interpolate

• Interpolators for spatial data

Source code

FirnCorr.interpolate.inpaint(xs: ndarray, ys: ndarray, zs: ndarray, N: int = 0, s0: int = 3, power: int = 2, epsilon: float = 2.0, **kwargs)

Inpaint over missing data in a two-dimensional array using a penalized least-squares method based on discrete cosine transforms [5, 17]

Parameters:

xs: np.ndarray

x-coordinates

ys: np.ndarray

y-coordinates

zs: np.ndarray

Data with masked values

N: int, default 0

Number of iterations (0 for nearest neighbors)

s0: int, default 3

Smoothing factor

power: int, default 2

Power for lambda function

epsilon: float, default 2.0

Relaxation factor

Returns:

z0: np.ndarray

Data with inpainted (filled) values

FirnCorr.interpolate.extrapolate(xs: ndarray, ys: ndarray, zs: ndarray, X: ndarray, Y: ndarray, fill_value: float = None, cutoff: int | float = inf, is_geographic: bool = True, **kwargs)

Nearest-neighbor (NN) extrapolation of valid model data using KD-trees

Parameters:

xs: np.ndarray

x-coordinates of firn model

ys: np.ndarray

y-coordinates of firn model

zs: np.ndarray

Firn model data

X: np.ndarray

Output x-coordinates

Y: np.ndarray

Output y-coordinates

fill_value: float, default np.nan

Invalid value

dtype: np.dtype, default np.float64

Output data type

cutoff: float, default np.inf

Return only neighbors within distance (kilometers)

Set to np.inf to extrapolate for all points

is_geographic: bool, default True

Input grid is in geographic coordinates

Returns:

data: xr.DataArray

Interpolated data

FirnCorr.interpolate._to_cartesian(x: ndarray, y: ndarray, is_geographic: bool = True)

Convert input coordinates to an array of points in a Cartesian coordinate system

Parameters:

x: np.ndarray

x-coordinates to be converted

y: np.ndarray

y-coordinates to be converted

is_geographic: bool, default True

Coordinates are geographic

Returns:

points: np.ndarray

Output points in Cartesian coordinates

FirnCorr.interpolate._build_tree(points: ndarray, **kwargs)

Build a KD-tree to search for the nearest-neighbors (NN)

Parameters:

points: np.ndarray

Input points in Cartesian coordinates

kwargs: dict

Keyword arguments for scipy.spatial.cKDTree

Returns:

tree: scipy.spatial.cKDTree

KD-tree from input points

FirnCorr.interpolate._nearest_neighbors(tree: cKDTree, points: ndarray, flattened: ndarray, cutoff: int | float = inf, fill_value: float = None, **kwargs)

Nearest-neighbor (NN) extrapolation of valid model data using KD-trees

Parameters:

tree: scipy.spatial.cKDTree

KD-tree of valid points for nearest-neighbor extrapolation

points: np.ndarray

Output points in Cartesian coordinates

flattened: np.ndarray

Valid data array to be extrapolated

cutoff: float, default np.inf

Return only neighbors within distance (kilometers)

fill_value: float, default None

Invalid value

dtype: np.dtype, default from input data

Output data type

Returns:

data: xr.DataArray

Extrapolated data