remove reduce arg

Author: hollymandel

xarray/core/dataarray.py

@@ -2217,20 +2217,15 @@ def interp(
         coords: Mapping[Any, Any] | None = None,
         method: InterpOptions = "linear",
         assume_sorted: bool = False,
-        reduce: bool = True,
         kwargs: Mapping[str, Any] | None = None,
         **coords_kwargs: Any,
     ) -> Self:
         """
         Interpolate a DataArray onto new coordinates.
 
         Performs univariate or multivariate interpolation of a Dataset onto new coordinates,
-        utilizing either NumPy or SciPy interpolation routines.
-
-        When interpolating along multiple dimensions, the process attempts to decompose the
-        interpolation into independent interpolations along one dimension at a time, unless
-        `reduce=False` is passed. The specific interpolation method and dimensionality
-        determine which interpolant is used:
+        utilizing either NumPy or SciPy interpolation routines. The specific interpolation
+        method and dimensionality determine which interpolant is used:
 
         1. **Interpolation along one dimension of 1D data (`method='linear'`)**
             - Uses :py:class:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
@@ -2269,10 +2264,6 @@ def interp(
             If False, values of x can be in any order and they are sorted
             first. If True, x has to be an array of monotonically increasing
             values.
-        reduce : bool, default: True
-            If True, the interpolation is decomposed into independent interpolations along one dimension at a time,
-            where the interpolation coordinates are independent. Setting this to be True alters the behavior of certain
-            multi-dimensional interpolants compared to the default SciPy output.
         kwargs : dict-like or None, default: None
             Additional keyword arguments passed to scipy's interpolator. Valid
             options and their behavior depend whether ``interp1d`` or
@@ -2289,8 +2280,9 @@ def interp(
         Notes
         -----
         - SciPy is required for certain interpolation methods.
-        - Allowing `reduce=True` (the default) may alter the behavior of interpolation along multiple dimensions
-            compared to the default behavior in SciPy.
+        - When interpolating along multiple dimensions with methods `linear` and `nearest`,
+            the process attempts to decompose the interpolation into independent interpolations
+            along one dimension at a time.
 
         See Also
         --------
@@ -2377,7 +2369,6 @@ def interp(
             method=method,
             kwargs=kwargs,
             assume_sorted=assume_sorted,
-            reduce=reduce,
             **coords_kwargs,
         )
         return self._from_temp_dataset(ds)
@@ -2387,16 +2378,13 @@ def interp_like(
         other: T_Xarray,
         method: InterpOptions = "linear",
         assume_sorted: bool = False,
-        reduce: bool = True,
         kwargs: Mapping[str, Any] | None = None,
     ) -> Self:
         """Interpolate this object onto the coordinates of another object,
         filling out of range values with NaN.
 
-        When interpolating along multiple dimensions, the process attempts to decompose the
-        interpolation into independent interpolations along one dimension at a time, unless
-        `reduce=False` is passed. The specific interpolation method and dimensionality
-        determine which interpolant is used:
+        The specific interpolation method and dimensionality determine which
+        interpolant is used:
 
         1. **Interpolation along one dimension of 1D data (`method='linear'`)**
             - Uses :py:class:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
@@ -2432,10 +2420,6 @@ def interp_like(
             in any order and they are sorted first. If True, interpolated
             coordinates are assumed to be an array of monotonically increasing
             values.
-        reduce : bool, default: True
-            If True, the interpolation is decomposed into independent interpolations along one dimension at a time,
-            where the interpolation coordinates are independent. Setting this to be True alters the behavior of certain
-            multi-dimensional interpolants compared to the default SciPy output.
         kwargs : dict, optional
             Additional keyword arguments passed to the interpolant.
 
@@ -2447,9 +2431,12 @@ def interp_like(
 
         Notes
         -----
-        scipy is required.
-        If the dataarray has object-type coordinates, reindex is used for these
-        coordinates instead of the interpolation.
+        - scipy is required.
+        - If the dataarray has object-type coordinates, reindex is used for these
+            coordinates instead of the interpolation.
+        - When interpolating along multiple dimensions with methods `linear` and `nearest`,
+            the process attempts to decompose the interpolation into independent interpolations
+            along one dimension at a time.
 
         See Also
         --------
@@ -2518,11 +2505,7 @@ def interp_like(
                 f"interp only works for a numeric type array. Given {self.dtype}."
             )
         ds = self._to_temp_dataset().interp_like(
-            other,
-            method=method,
-            kwargs=kwargs,
-            assume_sorted=assume_sorted,
-            reduce=reduce,
+            other, method=method, kwargs=kwargs, assume_sorted=assume_sorted
         )
         return self._from_temp_dataset(ds)
 

xarray/core/dataset.py

@@ -3884,7 +3884,6 @@ def interp(
         coords: Mapping[Any, Any] | None = None,
         method: InterpOptions = "linear",
         assume_sorted: bool = False,
-        reduce: bool = True,
         kwargs: Mapping[str, Any] | None = None,
         method_non_numeric: str = "nearest",
         **coords_kwargs: Any,
@@ -3893,12 +3892,8 @@ def interp(
         Interpolate a Dataset onto new coordinates.
 
         Performs univariate or multivariate interpolation of a Dataset onto new coordinates,
-        utilizing either NumPy or SciPy interpolation routines.
-
-        When interpolating along multiple dimensions, the process attempts to decompose the
-        interpolation into independent interpolations along one dimension at a time, unless
-        `reduce=False` is passed. The specific interpolation method and dimensionality
-        determine which interpolant is used:
+        utilizing either NumPy or SciPy interpolation routines. The specific interpolation
+        method and dimensionality determine which interpolant is used:
 
         1. **Interpolation along one dimension of 1D data (`method='linear'`)**
             - Uses :py:class:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
@@ -3938,10 +3933,6 @@ def interp(
             in any order and they are sorted first. If True, interpolated
             coordinates are assumed to be an array of monotonically increasing
             values.
-        reduce : bool, default: True
-            If True, the interpolation is decomposed into independent interpolations of minimal dimensionality such that
-            the interpolation coordinates are independent. Setting this to be True alters the behavior of certain
-            multi-dimensional interpolants compared to the default SciPy output.
         kwargs : dict, optional
             Additional keyword arguments passed to the interpolator. Valid
             options and their behavior depend which interpolant is used.
@@ -3961,10 +3952,9 @@ def interp(
         Notes
         -----
         - SciPy is required for certain interpolation methods.
-        - Allowing `reduce=True` (the default) may alter the behavior of interpolation along multiple dimensions
-            compared to the default behavior in SciPy.
-
-        See Also
+        - When interpolating along multiple dimensions with methods `linear` and `nearest`,
+            the process attempts to decompose the interpolation into independent interpolations
+            along one dimension at a time.
         --------
         scipy.interpolate.interp1d
         scipy.interpolate.interpn
@@ -4125,9 +4115,7 @@ def _validate_interp_indexer(x, new_x):
             if dtype_kind in "uifc":
                 # For normal number types do the interpolation:
                 var_indexers = {k: v for k, v in use_indexers.items() if k in var.dims}
-                variables[name] = missing.interp(
-                    var, var_indexers, method, reduce=reduce, **kwargs
-                )
+                variables[name] = missing.interp(var, var_indexers, method, **kwargs)
             elif dtype_kind in "ObU" and (use_indexers.keys() & var.dims):
                 # For types that we do not understand do stepwise
                 # interpolation to avoid modifying the elements.
@@ -4188,19 +4176,14 @@ def interp_like(
         other: T_Xarray,
         method: InterpOptions = "linear",
         assume_sorted: bool = False,
-        reduce: bool = True,
         kwargs: Mapping[str, Any] | None = None,
         method_non_numeric: str = "nearest",
     ) -> Self:
         """Interpolate this object onto the coordinates of another object.
 
         Performs univariate or multivariate interpolation of a Dataset onto new coordinates,
-        utilizing either NumPy or SciPy interpolation routines.
-
-        When interpolating along multiple dimensions, the process attempts to decompose the
-        interpolation into independent interpolations along one dimension at a time, unless
-        `reduce=False` is passed. The specific interpolation method and dimensionality
-        determine which interpolant is used:
+        utilizing either NumPy or SciPy interpolation routines. The specific interpolation
+        method and dimensionality determine which interpolant is used:
 
         1. **Interpolation along one dimension of 1D data (`method='linear'`)**
             - Uses :py:class:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
@@ -4239,10 +4222,6 @@ def interp_like(
             in any order and they are sorted first. If True, interpolated
             coordinates are assumed to be an array of monotonically increasing
             values.
-        reduce : bool, default: True
-            If True, the interpolation is decomposed into independent 1-dimensional interpolations such that
-            the interpolation coordinates are independent. Setting this to be True alters the behavior of certain
-            multi-dimensional interpolants compared to the default SciPy output.
         kwargs : dict, optional
             Additional keyword arguments passed to the interpolator. Valid
             options and their behavior depend which interpolant is use
@@ -4258,9 +4237,12 @@ def interp_like(
 
         Notes
         -----
-        scipy is required.
-        If the dataset has object-type coordinates, reindex is used for these
-        coordinates instead of the interpolation.
+        - scipy is required.
+        - If the dataset has object-type coordinates, reindex is used for these
+            coordinates instead of the interpolation.
+        - When interpolating along multiple dimensions with methods `linear` and `nearest`,
+            the process attempts to decompose the interpolation into independent interpolations
+            along one dimension at a time.
 
         See Also
         --------

xarray/core/missing.py

@@ -506,6 +506,7 @@ def _get_interpolator(
             interp_class = _import_interpolant("KroghInterpolator", method)
         elif method == "pchip":
             kwargs.update(axis=-1)
+            # pchip default behavior is to extrapolate
             kwargs.setdefault("extrapolate", False)
             interp_class = _import_interpolant("PchipInterpolator", method)
         elif method == "spline":
@@ -601,7 +602,7 @@ def _floatize_x(x, new_x):
     return x, new_x
 
 
-def interp(var, indexes_coords, method: InterpOptions, reduce: bool = True, **kwargs):
+def interp(var, indexes_coords, method: InterpOptions, **kwargs):
     """Make an interpolation of Variable
 
     Parameters
@@ -615,10 +616,6 @@ def interp(var, indexes_coords, method: InterpOptions, reduce: bool = True, **kw
         One of {'linear', 'nearest', 'zero', 'slinear', 'quadratic',
         'cubic'}. For multidimensional interpolation, only
         {'linear', 'nearest'} can be used.
-    reduce:
-        Decompose the interpolation along independent interpolation dimensions when
-        possible. This will likely improve performance over true multi-dimensional
-        interpolation but will alter the result for certain interpolators.
     **kwargs
         keyword arguments to be passed to scipy.interpolate
 
@@ -636,9 +633,8 @@ def interp(var, indexes_coords, method: InterpOptions, reduce: bool = True, **kw
 
     result = var
 
-    if reduce:
-        # decompose the interpolation into a succession of independent interpolation. This may
-        # affect the mathematical behavior of certain nd interpolants.
+    if method in ["linear", "nearest"]:
+        # decompose the interpolation into a succession of independent interpolation.
         indexes_coords = decompose_interp(indexes_coords)
     else:
         indexes_coords = [indexes_coords]