wradlib.ipol.Idw#

class wradlib.ipol.Idw(src, trg, nnearest=4, p=2.)[source]#

Inverse distance weighting interpolation in N dimensions.

Parameters:

  • src (numpy.ndarray) – ndarray of floats, shape (npoints, ndims) of cKDTree object Data point coordinates of the source points.

  • trg (numpy.ndarray) – ndarray of floats, shape (npoints, ndims) Data point coordinates of the target points.

  • nnearest (int) – max. number of neighbours to be considered

  • p (float) – inverse distance power used in 1/dist**p

  • remove_missing (bool) – If True masks NaN values in the data values, defaults to False

Keyword Arguments:

**kwargs (dict) – keyword arguments of ipclass (see class documentation)

Examples

See How to use wradlib’s ipol module for interpolation tasks?.

Note

Uses scipy.spatial.cKDTree

__init__(src, trg, nnearest=4, p=2.0, remove_missing=False, **kwargs)[source]#

Methods

__init__(src, trg[, nnearest, p, remove_missing])