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Hi Kai,
    thanks for this hint! I shall try and let you know the result. I 
suppose these parameters can be entered in the 'additional arguments box'!
Dieter

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Dieter Blaas,
Max F. Perutz Laboratories
Medical University of Vienna,
Inst. Med. Biochem., Vienna Biocenter (VBC),
Dr. Bohr Gasse 9/3,
A-1030 Vienna, Austria,
Tel: 0043 1 4277 61630,
Fax: 0043 1 4277 9616,
e-mail: [log in to unmask]
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Am 01.09.2014 00:20, schrieb Kai Zhang:
> Hi Dieter,
>
> Did you tried --only_flip_phases(or --ctf_intact_first_peak) and/or
> --strict_highres_exp? It helps a lot for uncleaned data sets with strong
> orientation preference.
>
> Kai
>
> On Sun, 2014-08-31 at 14:52 +0200, Dieter Blaas wrote:
>> Hi Sjors and all,
>>      I am trying to get a reasonable number of 2D class averages by
>> running a 2D-classification on an old icosahedral virus dataset (about
>> 10,000 images, 50 classes). After each run I select the classes with
>> features recognizable as viral, remove the trash, and run it again. I am
>> now at run 5 and I am still getting only 2 classes recognizable as viral
>> projections (in sum about 99% of the images) and about 40 classes
>> containing between 1 and 20 'bad' images. I have tried T=1, 1.5, and 2
>> and angular sampling 5 and 2.5 but regardless of these parameters it
>> seems to go on for ever (each new run finds new 'trash images' and I do
>> not get more than the 2 main classes (with the second one at about 3 %
>> only)). I admit that the dataset is skewed towards one single view but
>> shouldn't I get more than two main class averages ? Is there a way of
>> removing the trash and getting a reasonable number of views once for ever?
>> Thanks for hints, Dieter
>>
>> ------------------------------------------------------------------------
>> Dieter Blaas,
>> Max F. Perutz Laboratories
>> Medical University of Vienna,
>> Inst. Med. Biochem., Vienna Biocenter (VBC),
>> Dr. Bohr Gasse 9/3,
>> A-1030 Vienna, Austria,
>> Tel: 0043 1 4277 61630,
>> Fax: 0043 1 4277 9616,
>> e-mail: [log in to unmask]
>> ------------------------------------------------------------------------