Source Code for Module pipe_control.statistics

  1  ############################################################################### 
  2  #                                                                             # 
  3  # Copyright (C) 2015 Edward d'Auvergne                                        # 
  4  #                                                                             # 
  5  # This file is part of the program relax (http://www.nmr-relax.com).          # 
  6  #                                                                             # 
  7  # This program is free software: you can redistribute it and/or modify        # 
  8  # it under the terms of the GNU General Public License as published by        # 
  9  # the Free Software Foundation, either version 3 of the License, or           # 
 10  # (at your option) any later version.                                         # 
 11  #                                                                             # 
 12  # This program is distributed in the hope that it will be useful,             # 
 13  # but WITHOUT ANY WARRANTY; without even the implied warranty of              # 
 14  # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               # 
 15  # GNU General Public License for more details.                                # 
 16  #                                                                             # 
 17  # You should have received a copy of the GNU General Public License           # 
 18  # along with this program.  If not, see <http://www.gnu.org/licenses/>.       # 
 19  #                                                                             # 
 20  ############################################################################### 
 21   
 22  # Module docstring. 
 23  """Module for handling statistics.""" 
 24   
 25  # Python module imports. 
 26  import sys 
 27   
 28  # relax module imports. 
 29  from lib.io import write_data 
 30  from pipe_control.pipes import check_pipe 
 31  from specific_analyses.api import return_api 
 32   
 33   
 34 -def aic(): 
 35      """Calculate and store Akaike's Information Criterion (AIC) for each model.""" 
 36   
 37      # Checks. 
 38      check_pipe() 
 39   
 40      # The specific analysis API object. 
 41      api = return_api() 
 42   
 43      # Calculate the chi2. 
 44      print("Calculating the chi-squared value for the current parameter values.") 
 45      api.calculate() 
 46   
 47      # Loop over the base models. 
 48      print("\nStoring the model statistics.") 
 49      for model_info in api.model_loop(): 
 50          # Title printout. 
 51          api.print_model_title(model_info=model_info) 
 52   
 53          # Get the model statistics. 
 54          k, n, chi2 = api.model_statistics(model_info=model_info) 
 55   
 56          # Calculate the AIC value. 
 57          aic = chi2 + 2.0*k 
 58   
 59          # The model container. 
 60          container = api.get_model_container(model_info=model_info) 
 61   
 62          # Store the statistics. 
 63          container.chi2 = chi2 
 64          container.num_params = k 
 65          container.aic = aic 
 66   
 67          # Statistics printout. 
 68          data = [ 
 69              ["Chi-squared value:", "%20f" % chi2], 
 70              ["Number of parameters (k):", "%20i" % k], 
 71              ["Akaike's Information Criterion (AIC):", "%20f" % aic] 
 72          ] 
 73          write_data(out=sys.stdout, data=data) 
 74           
 75           
 76 -def model_statistics(): 
 77      """Calculate and store the model statistics.""" 
 78   
 79      # Checks. 
 80      check_pipe() 
 81   
 82      # The specific analysis API object. 
 83      api = return_api() 
 84   
 85      # Calculate the chi2. 
 86      print("Calculating the chi-squared value for the current parameter values.") 
 87      api.calculate() 
 88   
 89      # Loop over the base models. 
 90      print("\nStoring the model statistics.") 
 91      for model_info in api.model_loop(): 
 92          # Title printout. 
 93          api.print_model_title(model_info=model_info) 
 94   
 95          # Get the model statistics. 
 96          k, n, chi2 = api.model_statistics(model_info=model_info) 
 97   
 98          # The model container. 
 99          container = api.get_model_container(model_info=model_info) 
100   
101          # Store the values. 
102          container.chi2 = chi2 
103          container.num_params = k 
104          container.num_data_points = n 
105   
106          # Statistics printout. 
107          data = [ 
108              ['Chi-squared value:', "%20f" % chi2], 
109              ['Number of parameters (k):', "%20i" % k], 
110              ['Number of data points (n):', "%20i" % n] 
111          ] 
112          write_data(out=sys.stdout, data=data) 
113