specific_analyses.n_state

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). # 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 # Package docstring. 23 """Module for handling base data of the N-state model or structural ensemble analysis.""" 24 25 # Python module imports. 26 from math import pi, sqrt 27 from numpy.linalg import norm 28 from warnings import warn 29 30 # relax module imports. 31 from lib.check_types import is_float 32 from lib.errors import RelaxError, RelaxNoValueError, RelaxSpinTypeError 33 from lib.physical_constants import dipolar_constant, g1H, return_gyromagnetic_ratio 34 from lib.warnings import RelaxWarning 35 from pipe_control import align_tensor 36 from pipe_control.interatomic import interatomic_loop, return_interatom 37 from pipe_control.pcs import opt_uses_pcs 38 from pipe_control.rdc import opt_uses_rdc 39 from pipe_control.mol_res_spin import is_pseudoatom, return_spin, spin_loop 40 41

42 -def base_data_types():

43 """Determine all the base data types. 44 45 The base data types can include:: 46 - 'rdc', residual dipolar couplings. 47 - 'pcs', pseudo-contact shifts. 48 - 'noesy', NOE restraints. 49 - 'tensor', alignment tensors. 50 51 @return: A list of all the base data types. 52 @rtype: list of str 53 """ 54 55 # Array of data types. 56 list = [] 57 58 # RDC search. 59 for interatom in interatomic_loop(): 60 if hasattr(interatom, 'rdc'): 61 list.append('rdc') 62 break 63 64 # PCS search. 65 for spin in spin_loop(): 66 if hasattr(spin, 'pcs'): 67 list.append('pcs') 68 break 69 70 # Alignment tensor search. 71 if not ('rdc' in list or 'pcs' in list) and hasattr(cdp, 'align_tensors'): 72 list.append('tensor') 73 74 # NOESY data search. 75 if hasattr(cdp, 'noe_restraints'): 76 list.append('noesy') 77 78 # No data is present. 79 if not list: 80 raise RelaxError("Neither RDC, PCS, NOESY nor alignment tensor data is present.") 81 82 # Return the list. 83 return list

84 85

86 -def calc_ave_dist(atom1, atom2, exp=1):

87 """Calculate the average distances. 88 89 The formula used is:: 90 91 _N_ 92 / 1 \ \ 1/exp 93 <r> = | - > |p1i - p2i|^exp | 94 \ N /__ / 95 i 96 97 where i are the members of the ensemble, N is the total number of structural models, and p1 98 and p2 at the two atom positions. 99 100 101 @param atom1: The atom identification string of the first atom. 102 @type atom1: str 103 @param atom2: The atom identification string of the second atom. 104 @type atom2: str 105 @keyword exp: The exponent used for the averaging, e.g. 1 for linear averaging and -6 for 106 r^-6 NOE averaging. 107 @type exp: int 108 @return: The average distance between the two atoms. 109 @rtype: float 110 """ 111 112 # Get the spin containers. 113 spin1 = return_spin(atom1) 114 spin2 = return_spin(atom2) 115 116 # Loop over each model. 117 num_models = len(spin1.pos) 118 ave_dist = 0.0 119 for i in range(num_models): 120 # Distance to the minus sixth power. 121 dist = norm(spin1.pos[i] - spin2.pos[i]) 122 ave_dist = ave_dist + dist**(exp) 123 124 # Average. 125 ave_dist = ave_dist / num_models 126 127 # The exponent. 128 ave_dist = ave_dist**(1.0/exp) 129 130 # Return the average distance. 131 return ave_dist

132 133

134 -def num_data_points():

135 """Determine the number of data points used in the model. 136 137 @return: The number, n, of data points in the model. 138 @rtype: int 139 """ 140 141 # Determine the data type. 142 data_types = base_data_types() 143 144 # Init. 145 n = 0 146 147 # Spin loop. 148 for spin in spin_loop(): 149 # Skip deselected spins. 150 if not spin.select: 151 continue 152 153 # PCS data (skipping array elements set to None). 154 if 'pcs' in data_types: 155 if hasattr(spin, 'pcs'): 156 for pcs in spin.pcs: 157 if isinstance(pcs, float): 158 n = n + 1 159 160 # Interatomic data loop. 161 for interatom in interatomic_loop(): 162 # RDC data (skipping array elements set to None). 163 if 'rdc' in data_types: 164 if hasattr(interatom, 'rdc'): 165 for rdc in interatom.rdc: 166 if isinstance(rdc, float): 167 n = n + 1 168 169 # Alignment tensors. 170 if 'tensor' in data_types: 171 n = n + 5*len(cdp.align_tensors) 172 173 # Return the value. 174 return n

175 176

177 -def tensor_loop(red=False):

178 """Generator method for looping over the full or reduced tensors. 179 180 @keyword red: A flag which if True causes the reduced tensors to be returned, and if False 181 the full tensors are returned. 182 @type red: bool 183 @return: The tensor index and the tensor. 184 @rtype: (int, AlignTensorData instance) 185 """ 186 187 # Number of tensor pairs. 188 n = len(cdp.align_tensors.reduction) 189 190 # Alias. 191 data = cdp.align_tensors 192 list = data.reduction 193 194 # Full or reduced index. 195 if red: 196 index = 1 197 else: 198 index = 0 199 200 # Loop over the reduction list. 201 for i in range(n): 202 yield i, data[list[i][index]]

203

Source Code for Module specific_analyses.n_state_model.data