def assign_agents(cls, particle_num: int, state: np.array, model: Model):
"""
Assign the state of the particles to the
locations of the agents.
:param particle_num
:param state: The state of the particle to be assigned
:param model: The model to assign the state to
:type model: Return the model after having the agents assigned according to the state.
"""
model.set_state(state, sensor='location')
return model
def step_particle(cls, particle_num: int, model: Model, num_iter: int, particle_std: float, particle_shape: tuple):
"""
Step a particle, assign the locations of the
agents to the particle state with some noise, and
then use the new particle state to set the location
of the agents.
:param particle_num: The particle number to step
:param model: A pointer to the model object associated with the particle that needs to be stepped
:param num_iter: The number of iterations to step
:param particle_std: the particle noise standard deviation
:param particle_shape: the shape of the particle array
"""
# Force the model to re-seed its random number generator (otherwise each child process
# has the same generator https://stackoverflow.com/questions/14504866/python-multiprocessing-numpy-random
model.set_random_seed()
for i in range(num_iter):
model.step()
noise = np.random.normal(0, particle_std ** 2, size=particle_shape)
state = model.get_state(sensor='location') + noise
model.set_state(state, sensor='location')
return model, state