docs: def create_event_study_tear_sheet(factor_data,
                                    prices=None,
                                    avgretplot=(5, 15),
                                    rate_of_ret=True,
                                    n_bars=50):
      """
      Creates an event study tear sheet for analysis of a specific event.
  
      Parameters
      ----------
      factor_data : pd.DataFrame - MultiIndex
          A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
          containing the values for a single event, forward returns for each
          period, the factor quantile/bin that factor value belongs to, and
          (optionally) the group the asset belongs to.
      prices : pd.DataFrame, required only if 'avgretplot' is provided
          A DataFrame indexed by date with assets in the columns containing the
          pricing data.
          - See full explanation in utils.get_clean_factor_and_forward_returns
      avgretplot: tuple (int, int) - (before, after), optional
          If not None, plot event style average cumulative returns within a
          window (pre and post event).
      rate_of_ret : bool, optional
          Display rate of return instead of simple return in 'Mean Period Wise
          Return By Factor Quantile' and 'Period Wise Return By Factor Quantile'
          plots
      n_bars : int, optional
          Number of bars in event distribution plot
      """
  
      long_short = False
  
      plotting.plot_quantile_statistics_table(factor_data)
  
      gf = GridFigure(rows=1, cols=1)
      plotting.plot_events_distribution(events=factor_data['factor'],
                                        num_bars=n_bars,
                                        ax=gf.next_row())
      plt.show()
      gf.close()
  
      if prices is not None and avgretplot is not None:
  
          create_event_returns_tear_sheet(factor_data=factor_data,
                                          prices=prices,
                                          avgretplot=avgretplot,
                                          long_short=long_short,
                                          group_neutral=False,
                                          std_bar=True,
                                          by_group=False)
  
      factor_returns = perf.factor_returns(factor_data,
                                           demeaned=False,
                                           equal_weight=True)
  
      mean_quant_ret, std_quantile = \
          perf.mean_return_by_quantile(factor_data,
                                       by_group=False,
                                       demeaned=long_short)
      if rate_of_ret:
          mean_quant_ret = \
              mean_quant_ret.apply(utils.rate_of_return, axis=0,
                                   base_period=mean_quant_ret.columns[0])
  
      mean_quant_ret_bydate, std_quant_daily = \
          perf.mean_return_by_quantile(factor_data,
                                       by_date=True,
                                       by_group=False,
                                       demeaned=long_short)
      if rate_of_ret:
          mean_quant_ret_bydate = mean_quant_ret_bydate.apply(
              utils.rate_of_return, axis=0,
              base_period=mean_quant_ret_bydate.columns[0]
          )
  
      fr_cols = len(factor_returns.columns)
      vertical_sections = 2 + fr_cols * 1
      gf = GridFigure(rows=vertical_sections, cols=1)
  
      plotting.plot_quantile_returns_bar(mean_quant_ret,
                                         by_group=False,
                                         ylim_percentiles=None,
                                         ax=gf.next_row())
  
      plotting.plot_quantile_returns_violin(mean_quant_ret_bydate,
                                            ylim_percentiles=(1, 99),
                                            ax=gf.next_row())
  
      trading_calendar = factor_data.index.levels[0].freq
      if trading_calendar is None:
          trading_calendar = pd.tseries.offsets.BDay()
          warnings.warn(
              "'freq' not set in factor_data index: assuming business day",
              UserWarning
          )
  
      for p in factor_returns:
  
          plotting.plot_cumulative_returns(
              factor_returns[p],
              period=p,
              freq=trading_calendar,
              ax=gf.next_row()
          )
  
      plt.show()
      gf.close()