code: def create_full_tear_sheet(
      factor_data, long_short=True, group_neutral=False, by_group=False
  ):
      """
      Creates a full tear sheet for analysis and evaluating single
      return predicting (alpha) factor.
  
      Parameters
      ----------
      factor_data : pd.DataFrame - MultiIndex
          A MultiIndex DataFrame indexed by date (level 0) and asset (level 1),
          containing the values for a single alpha factor, forward returns for
          each period, the factor quantile/bin that factor value belongs to, and
          (optionally) the group the asset belongs to.
          - See full explanation in utils.get_clean_factor_and_forward_returns
      long_short : bool
          Should this computation happen on a long short portfolio?
          - See tears.create_returns_tear_sheet for details on how this flag
          affects returns analysis
      group_neutral : bool
          Should this computation happen on a group neutral portfolio?
          - See tears.create_returns_tear_sheet for details on how this flag
          affects returns analysis
          - See tears.create_information_tear_sheet for details on how this
          flag affects information analysis
      by_group : bool
          If True, display graphs separately for each group.
      """
  
      plotting.plot_quantile_statistics_table(factor_data)
      create_returns_tear_sheet(
          factor_data,
          long_short,
          group_neutral,
          by_group,
          set_context=False,
      )
      create_information_tear_sheet(
          factor_data, group_neutral, by_group, set_context=False
      )
      create_turnover_tear_sheet(factor_data, set_context=False)
  
file_path: alphalens/alphalens/tears.py
imports: import matplotlib.gridspec
  import matplotlib.pyplot
  import pandas
  import warnings
  from .None import plotting
  from .None import performance
  from .None import utils
label_desc: Use image node_id 5 for calling a global function with example usage: create_full_tear_sheet(factor_data, long_short, group_neutral, by_group) without return types