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).

__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 a bias correction object.

Methods

__init__([fit_or_bin, fit_func, ...])	Instantiate a bias correction object.
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.
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.
meta	Metadata dictionary of the coregistration.