r23843 - /branches/disp_spin_speed/target_functions/relax_disp.py -- June 11, 2014 - 16:19

Author: tlinnet
Date: Wed Jun 11 16:19:12 2014
New Revision: 23843

URL: http://svn.gna.org/viewcvs/relax?rev=23843&view=rev
Log:
Moved the mask which finds where to replace values into the __init__ 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=23843&r1=23842&r2=23843&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py     (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py     Wed Jun 11 
16:19:12 2014
@@ -427,7 +427,7 @@
             # The number of disp point can change per spectrometer, so we 
make the maximum size.
             self.values_a = deepcopy(zeros_a)
             self.errors_a = deepcopy(ones_a)
-            self.missing_a = zeros(self.numpy_array_shape)
+            missing_a = zeros(self.numpy_array_shape)
             
             self.cpmg_frqs_a = deepcopy(ones_a)
             self.num_disp_points_a = deepcopy(zeros_a)
@@ -474,13 +474,16 @@
                             for di in 
range(self.num_disp_points[ei][si][mi][oi]):
                                 if self.missing[ei][si][mi][oi][di]:
                                     self.has_missing = True
-                                    self.missing_a[ei][si][mi][oi][di] = 1.0
+                                    missing_a[ei][si][mi][oi][di] = 1.0
 
             # Make copy of values structure.
             self.back_calc_a = deepcopy(self.values_a)
 
             # Pre calculate frqs structure
             self.frqs_struct = self.frqs_a * self.disp_struct
+
+            # Find the numpy mask, which tells where values should be 
replaced.
+            self.mask_replace_blank = masked_equal(missing_a, 1.0)
 
 
     def calc_B14_chi2(self, R20A=None, R20B=None, dw=None, pA=None, 
kex=None):
@@ -574,11 +577,8 @@
 
         ## 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.
         if self.has_missing:
-            # Find the numpy mask, which tells where values should be 
replaced.
-            mask_replace = masked_equal(self.missing_a, 1.0)
-
             # Replace with values.
-            self.back_calc_a[mask_replace.mask] = 
self.values_a[mask_replace.mask]
+            self.back_calc_a[self.mask_replace_blank.mask] = 
self.values_a[self.mask_replace_blank.mask]
 
         ## Calculate the chi-squared statistic.
         return chi2_rankN(self.values_a, self.back_calc_a, self.errors_a)