foscat.alm

Classes

alm

Module Contents

class foscat.alm.alm(backend=None, lmax=24, nside=None, limit_range=10000000000.0)

lth

lph

matrix_shift_ph

ratio_mm

P_mm

A

B

Yp

Ym

ring_th(nside)

ring_ph(nside)

shift_ph(nside)

init_Ys(s, nside)

log(v)

double_factorial_log(n)

recurrence_fn(states, inputs)

Recurrence function for tf.scan. states: un tuple (U_{n-1}, U_{n-2}) de forme [m] inputs: a tuple (a_n(x), b_n) where a_n(x) has shape [m]

compute_legendre_m(x, m, lmax, nside)

compute_legendre_m_old2(x, m, lmax, nside)

compute_legendre_m_old(x, m, lmax, nside)

compute_legendre_spin2_m(co_th, si_th, m, lmax)

rfft2fft(val, axis=0)

irfft2fft(val, N, axis=0)

comp_tf(im, nside, realfft=False)

icomp_tf(i_im, nside, realfft=False)

anafast(im, map2=None, nest=False, spin=2, axes=0)

The anafast function computes the L1 and L2 norm power spectra.

Currently, it is not optimized for single-pass computation due to the relatively inefficient computation of (Y_{lm}). Nonetheless, it utilizes TensorFlow and can be integrated into gradient computations.

Input: - im: a vector of size ([N_image, 12 imes ext{Nside}^2]) for scalar data, or of size ([N_image, 2, 12 imes ext{Nside}^2]) for Q,U polar data, or of size ([N_image,3, 12 imes ext{Nside}^2]) for I,Q,U polar data. - map2 (optional): a vector of size ([12 imes ext{Nside}^2]) for scalar data, or of size ([3, 12 imes ext{Nside}^2]) for polar data. If provided, cross power spectra will be computed. - nest=True: alters the ordering of the input maps. - spin=2 for 1/2 spin data as Q and U. Spin=1 for seep fields

Output: -A tensor of size ([l_{ ext{max}} imes (l_{ ext{max}}-1)) formatted as ([6, ldots]), ordered as TT, EE, BB, TE, EB.TBanafast function computes L1 and L2 norm powerspctra.

map2alm(im, nest=False)

alm2map(nside, alm)

map2alm_spin(im_Q, im_U, spin=2, nest=False)