aaltoimaginglanguage / conpy Goto Github PK

Hi,
Thanks a lot for creating such a handy package!
I got a VertexConnectivity object 'con_clust' which shows that '<VertexConnectivity | n_sources=3331, n_conns=329, subject=fsaverage>', when I tried to parcel it using 'con_parc = con_clust.parcellate(labels, summary='degree', weight_by_degree=False)', the info of con_parc shows that: <LabelConnectivity | n_sources=68, n_conns=0, subject=fsaverage>.
I'm wondering why the n_conns in LabelConnectivity becomes 0 while in VertexConnectivity is not?

Todo:

• Get some example data going
• Get first proof of concept
• Design the API (=user interface: what functions, what parameters...)
• First implementation: test on example data
• Write unit tests
• Write example/tutorial to serve as documentation
• Merge into conpy

May I know the logic of removing score greater than 0.1? My initial believe that stronger correlation to eog should have higher value, hence need to be retained.

In line

when run 'pip install https://github.com/wmvanvliet/conpy/zipball/master', i got a error:
File "C:\Users\ADMINI~1\AppData\Local\Temp\pip-5qdm7gdd-build\setup.py", line 17, in
long_description=open('README.md').read(),
UnicodeDecodeError: 'gbk' codec can't decode byte 0x93 in position 4882: illegal multibyte sequence

how to solve it?

Hello, Reviewer 2 here, nice to meet you.

Thank you for the last round of changes. I'm running through the latest version now and everything seems to be working well so far. I will feedback once it has completed.

I have attached the squircle image from my previous last analysis. The cross-hemisphere connections shown in the manuscript are still present and very similar, though there seem to be more connections within the left hemisphere, particularly around the pre/post central gyri. Does this match what you found on your side?

Cheers,
Andrew
squircle.pdf

At the moment, conpy only supports connectivity between sensors/sources. A common use case is to compute coherence between the sensors/sources and an external sensor, for example an EMG sensor.

The API could look like this:

# Include the EMG sensors in the CSD matrix computations.
# (By default, only MEG and EEG is included)
csd_picks = mne.pick_types(epochs, meg=True, emg=True)
csd = mne.time_frequency.csd_epochs(epochs, picks=picks)

# Compute one-to-all connectivity pairs between the EMG sensor and all source vertices
pairs = conpy.sensor_to_source_pairs('EMG001', src)

# Specify during connectivity computations that the origin is not on the cortex,
# but the targets are
con = conpy.dics_connectivity(pairs, fwd, csd,
                              sources_on_cortex=False, targets_on_cortex=True)

Hello, I am very interested at your conpy package. And thanks for your sharing. But I run into an error when the code runs to line 185 ie."info.update(fwd['info'])". I tried to check the reason but failed. I suspect it may be a data path problem because my data_path、meg_path etc are not in the script folder. So could you help me to find what results in the problem.