def getRegions(point, regiondf):
res = 0
for index, row in regiondf.iterrows():
print(row['geometry'])
p = MultiPolygon(row['geometry'])
print(p)
if p.contains(point):
res = row["name"]
break
print(res)
return res
def print_closest_dis():
# Load data
df = pd.read_csv(
'https://raw.githubusercontent.com/trendct/dunkin-donuts-ct/master/dunkindonuts.csv'
)
ma_dunkin_donuts = df.loc[df['state'] == 'MA']
dunkin_donuts_coordinates = ma_dunkin_donuts[['lng', 'lat']].to_numpy()
with urlopen(
'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
) as response:
counties = json.load(response)
df = pd.json_normalize(counties['features'])
ma_geodata = pd.json_normalize(counties['features'])[
df['properties.STATE'] == '25']['geometry.coordinates'].to_numpy()
ma_coordinates = flatten(ma_geodata)
# Create Voronoi and Polygons
vor = Voronoi(dunkin_donuts_coordinates)
polygons = [Polygon(county) for county in ma_coordinates]
polygon = MultiPolygon(polygons)
biggestDistanceYet = 0
bestVertex = []
closest_dunkin_coord = []
# Calculate best vertex
for vertex in vor.vertices:
closest_dunkin_branch = closest_dunkin(vertex,
dunkin_donuts_coordinates)
minDis = distance.cdist([vertex], [closest_dunkin_branch])
if biggestDistanceYet < minDis and polygon.contains(Point(vertex)):
biggestDistanceYet = minDis
bestVertex = vertex
closest_dunkin_coord = closest_dunkin_branch
print(
"The furthest away you can get from a dunkin donuts is {} km at the coordinates {}. "
"The closest dunkin donuts would be at {}.".format(
haversine_np(bestVertex, closest_dunkin_coord), bestVertex,
closest_dunkin_coord))
# Plot everything
fig = voronoi_plot_2d(vor, show_vertices=False, line_width=0)
ax = fig.gca()
ax.add_patch(
plt.Circle(bestVertex,
biggestDistanceYet,
color='g',
fill=False,
linewidth=2))
plt.plot(bestVertex[0], bestVertex[1], 'o')
plt.ylim(40.5, 43)
for a in polygons:
x, y = a.exterior.xy
plt.plot(x, y)
ax.set_aspect('equal')
ax.set_ylabel('Latitude')
ax.set_xlabel('Longitude')
plt.show()
fig.savefig('vonoroi.png')
