Discrepancy between graph and tau estimate about pysynthdid HOT 1 OPEN

  def regression_df(self):
    
    date_var = self.df.index.name
    melted_df = self.df.reset_index().melt(id_vars=date_var)
    id_var = melted_df.columns[1]
    melted_df['treatment'] = melted_df[id_var].isin(self.treatment).values * melted_df[date_var].isin(self.Y_post_t.index).values
    
    # sdid
    omega_weights, lambda_weights = self.estimated_params()
    melted_df = melted_df.merge(omega_weights.iloc[:-1].rename(columns={'features':id_var,
                                                                      'sdid_weight':'omega_weight'}),on=id_var,how='outer')
    melted_df = melted_df.merge(lambda_weights.rename(columns={'time':date_var,
                                                                      'sdid_weight':'lambda_weight'}),on=date_var,how='outer')
    
    melted_df['sdid_weight'] = melted_df['omega_weight'].fillna(1/len(self.treatment)) * melted_df['lambda_weight'].fillna(1/len(self.Y_post_t))
    
    # sc
    omega_weights_ADH = self.estimated_params('sc')
    melted_df = melted_df.merge(omega_weights_ADH.rename(columns={'features':id_var}),on=id_var,how='outer')
    melted_df['sc_weight'] = melted_df['sc_weight'].fillna(1/len(self.treatment))
    
    melted_df = melted_df.set_index([id_var,date_var])
    
    return melted_df

  def hat_tau(self, model="sdid"):
  
    """
    # adjusted from github to perform weighted TWFE regression 
    """
    
    regression_df = self.regression_df()
    

    if model == "sdid":
          
        regression_df_noZeroWeight = regression_df.loc[regression_df['sdid_weight']>0] # weights must be strictly positive for PanelOLS; as they're zero weight, OK to drop
        
        FE = PanelOLS(regression_df_noZeroWeight['value'], regression_df_noZeroWeight['treatment'],
        		  entity_effects = True, 
        		  time_effects = True,
                  weights = regression_df_noZeroWeight['sdid_weight'])
              
        result = FE.fit()
        		  
        tau_est = result.params[0]

    elif model == "sc":
      
        regression_df_noZeroWeight = regression_df.loc[regression_df['sc_weight']>0] # weights must be strictly positive for PanelOLS; as they're zero weight, OK to drop
        
        FE = PanelOLS(regression_df_noZeroWeight['value'], regression_df_noZeroWeight['treatment'],
        		  time_effects = True,
                  weights = regression_df_noZeroWeight['sc_weight'])
              
        result = FE.fit()
        
        tau_est = result.params[0]