Allow just one location to search for nearby stations

panodata · Sep 3, 2020 · e7a7277 · e7a7277
1 parent 7821f73
commit e7a7277
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Showing 3 changed files with 47 additions and 56 deletions.
diff --git a/docs/pages/api.rst b/docs/pages/api.rst
@@ -18,8 +18,8 @@ The API for the historical weather data mainly consists of the following functio
     - calculates the close weather stations based on the coordinates for the requested
       data
     - either selected by rank (n stations) or by distance in km
-    - it returns a list of station ids that can be used to download the data plus the
-      distances
+    - it returns a DataFrame with meta data, distances [in km] and station ids that can be used to download the
+      data plus the
 
 - **DWDStationRequest:**
     - a class that can combine multiple periods/date ranges for any number of stations
@@ -97,18 +97,21 @@ Now, we know an approximate location, where we cant to get data for the temperat
 
 .. code-block:: Python
 
+    from datetime import datetime
     from wetterdienst import Parameter, PeriodType, TimeResolution
     from wetterdienst import get_nearby_stations
 
     get_nearby_stations(
-        [50., 51.4], [8.9, 9.3],
+        50., 8.9,
+        datetime(2020, 1, 1),
+        datetime(2020, 1, 20),
         Parameter.TEMPERATURE_AIR,
         TimeResolution.HOURLY,
         PeriodType.RECENT,
         num_stations_nearby=1
     )
 
-The function returns us the station ids (and distances), that we can use to get our
+The function returns a meta data DataFrame, where we can find weather station ids and distances to get our
 observation data.
 
 .. code-block:: Python

diff --git a/tests/additionals/test_geo_location.py b/tests/additionals/test_geo_location.py
@@ -27,19 +27,19 @@
 def test_get_nearby_stations():
     # Test for one nearest station
     nearest_station = get_nearby_stations(
-        [50.0, 51.4],
-        [8.9, 9.3],
+        50.0,
+        8.9,
         datetime(2020, 1, 1),
         datetime(2020, 1, 20),
         Parameter.TEMPERATURE_AIR,
         TimeResolution.HOURLY,
         PeriodType.RECENT,
         num_stations_nearby=1,
     )
-    nearest_station[0] = nearest_station[0].drop("TO_DATE", axis="columns")
+    nearest_station = nearest_station.drop("TO_DATE", axis="columns")
 
     pd.testing.assert_frame_equal(
-        nearest_station[0],
+        nearest_station,
         pd.DataFrame(
             [
                 [
@@ -70,18 +70,18 @@ def test_get_nearby_stations():
     )
 
     nearest_station = get_nearby_stations(
-        [50.0, 51.4],
-        [8.9, 9.3],
+        50.0,
+        8.9,
         datetime(2020, 1, 1),
         datetime(2020, 1, 20),
         Parameter.TEMPERATURE_AIR,
         TimeResolution.HOURLY,
         PeriodType.RECENT,
         max_distance_in_km=20,
     )
-    nearest_station[0] = nearest_station[0].drop("TO_DATE", axis="columns")
+    nearest_station = nearest_station.drop("TO_DATE", axis="columns")
     pd.testing.assert_frame_equal(
-        nearest_station[0],
+        nearest_station,
         pd.DataFrame(
             [
                 [
@@ -135,8 +135,8 @@ def test_get_nearby_stations():
 
     with pytest.raises(ValueError):
         get_nearby_stations(
-            [50.0, 51.4],
-            [8.9, 9.3],
+            50.0,
+            8.9,
             datetime(2020, 1, 1),
             datetime(2020, 1, 20),
             Parameter.TEMPERATURE_AIR,
@@ -148,8 +148,8 @@ def test_get_nearby_stations():
 
     with pytest.raises(ValueError):
         get_nearby_stations(
-            [50.0, 51.4],
-            [8.9, 9.3],
+            51.4,
+            9.3,
             datetime(2020, 1, 1),
             datetime(2020, 1, 20),
             Parameter.TEMPERATURE_AIR,
@@ -160,8 +160,8 @@ def test_get_nearby_stations():
 
     with pytest.raises(InvalidParameterCombination):
         get_nearby_stations(
-            [50.0, 51.4],
-            [8.9, 9.3],
+            51.4,
+            9.3,
             datetime(2020, 1, 1),
             datetime(2020, 1, 20),
             Parameter.SOIL,

diff --git a/wetterdienst/additionals/geo_location.py b/wetterdienst/additionals/geo_location.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 import pandas as pd
+import logging
 from scipy.spatial import cKDTree
 
 from wetterdienst.additionals.functions import (
@@ -22,24 +23,26 @@
 
 KM_EARTH_RADIUS = 6371
 
+logger = logging.getLogger(__name__)
+
 
 def get_nearby_stations(
-    latitudes: Union[List[float], np.array],
-    longitudes: Union[List[float], np.array],
+    latitude: float,
+    longitude: float,
     minimal_available_date: Union[datetime, str],
     maximal_available_date: Union[datetime, str],
     parameter: Union[Parameter, str],
     time_resolution: Union[TimeResolution, str],
     period_type: Union[PeriodType, str],
     num_stations_nearby: Optional[int] = None,
     max_distance_in_km: Optional[float] = None,
-) -> List[pd.DataFrame]:
+) -> pd.DataFrame:
     """
     Provides a list of weather station ids for the requested data
     Args:
-        latitudes: latitudes of locations to search for nearest
+        latitude: latitude of location to search for nearest
             weather station
-        longitudes: longitudes of locations to search for nearest
+        longitude: longitude of location to search for nearest
             weather station
         minimal_available_date: Start date of timespan where measurements
             should be available
@@ -53,7 +56,7 @@ def get_nearby_stations(
             distance to location in km
 
     Returns:
-        List of DataFrames with valid Stations in radius per requested location
+        DataFrames with valid Stations in radius per requested location
 
     """
     if (num_stations_nearby and max_distance_in_km) and (
@@ -84,13 +87,7 @@ def get_nearby_stations(
             f"{period_type.value} is invalid."
         )
 
-    if not isinstance(latitudes, list):
-        latitudes = np.array(latitudes)
-
-    if not isinstance(longitudes, list):
-        latitudes = np.array(longitudes)
-
-    coords = Coordinates(latitudes, longitudes)
+    coords = Coordinates(np.array(latitude), np.array(longitude))
 
     metadata = metadata_for_climate_observations(
         parameter, time_resolution, period_type
@@ -113,35 +110,26 @@ def get_nearby_stations(
     # Cast to np.array required for subset
     indices_nearest_neighbours = np.array(cast_to_list(indices_nearest_neighbours))
     distances_km = np.array(distances * KM_EARTH_RADIUS)
-    nearest_station_list = []
 
-    for distances_km_location, indices_nearest_neighbours_location in zip(
-        distances_km, indices_nearest_neighbours
-    ):
-        # Filter for distance based on calculated distances
-        if max_distance_in_km:
-            _in_max_distance_indices = np.where(
-                distances_km_location <= max_distance_in_km
-            )[0]
-            indices_nearest_neighbours_location = indices_nearest_neighbours_location[
-                _in_max_distance_indices
-            ]
-            distances_km_location = distances_km_location[_in_max_distance_indices]
-
-        metadata_location = metadata.loc[
-            indices_nearest_neighbours_location
-            if isinstance(indices_nearest_neighbours_location, (list, np.ndarray))
-            else [indices_nearest_neighbours_location],
-            :,
+    # Filter for distance based on calculated distances
+    if max_distance_in_km:
+        _in_max_distance_indices = np.where(
+            distances_km <= max_distance_in_km)[0]
+        indices_nearest_neighbours = indices_nearest_neighbours[
+            _in_max_distance_indices
         ]
-        metadata_location["DISTANCE_TO_LOCATION"] = distances_km_location
-        nearest_station_list.append(metadata_location)
+        distances_km = distances_km[_in_max_distance_indices]
+
+    metadata_location = metadata.loc[
+        indices_nearest_neighbours
+        if isinstance(indices_nearest_neighbours, (list, np.ndarray))
+        else [indices_nearest_neighbours], :]
+    metadata_location["DISTANCE_TO_LOCATION"] = distances_km
 
-    for _idx, _nearest_stations in enumerate(nearest_station_list):
-        if _nearest_stations.empty:
-            print(f"No weather station was found for {_idx+1}. location")
+    if metadata_location:
+        logger.warning(f"No weather station was found for {_idx+1}. location")
 
-    return nearest_station_list
+    return metadata_location
 
 
 def _derive_nearest_neighbours(