User Guide#

Everything you need to understand how healpix-resample works and how to configure it.

Output format#

All resamplers return a ResampleResults dataclass:

Field	Shape	Description
`cell_data`	`(K,)` or `(B, K)`	Resampled values on HEALPix cells
`cell_ids`	`(K,)`	HEALPix cell indices (nested scheme)
`cg_residual_norms`	`(iters,)`	CG convergence history (PSFResampler only)
`cg_niters`	scalar	Number of CG iterations (PSFResampler only)

Key parameters (all resamplers)#

Parameter	Default	Description
`level`	—	HEALPix resolution. `nside = 2**level`
`threshold`	`0.1`	Minimum weight sum to keep a cell
`sigma_m`	auto	Gaussian scale in metres (defaults to pixel size)
`out_cell_ids`	`None`	Restrict output to a specific subset of cells
`ellipsoid`	`"WGS84"`	Geodetic ellipsoid
`device`	auto	`"cpu"` or `"cuda"`. Auto-detected if not set.
`dtype`	`float64`	PyTorch dtype. Use `float32` for speed, `float64` for precision.

GPU usage#

Pass device="cuda" to any resampler to run on GPU. The operators M and MT stay in GPU memory between calls to resample(), so the cost is paid only once at construction time.

from healpix_resample import BilinearResampler
import torch

nr = BilinearResampler(
    lon_deg=lon, lat_deg=lat,
    level=13,
    device="cuda",
    dtype=torch.float32,
)

result = nr.resample(val)  # runs on GPU