old_contents: """The module provides an estimation process given the simulated data set and the initialization
  file."""
  import os
  
  from scipy.optimize import minimize
  import pandas as pd
  
  from development.estimation_auxiliary import minimizing_interface
  from development.estimation_auxiliary import optimizing_target
  from development.estimation_auxiliary import start_values
  from grmpy.read.read import read
  
  
  def estimate(init_file, type):
      """The function estimates the coefficients of the simulated data set."""
      assert os.path.isfile(init_file)
      # Import init file as dictionary
      dict_ = read(init_file)
  
      data_file = dict_['SIMULATION']['source'] + '.grmpy.txt'
  
      assert os.path.isfile(data_file)
  
      # Read data frame
      data = pd.read_table(data_file, delim_whitespace=True, header=0)
  
      # define starting values
      x0 = start_values(dict_, data, type)
      opts = {'maxiter': 100}
  
      opt_rslt = minimize(minimizing_interface, x0, args=(data, dict_), method='BFGS', options=opts)
      x_rslt, fun = opt_rslt['x'], opt_rslt['fun']
      success = opt_rslt['success']
  
      rslt = optimizing_target(x_rslt, dict_)
  
      rslt['fval'], rslt['success'] = fun, success
  
      # Finishing
      return rslt
  
new_contents: """The module provides an estimation process given the simulated data set and the initialization
  file."""
  import os
  
  from scipy.optimize import minimize
  import pandas as pd
  
  from development.estimation_auxiliary import minimizing_interface
  from development.estimation_auxiliary import optimizing_target
  from development.estimation_auxiliary import start_values
  from grmpy.read.read import read
  
  
  def estimate(init_file, type):
      """The function estimates the coefficients of the simulated data set."""
      # Import init file as dictionary
      assert os.path.isfile(init_file)
      dict_ = read(init_file)
  
      data_file = dict_['SIMULATION']['source'] + '.grmpy.txt'
      assert os.path.isfile(data_file)
  
      # Read data frame
      data = pd.read_table(data_file, delim_whitespace=True, header=0)
  
      # define starting values
      x0 = start_values(dict_, data, type)
      opts = {'maxiter': 100}
  
      opt_rslt = minimize(minimizing_interface, x0, args=(data, dict_), method='BFGS', options=opts)
      x_rslt, fun = opt_rslt['x'], opt_rslt['fun']
      success = opt_rslt['success']
  
      rslt = optimizing_target(x_rslt, dict_)
  
      rslt['fval'], rslt['success'] = fun, success
  
      # Finishing
      return rslt
  
subject: Update estimation.py