I've been trying to use the latex python script on the aic results file. I get
the syntax error below. Not sure if I'm doing something wrong or if there's
just a small problem with that line of code:
Latex()
----------------------------------------------------------------------------------------------------
Traceback (most recent call last):
File "/Applications/relax-1.3.1/relax-1.3/relax", line 408, in <module>
Relax()
File "/Applications/relax-1.3.1/relax-1.3/relax", line 125, in __init__
self.interpreter.run(self.script_file)
File "/Applications/relax-1.3.1/relax-1.3/prompt/interpreter.py",
line 270, in
run
return run_script(intro=self.__intro_string, local=self.local,
script_file=script_file, quit=self.__quit_flag,
show_script=self.__show_script,
raise_relax_error=self.__raise_relax_error)
File "/Applications/relax-1.3.1/relax-1.3/prompt/interpreter.py",
line 531, in
run_script
return console.interact(intro, local, script_file, quit,
show_script=show_script, raise_relax_error=raise_relax_error)
File "/Applications/relax-1.3.1/relax-1.3/prompt/interpreter.py",
line 427, in
interact_script
execfile(script_file, local)
File "latex_mf_table.py", line 171
for spin, spin_id in spin_loop(return_id=True)
SyntaxError: invalid syntax
Quoting Edward d'Auvergne <[EMAIL PROTECTED]>:
> On Mon, Oct 20, 2008 at 5:12 PM, Tyler Reddy <[EMAIL PROTECTED]> wrote:
>> Hello,
>>
>> 1)
>>
>> I have been using the multi model and model selection scripts in
>> relax 1.3.2 but
>> I have trouble displaying the output in a tabulated format. Both
>> scripts seem
>> to produce an xml document with various headers that isn't easy to read. It
>> looks like format='columnar' isn't supported. I wonder what other options I
>> have to look at this data? For some reason, I don't recall having
>> this problem
>> on a Linux machine a few weeks ago (using a Mac OS 10.4 at the moment), but
>> anyways it would be nice to get readable model-free output.
>
> The results file is now in XML format and the more readable 'columnar'
> format has been removed from the 1.3 line. With the change to the new
> XML results file all the contents of a data pipe, irrespective of what
> that data is, is packaged. So you can put data into this pipe
> yourself and it will save that information (for advanced users,
> complex python objects will need the to_xml() and from_xml() methods
> to package and unpackage the data). The reason for removing the
> 'columnar' format was that it was considered too inflexible for the
> changes occuring in the 1.3 line, it contained duplicate information,
> had numerical precision issues, and there were alternatives to easily
> view this data. You can use the value.display() and value.write()
> user functions to display and save the results for a single parameter.
> If needed, these user functions could be extended to accept a list of
> parameters.
>
> Then there is the sample_scripts/latex_mf_table.py sample script which
> will generate a LaTeX table of the model-free results. This file can
> be copied and modified - this requires learning a bit of python - to
> format and display the results any way you wish. And finally if
> anyone really wants to, and has the skills to, they can modify this
> sample script to recreate a version of the 'columnar' format. This
> could be added to the relax sample scripts, and if their skills are
> very advanced, then much code from the 1.2 relax versions can be
> recycled.
>
>
>> 2)
>>
>> The error input for the relaxation rate parameters is currently my
>> non-linear
>> curve fitting standard deviation. I'm not sure if that means subsequent
>> analysis will be completely incorrect? I guess it depends on the
>> comparison of
>> magnitude between these errors and the type of error that is propagated by
>> relax during its own curve-fitting (which I am unable to do at the moment).
>
> I'm guessing this is the sum of squared error value (SSE) from the
> fit. Or is it a regression coefficient or a chi-squared value? Did
> the fitting use a technique such as bootstrapping or jackknife
> simulations to estimate the parameter errors via propagation? Or did
> it use the covariance matrix? If it is the SSE, chi-squared, or
> regression coefficient then that value cannot be used. This will be
> wildly wrong and cause massive failure in model selection. It will
> cause big problems in optimisation, and if you are unlucky and have
> spaces with long, curved valleys or flat curved spaces leading to the
> minimum (that's model-free models m5 to m8 in most cases and not so
> uncommon in model m4) then the minimum can be squeezed and appear in
> another completely different region in the space. It will likely also
> cause model failure issues, which although removed by the eliminate()
> user function, might discount the best solution. I would guess that
> all of this will have a measurable affect on the final diffusion
> tensor as well and, if so, this will cause the appearance of
> artificial motions (my 2007 JBNMR paper at
> http://dx.doi.org/10.1039/b702202f explains these problems in detail).
> If one is not careful with the errors and they are significantly off,
> then the result is that the results may not be real. So I would only
> use the error if it comes from an established error propagation
> technique (i.e. from data to parameter error propagation).
>
> Regards,
>
> Edward
>
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