[Mne_analysis] computing peak frequency in source space

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Tue Oct 1 11:22:34 EDT 2013
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Luke,

can you move this conversation to a github issue?

Alex

On Tue, Oct 1, 2013 at 5:15 PM, Luke Bloy <luke.bloy at gmail.com> wrote:
> Is there a way to disable parallel jobs in the testing...
>
> nosetest hangs with the following error.
>
> Test cluster level permutations T-test ... [Parallel(n_jobs=2)]: Done   1
> out of   2 | elapsed:    0.1s remaining:    0.1s
> [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.1s finished
> [Parallel(n_jobs=2)]: Done   1 out of   2 | elapsed:    0.1s remaining:
> 0.1s
> [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.1s finished
> Process PoolWorker-401:
> Traceback (most recent call last):
>   File "/usr/lib64/python2.6/multiprocessing/process.py", line 232, in
> _bootstrap
> Process PoolWorker-402:
> Traceback (most recent call last):
>   File "/usr/lib64/python2.6/multiprocessing/process.py", line 232, in
> _bootstrap
>     self.run()
>   File "/usr/lib64/python2.6/multiprocessing/process.py", line 88, in run
>     self._target(*self._args, **self._kwargs)
>   File "/usr/lib64/python2.6/multiprocessing/pool.py", line 57, in worker
>     self.run()
>   File "/usr/lib64/python2.6/multiprocessing/process.py", line 88, in run
>     self._target(*self._args, **self._kwargs)
>   File "/usr/lib64/python2.6/multiprocessing/pool.py", line 57, in worker
>     task = get()
>   File "/usr/lib64/python2.6/multiprocessing/queues.py", line 352, in get
>     return recv()
> TypeError: type 'partial' takes at least one argument
>     task = get()
>   File "/usr/lib64/python2.6/multiprocessing/queues.py", line 352, in get
>     return recv()
> TypeError: type 'partial' takes at least one argument
>
> I'm running on a redhat 6.2 box with python 2.6.
>
> Thanks
> Luke
>
>
> On Tue, Oct 1, 2013 at 3:00 AM, Alexandre Gramfort
> <alexandre.gramfort at telecom-paristech.fr> wrote:
>>
>> hi Luke,
>>
>> > Is there a reason that compute_source_psd doesn't support MNE or is this
>> > just a bug?
>>
>> yes it is.
>>
>> would you be willing to give a try at fixing it?
>>
>> See the "How to contribute" page.
>>
>> http://martinos.org/mne/contributing.html
>>
>> Best,
>> Alex
>>
>> > The error I get comes from line 429 of time_frequency.py. basically
>> > noise_norm is None so noise_norm.size errors.
>> >
>> > AttributeError: 'NoneType' object has no attribute 'size'
>> >
>> > and in _assemble_kernel (inverse.py line 669) noise_norm is set to None
>> > if
>> > the method = 'MNE'
>> >
>> > it seems like line 429 is just trying to get the number of sources?
>> > Couldn't
>> > we get that from K? or am i missing something?
>> >
>> > Thanks,
>> > Luke
>> >
>> >
>> > On Mon, Sep 30, 2013 at 12:55 PM, Martin Luessi
>> > <mluessi at nmr.mgh.harvard.edu> wrote:
>> >>
>> >> Hi Luke,
>> >>
>> >> I think you want to either use compute_source_psd or (for raw data) or
>> >> compute_source_psd_epochs (for epoched data). The functions are in
>> >> mne.minimum_norm.time_frequency. There is an example here:
>> >>
>> >>
>> >>
>> >> http://martinos.org/mne/auto_examples/time_frequency/plot_source_power_spectrum.html
>> >>
>> >> I hope this helps.
>> >>
>> >> Martin
>> >>
>> >>
>> >> On 09/30/13 12:41, Luke Bloy wrote:
>> >>>
>> >>> Thanks for the quick reply Denis.
>> >>>
>> >>> I'm interested in the peak frequency within a band not the power
>> >>> timecourse of the band so source_band_induced_power isn't what I want.
>> >>> I
>> >>> also want to stick with MNE, as opposed to DICS or another beamformer,
>> >>> for localization since I will want to compare with mne/dspm power
>> >>> estimates.
>> >>>
>> >>> The other complicating factor is that I'm using resting state data (~~
>> >>> 5
>> >>> minutes), so most of the inverse operator code in python runs into
>> >>> memory problems. Otherwise I could just do apply_inverse and then work
>> >>> on the returned timecourses in the stc.
>> >>>
>> >>> once I have a time course for each source finding the peak frequency
>> >>> will be pretty straight forward using numpy.fft and numpy.fft.fftfreq.
>> >>>
>> >>> Hope this makes sense
>> >>> -Luke
>> >>>
>> >>>
>> >>>
>> >>> On Mon, Sep 30, 2013 at 12:14 PM, Denis-Alexander Engemann
>> >>> <denis.engemann at gmail.com <mailto:denis.engemann at gmail.com>> wrote:
>> >>>
>> >>>     Hi Luke,
>> >>>
>> >>>
>> >>>     On Mon, Sep 30, 2013 at 6:02 PM, Luke Bloy <luke.bloy at gmail.com
>> >>>     <mailto:luke.bloy at gmail.com>> wrote:
>> >>>
>> >>>         Hi all,
>> >>>
>> >>>         I am interested in computing the peak frequency within a band
>> >>>         for each source.
>> >>>
>> >>>         So my first question is does this already exists somewhere?
>> >>>
>> >>>
>> >>>
>> >>>     This example might be of interest.
>> >>>
>> >>>
>> >>>
>> >>> http://martinos.org/mne/auto_examples/time_frequency/plot_source_space_time_frequency.html#example-time-frequency-plot-source-space-time-frequency-py
>> >>>
>> >>>     Basically it returns source estimates per frequency band each of
>> >>>     which can be visualized on e.g. a cortical surface.
>> >>>
>> >>>     Another timely alternative is the DICS bearmformer recently added
>> >>> by
>> >>>     Roman:
>> >>>
>> >>>
>> >>>
>> >>> https://github.com/mne-tools/mne-python/blob/master/examples/inverse/plot_dics_source_power.py
>> >>>
>> >>>
>> >>>
>> >>> https://github.com/mne-tools/mne-python/blob/master/examples/inverse/plot_dics_beamformer.py
>> >>>
>> >>>     you can always use numpy.argmax and argsort functions to quickly
>> >>>     navigate through peaks inside the resulting arrays.
>> >>>
>> >>>
>> >>>         If not what would people suggest as a jumping off point for
>> >>>         developing it. I was thinking of following apply_inverse in
>> >>>         inverse.py until I get the final inverse operator (K in line
>> >>>         753) and then looping through each row (source) in K to
>> >>> compute
>> >>>         the time course and peak power and frequency. Any other
>> >>>         suggestions or downsides to this approach?
>> >>>
>> >>>
>> >>>     Maybe let's first see whether what is implemented so far gives you
>> >>>     what you're looking for.
>> >>>
>> >>>     I hope this helps + cheers,
>> >>>     Denis
>> >>>
>> >>>         Thanks,
>> >>>         Luke
>> >>>
>> >>>
>> >>>
>> >>>         _______________________________________________
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>> >>>
>> >>>         https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>> >>>
>> >>>
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>> >>>
>> >>>
>> >>>
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>> >>>
>> >>
>> >>
>> >> --
>> >> Martin Luessi, Ph.D.
>> >>
>> >> Research Fellow
>> >>
>> >> Department of Radiology
>> >> Athinoula A. Martinos Center for Biomedical Imaging
>> >> Massachusetts General Hospital
>> >> Harvard Medical School
>> >> 149 13th Street
>> >> Charlestown, MA 02129
>> >>
>> >> Fax: +1 617 726-7422
>> >
>> >
>> >
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