stilt.observations.fit_variogram#

stilt.observations.fit_variogram(lag, gamma, *, sigma=None)[source]#

Fit the exponential variogram to an empirical one.

Parameters:

lag (Union[_Buffer, _SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[bool | int | float | complex | str | bytes]]) – The empirical variogram, as variogram() returns it.
gamma (Union[_Buffer, _SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[bool | int | float | complex | str | bytes]]) – The empirical variogram, as variogram() returns it.
sigma (Optional[float]) – The error standard deviation. Given, the sill is fixed at sigma² and only the correlation scale is fitted, which is what Lin and Gerbig’s definition of the variogram implies and what you want when the sample standard deviation is known. None fits both.

Return type:

VariogramFit