def get_cities(lat, lon, radius, minpop=0):
region = od.radius_to_region(lat, lon, radius)
cities = get_cities_region(region, minpop=minpop)
clats = num.array([c.lat for c in cities])
clons = num.array([c.lon for c in cities])
dists = od.distance_accurate50m_numpy(lat, lon, clats, clons)
order = num.argsort(dists)
cities_sorted = [cities[i] for i in order if dists[i] < radius]
return cities_sorted
def get_cities(lat, lon, radius, minpop=0):
region = od.radius_to_region(lat, lon, radius)
cities = get_cities_region(region, minpop=minpop)
clats = num.array([c.lat for c in cities])
clons = num.array([c.lon for c in cities])
dists = od.distance_accurate50m_numpy(lat, lon, clats, clons)
order = num.argsort(dists)
cities_sorted = [cities[i] for i in order if dists[i] < radius]
return cities_sorted
def grid_points_in_error_ellipses(lat_ev, lon_ev, depth_ev, error_h, error_z,
path_models=None, gf_store_id=None):
pathlist = Path(path_models).glob('model_%s_*' % gf_store_id)
region = orthodrome.radius_to_region(lat_ev, lon_ev, error_h)
grid_points = []
for path in sorted(pathlist):
path = str(path)
model_coordinates = path.split("_")
lat = float(model_coordinates[3])
lon = float(model_coordinates[4])
depth = float(model_coordinates[5])
dists = orthodrome.distance_accurate50m_numpy(lat_ev, lon_ev, lat, lon)
if dists < error_h:
if depth_ev-error_z < depth and depth_ev+error_z > depth:
grid_points.append(path)
return grid_points