r22978 - /trunk/lib/dispersion/b14.py -- May 05, 2014 - 20:18

Author: tlinnet
Date: Mon May  5 20:18:43 2014
New Revision: 22978

URL: http://svn.gna.org/viewcvs/relax?rev=22978&view=rev
Log:
Replaced numpy.XX functions, with just the function.

sr #3154: (https://gna.org/support/?3154) Implementation of Baldwin (2014) 
B14 model - 2-site exact solution model for all time scales.

This follows the tutorial for adding relaxation dispersion models at:
http://wiki.nmr-relax.com/Tutorial_for_adding_relaxation_dispersion_models_to_relax#Debugging

Modified:
    trunk/lib/dispersion/b14.py

Modified: trunk/lib/dispersion/b14.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/lib/dispersion/b14.py?rev=22978&r1=22977&r2=22978&view=diff
==============================================================================
--- trunk/lib/dispersion/b14.py (original)
+++ trunk/lib/dispersion/b14.py Mon May  5 20:18:43 2014
@@ -99,7 +99,7 @@
 
 # Python module imports.
 import numpy
-from math import cos,sin, sqrt
+from numpy import arccosh, cos, cosh, log, sin, sinh, sqrt
 
 
 def r2eff_B14(r20a=None, r20b=None, pA=None, dw=None, kex=None, power=None, 
relax_time=None, tcp=None, back_calc=None, num_points=None):
@@ -191,16 +191,16 @@
     E1 = (complex(g3, -g4)) * tcp
 
     # Real.
-    ex0b = (f0 * numpy.cosh(E0) - f2 * numpy.cos(E2))
+    ex0b = (f0 * cosh(E0) - f2 * cos(E2))
 
     # Complex.
-    ex0c = (f0 * numpy.sinh(E0) - f2 * numpy.sin(E2) * complex(0, 1.0))
+    ex0c = (f0 * sinh(E0) - f2 * sin(E2) * complex(0, 1.0))
 
     # Complex.
-    ex1c = numpy.sinh(E1)
+    ex1c = sinh(E1)
 
     # Exact result for v2v3.
-    v3 = numpy.sqrt(ex0b**2 - 1)
+    v3 = sqrt(ex0b**2 - 1)
 
     y = numpy.power((ex0b - v3) / (ex0b + v3), ncyc)
 
@@ -209,7 +209,7 @@
     Tog = (((1 + y)/2 + (1 - y)/(2 * v3) * v2pPdN / N))
 
     # Estimate R2eff.
-    Minty = Rpre - ncyc/(Trel) * numpy.arccosh((ex0b).real) - 1/Trel * 
numpy.log((Tog.real))
+    Minty = Rpre - ncyc/(Trel) * arccosh((ex0b).real) - 1/Trel * 
log((Tog.real))
 
     # Loop over the time points, back calculating the R2eff values.
     for i in range(num_points):