Hi Edward,
Yes, R2E and R2G are the exchange-free R2 values. Sometimes also referred to
as R20 or R2infinity. They are the Redfield-relaxation part.
Currently, the functions do not use populations (pA, pB) but they use the
two exchange rates (forward, backward), rather than one rate constant and
one population. The two different approaches are in essence the same, as
populations can be calculated from forward/backward rates in a
straightforward manner.
We can easily change this if it is more in the logic of the existing code.
paul
Hello,
Another question I have is what are the parameters of this model? It
relates to the function arguments currently labelled as unknown in the
function docstring. I would like to implement step 2 of the tutorial
on adding relaxation dispersion models to relax (the
relax_disp.select_model user function front end, see
http://article.gmane.org/gmane.science.nmr.relax.devel/3907), but I'm
not sure how to list the parameters of the model for the user. Is
there a population parameter (pA or pB)? Are R2E and R2G the same as
the R20A and R20B rates (the R2 rates in the absence of exchange for
states A and B for a fixed magnetic field strength)?
Cheers,
Edward
On 11 July 2013 16:14, Edward d'Auvergne <edward@xxxxxxxxxxxxx> wrote:
Hi Paul,
I have now incorporated this code into relax (see
http://article.gmane.org/gmane.science.nmr.relax.scm/18025). Could
you please check the code, comments, and docstrings carefully and see
if there are any issues? Could you also tell me if anyone else should
be included in the copyright notice? It would be useful to replace
the 'Unknown' elements in the function docstring with basic
descriptions of the parameter and its Python object type. And any
suggestions for comments describing in basic NMR terms what the code
is doing (the physics of the propagations, for example) would also be
appreciated.
You can get the new code by typing the following in the base directory
of the checked out copy of the relax_disp branch:
$ svn up
There was an indentation problem that I have tried to solve. Also I
cannot determine where the mpower() function comes from - it appears
not to be part of numpy or scipy. Is this an outside function you
wrote? I have also made a number of significant speed ups of the code
(see article.gmane.org/gmane.science.nmr.relax.scm/18029). Note that
this cannot be selected as a model in the dispersion analysis yet.
Cheers,
Edward
On 11 July 2013 13:47, Paul Schanda <paul.schanda@xxxxxx> wrote:
Hi Edward,
Here is a function that does a numerical fit of CPMG. It uses an explicit
matrix that contains relaxation, exchange and chemical shift terms. It does
the 180deg pulses in the CPMG train with conjugate complex matrices.
It returns calculated R2eff values, so it can be used for numerical fitting
of CPMG.
It is an addition to the functions that I previously sent to you.
The approach of Bloch-McConnell can be found in chapter 3.1 of Palmer, A. G.
Chem Rev 2004, 104, 3623–3640.
I wrote this function (initially in MATLAB) in 2010.
I agree that this code is released under the GPL3 or higher licence.
Paul
def func1(R2E,R2G,fg,kge,keg, cpmgfreq,tcpmg):
kex=kge+keg
Rcalc_array=np.zeros(len(cpmgfreq))
Rr = -1.*np.matrix([[R2G, 0.],[0., R2E]])
Rex = -1.*np.matrix([[kge,-keg],[-kge, keg]])
RCS = complex(0.,-1.)*np.matrix([[0. ,0.],[0.,fg]])
R = Rr + Rex + RCS
cR = np.conj(R)
IGeq=keg/kex; IEeq=kge/kex
M0=np.matrix([[IGeq],[IEeq]])
for k in range(len(cpmgfreq)):
tcp=1./(4.*cpmgfreq[k])
prop_2
=np.dot(np.dot(sci.expm(R*tcp),sci.expm(cR*2.*tcp)),sci.expm(R*tcp))
prop_total =mpower(prop_2,cpmgfreq[k]*tcpmg)
Moft = prop_total*M0
Mgx=Moft[0].real/M0[0]
Rcalc=-(1./tcpmg)*math.log(Mgx);
Rcalc_array[k]=Rcalc
return Rcalc_array
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Re: numerical cpmg fit