mailRe: r23946 - /branches/disp_spin_speed/target_functions/relax_disp.py


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Posted by Edward d'Auvergne on June 13, 2014 - 19:07:
Hi,

For a more solid and permanent solution, I'd recommend not setting
self.back_calc earlier in __init__(), and just renaming all
self.back_calc_a to self.back_calc in the target function class.  This
will probably break some of the system tests until everything is
converted to your new design, but the dx.map user function will be
broken for those models anyway, even though it is not caught in the
test suite.

Regards,

Edward



On 13 June 2014 18:45,  <tlinnet@xxxxxxxxxxxxx> wrote:
Author: tlinnet
Date: Fri Jun 13 18:45:23 2014
New Revision: 23946

URL: http://svn.gna.org/viewcvs/relax?rev=23946&view=rev
Log:
Fix for system test Relax_disp.test_cpmg_synthetic_dx_map_points.

By just copying self.back_calc_a to self.back_calc, problem was solved.

In specific_analysis.relax_disp.optimisation in function back_calc_r2eff(), 
the
function gets the last values stores in the class function.

This is in:
class Disp_result_command(Result_command):
    """Class for processing the dispersion optimisation results.
With:
self.back_calc = back_calc

And back_calc_r2eff() have return model.back_calc

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion 
models for Clustered analysis.

Modified:
    branches/disp_spin_speed/target_functions/relax_disp.py

Modified: branches/disp_spin_speed/target_functions/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/target_functions/relax_disp.py?rev=23946&r1=23945&r2=23946&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py     (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py     Fri Jun 13 
18:45:23 2014
@@ -525,6 +525,7 @@

             # Make copy of values structure.
             self.back_calc_a = deepcopy(self.values_a)
+            self.back_calc = self.back_calc_a

             # Find the numpy mask, which tells where values should be 
replaced.
             self.mask_replace_blank = masked_equal(missing_a, 1.0)


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