r23951 - /branches/disp_spin_speed/target_functions/relax_disp.py -- June 15, 2014 - 08:53

Author: tlinnet
Date: Sun Jun 15 08:53:36 2014
New Revision: 23951

URL: http://svn.gna.org/viewcvs/relax?rev=23951&view=rev
Log:
Change to the target function to the model ns cpmg 2site 3d to use the 
reduced input to the lib function.

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=23951&r1=23950&r2=23951&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py     (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py     Sun Jun 15 
08:53:36 2014
@@ -627,15 +627,6 @@
         @rtype:         float
         """
 
-        # Once off parameter conversions.
-        pB = 1.0 - pA
-        k_BA = pA * kex
-        k_AB = pB * kex
-
-        # This is a vector that contains the initial magnetizations 
corresponding to the A and B state transverse magnetizations.
-        self.M0[1] = pA
-        self.M0[4] = pB
-
         # Chi-squared initialisation.
         chi2_sum = 0.0
 
@@ -650,7 +641,7 @@
                 dw_frq = dw[si] * self.frqs[0][si][mi]
 
                 # Back calculate the R2eff values.
-                r2eff_ns_cpmg_2site_3D(r180x=self.r180x, M0=self.M0, 
r20a=R20A[r20_index], r20b=R20B[r20_index], pA=pA, pB=pB, dw=dw_frq, 
k_AB=k_AB, k_BA=k_BA, inv_tcpmg=self.inv_relax_times[0][mi], 
tcp=self.tau_cpmg[0][mi], back_calc=self.back_calc[0][si][mi][0], 
num_points=self.num_disp_points[0][si][mi][0], power=self.power[0][mi])
+                r2eff_ns_cpmg_2site_3D(r180x=self.r180x, M0=self.M0, 
r20a=R20A[r20_index], r20b=R20B[r20_index], pA=pA, dw=dw_frq, kex=kex, 
inv_tcpmg=self.inv_relax_times[0][mi], tcp=self.tau_cpmg[0][mi], 
back_calc=self.back_calc[0][si][mi][0], 
num_points=self.num_disp_points[0][si][mi][0], power=self.power[0][mi])
 
                 # For all missing data points, set the back-calculated value 
to the measured values so that it has no effect on the chi-squared value.
                 for di in range(self.num_disp_points[0][si][mi][0]):