Multiple improvements on DWD Obs

CHANGELOG.md

@@ -2,6 +2,13 @@
 
 ## Development
 
+### Refactor
+- \[DWD Obs\] Make the download function more flexible using threadpool
+- \[DWD Obs\] Cleanup parser function 
+
+### Fix
+- \[DWD Obs\] Reduce unnecessary file index calls during retrieval of data for stations with multiple files
+
 ## 0.100.0 - 2025-01-06 
 
 ### Feature

tests/provider/dwd/observation/test_fileindex.py

@@ -59,7 +59,7 @@ def test_create_file_list_for_dwd_server(default_settings):
         station_id="00003",
         dataset=DwdObservationMetadata.minute_10.temperature_air,
         period=Period.HISTORICAL,
-        date_range="19930428_19991231",
+        date_ranges=["19930428_19991231"],
         settings=default_settings,
     ).to_list()
     assert remote_file_path == [

wetterdienst/provider/dwd/observation/api.py

@@ -21,7 +21,7 @@
 from wetterdienst.metadata.period import Period
 from wetterdienst.metadata.resolution import Resolution
 from wetterdienst.provider.dwd.observation.download import (
-    download_climate_observations_data_parallel,
+    download_climate_observations_data,
 )
 from wetterdienst.provider.dwd.observation.fileindex import (
     _create_file_index_for_dwd_server,
@@ -74,30 +74,31 @@ def _collect_station_parameter_or_dataset(
                 date_ranges = self._get_historical_date_ranges(
                     station_id, parameter_or_dataset, self.sr.stations.settings
                 )
-                for date_range in date_ranges:
-                    periods_and_date_ranges.append((period, date_range))
+                periods_and_date_ranges.append((period, date_ranges))
             else:
                 periods_and_date_ranges.append((period, None))
 
         parameter_data = []
 
-        for period, date_range in periods_and_date_ranges:
+        for period, date_ranges in periods_and_date_ranges:
             if period not in parameter_or_dataset.periods:
                 log.info(f"Skipping period {period} for {parameter_or_dataset.name}.")
                 continue
-            dataset_identifier = f"{parameter_or_dataset.resolution.value.name}/{parameter_or_dataset.name}/{station_id}/{period.value}/{date_range}"  # noqa: E501
+            dataset_identifier = (
+                f"{parameter_or_dataset.resolution.value.name}/{parameter_or_dataset.name}/{station_id}/{period.value}"  # noqa: E501
+            )
             log.info(f"Acquiring observation data for {dataset_identifier}.")
             remote_files = create_file_list_for_climate_observations(
                 station_id,
                 parameter_or_dataset,
                 period,
                 self.sr.stations.settings,
-                date_range,
+                date_ranges,
             )
             if remote_files.is_empty():
                 log.info(f"No files found for {dataset_identifier}. Station will be skipped.")
                 continue
-            filenames_and_files = download_climate_observations_data_parallel(remote_files, self.sr.stations.settings)
+            filenames_and_files = download_climate_observations_data(remote_files, self.sr.stations.settings)
             period_df = parse_climate_observations_data(filenames_and_files, parameter_or_dataset, period)
             parameter_data.append(period_df)
 

wetterdienst/provider/dwd/observation/download.py

@@ -22,40 +22,22 @@
 log = logging.getLogger(__name__)
 
 
-def download_climate_observations_data_parallel(
+def download_climate_observations_data(
     remote_files: pl.Series,
     settings: Settings,
 ) -> list[tuple[str, BytesIO]]:
-    """
-    Wrapper for ``_download_dwd_data`` to provide a multiprocessing feature.
-
-    :param remote_files:    List of requested files
-    :return:                List of downloaded files
-    """
-    with ThreadPoolExecutor() as p:
-        files_in_bytes = p.map(
-            lambda file: _download_climate_observations_data(remote_file=file, settings=settings),
-            remote_files,
-        )
-
+    if len(remote_files) > 1:
+        with ThreadPoolExecutor() as p:
+            files_in_bytes = p.map(
+                lambda file: _download_climate_observations_data(remote_file=file, settings=settings),
+                remote_files,
+            )
+    else:
+        files_in_bytes = [_download_climate_observations_data(remote_file=remote_files[0], settings=settings)]
     return list(zip(remote_files, files_in_bytes))
 
 
 def _download_climate_observations_data(remote_file: str, settings: Settings) -> BytesIO:
-    """
-    This function downloads the station data for which the link is
-    provided by the 'select_dwd' function. It checks the shortened filepath (just
-    the zipfile) for its parameters, creates the full filepath and downloads the
-    file(s) according to the set up folder.
-
-    Args:
-        remote_file: contains path to file that should be downloaded
-            and the path to the folder to store the files
-
-    Returns:
-        stores data on local file system
-
-    """
     return BytesIO(__download_climate_observations_data(remote_file=remote_file, settings=settings))
 
 

wetterdienst/provider/dwd/observation/fileindex.py

@@ -28,32 +28,18 @@ def create_file_list_for_climate_observations(
     dataset: DatasetModel,
     period: Period,
     settings: Settings,
-    date_range: str | None = None,
+    date_ranges: list[str] | None = None,
 ) -> pl.Series:
-    """
-    Function for selecting datafiles (links to archives) for given
-    station_ids, parameter, time_resolution and period_type under consideration of a
-    created list of files that are
-    available online.
-    Args:
-        station_id: station id for the weather station to ask for data
-        dataset: observation measure
-        resolution: frequency/granularity of measurement interval
-        period: recent or historical files
-        date_range:
-    Returns:
-        List of path's to file
+    """Create a list of files for a given station id, dataset and period.
+
+    Date ranges are used to reduce the number of files to be downloaded based on the date range of the files.
+    This is useful for hourly or more fine-grained data, where the number of files can be very large.
     """
     file_index = create_file_index_for_climate_observations(dataset, period, settings)
-
-    file_index = file_index.collect()
-
     file_index = file_index.filter(pl.col("station_id").eq(station_id))
-
-    if date_range:
-        file_index = file_index.filter(pl.col("date_range").eq(date_range))
-
-    return file_index.get_column("filename")
+    if date_ranges:
+        file_index = file_index.filter(pl.col("date_range").is_in(date_ranges))
+    return file_index.collect().get_column("filename")
 
 
 def create_file_index_for_climate_observations(