Author: bugman
Date: Tue Aug 12 11:45:44 2008
New Revision: 7168
URL: http://svn.gna.org/viewcvs/relax?rev=7168&view=rev
Log:
Big debugging of the N-state model setup, target, and gradient functions.
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=7168&r1=7167&r2=7168&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/n_state_model.py (original)
+++ branches/rdc_analysis/maths_fns/n_state_model.py Tue Aug 12 11:45:44 2008
@@ -257,7 +257,11 @@
for i in xrange(self.num_align):
for j in xrange(self.num_spins):
if isNaN(self.Dij[i, j]):
+ # Set the flag.
self.missing_Dij[i, j] = 1
+
+ # Change the NaN to zero.
+ self.Dij[i, j] = 0.0
# Missing data matrices (PCS).
if self.pcs_flag:
@@ -265,7 +269,11 @@
for i in xrange(self.num_align):
for j in xrange(self.num_spins):
if isNaN(self.deltaij[i, j]):
+ # Set the flag.
self.missing_deltaij[i, j] = 1
+
+ # Change the NaN to zero.
+ self.deltaij[i, j] = 0.0
# PCS function, gradient, and Hessian matrices.
self.deltaij_theta = zeros((self.num_align, self.num_spins),
float64)
@@ -523,20 +531,22 @@
# 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.dip_vect[j], self.N, self.A[i], weights=self.probs)
+ if not self.missing_Dij[i, j]:
+ 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]:
- self.Dij[i, j] = self.Dij_theta[i, j]
+ #if self.missing_Dij[i, j]:
+ # self.Dij[i, j] = self.Dij_theta[i, j]
# 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.pcs_vect[j], self.N, self.A[i],
weights=self.probs)
+ if not self.missing_deltaij[i, j]:
+ 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]:
- self.deltaij[i, j] = self.deltaij_theta[i, j]
+ #if self.missing_deltaij[i, j]:
+ # self.deltaij[i, j] = self.deltaij_theta[i, j]
# Calculate and sum the single alignment chi-squared value (for
the RDC).
if self.rdc_flag:
@@ -748,7 +758,7 @@
# Construct the Amn partial derivative components.
for j in xrange(self.num_spins):
# RDC.
- if self.rdc_flag:
+ if self.rdc_flag and not self.missing_Dij[i, j]:
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)
@@ -756,7 +766,7 @@
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:
+ if self.pcs_flag and not self.missing_deltaij[i, j]:
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)
@@ -771,12 +781,12 @@
# Loop over the spins.
for j in xrange(self.num_spins):
# Calculate the RDC for state c (this is the pc partial
derivative).
- if self.rdc_flag:
+ if self.rdc_flag and not self.missing_Dij[i, j]:
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.pcs_vect[j, c], self.A[i])
+ if self.pcs_flag and not self.missing_deltaij[i, j]:
+ self.ddeltaij_theta[param_index, i, j] =
pcs_tensor(self.pcs_const[i, j, c], 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):
r7168 - /branches/rdc_analysis/maths_fns/n_state_model.py