add Optional

xscen/aggregate.py

@@ -44,10 +44,10 @@ def _(s):
 def climatological_mean(
     ds: xr.Dataset,
     *,
-    window: int = None,
-    min_periods: int = None,
+    window: Optional[int] = None,
+    min_periods: Optional[int] = None,
     interval: int = 1,
-    periods: Union[list[str], list[list[str]]] = None,
+    periods: Optional[Union[list[str], list[list[str]]]] = None,
     to_level: Optional[str] = "climatology",
 ) -> xr.Dataset:
     """Compute the mean over 'year' for given time periods, respecting the temporal resolution of ds.
@@ -372,13 +372,13 @@ def spatial_mean(
     ds: xr.Dataset,
     method: str,
     *,
-    spatial_subset: bool = None,
-    call_clisops: bool = False,
-    region: Union[dict, str] = None,
-    kwargs: dict = None,
-    simplify_tolerance: float = None,
-    to_domain: str = None,
-    to_level: str = None,
+    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,
 ) -> xr.Dataset:
     """Compute the spatial mean using a variety of available methods.
 
@@ -703,10 +703,10 @@ def produce_horizon(
         ModuleType,
     ],
     *,
-    periods: Union[list[str], list[list[str]]] = None,
-    warminglevels: dict = None,
+    periods: Optional[Union[list[str], list[list[str]]]] = None,
+    warminglevels: Optional[dict] = None,
     to_level: Optional[str] = "horizons",
-    period: list = None,
+    period: Optional[list] = None,
 ) -> xr.Dataset:
     """Compute indicators, then the climatological mean, and finally unstack dates in order to have a single dataset with all indicators of different frequencies.
 

xscen/biasadjust.py

@@ -60,12 +60,12 @@ def train(
     period: list[str],
     *,
     method: str = "DetrendedQuantileMapping",
-    group: Union[sdba.Grouper, str, dict] = None,
-    xclim_train_args: dict = None,
+    group: Optional[Union[sdba.Grouper, str, dict]] = None,
+    xclim_train_args: Optional[dict] = None,
     maximal_calendar: str = "noleap",
-    adapt_freq: dict = None,
-    jitter_under: dict = None,
-    jitter_over: dict = None,
+    adapt_freq: Optional[dict] = None,
+    jitter_under: Optional[dict] = None,
+    jitter_over: Optional[dict] = None,
     align_on: Optional[str] = "year",
 ) -> xr.Dataset:
     """
@@ -191,11 +191,11 @@ def adjust(
     dsim: xr.Dataset,
     periods: Union[list[str], list[list[str]]],
     *,
-    xclim_adjust_args: dict = None,
+    xclim_adjust_args: Optional[dict] = None,
     to_level: str = "biasadjusted",
-    bias_adjust_institution: str = None,
-    bias_adjust_project: str = None,
-    moving_yearly_window: dict = None,
+    bias_adjust_institution: Optional[str] = None,
+    bias_adjust_project: Optional[str] = None,
+    moving_yearly_window: Optional[dict] = None,
     align_on: Optional[str] = "year",
 ) -> xr.Dataset:
     """

xscen/catalog.py

@@ -191,9 +191,9 @@ def __init__(
     def from_df(
         cls,
         data: Union[pd.DataFrame, os.PathLike, Sequence[os.PathLike]],
-        esmdata: Union[os.PathLike, dict] = None,
+        esmdata: Optional[Union[os.PathLike, dict]] = None,
         *,
-        read_csv_kwargs: Mapping[str, Any] = None,
+        read_csv_kwargs: Optional[Mapping[str, Any]] = None,
         name: str = "virtual",
         **intake_kwargs,
     ):
@@ -255,7 +255,7 @@ def _find_unique(series):
         else:
             return data.apply(_find_unique, result_type="reduce").to_dict()
 
-    def unique(self, columns: Union[str, Sequence[str]] = None):
+    def unique(self, columns: Optional[Union[str, Sequence[str]]] = None):
         """Return a series of unique values in the catalog.
 
         Parameters
@@ -396,7 +396,7 @@ def exists_in_cat(self, **columns) -> bool:
 
     def to_dataset(
         self,
-        concat_on: Union[list[str], str] = None,
+        concat_on: Optional[Union[list[str], str]] = None,
         create_ensemble_on: Optional[Union[list[str], str]] = None,
         calendar: Optional[str] = "standard",
         **kwargs,
@@ -528,7 +528,7 @@ def create(
         cls,
         filename: Union[os.PathLike, str],
         *,
-        project: dict = None,
+        project: Optional[dict] = None,
         overwrite: bool = False,
     ):
         r"""Create a new project catalog from some project metadata.
@@ -609,8 +609,8 @@ def __init__(
         df: Union[str, dict],
         *args,
         create: bool = False,
-        overwrite: bool = None,
-        project: dict = None,
+        overwrite: bool = False,
+        project: Optional[dict] = None,
         **kwargs,
     ):
         """Open or create a project catalog.
@@ -622,7 +622,7 @@ def __init__(
             If dict, this must be a dict representation of an ESM catalog.  See the notes below.
         create : bool
             If True, and if 'df' is a string, this will create an empty ProjectCatalog if none already exists.
-        overwrite : bool, optional
+        overwrite : bool
             If this and 'create' are True, this will overwrite any existing JSON and CSV file with an empty catalog.
         project : dict, optional
             Metadata to create the catalog, if required.
@@ -647,12 +647,14 @@ def __init__(
     # TODO: Implement a way to easily destroy part of the catalog to "reset" some steps
     def update(
         self,
-        df: Union[
-            DataCatalog,
-            intake_esm.esm_datastore,
-            pd.DataFrame,
-            pd.Series,
-            Sequence[pd.Series],
+        df: Optional[
+            Union[
+                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.
@@ -736,7 +738,7 @@ def update_from_ds(
         self,
         ds: xarray.Dataset,
         path: Union[os.PathLike, str],
-        info_dict: dict = None,
+        info_dict: Optional[dict] = None,
         **info_kwargs,
     ):
         """Update the catalog with new data and writes the new data to the csv file.
@@ -852,7 +854,7 @@ def _build_id(element: pd.Series, columns: list[str]):
 
 
 def generate_id(
-    df: Union[pd.DataFrame, xr.Dataset], id_columns: list = None
+    df: Union[pd.DataFrame, xr.Dataset], id_columns: Optional[list] = None
 ) -> pd.Series:
     """Create an ID from column entries.
 

xscen/catutils.py

@@ -13,7 +13,7 @@
 from functools import partial, reduce
 from multiprocessing import Pool
 from pathlib import Path
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 import cftime
 import netCDF4
@@ -109,7 +109,7 @@ def _find_assets(
     root: Union[str, os.PathLike],
     exts: set[str],
     lengths: set[int],
-    dirglob: str = None,
+    dirglob: Optional[str] = None,
 ):
     """Walk recursively over files in a directory, filtering according to a glob pattern, path depth and extensions.
 
@@ -180,9 +180,9 @@ def _name_parser(
     path: Union[os.PathLike, str],
     root: Union[os.PathLike, str],
     patterns: list[Union[str, parse.Parser]],
-    read_from_file: Union[list[str], dict] = None,
-    attrs_map: dict = None,
-    xr_open_kwargs: dict = None,
+    read_from_file: Optional[Union[list[str], dict]] = None,
+    attrs_map: Optional[dict] = None,
+    xr_open_kwargs: Optional[dict] = None,
 ) -> Union[dict, None]:
     """Extract metadata information from the file path.
 
@@ -255,11 +255,11 @@ def _name_parser(
 def _parse_dir(
     root: Union[os.PathLike, str],
     patterns: list[str],
-    dirglob: str = None,
-    checks: list[str] = None,
-    read_from_file: Union[list[str], dict] = None,
-    attrs_map: dict = None,
-    xr_open_kwargs: dict = None,
+    dirglob: Optional[str] = None,
+    checks: Optional[list[str]] = None,
+    read_from_file: Optional[Union[list[str], dict]] = None,
+    attrs_map: Optional[dict] = None,
+    xr_open_kwargs: Optional[dict] = None,
     progress: bool = False,
 ):
     """Iterate and parses files in a directory, filtering according to basic pattern properties and optional checks.
@@ -436,21 +436,21 @@ def parse_directory(
     directories: list[Union[str, os.PathLike]],
     patterns: list[str],
     *,
-    id_columns: list[str] = None,
+    id_columns: Optional[list[str]] = None,
     read_from_file: Union[
         bool,
         Sequence[str],
         tuple[Sequence[str], Sequence[str]],
         Sequence[tuple[Sequence[str], Sequence[str]]],
     ] = False,
-    homogenous_info: dict = None,
-    cvs: Union[str, os.PathLike, dict] = None,
-    dirglob: str = None,
-    xr_open_kwargs: Mapping[str, Any] = None,
+    homogenous_info: Optional[dict] = None,
+    cvs: Optional[Union[str, os.PathLike, dict]] = None,
+    dirglob: Optional[str] = None,
+    xr_open_kwargs: Optional[Mapping[str, Any]] = None,
     only_official_columns: bool = True,
     progress: bool = False,
     parallel_dirs: Union[bool, int] = False,
-    file_checks: list[str] = None,
+    file_checks: Optional[list[str]] = None,
 ) -> pd.DataFrame:
     r"""Parse files in a directory and return them as a pd.DataFrame.
 
@@ -698,7 +698,7 @@ def parse_directory(
 def parse_from_ds(
     obj: Union[str, os.PathLike, xr.Dataset],
     names: Sequence[str],
-    attrs_map: Mapping[str, str] = None,
+    attrs_map: Optional[Mapping[str, str]] = None,
     **xrkwargs,
 ):
     """Parse a list of catalog fields from the file/dataset itself.
@@ -1094,8 +1094,8 @@ def _build_path(
 @parse_config
 def build_path(
     data: Union[dict, xr.Dataset, xr.DataArray, pd.Series, DataCatalog, pd.DataFrame],
-    schemas: Union[str, os.PathLike, dict] = None,
-    root: Union[str, os.PathLike] = None,
+    schemas: Optional[Union[str, os.PathLike, dict]] = None,
+    root: Optional[Union[str, os.PathLike]] = None,
     **extra_facets,
 ) -> Union[Path, DataCatalog, pd.DataFrame]:
     r"""Parse the schema from a configuration and construct path using a dictionary of facets.

xscen/diagnostics.py

@@ -6,7 +6,7 @@
 from copy import deepcopy
 from pathlib import Path
 from types import ModuleType
-from typing import Union
+from typing import Optional, Union
 
 import numpy as np
 import xarray as xr
@@ -45,18 +45,18 @@ def _(s):
 def health_checks(
     ds: Union[xr.Dataset, xr.DataArray],
     *,
-    structure: dict = None,
-    calendar: str = None,
-    start_date: str = None,
-    end_date: str = None,
-    variables_and_units: dict = None,
-    cfchecks: dict = None,
-    freq: str = None,
-    missing: Union[dict, str, list] = None,
-    flags: dict = None,
-    flags_kwargs: dict = None,
+    structure: Optional[dict] = None,
+    calendar: Optional[str] = None,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    variables_and_units: Optional[dict] = None,
+    cfchecks: Optional[dict] = None,
+    freq: Optional[str] = None,
+    missing: Optional[Union[dict, str, list]] = None,
+    flags: Optional[dict] = None,
+    flags_kwargs: Optional[dict] = None,
     return_flags: bool = False,
-    raise_on: list = None,
+    raise_on: Optional[list] = None,
 ) -> Union[None, xr.Dataset]:
     """
     Perform a series of health checks on the dataset. Be aware that missing data checks and flag checks can be slow.
@@ -299,11 +299,11 @@ def properties_and_measures(
         Sequence[tuple[str, Indicator]],
         ModuleType,
     ],
-    period: list[str] = None,
+    period: Optional[list[str]] = None,
     unstack: bool = False,
-    rechunk: dict = None,
-    dref_for_measure: xr.Dataset = None,
-    change_units_arg: dict = None,
+    rechunk: Optional[dict] = None,
+    dref_for_measure: Optional[xr.Dataset] = None,
+    change_units_arg: Optional[dict] = None,
     to_level_prop: str = "diag-properties",
     to_level_meas: str = "diag-measures",
 ) -> tuple[xr.Dataset, xr.Dataset]:

xscen/ensembles.py

@@ -6,7 +6,7 @@
 from copy import deepcopy
 from itertools import chain, groupby
 from pathlib import Path
-from typing import Union
+from typing import Optional, Union
 
 import numpy as np
 import xarray as xr
@@ -27,8 +27,8 @@ def ensemble_stats(
     ],
     statistics: dict,
     *,
-    create_kwargs: dict = None,
-    weights: xr.DataArray = None,
+    create_kwargs: Optional[dict] = None,
+    weights: Optional[xr.DataArray] = None,
     common_attrs_only: bool = True,
     to_level: str = "ensemble",
 ) -> xr.Dataset:
@@ -146,9 +146,9 @@ def generate_weights(
     *,
     independence_level: str = "model",
     balance_experiments: bool = False,
-    attribute_weights: dict = None,
+    attribute_weights: Optional[dict] = None,
     skipna: bool = True,
-    v_for_skipna: str = None,
+    v_for_skipna: Optional[str] = None,
     standardize: bool = False,
     experiment_weights: bool = False,
 ) -> xr.DataArray: