Experimental Variography Analysis

variogram(object, attribute, region, geographic_azimuth, dip, pitch, lags, tol, atol=45.0, slice_width=None, slice_height=None, weights_attribute=None, chunk_size=None, backend=GeostatsBackend.PYTHON)

Given boundary input arguments (lag and its associated tolerance, angular reference and its associated tolerance), compute the variogram for a set of points (value associated with cartesian coordinates for a specific point)

Parameters:

Name	Type	Description	Default
object	GeoRefObject	Geo-ref Object.	required
attribute	str	Object attribute to compute variography on.	required
region	Optional[str]	Object region or condition to select data from.	required
geographic_azimuth	float	Azimuth angle, in degrees, comprised between 0 and 360.	required
dip	float	Dip angle, in degrees, comprised between 0 and 90.	required
pitch	float	Pitch angle, in degrees, comprised between -90 and 90.	required
lags	Vector	List of lags (in the same unit as the object, e.g. meters).	required
tol	Union[float, Vector]	Lag tolerance (in the same unit as the object, e.g. meters). Tolerance can be a Vector of tolerances or a single value used for each lag.	required
atol	float	Angular tolerance in degrees.	45.0
slice_width	Optional[float]	Slicing width (in the same unit as the object, e.g. meters).	None
slice_height	Optional[float]	Slicing height (in the same unit as the object, e.g. meters).	None
weights_attribute	Optional[str]	Weights attribute for ponderation, generally coming from declustering.	None
chunk_size	Optional[int]	The sought maximum number of points per distance matrix chunks. 1 Mb of RAM allows about 130 000 points in the computation matrix, corresponding to roughly 360 data points.	None
backend	GeostatsBackend	Computational backend used, for large number of lags, setting to JULIA will lead to faster results.	GeostatsBackend.PYTHON

Returns:

Type	Description
pd.DataFrame	A variogram dataframe, Isatis compliant output (rank, number of pairs, input lag, average distance, variogram value and indices)

generate_lags(lag, plag, nlags)

Generate lags array to be used with variogram

Parameters:

Name	Type	Description	Default
lag	float	Calculation lag	required
plag	float	Tolerance (percentage of lag)	required
nlags	int	Length of array to be generated	required

Returns:

Type	Description
Tuple[np.ndarray, float]	An array of lags and the tolerance.

generate_lags_from_interval(lag_intervals)

Generate lags array to be used with variogram.

Parameters:

Name	Type	Description	Default
lag_intervals	Vector	Boundary of each lag interval.	required

Raises:

Type	Description
ValueError	Intervals are not defined by 2 values (min and max).

Returns:

Type	Description
Tuple[np.ndarray, np.ndarray]	An array of lags and the tolerance.