wradlib.ipol.ExternalDriftKriging#

class wradlib.ipol.ExternalDriftKriging(src, trg, cov='1.0 Exp(10000.)', nnearest=12, src_drift=None, trg_drift=None, remove_missing=False, **kwargs)[source]#
ExternalDriftKriging(src, trg, cov=’1.0 Exp(10000.)’, nnearest=12,

drift_src=None, drift_trg=None)

Kriging with external drift

Parameters:

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

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

  • cov (str) – covariance (variogram) model string in the syntax gstat uses.

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

  • src_drift (numpy.ndarray) – ndarray of floats, shape (nsrcpoints,) values of the external drift at each source point

  • trg_drift (numpy.ndarray) – ndarray of floats, shape (ntrgpoints,) values of the external drift at each target point

See also

OrdinaryKriging

Note

After calling the object in order to get the interpolated values, the estimation variance of the system may be retrieved from the attribute estimation_variance. Accordingly, the interpolation weights can be retrieved from the attribute weights

If drift_src or drift_trg are not given on initialization, they must be provided when using the __call__ method. If any of them is given on initialization its values may be overridden by passing new values to the __call__ method.

Examples

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

__init__(src, trg, cov='1.0 Exp(10000.)', nnearest=12, src_drift=None, trg_drift=None, remove_missing=False, **kwargs)[source]#

Methods

__init__(src, trg[, cov, nnearest, ...])