[Mne_analysis] Could you help me find events from ".set" file?
Soan KIM
cheukusi.kim at gmail.com
Thu May 23 06:47:42 EDT 2019
External Email - Use Caution
Thank you for your reply.
It worked perfectly and you saved my day!
but could you take a look at another issue?
I get this error when I try to plot with the option, "spatial colors =
True" on Jupyter notebook. :
TypeError: get_tightbbox() got an unexpected keyword argument
'bbox_extra_artists'
Thank you.
-------------
My code :
evoked = epochs.average()
evoked.plot(spatial_colors=True)
Error :
TypeError Traceback (most recent call last)
/anaconda3/envs/mne/lib/python3.6/site-packages/IPython/core/formatters.py
in __call__(self, obj)
339 pass
340 else:
--> 341 return printer(obj)
342 # Finally look for special method names
343 method = get_real_method(obj, self.print_method)
/anaconda3/envs/mne/lib/python3.6/site-packages/IPython/core/pylabtools.py
in <lambda>(fig)
242
243 if 'png' in formats:
--> 244 png_formatter.for_type(Figure, lambda fig:
print_figure(fig, 'png', **kwargs))
245 if 'retina' in formats or 'png2x' in formats:
246 png_formatter.for_type(Figure, lambda fig:
retina_figure(fig, **kwargs))
/anaconda3/envs/mne/lib/python3.6/site-packages/IPython/core/pylabtools.py
in print_figure(fig, fmt, bbox_inches, **kwargs)
126
127 bytes_io = BytesIO()
--> 128 fig.canvas.print_figure(bytes_io, **kw)
129 data = bytes_io.getvalue()
130 if fmt == 'svg':
/anaconda3/envs/mne/lib/python3.6/site-packages/matplotlib/backends/backend_qt5agg.py
in print_figure(self, *args, **kwargs)
86
87 def print_figure(self, *args, **kwargs):
---> 88 super().print_figure(*args, **kwargs)
89 self.draw()
90
/anaconda3/envs/mne/lib/python3.6/site-packages/matplotlib/backend_bases.py
in print_figure(self, filename, dpi, facecolor, edgecolor, orientation,
format, bbox_inches, **kwargs)
2051 bbox_artists = kwargs.pop("bbox_extra_artists",
None)
2052 bbox_inches =
self.figure.get_tightbbox(renderer,
-> 2053 bbox_extra_artists=bbox_artists)
2054 pad = kwargs.pop("pad_inches", None)
2055 if pad is None:
/anaconda3/envs/mne/lib/python3.6/site-packages/matplotlib/figure.py in
get_tightbbox(self, renderer, bbox_extra_artists)
2274 bb.extend(
2275 ax.get_tightbbox(renderer,
bbox_extra_artists=bbox_extra_artists)
-> 2276 for ax in self.axes if ax.get_visible())
2277
2278 if len(bb) == 0:
/anaconda3/envs/mne/lib/python3.6/site-packages/matplotlib/figure.py in
<genexpr>(.0)
2274 bb.extend(
2275 ax.get_tightbbox(renderer,
bbox_extra_artists=bbox_extra_artists)
-> 2276 for ax in self.axes if ax.get_visible())
2277
2278 if len(bb) == 0:
TypeError: get_tightbbox() got an unexpected keyword argument
'bbox_extra_artists'
<Figure size 921x432 with 2 Axes>
2019년 5월 23일 (목) 오후 2:40, Brunner, Clemens (clemens.brunner at uni-graz.at) <
clemens.brunner at uni-graz.at>님이 작성:
> External Email - Use Caution
>
> Could you try mne.read_epochs_eeglab again? It looks like your .set file
> already contains the epochs, so this function should do the job. You
> certainly shouldn't have to construct your own info object, because this
> information should already be present in raw.info.
>
> Without the data file it is difficult to tell what the exact problem is,
> so feel free to send it to me if my suggestion doesn't work.
>
> Clemens
>
>
> > On 22.05.2019, at 17:59, Soan KIM <cheukusi.kim at gmail.com> wrote:
> >
> > External Email - Use Caution
> >
> >
> > Thank you so much!! It worked!
> > but I've faced with another problem making custom epochs. Could you give
> me some advice?
> >
> > Thank you!!
> >
> >
> > I see this error even though I picked 18 channels :
> >
> > ValueError: All picks must be < n_channels (30), got 30
> >
> > This is my code :
> >
> > event_id = {'S 4':4, 'S 6':6, 'S 7':7, 'S 1':1, 'S 5':5, 'S 3':3,
> 'S 2':2}
> > events= mne.events_from_annotations(raw)
> > n_channels = 32
> > sampling_rate = 500
> > info = mne.create_info(n_channels, sampling_rate)
> > channel_names = ['Fp1', 'Fp2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1',
> 'O2', 'F7', 'F8', 'T7', 'T8', 'P7', 'P8', 'Fz', 'Cz', 'Pz', 'FC1', 'FC2',
> 'CP1', 'CP2', 'FC5', 'FC6', 'CP5', 'CP6', 'TP9', 'TP10', 'EOG', 'M1', 'M2']
> > channel_types = ['eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg',
> 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg',
> 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg',
> 'eeg', 'eog', 'bio', 'bio']
> > sfreq = 500
> > info['description'] = 'My custom dataset'
> > info['bads'] = ['Fp1', 'Fp2', 'F7', 'F8', 'T7', 'T8', 'TP9', 'TP10']
> > include = ['F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2', 'P7', 'P8',
> 'FC1', 'FC2', 'CP1', 'CP2', 'FC5', 'FC6', 'CP5', 'CP6']
> > picks = mne.pick_channels(channel_names, include=include,
> exclude=info['bads'], ordered=False)
> > tmin= -0.2
> > tmax = 0.8
> > custom_epochs = mne.EpochsArray(raw, info, picks, events, event_id)
> >
> > 2019년 5월 22일 (수) 오후 10:26, Brunner, Clemens (clemens.brunner at uni-graz.at)
> <clemens.brunner at uni-graz.at>님이 작성:
> > External Email - Use Caution
> >
> >
> > Hi!
> >
> >
> >
> > Your markers are probably in raw.annotations. If you want to convert
> them to events, use mne.events_from_annotations(raw).
> >
> >
> >
> > Clemens
> >
> >
> >
> >
> >
> > From: mne_analysis-bounces at nmr.mgh.harvard.edu <
> mne_analysis-bounces at nmr.mgh.harvard.edu> On Behalf Of Soan KIM
> > Sent: Wednesday, 22 May 2019 14:04
> > To: mne_analysis at nmr.mgh.harvard.edu
> > Subject: [Mne_analysis] Could you help me find events from ".set" file?
> >
> >
> >
> > External Email - Use Caution
> >
> > Hello, I am a student learning MNE-python.
> > I have trouble finding events in my ".set" file. It appears empty.
> > I've also tried mne.io.read_epochs_eeglab with my another epoched
> > ".set" file, but the list of events is also empty.
> > To be specific, my raw eeg file was '.vhdr', '.vmrk', and
> > '.eeg.(BrainProduct)', with no stim_channel. And the data had no
> > problem when I pre-processed and ran statistics with EEGLAB and
> > ERPlab. Could you please help me?
> >
> > I attach my continuous eeg file('001.set' , '001.fdt'), and epoched
> one("001_sync.set", '001_sync.fdt")
> >
> >
> > Thank you.
> >
> > -------
> > This is how my file looks like.
> >
> > <Info | 17 non-empty fields
> > bads : list | 0 items
> > ch_names : list | Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, ...
> > chs : list | 30 items (EEG: 30)
> > comps : list | 0 items
> > custom_ref_applied : bool | False
> > dev_head_t : Transform | 3 items
> > dig : list | 29 items (29 EEG)
> > events : list | 0 items
> > highpass : float | 0.0 Hz
> > hpi_meas : list | 0 items
> > hpi_results : list | 0 items
> > lowpass : float | 125.0 Hz
> > meas_date : NoneType | unspecified
> > nchan : int | 30
> > proc_history : list | 0 items
> > projs : list | 0 items
> > sfreq : float | 250.0 Hz
> > acq_pars : NoneType
> > acq_stim : NoneType
> > ctf_head_t : NoneType
> > description : NoneType
> > dev_ctf_t : NoneType
> > experimenter : NoneType
> > file_id : NoneType
> > gantry_angle : NoneType
> > hpi_subsystem : NoneType
> > kit_system_id : NoneType
> > line_freq : NoneType
> > meas_id : NoneType
> > proj_id : NoneType
> > proj_name : NoneType
> > subject_info : NoneType
> > xplotter_layout : NoneType
> >
> > -----------
> >
> > This is my code :
> >
> > raw = mne.io.read_raw_eeglab('/001.set', preload=True)
> > event_id = {'B1(1)': 1, 'B2(2)':2, 'B3(3)':3, 'B4(4)':4, 'B5(5)':5,
> > 'B6(6)':6, 'B7(7)':7}
> > events= mne.find_events(raw)
> > data, times = raw[:, :]
> > data.shape
> > start, stop = raw.time_as_index([0, 503955])
> > data, times = raw[:, start:stop]
> > times.max()
> > picks = mne.pick_types(raw.info
> ,meg=False,eeg=True,stim=False,eog=False,ecg=False,misc=False)
> >
> > events = mne.find_events(raw, stim_channel=None, verbose=True)
> > events[:]
> >
> > array([], shape=(0, 3), dtype=int32)
> >
> > <~WRD000.jpg> 001.fdt<~WRD000.jpg>
> >
> > <~WRD000.jpg> 001.set<~WRD000.jpg>
> >
> > <~WRD000.jpg> 001_sync.fdt<~WRD000.jpg>
> >
> > <~WRD000.jpg> 001_sync.set<~WRD000.jpg>
> >
> >
> >
> > _______________________________________________
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> > Mne_analysis at nmr.mgh.harvard.edu
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> >
> >
> > --
> > 서울시 성북구 안암로 145 고려대학교 136-701
> > Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, Korea
> > _______________________________________________
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> > Mne_analysis at nmr.mgh.harvard.edu
> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
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--
서울시 성북구 안암로 145 고려대학교 136-701
Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, Korea
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