Use "xsdba" in docs and option

CHANGELOG.rst

@@ -16,6 +16,7 @@ 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`).
+* Changes to reflect the change of library name `xsdba` (:pull:`72`)
 
 .. _changes_0.2.0:
 

src/xsdba/adjustment.py

@@ -1308,19 +1308,19 @@ class OTC(Adjust):
         If `True`, a random location is picked uniformly inside their bin. Default is `True`.
     adapt_freq_thresh : dict or str, optional
         Threshold for frequency adaptation per variable.
-        See :py:class:`xclim.sdba.processing.adapt_freq` for details.
+        See :py:class:`xsdba.processing.adapt_freq` for details.
         Frequency adaptation is not applied to missing variables if is dict.
         Applied to all variables if is string.
     normalization : {None, 'standardize', 'max_distance', 'max_value'}
         Per-variable transformation applied before the distances are calculated.
         Default is "max_distance".
         See notes for details.
     group : Union[str, Grouper]
-        The grouping information. See :py:class:`xclim.sdba.base.Grouper` for details.
+        The grouping information. See :py:class:`xsdba.base.Grouper` for details.
         Default is "time", meaning a single adjustment group along dimension "time".
     pts_dim : str
         The name of the "multivariate" dimension. Defaults to "multivar", which is the
-        normal case when using :py:func:`xclim.sdba.base.stack_variables`.
+        normal case when using :py:func:`xsdba.base.stack_variables`.
 
     Notes
     -----
@@ -1445,7 +1445,7 @@ def _adjust(
 class dOTC(Adjust):
     r"""Dynamical Optimal Transport Correction.
 
-    This method is the dynamical version of :py:class:`~xclim.sdba.adjustment.OTC`, as presented by :cite:t:`robin_2019`.
+    This method is the dynamical version of :py:class:`~xsdba.adjustment.OTC`, as presented by :cite:t:`robin_2019`.
     The temporal evolution of the model is found for every point by mapping the historical to the future dataset with
     optimal transport. A mapping between historical and reference data is found in the same way, and the temporal evolution
     of model data is applied to their assigned reference.
@@ -1479,26 +1479,26 @@ class dOTC(Adjust):
         Applied to all variables if is string.
     adapt_freq_thresh : dict or str, optional
         Threshold for frequency adaptation per variable.
-        See :py:class:`xclim.sdba.processing.adapt_freq` for details.
+        See :py:class:`xsdba.processing.adapt_freq` for details.
         Frequency adaptation is not applied to missing variables if is dict.
         Applied to all variables if is string.
     normalization : {None, 'standardize', 'max_distance', 'max_value'}
         Per-variable transformation applied before the distances are calculated
         in the optimal transport. Default is "max_distance".
-        See :py:class:`~xclim.sdba.adjustment.OTC` for details.
+        See :py:class:`~xsdba.adjustment.OTC` for details.
     group : Union[str, Grouper]
-        The grouping information. See :py:class:`xclim.sdba.base.Grouper` for details.
+        The grouping information. See :py:class:`xsdba.base.Grouper` for details.
         Default is "time", meaning a single adjustment group along dimension "time".
     pts_dim : str
         The name of the "multivariate" dimension. Defaults to "multivar", which is the
-        normal case when using :py:func:`xclim.sdba.base.stack_variables`.
+        normal case when using :py:func:`xsdba.base.stack_variables`.
 
     Notes
     -----
     The simulated historical, simulated future and observed data sets :math:`X0`, :math:`X1` and :math:`Y0` are
     discretized and standardized using histograms whose bin length along dimension `k` is given by `bin_width[k]`.
     Mappings between :math:`Y0` and :math:`X0` on the one hand and between :math:`X0` and :math:`X1` on the other
-    are found by optimal transport (see :py:class:`~xclim.sdba.adjustment.OTC`). The latter mapping is used to
+    are found by optimal transport (see :py:class:`~xsdba.adjustment.OTC`). The latter mapping is used to
     compute the temporal evolution of model data. This evolution is computed additively or multiplicatively for
     each variable depending on its `kind`, and is applied to observed data with
 

src/xsdba/base.py

@@ -18,7 +18,7 @@
 from boltons.funcutils import wraps
 from xarray.core import dtypes
 
-from xsdba.options import OPTIONS, SDBA_ENCODE_CF
+from xsdba.options import OPTIONS, XSDBA_ENCODE_CF
 
 # TODO : Redistributes some functions in existing/new scripts
 
@@ -675,7 +675,7 @@ def _map_blocks(ds, **kwargs):  # noqa: C901
                 # duck empty calls dask if chunks is not None
                 tmpl[var] = duck_empty(dims, sizes, dtype=dtype, chunks=chunks)
 
-            if OPTIONS[SDBA_ENCODE_CF]:
+            if OPTIONS[XSDBA_ENCODE_CF]:
                 ds = ds.copy()
                 # Optimization to circumvent the slow pickle.dumps(cftime_array)
                 # List of the keys to avoid changing the coords dict while iterating over it.

src/xsdba/measures.py

@@ -28,7 +28,7 @@ class StatisticalMeasure(Indicator):
     """Base indicator class for statistical measures used when validating bias-adjusted outputs.
 
     Statistical measures use input data where the time dimension was reduced, usually by the computation
-    of a :py:class:`xclim.sdba.properties.StatisticalProperty` instance.  They usually take two arrays
+    of a :py:class:`xsdba.properties.StatisticalProperty` instance.  They usually take two arrays
     as input: "sim" and "ref", "sim" being measured against "ref". The two arrays must have identical
     coordinates on their common dimensions.
 

src/xsdba/options.py

@@ -7,20 +7,20 @@
 from collections.abc import Callable
 
 XSDBA_EXTRA_OUTPUT = "xsdba_extra_output"
-SDBA_ENCODE_CF = "sdba_encode_cf"
+XSDBA_ENCODE_CF = "xsdba_encode_cf"
 AS_DATASET = "as_dataset"
 
 MISSING_METHODS: dict[str, Callable] = {}
 
 OPTIONS = {
     XSDBA_EXTRA_OUTPUT: False,
-    SDBA_ENCODE_CF: False,
+    XSDBA_ENCODE_CF: False,
     AS_DATASET: False,
 }
 
 _VALIDATORS = {
     XSDBA_EXTRA_OUTPUT: lambda opt: isinstance(opt, bool),
-    SDBA_ENCODE_CF: lambda opt: isinstance(opt, bool),
+    XSDBA_ENCODE_CF: lambda opt: isinstance(opt, bool),
     AS_DATASET: lambda opt: isinstance(opt, bool),
 }
 
@@ -36,7 +36,7 @@ class set_options:
         docstring. When activated, `adjust` will return a Dataset with `scen` and those extra diagnostics
         For `processing` functions, see the doc, the output type might change, or not depending on the
         algorithm. Default: ``False``.
-    sdba_encode_cf : bool
+    xsdba_encode_cf : bool
         Whether to encode cf coordinates in the ``map_blocks`` optimization that most adjustment methods are based on.
         This should have no impact on the results, but should run much faster in the graph creation phase.
         If True, indicators output datasets. If False, they output DataArrays. Default :``False``.

tests/test_adjustment.py

@@ -633,7 +633,7 @@ def test_mon_u(
     @pytest.mark.parametrize("use_dask", [True, False])
     @pytest.mark.filterwarnings("ignore::RuntimeWarning")
     def test_add_dims(self, use_dask, gosset):
-        with set_options(sdba_encode_cf=use_dask):
+        with set_options(xsdba_encode_cf=use_dask):
             if use_dask:
                 chunks = {"location": -1}
             else:
@@ -680,7 +680,7 @@ class TestMBCn:
     @pytest.mark.parametrize("period_dim", [None, "period"])
     def test_simple(self, use_dask, group, window, period_dim, gosset):
         group, window, period_dim, use_dask = "time", 1, None, False
-        with set_options(sdba_encode_cf=use_dask):
+        with set_options(xsdba_encode_cf=use_dask):
             if use_dask:
                 chunks = {"location": -1}
             else:

tests/test_base.py

@@ -247,5 +247,5 @@ def func(ds, *, group):
             d = ds.mean("location")
             return d.rename("data").to_dataset()
 
-        with set_options(sdba_encode_cf=True):
+        with set_options(xsdba_encode_cf=True):
             func(ds.convert_calendar("noleap").tasmax, group=Grouper("time"))