Hi Charles,

Thanks for your reply.

The legacy Xplor "TENSOr" updates the tensor every time step from a current
structure and measured RDCs during calculation, and it is written in
Fortran. Is that right?

Is there the same function in RDCpot or others?

If I want to have the steric alignment tensor from its structure at every
timestep, similar as TENSOr, during simulated annealing, any suggestions?
Write this function into the Fortran code or C++ code then recompile?  Where
I can add this function?  Thanks a lot!!

Best wishes,

Jie-rong

2008/6/16 <Charles at schwieters.org>:

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> Hello Jie-rong--
>
> > I have some questions about tensor calculation and C++/Fortran code.
> >
> > Is there any difference between "calcTensor" in varTensorTools.py and
> > "TENSOr" (Sass, 2001) in conventional Xplor-NIH? Are they the same thing?
> >
>
> They are not the same.
>
> > Does the potential energy terms in python modules e.g. NOEPot, RDCPot,
> still
> > call the Fortran code of old X-plor to calculate the structure and
> penalty
> > function?  or these new potential functions are only coded in C++ (e.g.
> > NOEPot.cc) and nothing to do with the original Fortran?
> >
>
> They have nothing to do with the Fortran code.
>
> > If the calTensor is called at every cooling cycle to update the alignment
> > tensor during simulated annealing, would it be computationally expensive?
> > Since it calculates the tensor "outside" the core and put result back to
> the
> > core to do the structure calculation.  Will it be more efficient if the
> > function of calcTensor is in the source code and recompiled, or it
> doesn't
> > matter?
>
> Probably too slow, and also not necessary. It suffices to periodically
> recalculate the tensor (not every timestep).
>
> > (I am writing a python helper, which is similar to calcTensor.  Steric
> > alignment tensor can be obtained by sampling the structure over an
> > isotropically distributed space (The result is the same as PALES
> multiplied
> > by a constant).  The method is computationally expensive, so I am
> wondering
> > if it can be improved.)
>
> Most likely, it can be improved. The first task is to locate
> bottlenecks- you might use a Python profiler for this. Once the
> bottlenecks are identified, I might be able to give further advice.
>
> best regards--
> Charles
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