foscat.HealSpline
=================

.. py:module:: foscat.HealSpline


Classes
-------

.. autoapisummary::

   foscat.HealSpline.heal_spline


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

.. py:class:: heal_spline(level, gamma=1)

   .. py:attribute:: nside_store


   .. py:attribute:: spline_tree


   .. py:attribute:: gamma
      :value: 1



   .. py:attribute:: nside


   .. py:attribute:: th0_val


   .. py:attribute:: ph0_val


   .. py:attribute:: idx_th


   .. py:attribute:: spline_lat


   .. py:attribute:: f_interp_th


   .. py:method:: ang2weigths(th, ph, threshold=0.01, nest=False)


   .. py:method:: P(x, www, all_idx)


   .. py:method:: PT(y, www, all_idx, hit)


   .. py:method:: conjugate_gradient_normal_equation(data, x0, www, all_idx, max_iter=100, tol=1e-08, verbose=True)

      Solve (PᵗP)x = Pᵗy using explicit Conjugate Gradient without scipy.cg.

      Parameters:
      ----------
      P       : function(x) → forward operator (ℝⁿ → ℝᵐ)
      PT      : function(y) → adjoint operator (ℝᵐ → ℝⁿ)
      data    : array_like, observed data y ∈ ℝᵐ
      x0      : array_like, initial guess for x ∈ ℝⁿ
      max_iter: maximum number of iterations
      tol     : convergence tolerance on relative residual
      verbose : if True, print convergence info

      Returns:
      -------
      x       : estimated solution ∈ ℝⁿ



   .. py:method:: Fit(X, th, ph, threshold=0.01, nest=True, max_iter=100, tol=1e-08)


   .. py:method:: SetParam(nside, spline, heal_idx)


   .. py:method:: GetParam()


   .. py:method:: Transform(th, ph, threshold=0.01, nest=True)


   .. py:method:: FitTransform(X, th, ph, threshold=0.01, nest=True)