def main():
TRUE_WIMP = MockGen.TRUE_WIMP
events = []
with open('mock.txt') as f:
events = f.read().splitlines()
events = [float(num) for num in events]
# define events
E_thr = 6 * const.keV
# pick start
theta = np.zeros((N, 2))
# initialize MCMC parameters
theta[0] = [const.M_D, 1000 * const.sigma]
# initialize values for energy
Emin = 1 * const.keV
Emax = fn.max_recoil_energy()
del_Er = 1
E_r = np.arange(Emin, Emax, del_Er)
events = lik.find_indices(E_r, events)
# Metropolis Hastings loop
acceptance = []
for i in tqdm(range(1, N)):
proposed_theta = proposal(theta[i - 1])
prev_lik = lik.events_likelihood(E_r, events, theta[i - 1], const.AXe,
E_thr, del_Er)
new_lik = lik.events_likelihood(E_r, events, proposed_theta, const.AXe,
E_thr, del_Er)
ratio = new_lik - prev_lik
if ratio >= 0:
theta[i] = proposed_theta
acceptance.append(1)
elif np.log(np.random.rand()) < ratio:
theta[i] = proposed_theta
acceptance.append(1)
else:
theta[i] = theta[i - 1]
print(len(acceptance) / (N + 1))
f = open('data.txt', 'w')
for thetas in theta:
f.write(str(thetas) + '\n')
f.close()
