pyinterp.backends.xarray.RegularGridInterpolator

class pyinterp.backends.xarray.RegularGridInterpolator(array)

Bases: object

Interpolate on a regular grid in arbitrary dimensions.

Perform interpolation on a regular grid with uneven spacing support. Automatically detects geodetic coordinates (lon/lat) using CF conventions and temporal axes (datetime64).

The data must be defined on a regular grid; the grid spacing however may be uneven. Linear, nearest neighbors, inverse distance weighting, and bicubic interpolation are supported.

Parameters:

array (xr.DataArray) – The xarray DataArray defining the regular grid in n dimensions. Must be 2D, 3D, or 4D.

Raises:

NotImplementedError – if the number of dimensions in the array is less than 2 or more than 4.

Notes

Automatic Detection:

The interpolator automatically detects:

• Geodetic coordinates: If lon/lat are found via CF conventions (units attribute).

• Temporal axes: If a coordinate has dtype=’datetime64’, it will be treated as a temporal axis with proper interpolation

• Dimension count: Automatically selects Grid2D, Grid3D, or Grid4D

Geodetic Detection (CF Conventions):

Longitude axes are detected if the coordinate has units attribute matching: degrees_east, degree_east, degree_E, degrees_E, degreeE, or degreesE

Latitude axes are detected if the coordinate has units attribute matching: degrees_north, degree_north, degree_N, degrees_N, degreeN, or degreesN

Temporal Detection:

Any coordinate with dtype containing ‘datetime64’ is automatically treated as a temporal axis.

Examples

2D sea surface temperature

>>> sst = xr.open_dataarray("sst.nc")  # (lon, lat)
>>> interp = RegularGridInterpolator(sst)
>>> result = interp(
...     {"lon": [10.5, 20.3], "lat": [45.2, -30.1]}, method="bilinear"
... )

3D ocean temperature with depth

>>> temp = xr.open_dataarray("temp.nc")  # (lon, lat, depth)
>>> interp = RegularGridInterpolator(temp)
>>> result = interp(
...     {"lon": [10.5], "lat": [45.2], "depth": [25.0]},
...     method="bilinear",
... )

3D SST time series (automatic temporal handling)

>>> sst_time = xr.open_dataarray("sst_time.nc")  # (lon, lat, time)
>>> interp = RegularGridInterpolator(sst_time)
>>> result = interp(
...     {
...         "lon": [10.5],
...         "lat": [45.2],
...         "time": np.array(["2020-01-01"], dtype="datetime64"),
...     },
...     method="bilinear",
... )

Initialize the interpolator from an Xarray data array.

Parameters:

array (xr.DataArray) – The xarray DataArray to interpolate. Must be 2D, 3D, or 4D.

Raises:

NotImplementedError – If array is not 2D, 3D, or 4D.

Attributes

grid	Get the instance handling the regular grid for interpolations.
ndim	Get the number of array dimensions.

Special Methods

__call__(coords[, method])

Interpolate at coordinates.