code: def pure_profit_score(close: Series) -> Tuple[float, int]:
      """Pure Profit Score of a series.
  
      Args:
          close (pd.Series): Series of 'close's
  
      >>> result = ta.pure_profit_score(df.close)
      """
      close = verify_series(close)
      close_index = Series(0, index=close.reset_index().index)
  
      r = linear_regression(close_index, close)["r"]
      if r is not npNaN:
          return r * cagr(close)
      return 0
  
imports: from typing import Tuple
  from numpy import log
  from numpy import nan
  from numpy import sqrt
  from pandas import Series, Timedelta
  from ._core import verify_series
  from ._time import total_time
  from ._math import linear_regression, log_geometric_mean
  from pandas_ta import RATE
  from pandas_ta.performance import drawdown, log_return, percent_return
label_desc: Use image node_id 8 for calling a global function with example usage: pure_profit_score(close) and returns: int, unknown