amflss.py 文件源码

def plot_multiplicative(self, T, npaths=25, show_trend=True):
        """
        Plots for the multiplicative decomposition

        """
        # Pull out right sizes so we know how to increment
        nx, nk, nm = self.nx, self.nk, self.nm
        # Matrices for the multiplicative decomposition
        ?_tilde, H, g = self.multiplicative_decomp()

        # Allocate space (nm is the number of functionals - we want npaths for each)
        mpath_mult = np.empty((nm*npaths, T))
        mbounds_mult = np.empty((nm*2, T))
        spath_mult = np.empty((nm*npaths, T))
        sbounds_mult = np.empty((nm*2, T))
        tpath_mult = np.empty((nm*npaths, T))
        ypath_mult = np.empty((nm*npaths, T))

        # Simulate for as long as we wanted
        moment_generator = self.lss.moment_sequence()
        # Pull out population moments
        for t in range (T):
            tmoms = next(moment_generator)
            ymeans = tmoms[1]
            yvar = tmoms[3]

            # Lower and upper bounds - for each multiplicative functional
            for ii in range(nm):
                li, ui = ii*2, (ii+1)*2
                Mdist = lognorm(np.asscalar(np.sqrt(yvar[nx+nm+ii, nx+nm+ii])), 
                                scale=np.asscalar( np.exp( ymeans[nx+nm+ii]- \
                                                t*(.5)*np.expand_dims(np.diag(H @ H.T),1)[ii])))
                Sdist = lognorm(np.asscalar(np.sqrt(yvar[nx+2*nm+ii, nx+2*nm+ii])),
                                scale = np.asscalar( np.exp(-ymeans[nx+2*nm+ii])))
                mbounds_mult[li:ui, t] = Mdist.ppf([.01, .99])
                sbounds_mult[li:ui, t] = Sdist.ppf([.01, .99])

        # Pull out paths
        for n in range(npaths):
            x, y = self.lss.simulate(T)
            for ii in range(nm):
                ypath_mult[npaths*ii+n, :] = np.exp(y[nx+ii, :])
                mpath_mult[npaths*ii+n, :] = np.exp(y[nx+nm + ii, :] - np.arange(T)*(.5)*np.expand_dims(np.diag(H @ H.T),1)[ii])
                spath_mult[npaths*ii+n, :] = 1/np.exp(-y[nx+2*nm + ii, :])
                tpath_mult[npaths*ii+n, :] = np.exp(y[nx+3*nm + ii, :] + np.arange(T)*(.5)*np.expand_dims(np.diag(H @ H.T),1)[ii])

        mult_figs = []

        for ii in range(nm):
            li, ui = npaths*(ii), npaths*(ii+1)
            LI, UI = 2*(ii), 2*(ii+1)

            mult_figs.append(self.plot_given_paths(T, ypath_mult[li:ui,:], mpath_mult[li:ui,:], 
                                                   spath_mult[li:ui,:], tpath_mult[li:ui,:], 
                                                   mbounds_mult[LI:UI,:], sbounds_mult[LI:UI,:], 1, 
                                                   show_trend=show_trend))
            mult_figs[ii].suptitle( r'Multiplicative decomposition of $y_{%s}$' % str(ii+1), fontsize=14)

        return mult_figs