Various typing and signature fixes

docs/conf.py

@@ -16,6 +16,7 @@
 import datetime
 import json
 import os
+import pathlib
 import sys
 import warnings
 
@@ -67,13 +68,13 @@
 # Dump indicators to json. The json is added to the html output (html_extra_path)
 # It is read by _static/indsearch.js to populate the table in indicators.rst
 os.makedirs("_dynamic", exist_ok=True)
-with open("_dynamic/indicators.json", "w") as f:
+with pathlib.Path("_dynamic/indicators.json").open("w") as f:
     json.dump(indicators, f)
 
 
 # Dump variables information
-with open("variables.json", "w") as fout:
-    with open("../src/xclim/data/variables.yml") as fin:
+with pathlib.Path("variables.json").open("w") as fout:
+    with pathlib.Path("../src/xclim/data/variables.yml").open() as fin:
         data = yaml.safe_load(fin)
     json.dump(data, fout)
 

src/xclim/cli.py

@@ -518,7 +518,7 @@ def cli(ctx, **kwargs):  # numpydoc ignore=PR01
 
 @cli.result_callback()
 @click.pass_context
-def write_file(ctx, *args, **kwargs):  # numpydoc ignore=PR01
+def write_file(ctx, *_, **kwargs):  # numpydoc ignore=PR01
     """Write the output dataset to file."""
     if ctx.obj["output"] is not None:
         if ctx.obj["verbose"]:

src/xclim/core/bootstrapping.py

@@ -126,13 +126,17 @@ def bootstrap_func(compute_index_func: Callable, **kwargs) -> xarray.DataArray:
     :cite:cts:`zhang_avoiding_2005`
     """
     # Identify the input and the percentile arrays from the bound arguments
-    per_key = None
+    per_key, da_key = None, None
     for name, val in kwargs.items():
         if isinstance(val, DataArray):
             if "percentile_doy" in val.attrs.get("history", ""):
                 per_key = name
             else:
                 da_key = name
+    if da_key is None or per_key is None:
+        raise KeyError(
+            "The input data and the percentile DataArray must be provided as named arguments."
+        )
     # Extract the DataArray inputs from the arguments
     da: DataArray = kwargs.pop(da_key)
     per_da: DataArray | None = kwargs.pop(per_key, None)

src/xclim/core/dataflags.py

@@ -639,11 +639,11 @@ def data_flags(  # noqa: C901
 
     >>> from xclim.core.dataflags import data_flags
     >>> ds = xr.open_dataset(path_to_pr_file)
-    >>> flagged = data_flags(ds.pr, ds)
+    >>> flagged_multi = data_flags(ds.pr, ds)
     >>> # The next example evaluates only one data flag, passing specific parameters. It also aggregates the flags
     >>> # yearly over the "time" dimension only, such that a True means there is a bad data point for that year
     >>> # at that location.
-    >>> flagged = data_flags(
+    >>> flagged_single = data_flags(
     ...     ds.pr,
     ...     ds,
     ...     flags={"very_large_precipitation_events": {"thresh": "250 mm d-1"}},

src/xclim/core/indicator.py

@@ -873,7 +873,7 @@ def __call__(self, *args, **kwds):
             out_attrs.pop("units", None)
         else:
             out_attrs = {}
-        out_attrs = [out_attrs.copy() for i in range(self.n_outs)]
+        out_attrs = [out_attrs.copy() for _ in range(self.n_outs)]
 
         das, params = self._preprocess_and_checks(das, params)
 
@@ -943,7 +943,9 @@ def __call__(self, *args, **kwds):
             return outs[0]
         return tuple(outs)
 
-    def _parse_variables_from_call(self, args, kwds) -> tuple[OrderedDict, dict]:
+    def _parse_variables_from_call(
+        self, args, kwds
+    ) -> tuple[OrderedDict, OrderedDict, OrderedDict | dict]:
         """Extract variable and optional variables from call arguments."""
         # Bind call arguments to `compute` arguments and set defaults.
         ba = self.__signature__.bind(*args, **kwds)

src/xclim/core/units.py

@@ -1308,7 +1308,7 @@ def _check_output_has_units(
 
 # FIXME: This needs to be properly annotated for mypy compliance.
 # See: https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
-def declare_relative_units(**units_by_name) -> Callable:
+def declare_relative_units(**units_by_name: str) -> Callable:
     r"""
     Function decorator checking the units of arguments.
 
@@ -1317,7 +1317,7 @@ def declare_relative_units(**units_by_name) -> Callable:
 
     Parameters
     ----------
-    **units_by_name : dict
+    **units_by_name : str
         Mapping from the input parameter names to dimensions relative to other parameters.
         The dimensions can be a single parameter name as `<other_var>` or more complex expressions,
         such as `<other_var> * [time]`.
@@ -1430,7 +1430,7 @@ def declare_units(**units_by_name) -> Callable:
 
     Parameters
     ----------
-    **units_by_name : dict
+    **units_by_name : str
         Mapping from the input parameter names to their units or dimensionality ("[...]").
         If this decorates a function previously decorated with :py:func:`declare_relative_units`,
         the relative unit declarations are made absolute with the information passed here.

src/xclim/ensembles/_base.py

@@ -187,6 +187,7 @@ def ensemble_mean_std_max_min(
         ds_out[f"{v}_max"] = ens[v].max(dim="realization")
         ds_out[f"{v}_min"] = ens[v].min(dim="realization")
 
+        enough = None
         if min_members != 1:
             enough = ens[v].notnull().sum("realization") >= min_members
 

src/xclim/ensembles/_reduce.py

@@ -8,6 +8,7 @@
 
 from __future__ import annotations
 
+from typing import Any
 from warnings import warn
 
 import numpy as np
@@ -128,7 +129,7 @@ def kkz_reduce_ensemble(
     *,
     dist_method: str = "euclidean",
     standardize: bool = True,
-    **cdist_kwargs,
+    **cdist_kwargs: Any,
 ) -> list:
     r"""
     Return a sample of ensemble members using KKZ selection.
@@ -152,7 +153,7 @@ def kkz_reduce_ensemble(
     standardize : bool
         Whether to standardize the input before running the selection or not.
         Standardization consists in translation as to have a zero mean and scaling as to have a unit standard deviation.
-    **cdist_kwargs : dict
+    **cdist_kwargs : Any
         All extra arguments are passed as-is to `scipy.spatial.distance.cdist`, see its docs for more information.
 
     Returns

src/xclim/indices/_agro.py

@@ -1215,7 +1215,7 @@ def standardized_precipitation_index(
     ...     method="ML",
     ...     zero_inflated=True,
     ... )  # First getting params
-    >>> spi_3 = standardized_precipitation_index(pr, params=params)
+    >>> spi_3_fitted = standardized_precipitation_index(pr, params=params)
     """
     fitkwargs = fitkwargs or {}
     dist_methods = {"gamma": ["ML", "APP"], "fisk": ["ML", "APP"]}

src/xclim/indices/_threshold.py

@@ -1973,7 +1973,6 @@ def hot_spell_max_magnitude(
     thresh: Quantified = "25.0 degC",
     window: int = 3,
     freq: str = "YS",
-    op: str = ">",
     resample_before_rl: bool = True,
 ) -> xarray.DataArray:
     """
@@ -1993,8 +1992,6 @@ def hot_spell_max_magnitude(
         Minimum number of days with temperature above threshold to qualify as a heatwave.
     freq : str
         Resampling frequency.
-    op : {">", ">=", "gt", "ge"}
-        Comparison operation. Default: ">".
     resample_before_rl : bool
         Determines if the resampling should take place before or after the run
         length encoding (or a similar algorithm) is applied to runs.
@@ -3273,8 +3270,8 @@ def dry_spell_frequency(
     --------
     >>> from xclim.indices import dry_spell_frequency
     >>> pr = xr.open_dataset(path_to_pr_file).pr
-    >>> dsf = dry_spell_frequency(pr=pr, op="sum")
-    >>> dsf = dry_spell_frequency(pr=pr, op="max")
+    >>> dsf_sum = dry_spell_frequency(pr=pr, op="sum")
+    >>> dsf_max = dry_spell_frequency(pr=pr, op="max")
     """
     pram = rate2amount(convert_units_to(pr, "mm/d", context="hydro"), out_units="mm")
     return spell_length_statistics(
@@ -3481,8 +3478,8 @@ def wet_spell_frequency(
     --------
     >>> from xclim.indices import wet_spell_frequency
     >>> pr = xr.open_dataset(path_to_pr_file).pr
-    >>> dsf = wet_spell_frequency(pr=pr, op="sum")
-    >>> dsf = wet_spell_frequency(pr=pr, op="min")
+    >>> dsf_sum = wet_spell_frequency(pr=pr, op="sum")
+    >>> dsf_min = wet_spell_frequency(pr=pr, op="min")
     """
     pram = rate2amount(convert_units_to(pr, "mm/d", context="hydro"), out_units="mm")
     return spell_length_statistics(

src/xclim/indices/helpers.py

@@ -803,9 +803,7 @@ def make_hourly_temperature(tasmin: xr.DataArray, tasmax: xr.DataArray) -> xr.Da
     hourly = data.resample(time="h").ffill().isel(time=slice(0, -1))
 
     # To avoid "invalid value encountered in log" warning we set hours before sunset to 1
-    nighttime_hours = nighttime_hours = (
-        hourly.time.dt.hour + 1 - hourly.daylength
-    ).clip(1)
+    nighttime_hours = (hourly.time.dt.hour + 1 - hourly.daylength).clip(1)
 
     return xr.where(
         hourly.time.dt.hour < hourly.daylength,

src/xclim/indices/run_length.py

@@ -107,7 +107,7 @@ def resample_and_rl(
         Resampling frequency.
     dim : str
         The dimension along which to find runs.
-    **kwargs : dict
+    **kwargs : Any
         Keyword arguments needed in `compute`.
 
     Returns

src/xclim/sdba/_adjustment.py

@@ -339,9 +339,9 @@ def _npdft_adjust(sim, af_q, rots, quantiles, method, extrap):
 
 
 def mbcn_adjust(
-    ref: xr.Dataset,
-    hist: xr.Dataset,
-    sim: xr.Dataset,
+    ref: xr.DataArray,
+    hist: xr.DataArray,
+    sim: xr.DataArray,
     ds: xr.Dataset,
     pts_dims: Sequence[str],
     interp: str,
@@ -350,7 +350,7 @@ def mbcn_adjust(
     base_kws_vars: dict,
     adj_kws: dict,
     period_dim: str | None,
-) -> xr.DataArray:
+) -> xr.Dataset:
     """Perform the adjustment portion MBCn multivariate bias correction technique.
 
     The function :py:func:`mbcn_train` pre-computes the adjustment factors for each rotation
@@ -696,7 +696,7 @@ def npdf_transform(ds: xr.Dataset, **kwargs) -> xr.Dataset:
             hist : simulated timeseries on the reference period
             sim : Simulated timeseries on the projected period.
             rot_matrices : Random rotation matrices.
-    **kwargs : dict
+    **kwargs : Any
         pts_dim : multivariate dimension name
         base : Adjustment class
         base_kws : Kwargs for initialising the adjustment object

src/xclim/sdba/processing.py

@@ -852,14 +852,14 @@ def grouped_time_indexes(times, group):
         Time indexes of the blocks (built with a rolling window of `group.window` if any).
     """
 
-    def _get_group_complement(da, group):
+    def _get_group_complement(_da, _group):
         # complement of "dayofyear": "year", etc.
-        gr = group if isinstance(group, str) else group.name
-        if gr == "time.dayofyear":
-            return da.time.dt.year
-        if gr == "time.month":
-            return da.time.dt.strftime("%Y-%d")
-        raise NotImplementedError(f"Grouping {gr} not implemented.")
+        _gr = _group if isinstance(_group, str) else _group.name
+        if _gr == "time.dayofyear":
+            return _da.time.dt.year
+        if _gr == "time.month":
+            return _da.time.dt.strftime("%Y-%d")
+        raise NotImplementedError(f"Grouping {_gr} not implemented.")
 
     # does not work with group == "time.month"
     group = group if isinstance(group, Grouper) else Grouper(group)
@@ -871,14 +871,14 @@ def _get_group_complement(da, group):
     )
     if gr == "time.dayofyear":
         # time indices for each block with window = 1
-        g_idxs = timeind.groupby(gr).apply(
+        g_idxs = timeind.groupby(gr).map(
             lambda da: da.assign_coords(time=_get_group_complement(da, gr)).rename(
                 {"time": "year"}
             )
         )
         # time indices for each block with general window
         da = timeind.rolling(time=win, center=True).construct(window_dim=win_dim0)
-        gw_idxs = da.groupby(gr).apply(
+        gw_idxs = da.groupby(gr).map(
             lambda da: da.assign_coords(time=_get_group_complement(da, gr)).stack(
                 {win_dim: ["time", win_dim0]}
             )

src/xclim/sdba/utils.py

@@ -6,7 +6,7 @@
 from __future__ import annotations
 
 import itertools
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
 from warnings import warn
 
 import bottleneck as bn
@@ -80,7 +80,9 @@ def map_cdf(
     )
 
 
-def ecdf(x: xr.DataArray, value: float, dim: str = "time") -> xr.DataArray:
+def ecdf(
+    x: xr.DataArray, value: float, dim: str | Sequence[str] = "time"
+) -> xr.DataArray:
     """Return the empirical CDF of a sample at a given value.
 
     Parameters
@@ -948,7 +950,7 @@ def _skipna_correlation(data):
         # The output
         out = np.empty((nv, nv), dtype=coef.dtype)
         # A 2D mask of removed variables
-        M = (mask_omit)[:, np.newaxis] | (mask_omit)[np.newaxis, :]
+        M = mask_omit[:, np.newaxis] | mask_omit[np.newaxis, :]
         out[~M] = coef.flatten()
         out[M] = np.nan
         return out

src/xclim/testing/helpers.py

@@ -173,13 +173,12 @@ def add_doctest_filepaths() -> dict[str, Any]:
     dict[str, Any]
         A dictionary of xdoctest namespace objects.
     """
-    namespace: dict = {}
-    namespace["np"] = np
-    namespace["xclim"] = xclim
-    namespace["tas"] = test_timeseries(
-        np.random.rand(365) * 20 + 253.15, variable="tas"
-    )
-    namespace["pr"] = test_timeseries(np.random.rand(365) * 5, variable="pr")
+    namespace = {
+        "np": np,
+        "xclim": xclim,
+        "tas": test_timeseries(np.random.rand(365) * 20 + 253.15, variable="tas"),
+        "pr": test_timeseries(np.random.rand(365) * 5, variable="pr"),
+    }
     return namespace