def spawn_poisson_demand(timestep: int, demand_lambda: float, UserClass, rng: np.random.Generator = rng) -> Sequence[User]:
"""
One-step demand from homogeneous users, with demand size drawn from a Poisson distribution.
Args:
timestep (int): Current round
demand_lambda (float): Rate of arrival, the :math:`lambda` parameter of a Poisson distribution
UserClass (class): The user type
Returns:
Sequence[User]: An array of users
"""
demand_size = rng.poisson(demand_lambda)
new_users = [UserClass(timestep, rng=rng) for i in range(demand_size)]
return new_users
def spawn_poisson_heterogeneous_demand(timestep: int, demand_lambda: float, shares: Dict[type, float], rng: np.random.Generator = rng) -> Sequence[User]:
"""
One-step demand from heterogeneous users, with demand size drawn from a Poisson distribution.
Args:
timestep (int): Current round
demand_lambda (float): Rate of arrival, the :math:`lambda` parameter of a Poisson distribution
shares (Dict[type, float]): Keys are user classes (subclasses of :py:class:`abm1559.users.User`), values are the share of each user class to spawn this round. Shares are expected to sum to 1.
Returns:
Sequence[User]: An array of users
"""
new_users = []
demand_size = rng.poisson(demand_lambda)
sizes = shares_to_sizes(shares, demand_size)
for UserClass, size in sizes.items():
new_users += [UserClass(timestep, rng=rng) for i in range(size)]
return new_users