], dim=0)
-            confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-        return confidences
+            return {labels[i]: float(prediction[i]) for i in range(1000)}
 
     gr.Interface(
         fn=predict,

machine-learning/hm-kubeflow/pipelines/classify-mnist/src/main.py

@@ -28,8 +28,7 @@ def __init__(self) -> None:
             )
 
         def forward(self, x: torch.Tensor) -> torch.Tensor:
-            embedding = self.encoder(x)
-            return embedding
+            return self.encoder(x)
 
         def training_step(
             self,
@@ -45,8 +44,7 @@ def training_step(
             return loss
 
         def configure_optimizers(self) -> torch.optim.Optimizer:
-            optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
-            return optimizer
+            return torch.optim.Adam(self.parameters(), lr=1e-3)
 
     dataset = torchvision.datasets.MNIST(
         "data/",

machine-learning/hm-mlflow/experiments/classify-mnist/src/main.py

@@ -24,8 +24,7 @@ def __init__(self) -> None:
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        embedding = self.encoder(x)
-        return embedding
+        return self.encoder(x)
 
     def training_step(
         self,
@@ -41,8 +40,7 @@ def training_step(
         return loss
 
     def configure_optimizers(self) -> torch.optim.Optimizer:
-        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
-        return optimizer
+        return torch.optim.Adam(self.parameters(), lr=1e-3)
 
 
 def main() -> None: