def simBirth(self,which_agents):
'''
Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as
well as time variables t_age and t_cycle. Normalized assets and permanent income levels
are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc).
Parameters
----------
which_agents : np.array(Bool)
Boolean array of size self.AgentCount indicating which agents should be "born".
Returns
-------
None
'''
IndShockConsumerType.simBirth(self,which_agents)
if hasattr(self,'aLvlNow'):
self.aLvlNow[which_agents] = self.aNrmNow[which_agents]*self.pLvlNow[which_agents]
else:
self.aLvlNow = self.aNrmNow*self.pLvlNow
def simBirth(self, which_agents):
'''
Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as
well as time variables t_age and t_cycle. Normalized assets and permanent income levels
are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc).
Parameters
----------
which_agents : np.array(Bool)
Boolean array of size self.AgentCount indicating which agents should be "born".
Returns
-------
None
'''
IndShockConsumerType.simBirth(self, which_agents)
if hasattr(self, 'aLvlNow'):
self.aLvlNow[which_agents] = self.aNrmNow[
which_agents] * self.pLvlNow[which_agents]
else:
self.aLvlNow = self.aNrmNow * self.pLvlNow
