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datatree.DataTree.interp

DataTree.interp(coords: Mapping[Any, Any] | None = None, method: InterpOptions = 'linear', assume_sorted: bool = False, kwargs: Mapping[str, Any] | None = None, method_non_numeric: str = 'nearest', **coords_kwargs: Any) → Self

Interpolate a Dataset onto new coordinates

Performs univariate or multivariate interpolation of a Dataset onto new coordinates using scipy’s interpolation routines. If interpolating along an existing dimension, scipy.interpolate.interp1d is called. When interpolating along multiple existing dimensions, an attempt is made to decompose the interpolation into multiple 1-dimensional interpolations. If this is possible, scipy.interpolate.interp1d is called. Otherwise, scipy.interpolate.interpn() is called.

Parameters:

• coords (dict, optional) – Mapping from dimension names to the new coordinates. New coordinate can be a scalar, array-like or DataArray. 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", "krogh", "pchip", "spline", "akima"}, default: "linear") – String indicating which method to use for interpolation:

  • ‘linear’: linear interpolation. Additional keyword arguments are passed to numpy.interp()

  • ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘polynomial’: are passed to scipy.interpolate.interp1d(). If method='polynomial', the order keyword argument must also be provided.

  • ‘barycentric’, ‘krogh’, ‘pchip’, ‘spline’, ‘akima’: use their respective scipy.interpolate classes.

• assume_sorted (bool, default: False) – If False, values of coordinates that are interpolated over can be in any order and they are sorted first. If True, interpolated coordinates are assumed to be an array of monotonically increasing values.

• kwargs (dict, optional) – Additional keyword arguments passed to scipy’s interpolator. Valid options and their behavior depend whether interp1d or interpn is used.

• method_non_numeric ({"nearest", "pad", "ffill", "backfill", "bfill"}, optional) – Method for non-numeric types. Passed on to Dataset.reindex(). "nearest" is used by default.

• **coords_kwargs ({dim: coordinate, ...}, optional) – The keyword arguments form of coords. One of coords or coords_kwargs must be provided.

Returns:

interpolated (Dataset) – New dataset on the new coordinates.

Notes

scipy is required.

See also

scipy.interpolate.interp1d scipy.interpolate.interpn

xarray-tutorial:fundamentals/02.2_manipulating_dimensions

Tutorial material on manipulating data resolution using interp()

Examples

>>> ds = xr.Dataset(
...     data_vars={
...         "a": ("x", [5, 7, 4]),
...         "b": (
...             ("x", "y"),
...             [[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
...         ),
...     },
...     coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
... )
>>> ds
<xarray.Dataset> Size: 176B
Dimensions:  (x: 3, y: 4)
Coordinates:
  * x        (x) int64 24B 0 1 2
  * y        (y) int64 32B 10 12 14 16
Data variables:
    a        (x) int64 24B 5 7 4
    b        (x, y) float64 96B 1.0 4.0 2.0 9.0 2.0 7.0 6.0 nan 6.0 nan 5.0 8.0

1D interpolation with the default method (linear):

>>> ds.interp(x=[0, 0.75, 1.25, 1.75])
<xarray.Dataset> Size: 224B
Dimensions:  (x: 4, y: 4)
Coordinates:
  * y        (y) int64 32B 10 12 14 16
  * x        (x) float64 32B 0.0 0.75 1.25 1.75
Data variables:
    a        (x) float64 32B 5.0 6.5 6.25 4.75
    b        (x, y) float64 128B 1.0 4.0 2.0 nan 1.75 ... nan 5.0 nan 5.25 nan

1D interpolation with a different method:

>>> ds.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
<xarray.Dataset> Size: 224B
Dimensions:  (x: 4, y: 4)
Coordinates:
  * y        (y) int64 32B 10 12 14 16
  * x        (x) float64 32B 0.0 0.75 1.25 1.75
Data variables:
    a        (x) float64 32B 5.0 7.0 7.0 4.0
    b        (x, y) float64 128B 1.0 4.0 2.0 9.0 2.0 7.0 ... nan 6.0 nan 5.0 8.0

1D extrapolation:

>>> ds.interp(
...     x=[1, 1.5, 2.5, 3.5],
...     method="linear",
...     kwargs={"fill_value": "extrapolate"},
... )
<xarray.Dataset> Size: 224B
Dimensions:  (x: 4, y: 4)
Coordinates:
  * y        (y) int64 32B 10 12 14 16
  * x        (x) float64 32B 1.0 1.5 2.5 3.5
Data variables:
    a        (x) float64 32B 7.0 5.5 2.5 -0.5
    b        (x, y) float64 128B 2.0 7.0 6.0 nan 4.0 ... nan 12.0 nan 3.5 nan

2D interpolation:

>>> ds.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
<xarray.Dataset> Size: 184B
Dimensions:  (x: 4, y: 3)
Coordinates:
  * x        (x) float64 32B 0.0 0.75 1.25 1.75
  * y        (y) int64 24B 11 13 15
Data variables:
    a        (x) float64 32B 5.0 6.5 6.25 4.75
    b        (x, y) float64 96B 2.5 3.0 nan 4.0 5.625 ... nan nan nan nan nan