Author: bugman
Date: Sun Mar 23 13:31:02 2008
New Revision: 5178
URL: http://svn.gna.org/viewcvs/relax?rev=5178&view=rev
Log:
Updated the model-free assemble_scaling_matrix() 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=5178&r1=5177&r2=5178&view=diff
==============================================================================
--- 1.3/specific_fns/model_free/mf_minimise.py (original)
+++ 1.3/specific_fns/model_free/mf_minimise.py Sun Mar 23 13:31:02 2008
@@ -856,7 +856,7 @@
param_vector = self.assemble_param_vector(spin=spin,
sim_index=sim_index)
# Diagonal scaling.
- scaling_matrix = self.assemble_scaling_matrix(index=index,
scaling=scaling)
+ scaling_matrix = self.assemble_scaling_matrix(spin=spin,
scaling=scaling)
if scaling_matrix:
param_vector = dot(inv(scaling_matrix), param_vector)
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=5178&r1=5177&r2=5178&view=diff
==============================================================================
--- 1.3/specific_fns/model_free/model_free.py (original)
+++ 1.3/specific_fns/model_free/model_free.py Sun Mar 23 13:31:02 2008
@@ -317,19 +317,31 @@
return array(param_vector, float64)
- def assemble_scaling_matrix(self, index=None, scaling=1):
- """Function for creating the scaling matrix."""
+ def assemble_scaling_matrix(self, num_params=None, spin=None,
spin_id=None, scaling=True):
+ """Create and return the scaling matrix.
+
+ If the spin argument is supplied, then the spin_id argument will be
ignored.
+
+ @keyword num_params: The number of parameters in the model.
+ @type num_params: int
+ @keyword spin: The spin data container.
+ @type spin: SpinContainer instance
+ @keyword spin_id: The spin identification string.
+ @type spin_id: str
+ @return: An array of the parameter values of the
model-free model.
+ @rtype: numpy diagonal matrix
+ """
# Initialise.
- if len(self.param_vector) == 0:
- self.scaling_matrix = zeros((0, 0), float64)
+ if num_params == 0:
+ scaling_matrix = zeros((0, 0), float64)
else:
- self.scaling_matrix = identity(len(self.param_vector), float64)
+ scaling_matrix = identity(num_params, float64)
i = 0
- # No diagonal scaling.
+ # No diagonal scaling, so return the identity matrix.
if not scaling:
- return
+ return scaling_matrix
# tm, te, tf, and ts (must all be the same for diagonal scaling!).
ti_scaling = 1e-12
@@ -337,69 +349,74 @@
# Diffusion tensor parameters.
if self.param_set == 'diff' or self.param_set == 'all':
# Spherical diffusion.
- if relax_data_store.diff[self.run].type == 'sphere':
+ if cdp.diff_tensor.type == 'sphere':
# tm.
- self.scaling_matrix[i, i] = ti_scaling
+ scaling_matrix[i, i] = ti_scaling
# Increment i.
i = i + 1
# Spheroidal diffusion.
- elif relax_data_store.diff[self.run].type == 'spheroid':
+ elif cdp.diff_tensor.type == 'spheroid':
# tm, Da, theta, phi
- self.scaling_matrix[i, i] = ti_scaling
- self.scaling_matrix[i+1, i+1] = 1e7
- self.scaling_matrix[i+2, i+2] = 1.0
- self.scaling_matrix[i+3, i+3] = 1.0
+ scaling_matrix[i, i] = ti_scaling
+ scaling_matrix[i+1, i+1] = 1e7
+ scaling_matrix[i+2, i+2] = 1.0
+ scaling_matrix[i+3, i+3] = 1.0
# Increment i.
i = i + 4
# Ellipsoidal diffusion.
- elif relax_data_store.diff[self.run].type == 'ellipsoid':
+ elif cdp.diff_tensor.type == 'ellipsoid':
# tm, Da, Dr, alpha, beta, gamma.
- self.scaling_matrix[i, i] = ti_scaling
- self.scaling_matrix[i+1, i+1] = 1e7
- self.scaling_matrix[i+2, i+2] = 1.0
- self.scaling_matrix[i+3, i+3] = 1.0
- self.scaling_matrix[i+4, i+4] = 1.0
- self.scaling_matrix[i+5, i+5] = 1.0
+ scaling_matrix[i, i] = ti_scaling
+ scaling_matrix[i+1, i+1] = 1e7
+ scaling_matrix[i+2, i+2] = 1.0
+ scaling_matrix[i+3, i+3] = 1.0
+ scaling_matrix[i+4, i+4] = 1.0
+ scaling_matrix[i+5, i+5] = 1.0
# Increment i.
i = i + 6
# Model-free parameters.
if self.param_set != 'diff':
- # Loop over all residues.
- for j in xrange(len(relax_data_store.res[self.run])):
- # Skip unselected residues.
- if not relax_data_store.res[self.run][j].select:
+ # 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
- # Only add parameters for a single residue if index has a
value.
- if index != None and j != index:
- continue
-
# Loop over the model-free parameters.
- for k in
xrange(len(relax_data_store.res[self.run][j].params)):
+ for k in xrange(len(spin.params)):
# Local tm, te, tf, and ts (must all be the same for
diagonal scaling!).
- if relax_data_store.res[self.run][j].params[k] ==
'local_tm' or search('^t', relax_data_store.res[self.run][j].params[k]):
- self.scaling_matrix[i, i] = ti_scaling
+ if spin.params[k] == 'local_tm' or search('^t',
spin.params[k]):
+ scaling_matrix[i, i] = ti_scaling
# Rex.
- elif relax_data_store.res[self.run][j].params[k] ==
'Rex':
- self.scaling_matrix[i, i] = 1.0 / (2.0 * pi *
relax_data_store.res[self.run][j].frq[0]) ** 2
+ elif spin.params[k] == 'Rex':
+ scaling_matrix[i, i] = 1.0 / (2.0 * pi *
spin.frq[0]) ** 2
# Bond length.
- elif relax_data_store.res[self.run][j].params[k] == 'r':
- self.scaling_matrix[i, i] = 1e-10
+ elif spin.params[k] == 'r':
+ scaling_matrix[i, i] = 1e-10
# CSA.
- elif relax_data_store.res[self.run][j].params[k] ==
'CSA':
- self.scaling_matrix[i, i] = 1e-4
+ elif spin.params[k] == 'CSA':
+ scaling_matrix[i, i] = 1e-4
# Increment i.
i = i + 1
+
+ # Return the scaling matrix.
+ return scaling_matrix
def create_mc_data(self, run, i):
r5178 - in /1.3/specific_fns/model_free: mf_minimise.py model_free.py