feat(ruff): enable flake8-blind-except, flake8-boolean-trap, flake8-b…

…ugbear, flake8-builtins (#22609)

.ruff.toml

@@ -173,16 +173,20 @@ exclude = [
 # https://docs.astral.sh/ruff/rules/
 [lint]
 select = [
+  "A",     # flake8-builtins
   "AIR",   # Airflow
   "ANN",   # flake8-annotations
   "ASYNC", # flake8-async
+  "B",     # flake8-bugbear
+  "BLE",   # flake8-blind-except
   "C90",   # mccabe
   "COM",   # flake8-commas
   "D",     # pydocstyle
   "E",     # pycodestyle
   "EM",    # flake8-errmsg
   "EXE",   # flake8-executable
   "F",     # Pyflakes
+  "FBT",   # flake8-boolean-trap
   "FAST",  # FastAPI
   "FLY",   # flynt
   "FURB",  # Refurb
@@ -213,6 +217,8 @@ ignore = [
   "D203",    # Conflicts with D211
   "D212",
   "E501",
+  "FBT001",
+  "FBT002",
   "ISC001",
   "PLR0913",
   "PLR0915",

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

@@ -29,7 +29,7 @@ class PostgresFetcherConfig(FetcherConfig):
         description="can be overridden or complement parts of the DSN",
     )
     query: str = Field(..., description="the query")
-    fetch_one: bool = Field(False, description="fetch only one row")
+    fetch_one: bool = Field(False, description="fetch only one row")  # noqa: FBT003
     dict_key: str | None = Field(
         None,
         description="array of dict will map to dict with provided dict_key",

cloud-infrastructure/terraform/environments/production/aws/general/files/aws-glue/spark-scripts/src/hm_write_parquet_to_delta_table_adsb_2x_flight_trace_data.py

@@ -68,7 +68,7 @@ def add_dbflags_columns(
     for column_name, mask in columns_and_masks:
         df = df.withColumn(
             column_name,
-            when((col(flag_column_name).bitwiseAND(mask)) > 0, True).otherwise(False),
+            when((col(flag_column_name).bitwiseAND(mask)) > 0, True).otherwise(False),  # noqa: FBT003
         )
     return df
 
@@ -93,8 +93,8 @@ def add_trace_flags_columns(
         else:
             df = df.withColumn(
                 column_name,
-                when((col(flag_column_name).bitwiseAND(mask)) > 0, True).otherwise(
-                    False,
+                when((col(flag_column_name).bitwiseAND(mask)) > 0, True).otherwise(  # noqa: FBT003
+                    False,  # noqa: FBT003
                 ),
             )
     return df
@@ -111,8 +111,8 @@ def add_trace_on_ground_column(
             trace_on_ground_column_name,
             when(
                 col(trace_altitude_ft_column_name) == lit(ground_value),
-                True,
-            ).otherwise(False),
+                True,  # noqa: FBT003
+            ).otherwise(False),  # noqa: FBT003
         )
     return df
 

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

@@ -45,7 +45,7 @@ def __init__(self) -> None:
         self.table: Table | None = None
 
     @staticmethod
-    def download_file(client: httpx.Client, url: str, is_audio: bool = True) -> Path:
+    def download_file(client: httpx.Client, url: str, is_audio: bool) -> Path:
         filename = url.split("/")[-1]
         if not is_audio:
             filename = f"{filename}.jpg"
@@ -65,10 +65,12 @@ def download_file(client: httpx.Client, url: str, is_audio: bool = True) -> Path
     def download_all_files() -> tuple[list[Path], list[Path]]:
         with httpx.Client(follow_redirects=True) as client:
             audio_paths = [
-                ImageBindSearch.download_file(client, url, True) for url in AUDIO_URLS
+                ImageBindSearch.download_file(client, url, is_audio=True)
+                for url in AUDIO_URLS
             ]
             image_paths = [
-                ImageBindSearch.download_file(client, url, False) for url in IMAGE_URLS
+                ImageBindSearch.download_file(client, url, is_audio=False)
+                for url in IMAGE_URLS
             ]
             return audio_paths, image_paths
 

data-processing/hm-spark/applications/find-taxi-top-routes-sql/src/utils/zone.py

@@ -4,8 +4,8 @@
 def load_zones(spark: SparkSession, zone_data_path: str) -> DataFrame:
     return (
         spark.read.format("csv")
-        .option("inferSchema", True)
-        .option("header", True)
+        .option("inferSchema", True)  # noqa: FBT003
+        .option("header", True)  # noqa: FBT003
         .load(zone_data_path)
     )
 

data-processing/hm-spark/applications/find-taxi-top-routes/src/utils/zone.py

@@ -4,8 +4,8 @@
 def load_zones(spark: SparkSession, zone_data_path: str) -> DataFrame:
     return (
         spark.read.format("csv")
-        .option("inferSchema", True)
-        .option("header", True)
+        .option("inferSchema", True)  # noqa: FBT003
+        .option("header", True)  # noqa: FBT003
         .load(zone_data_path)
     )
 

data-visualization/iads/iads-data-manager/iads-config-reader/src/main.py

@@ -34,13 +34,13 @@ def process_config(iads_config_path: Path) -> None:
         iads_config = win32com.client.Dispatch("IadsConfigInterface.IadsConfig")
 
         show_version_from_file(iads_config, iads_config_path)
-        iads_config.Open(iads_config_path, True)
+        iads_config.Open(iads_config_path, True)  # noqa: FBT003
 
         execute_query(iads_config, "select * from Desktops")
         execute_query(iads_config, "select System.RowNumber from Desktops")
         execute_query(iads_config, "select Parameter from ParameterDefaults")
 
-        iads_config.Close(True)
+        iads_config.Close(True)  # noqa: FBT003
     except Exception:
         logger.exception("Failed to close IADS config")
     finally:

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

@@ -171,7 +171,7 @@ def get_iads_dataframe(
                 # Get IADS config
                 pythoncom.CoInitialize()
                 iads_config = win32com.client.Dispatch("IadsConfigInterface.IadsConfig")
-                iads_config.Open(str(temp_iads_config_path), False)
+                iads_config.Open(str(temp_iads_config_path), False)  # noqa: FBT003
 
                 # Get signals
                 query = "select Parameter from ParameterDefaults"

hardware-in-the-loop/national-instruments/veristand/hm-veristand/src/main.py

@@ -18,7 +18,7 @@ def engine_demo_basic(engine_power, desired_rpm) -> None:  # noqa: ANN001
 
 @nivs_rt_sequence
 def run_engine_demo() -> None:
-    engine_demo_basic(BooleanValue(True), DoubleValue(2500))
+    engine_demo_basic(BooleanValue(True), DoubleValue(2500))  # noqa: FBT003
 
 
 def run_non_deterministic() -> None:

hm-xxhash/src/main.py

@@ -7,14 +7,14 @@
 
 
 def get_file_xxh128(file_path: Path) -> str:
-    hash = xxhash.xxh128()
+    xxh128_hash = xxhash.xxh128()
     with open(file_path, "rb") as file:
         while True:
             data = file.read(8192)  # Read 8192 bytes at a time to use less memory
             if not data:
                 break
-            hash.update(data)
-    return hash.hexdigest()
+            xxh128_hash.update(data)
+    return xxh128_hash.hexdigest()
 
 
 def main() -> None:

machine-learning/convolutional-neural-network/src/train.py

@@ -13,7 +13,7 @@ def train(
     net.train()
     running_loss = 0.0
 
-    for i, data in enumerate(data_loader, 0):
+    for _i, data in enumerate(data_loader, 0):
         inputs, labels = data
 
         inputs = inputs.to(device)

machine-learning/graph-neural-network/src/main.py

@@ -23,7 +23,7 @@ def train(
     model.train()
     total_loss = 0
 
-    for step, batch in enumerate(tqdm(loader, desc="Iteration")):
+    for _step, batch in enumerate(tqdm(loader, desc="Iteration")):
         device_batch = batch.to(device)
 
         if device_batch.x.shape[0] == 1 or device_batch.batch[-1] == 0:
@@ -60,7 +60,7 @@ def evaluate(
     y_true = []
     y_pred = []
 
-    for step, batch in enumerate(tqdm(loader, desc="Iteration")):
+    for _step, batch in enumerate(tqdm(loader, desc="Iteration")):
         device_batch = batch.to(device)
 
         if device_batch.x.shape[0] == 1:

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

@@ -185,7 +185,7 @@ def __init__(
         # List of MLPs to transform virtual node at every layer
         self.mlp_virtualnode_list = torch.nn.ModuleList()
 
-        for layer in range(num_layer):
+        for _layer in range(num_layer):
             if gnn_type == "gin":
                 self.convs.append(GINConv(emb_dim))
             elif gnn_type == "gcn":
@@ -196,7 +196,7 @@ def __init__(
 
             self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))
 
-        for layer in range(num_layer - 1):
+        for _layer in range(num_layer - 1):
             self.mlp_virtualnode_list.append(
                 torch.nn.Sequential(
                     torch.nn.Linear(emb_dim, 2 * emb_dim),

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

@@ -41,8 +41,8 @@ def main() -> None:
             markdown_path = pdf_path.with_suffix(".md")
             markdown_path.write_text(markdown_content, encoding="utf-8")
             logger.info(f"Converted {pdf_path.name}")
-    except Exception as e:
-        logger.info(f"Error processing PDFs: {e}")
+    except Exception:
+        logger.exception("Error processing PDFs")
 
 
 if __name__ == "__main__":

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

@@ -13,10 +13,10 @@ def main() -> None:
     )
     labels = res.text.split("\n")
 
-    def predict(input: torch.Tensor) -> dict[str, float]:
-        input = transforms.ToTensor()(input).unsqueeze(0)
+    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(input)[0], dim=0)
+            prediction = torch.nn.functional.softmax(model(image_tensor)[0], dim=0)
             confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
         return confidences