Source Code for Module specific_analyses.n_state_model.data

  1  ############################################################################### 
  2  #                                                                             # 
  3  # Copyright (C) 2007-2013 Edward d'Auvergne                                   # 
  4  #                                                                             # 
  5  # This file is part of the program relax (http://www.nmr-relax.com).          # 
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 20  ############################################################################### 
 21   
 22  # Module docstring. 
 23  """The N-state model or structural ensemble analysis base data handling.""" 
 24   
 25  # Python module imports. 
 26  from numpy.linalg import norm 
 27   
 28  # relax module imports. 
 29  from lib.check_types import is_float 
 30  from lib.errors import RelaxError 
 31  from pipe_control.interatomic import interatomic_loop 
 32  from pipe_control.mol_res_spin import return_spin, spin_loop 
 33   
 34   
 35 -def base_data_types(): 
 36      """Determine all the base data types. 
 37   
 38      The base data types can include:: 
 39          - 'rdc', residual dipolar couplings. 
 40          - 'pcs', pseudo-contact shifts. 
 41          - 'noesy', NOE restraints. 
 42          - 'tensor', alignment tensors. 
 43   
 44      @return:    A list of all the base data types. 
 45      @rtype:     list of str 
 46      """ 
 47   
 48      # Array of data types. 
 49      list = [] 
 50   
 51      # RDC search. 
 52      for interatom in interatomic_loop(): 
 53          if hasattr(interatom, 'rdc'): 
 54              list.append('rdc') 
 55              break 
 56   
 57      # PCS search. 
 58      for spin in spin_loop(): 
 59          if hasattr(spin, 'pcs'): 
 60              list.append('pcs') 
 61              break 
 62   
 63      # Alignment tensor search. 
 64      if not ('rdc' in list or 'pcs' in list) and hasattr(cdp, 'align_tensors'): 
 65          list.append('tensor') 
 66   
 67      # NOESY data search. 
 68      if hasattr(cdp, 'noe_restraints'): 
 69          list.append('noesy') 
 70   
 71      # No data is present. 
 72      if not list: 
 73          raise RelaxError("Neither RDC, PCS, NOESY nor alignment tensor data is present.") 
 74   
 75      # Return the list. 
 76      return list 
 77   
 78   
 79 -def calc_ave_dist(atom1, atom2, exp=1): 
 80      r"""Calculate the average distances. 
 81   
 82      The formula used is:: 
 83   
 84                    _N_ 
 85                / 1 \                  \ 1/exp 
 86          <r> = | -  > |p1i - p2i|^exp | 
 87                \ N /__                / 
 88                     i 
 89   
 90      where i are the members of the ensemble, N is the total number of structural models, and p1 
 91      and p2 at the two atom positions. 
 92   
 93   
 94      @param atom1:   The atom identification string of the first atom. 
 95      @type atom1:    str 
 96      @param atom2:   The atom identification string of the second atom. 
 97      @type atom2:    str 
 98      @keyword exp:   The exponent used for the averaging, e.g. 1 for linear averaging and -6 for 
 99                      r^-6 NOE averaging. 
100      @type exp:      int 
101      @return:        The average distance between the two atoms. 
102      @rtype:         float 
103      """ 
104   
105      # Get the spin containers. 
106      spin1 = return_spin(spin_id=atom1) 
107      spin2 = return_spin(spin_id=atom2) 
108   
109      # Loop over each model. 
110      num_models = len(spin1.pos) 
111      ave_dist = 0.0 
112      for i in range(num_models): 
113          # Distance to the minus sixth power. 
114          dist = norm(spin1.pos[i] - spin2.pos[i]) 
115          ave_dist = ave_dist + dist**(exp) 
116   
117      # Average. 
118      ave_dist = ave_dist / num_models 
119   
120      # The exponent. 
121      ave_dist = ave_dist**(1.0/exp) 
122   
123      # Return the average distance. 
124      return ave_dist 
125   
126   
127 -def num_data_points(): 
128      """Determine the number of data points used in the model. 
129   
130      @return:    The number, n, of data points in the model. 
131      @rtype:     int 
132      """ 
133   
134      # Determine the data type. 
135      data_types = base_data_types() 
136   
137      # Init. 
138      n = 0 
139   
140      # Spin loop. 
141      for spin in spin_loop(skip_desel=True): 
142          # PCS data (skipping array elements set to None). 
143          if 'pcs' in data_types: 
144              if hasattr(spin, 'pcs'): 
145                  for id in spin.pcs: 
146                      if is_float(spin.pcs[id]): 
147                          n += 1 
148   
149      # Interatomic data loop. 
150      for interatom in interatomic_loop(skip_desel=True): 
151          # RDC data (skipping array elements set to None). 
152          if 'rdc' in data_types: 
153              if hasattr(interatom, 'rdc'): 
154                  for id in interatom.rdc: 
155                      if is_float(interatom.rdc[id]): 
156                          n += 1 
157   
158      # Alignment tensors. 
159      if 'tensor' in data_types: 
160          n += 5*len(cdp.align_tensors) 
161   
162      # Return the value. 
163      return n 
164   
165   
166 -def tensor_loop(red=False): 
167      """Generator method for looping over the full or reduced tensors. 
168   
169      @keyword red:   A flag which if True causes the reduced tensors to be returned, and if False 
170                      the full tensors are returned. 
171      @type red:      bool 
172      @return:        The tensor index and the tensor. 
173      @rtype:         (int, AlignTensorData instance) 
174      """ 
175   
176      # Number of tensor pairs. 
177      n = len(cdp.align_tensors.reduction) 
178   
179      # Alias. 
180      data = cdp.align_tensors 
181      list = data.reduction 
182   
183      # Full or reduced index. 
184      if red: 
185          index = 1 
186      else: 
187          index = 0 
188   
189      # Loop over the reduction list. 
190      for i in range(n): 
191          yield i, data[list[i][index]] 
192