Interpolation/Summary: Add progress bar

wetterdienst/core/timeseries/interpolate.py

@@ -13,6 +13,7 @@
 import utm
 from scipy.interpolate import LinearNDInterpolator
 from shapely.geometry import Point, Polygon
+from tqdm import tqdm
 
 from wetterdienst.core.timeseries.tools import _ParameterData, extract_station_values
 from wetterdienst.metadata.columns import Columns
@@ -45,7 +46,12 @@ def request_stations(
     distance = max(request.settings.ts_interpolation_station_distance.values())
     stations_ranked = request.filter_by_distance(latlon=(latitude, longitude), distance=distance)
     df_stations_ranked = stations_ranked.df
-    for station, result in zip(df_stations_ranked.iter_rows(named=True), stations_ranked.values.query()):
+    for station, result in tqdm(
+        zip(df_stations_ranked.iter_rows(named=True), stations_ranked.values.query()),
+        total=len(df_stations_ranked),
+        desc="querying stations for interpolation",
+        unit="station",
+    ):
         valid_station_groups_exists = not get_valid_station_groups(stations_dict, utm_x, utm_y).empty()
         # check if all parameters found enough stations and the stations build a valid station group
         if len(param_dict) > 0 and all(param.finished for param in param_dict.values()) and valid_station_groups_exists:

wetterdienst/core/timeseries/summarize.py

@@ -5,6 +5,7 @@
 from typing import TYPE_CHECKING
 
 import polars as pl
+from tqdm import tqdm
 
 from wetterdienst.core.timeseries.tools import _ParameterData, extract_station_values
 from wetterdienst.metadata.columns import Columns
@@ -29,7 +30,12 @@ def request_stations(request: TimeseriesRequest, latitude: float, longitude: flo
     distance = max(request.settings.ts_interpolation_station_distance.values())
     stations_ranked = request.filter_by_distance(latlon=(latitude, longitude), distance=distance)
     df_stations_ranked = stations_ranked.df
-    for station, result in zip(df_stations_ranked.iter_rows(named=True), stations_ranked.values.query()):
+    for station, result in tqdm(
+        zip(df_stations_ranked.iter_rows(named=True), stations_ranked.values.query()),
+        total=len(df_stations_ranked),
+        desc="querying stations for summary",
+        unit="station",
+    ):
         # check if all parameters found enough stations and the stations build a valid station group
         if len(param_dict) > 0 and all(param.finished for param in param_dict.values()):
             break