help precommit

src/xscen/aggregate.py

@@ -51,11 +51,11 @@ def _(s):
 def climatological_mean(
     ds: xr.Dataset,
     *,
-    window: Optional[int] = None,
-    min_periods: Optional[int] = None,
+    window: int | None = None,
+    min_periods: int | None = None,
     interval: int = 1,
-    periods: Optional[Union[list[str], list[list[str]]]] = None,
-    to_level: Optional[str] = "climatology",
+    periods: list[str] | list[list[str]] | None = None,
+    to_level: str | None = "climatology",
 ) -> xr.Dataset:
     """Compute the mean over 'year' for given time periods, respecting the temporal resolution of ds.
 
@@ -109,11 +109,11 @@ def climatological_mean(
 def climatological_op(  # noqa: C901
     ds: xr.Dataset,
     *,
-    op: Union[str, dict] = "mean",
-    window: Optional[int] = None,
-    min_periods: Optional[Union[int, float]] = None,
+    op: str | dict = "mean",
+    window: int | None = None,
+    min_periods: int | float | None = None,
     stride: int = 1,
-    periods: Optional[Union[list[str], list[list[str]]]] = None,
+    periods: list[str] | list[list[str]] | None = None,
     rename_variables: bool = True,
     to_level: str = "climatology",
     horizons_as_dim: bool = False,
@@ -506,11 +506,11 @@ def _ulinregress(x, y, **kwargs):
 @parse_config
 def compute_deltas(  # noqa: C901
     ds: xr.Dataset,
-    reference_horizon: Union[str, xr.Dataset],
+    reference_horizon: str | xr.Dataset,
     *,
-    kind: Union[str, dict] = "+",
+    kind: str | dict = "+",
     rename_variables: bool = True,
-    to_level: Optional[str] = "deltas",
+    to_level: str | None = "deltas",
 ) -> xr.Dataset:
     """Compute deltas in comparison to a reference time period, respecting the temporal resolution of ds.
 
@@ -702,13 +702,13 @@ def spatial_mean(  # noqa: C901
     ds: xr.Dataset,
     method: str,
     *,
-    spatial_subset: Optional[bool] = None,
-    call_clisops: Optional[bool] = False,
-    region: Optional[Union[dict, str]] = None,
-    kwargs: Optional[dict] = None,
-    simplify_tolerance: Optional[float] = None,
-    to_domain: Optional[str] = None,
-    to_level: Optional[str] = None,
+    spatial_subset: bool | None = None,
+    call_clisops: bool | None = False,
+    region: dict | str | None = None,
+    kwargs: dict | None = None,
+    simplify_tolerance: float | None = None,
+    to_domain: str | None = None,
+    to_level: str | None = None,
 ) -> xr.Dataset:
     """Compute the spatial mean using a variety of available methods.
 
@@ -1034,18 +1034,18 @@ def spatial_mean(  # noqa: C901
 @parse_config
 def produce_horizon(  # noqa: C901
     ds: xr.Dataset,
-    indicators: Union[
-        str,
-        os.PathLike,
-        Sequence[Indicator],
-        Sequence[tuple[str, Indicator]],
-        ModuleType,
-    ],
+    indicators: (
+        str
+        | os.PathLike
+        | Sequence[Indicator]
+        | Sequence[tuple[str, Indicator]]
+        | ModuleType
+    ),
     *,
-    periods: Optional[Union[list[str], list[list[str]]]] = None,
-    warminglevels: Optional[dict] = None,
-    to_level: Optional[str] = "horizons",
-    period: Optional[list] = None,
+    periods: list[str] | list[list[str]] | None = None,
+    warminglevels: dict | None = None,
+    to_level: str | None = "horizons",
+    period: list | None = None,
 ) -> xr.Dataset:
     """
     Compute indicators, then the climatological mean, and finally unstack dates in order
@@ -1095,7 +1095,7 @@ def produce_horizon(  # noqa: C901
     if periods is not None:
         all_periods.extend(standardize_periods(periods))
     if warminglevels is not None:
-        if isinstance(warminglevels["wl"], (int, float)):
+        if isinstance(warminglevels["wl"], int | float):
             all_periods.append(warminglevels)
         elif isinstance(warminglevels["wl"], list):
             template = deepcopy(warminglevels)

src/xscen/biasadjust.py

@@ -58,17 +58,17 @@ def _add_preprocessing_attr(scen, train_kwargs):
 def train(
     dref: xr.Dataset,
     dhist: xr.Dataset,
-    var: Union[str, list[str]],
+    var: str | list[str],
     period: list[str],
     *,
     method: str = "DetrendedQuantileMapping",
-    group: Optional[Union[sdba.Grouper, str, dict]] = None,
-    xclim_train_args: Optional[dict] = None,
+    group: sdba.Grouper | str | dict | None = None,
+    xclim_train_args: dict | None = None,
     maximal_calendar: str = "noleap",
-    adapt_freq: Optional[dict] = None,
-    jitter_under: Optional[dict] = None,
-    jitter_over: Optional[dict] = None,
-    align_on: Optional[str] = "year",
+    adapt_freq: dict | None = None,
+    jitter_under: dict | None = None,
+    jitter_over: dict | None = None,
+    align_on: str | None = "year",
 ) -> xr.Dataset:
     """
     Train a bias-adjustment.
@@ -194,13 +194,13 @@ def train(
 def adjust(
     dtrain: xr.Dataset,
     dsim: xr.Dataset,
-    periods: Union[list[str], list[list[str]]],
+    periods: list[str] | list[list[str]],
     *,
-    xclim_adjust_args: Optional[dict] = None,
+    xclim_adjust_args: dict | None = None,
     to_level: str = "biasadjusted",
-    bias_adjust_institution: Optional[str] = None,
-    bias_adjust_project: Optional[str] = None,
-    align_on: Optional[str] = "year",
+    bias_adjust_institution: str | None = None,
+    bias_adjust_project: str | None = None,
+    align_on: str | None = "year",
 ) -> xr.Dataset:
     """
     Adjust a simulation.

src/xscen/catalog.py

@@ -198,10 +198,10 @@ def __init__(
     @classmethod
     def from_df(
         cls,
-        data: Union[pd.DataFrame, os.PathLike, Sequence[os.PathLike]],
-        esmdata: Optional[Union[os.PathLike, dict]] = None,
+        data: pd.DataFrame | os.PathLike | Sequence[os.PathLike],
+        esmdata: os.PathLike | dict | None = None,
         *,
-        read_csv_kwargs: Optional[Mapping[str, Any]] = None,
+        read_csv_kwargs: Mapping[str, Any] | None = None,
         name: str = "virtual",
         **intake_kwargs,
     ):
@@ -263,7 +263,7 @@ def _find_unique(series):
         else:
             return data.apply(_find_unique, result_type="reduce").to_dict()
 
-    def unique(self, columns: Optional[Union[str, Sequence[str]]] = None):
+    def unique(self, columns: str | Sequence[str] | None = None):
         """Return a series of unique values in the catalog.
 
         Parameters
@@ -309,7 +309,7 @@ def search(self, **columns):
             )
         return cat
 
-    def drop_duplicates(self, columns: Optional[list[str]] = None):
+    def drop_duplicates(self, columns: list[str] | None = None):
         """Drop duplicates in the catalog based on a subset of columns.
 
         Parameters
@@ -404,10 +404,10 @@ def exists_in_cat(self, **columns) -> bool:
 
     def to_dataset(
         self,
-        concat_on: Optional[Union[list[str], str]] = None,
-        create_ensemble_on: Optional[Union[list[str], str]] = None,
-        ensemble_name: Optional[Union[list[str]]] = None,
-        calendar: Optional[str] = "standard",
+        concat_on: list[str] | str | None = None,
+        create_ensemble_on: list[str] | str | None = None,
+        ensemble_name: list[str] | None = None,
+        calendar: str | None = "standard",
         **kwargs,
     ) -> xr.Dataset:
         """
@@ -538,7 +538,7 @@ def preprocess(ds):
 
     def copy_files(
         self,
-        dest: Union[str, os.PathLike],
+        dest: str | os.PathLike,
         flat: bool = True,
         unzip: bool = False,
         zipzarr: bool = False,
@@ -636,9 +636,9 @@ class ProjectCatalog(DataCatalog):
     @classmethod
     def create(
         cls,
-        filename: Union[os.PathLike, str],
+        filename: os.PathLike | str,
         *,
-        project: Optional[dict] = None,
+        project: dict | None = None,
         overwrite: bool = False,
     ):
         r"""Create a new project catalog from some project metadata.
@@ -716,11 +716,11 @@ def create(
 
     def __init__(
         self,
-        df: Union[str, dict],
+        df: str | dict,
         *args,
         create: bool = False,
         overwrite: bool = False,
-        project: Optional[dict] = None,
+        project: dict | None = None,
         **kwargs,
     ):
         """
@@ -746,7 +746,7 @@ def __init__(
         The ‘df’ key must be a Pandas DataFrame containing content that would otherwise be in the CSV file.
         """
         if create:
-            if isinstance(df, (str, Path)) and (not Path(df).is_file() or overwrite):
+            if isinstance(df, str | Path) and (not Path(df).is_file() or overwrite):
                 self.create(df, project=project, overwrite=overwrite)
         super().__init__(df, *args, **kwargs)
         self.check_valid()
@@ -756,15 +756,13 @@ def __init__(
     # TODO: Implement a way to easily destroy part of the catalog to "reset" some steps
     def update(
         self,
-        df: Optional[
-            Union[
-                DataCatalog,
-                intake_esm.esm_datastore,
-                pd.DataFrame,
-                pd.Series,
-                Sequence[pd.Series],
-            ]
-        ] = None,
+        df: None | (
+            DataCatalog
+            | intake_esm.esm_datastore
+            | pd.DataFrame
+            | pd.Series
+            | Sequence[pd.Series]
+        ) = None,
     ):
         """Update the catalog with new data and writes the new data to the csv file.
 
@@ -846,8 +844,8 @@ def update(
     def update_from_ds(
         self,
         ds: xr.Dataset,
-        path: Union[os.PathLike, str],
-        info_dict: Optional[dict] = None,
+        path: os.PathLike | str,
+        info_dict: dict | None = None,
         **info_kwargs,
     ):
         """Update the catalog with new data and writes the new data to the csv file.
@@ -965,7 +963,7 @@ def _build_id(element: pd.Series, columns: list[str]):
 
 
 def generate_id(
-    df: Union[pd.DataFrame, xr.Dataset], id_columns: Optional[list] = None
+    df: pd.DataFrame | xr.Dataset, id_columns: list | None = None
 ) -> pd.Series:
     """Create an ID from column entries.
 
@@ -996,7 +994,7 @@ def generate_id(
     return df.apply(_build_id, axis=1, args=(id_columns,))
 
 
-def unstack_id(df: Union[pd.DataFrame, ProjectCatalog, DataCatalog]) -> dict:
+def unstack_id(df: pd.DataFrame | ProjectCatalog | DataCatalog) -> dict:
     """Reverse-engineer an ID using catalog entries.
 
     Parameters
@@ -1009,7 +1007,7 @@ def unstack_id(df: Union[pd.DataFrame, ProjectCatalog, DataCatalog]) -> dict:
     dict
         Dictionary with one entry per unique ID, which are themselves dictionaries of all the individual parts of the ID.
     """
-    if isinstance(df, (ProjectCatalog, DataCatalog)):
+    if isinstance(df, ProjectCatalog | DataCatalog):
         df = df.df
 
     out = {}
@@ -1038,7 +1036,7 @@ def unstack_id(df: Union[pd.DataFrame, ProjectCatalog, DataCatalog]) -> dict:
 
 def subset_file_coverage(
     df: pd.DataFrame,
-    periods: Union[list[str], list[list[str]]],
+    periods: list[str] | list[list[str]],
     *,
     coverage: float = 0.99,
     duplicates_ok: bool = False,