def query_radius(self,
location: np.ndarray,
r: float) -> np.ndarray:
"""
Selects the indices of the points that lie within a given distance from
a given location.
:param location: Location to query in [lat, lon] format
:param r: Radius in meters
:return: Array of indices
"""
lat = location[0]
lon = location[1]
d0 = gm.num_haversine(lat, lon, self.lat0, self.lon0)
d1 = gm.num_haversine(lat, lon, self.lat1, self.lon1)
i0 = np.searchsorted(self.sorted0, d0 - r)
i1 = np.searchsorted(self.sorted0, d0 + r)
match0 = self.idx0[i0:i1 + 1]
i0 = np.searchsorted(self.sorted1, d1 - r)
i1 = np.searchsorted(self.sorted1, d1 + r)
match1 = self.idx1[i0:i1 + 1]
intersect = np.intersect1d(match0, match1)
dist = gm.vec_haversine(self.lats[intersect],
self.lons[intersect],
lat, lon)
return intersect[dist <= r]
def query_knn(self, location: np.ndarray, k: int) -> (np.ndarray, np.ndarray):
idx = h3.geo_to_h3(location[0], location[1], self.h3res)
i = 0
indices = np.zeros(0, dtype=np.uint64)
ring = np.zeros(0, dtype=np.uint64)
while indices.shape[0] < k:
i += 2
k_ring = h3.k_ring(idx, i)
ring = np.setdiff1d(k_ring, ring, assume_unique=True)
i0 = np.searchsorted(self.h3arr, ring, side='left', sorter=self.h3idx)
i1 = np.searchsorted(self.h3arr, ring, side='right', sorter=self.h3idx)
indices = np.hstack((indices,
np.hstack([np.arange(i, j, dtype=np.uint64)
for i, j in zip(i0, i1) if i != j])))
idx = self.h3idx[indices]
dist = gm.vec_haversine(self.locations[idx, 0],
self.locations[idx, 1],
location[0], location[1])
dist_idx = np.argsort(dist)
return idx[dist_idx[:k]], dist[dist_idx[:k]]
def calculate_dx(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Calculates the consecutive distance in meters.
:param df: Input DataFrame
:return: DataFrame with added 'dx' column.
"""
lat0 = df[self.lat_col][:-1].to_numpy()
lon0 = df[self.lon_col][:-1].to_numpy()
lat1 = df[self.lat_col][1:].to_numpy()
lon1 = df[self.lon_col][1:].to_numpy()
dist = vec_haversine(lat0, lon0, lat1, lon1)
df[self.dx_col] = np.insert(dist, 0, 0.0)
return df
def query_radius(self,
location: np.ndarray,
r: float) -> (np.ndarray, np.ndarray):
"""
Selects the indices of the points that lie within a given distance from
a given location.
:param location: Location to query in [lat, lon] format
:param r: Radius in meters
:return: Array of indices
"""
lat = location[0, 0]
lon = location[0, 1]
dist = gm.vec_haversine(self.lats, self.lons, lat, lon)
return np.argwhere(dist <= r)
def query_radius(self,
location: np.ndarray,
r: float) -> np.ndarray:
edge_len = h3.edge_length(self.h3res, unit="m")
idx = h3.geo_to_h3(location[0], location[1], self.h3res)
ring = h3.k_ring(idx, 1 + int(round(r / edge_len)))
i0 = np.searchsorted(self.h3arr, ring, side='left', sorter=self.h3idx)
i1 = np.searchsorted(self.h3arr, ring, side='right', sorter=self.h3idx)
indices = np.hstack([np.arange(i, j) for i, j in zip(i0, i1) if i != j])
idx = self.h3idx[indices]
dist = gm.vec_haversine(self.locations[idx, 0], self.locations[idx, 1],
location[0], location[1])
return self.h3idx[indices[np.argwhere(dist <= r).ravel()]]
def query_knn(self, location: np.ndarray, k: int) -> (np.ndarray, np.ndarray):
lat = location[0]
lon = location[1]
d0 = gm.num_haversine(lat, lon, self.lat0, self.lon0)
d1 = gm.num_haversine(lat, lon, self.lat1, self.lon1)
r = math.sqrt(k / self.density) * 2.0
intersect = np.zeros(0)
while intersect.shape[0] < k:
s0 = np.searchsorted(self.sorted0, [d0 - r, d0 + r])
s1 = np.searchsorted(self.sorted1, [d1 - r, d1 + r])
intersect = np.intersect1d(self.idx0[s0[0]:s0[1] + 1],
self.idx1[s1[0]:s1[1] + 1],
assume_unique=True)
r *= 4
dist = gm.vec_haversine(self.lats[intersect],
self.lons[intersect],
lat, lon)
idx = np.argsort(dist)
return intersect[idx][:k], dist[idx[:k]]
def calculate_sorted_distances(latitudes, longitudes, lat, lon):
dist = gm.vec_haversine(latitudes, longitudes, lat, lon)
idx = np.argsort(dist)
return idx, dist[idx]
def query_knn(self, location: np.array, k: int) -> np.array:
dist = gm.vec_haversine(self.lats, self.lons,
location[0], location[1])
idx = np.argsort(dist)
return idx[:k], dist[idx[:k]]
