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xdem.coreg.BiasCorrND

xdem.coreg.BiasCorrND#

class xdem.coreg.BiasCorrND(fit_or_bin='bin', 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)[source]#

Bias-correction along N variables (e.g., simultaneously slope, curvature, aspect and elevation).

__init__(fit_or_bin='bin', 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)[source]#

Instantiate an N-D bias correction.

Parameters:

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

  • 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.

  • bias_var_names (Iterable[str]) – (Optional) For pipelines, explicitly define bias variables names to use during .fit().

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

Methods

__init__([fit_or_bin, fit_func, ...])

Instantiate an N-D bias correction.

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.

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.
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