def simulate(self, sim_length):
""" simulate the patient over the specified simulation length """
# random number generator for this patient
self._rng = rndClasses.RNG(self._id)
k = 0 # current time step
# while the patient is alive and simulation length is not yet reached
while self._stateMonitor.get_if_alive(
) and k * self._delta_t < sim_length:
# find the transition probabilities of the future states
trans_probs = self._param.get_transition_prob(
self._stateMonitor.get_current_state())
# create an empirical distribution
empirical_dist = rndClasses.Empirical(trans_probs)
# sample from the empirical distribution to get a new state
# (returns an integer from {0, 1, 2, ...})
new_state_index = empirical_dist.sample(self._rng)
# update health state
self._stateMonitor.update(k, P.HealthStats(new_state_index))
# increment time step
k += 1
