xarray.DataArray.interp#

DataArray.interp(coords=None, method='linear', assume_sorted=False, kwargs=None, **coords_kwargs)[source]#

Interpolate a DataArray onto new coordinates.

Performs univariate or multivariate interpolation of a Dataset onto new coordinates, 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.

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", "quintic", "polynomial", "pchip", "barycentric", "krogh", "akima", "makima"}) – Interpolation method to use (see descriptions above).

  • assume_sorted (bool, default: False) – 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.

  • kwargs (dict-like or None, default: None) – Additional keyword arguments passed to scipy’s interpolator. Valid options and their behavior depend whether interp1d or interpn is used.

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

Returns:

interpolated (DataArray) – New dataarray on the new coordinates.

Notes

  • SciPy is required for certain interpolation methods.

  • 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 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 scipy.interpolate.interp1d(), unless conditions permit the use of 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)
    4. Interpolation along multiple dimensions of multi-dimensional data

See also

scipy.interpolate

Manipulating Dimensions (Data Resolution)

Tutorial material on manipulating data resolution using interp()

Examples

>>> da = xr.DataArray(
...     data=[[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
...     dims=("x", "y"),
...     coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
... )
>>> da
<xarray.DataArray (x: 3, y: 4)> Size: 96B
array([[ 1.,  4.,  2.,  9.],
       [ 2.,  7.,  6., nan],
       [ 6., nan,  5.,  8.]])
Coordinates:
  * x        (x) int64 24B 0 1 2
  * y        (y) int64 32B 10 12 14 16

1D linear interpolation (the default):

>>> da.interp(x=[0, 0.75, 1.25, 1.75])
<xarray.DataArray (x: 4, y: 4)> Size: 128B
array([[1.  , 4.  , 2.  ,  nan],
       [1.75, 6.25, 5.  ,  nan],
       [3.  ,  nan, 5.75,  nan],
       [5.  ,  nan, 5.25,  nan]])
Coordinates:
  * y        (y) int64 32B 10 12 14 16
  * x        (x) float64 32B 0.0 0.75 1.25 1.75

1D nearest interpolation:

>>> da.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
<xarray.DataArray (x: 4, y: 4)> Size: 128B
array([[ 1.,  4.,  2.,  9.],
       [ 2.,  7.,  6., nan],
       [ 2.,  7.,  6., nan],
       [ 6., nan,  5.,  8.]])
Coordinates:
  * y        (y) int64 32B 10 12 14 16
  * x        (x) float64 32B 0.0 0.75 1.25 1.75

1D linear extrapolation:

>>> da.interp(
...     x=[1, 1.5, 2.5, 3.5],
...     method="linear",
...     kwargs={"fill_value": "extrapolate"},
... )
<xarray.DataArray (x: 4, y: 4)> Size: 128B
array([[ 2. ,  7. ,  6. ,  nan],
       [ 4. ,  nan,  5.5,  nan],
       [ 8. ,  nan,  4.5,  nan],
       [12. ,  nan,  3.5,  nan]])
Coordinates:
  * y        (y) int64 32B 10 12 14 16
  * x        (x) float64 32B 1.0 1.5 2.5 3.5

2D linear interpolation:

>>> da.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
<xarray.DataArray (x: 4, y: 3)> Size: 96B
array([[2.5  , 3.   ,   nan],
       [4.   , 5.625,   nan],
       [  nan,   nan,   nan],
       [  nan,   nan,   nan]])
Coordinates:
  * x        (x) float64 32B 0.0 0.75 1.25 1.75
  * y        (y) int64 24B 11 13 15
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