healpix_resample.NearestResampler#

class healpix_resample.NearestResampler(*args, Npt=9, **kwargs)[source]#

Nearest-neighbour HEALPix resampler — no sparse matrices.

Uses Npt KNN neighbours (default 9) to robustly find the nearest source for every HEALPix cell, even when the output grid is finer than the input.

Parameters:

  • Npt (int) – Number of HEALPix neighbours per source sample used by the KNN. Larger values cover more cells at the cost of more memory during construction. Default 9 is a good trade-off for most use cases.

  • All other parameters are forwarded to ``KNeighborsResampler``.

__init__(*args, Npt=9, **kwargs)[source]#

Pre-compute sparse operators.

Parameters:

  • lon_deg, lat_deg – unstructured sample coordinates in degrees, shape (N,)

  • Npt – number of nearest HEALPix cells used per sample

  • level – HEALPix level, nside = 2**level

  • sigma_m – Gaussian length scale (meters). If None, uses the HEALPix pixel scale sigma = sqrt(4*pi/(12*4**level))*R.

  • threshold – keep only HEALPix cells whose global weight sum >= threshold

  • nest – HEALPix indexing scheme

  • dtype/device – torch dtype/device for all matrices and computations

Methods

__init__(*args[, Npt])

Pre-compute sparse operators.

comp_matrix()

Build hi_k (K,) — nearest source index per HEALPix cell.

get_cell_ids()

invert(hval)

HEALPix cells → source samples.

resample(val, **_kwargs)

Source samples → HEALPix cells.