pyinterp.core.geodetic.RTree.inverse_distance_weighting#

RTree.inverse_distance_weighting(self: pyinterp.core.geodetic.RTree, lon: numpy.ndarray[numpy.float64[m, 1]], lat: numpy.ndarray[numpy.float64[m, 1]], radius: float | None = None, k: int = 9, p: int = 2, within: bool = True, num_threads: int = 0) → tuple#

Interpolation of the value at the requested position by inverse distance weighting method.

Parameters:

  • lon – The longitude of the points, in degrees, to be interpolated.

  • lat – The latitude of the points, in degrees, to be interpolated.

  • radius – The maximum radius of the search (m). Defaults The maximum distance between two points.

  • k – The number of nearest neighbors to be used for calculating the interpolated value. Defaults to 9.

  • p – The power parameters. Defaults to 2. within (bool, optional): If true, the method ensures that the neighbors found are located around the point of interest. In other words, this parameter ensures that the calculated values will not be extrapolated. Defaults to true.

  • within – If true, the method ensures that the neighbors found are located within the point of interest. Defaults to false.

  • 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 interpolated value and the number of neighbors used in the calculation.