Hi Edward.
I finally came around to make some plots and tables of the "failed" run and
even attached them to a bug report on GNA.
On 12.02.2013, at 15:09, Edward d'Auvergne <edward@xxxxxxxxxxxxx> wrote:
Rex of around 10^-18 (=nearly zero)
sigma_ex = Rex / omega**2
I extract Rex with the following command, I guess this is then the already
field-corrected value?
value.write( param = 'rex', file = 'example/rex.txt')
This is clearly a bug! For example on an 800, you should multiply
1e-18 with the value of ~2.6e17. Could you file a bug report for
this? A value of 1e-18 should give a significant, yet low, Rex value
of 0.15-0.3 rad.s^-1.
I'm not sure what kind of data / dump would be helpful as an attachment. I'm
also not sure if this is really a bug?
How could I test for that?
chi^2 and AIC values do not converge but differ by only a factor of 10^10
[in fact, the fluctuations are small, and in the range of approx 1e-10]
from each other in the last ~20 rounds.
How many rounds is it up to? If it runs infinitely, then maybe you
have run into a chaotic system. Now that would be fascinating!
Theoretically anyway, biologically it would be irrelevant.
Hooray chaos! I finally killed it after 190 rounds. That poor workstation
would have worked forever I guess.
I'm guessing you mean 1e-10. Can you see which models are changing?
Models are not changing at all.
Link to a plot of assigned models over the iterations (every plot corresponds
to a single residue, y axis corresponds to models 0-9):
https://gna.org/support/download.php?file_id=17287
Can you find any chi2 or AIC values which match between the rounds?
If you make a table of total parameter number, chi2, and AIC, can you
see any patterns?
Have a look for yourself: I can't see any patterns. Looks like random to me.
Link to tab-seperated table of parameters:
https://gna.org/support/download.php?file_id=17284
Link to plot of parameters over iterations:
https://gna.org/support/download.php?file_id=17286
Link to plot of parameters over iterations, zoomed in:
https://gna.org/support/download.php?file_id=17287
However I don't
think I've seen a problem which runs forever - that would just be
theoretically weird.
*sigh*
off-resonance "heating pulses" that make my R1 experiment just as warm as
the R2 experiment [...]
You shouldn't need to warm up your experiment to the level of the R2
in this way. [...] Actually you could end up with a temperature gradient
over the R1 evolution time - this would not be good.
You've been right, the new data I recorded with this technique do not look
right at all. I
The best way to do this is to
run the R1 experiment on a MeOH/ethylene glycol sample. Then
calibrate the temperature as you would normally calibrate a
spectrometer, just using shortened R1/R2/NOE pulse sequences.
As mentioned earlier, I use d4 methanol which gives sharp, not too strong
signals and really nicely fit, stable temperature calibration curves. After
starting the R2 I don't see any differences of the distance between OH/CH3
signals. (The peaks are a bit broader than in the simple 1D) This is true for
the individual delays during measurement (individual planes in the
pseudo-3D), as well as for before/after R2. The distance is the same
everywhere.
I looked into it similarly to what you suggested: more or less killed the
phase cycle and put half of the pulses off-resonance.
For the spins where m0 are selected, do their errors look larger than
the other spins?
I wouldn't say so:
https://dl.dropbox.com/u/4019316/boxplot.error.pdf
Or if you plot the I0 values from the relaxation
exponential curve-fitting, are these residues much lower than the
rest?
There definitely seems to be a tendency:
https://dl.dropbox.com/u/4019316/boxplot.pdf
Similar /enhanced plots can be found here now:
https://gna.org/support/download.php?file_id=17288
https://gna.org/support/download.php?file_id=17289
Those are quite interesting plots. Though I'm not sure why m0 is
selected so often. I've never seen such a phenomenon.
Maybe you should come around for a visit, talk to Peter Schmieder, Hartmut
Oschkinat and Phil Selenko and then you can crush my dreams of doing anything
useful with our system.
That would be fun.
I can only recommend switching to Sparky for this type of analysis.
You can use Topspin to split up the file and create a set of 2D fids.
These can then be used for processing in nmrPipe, if you like, and
converted to Sparky format.
I process with Topspin, (zero-fill for 8k, baseline correction, forward
prediction, set the right nc_proc, etc etc), convert to ucsf with bruk2ucsf,
corrected for the sfo1 with ucsfdata and imported the spectra one-by-one into
Sparky. Then copied my reference peak list onto all single spectra and saved
the resulting peak heights.
https://gna.org/support/download.php?file_id=17290
https://gna.org/support/download.php?file_id=17291
https://gna.org/support/download.php?file_id=17292
https://gna.org/support/download.php?file_id=17293
(some of the plots are truncated for outliers)
I don't see an indication that there are significant differences between
picking with sparky or picking with ccpn ...
Well, I was a pure biochemist before I looked at performing a
model-free analysis of a protein! So it's not impossible.
Kudos for that!
Regards
Martin
Re: m0 models