flownet.utils.kriging module

flownet.utils.kriging.execute(data: numpy.ndarray, n: int = 20, variogram_model: str = 'spherical', variogram_parameters: Optional[Dict] = None, n_lags: int = 6, anisotropy_scaling_z: float = 10.0, **kwargs) → Tuple[scipy.interpolate.interpolate.RegularGridInterpolator, scipy.interpolate.interpolate.RegularGridInterpolator]

Executes spatial kriging of input data and returns an scipy.interpolate.RegularInterpolator object.

Parameters

• data – 3xN np.ndarray with columns X, Y, Z, MEASUREMENT

• n – Number of kriged values in each direct. E.g, n = 10 -> 10x10x10 = 1000 values

• variogram_model – Variogram options as allowed in pykrige

• nlags – nlag as defined in pykrige

• anisotropy_scaling_z – anisotropy in the z-scale

• ... –

Returns

Tuple of RegularInterpolators for the kriged measurement and uncertainty in a 3D volume