r25280 - in /trunk/test_suite/shared_data/curve_fitting/numeric_gradient: integrate.log integrate.py -- August 26, 2014 - 12:24

Author: bugman
Date: Tue Aug 26 12:24:57 2014
New Revision: 25280

URL: http://svn.gna.org/viewcvs/relax?rev=25280&view=rev
Log:
Fix for the script for calculating the numerical gradient for an exponential 
curve.

The off-minimum derivative was not correctly calculated.


Modified:
    trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log
    trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py

Modified: 
trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log
URL: 
http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log?rev=25280&r1=25279&r2=25280&view=diff
==============================================================================
--- trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log 
  (original)
+++ trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.log 
  Tue Aug 26 12:24:57 2014
@@ -1,4 +1,4 @@
 The gradient at [1.0, 1000.0] is:
     [-1.0995282792650802e-09, 2.1826111665238544e-12]
 The gradient at [2.0, 500.0] is:
-    [722.67864120737488, -11.564651301654292]
+    [456.36655522098829, -10.8613338920982]

Modified: 
trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py?rev=25280&r1=25279&r2=25280&view=diff
==============================================================================
--- trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py  
  (original)
+++ trunk/test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py  
  Tue Aug 26 12:24:57 2014
@@ -63,8 +63,8 @@
 print("The gradient at %s is:\n    %s" % ([R, I0], [grad_R, grad_I]))
 
 # The numeric gradient off the minimum.
-R_off = 2.0
-I0_off = 500.0
-grad_R = derivative(func_R, R_off, dx=1e-5, order=11)
-grad_I = derivative(func_I, I0_off, dx=1e-5, order=11)
-print("The gradient at %s is:\n    %s" % ([R_off, I0_off], [grad_R, grad_I]))
+R = 2.0
+I0 = 500.0
+grad_R = derivative(func_R, R, dx=1e-5, order=11)
+grad_I = derivative(func_I, I0, dx=1e-5, order=11)
+print("The gradient at %s is:\n    %s" % ([R, I0], [grad_R, grad_I]))