aopp_deconv_tool.algorithm.interpolate.ssa_interp

module documentation

Uses SSA to get values for interpolation of data

Function ssa_deviations_interpolate_at_mask pass in a set of pixels
Function ssa_interpolate_at_mask Undocumented
Variable _lgr Undocumented
def ssa_deviations_interpolate_at_mask(ssa: SSA, mask: np.ndarray[[NM], bool], start: int | None = 0, stop: int | None = None, value: float = 0.5, show_plots: bool | int = 0):

Using SSA for interpolation. E.g.,
  1. pass in a set of pixels
  2. Calculate the 'difference from median of SSA component' score for each pixel

3) For each pixel, only combine SSA components when the |score| < 'some value' This should ensure that 'extreme' values for that pixel are ignored and the reconstructed pixel value is more similar to the surrounding pixels.

This should help when interpolating across a e.g., "hot" pixel as the components that contribute to the "hotness" will be ignored and the components that are more similar to the surroundings will contribute.

def ssa_interpolate_at_mask(data: np.ndarray[[NM], float], ssa: SSA, mask: np.ndarray[[NM], bool], start: int = 0, stop: int | None = None) -> np.ndarray[[NM], float]:

Undocumented

_lgr =

Undocumented