def noticeStimulus(self):
'''
Give each agent the opportunity to notice the future stimulus payment and
mentally account for it in their market resources.
'''
if self.T_til_check > 0:
self.T_til_check -= 1
updaters = drawBernoulli(self.AgentCount, p=self.UpdatePrb, seed=self.RNG.randint(0,2**31-1))
if self.T_til_check == 0:
updaters = np.ones(self.AgentCount, dtype=bool)
self.mNrmNow[updaters] += self.Stim_unnoticed[updaters]*self.StimLvl[updaters]/self.pLvlNow[updaters]*self.Rfree[0]**(-self.T_til_check)
self.Stim_unnoticed[updaters] = False
def getAdjust(self):
'''
Sets the attribute AdjustNow as a boolean array of size AgentCount, indicating
whether each agent is able to adjust their risky portfolio share this period.
Uses the attribute AdjustPrb to draw from a Bernoulli distribution.
Parameters
----------
None
Returns
-------
None
'''
self.AdjustNow = drawBernoulli(self.AgentCount,
p=self.AdjustPrb,
seed=self.RNG.randint(0, 2**31 - 1))
def getShocks(self):
'''
Determine which agents switch from employment to unemployment. All unemployed agents remain
unemployed until death.
Parameters
----------
None
Returns
-------
None
'''
employed = self.eStateNow == 1.0
N = int(np.sum(employed))
newly_unemployed = drawBernoulli(N,p=self.UnempPrb,seed=self.RNG.randint(0,2**31-1))
self.eStateNow[employed] = 1.0 - newly_unemployed
def test_drawBernoulli(self):
self.assertEqual(simulation.drawBernoulli(1)[0], False)