type checker fixes

pipeline/bundle_pdfs.py

@@ -316,7 +316,7 @@ def build_client_lookup(
     clients_obj = artifact.get("clients", [])
     clients = clients_obj if isinstance(clients_obj, list) else []
     lookup: Dict[tuple[str, str], dict] = {}
-    for client in clients:  # type: ignore[var-annotated]
+    for client in clients:
         sequence = client.get("sequence")  # type: ignore[attr-defined]
         client_id = client.get("client_id")  # type: ignore[attr-defined]
         lookup[(sequence, client_id)] = client  # type: ignore[typeddict-item]

pipeline/preprocess.py

@@ -47,7 +47,7 @@
 from hashlib import sha1
 from pathlib import Path
 from string import Formatter
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, overload
 import pandas as pd
 import yaml
 from babel.dates import format_date
@@ -95,6 +95,7 @@
 
 THRESHOLD = 80
 
+
 def convert_date_string(
     date_str: str | datetime | pd.Timestamp, locale: str = "en"
 ) -> str | None:
@@ -172,6 +173,7 @@ def format_iso_date_for_language(iso_date: str, language: str) -> str:
 
     return format_date(date_obj, format="long", locale=locale)
 
+
 def check_addresses_complete(df: pd.DataFrame) -> pd.DataFrame:
     """
     Check if address fields are complete in the DataFrame.
@@ -192,17 +194,13 @@ def check_addresses_complete(df: pd.DataFrame) -> pd.DataFrame:
     ]
 
     for col in address_cols:
-        df[col] = (
-            df[col]
-            .astype(str)
-            .str.strip()
-            .replace({"": pd.NA, "nan": pd.NA})
-        )
+        df[col] = df[col].astype(str).str.strip().replace({"": pd.NA, "nan": pd.NA})
 
     # Build combined address line
     df["ADDRESS"] = (
-        df["STREET_ADDRESS_LINE_1"].fillna("") + " " +
-        df["STREET_ADDRESS_LINE_2"].fillna("")
+        df["STREET_ADDRESS_LINE_1"].fillna("")
+        + " "
+        + df["STREET_ADDRESS_LINE_2"].fillna("")
     ).str.strip()
 
     df["ADDRESS"] = df["ADDRESS"].replace({"": pd.NA})
@@ -282,6 +280,7 @@ def over_16_check(date_of_birth, date_notice_delivery):
 
     return age >= 16
 
+
 def configure_logging(output_dir: Path, run_id: str) -> Path:
     """Configure file logging for the preprocessing step.
 
@@ -387,6 +386,7 @@ def read_input(file_path: Path) -> pd.DataFrame:
         LOG.error("Failed to read %s: %s", file_path, exc)
         raise
 
+
 def normalize(col: str) -> str:
     """Normalize formatting prior to matching."""
 
@@ -443,11 +443,10 @@ def map_columns(df: pd.DataFrame, required_columns=REQUIRED_COLUMNS):
 
     # Check each input column against required columns
     for input_col in normalized_input_cols:
-
         col_name, score, index = process.extractOne(
             query=input_col,
             choices=[normalize(req) for req in required_columns],
-            scorer=fuzz.partial_ratio
+            scorer=fuzz.partial_ratio,
         )
 
         # Remove column if it has a score of 0
@@ -460,18 +459,32 @@ def map_columns(df: pd.DataFrame, required_columns=REQUIRED_COLUMNS):
 
             # print colname and score for debugging
             print(f"Matching '{input_col}' to '{best_match}' with score {score}")
-        
+
     return df.rename(columns=col_map), col_map
 
+
+@overload
 def filter_columns(
     df: pd.DataFrame, required_columns: list[str] = REQUIRED_COLUMNS
-) -> pd.DataFrame:
+) -> pd.DataFrame: ...
+
+
+@overload
+def filter_columns(
+    df: None, required_columns: list[str] = REQUIRED_COLUMNS
+) -> None: ...
+
+
+def filter_columns(
+    df: pd.DataFrame | None, required_columns: list[str] = REQUIRED_COLUMNS
+) -> pd.DataFrame | None:
     """Filter dataframe to only include required columns."""
     if df is None or df.empty:
         return df
 
     return df[[col for col in df.columns if col in required_columns]]
 
+
 def ensure_required_columns(df: pd.DataFrame) -> pd.DataFrame:
     """Normalize column names and validate that all required columns are present.
 
@@ -767,7 +780,7 @@ def build_preprocess_result(
     sorted_df["SEQUENCE"] = [f"{idx + 1:05d}" for idx in range(len(sorted_df))]
 
     clients: List[ClientRecord] = []
-    for row in sorted_df.itertuples(index=False):  # type: ignore[attr-defined]
+    for row in sorted_df.itertuples(index=False):
         client_id = str(row.CLIENT_ID)  # type: ignore[attr-defined]
         sequence = row.SEQUENCE  # type: ignore[attr-defined]
         dob_iso = (

tests/unit/test_preprocess.py

@@ -112,6 +112,7 @@ def test_handles_non_alphabetic_characters(self):
         """Verify that non-letter characters are preserved."""
         assert preprocess.normalize("123 Name!") == "123 name!"
 
+
 @pytest.mark.unit
 class TestFilterColumns:
     """Unit tests for filter_columns() column filtering utility."""
@@ -143,7 +144,7 @@ def test_returns_empty_dataframe_when_no_required_columns_present(self):
 
     def test_handles_empty_dataframe(self):
         """Verify that an empty DataFrame is returned unchanged."""
-        df = pd.DataFrame(columns=["child_first_name", "child_last_name"])
+        df = pd.DataFrame(columns=pd.Index(["child_first_name", "child_last_name"]))
         result = preprocess.filter_columns(df, ["child_first_name"])
         assert result.empty
 
@@ -164,7 +165,10 @@ def test_order_of_columns_is_preserved(self):
         required = ["dob", "child_first_name"]
         result = preprocess.filter_columns(df, required)
 
-        assert list(result.columns) == ["child_first_name", "dob"] or list(result.columns) == required
+        assert (
+            list(result.columns) == ["child_first_name", "dob"]
+            or list(result.columns) == required
+        )
         # Either column order can appear depending on implementation; both are acceptable
 
     def test_ignores_required_columns_not_in_df(self):
@@ -176,6 +180,7 @@ def test_ignores_required_columns_not_in_df(self):
         assert "child_first_name" in result.columns
         assert "missing_column" not in result.columns
 
+
 @pytest.mark.unit
 class TestReadInput:
     """Unit tests for read_input function."""
@@ -785,5 +790,3 @@ def test_build_result_no_warning_for_unique_client_ids(
         # Should have NO warnings about duplicates
         duplicate_warnings = [w for w in result.warnings if "Duplicate client ID" in w]
         assert len(duplicate_warnings) == 0
-
-