remove data fetcher, fix park priority glitch, save pipeline output a…

…s parquet file in GCS

data/docker-compose.yml

@@ -74,19 +74,5 @@ services:
     extra_hosts:
       - host.docker.internal:host-gateway
 
-  data-fetcher:
-    build:
-      context: .
-      dockerfile: Dockerfile-fetch-data
-    depends_on:
-      - vacant-lots-proj
-    environment:
-      - VACANT_LOTS_DB
-    volumes:
-      - ./src:/usr/src/app
-      - /etc/timezone:/etc/timezone:ro
-      - /etc/localtime:/etc/localtime:ro
-    network_mode: 'host'
-
 volumes:
   database_volume:

data/src/classes/featurelayer.py

@@ -284,15 +284,25 @@ def create_centroid_gdf(self):
         self.centroid_gdf = self.gdf.copy()
         self.centroid_gdf.loc[:, "geometry"] = self.gdf["geometry"].centroid
 
-    def build_and_publish_pmtiles(self, tileset_id):
-        zoom_threshold = 13
+    def build_and_publish(self, tiles_file_id_prefix: str) -> None:
+        """
+        Builds PMTiles and a Parquet file from a GeoDataFrame and publishes them to Google Cloud Storage.
+
+        Args:
+            tiles_file_id_prefix (str): The ID prefix used for naming the PMTiles and Parquet files, coming from config.
+
+        Raises:
+            ValueError: Raised if the generated PMTiles file is smaller than the minimum allowed size, indicating a potential corruption or incomplete file.
+        """
+        zoom_threshold: int = 13
 
         # Export the GeoDataFrame to a temporary GeoJSON file
-        temp_geojson_points = f"tmp/temp_{tileset_id}_points.geojson"
-        temp_geojson_polygons = f"tmp/temp_{tileset_id}_polygons.geojson"
-        temp_pmtiles_points = f"tmp/temp_{tileset_id}_points.pmtiles"
-        temp_pmtiles_polygons = f"tmp/temp_{tileset_id}_polygons.pmtiles"
-        temp_merged_pmtiles = f"tmp/temp_{tileset_id}_merged.pmtiles"
+        temp_geojson_points: str = f"tmp/temp_{tiles_file_id_prefix}_points.geojson"
+        temp_geojson_polygons: str = f"tmp/temp_{tiles_file_id_prefix}_polygons.geojson"
+        temp_pmtiles_points: str = f"tmp/temp_{tiles_file_id_prefix}_points.pmtiles"
+        temp_pmtiles_polygons: str = f"tmp/temp_{tiles_file_id_prefix}_polygons.pmtiles"
+        temp_merged_pmtiles: str = f"tmp/temp_{tiles_file_id_prefix}_merged.pmtiles"
+        temp_parquet: str = f"tmp/{tiles_file_id_prefix}.parquet"
 
         # Reproject
         gdf_wm = self.gdf.to_crs(epsg=4326)
@@ -304,8 +314,36 @@ def build_and_publish_pmtiles(self, tileset_id):
         self.centroid_gdf = self.centroid_gdf.to_crs(epsg=4326)
         self.centroid_gdf.to_file(temp_geojson_points, driver="GeoJSON")
 
+        # Load the GeoJSON from the polygons, drop geometry, and save as Parquet
+        gdf_polygons = gpd.read_file(temp_geojson_polygons)
+        df_no_geom = gdf_polygons.drop(columns=["geometry"])
+
+        # Check if the DataFrame has fewer than 25,000 rows
+        num_rows, num_cols = df_no_geom.shape
+        if num_rows < 25000:
+            print(
+                f"Parquet file has {num_rows} rows, which is fewer than 25,000. Skipping upload."
+            )
+            return
+
+        # Save the DataFrame as Parquet
+        df_no_geom.to_parquet(temp_parquet)
+
+        # Upload Parquet to Google Cloud Storage
+        blob_parquet = bucket.blob(f"{tiles_file_id_prefix}.parquet")
+        try:
+            blob_parquet.upload_from_filename(temp_parquet)
+            parquet_size = os.stat(temp_parquet).st_size
+            parquet_size_mb = parquet_size / (1024 * 1024)
+            print(
+                f"Parquet upload successful! Size: {parquet_size} bytes ({parquet_size_mb:.2f} MB), Dimensions: {num_rows} rows, {num_cols} columns."
+            )
+        except Exception as e:
+            print(f"Parquet upload failed: {e}")
+            return
+
         # Command for generating PMTiles for points up to zoom level zoom_threshold
-        points_command = [
+        points_command: list[str] = [
             "tippecanoe",
             f"--output={temp_pmtiles_points}",
             f"--maximum-zoom={zoom_threshold}",
@@ -320,7 +358,7 @@ def build_and_publish_pmtiles(self, tileset_id):
         ]
 
         # Command for generating PMTiles for polygons from zoom level zoom_threshold
-        polygons_command = [
+        polygons_command: list[str] = [
             "tippecanoe",
             f"--output={temp_pmtiles_polygons}",
             f"--minimum-zoom={zoom_threshold}",
@@ -334,7 +372,7 @@ def build_and_publish_pmtiles(self, tileset_id):
         ]
 
         # Command for merging the two PMTiles files into a single output file
-        merge_command = [
+        merge_command: list[str] = [
             "tile-join",
             f"--output={temp_merged_pmtiles}",
             "--no-tile-size-limit",
@@ -347,20 +385,23 @@ def build_and_publish_pmtiles(self, tileset_id):
         for command in [points_command, polygons_command, merge_command]:
             subprocess.run(command)
 
-        write_files = [f"{tileset_id}_staging.pmtiles"]
+        write_files: list[str] = [f"{tiles_file_id_prefix}_staging.pmtiles"]
 
         if write_production_tiles_file:
-            write_files.append(f"{tileset_id}.pmtiles")
+            write_files.append(f"{tiles_file_id_prefix}.pmtiles")
 
-        # check whether the temp saved tiles files is big enough.
-        # If not then it might be corrupted so log error and don't upload to gcp.
-        file_size = os.stat(temp_merged_pmtiles).st_size
+        # Check whether the temp saved tiles files is big enough.
+        file_size: int = os.stat(temp_merged_pmtiles).st_size
         if file_size < min_tiles_file_size_in_bytes:
             raise ValueError(
-                f"{temp_merged_pmtiles} is {file_size} bytes in size but should be at least {min_tiles_file_size_in_bytes}.  Therefore, we are not uploading any files to the GCP bucket.  The file may be corrupt or incomplete."
+                f"{temp_merged_pmtiles} is {file_size} bytes in size but should be at least {min_tiles_file_size_in_bytes}. Therefore, we are not uploading any files to the GCP bucket. The file may be corrupt or incomplete."
             )
 
-        # Upload to Google Cloud Storage
+        # Upload PMTiles to Google Cloud Storage
         for file in write_files:
             blob = bucket.blob(file)
-            blob.upload_from_filename(temp_merged_pmtiles)
+            try:
+                blob.upload_from_filename(temp_merged_pmtiles)
+                print(f"PMTiles upload successful for {file}!")
+            except Exception as e:
+                print(f"PMTiles upload failed for {file}: {e}")

data/src/data_utils/park_priority.py

@@ -9,6 +9,7 @@
 from classes.featurelayer import FeatureLayer
 from config.config import USE_CRS
 from tqdm import tqdm
+import pyogrio
 
 
 def get_latest_shapefile_url() -> str:
@@ -32,17 +33,61 @@ def get_latest_shapefile_url() -> str:
         raise ValueError("Shapefile link not found on the page")
 
 
+def download_and_process_shapefile(
+    geojson_path: str, park_url: str, target_files: List[str], file_name_prefix: str
+) -> gpd.GeoDataFrame:
+    """
+    Downloads and processes the shapefile to create a GeoDataFrame for Philadelphia parks.
+
+    Args:
+        geojson_path (str): Path to save the GeoJSON file.
+        park_url (str): URL to download the shapefile.
+        target_files (List[str]): List of files to extract from the shapefile.
+        file_name_prefix (str): Prefix for the file names to be extracted.
+
+    Returns:
+        gpd.GeoDataFrame: GeoDataFrame containing the processed park data.
+    """
+    print("Downloading and processing park priority data...")
+    response: requests.Response = requests.get(park_url, stream=True)
+    total_size: int = int(response.headers.get("content-length", 0))
+
+    with tqdm(
+        total=total_size, unit="iB", unit_scale=True, desc="Downloading"
+    ) as progress_bar:
+        buffer: BytesIO = BytesIO()
+        for data in response.iter_content(1024):
+            size: int = buffer.write(data)
+            progress_bar.update(size)
+
+    with zipfile.ZipFile(buffer) as zip_ref:
+        for file_name in tqdm(target_files, desc="Extracting"):
+            zip_ref.extract(file_name, "tmp/")
+
+    print("Processing shapefile...")
+    pa_parks: gpd.GeoDataFrame = gpd.read_file(
+        "tmp/" + file_name_prefix + "_ParkPriorityAreas.shp"
+    )
+    pa_parks = pa_parks.to_crs(USE_CRS)
+
+    phl_parks: gpd.GeoDataFrame = pa_parks[pa_parks["ID"].str.startswith("42101")]
+    phl_parks = phl_parks.loc[:, ["ParkNeed", "geometry"]]
+
+    if isinstance(phl_parks, gpd.GeoDataFrame):
+        phl_parks.rename(columns={"ParkNeed": "park_priority"}, inplace=True)
+    else:
+        raise TypeError("Expected a GeoDataFrame, got Series or another type instead")
+
+    print(f"Writing filtered data to GeoJSON: {geojson_path}")
+    phl_parks.to_file(geojson_path, driver="GeoJSON")
+
+    return phl_parks
+
+
 def park_priority(primary_featurelayer: FeatureLayer) -> FeatureLayer:
     """
     Downloads and processes park priority data, then joins it with the primary feature layer.
 
-    This function performs the following steps:
-    1. Downloads the latest shapefile from TPL.
-    2. Extracts relevant files from the downloaded zip.
-    3. Processes the shapefile, filtering for Philadelphia County.
-    4. Creates a GeoJSON file with the processed data.
-    5. Joins the processed data with the primary feature layer.
-
     Args:
         primary_featurelayer (FeatureLayer): The primary feature layer to join with park priority data.
 
@@ -66,47 +111,22 @@ def park_priority(primary_featurelayer: FeatureLayer) -> FeatureLayer:
 
     os.makedirs("tmp/", exist_ok=True)
 
-    if not os.path.exists(geojson_path):
-        print("Downloading and processing park priority data...")
-        response: requests.Response = requests.get(park_url, stream=True)
-        total_size: int = int(response.headers.get("content-length", 0))
-
-        with tqdm(
-            total=total_size, unit="iB", unit_scale=True, desc="Downloading"
-        ) as progress_bar:
-            buffer: BytesIO = BytesIO()
-            for data in response.iter_content(1024):
-                size: int = buffer.write(data)
-                progress_bar.update(size)
-
-        with zipfile.ZipFile(buffer) as zip_ref:
-            for file_name in tqdm(target_files, desc="Extracting"):
-                zip_ref.extract(file_name, "tmp/")
-
-        print("Processing shapefile...")
-        pa_parks: gpd.GeoDataFrame = gpd.read_file(
-            "tmp/" + file_name_prefix + "_ParkPriorityAreas.shp"
-        )
-        pa_parks = pa_parks.to_crs(USE_CRS)
-
-        phl_parks: gpd.GeoDataFrame = pa_parks[pa_parks["ID"].str.startswith("42101")]
-        phl_parks = phl_parks.loc[:, ["ParkNeed", "geometry"]]
-
-        # Print the type of phl_parks for debugging
-        print(f"Type of phl_parks: {type(phl_parks)}")
-
-        if isinstance(phl_parks, gpd.GeoDataFrame):
-            phl_parks.rename(columns={"ParkNeed": "park_priority"}, inplace=True)
+    try:
+        if os.path.exists(geojson_path):
+            print(f"GeoJSON file already exists, loading from {geojson_path}")
+            phl_parks: gpd.GeoDataFrame = gpd.read_file(geojson_path)
         else:
-            raise TypeError(
-                "Expected a GeoDataFrame, got Series or another type instead"
+            raise pyogrio.errors.DataSourceError(
+                "GeoJSON file missing, forcing download."
             )
 
-        print(f"Writing filtered data to GeoJSON: {geojson_path}")
-        phl_parks.to_file(geojson_path, driver="GeoJSON")
-    else:
-        print(f"GeoJSON file already exists, loading from {geojson_path}")
-        phl_parks: gpd.GeoDataFrame = gpd.read_file(geojson_path)
+    except (pyogrio.errors.DataSourceError, ValueError) as e:
+        print(f"Error loading GeoJSON: {e}. Re-downloading and processing shapefile.")
+        if os.path.exists(geojson_path):
+            os.remove(geojson_path)  # Delete the corrupted GeoJSON if it exists
+        phl_parks = download_and_process_shapefile(
+            geojson_path, park_url, target_files, file_name_prefix
+        )
 
     park_priority_layer: FeatureLayer = FeatureLayer("Park Priority")
     park_priority_layer.gdf = phl_parks

data/src/script.py

@@ -99,7 +99,7 @@
 conn.commit()
 
 # Post to Mapbox
-dataset.build_and_publish_pmtiles(tiles_file_id_prefix)
+dataset.build_and_publish(tiles_file_id_prefix)
 
 # if we are reloading, run the diff report, then archive the backup and finally prune old archives
 if FORCE_RELOAD: