correct function signature of abstract method as_xarray

src/toargridding/grids.py

@@ -63,8 +63,8 @@ class GridDefinition(ABC):
         """
 
     @abstractmethod
-    def as_xarray(timeseries: dict[str, pd.DataFrame], metadata: pd.DataFrame) -> dict[str, xr.Dataset]:
-        """conversion of panda Dataframes to an xarray dataset
+    def as_xarray(self, data: AnalysisRequestResult) -> xr.Dataset:
+        """gridding of a request to the TOAR database
 
         This includes the required setup to store the results as netCDF file according to CF (https://cfconventions.org/cf-conventions/cf-conventions.html)
         """
@@ -105,8 +105,12 @@ class RegularGrid(GridDefinition):
         super().__init__()
         # TODO make sure only sensible resolutions
 
-        self.lat = Coordinate.from_resolution(Coordinates.latitude, lat_resolution, min=-90, max=90, wraps=False)
-        self.lon = Coordinate.from_resolution(Coordinates.longitude, lon_resolution, min=-180, max=180, wraps=True)
+        self.lat = Coordinate.from_resolution(
+            Coordinates.latitude, lat_resolution, min=-90, max=90, wraps=False
+        )
+        self.lon = Coordinate.from_resolution(
+            Coordinates.longitude, lon_resolution, min=-180, max=180, wraps=True
+        )
         spatial_shape = (self.lon.size, self.lat.size)
         spatial_size = self.lon.size * self.lat.size
         self.dims = [
@@ -115,7 +119,9 @@ class RegularGrid(GridDefinition):
             Coordinates.longitude.name,
         ]
 
-        self._as_xy_index = np.dstack(np.meshgrid(range(self.lat.size), range(self.lon.size))).reshape(-1, 2)
+        self._as_xy_index = np.dstack(
+            np.meshgrid(range(self.lat.size), range(self.lon.size))
+        ).reshape(-1, 2)
         self._as_i_index = np.arange(spatial_size).reshape(spatial_shape).T
 
     @property
@@ -135,13 +141,17 @@ class RegularGrid(GridDefinition):
             results of the request, including data, station coordinates and metadata of request
         """
 
-        data_grouped_by_cell = self.group_data_by_cell(data.stations_data, data.stations_coords)
+        data_grouped_by_cell = self.group_data_by_cell(
+            data.stations_data, data.stations_coords
+        )
         cell_statistics = self.get_cell_statistics(data_grouped_by_cell)
         dataset = self.create_dataset(cell_statistics, data.metadata)
 
         return dataset
 
-    def group_data_by_cell(self, data: pd.DataFrame, coords: pd.DataFrame) -> DataFrameGroupBy:
+    def group_data_by_cell(
+        self, data: pd.DataFrame, coords: pd.DataFrame
+    ) -> DataFrameGroupBy:
         """grouping of stations into cells
 
         This function converts the lat/lon coordinates of the stations into cell indices and groups stations belonging to one cell.
@@ -157,7 +167,9 @@ class RegularGrid(GridDefinition):
         cell_indices = self.as_cell_index(coords)
 
         # will convert cell_indices to float as some nans ar present
-        data_with_indices = data.join(cell_indices.to_frame(GridDefinition.cell_index_name), how="outer")
+        data_with_indices = data.join(
+            cell_indices.to_frame(GridDefinition.cell_index_name), how="outer"
+        )
 
         return data_with_indices.groupby(GridDefinition.cell_index_name)
 
@@ -180,7 +192,9 @@ class RegularGrid(GridDefinition):
 
         return stats
 
-    def create_dataset(self, cell_statistics: dict[str, pd.DataFrame], metadata: Metadata) -> xr.Dataset:
+    def create_dataset(
+        self, cell_statistics: dict[str, pd.DataFrame], metadata: Metadata
+    ) -> xr.Dataset:
         """creation of data set and filling with results from the gridding
 
         Parameters:
@@ -202,7 +216,9 @@ class RegularGrid(GridDefinition):
 
         gridded_ds = self.get_empty_grid(time, metadata)
         for variable, aggregated_data in cell_statistics.items():
-            data_array_dict = self.get_data_array_dict(time, aggregated_data, variable, metadata)
+            data_array_dict = self.get_data_array_dict(
+                time, aggregated_data, variable, metadata
+            )
             gridded_ds = gridded_ds.assign(data_array_dict)
 
         return gridded_ds
@@ -232,7 +248,9 @@ class RegularGrid(GridDefinition):
         gridded_variable = Variable.from_data(gridded_data, variable, metadata)
         return {variable.name: gridded_variable.as_data_array(self.dims)}
 
-    def create_gridded_data(self, time: Coordinate, grouped_timeseries: pd.DataFrame) -> np.array:
+    def create_gridded_data(
+        self, time: Coordinate, grouped_timeseries: pd.DataFrame
+    ) -> np.array:
         """converts the available cell data to a full lat/lon-temporal data cube.
 
         Parameters:
@@ -250,7 +268,9 @@ class RegularGrid(GridDefinition):
         values[...] = self.fill_value
 
         index = self._as_xy_index[grouped_timeseries.index.astype(int)]
-        values[:, index.T[0], index.T[1]] = grouped_timeseries.values.reshape(-1, time.size).T
+        values[:, index.T[0], index.T[1]] = grouped_timeseries.values.reshape(
+            -1, time.size
+        ).T
 
         return values
 
@@ -258,13 +278,17 @@ class RegularGrid(GridDefinition):
         """converts coordinates of stations into x and y indices of the regular grid"""
 
         id_x = self.coord_to_index(coords[self.lat.name], self.lat.min, self.lat.step)
-        id_y = self.coord_to_index(coords[self.lon.name], self.lon.min, self.lon.step, len(self.lon.data))
+        id_y = self.coord_to_index(
+            coords[self.lon.name], self.lon.min, self.lon.step, len(self.lon.data)
+        )
 
         id_i = self._as_i_index[id_x, id_y]
 
         return pd.Series(id_i, index=id_x.index)
 
-    def coord_to_index(self, coord: pd.Series, x0_axis: float, d_axis: float, maxBin4Wrap: int = None) -> np.array:
+    def coord_to_index(
+        self, coord: pd.Series, x0_axis: float, d_axis: float, maxBin4Wrap: int = None
+    ) -> np.array:
         """converts a coordinate into a bin index on one axis
 
         Parameters:
@@ -284,7 +308,9 @@ class RegularGrid(GridDefinition):
             ids[ids < 0] += maxBin4Wrap
         return ids
 
-    def get_empty_grid(self, time: Variable, metadata: Metadata) -> xr.Dataset:  # TODO make CF-compliant => docs
+    def get_empty_grid(
+        self, time: Variable, metadata: Metadata
+    ) -> xr.Dataset:  # TODO make CF-compliant => docs
         """creation of an empty dataset without data
 
         Sets up a dataset with its three axis: time, longitude and latitude.