r6201 - /1.3/specific_fns/relax_fit.py -- May 21, 2008 - 22:37

Author: bugman
Date: Wed May 21 22:36:57 2008
New Revision: 6201

URL: http://svn.gna.org/viewcvs/relax?rev=6201&view=rev
Log:
Updated the linear_constraints() method to the new design.


Modified:
    1.3/specific_fns/relax_fit.py

Modified: 1.3/specific_fns/relax_fit.py
URL: 
http://svn.gna.org/viewcvs/relax/1.3/specific_fns/relax_fit.py?rev=6201&r1=6200&r2=6201&view=diff
==============================================================================
--- 1.3/specific_fns/relax_fit.py (original)
+++ 1.3/specific_fns/relax_fit.py Wed May 21 22:36:57 2008
@@ -511,27 +511,27 @@
         return min_options
 
 
-    def linear_constraints(self, index=None):
-        """Function for setting up the linear constraint matrices A and b.
+    def linear_constraints(self, spin=None, scaling_matrix=None):
+        """Set up the relaxation curve fitting linear constraint matrices A 
and b.
 
         Standard notation
-        ~~~~~~~~~~~~~~~~~
-
-        The relaxation rate constraints are:
+        =================
+
+        The relaxation rate constraints are::
 
             Rx >= 0
 
-        The intensity constraints are:
+        The intensity constraints are::
 
             I0 >= 0
             Iinf >= 0
 
 
         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 |     |  Rx  |      |    0    |
             |         |     |      |      |         |
@@ -539,23 +539,25 @@
             |         |     |      |      |         |
             | 1  0  0 |     | Iinf |      |    0    |
 
+
+        @keyword spin:              The spin data container.
+        @type spin:                 SpinContainer instance
+        @keyword scaling_matrix:    The diagonal, square scaling matrix.
+        @type scaling_matrix:       numpy diagonal matrix
         """
 
         # Initialisation (0..j..m).
         A = []
         b = []
-        n = len(self.param_vector)
+        n = len(spin.params)
         zero_array = zeros(n, float64)
         i = 0
         j = 0
 
-        # Alias the residue specific data structure.
-        data = relax_data_store.res[self.run][index]
-
         # Loop over the parameters.
-        for k in xrange(len(data.params)):
+        for k in xrange(len(spin.params)):
             # Relaxation rate.
-            if data.params[k] == 'Rx':
+            if spin.params[k] == 'Rx':
                 # Rx >= 0.
                 A.append(zero_array * 0.0)
                 A[j][i] = 1.0
@@ -563,7 +565,7 @@
                 j = j + 1
 
             # Intensity parameter.
-            elif search('^I', data.params[k]):
+            elif search('^I', spin.params[k]):
                 # I0, Iinf >= 0.
                 A.append(zero_array * 0.0)
                 A[j][i] = 1.0