original_docstring: 
      Initialises the population with a user specified population.
      Parameters
      ----------
      init : np.ndarray
          Array specifying subset of the initial population. The array should
          have shape (M, len(x)), where len(x) is the number of parameters.
          The population is clipped to the lower and upper `bounds`.
      
docstring: Initialises the population with a user specified population.
  Parameters
  
  init : np.ndarray
  Array specifying subset of the initial population. The array should
  have shape (M, len(x)), where len(x) is the number of parameters.
  The population is clipped to the lower and upper `bounds`.
docstring_tokens: Initialises the population with a user specified population . Parameters init : np . ndarray Array specifying subset of the initial population . The array should have shape ( M len ( x )) where len ( x ) is the number of parameters . The population is clipped to the lower and upper ` bounds ` .
code: def init_population_array(self, init):
      """
      Initialises the population with a user specified population.
      Parameters
      ----------
      init : np.ndarray
          Array specifying subset of the initial population. The array should
          have shape (M, len(x)), where len(x) is the number of parameters.
          The population is clipped to the lower and upper `bounds`.
      """
      # make sure you're using a float array
      popn = np.asfarray(init)
  
      if (
          np.size(
              popn,
              0) < 5 or popn.shape[1] != self.parameter_count or len(
                  popn.shape) != 2):
        raise ValueError(
            "The population supplied needs to have shape (M, len(x)), where M > 4.")
  
      # scale values and clip to bounds, assigning to population
      self.population = np.clip(self._unscale_parameters(popn), 0, 1)
  
      self.num_population_members = np.size(self.population, 0)
  
      self.population_shape = (self.num_population_members,
                               self.parameter_count)
  
      # reset population energies
      self.population_energies = (np.ones(self.num_population_members) *
                                  np.inf)
  
      # reset number of function evaluations counter
      self._nfev = 0
code_tokens: def init_population_array ( self , init ) : popn = np . asfarray ( init ) if ( np . size ( popn , 0 ) < 5 or popn . shape [ 1 ] != self . parameter_count or len ( popn . shape ) != 2 ) : raise ValueError ( "The population supplied needs to have shape (M, len(x)), where M > 4." ) self . population = np . clip ( self . _unscale_parameters ( popn ) , 0 , 1 ) self . num_population_members = np . size ( self . population , 0 ) self . population_shape = ( self . num_population_members , self . parameter_count ) self . population_energies = ( np . ones ( self . num_population_members ) * np . inf ) self . _nfev = 0
short_docstring: Initialises the population with a user specified population.
short_docstring_tokens: Initialises the population with a user specified population .
comment: """
      Initialises the population with a user specified population.
      Parameters
      ----------
      init : np.ndarray
          Array specifying subset of the initial population. The array should
          have shape (M, len(x)), where len(x) is the number of parameters.
          The population is clipped to the lower and upper `bounds`.
      """$SEP$# make sure you're using a float array$SEP$# scale values and clip to bounds, assigning to population$SEP$# reset population energies$SEP$# reset number of function evaluations counter