xdem.coreg.BiasCorr

class xdem.coreg.BiasCorr(fit_or_bin='fit', fit_func='norder_polynomial', fit_optimizer=<function curve_fit>, bin_sizes=10, bin_statistic=<function nanmedian>, bin_apply_method='linear', bias_var_names=None, subsample=1.0)

Bias-correction (non-rigid alignment) simultaneously with any number and type of variables.

Variables for bias-correction can include the elevation coordinates (deramping, directional biases), terrain attributes (terrain corrections), or any other user-input variable (quality metrics, land cover).

The binning and/or fitting correction parameters are stored in the self.meta[“outputs”][“fitorbin”].

__init__(fit_or_bin='fit', fit_func='norder_polynomial', fit_optimizer=<function curve_fit>, bin_sizes=10, bin_statistic=<function nanmedian>, bin_apply_method='linear', bias_var_names=None, subsample=1.0)

Instantiate an N-dimensional bias correction using binning, fitting or both sequentially.

All fit arguments apply to “fit” and “bin_and_fit”, and bin arguments to “bin” and “bin_and_fit”.

Parameters:

• fit_or_bin (Union[Literal['bin_and_fit'], Literal['fit'], Literal['bin']]) – Whether to fit or bin, or both. Use “fit” to correct by optimizing a function or “bin” to correct with a statistic of central tendency in defined bins, or “bin_and_fit” to perform a fit on the binned statistics.

• fit_func (Union[Callable[..., ndarray[Any, dtype[floating[Any]]]], Literal['norder_polynomial'], Literal['nfreq_sumsin']]) – Function to fit to the bias with variables later passed in .fit().

• fit_optimizer (Callable[..., tuple[ndarray[Any, dtype[floating[Any]]], Any]]) – Optimizer to minimize the function.

• bin_sizes (int | dict[str, Union[int, Iterable[float]]]) – Size (if integer) or edges (if iterable) for binning variables later passed in .fit().

• bin_statistic (Callable[[ndarray[Any, dtype[floating[Any]]]], floating[Any]]) – Statistic of central tendency (e.g., mean) to apply during the binning.

• bin_apply_method (Union[Literal['linear'], Literal['per_bin']]) – Method to correct with the binned statistics, either “linear” to interpolate linearly between bins, or “per_bin” to apply the statistic for each bin.

Methods

__init__([fit_or_bin, fit_func, ...])	Instantiate an N-dimensional bias correction using binning, fitting or both sequentially.
apply(elev[, bias_vars, resample, ...])	Apply the estimated transform to a DEM.
copy()	Return an identical copy of the class.
error(reference_elev, to_be_aligned_elev[, ...])	Calculate the error of a coregistration approach.
fit(reference_elev, to_be_aligned_elev[, ...])	Estimate the coregistration transform on the given DEMs.
fit_and_apply(reference_elev, to_be_aligned_elev)	Estimate and apply the coregistration to a pair of elevation data.
info([as_str])	Summarize information about this coregistration.
residuals(reference_elev, to_be_aligned_elev)	Calculate the residual offsets (the difference) between two DEMs after applying the transformation.

Attributes

is_affine	Check if the transform be explained by a 3D affine transform.
is_translation
meta	Metadata dictionary of the coregistration.