Thank you, Edward!
I have submitted a bug report #17458. I will check the updated version
and get back to you if there are further problems.

Vitaly



On Mon, Jan 10, 2011 at 08:49, Edward d'Auvergne <[email protected]> wrote:
> Hi Vitaly,
>
> I've now fixed this bug in relax.  It would still be useful to have a
> bug report in case someone else encounters the same problem.  As I
> said in the commit message
> (https://mail.gna.org/public/relax-commits/2011-01/msg00114.html),
> this bug was recently introduced due to changes for BMRB support.  It
> affects relax 1.3.6, as well as the 1.3 line from revision r11678 to
> r12174.  As this bug is quite big affecting all users of model-free
> analysis, I will try to release a new version of relax very soon.  If
> you have a repository version, you can type 'svn up' to receive the
> bug fix.
>
> Cheers,
>
> Edward
>
>
>
> On 10 January 2011 11:00, Edward d'Auvergne <[email protected]> wrote:
>> Hi,
>>
>> I can confirm a recently introduced bug in relax whereby the prolate
>> tensor is actually oblate!  I will try to solve this as soon as
>> possible and release a new relax version.  Strangely this was not
>> caught by the relax test suite.  But if you look in the prolate
>> results file, you may see that the diffusion tensor is labelled as
>> 'oblate'.  Would you be able to submit a bug report for this?  This
>> will help other relax users who encounter the same problem.  The
>> submission form is located at
>> https://gna.org/bugs/?func=additem&group=relax.
>>
>> Cheers,
>>
>> Edward
>>
>>
>> On 6 January 2011 19:15, V.V. <[email protected]> wrote:
>>> Ok, looks like "diff_min.py" is already setup to do everything I
>>> wanted. :-) The problem with the prolate/oblate uncertainty still
>>> exists though. I have a feeling that the constraint for Da=>0 or <=0
>>> is removed by the statement fix('diff', fixed=False). As a workaround
>>> at the moment I use the following in the "diff_min.py":
>>>
>>>    fix('diff', fixed=False)
>>>    cdp.diff_tensor.spheroid_type = 'prolate'
>>>
>>> This seems to do the trick.
>>>
>>> Vitaly
>>>
>>>
>>>
>>>
>>> On Wed, Jan 5, 2011 at 16:14, V.V. <[email protected]> wrote:
>>>> Hello,
>>>>
>>>> I am trying to optimize the diffusion tensor outside of
>>>> "full_analysis.py", to avoid this prolate/oblate issue. I understand
>>>> that the first round should be models tm0-tm9, followed by model
>>>> selection, while the latter ones should alternate between the tensor
>>>> optimization and model-free parameters fitting. I am not entirely sure
>>>> how to start though. Should I:
>>>>
>>>> 1. Take the results from 'local_tm/aic', fix spins, initialize the
>>>> diffusion tensor and then run grid search + minimization, then fix the
>>>> tensor and optimize model-free parameters?
>>>>
>>>> or
>>>>
>>>> 2. Fix spins, initialize the diffusion tensor and perform m0-m9 runs
>>>> followed by model selection, and after that fix the tensor and
>>>> optimize MF? This seems to be what "diff_min.py" does.
>>>>
>>>> Thank you,
>>>> Vitaly
>>>>
>>>>
>>>> On Sat, Dec 18, 2010 at 23:25, V.V. <[email protected]> wrote:
>>>>> Hello,
>>>>>
>>>>> I have performed a "full_analysis" run, and the Chi2 values for
>>>>> prolate and oblate models were reported to be identical. I have
>>>>> checked the contents of the /prolate/round_**/opt, and the results
>>>>> file appears to contain the data for the oblate model:
>>>>>
>>>>> ================
>>>>> <diff_tensor desc="Diffusion tensor" type="spheroid">
>>>>> <Da ieee_754_byte_array="[144, 224, 168, 43, 17, 72, 82, 193]">
>>>>>                -4792388.6821824461
>>>>>            </Da>
>>>>> <fixed>
>>>>>                False
>>>>>            </fixed>
>>>>> <phi ieee_754_byte_array="[127, 197, 226, 164, 9, 205, 0, 64]">
>>>>>                2.1001160508828316
>>>>>            </phi>
>>>>> <spheroid_type>
>>>>>                'oblate'
>>>>>            </spheroid_type>
>>>>> <theta ieee_754_byte_array="[157, 42, 47, 69, 3, 109, 252, 63]">
>>>>>                1.7766144468793421
>>>>>            </theta>
>>>>> <tm ieee_754_byte_array="[131, 104, 243, 90, 176, 172, 60, 62]">
>>>>>                6.6763176508168525e-09
>>>>> </tm>
>>>>> </diff_tensor>
>>>>> ================
>>>>>
>>>>> So, does it mean that the system is converging to the oblate model in
>>>>> the prolate pipe? I thought the Da sign is constrained during the
>>>>> search.
>>>>>
>>>>> Thank you,
>>>>> Vitaly
>>>>>
>>>>
>>>
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