xdem.coreg.DirectionalBias

class xdem.coreg.DirectionalBias(angle=0, fit_or_bin='bin_and_fit', fit_func='nfreq_sumsin', fit_optimizer=None, bin_sizes=100, bin_statistic=<function nanmedian>, bin_apply_method='linear', subsample=1.0)

Bias correction for directional biases, for example along- or across-track of satellite angle.

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

__init__(angle=0, fit_or_bin='bin_and_fit', fit_func='nfreq_sumsin', fit_optimizer=None, bin_sizes=100, bin_statistic=<function nanmedian>, bin_apply_method='linear', subsample=1.0)

Instantiate a directional bias correction.

Parameters:

• angle (float) – Angle in which to perform the directional correction (degrees) with 0° corresponding to X axis direction and increasing clockwise.

• fit_or_bin (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 (Callable[..., NDArray[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[floating[Any]], Any]] | Literal['ols'] | None) – Optimizer to minimize the function. If None, use the default for the selected fit model.

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

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

• bin_apply_method (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.

• subsample (float | int) – Subsample the input for speed-up. <1 is parsed as a fraction. >1 is a pixel count.

Methods

__init__([angle, fit_or_bin, fit_func, ...])	Instantiate a directional bias correction.
apply(elev[, bias_vars, resample, ...])	Apply the estimated transform to a DEM.
copy()	Return an identical copy of the class.
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.

Attributes

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