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198 | import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
import warnings
warnings.filterwarnings("ignore")
plt.rcParams["font.sans-serif"] = ["Arial Unicode MS", "DejaVu Sans", "SimHei"]
plt.rcParams["axes.unicode_minus"] = False
def create_sample_data():
np.random.seed(42)
regions = {
"north_america": {
"lon_range": (-130, -60),
"lat_range": (25, 55),
"n_points": 150,
"speed_bias": 100,
},
"europe": {
"lon_range": (-10, 40),
"lat_range": (35, 65),
"n_points": 120,
"speed_bias": 80,
},
"australia": {
"lon_range": (110, 155),
"lat_range": (-45, -10),
"n_points": 80,
"speed_bias": 100,
},
"south_america": {
"lon_range": (-80, -35),
"lat_range": (-55, 10),
"n_points": 60,
"speed_bias": 50,
},
"africa": {
"lon_range": (10, 50),
"lat_range": (-30, 30),
"n_points": 40,
"speed_bias": 30,
},
"asia": {
"lon_range": (70, 140),
"lat_range": (20, 60),
"n_points": 80,
"speed_bias": 60,
},
}
all_lons = []
all_lats = []
all_speeds = []
for region_name, region_data in regions.items():
n = region_data["n_points"]
lon_min, lon_max = region_data["lon_range"]
lat_min, lat_max = region_data["lat_range"]
bias = region_data["speed_bias"]
lons = np.random.uniform(lon_min, lon_max, n)
lats = np.random.uniform(lat_min, lat_max, n)
speeds = np.random.exponential(bias, n)
speeds = np.random.exponential(bias, n)
all_lons.extend(lons)
all_lats.extend(lats)
all_speeds.extend(speeds)
return np.array(all_lons), np.array(all_lats), np.array(all_speeds)
def get_speed_category(speeds):
colors = []
categories = []
for speed in speeds:
if speed < 25:
colors.append("#7FC8F8")
categories.append("slow")
elif speed < 100:
colors.append("#FFA500")
categories.append("medium")
else:
colors.append("#90EE90")
categories.append("fast")
return colors, categories
def plot_coverage_map(
lons,
lats,
speeds,
title="Average throughput at each distinct location\nof Starlink NDT speed tests",
figsize=(14, 8),
save_path=None,
):
"""绘制地理覆盖图
参数:
- lons: 经度数组
- lats: 纬度数组
- speeds: 速度数组(Mbps)
- title: 图表标题
- figsize: 图表大小
- save_path: 保存路径(可选)
"""
fig = plt.figure(figsize=figsize)
ax = plt.axes(projection=ccrs.PlateCarree())
# 设置地图范围(全球)
ax.set_global()
# 添加地图特征
# 添加陆地(浅灰色填充)
ax.add_feature(cfeature.LAND, facecolor="#F5F5F5", edgecolor="none", zorder=1)
# 添加海洋(白色)
ax.add_feature(cfeature.OCEAN, facecolor="white", zorder=0)
# 添加国家边界(黑色细线)
ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor="#333333", zorder=2)
# 添加海岸线
ax.coastlines(linewidth=0.8, color="#333333", zorder=2)
# 获取颜色分类
colors, categories = get_speed_category(speeds)
# 绘制散点: 使用较小的点和一定的透明度来处理重叠
scatter = ax.scatter(
lons,
lats,
c=colors,
s=20, # 点的大小
alpha=0.6, # 透明度
edgecolors="none",
transform=ccrs.PlateCarree(),
zorder=3,
)
# 添加图例
from matplotlib.patches import Patch
legend_elements = [
Patch(facecolor="#7FC8F8", label="[0,25) Mbps"),
Patch(facecolor="#FFA500", label="[25,100) Mbps"),
Patch(facecolor="#90EE90", label="[100,∞) Mbps"),
]
# 将图例放在左下角
ax.legend(
handles=legend_elements,
loc="lower left",
frameon=True,
facecolor="white",
edgecolor="black",
fontsize=10,
title="Mbps",
)
# 添加标题
plt.title(title, fontsize=14, fontweight="bold", pad=20)
# 调整布局
plt.tight_layout()
# 保存图形
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches="tight", facecolor="white")
print(f"图形已保存到: {save_path}")
return fig, ax
def main():
lons, lats, speeds = create_sample_data()
print(f"生成了 {len(lons)} 个测试点")
print(f"速度范围: {speeds.min():.2f} - {speeds.max():.2f} Mbps")
fig1, ax1 = plot_coverage_map(
lons, lats, speeds, save_path="./starlink_coverage_detailed.png"
)
plt.show()
if __name__ == "__main__":
main()
|
