old_contents: """
  *******************************************************
  *
  *  get_Threshold - EXTRACT THRESHOLD FROM FITTED PARAMETERS
  *  
  *  License:      Apache 2.0
  *  Written by:   Michael Slugocki
  *  Created on:   April 18, 2018
  *  Last updated: September 12, 2018
  *
  *******************************************************
  """
  
  #################################################################
  #  IMPORT MODULES
  #################################################################
  import numpy as np
  from .psyFunction import psyfunction as _psyfunction
  
  
  #################################################################
  #  EXTRACT THRESHOLD FROM FITTED PARAMETERS
  #################################################################
  def get_threshold(data, options, metrics, threshold_pc, estimate_type = 'MAP'):
      """Check options provided ny user, or assigned by defaults
      are valid arguments.
      
      Keyword arguments:
      data -- m x 3 numpy array
      metrics -- contain important metrics about fitted model (dictionary)
      options -- contains all options used to fit model (dictionary)
      threshold_pc -- proportion correct to define threshold (float)
      estimate_type -- determines which metric to use for parameters of model (string)
      """
      # Extract threshold at specified level of propertion correct
      # using numerical approximation 
      def _find_nearest(y, x, value):
          idx = (np.abs(y-value)).argmin()
          return x[idx]
      
      x_est = np.linspace(data[:, 0].min(), data[:, 0].max(), 1e4)
      
      if estimate_type not in ('MAP', 'Mean'):
          raise ValueError('Please provide appropriate argument for estimate type (e.g., MAP, Mean)')
          
      # Determine which values to use for vector of parameters 
      param_guess = np.zeros(4)
      counter = 0
      for keys in options['param_free']:
          if keys is True:
              param_guess[counter] = metrics[estimate_type][counter]
          elif keys is False: 
              param_guess[counter] = options['param_ests'][counter]
          counter += 1
      
      y_pred = _psyfunction(x_est, 
                            param_guess[0],
                            param_guess[1],
                            param_guess[2],
                            param_guess[3],
                            options['sigmoid_type'])
      
      threshold = _find_nearest(y_pred, x_est, threshold_pc)
          
      return threshold
  
new_contents: """
  *******************************************************
  *
  *  get_Threshold - EXTRACT THRESHOLD FROM FITTED PARAMETERS
  *  
  *  License:      Apache 2.0
  *  Written by:   Michael Slugocki
  *  Created on:   April 18, 2018
  *  Last updated: September 12, 2018
  *
  *******************************************************
  """
  
  #################################################################
  #  IMPORT MODULES
  #################################################################
  import numpy as np
  from .psyFunction import psyfunction as _psyfunction
  
  
  #################################################################
  #  EXTRACT THRESHOLD FROM FITTED PARAMETERS
  #################################################################
  def get_threshold(data, options, metrics, threshold_pc, estimate_type = 'MAP'):
      """Check options provided ny user, or assigned by defaults
      are valid arguments.
      
      Keyword arguments:
      data -- m x 3 numpy array
      metrics -- contain important metrics about fitted model (dictionary)
      options -- contains all options used to fit model (dictionary)
      threshold_pc -- proportion correct to define threshold (float)
      estimate_type -- determines which metric to use for parameters of model (string)
      """
      # Extract threshold at specified level of propertion correct
      # using numerical approximation 
      def _find_nearest(y, x, value):
          idx = (np.abs(y-value)).argmin()
          return x[idx]
      
      x_est = np.linspace(data[:, 0].min(), data[:, 0].max(), int(1e4))
      
      if estimate_type not in ('MAP', 'Mean'):
          raise ValueError('Please provide appropriate argument for estimate type (e.g., MAP, Mean)')
          
      # Determine which values to use for vector of parameters 
      param_guess = np.zeros(4)
      counter = 0
      for keys in options['param_free']:
          if keys is True:
              param_guess[counter] = metrics[estimate_type][counter]
          elif keys is False: 
              param_guess[counter] = options['param_ests'][counter]
          counter += 1
      
      y_pred = _psyfunction(x_est, 
                            param_guess[0],
                            param_guess[1],
                            param_guess[2],
                            param_guess[3],
                            options['sigmoid_type'])
      
      threshold = _find_nearest(y_pred, x_est, threshold_pc)
          
      return threshold
  
subject: Fix type error due to float use in `np.linspace()`.