xarray.Dataset.interp
xarray.Dataset.interp¶
-
Dataset.
interp
(coords=None, method='linear', assume_sorted=False, kwargs=None, method_non_numeric='nearest', **coords_kwargs)[source]¶ Multidimensional interpolation of Dataset.
- 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 (
str
, optional) – {“linear”, “nearest”} for multidimensional array, {“linear”, “nearest”, “zero”, “slinear”, “quadratic”, “cubic”} for 1-dimensional array. “linear” is used by default.assume_sorted (
bool
, optional) – 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 on if 1-dimensional or multi-dimensional interpolation is used.method_non_numeric (
{"nearest", "pad", "ffill", "backfill", "bfill"}
, optional) – Method for non-numeric types. Passed on toDataset.reindex()
."nearest"
is used by default.**coords_kwargs (
{dim: coordinate, ...}
, optional) – The keyword arguments form ofcoords
. One of coords or coords_kwargs must be provided.
- Returns
interpolated (
Dataset
) – New dataset on the new coordinates.
Notes
scipy is required.
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> Dimensions: (x: 3, y: 4) Coordinates: * x (x) int64 0 1 2 * y (y) int64 10 12 14 16 Data variables: a (x) int64 5 7 4 b (x, y) float64 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> Dimensions: (x: 4, y: 4) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 0.0 0.75 1.25 1.75 Data variables: a (x) float64 5.0 6.5 6.25 4.75 b (x, y) float64 1.0 4.0 2.0 nan 1.75 6.25 ... 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> Dimensions: (x: 4, y: 4) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 0.0 0.75 1.25 1.75 Data variables: a (x) float64 5.0 7.0 7.0 4.0 b (x, y) float64 1.0 4.0 2.0 9.0 2.0 7.0 ... 6.0 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> Dimensions: (x: 4, y: 4) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 1.0 1.5 2.5 3.5 Data variables: a (x) float64 7.0 5.5 2.5 -0.5 b (x, y) float64 2.0 7.0 6.0 nan 4.0 nan ... 4.5 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> Dimensions: (x: 4, y: 3) Coordinates: * x (x) float64 0.0 0.75 1.25 1.75 * y (y) int64 11 13 15 Data variables: a (x) float64 5.0 6.5 6.25 4.75 b (x, y) float64 2.5 3.0 nan 4.0 5.625 nan nan nan nan nan nan nan