Fix tfl scraping

pipeline/journey_times/__main__.py

@@ -12,6 +12,7 @@ from .config import (
     OUTPUT_DIR,
     MAX_DISTANCE_KM,
 )
+from .models import JourneyResult
 from .results import CheckpointSaver, results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
 from pipeline.utils import haversine_km_expr
@@ -63,12 +64,44 @@ def main():
         ),
     )
 
+    # 25556/76273 
+
+    # Resume from checkpoint if one exists
+    checkpoint_path = checkpoint_saver._checkpoint_path()
+    prior_results: list[JourneyResult] = []
+    if checkpoint_path.exists():
+        checkpoint_df = pl.read_parquet(checkpoint_path)
+        completed_postcodes = set(
+            checkpoint_df.filter(pl.col("public_transport_easy_minutes").is_not_null())["postcode"].to_list()
+        )
+        prior_results = [
+            JourneyResult(
+                postcode=row["postcode"],
+                public_transport_easy_minutes=row["public_transport_easy_minutes"],
+                public_transport_quick_minutes=row["public_transport_quick_minutes"],
+                cycling_minutes=row["cycling_minutes"],
+                error=row["error"],
+            )
+            for row in checkpoint_df.iter_rows(named=True)
+        ]
+        checkpoint_saver.results = prior_results
+        checkpoint_saver._last_save_count = len(prior_results)
+        postcode_data = [
+            (pc, lat, lon)
+            for pc, lat, lon in postcode_data
+            if pc not in completed_postcodes
+        ]
+        print(
+            f"Resumed from checkpoint: {len(prior_results):,} already done, "
+            f"{len(postcode_data):,} remaining"
+        )
+
     def on_result(result):
         pbar.update(1)
         checkpoint_saver.add_result(result)
 
     with tqdm(total=len(postcode_data), desc="Fetching journeys") as pbar:
-        results = asyncio.run(
+        new_results = asyncio.run(
             fetch_journey_times(
                 postcode_data,
                 destination,
@@ -79,11 +112,12 @@ def main():
             )
         )
 
-    results_df = results_to_dataframe(results)
+    all_results = prior_results + new_results
+    results_df = results_to_dataframe(all_results)
 
-    postcodes_processed = [pc for pc, _, _ in postcode_data]
+    all_postcodes = {r.postcode for r in all_results}
     coords_df = postcodes_df.filter(
-        pl.col("postcode").is_in(postcodes_processed)
+        pl.col("postcode").is_in(all_postcodes)
     ).select(["postcode", "lat", "long"])
     results_df = coords_df.join(results_df, on="postcode", how="left")
 
@@ -94,7 +128,7 @@ def main():
     )
 
     successful = results_df.filter(pl.col("cycling_minutes").is_not_null()).height
-    print(f"Completed: {successful}/{len(results)} successful")
+    print(f"Completed: {successful}/{len(all_results)} successful")
 
     parquet_path = save_results(results_df, destination.name)
     checkpoint_saver.cleanup_checkpoint()

pipeline/journey_times/tfl_client.py

@@ -219,7 +219,9 @@ async def fetch_journey_times(
 
     # TFL API authentication via app_key query parameter
     tfl_token = os.environ.get("TFL_TOKEN")
-    params = {"app_key": tfl_token} if tfl_token else {}
+    if not tfl_token:
+        raise RuntimeError("TFL_TOKEN environment variable not set")
+    params = {"app_key": tfl_token}
 
     async with httpx.AsyncClient(
         base_url=BASE_URL,