FIX: use "krogh" as interpolator method-string instead of "krog" (#8187)

* FIX: use "krogh" as interpolator method-string instead of "krog"

* Update xarray/tests/test_missing.py

Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com>

* Update xarray/core/missing.py

Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

---------

Co-authored-by: Illviljan <14371165+Illviljan@users.noreply.github.com>
Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

xarray/core/dataarray.py

@@ -3380,7 +3380,7 @@ def interpolate_na(
         dim : Hashable or None, optional
             Specifies the dimension along which to interpolate.
         method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial", \
-            "barycentric", "krog", "pchip", "spline", "akima"}, default: "linear"
+            "barycentric", "krogh", "pchip", "spline", "akima"}, default: "linear"
             String indicating which method to use for interpolation:
 
             - 'linear': linear interpolation. Additional keyword
@@ -3389,7 +3389,7 @@ def interpolate_na(
               are passed to :py:func:`scipy.interpolate.interp1d`. If
               ``method='polynomial'``, the ``order`` keyword argument must also be
               provided.
-            - 'barycentric', 'krog', 'pchip', 'spline', 'akima': use their
+            - 'barycentric', 'krogh', 'pchip', 'spline', 'akima': use their
               respective :py:class:`scipy.interpolate` classes.
 
         use_coordinate : bool or str, default: True

xarray/core/dataset.py

@@ -3730,7 +3730,7 @@ def interp(
             If DataArrays are passed as new coordinates, their dimensions are
             used for the broadcasting. Missing values are skipped.
         method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial", \
-            "barycentric", "krog", "pchip", "spline", "akima"}, default: "linear"
+            "barycentric", "krogh", "pchip", "spline", "akima"}, default: "linear"
             String indicating which method to use for interpolation:
 
             - 'linear': linear interpolation. Additional keyword
@@ -3739,7 +3739,7 @@ def interp(
               are passed to :py:func:`scipy.interpolate.interp1d`. If
               ``method='polynomial'``, the ``order`` keyword argument must also be
               provided.
-            - 'barycentric', 'krog', 'pchip', 'spline', 'akima': use their
+            - 'barycentric', 'krogh', 'pchip', 'spline', 'akima': use their
               respective :py:class:`scipy.interpolate` classes.
 
         assume_sorted : bool, default: False
@@ -4006,7 +4006,7 @@ def interp_like(
             names to an 1d array-like, which provides coordinates upon
             which to index the variables in this dataset. Missing values are skipped.
         method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial", \
-            "barycentric", "krog", "pchip", "spline", "akima"}, default: "linear"
+            "barycentric", "krogh", "pchip", "spline", "akima"}, default: "linear"
             String indicating which method to use for interpolation:
 
             - 'linear': linear interpolation. Additional keyword
@@ -4015,7 +4015,7 @@ def interp_like(
               are passed to :py:func:`scipy.interpolate.interp1d`. If
               ``method='polynomial'``, the ``order`` keyword argument must also be
               provided.
-            - 'barycentric', 'krog', 'pchip', 'spline', 'akima': use their
+            - 'barycentric', 'krogh', 'pchip', 'spline', 'akima': use their
               respective :py:class:`scipy.interpolate` classes.
 
         assume_sorted : bool, default: False
@@ -6371,7 +6371,7 @@ def interpolate_na(
         dim : Hashable or None, optional
             Specifies the dimension along which to interpolate.
         method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial", \
-            "barycentric", "krog", "pchip", "spline", "akima"}, default: "linear"
+            "barycentric", "krogh", "pchip", "spline", "akima"}, default: "linear"
             String indicating which method to use for interpolation:
 
             - 'linear': linear interpolation. Additional keyword
@@ -6380,7 +6380,7 @@ def interpolate_na(
               are passed to :py:func:`scipy.interpolate.interp1d`. If
               ``method='polynomial'``, the ``order`` keyword argument must also be
               provided.
-            - 'barycentric', 'krog', 'pchip', 'spline', 'akima': use their
+            - 'barycentric', 'krogh', 'pchip', 'spline', 'akima': use their
               respective :py:class:`scipy.interpolate` classes.
 
         use_coordinate : bool or Hashable, default: True

xarray/core/missing.py

@@ -501,7 +501,7 @@ def _get_interpolator(
             )
         elif method == "barycentric":
             interp_class = _import_interpolant("BarycentricInterpolator", method)
-        elif method == "krog":
+        elif method in ["krogh", "krog"]:
             interp_class = _import_interpolant("KroghInterpolator", method)
         elif method == "pchip":
             interp_class = _import_interpolant("PchipInterpolator", method)

xarray/core/types.py

@@ -189,7 +189,7 @@ def copy(
 Interp1dOptions = Literal[
     "linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"
 ]
-InterpolantOptions = Literal["barycentric", "krog", "pchip", "spline", "akima"]
+InterpolantOptions = Literal["barycentric", "krogh", "pchip", "spline", "akima"]
 InterpOptions = Union[Interp1dOptions, InterpolantOptions]
 
 DatetimeUnitOptions = Literal[

xarray/tests/test_missing.py

@@ -117,8 +117,8 @@ def test_interpolate_pd_compat():
 
 
 @requires_scipy
-@pytest.mark.parametrize("method", ["barycentric", "krog", "pchip", "spline", "akima"])
-def test_scipy_methods_function(method):
+@pytest.mark.parametrize("method", ["barycentric", "krogh", "pchip", "spline", "akima"])
+def test_scipy_methods_function(method) -> None:
     # Note: Pandas does some wacky things with these methods and the full
     # integration tests won't work.
     da, _ = make_interpolate_example_data((25, 25), 0.4, non_uniform=True)