foscat.alm
==========

.. py:module:: foscat.alm


Classes
-------

.. autoapisummary::

   foscat.alm.alm


Module Contents
---------------

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

   .. py:attribute:: lth


   .. py:attribute:: lph


   .. py:attribute:: matrix_shift_ph


   .. py:attribute:: ratio_mm


   .. py:attribute:: P_mm


   .. py:attribute:: A


   .. py:attribute:: B


   .. py:attribute:: Yp


   .. py:attribute:: Ym


   .. py:method:: ring_th(nside)


   .. py:method:: ring_ph(nside)


   .. py:method:: shift_ph(nside)


   .. py:method:: init_Ys(s, nside)


   .. py:method:: log(v)


   .. py:method:: double_factorial_log(n)


   .. py:method:: 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]



   .. py:method:: compute_legendre_m(x, m, lmax, nside)


   .. py:method:: compute_legendre_m_old2(x, m, lmax, nside)


   .. py:method:: compute_legendre_m_old(x, m, lmax, nside)


   .. py:method:: compute_legendre_spin2_m(co_th, si_th, m, lmax)


   .. py:method:: rfft2fft(val, axis=0)


   .. py:method:: irfft2fft(val, N, axis=0)


   .. py:method:: comp_tf(im, nside, realfft=False)


   .. py:method:: icomp_tf(i_im, nside, realfft=False)


   .. py:method:: 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.




   .. py:method:: map2alm(im, nest=False)


   .. py:method:: alm2map(nside, alm)


   .. py:method:: map2alm_spin(im_Q, im_U, spin=2, nest=False)