Interpolate (metpy.interpolate)

Provides tools for interpolating data.

Functions

natural_neighbor_to_grid(xp, yp, variable, ...)	Generate a natural neighbor interpolation of the given points to a regular grid.
inverse_distance_to_grid(xp, yp, variable, ...)	Generate an inverse distance interpolation of the given points to a regular grid.
interpolate_to_grid(x, y, z[, interp_type, ...])	Interpolate given (x,y), observation (z) pairs to a grid based on given parameters.
interpolate_to_isosurface(level_var, ...[, ...])	Linear interpolation of a variable to a given vertical level from given values.
interpolate_nans_1d(x, y[, kind])	Interpolate NaN values in y.
interpolate_1d(x, xp, *args[, axis, ...])	Interpolates data with any shape over a specified axis.
log_interpolate_1d(x, xp, *args[, axis, ...])	Interpolates data with logarithmic x-scale over a specified axis.
natural_neighbor_to_points(points, values, xi)	Generate a natural neighbor interpolation to the given points.
inverse_distance_to_points(points, values, xi, r)	Generate an inverse distance weighting interpolation to the given points.
interpolate_to_points(points, values, xi[, ...])	Interpolate unstructured point data to the given points.
interpolate_to_slice(data, points[, interp_type])	Obtain an interpolated slice through data using xarray.
geodesic(crs, start, end, steps)	Construct a geodesic path between two points.
cross_section(data, start, end[, steps, ...])	Obtain an interpolated cross-sectional slice through gridded data.
remove_observations_below_value(x, y, z[, val])	Remove all x, y, and z where z is less than val.
remove_nan_observations(x, y, z)	Remove all x, y, and z where z is nan.
remove_repeat_coordinates(x, y, z)	Remove all x, y, and z where (x,y) is repeated and keep the first occurrence only.