Author: bugman
Date: Wed Jul 9 15:35:23 2008
New Revision: 6884
URL: http://svn.gna.org/viewcvs/relax?rev=6884&view=rev
Log:
Modified the N_state_opt class to handle both alignment tensor and RDC
optimisation.
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=6884&r1=6883&r2=6884&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/n_state_model.py (original)
+++ branches/rdc_analysis/maths_fns/n_state_model.py Wed Jul 9 15:35:23 2008
@@ -67,31 +67,79 @@
self.N = N
self.params = 1.0 * init_params # Force a copy of the data to be
stored.
self.total_num_params = len(init_params)
- self.full_tensors = array(full_tensors, float64)
- self.num_tensors = len(self.full_tensors)
- self.red_data = red_data
- self.red_errors = red_errors
- self.full_in_ref_frame = full_in_ref_frame
- # Initialise some empty numpy objects for storage of:
- # R: the transient rotation matricies.
- # RT: the transposes of the rotation matricies.
- # red_bc: the back-calculated reduced alignment tensors.
- # red_bc_vector: the back-calculated reduced alignment tensors in
vector form {Sxx, Syy, Sxy, Sxz, Syz}.
- self.R = zeros((self.N,3,3), float64)
- self.RT = zeros((self.N,3,3), float64)
- self.red_bc = zeros((self.num_tensors,3,3), float64)
- self.red_bc_vector = zeros(self.num_tensors*5, float64)
+ # Using alignment tensors.
+ if full_tensors:
+ # Some checks.
+ if red_data == None and not len(red_data):
+ raise RelaxError, "The red_data argument " + `red_data` + "
must be supplied."
+ if red_errors == None and not len(red_errors):
+ raise RelaxError, "The red_errors argument " + `red_errors`
+ " must be supplied."
+ if full_in_ref_frame == None and not len(full_in_ref_frame):
+ raise RelaxError, "The full_in_ref_frame argument " +
`full_in_ref_frame` + " must be supplied."
+
+ # Tensor set up.
+ self.full_tensors = array(full_tensors, float64)
+ self.num_tensors = len(self.full_tensors)
+ self.red_data = red_data
+ self.red_errors = red_errors
+ self.full_in_ref_frame = full_in_ref_frame
+
+ # Initialise some empty numpy objects for storage of:
+ # R: the transient rotation matricies.
+ # RT: the transposes of the rotation matricies.
+ # red_bc: the back-calculated reduced alignment tensors.
+ # red_bc_vector: the back-calculated reduced alignment tensors
in vector form {Sxx, Syy, Sxy, Sxz, Syz}.
+ self.R = zeros((self.N,3,3), float64)
+ self.RT = zeros((self.N,3,3), float64)
+ self.red_bc = zeros((self.num_tensors,3,3), float64)
+ self.red_bc_vector = zeros(self.num_tensors*5, float64)
+
+ # Set the target function.
+ self.func = self.func_tensors
+
+ # Using RDCs.
+ elif rdcs:
+ # Some checks.
+ if xh_vect == None and not len(xh_vect):
+ raise RelaxError, "The xh_vect argument " + `xh_vect` + "
must be supplied."
+
+ # RDC set up.
+ self.num_spins = len(rdcs)
+ self.rdcs = []
+ self.xh_vect = []
+ for i in xrange(self.num_spins):
+ self.rdcs.append(array(rdcs[i], float64))
+ self.xh_vect.append(array(xh_vect[i], float64))
+
+ # Set the target function.
+ self.func = self.func_rdcs
- def func(self, params):
- """The target function for optimisation.
+ def func_rdcs(self, params):
+ """The target function for optimisation using RDCs.
+
+ This function should be passed to the optimisation algorithm. It
accepts, as an array, a
+ vector of parameter values and, using these, returns the single
chi-squared value
+ corresponding to that coordinate in the parameter space. If no RDC
errors are supplied,
+ then the SSE (the sum of squares error) value is returned instead.
The chi-squared is
+ simply the SSE normalised to unit variance (the SSE divided by the
error squared).
+
+ @param params: The vector of parameter values.
+ @type params: list of float
+ @return: The chi-squared or SSE value.
+ @rtype: float
+ """
+
+
+ def func_tensors(self, params):
+ """The target function for optimisation using alignment tensors.
This function should be passed to the optimisation algorithm. It
accepts, as an array, a
vector of parameter values and, using these, returns the single
chi-squared value
corresponding to that coordinate in the parameter space. If no
tensor errors are supplied,
then the SSE (the sum of squares error) value is returned instead.
The chi-squared is
- simply the SSE normalised to unit variance (the SS divided by the
error squared).
+ simply the SSE normalised to unit variance (the SSE divided by the
error squared).
@param params: The vector of parameter values.
@type params: list of float
r6884 - /branches/rdc_analysis/maths_fns/n_state_model.py