chore: Auto-format and lint fixes (Ruff)

fgi_stac/metadata.py

@@ -125,8 +125,10 @@ def build_metadata_block(
     )
 
     organisation_path = (
-        default_publisher if "/" in clean(default_publisher) else ""
-    ) or clean(dataspot_meta["publisher_path"]) or clean(producer_organization)
+        (default_publisher if "/" in clean(default_publisher) else "")
+        or clean(dataspot_meta["publisher_path"])
+        or clean(producer_organization)
+    )
 
     return {
         "default": {

fgi_stac/publish_dataset.py

@@ -35,6 +35,7 @@
     write_metadata_snapshot_file,
 )
 from common import change_tracking
+from dataspot_api import dataspot_metadata
 from dataspot_auth import DataspotAuth
 from http_client import HTTP_LIMITS, HTTP_TIMEOUT, with_http_retry
 from huwise_utils_py import (
@@ -45,12 +46,10 @@
 )
 from huwise_utils_py.config import HuwiseConfig
 from huwise_utils_py.http import HttpClient
-from dataspot_api import dataspot_metadata
-from dataspot_auth import DataspotAuth
 from metadata import (
+    DEFAULT_ATTRIBUTIONS,
     DEFAULT_CONTACT_EMAIL,
     DEFAULT_CONTACT_NAME,
-    DEFAULT_ATTRIBUTIONS,
     DEFAULT_GEOGRAPHIC_REFERENCE,
     DEFAULT_LICENSE,
     DEFAULT_RIGHTS,
@@ -190,6 +189,8 @@ def _processor_field_targets_stale(
         if expected_label and clean_text(existing.get("field_label")) != expected_label:
             return True
     return False
+
+
 THEME_MAP_DATA_BS_CH = {
     "arbeit, erwerb": "20bb143",
     "bau- und wohnungswesen": "c813f26",
@@ -1031,9 +1032,7 @@ def _set_template_field(template: str, field: str, value: Any) -> None:
         matches_last_push = last_push is not None and normalized_existing == _normalize_metadata_compare_value(
             last_push
         )
-        publisher_existing = _normalize_metadata_compare_value(
-            template_payloads.get("default", {}).get("publisher")
-        )
+        publisher_existing = _normalize_metadata_compare_value(template_payloads.get("default", {}).get("publisher"))
         prefilled_as_publisher = (
             (resolved_template, field) == ("custom", "publizierende_organisation")
             and normalized_existing

kapo_smileys/etl.py

@@ -76,17 +76,13 @@ def parse_messdaten(df_einsatz_days, df_einsaetze):
                 messdaten_columns = df_all_pro_standort.columns
 
             total_all_rows += len(df_all_pro_standort)
-            df_all_pro_standort.to_csv(
-                export_file_all_unfiltered, mode="a", header=first_all_csv, index=False
-            )
+            df_all_pro_standort.to_csv(export_file_all_unfiltered, mode="a", header=first_all_csv, index=False)
             first_all_csv = False
 
             df_filtered = df_all_pro_standort[df_all_pro_standort["Zyklus"].isin(zyklus_filter)]
             if not df_filtered.empty:
                 total_filtered_rows += len(df_filtered)
-                df_filtered.to_csv(
-                    export_file_filtered, mode="a", header=first_filtered_csv, index=False
-                )
+                df_filtered.to_csv(export_file_filtered, mode="a", header=first_filtered_csv, index=False)
                 first_filtered_csv = False
             del df_filtered
 
@@ -102,14 +98,11 @@ def parse_messdaten(df_einsatz_days, df_einsaetze):
 
         if first_filtered_csv:
             logging.warning(
-                f"No datapoints for cycles {previous_zyklus} and {current_zyklus}; "
-                f"writing empty {export_file_filtered}"
+                f"No datapoints for cycles {previous_zyklus} and {current_zyklus}; writing empty {export_file_filtered}"
             )
             pd.DataFrame(columns=messdaten_columns).to_csv(export_file_filtered, index=False)
 
-        logging.info(
-            f"Saved unfiltered data with {total_all_rows} datapoints to {export_file_all_unfiltered}"
-        )
+        logging.info(f"Saved unfiltered data with {total_all_rows} datapoints to {export_file_all_unfiltered}")
         logging.info(f"Extracting data for cycles {previous_zyklus} and {current_zyklus}")
         logging.info(
             f"Filtered data contains {total_filtered_rows} datapoints out of {total_all_rows} total "
@@ -456,9 +449,7 @@ def init_sqlite(conn):
 def append_to_sqlite(df, conn):
     df_einsatzplan, df_einzelmessungen = _prepare_sqlite_frames(df)
     df_einsatzplan.to_sql("Einsatzplan", conn, if_exists="append", index=False)
-    df_einzelmessungen.to_sql(
-        "Einzelmessungen", conn, if_exists="append", index=False, chunksize=SQLITE_CHUNK_SIZE
-    )
+    df_einzelmessungen.to_sql("Einzelmessungen", conn, if_exists="append", index=False, chunksize=SQLITE_CHUNK_SIZE)
 
 
 def finalize_sqlite(conn):

mobilitaet_mikromobilitaet_stats/etl.py

@@ -14,10 +14,10 @@
 import common
 import geopandas as gpd
 import pandas as pd
-from shapely import wkt
-from shapely.geometry import mapping
 from common import FTP_PASS, FTP_SERVER, FTP_USER
 from dateutil.relativedelta import relativedelta
+from shapely import wkt
+from shapely.geometry import mapping
 
 CONFIGS = {
     "bezirke": {
@@ -126,6 +126,7 @@ def _ods_export_cols(output_cols):
     """Huwise/ODS export: no geometry (full geometries stay in SQLite for Datasette)."""
     return [c for c in output_cols if c != "geometry"]
 
+
 DEDUPE_COLS_416 = [
     "date",
     "bez_id",
@@ -344,10 +345,7 @@ def append_stats_to_sqlite(df_stats, db_path, table_name, dedupe_cols, output_co
         df_all = _cast_bez_id_column(df_all)
         df_all.to_sql(table_name, conn, if_exists="replace", index=False)
         conn.commit()
-    logging.info(
-        f"SQLite {table_name}: {len(df_all)} fact rows, "
-        f"{len(df_bezirke)} bezirke update(s) in {work_path}"
-    )
+    logging.info(f"SQLite {table_name}: {len(df_all)} fact rows, {len(df_bezirke)} bezirke update(s) in {work_path}")
 
 
 def _filter_by_rolling_window(df, is_monthly):
@@ -433,9 +431,7 @@ def _zip_csv_for_ods(csv_path):
     logging.info(f"File {csv_path} size is {csv_mb:.2f} MB")
     logging.info(f"Created compressed file: {zip_path}")
     if csv_mb > 0:
-        logging.info(
-            f"Compressed file size: {zip_mb:.2f} MB (compression ratio: {zip_mb / csv_mb * 100:.1f}%)"
-        )
+        logging.info(f"Compressed file size: {zip_mb:.2f} MB (compression ratio: {zip_mb / csv_mb * 100:.1f}%)")
     else:
         logging.info(f"Compressed file size: {zip_mb:.2f} MB")
     if zip_mb > ODS_SIZE_LIMIT_MB:

staka_vernehmlassungen/etl.py

@@ -1099,9 +1099,7 @@ def process_textrueckmeldungen():
     for filename in os.listdir(textrueckmeldungen_path):
         if filename.endswith((".xlsx", ".xls")):
             if "zustimmungsmessung" in _normalize_column_name(Path(filename).stem):
-                logging.warning(
-                    f"Skipping Zustimmungsmessung file (aggregate format not supported): {filename}"
-                )
+                logging.warning(f"Skipping Zustimmungsmessung file (aggregate format not supported): {filename}")
                 continue
             file_path = os.path.join(textrueckmeldungen_path, filename)
             try: