xarray.DataArray.interp (original) (raw)

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 orinterpn 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)
     • Depending on the method, the following interpolants from scipy.interpolate are used:
       * “pchip”: scipy.interpolate.PchipInterpolator
       * “barycentric”: scipy.interpolate.BarycentricInterpolator
       * “krogh”: scipy.interpolate.KroghInterpolator
       * “akima” or “makima”: scipy.interpolate.Akima1dInterpolator
       (makima is handled by passing the makima flag).
  4. Interpolation along multiple dimensions of multi-dimensional data
     • Uses scipy.interpolate.interpn() for methods {“linear”, “nearest”, “slinear”,
       “cubic”, “quintic”, “pchip”}.

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