class darkmappy.wavelet_wrappers.S2letWavelets(L, B=2, J_min=0, N=1, forward_transform=True, upsample=0)

Linear operator wrapper for Spherical wavelet transforms using pys2let.

All variations of the wavelet transform are supported.

__init__(L, B=2, J_min=0, N=1, forward_transform=True, upsample=0)

Construct Spherical Wavelet transform class to hold all forward, inverse, and corresponding adjoint wavelet transforms.

Parameters

• L (int) – Spherical harmonic bandlimit

• B (int) – Wavelet tiling spread (2 = dyadic)

• J_min (int) – Smallest wavelet scale

• N (int) – directional samples (wigner space)

• upsample (bint) – downscale wavelet scale maps.

• Reality (bint) – Reality of signal.

Raises

• ValueError – Raised if L is not positive

• ValueError – Raised if B is less than 1.

• ValueError – Raised if N is less than 1.

• ValueError – Raised if N or B is more than L.

• WarningLog – Raised if L is very large.

forward(f, spin=0)

Compute the forward spherical wavelet transform.

Parameters

• f (np.complexarray) – Realspace Signal

• spin (int) – spin of field f

Raises

ValueError – Raised if signal is nan

forward_adjoint(fws, spin=0)

Compute the forward_adjoint spherical wavelet transform.

Parameters

• f_ws (np.complexarray) – Wavelet + scaling coefficients

• spin (int) – spherical harmonic spin

Raises

• ValueError – Raised if wavelet coefficients nan

• ValueError – Raised if scaling coefficients nan

inverse(fws, spin=0)

Compute the inverse spherical wavelet transform.

Parameters

• f_ws (np.complexarray) – Wavelet + scaling coefficients

• spin (int) – spherical harmonic spin

Raises

• ValueError – Raised if wavelet coefficients nan

• ValueError – Raised if scaling coefficients nan

inverse_adjoint(f, spin=0)

Compute the inverse adjoint spherical wavelet transform.

Parameters

• f (np.complexarray) – Realspace Signal

• spin (int) – spin of field f

Raises

ValueError – Raised if signal is nan