foscat.scat_cov_map
===================

.. py:module:: foscat.scat_cov_map


Classes
-------

.. autoapisummary::

   foscat.scat_cov_map.scat_cov_map
   foscat.scat_cov_map.funct


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

.. py:class:: scat_cov_map(S2, S0, S3, S4, S1=None, S3P=None, backend=None)

   Bases: :py:obj:`foscat.scat_cov.funct`


   .. py:attribute:: S2


   .. py:attribute:: S0


   .. py:attribute:: S1
      :value: None



   .. py:attribute:: S3


   .. py:attribute:: S3P
      :value: None



   .. py:attribute:: S4


   .. py:attribute:: backend
      :value: None



   .. py:attribute:: bk_type
      :value: 'SCAT_COV_MAP2D'



   .. py:method:: fill(im, nullval=hp.UNSEEN)


.. py:class:: funct(*args, **kwargs)

   Bases: :py:obj:`foscat.scat_cov.funct`


   .. py:method:: eval(image1, image2=None, mask=None, norm=None, calc_var=False, out_nside=None)

      Calculates the scattering correlations for a batch of images. Mean are done over pixels.
      mean of modulus:
                      S1 = <|I * Psi_j3|>
           Normalization : take the log
      power spectrum:
                      S2 = <|I * Psi_j3|^2>
          Normalization : take the log
      orig. x modulus:
                      S3 = < (I * Psi)_j3 x (|I * Psi_j2| * Psi_j3)^* >
           Normalization : divide by (S2_j2 * S2_j3)^0.5
      modulus x modulus:
                      S4 = <(|I * psi1| * psi3)(|I * psi2| * psi3)^*>
           Normalization : divide by (S2_j1 * S2_j2)^0.5
      :Parameters: * **image1** (:py:class:`tensor`) -- Image on which we compute the scattering coefficients [Nbatch, Npix, 1, 1]
                   * **image2** (:py:class:`tensor`) -- Second image. If not None, we compute cross-scattering covariance coefficients.
                   * **mask**
                   * **norm** (:py:obj:`None` or :py:class:`str`) -- If None no normalization is applied, if 'auto' normalize by the reference S2,
                     if 'self' normalize by the current S2.
                   * **spin** (:py:class:`Integer`) -- If different from 0 compute spinned data (U,V to Divergence/Rotational spin==1) or (Q,U to E,B spin=2).
                     This implies that the input data is 2*12*nside^2.

      :returns: :py:class:`S1`, :py:class:`S2`, :py:class:`S3`, :py:class:`S4 normalized`



   .. py:method:: scat_coeffs_apply(scat, method, no_order_1=False, no_order_2=False, no_order_3=False)


   .. py:method:: scat_ud_grade_2(scat, no_order_1=False, no_order_2=False, no_order_3=False)


   .. py:method:: iso_mean(scat, no_order_1=False, no_order_2=False, no_order_3=False)


   .. py:method:: fft_ang(scat, nharm=1, imaginary=False, no_order_1=False, no_order_2=False, no_order_3=False)