healpix_geo.ring.bilinear_interpolation#

healpix_geo.ring.bilinear_interpolation(longitude, latitude, depth, *, ellipsoid='sphere', num_threads=0)#

Get the cell ids and weights necessary to bilinearly interpolate the given values.

Parameters:

longitude (numpy.ndarray) – The longitudes of the input points, in degrees.
latitude (numpy.ndarray) – The latitudes of the input points, in degrees.
depth (int) – The depth of the HEALPix cells.
ellipsoid (ellipsoid-like, default: "sphere") – Reference ellipsoid to evaluate healpix on. If the reference ellipsoid is spherical, this will return the same result as cdshealpix.nested.vertices().
num_threads (int, optional) – Specifies the number of threads to use for the computation. Default to 0 means it will choose the number of threads based on the RAYON_NUM_THREADS environment variable (if set), or the number of logical CPUs (otherwise)

Returns:

cell_ids (numpy.ndarray) – The neighbours above and below the given points as a \(N\) x \(4\) masked array.
weights (numpy.ndarray) – The associated weights as a \(N\) x \(4\) masked array.

Raises:

ValueError – When the HEALPix cell indexes given have values out of \([0, 4^{29 - depth})\).

Examples

>>> from healpix_geo.ring import bilinear_interpolation
>>> import numpy as np

Define coordinates

>>> lon = np.array([-15.0, -10.0, -5.0, 0.0, 5.0])
>>> lat = np.array([30.0, 35.0, 40.0, 45.0, 50.0])

Compute interpolation weights

>>> cell_ids, weights = bilinear_interpolation(lon, lat, depth=6, ellipsoid="WGS84")
>>> cell_ids
MArray(
    array([[12661, 12405, 12404, 12149],
           [10872, 10617, 10616, 10360],
           [ 9340,  9085,  9084,  8828],
           [ 7320,  7080,  7563,  7319],
           [ 5943,  5727,  5726,  5514]], dtype=uint64),
    array([[False, False, False, False],
           [False, False, False, False],
           [False, False, False, False],
           [False, False, False, False],
           [False, False, False, False]])
)
>>> weights
MArray(
    array([[0.22596183, 0.68795133, 0.02128467, 0.06480216],
           [0.15244623, 0.78751507, 0.00973729, 0.05030142],
           [0.04859157, 0.12318081, 0.23429192, 0.5939357 ],
           [0.32719215, 0.17280785, 0.32719215, 0.17280785],
           [0.14255714, 0.34522269, 0.1497    , 0.36252017]]),
    array([[False, False, False, False],
           [False, False, False, False],
           [False, False, False, False],
           [False, False, False, False],
           [False, False, False, False]])
)