from MCFF.mcmc import mcmc_generator
from MCFF.ising_model import show_state
### Simulation Inputs ###
N = 500 # Use an NxN system
initial_state = np.random.choice(
np.array([-1, 1], dtype=np.int8), size=(N, N)
) # the intial state to use
### Simulation Code ###
critical_states = [
s for s in mcmc_generator(initial_state, steps=5, stepsize=5*N**2, T= 3.5)
]
fig, axes = plt.subplots(
ncols=len(critical_states), figsize=(5 * len(critical_states), 5)
)
for s, ax in zip(critical_states, axes):
show_state(s, ax=ax)
