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.