mc_ising 2

Query

import numpy as np

def run_mc(n_steps, n_sites, T, J=1, h=0):
    """
    This function performs metropolis monte carlo sampling of the 1d ising model at temperature T
    and returns the trajectory
    """
    [insert]
    return state_traj

Output

import numpy as np

def run_mc(n_steps, n_sites, T, J=1, h=0):
    """
    This function performs metropolis monte carlo sampling of the 1d ising model at temperature T
    and returns the trajectory
    """
    state_traj = np.zeros((n_steps, n_sites))
    state = np.random.choice([-1, 1], size=n_sites)
    state_traj[0, :] = state
    for i in range(1, n_steps):
        site = np.random.randint(0, n_sites)
        delta_E = 2 * state[site] * (J * (state[(site + 1) % n_sites] + state[(site - 1) % n_sites]) + h)
        if delta_E <= 0:
            state[site] *= -1
        elif np.random.rand() < np.exp(-delta_E / T):
            state[site] *= -1
        state_traj[i, :] = state
    return state_traj

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