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]

            states_0_upper_95 = self.states[0] + 1.98*np.sqrt(self.states_var[0])
            states_0_lower_95 = self.states[0] - 1.98*np.sqrt(self.states_var[0])
            states_1_upper_95 = self.states[1] + 1.98*np.sqrt(self.states_var[1])
            states_1_lower_95 = self.states[1] - 1.98*np.sqrt(self.states_var[1])

            plt.figure(figsize=figsize) 
            
            plt.subplot(2, 2, 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_0_lower_95), self.link(states_0_upper_95), alpha=0.15,label='95% C.I.')   

            plt.plot(date_index,self.data,label='Data')
            plt.plot(date_index,self.link(self.states[0]),label="Smoothed",c='black')
            plt.legend(loc=2)
            
            plt.subplot(2, 2, 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_0_lower_95), self.link(states_0_upper_95), alpha=0.15,label='95% C.I.')   

            plt.plot(date_index,self.link(self.states[0]),label='Smoothed State')
            plt.legend(loc=2)
            
            plt.subplot(2, 2, 3)
            plt.title(self.data_name + " Trend")    

            if intervals == True:
                alpha =[0.15*i/float(100) for i in range(50,12,-2)]
                plt.fill_between(date_index, states_1_lower_95, states_1_upper_95, alpha=0.15,label='95% C.I.') 

            plt.plot(date_index,self.states[1],label='Smoothed State')
            plt.legend(loc=2)
            
            plt.show()