# Import relevant modules.
import numpy as np 
import ProxNest 

# Load your data and set parameters.
data = np.load(<path to your data.npy>)
params = params    # Parameters of the prior resampling optimisation problem.
options = options  # Options associated with the sampling strategy.

# Construct your forward model (phi) and wavelet operators (psi).
phi = ProxNest.operators.sensing_operators.Identity()
psi = ProxNest.operators.wavelet_operators.db_wavelets(["db6"], 2, (dim, dim))

# Define proximal operators for both your likelihood and prior.
proxH = lambda x, T : ProxNest.operators.proximal_operators.l1_projection(x, T, delta, Psi=psi)
proxB = lambda x, tau: ProxNest.optimisations.l2_ball_proj.sopt_fast_proj_B2(x, tau, params)

# Write a lambda function to evaluate your likelihood term (here a Gaussian)
LogLikeliL = lambda sol : - np.linalg.norm(y-phi.dir_op(sol), 'fro')**2/(2*sigma**2)

# Perform proximal nested sampling
BayEvi, XTrace = ProxNest.sampling.proximal_nested.ProxNestedSampling(
    np.abs(phi.adj_op(data)), LogLikeliL, proxH, proxB, params, options
    )

Live = options["samplesL"] # Number of live samples
Disc = options["samplesD"] # Number of discarded samples

# BayEvi is a tuple containing two values:
BayEvi[0] = 'Estimate of Bayesian evidence (float).'
BayEvi[1] = 'Variance of Bayesian evidence estimate (float).'

# XTrace is a dictionary containing the np.ndarrays:
XTrace['Liveset'] = 'Set of live samples (shape: Live, dim, dim).'
XTrace['LivesetL'] = 'Likelihood of live samples (shape: Live).'

XTrace['Discard'] = 'Set of discarded samples (shape: Disc, dim, dim).'
XTrace['DiscardL'] = 'Likelihood of discarded samples (shape: Disc).'
XTrace['DiscardW'] = 'Weights of discarded samples (shape: Disc).'

XTrace['DiscardPostProb'] = 'Posterior probability of discarded samples (shape: Disc)'
XTrace['DiscardPostMean'] = 'Posterior mean solution (shape: dim, dim)'