Matrice Precomputes#

s2wav.transforms.construct.generate_full_precomputes(L: int, N: int, J_min: int = 0, lam: float = 2.0, sampling: str = 'mw', nside: int | None = None, forward: bool = False, reality: bool = False, nospherical: bool = False, using_torch: bool = False) → List[Array]#

Generates a list of precompute arrays associated with the underlying Wigner transforms.

Parameters:

L (int) – Harmonic bandlimit.
N (int, optional) – Upper azimuthal band-limit. Defaults to 1.
J_min (int, optional) – Lowest frequency wavelet scale to be used. Defaults to 1.
lam (float, optional) – Wavelet parameter which determines the scale factor between consecutive wavelet scales. Note that \(\lambda = 2\) indicates dyadic wavelets. Defaults to 2.
sampling (str, optional) – Spherical sampling scheme from {“mw”,”mwss”, “dh”, “healpix”}. Defaults to “mw”.
nside (int, optional) – HEALPix Nside resolution parameter. Only required if sampling=”healpix”. Defaults to None.
forward (bool, optional) – _description_. Defaults to False.
reality (bool, optional) – Whether \(f \in \mathbb{R}\), if True exploits conjugate symmetry of harmonic coefficients. Defaults to False.
nospherical (bool, optional) – Whether to only compute Wigner precomputes. Defaults to False.
using_torch (bool, optional) – Desired frontend functionality. Defaults to False.

Returns:

Precomputed recursion arrays for underlying Wigner transforms.

Return type:

List[jnp.ndarray]

s2wav.transforms.construct.generate_wigner_precomputes(L: int, N: int, J_min: int = 0, lam: float = 2.0, sampling: str = 'mw', nside: int | None = None, forward: bool = False, reality: bool = False, using_torch: bool = False) → List[Array]#