Hi Edward
Thanks for your help.
I have another question about reduced spectral density mapping.
With the script jw_mapping.py, one has to select the frequency
(jw_mapping.set_frq()). I would like to know if it is possible to select
datasets at multiple fields and then optimize everything together...
Would this lead to better values as is the case with the model-free
approach ?
I tried by simply putting three fields :
===============================================================
jw_mapping.set_frq(name, frq=499.719 * 1e6, frq=599.739 * 1e6,
frq=799.744 * 1e6)
===============================================================
but as I thought, ended up with an error :
===============================================================
SyntaxError: duplicate keyword argument
===============================================================
Of course...
Thanks for help !
Séb :)
Edward d'Auvergne wrote:
> On 11/29/06, Sebastien Morin <[EMAIL PROTECTED]> wrote:
>> Hi everyone !
>>
>> I just started using the jw_mapping.py script to get spectral densities
>> out of my data.
>>
>> I have some questions :
>>
>> 1.
>> Reading about reduced spectral density mapping, I thought one would
>> extract J(0), J(wN) and J(0.87wH). However, when reading the results
>> file from the jw_mapping.py script, I see J(0), J(wN) and J(wH)... Is it
>> the same thing in this case or is relax using another approach than the
>> one giving J(0.87wH) ?
>
> The J(0.87wH), J(0.921wH), and J(0.955wH) terms are from the paper of
> Farrow et al., (1995b) JBNMR, 6, 153. There are three methods in that
> paper for determining the spectral density values. The technique
> currently used in relax is 'method 1', which is the same as the other
> Farrow et al., 1995 publication (Farrow et al., (1995a) Biochem, 34,
> 886) and the Lefevre et al., (1996) Biochem, 25, 2674 publication. If
> you're not worried about precision in the x-intercept of the spectral
> density graphs, the frequency, then essentially wH == 0.87wH.
> Currently I only have the Lefevre reference on the website, but this
> needs updating.
>
>
>> 2.
>> I would like to know what are the units of spectral densities in the
>> results file when using jw_mapping.py. I get values ranging from 1e-10
>> to 1e-13, is it what one would get ?
>
> The units are the standard seconds per radian. Nanosecond or
> picosecond per radian is what is normally plotted.
>
>
>> 3.
>> I would like to know if it is possible to get the spectral densities
>> using data from more than one field at the same time or must one get
>> fits from data at each field separately ?
>
> If you'd like to do something a bit fancier, there are methods 2 and 3
> of Farrow et al., 1995b or the techniques used in Butterwick et al.,
> 2004 (as well as many other variants). Anyway, each field strength
> data set is treated separately yet the multiple frequencies can be
> used to extract one J(0). These additional techniques are most
> welcome to be added to relax!
>
>
>> 4.
>> In the results file, when using the script jw_mapping.py, what does
>> 'remap_table' stand for ? With the few tests I made, I always get
>> [0,0,0]...
>
> The 'remap_table' is an internal relax data structure. It links the
> NMR data to the frequency data. If you have data at two field
> strengths, then the first field is '0' and the second '1'. Hence
> you'll have a remap table of [0,0,0,1,1,1] if the R1, R2, and NOE have
> been collected. 'ri_lables', 'remap_table', 'frq_labels', and
> 'frequencies' are used to store info about the relaxation data (so it
> can easily later be read back into relax).
>
>
>> 5.
>> I would like to propose that fitted values and errors be output
>> separately from the monte carlo simulations when using the jw_mapping.py
>> script. This would render analysis easier as the file at which one would
>> look more closely would be the values and errors one. This file would be
>> more practical, also, if the values and errors would be on the same
>> line. This would reduce the amount of gawk, sort, tail, head, etc to
>> use...
>
> The results file is designed to contain absolutely every last bit of
> data associated with a run, it's just one massive repository of data.
> The best way to view this file is using the command 'less -S
> results.bz2'. However instead of manually extracting the data from
> this file, it can be read back into relax and user functions can be
> used to create files containing the specific values with just their
> errors using 'value.write()' or viewed using 'value.display()',
> 'grace.view()', 'grace.write()', or 'molmol.write()'. You can use
> these with the any value, any simulation, or any x-y data combination
> you can think of. The documentation associated with these functions
> will tell you all the possible combinations.
>
> I hope this helps,
>
> Edward
>
--
______________________________________
_______________________________________________
| |
|| Sebastien Morin ||
||| Etudiant au PhD en biochimie |||
|||| Laboratoire de resonance magnetique nucleaire ||||
||||| Dr Stephane Gagne |||||
|||| CREFSIP (Universite Laval, Quebec, CANADA) ||||
||| 1-418-656-2131 #4530 |||
|| ||
|_______________________________________________|
______________________________________
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