def simBirth(self, which_agents): ''' Makes new consumers for the given indices. Slightly extends base method by also setting pLvlErrNow = 1.0 for new agents, indicating that they correctly perceive their productivity. Parameters ---------- which_agents : np.array(Bool) Boolean array of size self.AgentCount indicating which agents should be "born". Returns ------- None ''' AggShockConsumerType.simBirth(self, which_agents) if hasattr(self, 'pLvlErrNow'): self.pLvlErrNow[which_agents] = 1.0 else: self.pLvlErrNow = np.ones(self.AgentCount)
def simBirth(self, which_agents): ''' Makes new consumers for the given indices. Slightly extends base method by also setting pLvlErrNow = 1.0 for new agents, indicating that they correctly perceive their productivity. Parameters ---------- which_agents : np.array(Bool) Boolean array of size self.AgentCount indicating which agents should be "born". Returns ------- None ''' AggShockConsumerType.simBirth(self, which_agents) if hasattr(self, 'pLvlErrNow'): self.pLvlErrNow[which_agents] = 1.0 else: # This only triggers at the beginning of the very first simulated period self.pLvlErrNow = np.ones(self.AgentCount) self.t_since_update = np.zeros(self.AgentCount, dtype=int)