inverse_distance¶

metpy.gridding.
inverse_distance
(xp, yp, variable, grid_x, grid_y, r, gamma=None, kappa=None, min_neighbors=3, kind='cressman')[source]¶ Generate an inverse distance interpolation of the given points to a regular grid.
Values are assigned to the given grid using inverse distance weighting based on either [Cressman1959] or [Barnes1964]. The Barnes implementation used here based on [Koch1983].
Parameters:  xp ((N, ) ndarray) – xcoordinates of observations.
 yp ((N, ) ndarray) – ycoordinates of observations.
 variable ((N, ) ndarray) – observation values associated with (xp, yp) pairs. IE, variable[i] is a unique observation at (xp[i], yp[i]).
 grid_x ((M, 2) ndarray) – Meshgrid associated with x dimension.
 grid_y ((M, 2) ndarray) – Meshgrid associated with y dimension.
 r (float) – Radius from grid center, within which observations are considered and weighted.
 gamma (float) – Adjustable smoothing parameter for the barnes interpolation. Default None.
 kappa (float) – Response parameter for barnes interpolation. Default None.
 min_neighbors (int) – Minimum number of neighbors needed to perform barnes or cressman interpolation for a point. Default is 3.
 kind (str) – Specify what inverse distance weighting interpolation to use. Options: ‘cressman’ or ‘barnes’. Default ‘cressman’
Returns: img ((M, N) ndarray) – Interpolated values on a 2dimensional grid
See also
inverse_distance_to_points()
Deprecated since version 0.9.0: Function has been renamed to inverse_distance_to_grid and will be removed from MetPy in 0.12.0.