r23746 - /branches/disp_spin_speed/target_functions/relax_disp.py -- June 08, 2014 - 23:44

Author: tlinnet
Date: Sun Jun  8 23:44:45 2014
New Revision: 23746

URL: http://svn.gna.org/viewcvs/relax?rev=23746&view=rev
Log:
Changed all the creation of special numpy arrays to be of float64 type.

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=23746&r1=23745&r2=23746&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py     (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py     Sun Jun  8 
23:44:45 2014
@@ -409,16 +409,16 @@
             # All numpy arrays have to have same shape to allow to multiply 
together.
             # The dimensions should be [ei][si][mi][oi][di]. 
[Experiment][spins][spec. frq][offset][disp points].
             # The number of disp point can change per spectrometer, so we 
make the maximum size.
-            self.R20A_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.R20B_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.pA_a = np.zeros(back_calc_shape + 
[self.max_num_disp_points])
-            self.dw_frq_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.kex_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.cpmg_frqs_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.num_disp_points_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.back_calc_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.errors_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
-            self.values_a = np.ones(back_calc_shape + 
[self.max_num_disp_points])
+            self.R20A_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.R20B_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.pA_a = np.zeros(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.dw_frq_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.kex_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.cpmg_frqs_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.num_disp_points_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.back_calc_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.errors_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
+            self.values_a = np.ones(back_calc_shape + 
[self.max_num_disp_points], float64)
             self.has_missing = False
 
             # Loop over the experiment types.