[Mne_analysis] computing peak frequency in source space
Luke Bloy
luke.bloy at gmail.com
Tue Oct 1 11:15:40 EDT 2013
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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> >>>
> >>
> >>
> >> --
> >> 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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