Source Code for Module functions.chi2

 1 -class chi2: 
 2 -        def __init__(self): 
 3                  "Function to calculate the chi-squared value." 
 4   
 5   
 6 -        def chi2(self, params, diff_type, diff_params, model, relax_data, errors, print_flag=0): 
 7                  """Function to calculate the chi-squared value. 
 8   
 9                  Function arguments 
10                  ~~~~~~~~~~~~~~~~~~ 
11   
12                  1:  params - a list containing the parameter values specific for the given model. 
13                  The order of parameters must be as follows: 
14                          m1 - {S2} 
15                          m2 - {S2, te} 
16                          m3 - {S2, Rex} 
17                          m4 - {S2, te, Rex} 
18                          m5 - {S2f, S2s, ts} 
19                  2:  diff_type - string.  The diffusion tensor, ie 'iso', 'axial', 'aniso' 
20                  3:  diff_params - array.  An array with the diffusion parameters 
21                  4:  model - string.  The model 
22                  5:  relax_data - array.  An array containing the experimental relaxation values. 
23                  6:  errors - array.  An array containing the experimental errors. 
24   
25   
26                  The chi-sqared equation 
27                  ~~~~~~~~~~~~~~~~~~~~~~~ 
28   
29                  Data structure:  self.data.chi2 
30                  Type:  Double precision floating point value. 
31                  Dependencies:  self.data.ri 
32                  Required by:  None 
33   
34   
35                  Formula 
36                  ~~~~~~~ 
37                          _n_ 
38                          \    (Ri - Ri()) ** 2 
39                  Chi2  =  >   ---------------- 
40                          /__    sigma_i ** 2 
41                          i=1 
42   
43                  where: 
44                          Ri are the values of the measured relaxation data set. 
45                          Ri() are the values of the back calculated relaxation data set. 
46                          sigma_i are the values of the error set. 
47   
48                  Returned is the chi-squared value. 
49                  """ 
50   
51                  # debug. 
52                  if print_flag == 2: 
53                          print "\n< chi2 >" 
54                          print "Params: " + `params` 
55   
56                  self.data.params = params 
57                  self.data.diff_type = diff_type 
58                  self.data.diff_params = diff_params 
59                  self.data.model = model 
60                  self.data.relax_data = relax_data 
61                  self.data.errors = errors 
62   
63                  # Test to see if relaxation values and spectral density values have previously been calculated for the current parameter values, 
64                  # ie, if the gradient or hessian has been called before the function evaluation.  If not, calculate the relaxation values which 
65                  # will then calculate the spectral density values, and store the current data and parameter 
66                  # values in self.data.relax_test. 
67                  test = [ self.data.relax_data[:], self.data.errors[:], self.data.params.tolist(), self.data.model ] 
68                  if test != self.data.relax_test: 
69                          self.init_param_types() 
70                          self.Ri() 
71                          self.data.relax_test = test[:] 
72   
73                  # Calculate the chi-squared statistic. 
74                  self.data.chi2 = 0.0 
75                  for i in range(len(self.data.relax_data)): 
76                          if self.data.errors[i] != 0.0: 
77                                  self.data.chi2 = self.data.chi2 + ((self.data.relax_data[i] - self.data.ri[i]) / self.data.errors[i])**2 
78                          else: 
79                                  self.data.chi2 = 1e99 
80                                  continue 
81   
82                  # debug. 
83                  if print_flag == 2: 
84                          print "J(w): " + `self.data.jw` 
85                          print "Ri: " + `self.data.ri` 
86                          print "chi2: " + `self.data.chi2` 
87                          print "\n" 
88   
89                  return self.data.chi2 
90