moving list of interpolants

pydata · Oct 22, 2024 · fc4c8a2 · fc4c8a2
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diff --git a/xarray/core/dataarray.py b/xarray/core/dataarray.py
@@ -2224,30 +2224,7 @@ def interp(
         Interpolate a DataArray onto new coordinates.
 
         Performs univariate or multivariate interpolation of a Dataset onto new coordinates,
-        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:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
-
-        2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
-                use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
-                (as in the case of `method='linear'` for 1D data).
-            - If `method='polynomial'`, the `order` keyword argument must also be provided. In this case,
-                :py:func:`scipy.interpolate.interp1d` is called with `kind=order`.
-
-        3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
-                - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
-                - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
-                - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
-                - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
-                    (`makima` is handled by passing `makima` to `method`).
-
-        4. **Interpolation along multiple dimensions of multi-dimensional data**
-            - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
-                "cubic", "quintic", "pchip"}.
+        utilizing either NumPy or SciPy interpolation routines.
 
         Out-of-range values are filled with NaN, unless specified otherwise via `kwargs` to the numpy/scipy interpolant.
 
@@ -2284,6 +2261,29 @@ def interp(
         - 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.
+        - The specific interpolation method and dimensionality determine which
+            interpolant is used:
+
+            1. **Interpolation along one dimension of 1D data (`method='linear'`)**
+                - Uses :py:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
+
+            2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
+                    use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
+                    (as in the case of `method='linear'` for 1D data).
+                - If `method='polynomial'`, the `order` keyword argument must also be provided.
+
+            3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
+                    - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
+                    - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
+                    - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
+                    - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
+                        (`makima` is handled by passing the `makima` flag).
+
+            4. **Interpolation along multiple dimensions of multi-dimensional data**
+                - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
+                    "cubic", "quintic", "pchip"}.
 
         See Also
         --------
@@ -2384,30 +2384,6 @@ def interp_like(
         """Interpolate this object onto the coordinates of another object,
         filling out of range values with NaN.
 
-        The specific interpolation method and dimensionality determine which
-        interpolant is used:
-
-        1. **Interpolation along one dimension of 1D data (`method='linear'`)**
-            - Uses :py:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
-
-        2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
-                use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
-                (as in the case of `method='linear'` for 1D data).
-            - If `method='polynomial'`, the `order` keyword argument must also be provided.
-
-        3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
-                - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
-                - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
-                - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
-                - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
-                    (`makima` is handled by passing the `makima` flag).
-
-        4. **Interpolation along multiple dimensions of multi-dimensional data**
-            - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
-                "cubic", "quintic", "pchip"}.
-
         Parameters
         ----------
         other : Dataset or DataArray
@@ -2439,6 +2415,29 @@ def interp_like(
         - 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.
+        - The specific interpolation method and dimensionality determine which
+        interpolant is used:
+
+            1. **Interpolation along one dimension of 1D data (`method='linear'`)**
+                - Uses :py:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
+
+            2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
+                    use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
+                    (as in the case of `method='linear'` for 1D data).
+                - If `method='polynomial'`, the `order` keyword argument must also be provided.
+
+            3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
+                    - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
+                    - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
+                    - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
+                    - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
+                        (`makima` is handled by passing the `makima` flag).
+
+            4. **Interpolation along multiple dimensions of multi-dimensional data**
+                - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
+                    "cubic", "quintic", "pchip"}.
 
         See Also
         --------

diff --git a/xarray/core/dataset.py b/xarray/core/dataset.py
@@ -3892,30 +3892,7 @@ 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. The specific interpolation
-        method and dimensionality determine which interpolant is used:
-
-        1. **Interpolation along one dimension of 1D data (`method='linear'`)**
-            - Uses :py:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
-
-        2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
-                use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
-                (as in the case of `method='linear'` for 1D data).
-            - If `method='polynomial'`, the `order` keyword argument must also be provided. In this case,
-                :py:func:`scipy.interpolate.interp1d` is called with `kind=order`.
-
-        3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
-                - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
-                - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
-                - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
-                - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
-                    (`makima` is handled by passing `makima` to `method`).
-
-        4. **Interpolation along multiple dimensions of multi-dimensional data**
-            - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
-                "cubic", "quintic", "pchip"}.
+        utilizing either NumPy or SciPy interpolation routines.
 
         Out-of-range values are filled with NaN, unless specified otherwise via `kwargs` to the numpy/scipy interpolant.
 
@@ -3956,6 +3933,30 @@ def interp(
         - 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.
+        - The specific interpolation method and dimensionality determine which
+            interpolant is used:
+
+            1. **Interpolation along one dimension of 1D data (`method='linear'`)**
+                - Uses :py:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
+
+            2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
+                    use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
+                    (as in the case of `method='linear'` for 1D data).
+                - If `method='polynomial'`, the `order` keyword argument must also be provided.
+
+            3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
+                    - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
+                    - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
+                    - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
+                    - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
+                        (`makima` is handled by passing the `makima` flag).
+
+            4. **Interpolation along multiple dimensions of multi-dimensional data**
+                - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
+                    "cubic", "quintic", "pchip"}.
+
         --------
         scipy.interpolate.interp1d
         scipy.interpolate.interpn
@@ -4183,30 +4184,7 @@ def interp_like(
         """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. The specific interpolation
-        method and dimensionality determine which interpolant is used:
-
-        1. **Interpolation along one dimension of 1D data (`method='linear'`)**
-            - Uses :py:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
-
-        2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
-                use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
-                (as in the case of `method='linear'` for 1D data).
-            - If `method='polynomial'`, the `order` keyword argument must also be provided. In this case,
-                :py:func:`scipy.interpolate.interp1d` is called with `kind=order`.
-
-        3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
-            - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
-                - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
-                - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
-                - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
-                - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
-                    (`makima` is handled by passing `makima` to `method`).
-
-        4. **Interpolation along multiple dimensions of multi-dimensional data**
-            - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
-                "cubic", "quintic", "pchip"}.
+        utilizing either NumPy or SciPy interpolation routines.
 
         Out-of-range values are filled with NaN, unless specified otherwise via `kwargs` to the numpy/scipy interpolant.
 
@@ -4245,6 +4223,29 @@ def interp_like(
         - 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.
+        - The specific interpolation method and dimensionality determine which
+            interpolant is used:
+
+            1. **Interpolation along one dimension of 1D data (`method='linear'`)**
+                - Uses :py:func:`numpy.interp`, unless `fill_value='extrapolate'` is provided via `kwargs`.
+
+            2. **Interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Methods {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "quintic", "polynomial"}
+                    use :py:func:`scipy.interpolate.interp1d`, unless conditions permit the use of :py:func:`numpy.interp`
+                    (as in the case of `method='linear'` for 1D data).
+                - If `method='polynomial'`, the `order` keyword argument must also be provided.
+
+            3. **Special interpolants for interpolation along one dimension of N-dimensional data (N ≥ 1)**
+                - Depending on the `method`, the following interpolants from :py:class:`scipy.interpolate` are used:
+                    - `"pchip"`: :py:class:`scipy.interpolate.PchipInterpolator`
+                    - `"barycentric"`: :py:class:`scipy.interpolate.BarycentricInterpolator`
+                    - `"krogh"`: :py:class:`scipy.interpolate.KroghInterpolator`
+                    - `"akima"` or `"makima"`: :py:class:`scipy.interpolate.Akima1dInterpolator`
+                        (`makima` is handled by passing the `makima` flag).
+
+            4. **Interpolation along multiple dimensions of multi-dimensional data**
+                - Uses :py:func:`scipy.interpolate.interpn` for methods {"linear", "nearest", "slinear",
+                    "cubic", "quintic", "pchip"}.
 
         See Also
         --------