[Mne_analysis] [mne-python] tfr_morlet to return single epochs

[Alexandre Gramfort]

Hi Mads,

there was 2 problems:

- epochs[0] was a bad epoch so you need to preload epochs or call
epochs.drop_bad_epochs() to make sure epochs[0] is good.

- a bug in mne-python I just fixed
https://github.com/mne-tools/mne-python/pull/1543

I will be in our bug fix release of 0.8 in the next few weeks. You can
also use current development version.

thanks for the bug report

Alex

On Fri, Aug 29, 2014 at 8:25 PM, Mads Jensen <mje.mads at gmail.com> wrote:
> hi Alex,
>
> thanks for the reply. I afraid cannot make it work, I get the following
> error:
>
> Traceback (most recent call last):
>
>    File "<ipython-input-9-1e05100b604d>", line 30, in <module>
>      return_itc=True, decim=3, n_jobs=1)
>
>    File
> "/home/mje/Toolboxes/anaconda/lib/python2.7/site-packages/mne-0.9.git-py2.7.egg/mne/time_frequency/tfr.py",
> line 892, in tfr_morlet
>      data = data[:, picks, :]
>
> IndexError: too many indice
>
>
> I just used to the code from the example
> "time_frequency/plot_time_frequency_sensors.py" and changed the:
> power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
> use_fft=False,
> to:
> power, itc = tfr_morlet(epochs[0], freqs=freqs, n_cycles=n_cycles,
> use_fft=False,
>                          return_itc=True, decim=3, n_jobs=1)
>             return_itc=True, decim=3, n_jobs=1)
>
> the full code is below,
>
> any thoughts?
> best regards,
> mads
>
> ***
> import numpy as np
> import mne
> from mne import io
> from mne.time_frequency import tfr_morlet
> from mne.datasets import somato
>
> ###############################################################################
> # Set parameters
> data_path = somato.data_path()
> raw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'
> event_id, tmin, tmax = 1, -1., 3.
>
> # Setup for reading the raw data
> raw = io.Raw(raw_fname)
> baseline = (None, 0)
> events = mne.find_events(raw, stim_channel='STI 014')
>
> # picks MEG gradiometers
> picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,
> stim=False)
>
> epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
>                      baseline=baseline, reject=dict(grad=4000e-13,
> eog=350e-6))
>
> ###############################################################################
> # Calculate power and intertrial coherence
>
> freqs = np.arange(6, 30, 3)  # define frequencies of interest
> n_cycles = freqs / 2.  # different number of cycle per frequency
> power, itc = tfr_morlet(epochs[0], freqs=freqs, n_cycles=n_cycles,
> use_fft=False,
>                          return_itc=True, decim=3, n_jobs=1)
> ***
>
>
> On 29/08/14 18:32, Alexandre Gramfort wrote:
>> hi Mads,
>>
>> yes you can. Just call tfr_morlet on epochs[k] to compute it on one epochs.
>>
>> You can index epochs like arrays with MNE-Python
>>
>> Alex
>>
>>
>>
>> On Fri, Aug 29, 2014 at 1:41 PM, Mads Jensen <mje.mads at gmail.com> wrote:
>>> Dear mne-list,
>>>
>>> I would like to make a morlet wavelet on single epochs but as I
>>> understand the tfr_morlet, it returns the average power for all the
>>> epochs. If this is correct is there a way to make tfr_morlet return the
>>> single epochs or is there another function that can?
>>>
>>> thanks,
>>> mads
>>>
>>>
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