foscat.HealSpline

Classes

heal_spline

Module Contents

class foscat.HealSpline.heal_spline(level, gamma=1)

nside_store

spline_tree

gamma = 1

nside

th0_val

ph0_val

idx_th

spline_lat

f_interp_th

ang2weigths(th, ph, threshold=0.01, nest=False)

P(x, www, all_idx)

PT(y, www, all_idx, hit)

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

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

SetParam(nside, spline, heal_idx)

GetParam()

Transform(th, ph, threshold=0.01, nest=True)

FitTransform(X, th, ph, threshold=0.01, nest=True)