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# 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 DataArray onto new coordinates using scipyâ€™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) â€“ 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"}, default: "linear") â€“ The method used to interpolate. The method should be supported by the scipy interpolator:

• interp1d: {â€ślinearâ€ť, â€śnearestâ€ť, â€śzeroâ€ť, â€śslinearâ€ť, â€śquadraticâ€ť, â€ścubicâ€ť, â€śpolynomialâ€ť}

• interpn: {â€ślinearâ€ť, â€śnearestâ€ť}

If "polynomial" is passed, the order keyword argument must also be provided.

• assume_sorted (bool, default: False) â€“ 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) â€“ Additional keyword arguments passed to scipyâ€™s interpolator. Valid options and their behavior depend whether interp1d or interpn is used.

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

Returns:

interpolated (DataArray) â€“ New dataarray on the new coordinates.

Notes

scipy is required.

scipy.interpolate.interp1d scipy.interpolate.interpn

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)>
array([[ 1.,  4.,  2.,  9.],
[ 2.,  7.,  6., nan],
[ 6., nan,  5.,  8.]])
Coordinates:
* x        (x) int64 0 1 2
* y        (y) int64 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)>
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 10 12 14 16
* x        (x) float64 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)>
array([[ 1.,  4.,  2.,  9.],
[ 2.,  7.,  6., nan],
[ 2.,  7.,  6., nan],
[ 6., nan,  5.,  8.]])
Coordinates:
* y        (y) int64 10 12 14 16
* x        (x) float64 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)>
array([[ 2. ,  7. ,  6. ,  nan],
[ 4. ,  nan,  5.5,  nan],
[ 8. ,  nan,  4.5,  nan],
[12. ,  nan,  3.5,  nan]])
Coordinates:
* y        (y) int64 10 12 14 16
* x        (x) float64 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)>
array([[2.5  , 3.   ,   nan],
[4.   , 5.625,   nan],
[  nan,   nan,   nan],
[  nan,   nan,   nan]])
Coordinates:
* x        (x) float64 0.0 0.75 1.25 1.75
* y        (y) int64 11 13 15