geolime.geostats.models.autofit

Classes:

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

Nugget(dim, angles, scales, convention)

Spherical(dim, angles, scales, convention)

Data:

List

The central part of internal API.

Functions:

fill_cov(param, cov)

Append additional information to covariance element (angles and scales)

init_params(cov, vario[, constraint])

Initialize parameters for variogram model fitting

model_fit(variograms[, cov, constraint])

Fit a variogram model from an experimental semivariogram

residual(p, vario, cov, nlags)

Residuals to be minimized for model fitting
geolime.geostats.models.autofit.fill_cov(param: numpy.ndarray, cov: geolime.geostats.models.covariance.Covariance)

Append additional information to covariance element (angles and scales)

Parameters

  • param (type) – Model parameters

  • cov (Covariance) – Covariance element to be applied

Returns

Return type

None

geolime.geostats.models.autofit.init_params(cov: geolime.geostats.models.covariance.Covariance, vario: List[pandas.core.frame.DataFrame], constraint: int = None)

Initialize parameters for variogram model fitting

Parameters

  • cov (Covariance) – Covariance to be modelled

  • vario (List[pd.DataFrame]) – List of experimental variograms

  • constraint (int) – Constraint to be applied

Returns

Parameters

Return type

np.ndarray

geolime.geostats.models.autofit.model_fit(variograms: List[pandas.core.frame.DataFrame], cov: geolime.geostats.models.covariance.Covariance = <geolime.geostats.models.covariance.Spherical object>, constraint: float = None)

Fit a variogram model from an experimental semivariogram

Parameters

  • variograms (pd.DataFrame) – Experimental semivariogram

  • cov (Covariance) – Covariance model

  • constraint (int) – Constraint sill to be applied

Returns

The fitted covariance model

Return type

Covariance

geolime.geostats.models.autofit.residual(p: numpy.ndarray, vario: pandas.core.frame.DataFrame, cov: geolime.geostats.models.covariance.Covariance, nlags: int)

Residuals to be minimized for model fitting

Parameters

  • p (np.ndarray) – Parameters

  • vario (pd.DataFrame) – Experimental variogram

  • cov (Covariance) – Covariance that will be ajuste for model fitting

  • nlags (int) – Number of lag samples

Returns

Residuals array

Return type

np.ndarray