Author: bugman
Date: Tue Aug 12 10:58:28 2008
New Revision: 7167
URL: http://svn.gna.org/viewcvs/relax?rev=7167&view=rev
Log:
Bug fix for the target function.
The RDC vector and PCS vectors are not the same!
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=7167&r1=7166&r2=7167&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/n_state_model.py (original)
+++ branches/rdc_analysis/maths_fns/n_state_model.py Tue Aug 12 10:58:28 2008
@@ -147,7 +147,8 @@
self.params = 1.0 * init_params # Force a copy of the data to be
stored.
self.deltaij = pcs
self.Dij = rdcs
- self.mu = xh_vect
+ self.dip_vect = xh_vect
+ self.pcs_vect = pcs_vect
self.pcs_const = pcs_const
self.dip_const = dip_const
self.total_num_params = len(init_params)
@@ -522,7 +523,7 @@
# The back calculated RDC.
if self.rdc_flag:
# Calculate the average RDC.
- self.Dij_theta[i, j] = ave_rdc_tensor(self.dip_const[j],
self.mu[j], self.N, self.A[i], weights=self.probs)
+ self.Dij_theta[i, j] = ave_rdc_tensor(self.dip_const[j],
self.dip_vect[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_Dij[i, j]:
@@ -531,7 +532,7 @@
# The back calculated PCS.
if self.pcs_flag:
# Calculate the average PCS.
- self.deltaij_theta[i, j] =
ave_pcs_tensor(self.pcs_const[i, 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.pcs_vect[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]:
@@ -748,19 +749,19 @@
for j in xrange(self.num_spins):
# RDC.
if self.rdc_flag:
- self.dDij_theta[i*5, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.mu[j], self.N, self.dA[0],
weights=self.probs)
- self.dDij_theta[i*5+1, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.mu[j], self.N, self.dA[1],
weights=self.probs)
- self.dDij_theta[i*5+2, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.mu[j], self.N, self.dA[2],
weights=self.probs)
- self.dDij_theta[i*5+3, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.mu[j], self.N, self.dA[3],
weights=self.probs)
- self.dDij_theta[i*5+4, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.mu[j], self.N, self.dA[4],
weights=self.probs)
+ self.dDij_theta[i*5, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.dip_vect[j], self.N,
self.dA[0], weights=self.probs)
+ self.dDij_theta[i*5+1, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.dip_vect[j], self.N,
self.dA[1], weights=self.probs)
+ self.dDij_theta[i*5+2, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.dip_vect[j], self.N,
self.dA[2], weights=self.probs)
+ self.dDij_theta[i*5+3, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.dip_vect[j], self.N,
self.dA[3], weights=self.probs)
+ self.dDij_theta[i*5+4, i, j] =
ave_rdc_tensor_dDij_dAmn(self.dip_const[j], self.dip_vect[j], self.N,
self.dA[4], weights=self.probs)
# PCS.
if self.pcs_flag:
- 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)
+ self.ddeltaij_theta[i*5, i, j] =
ave_pcs_tensor_ddeltaij_dAmn(self.pcs_const[i, j], self.pcs_vect[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.pcs_vect[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.pcs_vect[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.pcs_vect[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.pcs_vect[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):
@@ -771,11 +772,11 @@
for j in xrange(self.num_spins):
# Calculate the RDC for state c (this is the pc partial
derivative).
if self.rdc_flag:
- self.dDij_theta[param_index, i, j] =
rdc_tensor(self.dip_const[j], self.mu[j, c], self.A[i])
+ self.dDij_theta[param_index, i, j] =
rdc_tensor(self.dip_const[j], self.dip_vect[j, c], self.A[i])
# Calculate the PCS for state c (this is the pc partial
derivative).
if self.pcs_flag:
- self.ddeltaij_theta[param_index, i, j] =
pcs_tensor(self.dip_const[j], self.mu[j, c], self.A[i])
+ self.ddeltaij_theta[param_index, i, j] =
pcs_tensor(self.dip_const[j], self.pcs_vect[j, c], self.A[i])
# Construct the chi-squared gradient element for parameter k,
alignment i.
for k in xrange(self.total_num_params):
r7167 - /branches/rdc_analysis/maths_fns/n_state_model.py