Plots the fit of the model

        Returns
        ----------
        None (plots data and the fit)

def plot_fit(self,intervals=True,**kwargs):
        """ Plots the fit of the model

        Returns
        ----------
        None (plots data and the fit)
        """
        import matplotlib.pyplot as plt
        import seaborn as sns

        figsize = kwargs.get('figsize',(10,7))

        if self.latent_variables.estimated is False:
            raise Exception("No latent variables estimated!")
        else:
            date_index = copy.deepcopy(self.index)
            date_index = date_index[self.integ:self.data_original.shape[0]+1]
            t_params = self.transform_z()

            if self.model_name2 == 'Skewt':
                states_upper_95 = (self.states + 1.98*np.sqrt(self.states_ses)) + ((t_params[-3] - (1.0/t_params[-3]))*t_params[-2]*gas.SkewtScore.tv_variate_exp(t_params[-1]))
                states_lower_95 = (self.states - 1.98*np.sqrt(self.states_ses)) + ((t_params[-3] - (1.0/t_params[-3]))*t_params[-2]*gas.SkewtScore.tv_variate_exp(t_params[-1])) 
                mean_states = self.states + ((t_params[-3] - (1.0/t_params[-3]))*t_params[-2]*gas.SkewtScore.tv_variate_exp(t_params[-1]))
            else:
                states_upper_95 = (self.states + 1.98*np.sqrt(self.states_ses)) 
                states_lower_95 = (self.states - 1.98*np.sqrt(self.states_ses))
                mean_states = self.states

            plt.figure(figsize=figsize) 
            
            plt.subplot(2, 1, 1)
            plt.title(self.data_name + " Raw and Smoothed") 

            if intervals == True:
                alpha =[0.15*i/float(100) for i in range(50,12,-2)]
                plt.fill_between(date_index, self.link(states_lower_95), self.link(states_upper_95), alpha=0.15,label='95% C.I.')   

            plt.plot(date_index, self.data, label='Data')
            plt.plot(date_index, self.link(mean_states), label='Smoothed', c='black')          
            plt.legend(loc=2)
            
            plt.subplot(2, 1, 2)
            plt.title(self.data_name + " Local Level")  

            if intervals == True:
                alpha =[0.15*i/float(100) for i in range(50,12,-2)]
                plt.fill_between(date_index, self.link(states_lower_95), self.link(states_upper_95), alpha=0.15,label='95% C.I.')   

            plt.plot(date_index, self.link(mean_states), label='Smoothed State')
            plt.legend(loc=2)
            plt.show()