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]#
Bases:
IpolBase
- 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 syntaxgstat
uses.nnearest (
int
) – max. number of neighbours to be consideredsrc_drift (
numpy.ndarray
) – ndarray of floats, shape (nsrcpoints,) values of the external drift at each source pointtrg_drift (
numpy.ndarray
) – ndarray of floats, shape (ntrgpoints,) values of the external drift at each target point
See also
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?.
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Evaluate interpolator for values given at the source points. |