Author: bugman
Date: Sun Jan 13 20:02:17 2008
New Revision: 4693
URL: http://svn.gna.org/viewcvs/relax?rev=4693&view=rev
Log:
Converted the assemble_param_vector() model-free method to the new relax
design.
Modified:
1.3/specific_fns/model_free/model_free.py
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=4693&r1=4692&r2=4693&view=diff
==============================================================================
--- 1.3/specific_fns/model_free/model_free.py (original)
+++ 1.3/specific_fns/model_free/model_free.py Sun Jan 13 20:02:17 2008
@@ -150,167 +150,161 @@
param_names = param_names + spin.params
- def assemble_param_vector(self, index=None, sim_index=None,
param_set=None):
- """Function for assembling various pieces of data into a Numeric
parameter array."""
+ def assemble_param_vector(self, model_type, spin_id=None,
sim_index=None):
+ """Assemble the model-free parameter vector (as numpy array).
+
+ @param model_type: The model-free model type. This must be one of
'mf', 'local_tm',
+ 'diff', or 'all'.
+ @type model_type: str
+ @param spin_id: The spin identification string.
+ @type spin_id: str
+ @return: An array of the parameter values of the
model-free model.
+ @rtype: numpy array
+ """
# Initialise.
param_vector = []
- if param_set == None:
- param_set = self.param_set
-
- # Monte Carlo diffusion tensor parameters.
- if sim_index != None and (param_set == 'diff' or param_set == 'all'):
- # Spherical diffusion.
- if relax_data_store.diff[self.run].type == 'sphere':
-
param_vector.append(relax_data_store.diff[self.run].tm_sim[sim_index])
-
- # Spheroidal diffusion.
- elif relax_data_store.diff[self.run].type == 'spheroid':
-
param_vector.append(relax_data_store.diff[self.run].tm_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].Da_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].theta_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].phi_sim[sim_index])
-
- # Ellipsoidal diffusion.
- elif relax_data_store.diff[self.run].type == 'ellipsoid':
-
param_vector.append(relax_data_store.diff[self.run].tm_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].Da_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].Dr_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].alpha_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].beta_sim[sim_index])
-
param_vector.append(relax_data_store.diff[self.run].gamma_sim[sim_index])
+
+ # Alias the current data pipe.
+ cdp = relax_data_store[relax_data_store.current_pipe]
# Diffusion tensor parameters.
- elif param_set == 'diff' or param_set == 'all':
- # Spherical diffusion.
- if relax_data_store.diff[self.run].type == 'sphere':
- param_vector.append(relax_data_store.diff[self.run].tm)
-
- # Spheroidal diffusion.
- elif relax_data_store.diff[self.run].type == 'spheroid':
- param_vector.append(relax_data_store.diff[self.run].tm)
- param_vector.append(relax_data_store.diff[self.run].Da)
- param_vector.append(relax_data_store.diff[self.run].theta)
- param_vector.append(relax_data_store.diff[self.run].phi)
-
- # Ellipsoidal diffusion.
- elif relax_data_store.diff[self.run].type == 'ellipsoid':
- param_vector.append(relax_data_store.diff[self.run].tm)
- param_vector.append(relax_data_store.diff[self.run].Da)
- param_vector.append(relax_data_store.diff[self.run].Dr)
- param_vector.append(relax_data_store.diff[self.run].alpha)
- param_vector.append(relax_data_store.diff[self.run].beta)
- param_vector.append(relax_data_store.diff[self.run].gamma)
+ if model_type == 'diff' or model_type == 'all':
+ # Normal parameters.
+ if sim_index == None:
+ # Spherical diffusion.
+ if cdp.diff.type == 'sphere':
+ param_vector.append(cdp.diff.tm)
+
+ # Spheroidal diffusion.
+ elif cdp.diff.type == 'spheroid':
+ param_vector.append(cdp.diff.tm)
+ param_vector.append(cdp.diff.Da)
+ param_vector.append(cdp.diff.theta)
+ param_vector.append(cdp.diff.phi)
+
+ # Ellipsoidal diffusion.
+ elif cdp.diff.type == 'ellipsoid':
+ param_vector.append(cdp.diff.tm)
+ param_vector.append(cdp.diff.Da)
+ param_vector.append(cdp.diff.Dr)
+ param_vector.append(cdp.diff.alpha)
+ param_vector.append(cdp.diff.beta)
+ param_vector.append(cdp.diff.gamma)
+
+ # Monte Carlo diffusion tensor parameters.
+ else:
+ # Spherical diffusion.
+ if cdp.diff.type == 'sphere':
+ param_vector.append(cdp.diff.tm_sim[sim_index])
+
+ # Spheroidal diffusion.
+ elif cdp.diff.type == 'spheroid':
+ param_vector.append(cdp.diff.tm_sim[sim_index])
+ param_vector.append(cdp.diff.Da_sim[sim_index])
+ param_vector.append(cdp.diff.theta_sim[sim_index])
+ param_vector.append(cdp.diff.phi_sim[sim_index])
+
+ # Ellipsoidal diffusion.
+ elif cdp.diff.type == 'ellipsoid':
+ param_vector.append(cdp.diff.tm_sim[sim_index])
+ param_vector.append(cdp.diff.Da_sim[sim_index])
+ param_vector.append(cdp.diff.Dr_sim[sim_index])
+ param_vector.append(cdp.diff.alpha_sim[sim_index])
+ param_vector.append(cdp.diff.beta_sim[sim_index])
+ param_vector.append(cdp.diff.gamma_sim[sim_index])
# Model-free parameters (residue specific parameters).
- if param_set != 'diff':
- for i in xrange(len(relax_data_store.res[self.run])):
+ if model_type != 'diff':
+ # Loop over the spins.
+ for spin in spin_loop(spin_id):
# Skip unselected residues.
- if not relax_data_store.res[self.run][i].select:
+ if not spin.select:
continue
- # Only add parameters for a single residue if index has a
value.
- if (param_set == 'mf' or param_set == 'local_tm') and index
!= None and i != index:
- continue
-
# Loop over the model-free parameters.
- for j in
xrange(len(relax_data_store.res[self.run][i].params)):
+ for i in xrange(len(spin.params)):
# local tm.
- if relax_data_store.res[self.run][i].params[j] ==
'local_tm':
- if relax_data_store.res[self.run][i].local_tm ==
None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].local_tm_sim[sim_index])
+ if spin.params[i] == 'local_tm':
+ if sim_index == None:
+ param_vector.append(spin.local_tm)
else:
-
param_vector.append(relax_data_store.res[self.run][i].local_tm)
+ param_vector.append(spin.local_tm_sim[sim_index])
# S2.
- elif relax_data_store.res[self.run][i].params[j] == 'S2':
- if relax_data_store.res[self.run][i].s2 == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].s2_sim[sim_index])
+ elif spin.params[i] == 'S2':
+ if sim_index == None:
+ param_vector.append(spin.s2)
else:
-
param_vector.append(relax_data_store.res[self.run][i].s2)
+ param_vector.append(spin.s2_sim[sim_index])
# S2f.
- elif relax_data_store.res[self.run][i].params[j] ==
'S2f':
- if relax_data_store.res[self.run][i].s2f == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].s2f_sim[sim_index])
+ elif spin.params[i] == 'S2f':
+ if sim_index == None:
+ param_vector.append(spin.s2f)
else:
-
param_vector.append(relax_data_store.res[self.run][i].s2f)
+ param_vector.append(spin.s2f_sim[sim_index])
# S2s.
- elif relax_data_store.res[self.run][i].params[j] ==
'S2s':
- if relax_data_store.res[self.run][i].s2s == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].s2s_sim[sim_index])
+ elif spin.params[i] == 'S2s':
+ if sim_index == None:
+ param_vector.append(spin.s2s)
else:
-
param_vector.append(relax_data_store.res[self.run][i].s2s)
+ param_vector.append(spin.s2s_sim[sim_index])
# te.
- elif relax_data_store.res[self.run][i].params[j] == 'te':
- if relax_data_store.res[self.run][i].te == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].te_sim[sim_index])
+ elif spin.params[i] == 'te':
+ if sim_index == None:
+ param_vector.append(spin.te)
else:
-
param_vector.append(relax_data_store.res[self.run][i].te)
+ param_vector.append(spin.te_sim[sim_index])
# tf.
- elif relax_data_store.res[self.run][i].params[j] == 'tf':
- if relax_data_store.res[self.run][i].tf == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].tf_sim[sim_index])
+ elif spin.params[i] == 'tf':
+ if sim_index == None:
+ param_vector.append(spin.tf)
else:
-
param_vector.append(relax_data_store.res[self.run][i].tf)
+ param_vector.append(spin.tf_sim[sim_index])
# ts.
- elif relax_data_store.res[self.run][i].params[j] == 'ts':
- if relax_data_store.res[self.run][i].ts == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].ts_sim[sim_index])
+ elif spin.params[i] == 'ts':
+ if sim_index == None:
+ param_vector.append(spin.ts)
else:
-
param_vector.append(relax_data_store.res[self.run][i].ts)
+ param_vector.append(spin.ts_sim[sim_index])
# Rex.
- elif relax_data_store.res[self.run][i].params[j] ==
'Rex':
- if relax_data_store.res[self.run][i].rex == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].rex_sim[sim_index])
+ elif spin.params[i] == 'Rex':
+ if sim_index == None:
+ param_vector.append(spin.rex)
else:
-
param_vector.append(relax_data_store.res[self.run][i].rex)
+ param_vector.append(spin.rex_sim[sim_index])
# r.
- elif relax_data_store.res[self.run][i].params[j] == 'r':
- if relax_data_store.res[self.run][i].r == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].r_sim[sim_index])
+ elif spin.params[i] == 'r':
+ if sim_index == None:
+ param_vector.append(spin.r)
else:
-
param_vector.append(relax_data_store.res[self.run][i].r)
+ param_vector.append(spin.r_sim[sim_index])
# CSA.
- elif relax_data_store.res[self.run][i].params[j] ==
'CSA':
- if relax_data_store.res[self.run][i].csa == None:
- param_vector.append(0.0)
- elif sim_index != None:
-
param_vector.append(relax_data_store.res[self.run][i].csa_sim[sim_index])
+ elif spin.params[i] == 'CSA':
+ if sim_index == None:
+ param_vector.append(spin.csa)
else:
-
param_vector.append(relax_data_store.res[self.run][i].csa)
+ param_vector.append(spin.csa_sim[sim_index])
# Unknown parameter.
else:
raise RelaxError, "Unknown parameter."
- # Return a Numeric array.
- return array(param_vector, Float64)
+ # Replace all instances of None with 0.0 to allow the list to be
converted to a numpy array.
+ for i in xrange(len(param_vector)):
+ if param_vector[i] == None:
+ param_vector[i] = 0.0
+
+ # Return a numpy array.
+ return array(param_vector, float64)
def assemble_scaling_matrix(self, index=None, scaling=1):
r4693 - /1.3/specific_fns/model_free/model_free.py