geolime.geostats.models.model

Classes:

Covariance(dim, angles, 0.0, 0.0], scales, …)

Enum(value)

Generic enumeration.

PatternGenerator()

Functions:

cvv(cov[, pattern_generator, lag, method])

Applies a covariance model to a given grid for variance calculus

model_regularize(variogram, model, …[, …])

Semi-variogram regularization from a covariance model.
geolime.geostats.models.model.model_regularize(variogram: pandas.core.frame.DataFrame, model: geolime.geostats.models.covariance.Covariance, pattern_generator: geolime.geostats.patterns.pattern_generator.PatternGenerator, method: enum.Enum = 'semivariogram', nlag: int = 100, border_ratio: float = 1, normalize: bool = False)

Semi-variogram regularization from a covariance model. Regularization can be used to explore the change of support.

Parameters

  • variogram (pd.DataFrame) – Semi experimental variogram

  • model (Covariance) – Covariance model to be applied

  • pattern_generator (PatternGenerator) – Optional points pattern to consider for cvv calculation E.g. refinment pattern of a grid cell into [x, x, x]

  • method (Enum) – “covariogram” or “semivariogram”

  • nlag (int) – Number of lag samples

  • border_ratio (float) – Border extent ratio

  • normalize (bool) – Apply normalization if True

Returns

Pseudo-experimental variogram computed in one direction

Return type

pd.DataFrame