Spotter Network Chaser Map

chaser_map.py

import urllib.request
import numpy as np
from scipy.spatial import cKDTree
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from scipy.ndimage import gaussian_filter
from datetime import datetime
import matplotlib.pyplot as plt
fp = urllib.request.urlopen("http://www.spotternetwork.org/feeds/gr-no.txt")
objs = []
for line in fp:
    if b"Object" in line:
        objs.append(line.decode("utf-8")[7:].strip().split(","))
kd = cKDTree(np.array(objs, dtype=float)[:, ::-1])
lons = np.arange(-102, -95, 0.1)
lats = np.arange(33, 40, 0.1)
lon_grid, lat_grid = np.meshgrid(lons, lats)
points = np.vstack([lon_grid.ravel(), lat_grid.ravel()]).T
print(points.shape)
matches = kd.query_ball_point(points, 0.2)
match_grid = np.array([len(x) for x in matches]).reshape(lon_grid.shape)
proj = ccrs.PlateCarree()
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(1, 1, 1, projection=proj)
countries = cfeature.NaturalEarthFeature("cultural", "admin_0_countries", "50m", facecolor="None", edgecolor="k")
states = cfeature.NaturalEarthFeature("cultural", "admin_1_states_provinces", "50m", facecolor="None", edgecolor="k")
ax.add_feature(countries)
ax.add_feature(states)
plt.pcolormesh(lon_grid, lat_grid, np.ma.array(match_grid, mask=match_grid<=2))
plt.colorbar()
valid = datetime.utcnow()
valid_str = valid.strftime("%Y-%m-%d %H:%M")
valid_out = valid.strftime("%Y%m%d_%H%M")
plt.title(f"Spotter Network Chaser Neighborhood Counts Valid {valid_str}")
plt.savefig(f"chaser_hist_{valid_out}.png", dpi=200, bbox_inches="tight")

Here is a short script for grabbing and plotting spotter network reports as a neighborhood map.
Requires

• numpy
• scipy
• cartopy
• matplotlib