pyinterp.fill.loess

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pyinterp.fill.loess#

pyinterp.fill.loess(mesh: Grid4D, nx: int = 3, ny: int = 3, value_type: str | None = None, num_threads: int = 0) → NDArray4D[source]#
pyinterp.fill.loess(mesh: Grid3D, nx: int = 3, ny: int = 3, value_type: str | None = None, num_threads: int = 0) → NDArray3D
pyinterp.fill.loess(mesh: Grid2D, nx: int = 3, ny: int = 3, value_type: str | None = None, num_threads: int = 0) → NDArray2D

Filter values using a locally weighted regression function (LOESS).

Apply LOESS filtering to fill or smooth grid values using the tri-cube weight function: \(w(x)=(1-|d|^3)^3\).

Parameters:

  • mesh – Grid function on a uniform 2-dimensional grid to be filled.

  • nx – Number of points of the half-window to be taken into account along the X-axis. Defaults to 3.

  • ny – Number of points of the half-window to be taken into account along the Y-axis. Defaults to 3.

  • value_type – Type of values processed by the filter. Supported are undefined, defined, all. Default to undefined.

  • num_threads – The number of threads to use for the computation. If 0 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. Defaults to 0.

Returns:

The grid will have NaN filled with extrapolated values.

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