Hi,
You will soon see my commits about sorting the models.
I'm still catching up on your huge number of changes :)
And here we would probably have to discuss a little.
It's best to discuss ideas before implementing them, as it's then far
easier to change to the best course. It's far more difficult once
code and time has been invested into one solution when a better
solution could have been first implemented.
There is a huge time potential to nest parameters from earlier models.
This was a major point in our paper
(http://dx.doi.org/10.1093/bioinformatics/btu166, and mentioned in the
abstract) which I can now see has volume and page numbers :)
And so by common sense, it would/could be best to nest from numerical
solutions, since they are more precise.
But they are terrible slow.
This is not a good idea :S Well, the analytic models are
approximations anyway so there is a bias in the parameter values, and
this destroys any value the higher precision of the numeric models
could give.
Unless they are these special hybrid version.
Hybrid, I don't understand?
The word "silico", I took from:
A.J. Baldwin (2014). An exact solution for R2,eff in CPMG experiments
in the case of two site chemical exchange. J. Magn. Reson., 2014.
(10.1016/j.jmr.2014.02.023).
I think it is a very good representation.
"Silico" is "fast", and precise.
Hmmm, maybe Nikolai should be asked about this. I believe he would
have a strong opinion about this categorisation of his model, and I
don't remember him ever using such terminology. Andy used the text
"derived in silico" in the introduction which is quite different, as
'in silico' just means on a computer. Most modern analytic models
would also be derived using Mathametica, Maxima, etc. on a computer
and could equally be said to be 'in silico' derived. I bet Andy also
used symbolic computational software for his paper ;)
But "impossible" to read and understand from the code.
I think that "silico" is a good way to represent a "subset" of
numerical solutions.
I would rather describe a model as being "silico", than to add meta
data about "how fast" it is.
Why is this differential catagorisation needed at all? I know this
would invalidate a lot of work, but would it not be better to have
hard coded nested model defaults? I.e. a dictionary structure as you
have already used in the variables module, with one entry per model.
Regards,
Edward
Re: r24983 - in /branches/R1_fitting: specific_analyses/relax_disp/ test_suite/unit_tests/_specific_analyses/_relax_disp/