Source Code for Module functions.ri_prime

  1  from Numeric import Float64, zeros 
  2  from re import match 
  3   
  4 -class Ri_prime: 
  5 -        def __init__(self): 
  6                  "Function for back calculating the transformed relaxation values R1, R2, and sigma_noe." 
  7   
  8   
  9 -        def Ri_prime(self): 
 10                  """Function for back calculation of the transformed relaxation values R1, R2, and sigma_noe. 
 11   
 12                  The transformed relaxation equations 
 13                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
 14   
 15                  Data structure:  self.data.ri_prime 
 16                  Dimension:  1D, (transformed relaxation data) 
 17                  Type:  Numeric array, Float64 
 18                  Dependencies:  self.data.jw 
 19                  Required by:  self.data.ri, self.data.dri, self.data.d2ri 
 20   
 21   
 22                  Formulae 
 23                  ~~~~~~~~ 
 24   
 25                  Components 
 26                  ~~~~~~~~~~ 
 27                                1   / mu0  \ 2  (gH.gN.h_bar)**2 
 28                          d  =  - . | ---- |  . ---------------- 
 29                                4   \ 4.pi /         <r**6> 
 30   
 31                                (wN.csa)**2 
 32                          c  =  ----------- 
 33                                     3 
 34   
 35                          J_R1_d  =  J(wH-wN) + 3J(wN) + 6J(wH+wN) 
 36   
 37                          J_R1_c  =  J(wN) 
 38   
 39                          J_R2_d  =  4J(0) + J(wH-wN) + 3J(wN) + 6J(wH) + 6J(wH+wN) 
 40   
 41                          J_R2_c  =  4J(0) + 3J(wN) 
 42   
 43                          J_sigma_noe  =  6J(wH+wN) - J(wH-wN) 
 44   
 45   
 46                  Relaxation equations 
 47                  ~~~~~~~~~~~~~~~~~~~~ 
 48   
 49                          R1()  =  d . J_R1_d  +  c . J_R1_c 
 50   
 51   
 52                                   d              c 
 53                          R2()  =  - . J_R2_d  +  - . J_R2_c  +  Rex 
 54                                   2              6 
 55   
 56   
 57                          sigma_noe()  =  d . J_sigma_noe 
 58   
 59                  """ 
 60   
 61                  # Calculate the spectral density values. 
 62                  self.Jw() 
 63   
 64                  # Initialise the components of the transformed relaxation equations. 
 65                  self.data.dip_comps = zeros((self.mf.data.num_ri), Float64) 
 66                  self.data.j_dip_comps = zeros((self.mf.data.num_ri), Float64) 
 67                  self.data.csa_comps = zeros((self.mf.data.num_ri), Float64) 
 68                  self.data.j_csa_comps = zeros((self.mf.data.num_ri), Float64) 
 69                  if match('m[34]', self.data.model): 
 70                          self.data.rex_comps = zeros((self.mf.data.num_ri), Float64) 
 71   
 72                  # Calculate the components of the transformed relaxation equations. 
 73                  for i in range(self.mf.data.num_ri): 
 74                          frq_num = self.mf.data.remap_table[i] 
 75   
 76                          # R1 components. 
 77                          if self.mf.data.data_types[i]  == 'R1': 
 78                                  self.data.dip_comps[i] = self.mf.data.dipole_const 
 79                                  self.data.j_dip_comps[i] = self.data.jw[frq_num, 2] + 3.0*self.data.jw[frq_num, 1] + 6.0*self.data.jw[frq_num, 4] 
 80                                  self.data.csa_comps[i] = self.mf.data.csa_const[frq_num] 
 81                                  self.data.j_csa_comps[i] = self.data.jw[frq_num, 1] 
 82   
 83                          # R2 components. 
 84                          elif self.mf.data.data_types[i] == 'R2': 
 85                                  self.data.dip_comps[i] = self.mf.data.dipole_const / 2.0 
 86                                  self.data.j_dip_comps[i] = 4.0*self.data.jw[frq_num, 0] + self.data.jw[frq_num, 2] + 3.0*self.data.jw[frq_num, 1] + 6.0*self.data.jw[frq_num, 3] + 6.0*self.data.jw[frq_num, 4] 
 87                                  self.data.csa_comps[i] = self.mf.data.csa_const[frq_num] / 6.0 
 88                                  self.data.j_csa_comps[i] = 4.0*self.data.jw[frq_num, 0] + 3.0*self.data.jw[frq_num, 1] 
 89                                  if self.data.model == 'm3': 
 90                                          self.data.rex_comps[i] = self.data.params[1] * (1e-8 * self.mf.data.frq[frq_num])**2 
 91                                  elif self.data.model == 'm4': 
 92                                          self.data.rex_comps[i] = self.data.params[2] * (1e-8 * self.mf.data.frq[frq_num])**2 
 93   
 94                          # sigma_noe components. 
 95                          elif self.mf.data.data_types[i] == 'NOE': 
 96                                  self.data.dip_comps[i] = self.mf.data.dipole_const 
 97                                  self.data.j_dip_comps[i] = 6.0*self.data.jw[frq_num, 4] - self.data.jw[frq_num, 2] 
 98   
 99                  # Initialise the transformed relaxation values. 
100                  self.data.ri_prime = zeros((self.mf.data.num_ri), Float64) 
101   
102                  # Calculate the transformed relaxation values. 
103                  if match('m[34]', self.data.model): 
104                          self.data.ri_prime = self.data.dip_comps * self.data.j_dip_comps + self.data.csa_comps * self.data.j_csa_comps + self.data.rex_comps 
105                  else: 
106                          self.data.ri_prime = self.data.dip_comps * self.data.j_dip_comps + self.data.csa_comps * self.data.j_csa_comps 
107