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)
