r23682 - /branches/frame_order_cleanup/specific_analyses/frame_order/api.py -- June 06, 2014 - 10:10

Author: bugman
Date: Fri Jun  6 10:10:00 2014
New Revision: 23682

URL: http://svn.gna.org/viewcvs/relax?rev=23682&view=rev
Log:
Fix for a number of the frame order models which do not have parameter 
constraints.

This change to the grid_search() API method is similar to the change for the 
minimise() method
(r23211).  The linear_constraint() function was returning A, b = [], [] for 
these models, but these
empty numpy arrays were causing the dot product with A to fail in the 
grid_search() API method.
These values are now caught and the constraint algorithm turned off.


Modified:
    branches/frame_order_cleanup/specific_analyses/frame_order/api.py

Modified: branches/frame_order_cleanup/specific_analyses/frame_order/api.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/frame_order_cleanup/specific_analyses/frame_order/api.py?rev=23682&r1=23681&r2=23682&view=diff
==============================================================================
--- branches/frame_order_cleanup/specific_analyses/frame_order/api.py   
(original)
+++ branches/frame_order_cleanup/specific_analyses/frame_order/api.py   Fri 
Jun  6 10:10:00 2014
@@ -479,10 +479,19 @@
                 else:
                     indices[j] = 0
 
+        # Linear constraints.
+        A, b = None, None
+        if constraints:
+            A, b = linear_constraints(scaling_matrix=scaling_matrix)
+
+        # Constraint flag set but no constraints present.
+        if A != None and len(A) == 0:
+            if verbosity:
+                warn(RelaxWarning("The '%s' model parameters are not 
constrained, turning the linear constraint algorithm off." % cdp.model))
+            constraints = False
+
         # Eliminate all points outside of constraints (useful for the 
pseudo-ellipse models).
         if constraints:
-            A, b = linear_constraints(scaling_matrix=scaling_matrix)
-
             # Construct a new point array.
             new_pts = []
             for i in range(total_pts):