Source Copenhagen transit data from Rejseplanen GTFS
- Replace OSM Overpass transit data with the Rejseplanen GTFS feed (routes keyed by (agency, short name); styles from modes.yaml) - Draw one shape per (style, ref, direction), choosing the shape that serves the most in-area stops so lines pass the stops we show - Collapse stops by names (verified unambiguous); prune stops whose serving refs have no drawn line within 300 m - Patch Københavns Havn + Nordhavn into the area polygon so ferry routes and sub-harbour metro tunnels survive clipping; 100 m buffer closes relation boundary slivers - Drop OSM download pipeline; keep tiled basemap
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marvin
@@ -1,41 +1,150 @@
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#!/usr/bin/env python3
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"""Convert raw GTFS txt files into portable GeoPackage layers + a colour table.
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STATUS: BLOCKED on download_gtfs.py (no feed URL yet).
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Intended outputs (data/processed):
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gtfs_shapes.gpkg (shapes.txt -> LineString per shape_id, EPSG:4326)
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Inputs (data/raw/gtfs): routes.txt, trips.txt, stops.txt, shapes.txt
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Outputs (data/processed):
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gtfs_shapes.gpkg (shapes.txt -> LineString per shape_id, EPSG:4326,
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each shape tagged with its route_id)
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gtfs_stops.gpkg (stops.txt -> Point per stop, EPSG:4326)
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route_colors.csv (route_id, route_short_name, route_type, route_color)
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route_colors.csv (route_id, agency_id, route_short_name, route_long_name,
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route_type, route_color, route_text_color)
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Uses partridge for fast GTFS parsing. prepare.py reads these when present and
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falls back to OSM-only data when absent.
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The feed is nationwide, so everything is pre-filtered to the Copenhagen area:
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stops by location, shapes to those intersecting the area polygon (or a
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fallback bbox when data/processed/area.gpkg does not exist yet), and routes
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to those having at least one surviving shape. prepare.py clips precisely to
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the area later.
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Note: this feed leaves route_color empty for the Copenhagen operators, so
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prepare.py will mostly fall through to OSM colour tags / the styling palette.
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stop_times.txt is not needed here and is intentionally not parsed (220 MB).
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"""
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import sys
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from pathlib import Path
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import geopandas as gpd
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import pandas as pd
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from shapely.geometry import LineString, box
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from shapely.geometry.base import BaseGeometry
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from _common import GTFS_RAW, PROCESSED
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# Generous Copenhagen bbox (covers København, Frederiksberg, Amager, and
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# immediate surroundings: airport, Hellerup, Lyngby, Brøndby, ...).
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CPH_BBOX = box(12.30, 55.55, 12.75, 55.85)
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def load_area() -> BaseGeometry:
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"""City Pass area polygon if build_area has run, else the fallback bbox."""
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area_path = PROCESSED / "area.gpkg"
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if area_path.exists():
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return gpd.read_file(area_path).geometry.union_all()
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print(f"no {area_path}; using fallback Copenhagen bbox", flush=True)
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return CPH_BBOX
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def build_stops(area: BaseGeometry) -> gpd.GeoDataFrame:
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stops = pd.read_csv(
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GTFS_RAW / "stops.txt",
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usecols=["stop_id", "stop_name", "stop_lat", "stop_lon",
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"location_type", "parent_station"],
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dtype={"stop_id": str, "parent_station": str},
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)
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minx, miny, maxx, maxy = area.bounds
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in_bbox = (
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stops["stop_lat"].between(miny, maxy)
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& stops["stop_lon"].between(minx, maxx)
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)
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stops = stops[in_bbox].copy()
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g = gpd.GeoDataFrame(
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stops,
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geometry=gpd.points_from_xy(stops["stop_lon"], stops["stop_lat"]),
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crs="EPSG:4326",
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)
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return g[g.intersects(area)].drop(columns=["stop_lat", "stop_lon"])
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def build_shapes(area: BaseGeometry) -> gpd.GeoDataFrame:
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pts = pd.read_csv(GTFS_RAW / "shapes.txt", dtype={"shape_id": str})
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minx, miny, maxx, maxy = area.bounds
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hits = pts[
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pts["shape_pt_lat"].between(miny, maxy)
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& pts["shape_pt_lon"].between(minx, maxx)
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]
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pts = pts[pts["shape_id"].isin(set(hits["shape_id"].unique()))]
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pts = pts.sort_values(["shape_id", "shape_pt_sequence"])
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lines = (
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pts.groupby("shape_id")
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.apply(
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lambda g: LineString(zip(g["shape_pt_lon"], g["shape_pt_lat"])),
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include_groups=False,
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)
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.rename("geometry")
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.reset_index()
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)
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g = gpd.GeoDataFrame(lines, geometry="geometry", crs="EPSG:4326")
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return g[g.intersects(area)]
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def build_routes(
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shapes: gpd.GeoDataFrame,
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) -> tuple[gpd.GeoDataFrame, pd.DataFrame]:
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"""Tag shapes with route_id and keep routes having >= 1 surviving shape."""
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trips = pd.read_csv(
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GTFS_RAW / "trips.txt",
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usecols=["route_id", "shape_id"],
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dtype=str,
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).dropna(subset=["shape_id"])
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shape_to_route = (
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trips[trips["shape_id"].isin(set(shapes["shape_id"]))]
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.drop_duplicates("shape_id")
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.set_index("shape_id")["route_id"]
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)
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shapes = shapes.copy()
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shapes["route_id"] = shapes["shape_id"].map(shape_to_route)
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routes = pd.read_csv(GTFS_RAW / "routes.txt", dtype=str).fillna("")
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routes = routes[
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routes["route_id"].isin(set(shape_to_route.unique()))
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].copy()
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return shapes, routes[
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[
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"route_id", "agency_id", "route_short_name", "route_long_name",
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"route_type", "route_color", "route_text_color",
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]
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]
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def main():
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if not (GTFS_RAW / "routes.txt").exists():
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print(
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"gtfs_to_geopackage: BLOCKED — no GTFS data found in "
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f"{GTFS_RAW}. Run download_gtfs.py first once a feed URL is set.",
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"gtfs_to_geopackage: no GTFS data found in "
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f"{GTFS_RAW}. Run download_gtfs.py first.",
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file=sys.stderr,
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)
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sys.exit(2)
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# TODO (when GTFS available):
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# import partridge as pt
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# import geopandas as gpd
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# from shapely.geometry import LineString, Point
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# feed = pt.load_geo_feed(str(GTFS_RAW))
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# shapes -> gtfs_shapes.gpkg (LineString per shape_id)
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# stops -> gtfs_stops.gpkg
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# routes -> route_colors.csv (route_id, route_short_name, route_type, route_color)
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print("gtfs_to_geopackage: not yet implemented (GTFS feed unavailable).")
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sys.exit(2)
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PROCESSED.mkdir(parents=True, exist_ok=True)
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area = load_area()
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stops = build_stops(area)
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print(f"stops in area: {len(stops)}", flush=True)
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shapes = build_shapes(area)
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print(f"shapes in area: {len(shapes)}", flush=True)
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shapes, routes = build_routes(shapes)
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print(f"routes in area: {len(routes)}", flush=True)
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stops.to_file(PROCESSED / "gtfs_stops.gpkg", driver="GPKG", layer="stops")
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shapes.to_file(
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PROCESSED / "gtfs_shapes.gpkg", driver="GPKG", layer="shapes"
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)
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routes.to_csv(PROCESSED / "route_colors.csv", index=False)
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print(
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"wrote gtfs_stops.gpkg, gtfs_shapes.gpkg, route_colors.csv "
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f"into {PROCESSED}",
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flush=True,
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)
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if __name__ == "__main__":
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