Remove outdated imports and old FutureWarnings (#461)

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### Pull Request Checklist:
- [x] This PR addresses an already opened issue (for bug fixes /
features)
    - This PR fixes #xyz
- [x] (If applicable) Documentation has been added / updated (for bug
fixes / features).
- [x] (If applicable) Tests have been added.
- [x] This PR does not seem to break the templates.
- [x] CHANGELOG.rst has been updated (with summary of main changes).
- [x] Link to issue (:issue:`number`) and pull request (:pull:`number`)
has been added.

### What kind of change does this PR introduce?

* Removes `Union, Optional` imports that aren't used anymore.
* Removes deprecated functions and arguments that are 10+ months old. 

### Does this PR introduce a breaking change?

* Only for people still using the deprecated functions.


### Other information:

CHANGELOG.rst

@@ -44,6 +44,7 @@ Breaking changes
 * `convert_calendar` in ``clean_up`` now uses `xarray` instead of `xclim`. Keywords aren't compatible between the two, but given that `xclim` will abandon its function, no backwards compatibility was sought. (:pull:`450`).
 * `attrs_to_remove` and `remove_all_attrs_except` in ``clean_up`` now use real regex. It should not be too breaking since a `fullmatch()` is used, but `*` is now `.*`. (:pull:`450`).
 * Python 3.9 is no longer supported. (:pull:`456`).
+* Functions and arguments that were deprecated in `xscen` v0.8.0 or earlier have been removed. (:pull:`461`).
 
 Internal changes
 ^^^^^^^^^^^^^^^^

src/xscen/aggregate.py

@@ -8,7 +8,6 @@
 from copy import deepcopy
 from pathlib import Path
 from types import ModuleType
-from typing import Optional, Union
 
 import geopandas as gpd
 import numpy as np
@@ -34,7 +33,6 @@
 logger = logging.getLogger(__name__)
 
 __all__ = [
-    "climatological_mean",
     "climatological_op",
     "compute_deltas",
     "produce_horizon",
@@ -47,64 +45,6 @@ def _(s):
     return s
 
 
-@parse_config
-def climatological_mean(
-    ds: xr.Dataset,
-    *,
-    window: int | None = None,
-    min_periods: int | None = None,
-    interval: int = 1,
-    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.
-
-    Parameters
-    ----------
-    ds : xr.Dataset
-        Dataset to use for the computation.
-    window : int, optional
-        Number of years to use for the time periods.
-        If left at None and periods is given, window will be the size of the first period.
-        If left at None and periods is not given, the window will be the size of the input dataset.
-    min_periods : int, optional
-        For the rolling operation, minimum number of years required for a value to be computed.
-        If left at None and the xrfreq is either QS or AS and doesn't start in January, min_periods will be one less than window.
-        If left at None, it will be deemed the same as 'window'.
-    interval : int
-        Interval (in years) at which to provide an output.
-    periods : list of str or list of lists of str, optional
-        Either [start, end] or list of [start, end] of continuous periods to be considered.
-        This is needed when the time axis of ds contains some jumps in time.
-        If None, the dataset will be considered continuous.
-    to_level : str, optional
-        The processing level to assign to the output.
-        If None, the processing level of the inputs is preserved.
-
-    Returns
-    -------
-    xr.Dataset
-        Returns a Dataset of the climatological mean, by calling climatological_op with option op=='mean'.
-
-    """
-    warnings.warn(
-        "xs.climatological_mean is deprecated and will be abandoned in a future release. "
-        "Use xs.climatological_op with option op=='mean' instead.",
-        category=FutureWarning,
-    )
-    return climatological_op(
-        ds,
-        op="mean",
-        window=window,
-        min_periods=min_periods,
-        stride=interval,
-        periods=periods,
-        rename_variables=False,
-        to_level=to_level,
-        horizons_as_dim=False,
-    )
-
-
 @parse_config
 def climatological_op(  # noqa: C901
     ds: xr.Dataset,
@@ -767,24 +707,6 @@ def spatial_mean(  # noqa: C901
     xarray.Dataset.mean, xarray.Dataset.interp, xesmf.SpatialAverager
     """
     kwargs = kwargs or {}
-    if method == "mean":
-        warnings.warn(
-            "xs.spatial_mean with method=='mean' is deprecated and will be abandoned in a future release. "
-            "Use method=='cos-lat' instead for a more robust but similar method.",
-            category=FutureWarning,
-        )
-    elif method == "interp_coord":
-        warnings.warn(
-            "xs.spatial_mean with method=='interp_coord' is deprecated. Use method=='interp_centroid' instead.",
-            category=FutureWarning,
-        )
-        method = "interp_centroid"
-    if call_clisops:
-        warnings.warn(
-            "call_clisops has been renamed and is deprecated. Use spatial_subset instead.",
-            category=FutureWarning,
-        )
-        spatial_subset = call_clisops
 
     if region == "global":
         region = {
@@ -798,21 +720,6 @@ def spatial_mean(  # noqa: C901
         else:
             region["lon_bnds"] = [-180, 180]
 
-    if (
-        (region is not None)
-        and (region["method"] in region)
-        and (isinstance(region[region["method"]], dict))
-    ):
-        warnings.warn(
-            "You seem to be using a deprecated version of region. Please use the new formatting.",
-            category=FutureWarning,
-        )
-        region = deepcopy(region)
-        if "buffer" in region:
-            region["tile_buffer"] = region.pop("buffer")
-        _kwargs = region.pop(region["method"])
-        region.update(_kwargs)
-
     if (
         (region is not None)
         and (spatial_subset is None)
@@ -873,19 +780,6 @@ def spatial_mean(  # noqa: C901
             f"weighted mean(dim={[d for d in ds.cf.axes['X'] + ds.cf.axes['Y']]}) using a 'cos-lat' approximation of areacella (in deg2)"
         )
 
-    # This simply calls .mean() over the spatial dimensions
-    elif method == "mean":
-        if "dim" not in kwargs:
-            kwargs["dim"] = ds.cf.axes["X"] + ds.cf.axes["Y"]
-
-        ds_agg = ds.mean(keep_attrs=True, **kwargs)
-
-        # Prepare the History field
-        new_history = (
-            f"[{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] "
-            f"xarray.mean(dim={kwargs['dim']}) - xarray v{xr.__version__}"
-        )
-
     # This calls .interp() to a pair of coordinates
     elif method == "interp_centroid":
         # Find the centroid
@@ -896,7 +790,7 @@ def spatial_mean(  # noqa: C901
                 kwargs[ds.cf.axes["Y"][0]] = ds[ds.cf.axes["Y"][0]].mean().values
         else:
             if region["method"] == "gridpoint":
-                if len(region["lon"] != 1):
+                if len(region["lon"]) != 1:
                     raise ValueError(
                         "Only a single location should be used with interp_centroid."
                     )
@@ -1045,7 +939,6 @@ def produce_horizon(  # noqa: C901
     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
@@ -1084,12 +977,6 @@ def produce_horizon(  # noqa: C901
             "If you want to use produce_horizon for multiple warming levels, "
             "extract the full time series and use the `warminglevels` argument instead."
         )
-    if period is not None:
-        warnings.warn(
-            "The 'period' argument is deprecated and will be removed in a future version. Use 'periods' instead.",
-            category=FutureWarning,
-        )
-        periods = [standardize_periods(period, multiple=False)]
 
     all_periods = []
     if periods is not None:

src/xscen/biasadjust.py

@@ -2,7 +2,6 @@
 
 import logging
 from copy import deepcopy
-from typing import Optional, Union
 
 import xarray as xr
 import xclim as xc

src/xscen/catalog.py

@@ -12,7 +12,7 @@
 from functools import reduce
 from operator import or_
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any
 
 import fsspec as fs
 import intake_esm

src/xscen/catutils.py

@@ -15,7 +15,7 @@
 from itertools import chain, combinations, product
 from multiprocessing import Pool
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any
 
 import cftime
 import netCDF4

src/xscen/config.py

@@ -52,7 +52,7 @@
 from copy import deepcopy
 from functools import wraps
 from pathlib import Path
-from typing import Any, Optional
+from typing import Any
 
 import xarray as xr
 import xclim as xc

src/xscen/diagnostics.py

@@ -7,7 +7,6 @@
 from copy import deepcopy
 from pathlib import Path
 from types import ModuleType
-from typing import Optional, Union
 
 import numpy as np
 import xarray as xr

src/xscen/ensembles.py

@@ -7,7 +7,6 @@
 from copy import deepcopy
 from itertools import chain, groupby
 from pathlib import Path
-from typing import Optional, Union
 
 import numpy as np
 import xarray as xr
@@ -253,7 +252,6 @@ def generate_weights(  # noqa: C901
     skipna: bool = True,
     v_for_skipna: str | None = None,
     standardize: bool = False,
-    experiment_weights: bool = False,
 ) -> xr.DataArray:
     """Use realization attributes to automatically generate weights along the 'realization' dimension.
 
@@ -291,8 +289,6 @@ def generate_weights(  # noqa: C901
         Variable to use for skipna=False. If None, the first variable in the first dataset is used.
     standardize : bool
         If True, the weights are standardized to sum to 1 (per timestep/horizon, if skipna=False).
-    experiment_weights : bool
-        Deprecated. Use balance_experiments instead.
 
     Notes
     -----
@@ -309,23 +305,9 @@ def generate_weights(  # noqa: C901
     xr.DataArray
         Weights along the 'realization' dimension, or 2D weights along the 'realization' and 'time/horizon' dimensions if skipna=False.
     """
-    if experiment_weights is True:
-        warnings.warn(
-            "`experiment_weights` has been renamed and will be removed in a future release. Use `balance_experiments` instead.",
-            category=FutureWarning,
-        )
-        balance_experiments = True
-
     if isinstance(datasets, list):
         datasets = {i: datasets[i] for i in range(len(datasets))}
 
-    if independence_level == "all":
-        warnings.warn(
-            "The independence level 'all' is deprecated and will be removed in a future version. Use 'model' instead.",
-            category=FutureWarning,
-        )
-        independence_level = "model"
-
     if independence_level not in ["model", "GCM", "institution"]:
         raise ValueError(
             f"'independence_level' should be between 'model', 'GCM', and 'institution', received {independence_level}."

src/xscen/extract.py

@@ -9,7 +9,6 @@
 from collections.abc import Callable, Sequence
 from copy import deepcopy
 from pathlib import Path
-from typing import Optional, Union
 
 import numpy as np
 import pandas as pd

src/xscen/indicators.py

@@ -6,7 +6,6 @@
 from functools import partial
 from pathlib import Path
 from types import ModuleType
-from typing import Optional, Union
 
 import pandas as pd
 import xarray as xr

src/xscen/io.py

@@ -8,7 +8,6 @@
 from collections.abc import Sequence
 from inspect import signature
 from pathlib import Path
-from typing import Optional, Union
 from zipfile import ZipFile
 
 import h5py

src/xscen/reduce.py

@@ -1,7 +1,6 @@
 """Functions to reduce an ensemble of simulations."""
 
 import warnings
-from typing import Optional, Union
 
 import numpy as np
 import xarray as xr
@@ -37,7 +36,7 @@ def build_reduction_data(
         2D DataArray of dimensions "realization" and "criteria", to be used as input for ensemble reduction.
     """
     warnings.warn(
-        "This function will be dropped in a future version, as it is now redundant with xclim.ensembles.make_criteria."
+        "This function will be dropped in v0.11.0, as it is now redundant with xclim.ensembles.make_criteria."
         "Either use xclim.ensembles.make_criteria directly (preceded by xclim.ensembles.create_ensemble if needed) or "
         "use xscen's reduce_ensemble function to build the criteria and reduce the ensemble in one step.",
         FutureWarning,
@@ -128,7 +127,7 @@ def reduce_ensemble(
     You can use py:func:`xscen.utils.unstack_dates` on seasonal or monthly indicators to this end.
     """
     warnings.warn(
-        "This function has been moved to xscen.ensembles.reduce_ensemble. This version will be dropped in a future release.",
+        "This function has been moved to xscen.ensembles.reduce_ensemble. This version will be dropped in v0.11.0.",
         FutureWarning,
     )
     return reduce_ensemble(

src/xscen/regrid.py

@@ -6,7 +6,6 @@
 import warnings
 from copy import deepcopy
 from pathlib import Path
-from typing import Optional, Union
 
 import cartopy.crs as ccrs
 import cf_xarray as cfxr

src/xscen/scripting.py

@@ -15,7 +15,6 @@
 from io import BytesIO
 from pathlib import Path
 from traceback import format_exception
-from typing import Optional, Union
 
 import xarray as xr
 from matplotlib.figure import Figure

src/xscen/spatial.py

@@ -6,7 +6,6 @@
 import warnings
 from collections.abc import Sequence
 from pathlib import Path
-from typing import Optional, Union
 
 import clisops.core.subset
 import dask

src/xscen/testing.py

@@ -1,7 +1,5 @@
 """Testing utilities for xscen."""
 
-from typing import Optional, Union
-
 import cartopy.crs as ccrs
 import numpy as np
 import pandas as pd

src/xscen/utils.py

@@ -15,7 +15,7 @@
 from itertools import chain
 from pathlib import Path
 from types import ModuleType
-from typing import Optional, TextIO, Union
+from typing import TextIO
 
 import cftime
 import flox.xarray