Author: bugman
Date: Sun Mar 23 15:05:06 2008
New Revision: 5189
URL: http://svn.gna.org/viewcvs/relax?rev=5189&view=rev
Log:
Converted the model-free linear_constraints() method to the new relax design.
Modified:
1.3/specific_fns/model_free/mf_minimise.py
1.3/specific_fns/model_free/model_free.py
Modified: 1.3/specific_fns/model_free/mf_minimise.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/specific_fns/model_free/mf_minimise.py?rev=5189&r1=5188&r2=5189&view=diff
==============================================================================
--- 1.3/specific_fns/model_free/mf_minimise.py (original)
+++ 1.3/specific_fns/model_free/mf_minimise.py Sun Mar 23 15:05:06 2008
@@ -482,7 +482,7 @@
The number of elements in the array must
equal to the number of
parameters in the model.
@type inc: array of int
- @keyword scaling_matrix: The diagonal scaling matrix.
+ @keyword scaling_matrix: The diagonal, square scaling matrix.
@type scaling_matrix: numpy diagonal matrix
"""
@@ -888,7 +888,7 @@
# Linear constraints.
if constraints:
- A, b = self.linear_constraints(index=index)
+ A, b = self.linear_constraints(num_params,
param_set=param_set, spin=spin, scaling_matrix=scaling_matrix)
# Print out.
if verbosity >= 1:
Modified: 1.3/specific_fns/model_free/model_free.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/specific_fns/model_free/model_free.py?rev=5189&r1=5188&r2=5189&view=diff
==============================================================================
--- 1.3/specific_fns/model_free/model_free.py (original)
+++ 1.3/specific_fns/model_free/model_free.py Sun Mar 23 15:05:06 2008
@@ -1130,25 +1130,25 @@
return True
- def linear_constraints(self, index=None):
- """Function for setting up the model-free linear constraint matrices
A and b.
+ def linear_constraints(self, num_params, param_set=None, spin=None,
spin_id=None, scaling_matrix=None):
+ """Set up the model-free linear constraint matrices A and b.
Standard notation
- ~~~~~~~~~~~~~~~~~
-
- The order parameter constraints are:
+ =================
+
+ The order parameter constraints are::
0 <= S2 <= 1
0 <= S2f <= 1
0 <= S2s <= 1
By substituting the formula S2 = S2f.S2s into the above
inequalities, the additional two
- inequalities can be derived:
+ inequalities can be derived::
S2 <= S2f
S2 <= S2s
- Correlation time constraints are:
+ Correlation time constraints are::
te >= 0
tf >= 0
@@ -1158,7 +1158,7 @@
te, tf, ts <= 2 * tm
- Additional constraints used include:
+ Additional constraints used include::
Rex >= 0
0.9e-10 <= r <= 2e-10
@@ -1166,8 +1166,9 @@
Rearranged notation
- ~~~~~~~~~~~~~~~~~~~
- The above ineqality constraints can be rearranged into:
+ ===================
+
+ The above inequality constraints can be rearranged into::
S2 >= 0
-S2 >= -1
@@ -1189,10 +1190,10 @@
Matrix notation
- ~~~~~~~~~~~~~~~
+ ===============
In the notation A.x >= b, where A is an matrix of coefficients, x is
an array of parameter
- values, and b is a vector of scalars, these inequality constraints
are:
+ values, and b is a vector of scalars, these inequality constraints
are::
| 1 0 0 0 0 0 0 0 0 | | 0 |
| | | |
@@ -1228,7 +1229,22 @@
| | | |
| 0 0 0 0 0 0 0 0 -1 | | 0 |
+
+ @param num_params: The number of parameters in the model.
+ @type num_params: int
+ @keyword param_set: The parameter set, one of 'all', 'diff',
'mf', or 'local_tm'.
+ @type param_set: str
+ @keyword spin: The spin data container. If this
argument is supplied, then the
+ spin_id argument will be ignored.
+ @type spin: SpinContainer instance
+ @keyword spin_id: The spin identification string.
+ @type spin_id: str
+ @keyword scaling_matrix: The diagonal, square scaling matrix.
+ @type scaling_matrix: numpy diagonal matrix
"""
+
+ # Alias the current data pipe.
+ cdp = relax_data_store[relax_data_store.current_pipe]
# Upper limit flag for correlation times.
upper_time_limit = 1
@@ -1236,42 +1252,41 @@
# Initialisation (0..j..m).
A = []
b = []
- n = len(self.param_vector)
- zero_array = zeros(n, float64)
+ zero_array = zeros(num_params, float64)
i = 0
j = 0
# Diffusion tensor parameters.
- if self.param_set != 'mf' and
relax_data_store.diff.has_key(self.run):
+ if param_set != 'mf' and diffusion_tensor.diff_data_exists():
# Spherical diffusion.
- if relax_data_store.diff[self.run].type == 'sphere':
+ if cdp.diff_tensor.type == 'sphere':
# 0 <= tm <= 200 ns.
A.append(zero_array * 0.0)
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(0.0 / self.scaling_matrix[i, i])
- b.append(-200.0 * 1e-9 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
+ b.append(-200.0 * 1e-9 / scaling_matrix[i, i])
i = i + 1
j = j + 2
# Spheroidal diffusion.
- elif relax_data_store.diff[self.run].type == 'spheroid':
+ elif cdp.diff_tensor.type == 'spheroid':
# 0 <= tm <= 200 ns.
A.append(zero_array * 0.0)
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(0.0 / self.scaling_matrix[i, i])
- b.append(-200.0 * 1e-9 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
+ b.append(-200.0 * 1e-9 / scaling_matrix[i, i])
i = i + 1
j = j + 2
# Prolate diffusion, Da >= 0.
- if relax_data_store.diff[self.run].spheroid_type ==
'prolate':
+ if cdp.diff_tensor.spheroid_type == 'prolate':
A.append(zero_array * 0.0)
A[j][i] = 1.0
- b.append(0.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
i = i + 1
j = j + 1
@@ -1279,10 +1294,10 @@
i = i + 2
# Oblate diffusion, Da <= 0.
- elif relax_data_store.diff[self.run].spheroid_type ==
'oblate':
+ elif cdp.diff_tensor.spheroid_type == 'oblate':
A.append(zero_array * 0.0)
A[j][i] = -1.0
- b.append(0.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
i = i + 1
j = j + 1
@@ -1294,21 +1309,21 @@
i = i + 3
# Ellipsoidal diffusion.
- elif relax_data_store.diff[self.run].type == 'ellipsoid':
+ elif cdp.diff_tensor.type == 'ellipsoid':
# 0 <= tm <= 200 ns.
A.append(zero_array * 0.0)
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(0.0 / self.scaling_matrix[i, i])
- b.append(-200.0 * 1e-9 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
+ b.append(-200.0 * 1e-9 / scaling_matrix[i, i])
i = i + 1
j = j + 2
# Da >= 0.
A.append(zero_array * 0.0)
A[j][i] = 1.0
- b.append(0.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
i = i + 1
j = j + 1
@@ -1317,8 +1332,8 @@
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(0.0 / self.scaling_matrix[i, i])
- b.append(-1.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
+ b.append(-1.0 / scaling_matrix[i, i])
i = i + 1
j = j + 2
@@ -1326,55 +1341,57 @@
i = i + 3
# Model-free parameters.
- if self.param_set != 'diff':
- # Loop over all residues.
- for k in xrange(len(relax_data_store.res[self.run])):
- # Skip unselected residues.
- if not relax_data_store.res[self.run][k].select:
- continue
-
- # Only add parameters for a single residue if index has a
value.
- if index != None and k != index:
+ if param_set != 'diff':
+ # The loop.
+ if spin:
+ loop = [spin]
+ else:
+ loop = spin_loop(spin_id)
+
+ # Loop over the spins.
+ for spin in loop:
+ # Skip unselected spins.
+ if not spin.select:
continue
# Save current value of i.
old_i = i
# Loop over the model-free parameters.
- for l in
xrange(len(relax_data_store.res[self.run][k].params)):
+ for l in xrange(len(spin.params)):
# Local tm.
- if relax_data_store.res[self.run][k].params[l] ==
'local_tm':
+ if spin.params[l] == 'local_tm':
if upper_time_limit:
# 0 <= tm <= 200 ns.
A.append(zero_array * 0.0)
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(0.0 / self.scaling_matrix[i, i])
- b.append(-200.0 * 1e-9 / self.scaling_matrix[i,
i])
+ b.append(0.0 / scaling_matrix[i, i])
+ b.append(-200.0 * 1e-9 / scaling_matrix[i, i])
j = j + 2
else:
# 0 <= tm.
A.append(zero_array * 0.0)
A[j][i] = 1.0
- b.append(0.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
j = j + 1
# Order parameters {S2, S2f, S2s}.
- elif match('S2',
relax_data_store.res[self.run][k].params[l]):
+ elif match('S2', spin.params[l]):
# 0 <= S2 <= 1.
A.append(zero_array * 0.0)
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(0.0 / self.scaling_matrix[i, i])
- b.append(-1.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
+ b.append(-1.0 / scaling_matrix[i, i])
j = j + 2
# S2 <= S2f and S2 <= S2s.
- if relax_data_store.res[self.run][k].params[l] ==
'S2':
- for m in
xrange(len(relax_data_store.res[self.run][k].params)):
- if
relax_data_store.res[self.run][k].params[m] == 'S2f' or
relax_data_store.res[self.run][k].params[m] == 'S2s':
+ if spin.params[l] == 'S2':
+ for m in xrange(len(spin.params)):
+ if spin.params[m] == 'S2f' or spin.params[m]
== 'S2s':
A.append(zero_array * 0.0)
A[j][i] = -1.0
A[j][old_i+m] = 1.0
@@ -1382,17 +1399,17 @@
j = j + 1
# Correlation times {te, tf, ts}.
- elif match('t[efs]',
relax_data_store.res[self.run][k].params[l]):
+ elif match('t[efs]', spin.params[l]):
# te, tf, ts >= 0.
A.append(zero_array * 0.0)
A[j][i] = 1.0
- b.append(0.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
j = j + 1
# tf <= ts.
- if relax_data_store.res[self.run][k].params[l] ==
'ts':
- for m in
xrange(len(relax_data_store.res[self.run][k].params)):
- if
relax_data_store.res[self.run][k].params[m] == 'tf':
+ if spin.params[l] == 'ts':
+ for m in xrange(len(spin.params)):
+ if spin.params[m] == 'tf':
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j][old_i+m] = -1.0
@@ -1401,11 +1418,11 @@
# te, tf, ts <= 2 * tm. (tf not needed because tf
<= ts).
if upper_time_limit:
- if not
relax_data_store.res[self.run][k].params[l] == 'tf':
- if self.param_set == 'mf':
+ if not spin.params[l] == 'tf':
+ if param_set == 'mf':
A.append(zero_array * 0.0)
A[j][i] = -1.0
- b.append(-2.0 *
relax_data_store.diff[self.run].tm / self.scaling_matrix[i, i])
+ b.append(-2.0 * cdp.diff_tensor.tm /
scaling_matrix[i, i])
else:
A.append(zero_array * 0.0)
A[j][0] = 2.0
@@ -1415,38 +1432,38 @@
j = j + 1
# Rex.
- elif relax_data_store.res[self.run][k].params[l] ==
'Rex':
+ elif spin.params[l] == 'Rex':
A.append(zero_array * 0.0)
A[j][i] = 1.0
- b.append(0.0 / self.scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
j = j + 1
# Bond length.
- elif relax_data_store.res[self.run][k].params[l] == 'r':
+ elif spin.params[l] == 'r':
# 0.9e-10 <= r <= 2e-10.
A.append(zero_array * 0.0)
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(0.9e-10 / self.scaling_matrix[i, i])
- b.append(-2e-10 / self.scaling_matrix[i, i])
+ b.append(0.9e-10 / scaling_matrix[i, i])
+ b.append(-2e-10 / scaling_matrix[i, i])
j = j + 2
# CSA.
- elif relax_data_store.res[self.run][k].params[l] ==
'CSA':
+ elif spin.params[l] == 'CSA':
# -300e-6 <= CSA <= 0.
A.append(zero_array * 0.0)
A.append(zero_array * 0.0)
A[j][i] = 1.0
A[j+1][i] = -1.0
- b.append(-300e-6 / self.scaling_matrix[i, i])
- b.append(0.0 / self.scaling_matrix[i, i])
+ b.append(-300e-6 / scaling_matrix[i, i])
+ b.append(0.0 / scaling_matrix[i, i])
j = j + 2
# Increment i.
i = i + 1
- # Convert to Numeric data structures.
+ # Convert to numpy data structures.
A = array(A, float64)
b = array(b, float64)
r5189 - in /1.3/specific_fns/model_free: mf_minimise.py model_free.py