def updatePrefShockProcess(self):
        '''
        Make a discrete preference shock structure for each period in the cycle
        for this agent type, storing them as attributes of self for use in the
        solution (and other methods).

        Parameters
        ----------
        none

        Returns
        -------
        none
        '''
        time_orig = self.time_flow
        self.timeFwd()

        PrefShkDstn = [] # discrete distributions of preference shocks
        for t in range(len(self.PrefShkStd)):
            PrefShkStd = self.PrefShkStd[t]
            PrefShkDstn.append(approxMeanOneLognormal(N=self.PrefShkCount,
                                                      sigma=PrefShkStd,tail_N=self.PrefShk_tail_N))

        # Store the preference shocks in self (time-varying) and restore time flow
        self.PrefShkDstn = PrefShkDstn
        self.addToTimeVary('PrefShkDstn')
        if not time_orig:
            self.timeRev()
def updatePrefShockProcess ( self ) : time_orig = self . time_flow self . timeFwd ( ) PrefShkDstn = [ ] for t in range ( len ( self . PrefShkStd ) ) : PrefShkStd = self . PrefShkStd [ t ] PrefShkDstn . append ( approxMeanOneLognormal ( N = self . PrefShkCount , sigma = PrefShkStd , tail_N = self . PrefShk_tail_N ) ) self . PrefShkDstn = PrefShkDstn self . addToTimeVary ( 'PrefShkDstn' ) if not time_orig : self . timeRev ( )
Make a discrete preference shock structure for each period in the cycle
        for this agent type, storing them as attributes of self for use in the
        solution (and other methods).

        Parameters
        ----------
        none

        Returns
        -------
        none