Source Code for Module specific_analyses.n_state_model.data

  1  ############################################################################### 
  2  #                                                                             # 
  3  # Copyright (C) 2007-2014 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  """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.errors import RelaxError 
 30  from pipe_control.interatomic import interatomic_loop 
 31  from pipe_control.mol_res_spin import return_spin, spin_loop 
 32   
 33   
 34 -def base_data_types(): 
 35      """Determine all the base data types. 
 36   
 37      The base data types can include:: 
 38          - 'rdc', residual dipolar couplings. 
 39          - 'pcs', pseudo-contact shifts. 
 40          - 'noesy', NOE restraints. 
 41          - 'tensor', alignment tensors. 
 42   
 43      @return:    A list of all the base data types. 
 44      @rtype:     list of str 
 45      """ 
 46   
 47      # Array of data types. 
 48      list = [] 
 49   
 50      # RDC search. 
 51      for interatom in interatomic_loop(): 
 52          if hasattr(interatom, 'rdc'): 
 53              list.append('rdc') 
 54              break 
 55   
 56      # PCS search. 
 57      for spin in spin_loop(): 
 58          if hasattr(spin, 'pcs'): 
 59              list.append('pcs') 
 60              break 
 61   
 62      # Alignment tensor search. 
 63      if not ('rdc' in list or 'pcs' in list) and hasattr(cdp, 'align_tensors'): 
 64          list.append('tensor') 
 65   
 66      # NOESY data search. 
 67      if hasattr(cdp, 'noe_restraints'): 
 68          list.append('noesy') 
 69   
 70      # No data is present. 
 71      if not list: 
 72          raise RelaxError("Neither RDC, PCS, NOESY nor alignment tensor data is present.") 
 73   
 74      # Return the list. 
 75      return list 
 76   
 77   
 78 -def calc_ave_dist(atom1, atom2, exp=1): 
 79      """Calculate the average distances. 
 80   
 81      The formula used is:: 
 82   
 83                    _N_ 
 84                / 1 \                  \ 1/exp 
 85          <r> = | -  > |p1i - p2i|^exp | 
 86                \ N /__                / 
 87                     i 
 88   
 89      where i are the members of the ensemble, N is the total number of structural models, and p1 
 90      and p2 at the two atom positions. 
 91   
 92   
 93      @param atom1:   The atom identification string of the first atom. 
 94      @type atom1:    str 
 95      @param atom2:   The atom identification string of the second atom. 
 96      @type atom2:    str 
 97      @keyword exp:   The exponent used for the averaging, e.g. 1 for linear averaging and -6 for 
 98                      r^-6 NOE averaging. 
 99      @type exp:      int 
100      @return:        The average distance between the two atoms. 
101      @rtype:         float 
102      """ 
103   
104      # Get the spin containers. 
105      spin1 = return_spin(atom1) 
106      spin2 = return_spin(atom2) 
107   
108      # Loop over each model. 
109      num_models = len(spin1.pos) 
110      ave_dist = 0.0 
111      for i in range(num_models): 
112          # Distance to the minus sixth power. 
113          dist = norm(spin1.pos[i] - spin2.pos[i]) 
114          ave_dist = ave_dist + dist**(exp) 
115   
116      # Average. 
117      ave_dist = ave_dist / num_models 
118   
119      # The exponent. 
120      ave_dist = ave_dist**(1.0/exp) 
121   
122      # Return the average distance. 
123      return ave_dist 
124   
125   
126 -def num_data_points(): 
127      """Determine the number of data points used in the model. 
128   
129      @return:    The number, n, of data points in the model. 
130      @rtype:     int 
131      """ 
132   
133      # Determine the data type. 
134      data_types = base_data_types() 
135   
136      # Init. 
137      n = 0 
138   
139      # Spin loop. 
140      for spin in spin_loop(): 
141          # Skip deselected spins. 
142          if not spin.select: 
143              continue 
144   
145          # PCS data (skipping array elements set to None). 
146          if 'pcs' in data_types: 
147              if hasattr(spin, 'pcs'): 
148                  for pcs in spin.pcs: 
149                      if isinstance(pcs, float): 
150                          n = n + 1 
151   
152      # Interatomic data loop. 
153      for interatom in interatomic_loop(): 
154          # RDC data (skipping array elements set to None). 
155          if 'rdc' in data_types: 
156              if hasattr(interatom, 'rdc'): 
157                  for rdc in interatom.rdc: 
158                      if isinstance(rdc, float): 
159                          n = n + 1 
160   
161      # Alignment tensors. 
162      if 'tensor' in data_types: 
163          n = n + 5*len(cdp.align_tensors) 
164   
165      # Return the value. 
166      return n 
167   
168   
169 -def tensor_loop(red=False): 
170      """Generator method for looping over the full or reduced tensors. 
171   
172      @keyword red:   A flag which if True causes the reduced tensors to be returned, and if False 
173                      the full tensors are returned. 
174      @type red:      bool 
175      @return:        The tensor index and the tensor. 
176      @rtype:         (int, AlignTensorData instance) 
177      """ 
178   
179      # Number of tensor pairs. 
180      n = len(cdp.align_tensors.reduction) 
181   
182      # Alias. 
183      data = cdp.align_tensors 
184      list = data.reduction 
185   
186      # Full or reduced index. 
187      if red: 
188          index = 1 
189      else: 
190          index = 0 
191   
192      # Loop over the reduction list. 
193      for i in range(n): 
194          yield i, data[list[i][index]] 
195