ExternalDriftKriging.__call__(vals, *, src_drift=None, trg_drift=None)[source]#

Evaluate interpolator for values given at the source points.

You can interpolate multiple datasets of source values (vals) at once: the vals array should have the shape (number of source points, number of source datasets). If you want to interpolate only one set of source values, vals can have the shape (number of source points, 1) or just (number of source points, ) - which is a flat/1-D array. The output will have the same number of dimensions as vals, i.e. it will be a flat 1-D array in case vals is a 1-D array.


vals (numpy.ndarray) – ndarray of float, shape (numsourcepoints, numfields) Values at the source points from which to interpolate Several fields may be calculated at once by passing them along the second dimension. Only this second dimension is implemented. You’ll have to reshape a more complex array for the function to work.


output (numpy.ndarray) – ndarray of float with shape (numtargetpoints, numfields)