Ruff formatting

pyaerocom/aeroval/modelmaps_engine.py

@@ -101,20 +101,14 @@ def _run_model(self, model_name: str, var_list):
             ) as e:
                 if self.raise_exceptions:
                     raise
-                logger.warning(
-                    f"Failed to process maps for {model_name} {var} data. Reason: {e}."
-                )
+                logger.warning(f"Failed to process maps for {model_name} {var} data. Reason: {e}.")
         return files
 
     def _check_dimensions(self, data: GriddedData) -> "GriddedData":
         if not data.has_latlon_dims:
-            raise DataDimensionError(
-                "data needs to have latitude an longitude dimension"
-            )
+            raise DataDimensionError("data needs to have latitude an longitude dimension")
         elif not data.has_time_dim:
-            raise DataDimensionError(
-                "data needs to have latitude an longitude dimension"
-            )
+            raise DataDimensionError("data needs to have latitude an longitude dimension")
         if data.ndim == 4:
             data = data.extract_surface_level()
         return data
@@ -153,14 +147,10 @@ def _process_map_var(self, model_name, var, reanalyse_existing):
         var_ranges_defaults = self.cfg.var_scale_colmap
         if var in var_ranges_defaults.keys():
             cmapinfo = var_ranges_defaults[var]
-            varinfo = VarinfoWeb(
-                var, cmap=cmapinfo["colmap"], cmap_bins=cmapinfo["scale"]
-            )
+            varinfo = VarinfoWeb(var, cmap=cmapinfo["colmap"], cmap_bins=cmapinfo["scale"])
         else:
             cmapinfo = var_ranges_defaults["default"]
-            varinfo = VarinfoWeb(
-                var, cmap=cmapinfo["colmap"], cmap_bins=cmapinfo["scale"]
-            )
+            varinfo = VarinfoWeb(var, cmap=cmapinfo["colmap"], cmap_bins=cmapinfo["scale"])
 
         data = self._check_dimensions(data)
 
@@ -184,9 +174,7 @@ def _process_map_var(self, model_name, var, reanalyse_existing):
 
         data.check_unit()
         # first calcualate and save geojson with contour levels
-        contourjson = calc_contour_json(
-            data, cmap=varinfo.cmap, cmap_bins=varinfo.cmap_bins
-        )
+        contourjson = calc_contour_json(data, cmap=varinfo.cmap, cmap_bins=varinfo.cmap_bins)
 
         # now calculate pixel data json file (basically a json file
         # containing monthly mean timeseries at each grid point at
@@ -198,9 +186,7 @@ def _process_map_var(self, model_name, var, reanalyse_existing):
             lat_res = self.cfg.modelmaps_opts.maps_res_deg["lat_res_deg"]
             lon_res = self.cfg.modelmaps_opts.maps_res_deg["lon_res_deg"]
 
-        datajson = griddeddata_to_jsondict(
-            data, lat_res_deg=lat_res, lon_res_deg=lon_res
-        )
+        datajson = griddeddata_to_jsondict(data, lat_res_deg=lat_res, lon_res_deg=lon_res)
 
         with self.avdb.lock():
             self.avdb.put_gridded_map(
@@ -230,9 +216,7 @@ def _get_maps_freq(self) -> TsType:
         TSType
         """
         maps_freq = TsType(self.cfg.modelmaps_opts.maps_freq)
-        if (
-            maps_freq == "coarsest"
-        ):  # TODO: Implement this in terms of a TsType object. #1267
+        if maps_freq == "coarsest":  # TODO: Implement this in terms of a TsType object. #1267
             freq = min(TsType(fq) for fq in self.cfg.time_cfg.freqs)
             freq = min(freq, self.cfg.time_cfg.main_freq)
         else: