feat: add download date and restructure columns in FAA master conversion

src/derive_from_faa_master_txt.py

@@ -12,17 +12,54 @@ def convert_faa_master_txt_to_csv(zip_path: Path, csv_path: Path, date: str = No
     if date is not None:
         df.insert(0, "download_date", date)
     
-    col = "transponder_code_hex"
-    df = df[[col] + [c for c in df.columns if c != col]]
-    df = df.rename(columns={"transponder_code_hex": "icao"})
     registrant = pd.json_normalize(df["registrant"]).add_prefix("registrant_")
     df = df.drop(columns="registrant").join(registrant)
+    
+    # Move transponder_code_hex to second column (after registration_number)
+    cols = df.columns.tolist()
+    cols.remove("transponder_code_hex")
+    cols.insert(1, "transponder_code_hex")
+    df = df[cols]
+    
     df = df.rename(columns={"aircraft_type": "aircraft_type_2"})
     aircraft = pd.json_normalize(df["aircraft"].where(df["aircraft"].notna(), {})).add_prefix("aircraft_")
     df = df.drop(columns="aircraft").join(aircraft)
     df = df.rename(columns={"engine_type": "engine_type_2"})
     engine = pd.json_normalize(df["engine"].where(df["engine"].notna(), {})).add_prefix("engine_")
     df = df.drop(columns="engine").join(engine)
-    df = df.sort_values(by=["icao"])
+    certification = pd.json_normalize(df["certification"].where(df["certification"].notna(), {})).add_prefix("certificate_")
+    df = df.drop(columns="certification").join(certification)
+    
+    # Create planequery_airframe_id
+    df["planequery_airframe_id"] = (
+        normalize(df["aircraft_manufacturer"])
+        + "|"
+        + normalize(df["aircraft_model"])
+        + "|"
+        + normalize(df["serial_number"])
+    )
+    
+    # Move planequery_airframe_id to come after registration_number
+    cols = df.columns.tolist()
+    cols.remove("planequery_airframe_id")
+    reg_idx = cols.index("registration_number")
+    cols.insert(reg_idx + 1, "planequery_airframe_id")
+    df = df[cols]
+    
     df.to_csv(csv_path, index=False)
     return df
+
+
+
+def normalize(s: pd.Series) -> pd.Series:
+    return (
+        s.fillna("")
+         .astype(str)
+         .str.upper()
+         .str.strip()
+         # collapse whitespace
+         .str.replace(r"\s+", " ", regex=True)
+         # remove characters that cause false mismatches
+         .str.replace(r"[^\w\-]", "", regex=True)
+    )
+