def nearby_number(
self,
latitude: float,
longitude: float,
num_stations_nearby: int,
) -> pd.DataFrame:
"""
Wrapper for get_nearby_stations_by_number using the given parameter set. Returns
nearest stations defined by number.
Args:
latitude: latitude in degrees
longitude: longitude in degrees
num_stations_nearby: number of stations to be returned, greater 0
Returns:
pandas.DataFrame with station information for the selected stations
"""
if num_stations_nearby <= 0:
raise ValueError("'num_stations_nearby' has to be at least 1.")
coords = Coordinates(np.array(latitude), np.array(longitude))
metadata = self.all()
metadata = metadata.reset_index(drop=True)
distances, indices_nearest_neighbours = derive_nearest_neighbours(
metadata.LAT.values, metadata.LON.values, coords, num_stations_nearby
)
distances = pd.Series(distances)
indices_nearest_neighbours = pd.Series(indices_nearest_neighbours)
# If num_stations_nearby is higher then the actual amount of stations
# further indices and distances are added which have to be filtered out
distances = distances[: min(metadata.shape[0], num_stations_nearby)]
indices_nearest_neighbours = indices_nearest_neighbours[
: min(metadata.shape[0], num_stations_nearby)
]
distances_km = np.array(distances * KM_EARTH_RADIUS)
metadata_location = metadata.iloc[indices_nearest_neighbours, :].reset_index(
drop=True
)
metadata_location[DWDMetaColumns.DISTANCE_TO_LOCATION.value] = distances_km
if metadata_location.empty:
logger.warning(
f"No weather stations were found for coordinate "
f"{latitude}°N and {longitude}°E "
)
return metadata_location
def filter_by_rank(
self,
latitude: float,
longitude: float,
rank: int,
) -> StationsResult:
"""
Wrapper for get_nearby_stations_by_number using the given parameter set. Returns
nearest stations defined by number.
:param latitude: latitude in degrees
:param longitude: longitude in degrees
:param rank: number of stations to be returned, greater 0
:return: pandas.DataFrame with station information for the selected stations
"""
rank = int(rank)
if rank <= 0:
raise ValueError("'num_stations_nearby' has to be at least 1.")
coords = Coordinates(np.array(latitude), np.array(longitude))
df = self.all().df.reset_index(drop=True)
distances, indices_nearest_neighbours = derive_nearest_neighbours(
df[Columns.LATITUDE.value].values,
df[Columns.LONGITUDE.value].values,
coords,
rank,
)
distances = pd.Series(distances)
indices_nearest_neighbours = pd.Series(indices_nearest_neighbours)
# If num_stations_nearby is higher then the actual amount of stations
# further indices and distances are added which have to be filtered out
distances = distances[: min(df.shape[0], rank)]
indices_nearest_neighbours = indices_nearest_neighbours[
: min(df.shape[0], rank)
]
distances_km = np.array(distances * EARTH_RADIUS_KM)
df = df.iloc[indices_nearest_neighbours, :].reset_index(drop=True)
df[Columns.DISTANCE.value] = distances_km
if df.empty:
log.warning(
f"No weather stations were found for coordinate "
f"{latitude}°N and {longitude}°E and number {rank}"
)
result = StationsResult(self, df.reset_index(drop=True))
return result
def test_derive_nearest_neighbours():
coords = Coordinates(np.array([50.0, 51.4]), np.array([8.9, 9.3]))
metadata = pd.read_json(METADATA_FILE)
distances, indices_nearest_neighbours = derive_nearest_neighbours(
metadata.LAT.values, metadata.LON.values, coords)
np.testing.assert_array_almost_equal(distances,
np.array([0.00182907, 0.00227919]))
np.testing.assert_array_almost_equal(indices_nearest_neighbours,
np.array([432, 655]))
def filter_by_rank(
self,
latitude: float,
longitude: float,
rank: int,
) -> StationsResult:
"""
Wrapper for get_nearby_stations_by_number using the given parameter set. Returns
nearest stations_result defined by number.
:param latitude: latitude in degrees
:param longitude: longitude in degrees
:param rank: number of stations_result to be returned, greater 0
:return: pandas.DataFrame with station information for the selected stations_result
"""
rank = int(rank)
if rank <= 0:
raise ValueError("'num_stations_nearby' has to be at least 1.")
coords = Coordinates(np.array(latitude), np.array(longitude))
df = self.all().df.reset_index(drop=True)
distances, indices_nearest_neighbours = derive_nearest_neighbours(
df[Columns.LATITUDE.value].values,
df[Columns.LONGITUDE.value].values,
coords,
min(rank, df.shape[0]),
)
df = df.iloc[indices_nearest_neighbours.flatten(), :].reset_index(
drop=True)
df[Columns.DISTANCE.value] = pd.Series(distances.flatten() *
EARTH_RADIUS_KM,
dtype=float)
if df.empty:
log.warning(
f"No weather stations_result were found for coordinate "
f"{latitude}°N and {longitude}°E and number {rank}")
return StationsResult(self, df.reset_index(drop=True))
def test_derive_nearest_neighbours():
coords = Coordinates(np.array([50.0, 51.4]), np.array([8.9, 9.3]))
metadata = pd.DataFrame({
"station_id": [4371, 4373, 4411, 13904, 13965, 15207],
"latitude": [52.1042, 52.8568, 49.9195, 55.0, 48.2639, 51.2835],
"longitude": [8.7521, 11.1319, 8.9671, 6.3333, 8.8134, 9.359],
})
distances, indices_nearest_neighbours = derive_nearest_neighbours(
latitudes=metadata["latitude"].values,
longitudes=metadata["longitude"].values,
coordinates=coords,
number_nearby=1,
)
np.testing.assert_array_almost_equal(distances,
np.array([[0.001594], [0.002133]]))
np.testing.assert_array_almost_equal(indices_nearest_neighbours,
np.array([[2], [5]]))