hichamjanati

import numpy as np
import time

def estimate_prob_bob_win(n_situations, seed=None, timeout=10):
    rng = np.random.default_rng(seed)

    bob_wins = 0
    n_games = 0

    t_start = time.time()

hichamjanati

import numpy as np
import warnings
from scipy import sparse
from scipy.stats import norm as gaussian


def make_gaussians(grid, n_hists, loc=None, scale=None, normed=True,
                   mass=None):
    """Generate random gaussian histograms.
    """

hichamjanati

import pyvista as pv
import numpy as np


make_gif = True

# increase n_points for a higher resolution
n_points = 100
xmin, xmax = -1.2, 1.2
bounds = 1.25 * np.array([xmin, xmax, xmin, xmax, 0., 0.])

hichamjanati

"""
================================================================
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
================================================================

Runs an (ir)MxNE (L1/L2 [1]_ or L0.5/L2 [2]_ mixed norm) inverse solver.
L0.5/L2 is done with irMxNE which allows for sparser
source estimates with less amplitude bias due to the non-convexity
of the L0.5/L2 mixed norm penalty.