docstring fixes

src/xsdba/_adjustment.py

@@ -693,29 +693,28 @@ def npdf_transform(ds: xr.Dataset, **kwargs) -> xr.Dataset:
     ----------
     ds : xr.Dataset
         Dataset variables:
-            ref : Reference multivariate timeseries
-            hist : simulated timeseries on the reference period
+            ref : Reference multivariate timeseries.
+            hist : simulated timeseries on the reference period.
             sim : Simulated timeseries on the projected period.
             rot_matrices : Random rotation matrices.
     \*\*kwargs
-        pts_dim : multivariate dimension name
-        base : Adjustment class
-        base_kws : Kwargs for initialising the adjustment object
-        adj_kws : Kwargs of the `adjust` call
+        pts_dim : multivariate dimension name.
+        base : Adjustment class.
+        base_kws : Kwargs for initialising the adjustment object.
+        adj_kws : Kwargs of the `adjust` call.
         n_escore : Number of elements to include in the e_score test (0 for all, < 0 to skip).
 
     Returns
     -------
     xr.Dataset
         Dataset variables:
-            scenh : Scenario in the reference period (source `hist` transferred to target `ref` inside training)
-            scens : Scenario in the projected period (source `sim` transferred to target `ref` outside training)
+            scenh : Scenario in the reference period (source `hist` transferred to target `ref` inside training).
+            scens : Scenario in the projected period (source `sim` transferred to target `ref` outside training).
             escores : Index estimating the dissimilarity between `scenh` and `hist`.
 
     Notes
     -----
     If `n_escore` is negative, `escores` will be filled with NaNs.
-
     """
     ref = ds.ref.rename(time_hist="time")
     hist = ds.hist.rename(time_hist="time")

src/xsdba/_processing.py

@@ -1,4 +1,4 @@
-"""
+"""# noqa: SS01
 Compute Functions Submodule
 ===========================
 
@@ -35,24 +35,23 @@ def _adapt_freq(
     Parameters
     ----------
     ds : xr.Dataset
-        With variables :  "ref", Target/reference data, usually observed data and "sim", Simulated data.
+        With variables : "ref", Target/reference data, usually observed data and "sim", Simulated data.
     dim : str, or sequence of strings
-        Dimension name(s). If more than one, the probabilities and quantiles are computed within all the dimensions.
-        If `window` is in the names, it is removed before the correction
-        and the final timeseries is corrected along dim[0] only.
+        Dimension name(s).
+        If more than one, the probabilities and quantiles are computed within all the dimensions.
+        If `window` is in the names, it is removed before the correction and the final timeseries is corrected along dim[0] only.
     group : Union[str, Grouper]
-        Grouping information, see base.Grouper
+        Grouping information, see base.Grouper.
     thresh : float
         Threshold below which values are considered zero.
 
     Returns
     -------
     xr.Dataset, with the following variables:
-
       - `sim_adj`: Simulated data with the same frequency of values under threshold than ref.
         Adjustment is made group-wise.
-      - `pth` : For each group, the smallest value of sim that was not frequency-adjusted. All values smaller were
-        either left as zero values or given a random value between thresh and pth.
+      - `pth` : For each group, the smallest value of sim that was not frequency-adjusted.
+        All values smaller were either left as zero values or given a random value between thresh and pth.
         NaN where frequency adaptation wasn't needed.
       - `dP0` : For each group, the percentage of values that were corrected in sim.
     """
@@ -125,7 +124,7 @@ def _normalize(
     Returns
     -------
     xr.Dataset
-        Group-wise anomaly of x
+        Group-wise anomaly of 'x'.
 
     Notes
     -----

src/xsdba/calendar.py

@@ -1,4 +1,4 @@
-"""
+"""# noqa: SS01
 Calendar Handling Utilities
 ===========================
 
@@ -435,11 +435,11 @@ def percentile_doy(
     Parameters
     ----------
     arr : xr.DataArray
-      Input data, a daily frequency (or coarser) is required.
+        Input data, a daily frequency (or coarser) is required.
     window : int
-      Number of time-steps around each day of the year to include in the calculation.
+        Number of time-steps around each day of the year to include in the calculation.
     per : float or sequence of floats
-      Percentile(s) between [0, 100]
+        Percentile(s) between [0, 100].
     alpha : float
         Plotting position parameter.
     beta : float
@@ -541,16 +541,16 @@ def compare_offsets(freqA: str, op: str, freqB: str) -> bool:
     Parameters
     ----------
     freqA : str
-        RHS Date offset string ('YS', '1D', 'QS-DEC', ...)
+        RHS Date offset string ('YS', '1D', 'QS-DEC', ...).
     op : {'<', '<=', '==', '>', '>=', '!='}
         Operator to use.
     freqB : str
-        LHS Date offset string ('YS', '1D', 'QS-DEC', ...)
+        LHS Date offset string ('YS', '1D', 'QS-DEC', ...).
 
     Returns
     -------
     bool
-        freqA op freqB
+        Either freqA op freqB.
     """
     from .xclim_submodules.generic import (  # pylint: disable=import-outside-toplevel
         get_op,
@@ -579,23 +579,20 @@ def parse_offset(freq: str) -> tuple[int, str, bool, str | None]:
     Parameters
     ----------
     freq : str
-      Frequency offset.
+        Frequency offset.
 
     Returns
     -------
     multiplier : int
-        Multiplier of the base frequency. "[n]W" is always replaced with "[7n]D",
-        as xarray doesn't support "W" for cftime indexes.
+        Multiplier of the base frequency.
+        "[n]W" is always replaced with "[7n]D", as xarray doesn't support "W" for cftime indexes.
     offset_base : str
         Base frequency.
     is_start_anchored : bool
-        Whether coordinates of this frequency should correspond to the beginning of the period (`True`)
-        or its end (`False`). Can only be False when base is Y, Q or M; in other words, xsdba assumes frequencies finer
-        than monthly are all start-anchored.
+        Whether coordinates of this frequency should correspond to the beginning of the period (`True`) or its end (`False`).
+        Can only be False when base is Y, Q or M; in other words, xsdba assumes frequencies finer than monthly are all start-anchored.
     anchor : str, optional
-        Anchor date for bases Y or Q. As xarray doesn't support "W",
-        neither does xsdba (anchor information is lost when given).
-
+        Anchor date for bases Y or Q. As xarray doesn't support "W", neither does xsdba (anchor information is lost when given).
     """
     # Useful to raise on invalid freqs, convert Y to A and get default anchor (A, Q)
     offset = pd.tseries.frequencies.to_offset(freq)
@@ -1236,17 +1233,17 @@ def select_time(
         Input data.
     drop : bool
         Whether to drop elements outside the period of interest or to simply mask them (default).
-    season : string or sequence of strings, optional
+    season : str or sequence of str, optional
         One or more of 'DJF', 'MAM', 'JJA' and 'SON'.
-    month : integer or sequence of integers, optional
-        Sequence of month numbers (January = 1 ... December = 12)
-    doy_bounds : 2-tuple of integers, optional
+    month : int or sequence of int, optional
+        Sequence of month numbers (January = 1 ... December = 12).
+    doy_bounds : 2-tuple of int, optional
         The bounds as (start, end) of the period of interest expressed in day-of-year, integers going from
         1 (January 1st) to 365 or 366 (December 31st).
         If calendar awareness is needed, consider using ``date_bounds`` instead.
-    date_bounds : 2-tuple of strings, optional
+    date_bounds : 2-tuple of str, optional
         The bounds as (start, end) of the period of interest expressed as dates in the month-day (%m-%d) format.
-    include_bounds : bool or 2-tuple of booleans
+    include_bounds : bool or 2-tuple of bool
         Whether the bounds of `doy_bounds` or `date_bounds` should be inclusive or not.
         Either one value for both or a tuple. Default is True, meaning bounds are inclusive.
 
@@ -1343,7 +1340,7 @@ def _get_doys(_start, _end, _inclusive):
 
 
 def _month_is_first_period_month(time, freq):
-    """Returns True if the given time is from the first month of freq."""
+    """Return True if the given time is from the first month of freq."""
     if isinstance(time, cftime.datetime):
         frq_monthly = xr.coding.cftime_offsets.to_offset("MS")
         frq = xr.coding.cftime_offsets.to_offset(freq)

src/xsdba/datachecks.py

@@ -1,4 +1,4 @@
-"""
+"""# noqa: SS01
 Data Checks
 ===========
 

src/xsdba/formatting.py

@@ -627,8 +627,8 @@ def generate_indicator_docstring(ind) -> str:
 
     Parameters
     ----------
-    ind
-        Indicator instance
+    ind : Indicator
+        An Indicator instance.
 
     Returns
     -------

src/xsdba/indicator.py

@@ -1,4 +1,4 @@
-"""
+"""# noqa: SS01
 Indicator Utilities
 ===================
 
@@ -9,8 +9,8 @@
 There are many ways to construct indicators. A good place to start is
 `this notebook <notebooks/extendxclim.ipynb#Defining-new-indicators>`_.
 
-Dictionary and YAML parser
---------------------------
+Dictionary and YAML parser # noqa: GL06
+-------------------------- # noqa: GL06
 
 To construct indicators dynamically, xclim can also use dictionaries and parse them from YAML files.
 This is especially useful for generating whole indicator "submodules" from files.
@@ -73,7 +73,6 @@
           <var name in compute> : <variable official name>
           ...
 
-
 .. code-block:: restructuredtext
 
     Parameters  # noqa: D214
@@ -99,7 +98,6 @@
 As xclim has strict definitions of possible input variables (see :py:data:`xclim.core.utils.variables`),
 the mapping of `data.input` simply links an argument name from the function given in "compute"
 to one of those official variables.
-
 """
 
 from __future__ import annotations
@@ -515,7 +513,6 @@ def _parse_indice(compute, passed_parameters):  # noqa: F841
         'passed_parameters' is only needed when compute is a generic function
         (not decorated by `declare_units`) and it takes a string parameter. In that case
         we need to check if that parameter has units (which have been passed explicitly).
-
         """
         docmeta = parse_doc(compute.__doc__)
         params_dict = docmeta.pop("parameters", {})  # override parent's parameters
@@ -951,7 +948,7 @@ def _assign_named_args(self, ba):
                         )
 
     def _postprocess(self, outs, das, params):
-        """Actions to done after computing."""
+        """Run post-computation actions."""
         return outs
 
     def _bind_call(self, func, **das):
@@ -1162,7 +1159,6 @@ def json(self, args=None):
         Notes
         -----
         This is meant to be used by a third-party library wanting to wrap this class into another interface.
-
         """
         names = ["identifier", "title", "abstract", "keywords"]
         out = {key: getattr(self, key) for key in names}

src/xsdba/locales.py

@@ -100,7 +100,7 @@ def get_local_dict(locale: str | Sequence[str] | tuple[str, dict]) -> tuple[str,
 
     Parameters
     ----------
-    locale: str or sequence of str
+    locale : str or sequence of str
         IETF language tag or a tuple of the language tag and a translation dict, or a tuple of the language
         tag and a path to a json file defining translation of attributes.
 
@@ -112,7 +112,7 @@ def get_local_dict(locale: str | Sequence[str] | tuple[str, dict]) -> tuple[str,
     Returns
     -------
     str
-        The best fitting locale string
+        The best fitting locale string.
     dict
         The available translations in this locale.
     """
@@ -284,7 +284,7 @@ def generate_local_dict(locale: str, init_english: bool = False) -> dict:
     Parameters
     ----------
     locale : str
-        Locale in the IETF format
+        Locale in the IETF format.
     init_english : bool
         If True, fills the initial dictionary with the english versions of the attributes.
         Defaults to False.

src/xsdba/nbutils.py

@@ -40,7 +40,7 @@ def _get_indexes(
     Returns
     -------
     array-like, array-like
-        A tuple of virtual_indexes neighbouring indexes (previous and next)
+        A tuple of virtual_indexes neighbouring indexes (previous and next).
 
     Notes
     -----

src/xsdba/processing.py

@@ -1,5 +1,5 @@
 # pylint: disable=missing-kwoa
-"""
+"""# noqa: SS01
 Pre- and Post-Processing Submodule
 ==================================
 """
@@ -556,7 +556,7 @@ def to_additive_space(
 
     See Also
     --------
-    from_additive_space : for the inverse transformation.
+    from_additive_space : For the inverse transformation.
     jitter_under_thresh : Remove values exactly equal to the lower bound.
     jitter_over_thresh : Remove values exactly equal to the upper bound.
 
@@ -654,12 +654,11 @@ def from_additive_space(
 
     See Also
     --------
-    to_additive_space : for the original transformation.
+    to_additive_space : For the original transformation.
 
     References
     ----------
     :cite:cts:`sdba-alavoine_distinct_2022`.
-
     """
     if trans is None and lower_bound is None and units is None:
         try:

src/xsdba/utils.py

@@ -50,17 +50,18 @@ def map_cdf(
     Parameters
     ----------
     ds : xr.Dataset
-      Variables: x, Values from which to pick,
-      y, Reference values giving the ranking
+        Variables:
+            x, Values from which to pick,
+            y, Reference values giving the ranking.
     y_value : float, array
-      Value within the support of `y`.
+        Value within the support of `y`.
     dim : str
-      Dimension along which to compute quantile.
+        Dimension along which to compute quantile.
 
     Returns
     -------
     array
-      Quantile of `x` with the same CDF as `y_value` in `y`.
+        Quantile of `x` with the same CDF as `y_value` in `y`.
     """
     return xr.apply_ufunc(
         map_cdf_1d,
@@ -826,19 +827,18 @@ def rand_rot_matrix(
 
     Parameters
     ----------
-    crd: xr.DataArray
-      1D coordinate DataArray along which the rotation occurs.
-      The output will be square with the same coordinate replicated,
-      the second renamed to `new_dim`.
+    crd : xr.DataArray
+        1D coordinate DataArray along which the rotation occurs.
+        The output will be square with the same coordinate replicated, the second renamed to `new_dim`.
     num : int
-      If larger than 1 (default), the number of matrices to generate, stacked along a "matrices" dimension.
+        If larger than 1 (default), the number of matrices to generate, stacked along a "matrices" dimension.
     new_dim : str
-      Name of the new "prime" dimension, defaults to the same name as `crd` + "_prime".
+        Name of the new "prime" dimension, defaults to the same name as `crd` + "_prime".
 
     Returns
     -------
     xr.DataArray
-      float, NxN if num = 1, numxNxN otherwise, where N is the length of crd.
+        A float, NxN if num = 1, numxNxN otherwise, where N is the length of crd.
 
     References
     ----------