Author: tlinnet
Date: Mon May 5 20:18:30 2014
New Revision: 22971
URL: http://svn.gna.org/viewcvs/relax?rev=22971&view=rev
Log:
Pretty up the code, making space between "=".
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=22971&r1=22970&r2=22971&view=diff
==============================================================================
--- trunk/lib/dispersion/b14.py (original)
+++ trunk/lib/dispersion/b14.py Mon May 5 20:18:30 2014
@@ -138,43 +138,43 @@
#########################################################################
##### Baldwins code.
#########################################################################
- pa=(1-pb)
- keg=kex * (1-pb)
- kge=kex * pb
- deltaR2=r20a-r20b
+ pa = (1-pb)
+ keg = kex * (1-pb)
+ kge = kex * pb
+ deltaR2 = r20a-r20b
alpha_m = r20a - r20b + kge - keg
# This is not used
- #nu_cpmg=ncyc/Trel
- #tcp=Trel/(4.0 * ncyc) #time for one free precession element
+ #nu_cpmg = ncyc/Trel
+ #tcp = Trel/(4.0 * ncyc) #time for one free precession element
#########################################################################
#get the real and imaginary components of the exchange induced shift
- g1=2 * dw * alpha_m #same as carver richards
zeta
- g2=alpha_m**2+4 * keg * kge-dw**2 #same as carver richards psi
- g3=1/sqrt(2) * sqrt(g2+sqrt(g1**2+g2**2)) #trig faster than square
roots
- g4=1/sqrt(2) * sqrt(-g2+sqrt(g1**2+g2**2)) #trig faster than square
roots
+ g1 = 2 * dw * alpha_m #same as carver
richards zeta
+ g2 = alpha_m**2+4 * keg * kge-dw**2 #same as carver richards psi
+ g3 = 1/sqrt(2) * sqrt(g2+sqrt(g1**2+g2**2)) #trig faster than square
roots
+ g4 = 1/sqrt(2) * sqrt(-g2+sqrt(g1**2+g2**2)) #trig faster than square
roots
#########################################################################
#time independent factors
- N=complex(kge+g3-kge,g4) #N=oG+oE
- NNc=(g3**2+g4**2)
- f0=(dw**2+g3**2)/(NNc) #f0
- f2=(dw**2-g4**2)/(NNc) #f2
- #t1=(-dw+g4) * (complex(-dw,-g3))/(NNc) #t1
- t2=(dw+g4) * (complex(dw,-g3))/(NNc) #t2
- t1pt2=complex(2 * dw**2,g1)/(NNc) #t1+t2
- oGt2=complex((-deltaR2+keg-kge-g3),(dw-g4)) * t2 #-2 * oG * t2
- Rpre=(r20a+r20b+kex)/2.0 #-1/Trel * log(LpreDyn)
- E0= 2.0 * tcp * g3 #derived from relaxation #E0=-2.0 * tcp *
(f00R-f11R)
- E2= 2.0 * tcp * g4 #derived from chemical shifts #E2=complex(0,-2.0 *
tcp * (f00I-f11I))
- E1=(complex(g3,-g4)) * tcp #mixed term (complex) (E0-iE2)/2
- ex0b=(f0 * numpy.cosh(E0)-f2 * numpy.cos(E2)) #real
- ex0c=(f0 * numpy.sinh(E0)-f2 * numpy.sin(E2) * complex(0,1.)) #complex
- ex1c=(numpy.sinh(E1)) #complex
- v3=numpy.sqrt(ex0b**2-1) #exact result for v2v3
- y=numpy.power((ex0b-v3)/(ex0b+v3),ncyc)
- v2pPdN=(( complex(-deltaR2+kex,dw) ) * ex0c+(-oGt2-kge * t1pt2) * 2 *
ex1c) #off diagonal common factor. sinh fuctions
- Tog=(((1+y)/2+(1-y)/(2 * v3) * (v2pPdN)/N))
- Minty=Rpre-ncyc/(Trel) * numpy.arccosh((ex0b).real)-1/Trel *
numpy.log((Tog.real)) #estimate R2eff
+ N = complex(kge+g3-kge,g4) #N = oG+oE
+ NNc = (g3**2+g4**2)
+ f0 = (dw**2+g3**2)/(NNc) #f0
+ f2 = (dw**2-g4**2)/(NNc) #f2
+ #t1 = (-dw+g4) * (complex(-dw,-g3))/(NNc) #t1
+ t2 = (dw+g4) * (complex(dw,-g3))/(NNc) #t2
+ t1pt2 = complex(2 * dw**2,g1)/(NNc) #t1+t2
+ oGt2 = complex((-deltaR2+keg-kge-g3),(dw-g4)) * t2 #-2 * oG * t2
+ Rpre = (r20a+r20b+kex)/2.0 #-1/Trel * log(LpreDyn)
+ E0 = 2.0 * tcp * g3 #derived from relaxation #E0 = -2.0 * tcp *
(f00R-f11R)
+ E2 = 2.0 * tcp * g4 #derived from chemical shifts #E2 =
complex(0,-2.0 * tcp * (f00I-f11I))
+ E1 = (complex(g3,-g4)) * tcp #mixed term (complex) (E0-iE2)/2
+ ex0b = (f0 * numpy.cosh(E0)-f2 * numpy.cos(E2)) #real
+ ex0c = (f0 * numpy.sinh(E0)-f2 * numpy.sin(E2) * complex(0,1.)) #complex
+ ex1c = (numpy.sinh(E1)) #complex
+ v3 = numpy.sqrt(ex0b**2-1) #exact result for v2v3
+ y = numpy.power((ex0b-v3)/(ex0b+v3),ncyc)
+ v2pPdN = (( complex(-deltaR2+kex,dw) ) * ex0c+(-oGt2-kge * t1pt2) * 2 *
ex1c) #off diagonal common factor. sinh fuctions
+ Tog = (((1+y)/2+(1-y)/(2 * v3) * (v2pPdN)/N))
+ Minty = Rpre-ncyc/(Trel) * numpy.arccosh((ex0b).real)-1/Trel *
numpy.log((Tog.real)) #estimate R2eff
# Loop over the time points, back calculating the R2eff values.
for i in range(num_points):
r22971 - /trunk/lib/dispersion/b14.py