xdem.spatialstats.two_step_standardization

xdem.spatialstats.two_step_standardization(dvalues, list_var, unscaled_error_fun, spread_statistic=<function nmad>, fac_spread_outliers=7)

Standardize the proxy differenced values using the modelled heteroscedasticity, re-scaled to the spread statistic, and generate the final standardization function.

Parameters:

• dvalues (ndarray[tuple[int, ...], dtype[floating[Any]]]) – Proxy values as array of size (N,) (i.e., differenced values where signal should be zero such as elevation differences on stable terrain)

• list_var (list[ndarray[tuple[int, ...], dtype[floating[Any]]]]) – List of size (L) of explanatory variables array of size (N,)

• unscaled_error_fun (Callable[[tuple[Union[Buffer, _SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[bool | int | float | complex | str | bytes]], ...]], ndarray[tuple[int, ...], dtype[floating[Any]]]]) – Function of the spread with explanatory variables not yet re-scaled

• spread_statistic (Callable[[ndarray[tuple[int, ...], dtype[floating[Any]]]], floating[Any]]) – Statistic to be computed for the spread; defaults to nmad

• fac_spread_outliers (float | None) – Exclude outliers outside this spread after standardizing; pass None to ignore.

Return type:

tuple[ndarray[tuple[int, ...], dtype[floating[Any]]], Callable[[tuple[Union[Buffer, _SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[bool | int | float | complex | str | bytes]], ...]], ndarray[tuple[int, ...], dtype[floating[Any]]]]]

Returns:

Standardized values array of size (N,), Function to destandardize