def ModelLayerCoordList(grnd, azimuth, elevation_angle, layer_hgts):
layer_num = [0]
lat_layer_coord = [grnd.lat]
lon_layer_coord = [grnd.lon]
for layer in range(layer_hgts.shape[0])[1:]:
# Find the height of the model cell then convert to km
lat_index = grnd.lat_min_index - GetCoordValue(lat_layer_coord[-1],
lat_or_lon='lat')
lon_index = grnd.lon_min_index - GetCoordValue(lon_layer_coord[-1],
lat_or_lon='lon')
cell_height = layer_hgts[layer, lat_index, lon_index] / 1000
horizontal_dist = cell_height / np.tan(np.deg2rad(elevation_angle))
# Set a geographic point for the origin of the layer
origin = geopy.Point(lat_layer_coord[-1], lon_layer_coord[-1])
# Use geopy to calculate the destination
destination = geodist(kilometers=horizontal_dist).destination(
origin, azimuth)
layer_num.append(layer)
lat_layer_coord.append(destination.latitude)
lon_layer_coord.append(destination.longitude)
coord_df = pd.DataFrame(
{
'LatEntry': lat_layer_coord,
'LonEntry': lon_layer_coord
},
index=layer_num)
return coord_df
def ringScooter(self, scooter):
assert self.authorization is not None
birdDistance = geodist((self.latitude,self.longitude), (scooter["location"]["latitude"], scooter["location"]["longitude"]))
if self.radius == 0 or self.radius >= birdDistance.m:
url = "https://{}/bird/chirp".format(self.domain)
headers = {
'location': self.getLocationHeader(),
'authorization': "Bird {}".format(self.authorization),
'content-type': 'application/json; charset=UTF-8'
}
headers.update(self.deviceHeaders)
payload = {
'alarm': False,
'bird_id': scooter['id']
}
r = requests.put(url, headers=headers, data=json.dumps(payload))
payload['alarm'] = True
r = requests.put(url, headers=headers, data=json.dumps(payload))
return r.status_code, birdDistance
else:
return 0, birdDistance
def get_speed(row):
prev_coords = (row['prev_latitude'], row['prev_longitude'])
curr_coords = (row['latitude'], row['longitude'])
delta = row['timestamp'] - row['prev_timestamp']
if pd.isnull(delta):
return np.nan
time = abs(delta.total_seconds())
if np.isnan(prev_coords[0]) or np.isnan(prev_coords[1]) or np.isnan(
curr_coords[0]) or np.isnan(curr_coords[1]):
return np.nan
if time == 0:
return np.nan
return geodist(curr_coords, prev_coords).meters / time
def _test_get_displacement_vector():
prms, _ = mepo2.smallnetv5_fc5_pose_euler_5dof_crp192_rawImSz256_lossl1()
grps = get_groups(prms, '0001')
errs = []
gAll, aAll = [], []
for g in grps:
N = g.num
perm = np.random.permutation(N)
if N < 2:
continue
p1, p2 = perm[0], perm[1]
pt1 = g.data[p1].pts.camera[0:3]
pt2 = g.data[p2].pts.camera[0:3]
gDist = geodist(pt1[0:2], pt2[0:2]).meters
x, y, z = get_displacement_vector(pt1, pt2)
aDist = np.sqrt(x * x + y * y)
errs.append(gDist - aDist)
gAll.append(gDist)
aAll.append(aDist)
errs, gAll, aAll = np.array(errs), np.array(gAll), np.array(aAll)
rErr = np.abs(errs) / np.minimum(np.abs(gAll), np.abs(aAll))
print 'Mean relative Error: %f, sd relative error: %f' % (np.mean(rErr),
np.std(rErr))
return errs, rErr, gAll, aAll
def get_distance_between_groups(grp1, grp2): lb1, lb2 = grp1.data[0], grp2.data[0] tPt1 = lb1.label.pts.target tPt2 = lb2.label.pts.target tDist = geodist(tPt1, tPt2).meters return tDist