Author: bugman
Date: Mon Dec 6 19:16:39 2010
New Revision: 11706
URL: http://svn.gna.org/viewcvs/relax?rev=11706&view=rev
Log:
Bug fix for a number of missing ':' characters, and comment improvements.
Modified:
branches/cst/maths_fns/mf.py
Modified: branches/cst/maths_fns/mf.py
URL:
http://svn.gna.org/viewcvs/relax/branches/cst/maths_fns/mf.py?rev=11706&r1=11705&r2=11706&view=diff
==============================================================================
--- branches/cst/maths_fns/mf.py (original)
+++ branches/cst/maths_fns/mf.py Mon Dec 6 19:16:39 2010
@@ -259,8 +259,7 @@
# The ratio of gyromagnetic ratios.
g_ratio = gh[i] / gx[i]
-
- # loop over interactions
+ # Loop over the relaxation interactions.
for j in xrange(self.num_interactions[i]):
self.data[i].append(Data())
self.data[i][j].interactions=interactions[i][j]
@@ -412,7 +411,8 @@
if self.scaling_flag:
params = dot(params, self.scaling_matrix)
- for j in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for j in xrange(self.num_interactions[0]):
# Direction cosine calculations.
if self.diff_data.calc_di:
self.diff_data.calc_di(data[j], self.diff_data)
@@ -467,7 +467,8 @@
# Diffusion tensor parameters.
self.diff_data.params = params[0:1]
- for j in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for j in xrange(self.num_interactions[0]):
# Diffusion tensor weight calculations.
self.diff_data.calc_ci(data[j], self.diff_data)
@@ -527,7 +528,9 @@
for i in xrange(self.num_spins):
# Set self.data[i] to data.
data = self.data[i]
- for j in xrange(self.num_interactions[i])
+
+ # Loop over the relaxation interactions.
+ for j in xrange(self.num_interactions[i]):
# Direction cosine calculations.
if self.diff_data.calc_di:
self.diff_data.calc_di(data[j], self.diff_data)
@@ -594,7 +597,9 @@
for i in xrange(self.num_spins):
# Set self.data[i] to data.
data = self.data[i]
- for j in xrange(self.num_interactions[i])
+
+ # Loop over the relaxation interactions.
+ for j in xrange(self.num_interactions[i]):
# Direction cosine calculations.
if self.diff_data.calc_di:
self.diff_data.calc_di(data[j], self.diff_data)
@@ -657,16 +662,16 @@
if self.scaling_flag:
params = dot(params, self.scaling_matrix)
- # Loop over the interactions
- for k in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for k in xrange(self.num_interactions[0]):
# Calculate the spectral density gradient components.
if data[k].calc_djw_comps:
data[k].calc_djw_comps(data[k], params)
# Loop over the gradient.
for j in xrange(data.total_num_params):
- # Loop over the interactions
- for k in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for k in xrange(self.num_interactions[0]):
# Calculate the spectral density gradients.
if data[k].calc_djw[j]:
data[k].djw = data[k].calc_djw[j](data[k], params, j)
@@ -719,8 +724,8 @@
# Diffusion tensor parameters.
self.diff_data.params = params[0:1]
- # Loop over the interactions
- for k in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for k in xrange(self.num_interactions[0]):
# Calculate the spectral density gradient components.
if data.calc_djw_comps:
data.calc_djw_comps(data[k], params)
@@ -730,8 +735,8 @@
# Loop over the gradient.
for j in xrange(data.total_num_params):
- # Loop over the interactions
- for k in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for k in xrange(self.num_interactions[0]):
# Calculate the spectral density gradients.
if data[k].calc_djw[j]:
data[k].djw = data[k].calc_djw[j](data[k], params, j)
@@ -802,16 +807,16 @@
# Diffusion tensor correlation times.
self.diff_data.calc_dti(data, self.diff_data)
- # Loop over the interactions
- for k in xrange(self.num_interactions[i])
+ # Loop over the relaxation interactions.
+ for k in xrange(self.num_interactions[i]):
# Calculate the spectral density gradient components.
if data[k].calc_djw_comps:
data[k].calc_djw_comps(data[k], data[k].param_values)
# Loop over the gradient.
for j in xrange(data.total_num_params):
- # Loop over the interactions
- for k in xrange(self.num_interactions[i])
+ # Loop over the relaxation interactions.
+ for k in xrange(self.num_interactions[i]):
# Calculate the spectral density gradients.
if data[k].calc_djw[j]:
data[k].djw = data[k].calc_djw[j](data[k],
data[k].param_values, j)
@@ -893,8 +898,8 @@
# Loop over the gradient.
for j in xrange(data.total_num_params):
- # Loop over the interactions
- for k in xrange(self.num_interactions[i])
+ # Loop over the relaxation interactions.
+ for k in xrange(self.num_interactions[i]):
# Calculate the spectral density gradients.
if data[k].calc_djw[j]:
data[k].djw = data[k].calc_djw[j](data[k], params, j)
@@ -953,8 +958,8 @@
# Loop over the lower triangle of the Hessian.
for j in xrange(data.total_num_params):
for k in xrange(j + 1):
- # Loop over the interactions
- for m in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for m in xrange(self.num_interactions[0]):
# Calculate the spectral density Hessians.
if data[m].calc_d2jw[j][k]:
data[m].d2jw = data[m].calc_d2jw[j][k](data[m],
params, j, k)
@@ -1008,8 +1013,8 @@
# Loop over the lower triangle of the Hessian.
for j in xrange(data.total_num_params):
for k in xrange(j + 1):
- # Loop over the interactions
- for m in xrange(self.num_interactions[0])
+ # Loop over the relaxation interactions.
+ for m in xrange(self.num_interactions[0]):
# Calculate the spectral density Hessians.
if data[m].calc_d2jw[j][k]:
data[m].d2jw = data[m].calc_d2jw[j][k](data[m],
params, j, k)
@@ -1081,8 +1086,8 @@
# Loop over the lower triangle of the Hessian.
for j in xrange(data.total_num_params):
for k in xrange(j + 1):
- # Loop over the interactions
- for m in xrange(self.num_interactions[i])
+ # Loop over the relaxation interactions.
+ for m in xrange(self.num_interactions[i]):
# Calculate the spectral density Hessians.
if data[m].calc_d2jw[j][k]:
data[m].d2jw = data[m].calc_d2jw[j][k](data[m],
data[m].param_values, j, k)
@@ -1157,8 +1162,8 @@
# Loop over the lower triangle of the Hessian.
for j in xrange(data.total_num_params):
for k in xrange(j + 1):
- # Loop over the interactions
- for m in xrange(self.num_interactions[i])
+ # Loop over the relaxation interactions.
+ for m in xrange(self.num_interactions[i]):
# Calculate the spectral density Hessians.
if data[m].calc_d2jw[j][k]:
data[m].d2jw = data[m].calc_d2jw[j][k](data[m],
params, j, k)
r11706 - /branches/cst/maths_fns/mf.py