Import from typing

.pre-commit-config.yaml

@@ -67,7 +67,6 @@ repos:
           flake8-builtins,
           flake8-bugbear,
           flake8-comprehensions,
-          flake8-docstrings,
           flake8-eradicate,
           flake8-mutable,
           flake8-pytest-style,

climetlab_maelstrom_power_production/dataset.py

@@ -1,5 +1,5 @@
 import abc
-import typing as t
+from typing import Optional
 
 import climetlab as cml  # type: ignore
 import pandas as pd  # type: ignore
@@ -24,7 +24,7 @@ class AbstractDataset(cml.Dataset):
     )
     dataset = None
     _as_dataframe = None
-    _merger: t.Optional[merger.AbstractMerger] = None
+    _merger: Optional[merger.AbstractMerger] = None
 
     @property
     @abc.abstractmethod
@@ -46,7 +46,7 @@ def _get_data(self) -> url.Url:
         """Get the data of the dataset."""
 
     @property
-    def merger(self) -> t.Optional[merger.AbstractMerger]:
+    def merger(self) -> Optional[merger.AbstractMerger]:
         """Get the merger for the source files."""
         return self._merger
 

climetlab_maelstrom_power_production/weather/abc.py

@@ -9,7 +9,7 @@
 import abc
 import datetime
 import itertools
-import typing as t
+from typing import Optional, Union
 
 import climetlab as cml  # type: ignore
 import pandas as pd  # type: ignore
@@ -58,7 +58,7 @@ class Weather(dataset.AbstractDataset):
     model_timestamp_2 = MODEL_TIMESTAMP_2
     dates = AVAILABLE_DATA_DATES
 
-    def __init__(self, date: t.Union[str, t.List[str]] = None):
+    def __init__(self, date: Optional[Union[str, list[str]]] = None):
         """Initialize and load the dataset."""
         self.date = _convert_dates(date) if date is not None else self.dates
         self._merger = merger.WeatherMerger()
@@ -67,7 +67,7 @@ def __init__(self, date: t.Union[str, t.List[str]] = None):
 
     @property
     @abc.abstractmethod
-    def type(self) -> str:
+    def type(self) -> str:  # noqa: A003
         """Return the type of the weather data.
 
         Either ml (model level) or pl (pressure level).
@@ -77,7 +77,7 @@ def type(self) -> str:
     @property
     def merger(
         self,
-    ) -> t.Optional[climetlab_maelstrom_power_production.merger.AbstractMerger]:
+    ) -> Optional[climetlab_maelstrom_power_production.merger.AbstractMerger]:
         """Get the merger for the weather data."""
         return self._merger
 
@@ -88,7 +88,7 @@ def _get_data(self) -> cml.Source:
             date_with_model_timestamp=dates_with_model_timestamps,
         )
 
-    def _add_timestamps_to_each_date(self) -> t.List[str]:
+    def _add_timestamps_to_each_date(self) -> list[str]:
         dates = (date.strftime(DATE_FORMAT_REMOTE) for date in self.date)
         timestamps = (self.model_timestamp_1, self.model_timestamp_2)
         dates_with_model_timestamps = (
@@ -98,7 +98,7 @@ def _add_timestamps_to_each_date(self) -> t.List[str]:
         return list(dates_with_model_timestamps)
 
 
-def _convert_dates(dates: t.Union[str, t.List[str]]) -> t.List[datetime.datetime]:
+def _convert_dates(dates: Union[str, list[str]]) -> list[datetime.datetime]:
     if isinstance(dates, str):
         dates_as_datetime = [_convert_to_datetime(dates)]  # type: ignore
     else:
@@ -116,7 +116,7 @@ def _convert_to_datetime(date: str) -> datetime.datetime:
         )
 
 
-def _check_dates_availability(dates: t.List[datetime.datetime]) -> None:
+def _check_dates_availability(dates: list[datetime.datetime]) -> None:
     for date in dates:
         if not _date_is_available(date):
             start = AVAILABLE_DATA_DATES[0].strftime(DATE_FORMAT)

climetlab_maelstrom_power_production/weather/merger.py

@@ -6,7 +6,7 @@
 # granted to it by virtue of its status as an intergovernmental organisation
 # nor does it submit to any jurisdiction.
 #
-import typing as t
+from typing import Optional
 
 import pandas as pd  # type: ignore
 import xarray as xr
@@ -20,7 +20,7 @@ class WeatherMerger(merger.AbstractMerger):
     # Coordinate to use for merging multiple datasets.
     concat_dim = "time"
 
-    def __init__(self, options: t.Optional[dict] = None):
+    def __init__(self, options: Optional[dict] = None):
         """Initialize the merger."""
         self.options = options or {}
 

notebooks/utils/data.py

@@ -1,18 +1,17 @@
-import re
-import typing as t
 from datetime import datetime
+from typing import Dict, List, Set, Tuple, Union
 
 import numpy as np
 import pandas as pd
 import xarray as xr
 
-Number = t.Union[int, float]
+Number = Union[int, float]
 
 
 def get_closest_grid_point_to_wind_turbine(
     production_data: xr.Dataset,
     data: xr.Dataset,
-) -> t.Dict[str, Number]:
+) -> dict[str, Number]:
     """Get the grid point in the data closest to the wind turbine.
 
     Parameters
@@ -41,7 +40,7 @@ def get_closest_grid_point_to_wind_turbine(
     return {"longitude": longitude, "latitude": latitude}
 
 
-def _get_wind_turbine_coordinates(ds: xr.Dataset) -> t.Tuple[Number, Number]:
+def _get_wind_turbine_coordinates(ds: xr.Dataset) -> tuple[Number, Number]:
     """Get the coordinates (longitude, latitude) of a wind turbine from the production data.
 
     Parameters
@@ -81,8 +80,8 @@ def _get_grid_coordinates(ds: xr.Dataset, coordinate: str) -> np.ndarray:
 
 
 def _get_closest_grid_point(
-    grid: t.List[np.ndarray], coordinates: t.Tuple[Number, ...]
-) -> t.Tuple[t.Union[int, float], ...]:
+    grid: list[np.ndarray], coordinates: tuple[Number, ...]
+) -> tuple[Union[int, float], ...]:
     """Get the grid point that is closest to the given coordinates.
 
     Parameters
@@ -122,15 +121,15 @@ def _get_closest_coordinate(coordinates: np.ndarray, value: Number) -> Number:
 
 def resample_and_clear_production_data_to_hourly_timeseries(
     production_data: xr.Dataset,
-    dates: t.List[datetime],
+    dates: list[datetime],
 ) -> xr.DataArray:
     """Resample the production data to an hourly timeseries and clear negative values.
 
     Parameters
     ----------
     production_data : pandas.DataFrame
         Production data from the `maelstrom-power-production` dataset.
-    dates : t.List[datetime]
+    dates : List[datetime]
         Dates from the weather data to get all matching timestamps of the resample.
 
     Returns
@@ -170,8 +169,8 @@ def _resample_production_data_to_hourly_timeseries(
 
 
 def _get_intersection_of_dates(
-    left: pd.DataFrame, right: t.List[datetime]
-) -> t.Set[pd.Timestamp]:
+    left: pd.DataFrame, right: list[datetime]
+) -> set[pd.Timestamp]:
     return sorted(set(left.index) & set(right))
 
 

notebooks/utils/density.py

@@ -1,5 +1,5 @@
 """Functionality for calculating the density of air."""
-import typing as t
+from typing import Dict, Optional
 
 import numpy as np
 import xarray as xr
@@ -22,7 +22,7 @@
 
 
 def calculate_air_density(
-    grid_point: t.Dict[str, float],
+    grid_point: dict[str, float],
     model_level: int,
     model_level_data: xr.Dataset,
     surface_data: xr.Dataset,
@@ -32,7 +32,7 @@ def calculate_air_density(
 
     Parameters
     ----------
-    grid_point : t.Tuple[float, float]
+    grid_point : Dict[str, float]
         Grid point for which to calculate the air density.
     model_level : int
         Model level for which to calculate the air density.
@@ -70,7 +70,7 @@ def _calculate_density(
     temperature: xr.DataArray,
     pressure: xr.DataArray,
     specific_humidity: xr.DataArray,
-    relative_humidity: t.Optional[xr.DataArray] = None,
+    relative_humidity: Optional[xr.DataArray] = None,
 ) -> xr.DataArray:
     """Calculate air density.
 

notebooks/utils/model.py

@@ -1,5 +1,6 @@
 import datetime
-from typing import Iterator, List
+from collections.abc import Iterator
+from typing import List
 
 import numpy as np
 import xarray as xr
@@ -58,7 +59,7 @@ def predict_power_production(
     return np.array(production)
 
 
-def _prepare_x(time_coordinate_name: str, *args: xr.DataArray) -> List:
+def _prepare_x(time_coordinate_name: str, *args: xr.DataArray) -> list:
     intersecting_dates = _get_intersecting_dates(time_coordinate_name, *args)
     intersecting_data = _select_subset(intersecting_dates, time_coordinate_name, *args)
     return list(zip(*intersecting_data))
@@ -77,7 +78,7 @@ def _get_intersecting_dates(
 
 
 def _select_subset(
-    dates: List[datetime.datetime], time_coordinate_name: str, *args
+    dates: list[datetime.datetime], time_coordinate_name: str, *args
 ) -> Iterator[np.ndarray]:
     for arg in args:
         yield arg.sel({time_coordinate_name: dates}).values

notebooks/utils/time.py

@@ -1,26 +1,26 @@
 import datetime
-import typing as t
+from typing import Callable, List, Tuple
 
 import numpy as np
 import xarray as xr
 
 
-def get_dates_from_time_coordinate(data: xr.Dataset) -> t.List[datetime.datetime]:
+def get_dates_from_time_coordinate(data: xr.Dataset) -> list[datetime.datetime]:
     """Get the time index/coordinate as a list of datetimes."""
     return data.coords["time"].to_index().to_list()
 
 
 def get_time_of_day_and_year(
-    dates: t.List[datetime.datetime], time_coord_name: str = "time"
-) -> t.Tuple[xr.DataArray, xr.DataArray]:
+    dates: list[datetime.datetime], time_coord_name: str = "time"
+) -> tuple[xr.DataArray, xr.DataArray]:
     """Calculate the time of day and year for given datetimes."""
     time_of_day = get_time_of_day(dates, time_coord_name=time_coord_name)
     time_of_year = get_time_of_year(dates, time_coord_name=time_coord_name)
     return time_of_day, time_of_year
 
 
 def get_time_of_day(
-    dates: t.List[datetime.datetime], time_coord_name: str = "time"
+    dates: list[datetime.datetime], time_coord_name: str = "time"
 ) -> xr.DataArray:
     """Calculate relative time of day for datetimes."""
     converted = _apply_to_dates(_time_of_day, dates=dates)
@@ -31,7 +31,7 @@ def get_time_of_day(
 
 
 def get_time_of_year(
-    dates: t.List[datetime.datetime], time_coord_name: str = "time"
+    dates: list[datetime.datetime], time_coord_name: str = "time"
 ) -> xr.DataArray:
     """Calculate relative time of year for datetimes."""
     converted = _apply_to_dates(_time_of_year, dates=dates)
@@ -41,11 +41,11 @@ def get_time_of_year(
     )
 
 
-def _apply_to_dates(func: t.Callable, dates: t.List[datetime.datetime]) -> np.ndarray:
+def _apply_to_dates(func: Callable, dates: list[datetime.datetime]) -> np.ndarray:
     return np.array(list(map(func, dates)))
 
 
-def _time_of_day(dt: datetime) -> float:
+def _time_of_day(dt: datetime.datetime) -> float:
     """Convert a given datetime `dt` to a point on the unit circle, where the 'period' corresponds to one day.
 
     :param datetime.datetime dt: input datetime UTC
@@ -56,7 +56,7 @@ def _time_of_day(dt: datetime) -> float:
     return seconds_since_midnight / 86400
 
 
-def _time_of_year(dt: datetime) -> float:
+def _time_of_year(dt: datetime.datetime) -> float:
     """Convert a given datetime `dt` to a point on the unit circle, where the 'period' corresponds to one year.
 
     :param datetime.datetime dt: input datetime (preferably UTC)

notebooks/utils/wind.py

@@ -1,19 +1,19 @@
-import typing as t
+from typing import Dict, Tuple
 
 import numpy as np
 import xarray as xr
 
 
 def calculate_absolute_wind_speed_and_wind_direction(
-    grid_point: t.Dict[str, float],
+    grid_point: dict[str, float],
     model_level: int,
     model_level_data: xr.Dataset,
-) -> t.Tuple[xr.DataArray, xr.DataArray]:
+) -> tuple[xr.DataArray, xr.DataArray]:
     """Calculate the absolute wind speed and wind direction.
 
     Parameters
     ----------
-    grid_point : t.Tuple[float, float]
+    grid_point : Dict[str, float]
         Grid point for which to calculate the wind properties.
     model_level : int
         Model level for which to calculate the wind properties.
@@ -23,7 +23,7 @@ def calculate_absolute_wind_speed_and_wind_direction(
 
     Returns
     -------
-    t.Tuple[pandas.Series, pandas.Series]
+    Tuple[xr.DataArray, xr.DataArray]
         Absolute wind speed and wind direction (angle relative to longitude).
 
     """