Merge branch 'naming_conventions' of https://github.com/Ouranosinc/xsdba

 into naming_conventions

src/xsdba/_adjustment.py

@@ -1,5 +1,6 @@
 # pylint: disable=no-value-for-parameter
-"""# noqa: SS01
+"""
+# noqa: SS01
 Adjustment Algorithms
 =====================
 
@@ -49,7 +50,8 @@ def dqm_train(
     adapt_freq_thresh: str | None = None,
     jitter_under_thresh_value: str | None = None,
 ) -> xr.Dataset:
-    """Train step on one group.
+    """
+    Train step on one group.
 
     Parameters
     ----------
@@ -111,7 +113,8 @@ def eqm_train(
     adapt_freq_thresh: str | None = None,
     jitter_under_thresh_value: str | None = None,
 ) -> xr.Dataset:
-    """EQM: Train step on one group.
+    """
+    EQM: Train step on one group.
 
     Parameters
     ----------
@@ -154,7 +157,8 @@ def eqm_train(
 
 
 def _npdft_train(ref, hist, rots, quantiles, method, extrap, n_escore, standardize):
-    r"""Npdf transform to correct a source `hist` into target `ref`.
+    r"""
+    Npdf transform to correct a source `hist` into target `ref`.
 
     Perform a rotation, bias correct `hist` into `ref` with QuantileDeltaMapping, and rotate back.
     Do this iteratively over all rotations `rots` and conserve adjustment factors `af_q` in each iteration.
@@ -212,7 +216,8 @@ def mbcn_train(
     extrapolation: str,
     n_escore: int,
 ) -> xr.Dataset:
-    """Npdf transform training.
+    """
+    Npdf transform training.
 
     Adjusting factors obtained for each rotation in the npdf transform and conserved to be applied in
     the adjusting step in :py:func:`mcbn_adjust`.
@@ -299,7 +304,8 @@ def mbcn_train(
 
 
 def _npdft_adjust(sim, af_q, rots, quantiles, method, extrap):
-    """Npdf transform adjusting.
+    """
+    Npdf transform adjusting.
 
     Adjusting factors `af_q` obtained in the training step are applied on the simulated data `sim` at each iterated
     rotation, see :py:func:`_npdft_train`.
@@ -351,7 +357,8 @@ def mbcn_adjust(
     adj_kws: dict,
     period_dim: str | None,
 ) -> xr.DataArray:
-    """Perform the adjustment portion MBCn multivariate bias correction technique.
+    """
+    Perform the adjustment portion MBCn multivariate bias correction technique.
 
     The function :py:func:`mbcn_train` pre-computes the adjustment factors for each rotation
     in the npdf portion of the MBCn algorithm. The rest of adjustment is performed here
@@ -470,7 +477,8 @@ def mbcn_adjust(
 def qm_adjust(
     ds: xr.Dataset, *, group: Grouper, interp: str, extrapolation: str, kind: str
 ) -> xr.Dataset:
-    """QM (DQM and EQM): Adjust step on one block.
+    """
+    QM (DQM and EQM): Adjust step on one block.
 
     Parameters
     ----------
@@ -517,7 +525,8 @@ def dqm_adjust(
     extrapolation: str,
     detrend: int | PolyDetrend,
 ) -> xr.Dataset:
-    """DQM adjustment on one block.
+    """
+    DQM adjustment on one block.
 
     Parameters
     ----------
@@ -576,7 +585,8 @@ def dqm_adjust(
 
 @map_blocks(reduces=[Grouper.PROP, "quantiles"], scen=[], sim_q=[])
 def qdm_adjust(ds: xr.Dataset, *, group, interp, extrapolation, kind) -> xr.Dataset:
-    """QDM: Adjust process on one block.
+    """
+    QDM: Adjust process on one block.
 
     Parameters
     ----------
@@ -605,7 +615,8 @@ def qdm_adjust(ds: xr.Dataset, *, group, interp, extrapolation, kind) -> xr.Data
     hist_thresh=[Grouper.PROP],
 )
 def loci_train(ds: xr.Dataset, *, group, thresh) -> xr.Dataset:
-    """LOCI: Train on one block.
+    """
+    LOCI: Train on one block.
 
     Parameters
     ----------
@@ -631,7 +642,8 @@ def loci_train(ds: xr.Dataset, *, group, thresh) -> xr.Dataset:
 
 @map_blocks(reduces=[Grouper.PROP], scen=[])
 def loci_adjust(ds: xr.Dataset, *, group, thresh, interp) -> xr.Dataset:
-    """LOCI: Adjust on one block.
+    """
+    LOCI: Adjust on one block.
 
     Parameters
     ----------
@@ -652,7 +664,8 @@ def loci_adjust(ds: xr.Dataset, *, group, thresh, interp) -> xr.Dataset:
 
 @map_groups(af=[Grouper.PROP])
 def scaling_train(ds: xr.Dataset, *, dim, kind) -> xr.Dataset:
-    """Scaling: Train on one group.
+    """
+    Scaling: Train on one group.
 
     Parameters
     ----------
@@ -670,7 +683,8 @@ def scaling_train(ds: xr.Dataset, *, dim, kind) -> xr.Dataset:
 
 @map_blocks(reduces=[Grouper.PROP], scen=[])
 def scaling_adjust(ds: xr.Dataset, *, group, interp, kind) -> xr.Dataset:
-    """Scaling: Adjust on one block.
+    """
+    Scaling: Adjust on one block.
 
     Parameters
     ----------
@@ -686,7 +700,8 @@ def scaling_adjust(ds: xr.Dataset, *, group, interp, kind) -> xr.Dataset:
 
 
 def npdf_transform(ds: xr.Dataset, **kwargs) -> xr.Dataset:
-    r"""N-pdf transform : Iterative univariate adjustment in random rotated spaces.
+    r"""
+    N-pdf transform : Iterative univariate adjustment in random rotated spaces.
 
     Parameters
     ----------
@@ -836,7 +851,8 @@ def extremes_train(
     dist,
     quantiles: np.ndarray,
 ) -> xr.Dataset:
-    """Train extremes for a given variable series.
+    """
+    Train extremes for a given variable series.
 
     Parameters
     ----------
@@ -901,7 +917,8 @@ def extremes_adjust(
     extrapolation: str,
     cluster_thresh: float,
 ) -> xr.Dataset:
-    """Adjust extremes to reflect many distribution factors.
+    """
+    Adjust extremes to reflect many distribution factors.
 
     Parameters
     ----------
@@ -964,7 +981,8 @@ def _otc_adjust(
     jitter_inside_bins: bool = True,
     normalization: str | None = "max_distance",
 ):
-    """Optimal Transport Correction of the bias of X with respect to Y.
+    """
+    Optimal Transport Correction of the bias of X with respect to Y.
 
     Parameters
     ----------
@@ -1063,7 +1081,8 @@ def otc_adjust(
     adapt_freq_thresh: dict | None = None,
     normalization: str | None = "max_distance",
 ):
-    """Optimal Transport Correction of the bias of `hist` with respect to `ref`.
+    """
+    Optimal Transport Correction of the bias of `hist` with respect to `ref`.
 
     Parameters
     ----------
@@ -1162,7 +1181,8 @@ def _dotc_adjust(
     kind: dict | None = None,
     normalization: str | None = "max_distance",
 ):
-    """Dynamical Optimal Transport Correction of the bias of X with respect to Y.
+    """
+    Dynamical Optimal Transport Correction of the bias of X with respect to Y.
 
     Parameters
     ----------
@@ -1292,7 +1312,8 @@ def dotc_adjust(
     adapt_freq_thresh: dict | None = None,
     normalization: str | None = "max_distance",
 ):
-    """Dynamical Optimal Transport Correction of the bias of X with respect to Y.
+    """
+    Dynamical Optimal Transport Correction of the bias of X with respect to Y.
 
     Parameters
     ----------

src/xsdba/_processing.py

@@ -1,4 +1,5 @@
-"""# noqa: SS01
+"""
+# noqa: SS01
 Compute Functions Submodule
 ===========================
 
@@ -107,7 +108,8 @@ def _normalize(
     dim: Sequence[str],
     kind: str = ADDITIVE,
 ) -> xr.Dataset:
-    """Normalize an array by removing its mean.
+    """
+    Normalize an array by removing its mean.
 
     Parameters
     ----------
@@ -143,7 +145,8 @@ def _normalize(
 
 @map_groups(reordered=[Grouper.DIM], main_only=False)
 def _reordering(ds: xr.Dataset, *, dim: str) -> xr.Dataset:
-    """Group-wise reordering.
+    """
+    Group-wise reordering.
 
     Parameters
     ----------

src/xsdba/detrending.py

@@ -1,4 +1,5 @@
-"""# noqa: SS01
+"""
+# noqa: SS01
 Detrending Objects Utilities
 ============================
 """
@@ -14,7 +15,8 @@
 
 
 class BaseDetrend(ParametrizableWithDataset):
-    """Base class for detrending objects.
+    """
+    Base class for detrending objects.
 
     Defines three methods:
 
@@ -34,7 +36,8 @@ class BaseDetrend(ParametrizableWithDataset):
 
     @parse_group
     def __init__(self, *, group: Grouper | str = "time", kind: str = "+", **kwargs):
-        """Initialize Detrending object.
+        """
+        Initialize Detrending object.
 
         Parameters
         ----------
@@ -52,7 +55,8 @@ def fitted(self):
         return hasattr(self, "ds")
 
     def fit(self, da: xr.DataArray):
-        """Extract the trend of a DataArray along a specific dimension.
+        """
+        Extract the trend of a DataArray along a specific dimension.
 
         Returns a new object that can be used for detrending and retrending.
         Fitted objects are unique to the fitted coordinate used.
@@ -64,7 +68,8 @@ def fit(self, da: xr.DataArray):
         return new
 
     def _get_trend(self, da: xr.DataArray):
-        """Compute the trend along the self.group.dim as found on da.
+        """
+        Compute the trend along the self.group.dim as found on da.
 
         If da is a DataArray (and has a `dtype` attribute), the trend is cast to have the same dtype.
 
@@ -148,7 +153,8 @@ def _meandetrend_get_trend(da, *, dim, kind):
 
 
 class PolyDetrend(BaseDetrend):
-    """Detrend time series using a polynomial regression.
+    """
+    Detrend time series using a polynomial regression.
 
     Attributes
     ----------
@@ -193,7 +199,8 @@ def _polydetrend_get_trend(da, *, dim, degree, preserve_mean, kind):
 
 
 class LoessDetrend(BaseDetrend):
-    """Detrend time series using a LOESS regression.
+    """
+    Detrend time series using a LOESS regression.
 
     The fit is a piecewise linear regression. For each point, the contribution of all
     neighbors is weighted by a bell-shaped curve (gaussian) with parameters sigma (std).
@@ -278,7 +285,8 @@ def _loessdetrend_get_trend(
 
 
 class RollingMeanDetrend(BaseDetrend):
-    """Detrend time series using a rolling mean.
+    """
+    Detrend time series using a rolling mean.
 
     Attributes
     ----------

src/xsdba/loess.py

@@ -1,4 +1,5 @@
-"""# noqa: SS01
+"""
+# noqa: SS01
 LOESS Smoothing Submodule
 =========================
 """
@@ -57,7 +58,8 @@ def _loess_nb(
     dx=0,
     skipna=True,
 ):  # pragma: no cover
-    """1D Locally weighted regression: fits a nonparametric regression curve to a scatter plot.
+    """
+    1D Locally weighted regression: fits a nonparametric regression curve to a scatter plot.
 
     The arrays x and y contain an equal number of elements; each pair (x[i], y[i]) defines
     a data point in the scatter plot. The function returns the estimated (smooth) values of y.
@@ -185,7 +187,8 @@ def loess_smoothing(
     equal_spacing: bool | None = None,
     skipna: bool = True,
 ):
-    r"""Locally weighted regression in 1D: fits a nonparametric regression curve to a scatter plot.
+    r"""
+    Locally weighted regression in 1D: fits a nonparametric regression curve to a scatter plot.
 
     Returns a smoothed curve along given dimension. The regression is computed for each point using a subset of
     neighbouring points as given from evaluating the weighting function locally.