circles.py

import random
import time
import pandas as pd
import cartopy
import cartopy.feature as cf
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import shapely.geometry as sgeom
import numpy as np
from cartopy.geodesic import Geodesic


# generate some fake data
def generate_data(count):
    data ={'id':[],'lat':[],'lon':[],'timestamp':[],'radio_horizon':[]}
    for i in range(count):
        
            data["id"].append(i),
            data["lat"].append(random.uniform(45.0,60.0)),
            data["lon"].append(random.uniform(0.0,30.0)),
            data["timestamp"].append(int(time.time())),
            data["radio_horizon"].append(random.uniform(50000.0,100000.0))
        

    return pd.DataFrame(data)


def draw_map():

    gd = Geodesic()

    # number of entries in data list
    samples = 1000
    # alpha value for drawing the circles
    alpha = max(1.0/samples,0.01) # seem like the alpha cant go below 0.01
    data = generate_data(samples)

    # projection setup
    # https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#platecarree
    src_crs = ccrs.PlateCarree()
    lcc = ccrs.LambertConformal(central_longitude=data.lon.mean(), central_latitude=data.lat.mean())

    # figure setup
    fig = plt.figure(figsize=(7,7))
    ax = fig.add_subplot(111, projection=lcc)
    # add coasts and countries
    ax.add_feature(cf.COASTLINE)
    ax.add_feature(cf.BORDERS)

    # create polygons for circles
    circles = []
    for lon, lat,radio_horizon in zip(data.lon, data.lat, data.radio_horizon):
        polygon = gd.circle(lon=lon, lat=lat, radius=radio_horizon)
        circles.append(sgeom.Polygon(polygon))

    # add them to the map
    ax.add_geometries(circles, crs=src_crs, alpha=alpha,  color="000000")

    # crop the map based on data
    ax.set_extent([data.lon.min(), data.lon.max(), data.lat.min(), data.lat.max()])

    # save the map
    plt.savefig('circles', dpi=300)

draw_map()

folium.py

def generate_data(count):
    data ={'id':[],'lat':[],'lon':[],'timestamp':[],'radio_horizon':[]}
    for i in range(count):
        
            data["id"].append(i),
            data["lat"].append(random.uniform(45.0,60.0)),
            data["lon"].append(random.uniform(0.0,30.0)),
            data["timestamp"].append(int(time.time())),
            data["radio_horizon"].append(random.uniform(100000.0,200000.0))
        

    return pd.DataFrame(data)

import random
import time
import pandas as pd
import io
import folium
import folium.plugins


samples = 10000
data = generate_data(samples)
map = folium.Map([data.lat.mean(), data.lon.mean()], zoom_start=4)

cluster = folium.plugins.MarkerCluster().add_to(map)

for lat, lon, radio_horizon in zip(data.lat, data.lon, data.radio_horizon):
    print(lat, lon, radio_horizon)

    # https://python-visualization.github.io/folium/latest/reference.html#folium.vector_layers.Circle
    folium.vector_layers.Circle(
        location=[lat, lon],
        tooltip=str(radio_horizon),
        radius=radio_horizon,
        color='#3186cc',
        fill=True,
        fill_color='#3186cc',
        opacity=1.0/samples
    ).add_to(cluster)

map.save("map.html")