| """Build static geography features for the 210 challenge cities. |
| |
| Inputs (raw/): cities_elev.csv (ID, Name, Lat, Lon, elevation_m) -- Open-Meteo elevation API (static DEM, no weather) |
| coast50.geojson -- Natural Earth 50m coastline (public domain) |
| Output: city_features.csv |
| |
| All inputs are static geography, so they are allowed by the challenge rules. |
| The current axes are HAND-DRAWN APPROXIMATIONS (see below), not an official dataset. |
| """ |
| import json |
| import numpy as np |
| import pandas as pd |
|
|
| R = 6371.0 |
|
|
|
|
| def hav(lat1, lon1, lat2, lon2): |
| p1, p2 = np.radians(lat1), np.radians(lat2) |
| a = np.sin((p2 - p1) / 2) ** 2 + np.cos(p1) * np.cos(p2) * np.sin(np.radians(lon2 - lon1) / 2) ** 2 |
| return 2 * R * np.arcsin(np.sqrt(a)) |
|
|
|
|
| def densify(pts, step_deg=0.05): |
| out = [] |
| for (x0, y0), (x1, y1) in zip(pts[:-1], pts[1:]): |
| n = max(2, int(max(abs(x1 - x0), abs(y1 - y0)) / step_deg)) |
| out += list(zip(np.linspace(x0, x1, n), np.linspace(y0, y1, n))) |
| return np.array(out) |
|
|
|
|
| |
| GULF_STREAM = [(-80.0, 25.0), (-79.9, 27.0), (-79.5, 29.0), (-78.5, 31.0), (-76.5, 33.5), |
| (-75.3, 35.2), (-72.0, 37.5), (-69.0, 39.5), (-65.0, 40.8)] |
| |
| CALIFORNIA = [(-127.0, 48.0), (-127.0, 45.0), (-126.0, 42.0), (-125.0, 39.0), |
| (-123.5, 36.5), (-121.5, 34.0), (-119.5, 32.0), (-117.5, 30.0)] |
| |
| LABRADOR = [(-55.0, 50.0), (-52.0, 46.0), (-56.0, 43.0), (-62.0, 41.5), (-68.0, 40.0), (-72.0, 38.5)] |
|
|
|
|
| def min_dist(lat, lon, line): |
| return float(hav(lat, lon, line[:, 1], line[:, 0]).min()) |
|
|
|
|
| def main(): |
| cities = pd.read_csv("raw/cities_elev.csv") |
| gj = json.load(open("raw/coast50.geojson")) |
| segs = [] |
| for f in gj["features"]: |
| g = f["geometry"] |
| lines = [g["coordinates"]] if g["type"] == "LineString" else g["coordinates"] |
| for ln in lines: |
| a = np.array(ln) |
| if len(a) > 1 and a[:, 0].max() > -140 and a[:, 0].min() < -50 and a[:, 1].max() > 10 and a[:, 1].min() < 60: |
| segs.append(densify([tuple(p) for p in a])) |
| coast = np.vstack(segs) |
| coast = coast[(coast[:, 0] > -140) & (coast[:, 0] < -50) & (coast[:, 1] > 10) & (coast[:, 1] < 60)] |
|
|
| gs, ca, lb = densify(GULF_STREAM), densify(CALIFORNIA), densify(LABRADOR) |
| rows = [] |
| for _, c in cities.iterrows(): |
| d_gs = min_dist(c.Lat, c.Lon, gs) |
| d_ca = min_dist(c.Lat, c.Lon, ca) |
| d_lb = min_dist(c.Lat, c.Lon, lb) |
| rows.append(dict( |
| ID=c.ID, Name=c.Name, Lat=c.Lat, Lon=c.Lon, |
| elevation_m=max(c.elevation_m, 0), |
| dist_coast_km=min_dist(c.Lat, c.Lon, coast), |
| dist_gulf_stream_km=d_gs, |
| dist_california_current_km=d_ca, |
| dist_labrador_km=d_lb, |
| )) |
| f = pd.DataFrame(rows) |
| |
| f["w_coast"] = np.exp(-f.dist_coast_km / 150) |
| f["w_gulf_stream"] = np.exp(-f.dist_gulf_stream_km / 300) * (f.Lon > -90) |
| f["w_calif_current"] = np.exp(-f.dist_california_current_km / 300) * (f.Lon < -110) |
| f.to_csv("city_features.csv", index=False) |
| print(f.describe().T[["min", "50%", "max"]]) |
| print(f.sort_values("dist_coast_km").head(3)[["Name", "dist_coast_km"]]) |
| print(f.sort_values("dist_coast_km").tail(3)[["Name", "dist_coast_km"]]) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
