Source Code for Module relax_fit

 1  # Relaxation curve fitting. 
 2   
 3  import sys 
 4   
 5  from common_ops import common_ops 
 6   
 7   
 8 -class relax_fit(common_ops): 
 9 -        def __init__(self): 
10                  "Relaxation curve fitting." 
11   
12                  raise NameError, "End of code." 
13   
14   
15 -        def curvefit_input(self): 
16                  # Loop through each time point. 
17                  for spectra in range(len(self.usr_param.input_info)): 
18                          print '\nProcessing data for time point ' + self.xmf.r1_data.input[i][0] + ' sec' 
19                          for j in range(len(self.xmf.r1_data.input[i])):     # Go through the three columns of the input file. 
20                                  if j == 0:           # The time column. 
21                                          self.xmf.r1_data.times.append(self.xmf.r1_data.input[i][0])     # Add the time to the 'times' array. 
22                                          self.xmf.r1_data.input_data[i][0] = self.xmf.r1_data.input[i][0] 
23                                          continue     # Go to the next column. 
24                                  file = self.xmf.r1_data.input[i][j] 
25                                  self.xmf.r1_data.input_data[i][j] = self.xmf.file_ops.extract_data(file) 
26                                  equal_size(len(self.xmf.r1_data.input_data[0][1]),len(self.xmf.r1_data.input_data[i][j]))     # Test if both are the same length. 
27                                  if j == 2:           # The duplicate spectra column. 
28                                          diff_list = []     # List of peak intensity differences for backbone peaks. 
29                                  bb_no = 0            # Number of backbone peaks. 
30                                  indole_no = 0        # Number of tryptophane indole peaks. 
31                                  for k in range(len(self.xmf.r1_data.input_data[i][j])):     # Go through the lines of each spectra. 
32                                          if non_residue(self.xmf.r1_data.input_data[i][j][k]): 
33                                                  continue     # Skip all non-residue lines. 
34                                          test_res_no(self.xmf.r1_data.input_data[0][1][k][0],self.xmf.r1_data.input_data[i][j][k][0])     # Test if residue numbers match. 
35                                          if backbone(self.xmf.r1_data.input_data[i][j][k][4]):     # Backbone peak. 
36                                                  if unresolved_peak(self.xmf.r1_data.input_data[i][j][k][5],'test/R1/unresolved'): 
37                                                          continue     # Skip unresolved peaks. 
38                                                  self.xmf.r1_data.bb_intense = create_2Dint(i, j, k, self.xmf.r1_data.input_data, self.xmf.r1_data.bb_intense, bb_no) 
39                                                  if j == 2:     # Duplicate spectra data set. 
40                                                          diff_val = float(self.xmf.r1_data.input_data[i][1][k][10]) - float(self.xmf.r1_data.input_data[i][2][k][10]) 
41                                                          diff_list.append(diff_val) 
42                                                  bb_no = bb_no + 1 
43                                          elif indole(self.xmf.r1_data.input_data[i][j][k][4]):     # Tryptophane indole peak. 
44                                                  if unresolved_peak(self.xmf.r1_data.input_data[i][j][k][5],'R1/indole_unresolved'): 
45                                                          continue     # Skip unresolved peaks. 
46                                                  self.xmf.r1_data.indole_intense = create_2Dint(i, j, k, self.xmf.r1_data.input_data, self.xmf.r1_data.indole_intense, indole_no) 
47                                                  indole_no = indole_no + 1 
48                                  if j == 2: 
49                                          self.xmf.r1_data.diff.append(diff_list) 
50                  self.xmf.r1_data.bb_intense.sort() 
51                  self.xmf.r1_data.indole_intense.sort() 
52                  sumSd = 0     # The sum of all the standard deviations. 
53                  for set in range(len(self.xmf.r1_data.diff)): 
54                          sd = stand_dev(self.xmf.r1_data.diff[set]) 
55                          print '\nThe standard deviation is: ' + `sd` 
56                          sumSd = sumSd + sd 
57                  aveSd = sumSd / len(self.xmf.r1_data.diff) 
58                  print '\nThe average standard deviation is: ' + `aveSd` 
59   
60                  print '\nCreating grace files\n' 
61                  self.xmf.grace.intensity('R1_backbone', self.xmf.r1_data.bb_intense, self.xmf.r1_data.times, aveSd) 
62                  self.xmf.grace.intensity('R1_trp_indole', self.xmf.r1_data.indole_intense, self.xmf.r1_data.times, aveSd) 
63                  rx_log('r1.log', self.xmf.r1_data.times, self.xmf.r1_data.bb_intense, self.xmf.r1_data.indole_intense, self.xmf.r1_data.diff, aveSd) 
64   
65 -        def test_res_no(self, first_no, second_no): 
66                  if first_no != second_no: 
67                          text = 'Residues don\'t line up\n' 
68                          print str(first_no) + ' != ' + str(second_no) 
69                          while 1: 
70                                  pass 
71