Author: bugman
Date: Thu Aug 7 16:53:06 2008
New Revision: 7083
URL: http://svn.gna.org/viewcvs/relax?rev=7083&view=rev
Log:
Modified the dfunc_population() gradient function to handle both RDCs and
PCSs.
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=7083&r1=7082&r2=7083&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/n_state_model.py (original)
+++ branches/rdc_analysis/maths_fns/n_state_model.py Thu Aug 7 16:53:06 2008
@@ -516,10 +516,10 @@
where:
- theta is the parameter vector,
- - Dij are the measured RDCs,
- - Dij(theta) are the back calculated RDCs,
- - sigma_ij are the RDC errors,
- - dDij(theta)/dthetak is the RDC gradient for parameter k.
+ - Dij are the measured RDCs or PCSs,
+ - Dij(theta) are the back calculated RDCs or PCSs,
+ - sigma_ij are the RDC or PCS errors,
+ - dDij(theta)/dthetak is the RDC or PCS gradient for parameter k.
The RDC gradient
@@ -560,6 +560,44 @@
Amn.
+ The PCS gradient
+ ----------------
+
+ This gradient is also different for the various parameter types.
+
+ pc partial derivative
+ ~~~~~~~~~~~~~~~~~~~~~
+
+ The population parameter partial derivative is::
+
+ ddeltaij(theta) T
+ --------------- = djc . mu_jc . Ai . mu_jc,
+ dpc
+
+ where:
+ - djc is the pseudocontact shift constant for spin j and state c,
+ - mu_jc is the unit vector corresponding to spin j and state c,
+ - Ai is the alignment tensor.
+
+ Amn partial derivative
+ ~~~~~~~~~~~~~~~~~~~~~~
+
+ The alignment tensor element partial derivative is::
+
+ _N_
+ ddelta_ij(theta) \ T dAi
+ ---------------- = > pc . djc . mu_jc . ---- . mu_jc,
+ dAmn /__ dAmn
+ c=1
+
+ where:
+ - djc is the pseudocontact shift constant for spin j and state c,
+ - pc is the weight or probability associated with state c,
+ - mu_jc is the unit vector corresponding to spin j and state c,
+ - dAi/dAmn is the partial derivative of the alignment tensor
with respect to element
+ Amn.
+
+
The alignment tensor gradient
-----------------------------
@@ -593,13 +631,18 @@
======================
There are a number of data structures calculated by this function
and stored for subsequent
- use in the Hessian function. This include the back calculated RDC
gradient and the
+ use in the Hessian function. This include the back calculated RDC
and PCS gradients and the
alignment tensor gradients.
dDij(theta)/dthetak
-------------------
The back calculated RDC gradient. This is a rank-3 tensor with
indices {k, i, j}.
+
+ ddeltaij(theta)/dthetak
+ -------------------
+
+ The back calculated PCS gradient. This is a rank-3 tensor with
indices {k, i, j}.
dAi/dAmn
--------
@@ -623,15 +666,25 @@
# Loop over each alignment.
for i in xrange(self.num_align):
- # Construct the Amn partial derivative part of the RDC gradient.
+ # Construct the Amn partial derivative components.
for j in xrange(self.num_spins):
- 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)
-
- # Construct the pc partial derivative part of the RDC gradient,
looping over each state.
+ # 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)
+
+ # 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)
+
+ # Construct the pc partial derivative gradient components,
looping over each state.
for c in xrange(self.N - 1):
# Index in the parameter array.
param_index = self.num_align_params + c
@@ -639,17 +692,28 @@
# Loop over the spins.
for j in xrange(self.num_spins):
# Calculate the RDC for state c (this is the pc partial
derivative).
- self.dDij_theta[param_index, i, j] =
rdc_tensor(self.dip_const[j], self.mu[j, c], self.A[i])
+ if self.rdc_flag:
+ self.dDij_theta[param_index, i, j] =
rdc_tensor(self.dip_const[j], self.mu[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])
# Construct the chi-squared gradient element for parameter k,
alignment i.
for k in xrange(self.total_num_params):
- self.dchi2[k] = self.dchi2[k] + dchi2_element(self.Dij[i],
self.Dij_theta[i], self.dDij_theta[k, i], self.rdc_sigma_ij[i])
+ # RDC part of the chi-squared gradient.
+ if self.rdc_flag:
+ self.dchi2[k] = self.dchi2[k] +
dchi2_element(self.Dij[i], self.Dij_theta[i], self.dDij_theta[k, i],
self.rdc_sigma_ij[i])
+
+ # PCS part of the chi-squared gradient.
+ if self.pcs_flag:
+ self.dchi2[k] = self.dchi2[k] +
dchi2_element(self.deltaij[i], self.deltaij_theta[i], self.ddeltaij_theta[k,
i], self.pcs_sigma_ij[i])
# Diagonal scaling.
if self.scaling_flag:
self.dchi2 = dot(self.dchi2, self.scaling_matrix)
- # Return a copy of the gradient.
+ # The gradient.
return self.dchi2
r7083 - /branches/rdc_analysis/maths_fns/n_state_model.py