xarray.DataArray.interp¶
-
DataArray.
interp
(coords=None, method='linear', assume_sorted=False, kwargs=None, **coords_kwargs)[source]¶ Multidimensional interpolation of variables.
- Parameters
coords (
dict
, optional) – Mapping from dimension names to the new coordinates. New coordinate can be an scalar, array-like or DataArray. If DataArrays are passed as new coordinates, their dimensions are used for the broadcasting. Missing values are skipped.method (
str
, default:"linear"
) – The method used to interpolate. Choose from{“linear”, “nearest”} for multidimensional array,
{“linear”, “nearest”, “zero”, “slinear”, “quadratic”, “cubic”} for 1-dimensional array.
assume_sorted (
bool
, optional) – 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
) – Additional keyword arguments passed to scipy’s interpolator. Valid options and their behavior depend on if 1-dimensional or multi-dimensional interpolation is used.**coords_kwargs (
{dim: coordinate, ...}
, optional) – The keyword arguments form ofcoords
. One of coords or coords_kwargs must be provided.
- Returns
interpolated (
DataArray
) – New dataarray on the new coordinates.
Notes
scipy is required.
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