Use "xsdba" in docs and option

CHANGELOG.rst

@@ -20,7 +20,9 @@ Changes
 Fixes
 ^^^^^
 * Gave credits to the package to all previous contributors of ``xclim.sdba`` (:issue:`58`, :pull:`59`).
+* Pin `sphinx-codeautolink` to fix ReadTheDocs and correct some docs errors (:pull:`40`).
 * Removed reliance on the `netcdf4` package for testing purposes. The `h5netcdf` engine is now used for file IO operations. (:pull:`71`).
+* Changes to reflect the change of library name `xsdba` (:pull:`72`)
 
 .. _changes_0.2.0:
 

docs/notebooks/advanced_example.ipynb

@@ -314,7 +314,7 @@
     "\n",
     "<!-- TODO : check xsdba_extra_output works -->\n",
     "\n",
-    "To fully understand what is happening during the bias-adjustment process, `xsdba` can output _diagnostic_ variables, giving more visibility to what the adjustment is doing behind the scene. This behaviour, a `verbose` option, is controlled by the `xsdba_extra_output` option, set with `xsdba.set_options`. When `True`, `train` calls are instructed to include additional variables to the training datasets. In addition, the `adjust` calls will always output a dataset, with `scen` and, depending on the algorithm, other diagnostics variables. See the documentation of each `Adjustment` objects to see what extra variables are available.\n",
+    "To fully understand what is happening during the bias-adjustment process, `xsdba` can output _diagnostic_ variables, giving more visibility to what the adjustment is doing behind the scene. This behaviour, a `verbose` option, is controlled by the `extra_output` option, set with `xsdba.set_options`. When `True`, `train` calls are instructed to include additional variables to the training datasets. In addition, the `adjust` calls will always output a dataset, with `scen` and, depending on the algorithm, other diagnostics variables. See the documentation of each `Adjustment` objects to see what extra variables are available.\n",
     "\n",
     "For the moment, this feature is still under construction and only a few `Adjustment` actually provide these extra outputs. Please open issues on the GitHub repo if you have needs or ideas of interesting diagnostic variables.\n",
     "\n",
@@ -329,7 +329,7 @@
    "source": [
     "from xsdba import set_options\n",
     "\n",
-    "with set_options(xsdba_extra_output=True):\n",
+    "with set_options(extra_output=True):\n",
     "    QDM = QuantileDeltaMapping.train(\n",
     "        ref, hist, nquantiles=15, kind=\"+\", group=\"time.dayofyear\"\n",
     "    )\n",

pyproject.toml

@@ -324,7 +324,8 @@ line-ending = "auto"
 
 [tool.ruff.lint]
 extend-select = [
-  "RUF022" # unsorted-dunder-all
+  "RUF022", # unsorted-dunder-all
+  "D213" # multi-line-summary-second-line
 ]
 ignore = [
   "COM", # commas

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
@@ -425,7 +432,7 @@ def mbcn_adjust(
         scen_block = xr.zeros_like(sim[{"time": ind_gw}])
         for iv, v in enumerate(sim[pts_dims[0]].values):
             sl = {"time": ind_gw, pts_dims[0]: iv}
-            with set_options(xsdba_extra_output=False):
+            with set_options(extra_output=False):
                 ADJ = base.train(
                     ref[sl], hist[sl], **base_kws_vars[v], skip_input_checks=True
                 )
@@ -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
     ----------