running 01_gDCNN
Hi Jürgen, Running the code adapted to run in my env. I get the following error, which I don't understand.
(mne) maximilien.chaumon@icm-meg-le41 ~/liensNet/00_max/gDCNN/gdcnn $ python 01_gDCNN_label_ica.py
Opening raw data file /network/lustre/iss01/cenir/analyse/meeg/00_max/gDCNN/data/meg_raw/Paris/SELFI/selfi01_s01/170124/run1_tsss.fif...
/network/lustre/iss01/cenir/analyse/meeg/00_max/gDCNN/gdcnn/dcnn_utils.py:384: RuntimeWarning: This filename (/network/lustre/iss01/cenir/analyse/meeg/00_max/gDCNN/data/meg_raw/Paris/SELFI/selfi01_s01/170124/run1_tsss.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz or _meg.fif
raw = mne.io.Raw(fn,preload=preload)
Range : 258000 ... 558999 = 258.000 ... 558.999 secs
Ready.
Current compensation grade : 0
Reading 0 ... 300999 = 0.000 ... 300.999 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 2 - 40 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 2.00
- Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 1.00 Hz)
- Upper passband edge: 40.00 Hz
- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)
- Filter length: 1651 samples (1.651 sec)
[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done 93 tasks | elapsed: 1.9s
[Parallel(n_jobs=2)]: Done 306 out of 306 | elapsed: 4.1s finished
Trigger channel has a non-zero initial value of 4 (consider using initial_event=True to detect this event)
[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done 86 tasks | elapsed: 1.8s
[Parallel(n_jobs=2)]: Done 323 out of 323 | elapsed: 6.7s finished
Trigger channel has a non-zero initial value of 4 (consider using initial_event=True to detect this event)
Fitting ICA to data using 306 channels (please be patient, this may take a while)
Inferring max_pca_components from picks
Selecting by number: 40 components
/home/maximilien.chaumon/anaconda3/envs/mne/lib/python3.6/site-packages/sklearn/decomposition/fastica_.py:118: UserWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
warnings.warn('FastICA did not converge. Consider increasing '
Fitting ICA took 8.7s.
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 8 - 25 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 8.00
- Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 7.00 Hz)
- Upper passband edge: 25.00 Hz
- Upper transition bandwidth: 6.25 Hz (-6 dB cutoff frequency: 28.12 Hz)
- Filter length: 329 samples (1.650 sec)
[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done 40 out of 40 | elapsed: 0.1s finished
Setting up low-pass filter at 20 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal lowpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Upper passband edge: 20.00 Hz
- Upper transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 22.50 Hz)
- Filter length: 133 samples (0.667 sec)
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal allpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Filter length: 1 samples (0.005 sec)
Traceback (most recent call last):
File "01_gDCNN_label_ica.py", line 62, in <module>
fgdcnn = dcnn.label_ica(save=True)
File "/network/lustre/iss01/cenir/analyse/meeg/00_max/gDCNN/gdcnn/dcnn_main.py", line 501, in label_ica
self.ica.update_topo_images(res=self.model['res_space'])
File "/network/lustre/iss01/cenir/analyse/meeg/00_max/gDCNN/gdcnn/dcnn_base.py", line 1291, in update_topo_images
res=res)
File "/network/lustre/iss01/cenir/analyse/meeg/00_max/gDCNN/gdcnn/dcnn_base.py", line 1161, in update_images
n_components=n_components)
File "/network/lustre/iss01/cenir/analyse/meeg/00_max/gDCNN/gdcnn/dcnn_base.py", line 1123, in _get_images
_prepare_topomap_plot(ica, 'mag', None, sphere=None)
TypeError: _prepare_topomap_plot() got multiple values for argument 'sphere'
(mne) maximilien.chaumon@icm-meg-le41 ~/liensNet/00_max/gDCNN/gdcnn $