r25522 - /branches/est_par_error/test_suite/shared_data/dispersion/estimate_par_err/tsmfk01/tsmfk01_der.py -- September 02, 2014 - 00:16

Author: tlinnet
Date: Tue Sep  2 00:16:37 2014
New Revision: 25522

URL: http://svn.gna.org/viewcvs/relax?rev=25522&view=rev
Log:
Fix for derivation script.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR 
estimation from Jacobian and Co-variance matrix of dispersion models.

Modified:
    
branches/est_par_error/test_suite/shared_data/dispersion/estimate_par_err/tsmfk01/tsmfk01_der.py

Modified: 
branches/est_par_error/test_suite/shared_data/dispersion/estimate_par_err/tsmfk01/tsmfk01_der.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/est_par_error/test_suite/shared_data/dispersion/estimate_par_err/tsmfk01/tsmfk01_der.py?rev=25522&r1=25521&r2=25522&view=diff
==============================================================================
--- 
branches/est_par_error/test_suite/shared_data/dispersion/estimate_par_err/tsmfk01/tsmfk01_der.py
    (original)
+++ 
branches/est_par_error/test_suite/shared_data/dispersion/estimate_par_err/tsmfk01/tsmfk01_der.py
    Tue Sep  2 00:16:37 2014
@@ -33,9 +33,9 @@
 # Try again.
 
 # The vectorial function.
-X = Matrix([r20a + k_AB - k_AB * sin(dw * tcp) / dw * tcp])
+X = Matrix([r20a + k_AB - k_AB * sin(dw * tcp) / (dw * tcp)])
 # What to derive for.
-Y = Matrix([r20a, dw, k_AB, tcp])
+Y = Matrix([r20a, dw, k_AB])
 
 # Make the Jacobian
 Jacobian = X.jacobian(Y)