Author: bugman
Date: Mon Aug 11 16:01:02 2008
New Revision: 7150
URL: http://svn.gna.org/viewcvs/relax?rev=7150&view=rev
Log:
Fixes for the PCS constant, as it is dependent on the indices i, j and c.
These are:
i, the experiment or alignment index,
j, the spin index,
c, the state index.
Modified:
branches/rdc_analysis/maths_fns/n_state_model.py
Modified: branches/rdc_analysis/maths_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/maths_fns/n_state_model.py?rev=7150&r1=7149&r2=7150&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/n_state_model.py (original)
+++ branches/rdc_analysis/maths_fns/n_state_model.py Mon Aug 11 16:01:02 2008
@@ -437,13 +437,13 @@
The PCS equation is::
_N_
- \ T
- delta_ij(theta) = > pc . djc . mu_jc . Ai . mu_jc,
+ \ T
+ delta_ij(theta) = > pc . dijc . mu_jc . Ai . mu_jc,
/__
c=1
where:
- - djc is the PCS constant for spin j and state c,
+ - djci is the PCS constant for spin j, state c and experiment or
alignment i,
- N is the total number of states or structures,
- pc is the weight or probability associated with state c,
- mu_jc is the unit vector corresponding to spin j and state c,
@@ -451,16 +451,17 @@
The PCS constant is defined as::
- mu0 15kT 1
- dj = --- ----- ---- ,
- 4pi Bo**2 r**3
+ mu0 15kT 1
+ dijc = --- ----- ---- ,
+ 4pi Bo**2 r**3
where:
- mu0 is the permeability of free space,
- k is Boltzmann's constant,
- - T is the absolute temperature,
- - Bo is the magnetic field strength,
- - r is the distance between the paramagnetic centre (electron
spin) and the nuclear spin.
+ - T is the absolute temperature (different for each experiment),
+ - Bo is the magnetic field strength (different for each
experiment),
+ - r is the distance between the paramagnetic centre (electron
spin) and the nuclear spin
+ (different for each spin and state).
Stored data structures
@@ -521,7 +522,7 @@
# The back calculated PCS.
if self.pcs_flag:
# Calculate the average PCS.
- self.deltaij_theta[i, j] =
ave_pcs_tensor(self.pcs_const[j], self.mu[j], self.N, self.A[i],
weights=self.probs)
+ self.deltaij_theta[i, j] =
ave_pcs_tensor(self.pcs_const[i, j], self.mu[j], self.N, self.A[i],
weights=self.probs)
# Replace missing data with the back calculated value
(to give a zero chi-squared for the missing element).
if self.missing_deltaij[i, j]:
@@ -638,8 +639,8 @@
The population parameter partial derivative is::
- ddeltaij(theta) T
- --------------- = djc . mu_jc . Ai . mu_jc,
+ ddeltaij(theta) T
+ --------------- = dijc . mu_jc . Ai . mu_jc,
dpc
where:
@@ -746,11 +747,11 @@
# PCS.
if self.pcs_flag:
- self.ddeltaij_theta[i*5, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.dip_const[j], self.mu[j], self.N,
self.dA[0], weights=self.probs)
- self.ddeltaij_theta[i*5+1, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.dip_const[j], self.mu[j], self.N,
self.dA[1], weights=self.probs)
- self.ddeltaij_theta[i*5+2, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.dip_const[j], self.mu[j], self.N,
self.dA[2], weights=self.probs)
- self.ddeltaij_theta[i*5+3, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.dip_const[j], self.mu[j], self.N,
self.dA[3], weights=self.probs)
- self.ddeltaij_theta[i*5+4, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.dip_const[j], self.mu[j], self.N,
self.dA[4], weights=self.probs)
+ self.ddeltaij_theta[i*5, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.pcs_const[i, j], self.mu[j], self.N,
self.dA[0], weights=self.probs)
+ self.ddeltaij_theta[i*5+1, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.pcs_const[i, j], self.mu[j], self.N,
self.dA[1], weights=self.probs)
+ self.ddeltaij_theta[i*5+2, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.pcs_const[i, j], self.mu[j], self.N,
self.dA[2], weights=self.probs)
+ self.ddeltaij_theta[i*5+3, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.pcs_const[i, j], self.mu[j], self.N,
self.dA[3], weights=self.probs)
+ self.ddeltaij_theta[i*5+4, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.pcs_const[i, j], self.mu[j], self.N,
self.dA[4], weights=self.probs)
# Construct the pc partial derivative gradient components,
looping over each state.
for c in xrange(self.N - 1):
r7150 - /branches/rdc_analysis/maths_fns/n_state_model.py