[Mne_analysis] mn.time_frequency.tfr_array_stockwell

[Paul Fishback]

        External Email - Use Caution        

Hi Alex,

Thanks so much for responding to my message. I tried what you suggested,
but I'm still getting an error:

raw=mn.io.read_raw_edf('/Users/fishbacp/Desktop/11.edf', preload=True)
Fs=raw.info['sfreq']

data,times=raw[:,:]
print('data: '+str(data.shape)) #This indicates the data shape is
46-by-123200, which makes sense for my recording: 46 channels, 10 minute
time length, and sampling frequency 200 Hz.

signal = data[[0]]
print('signal: '+str(signal.shape))    # This produces 1-by-123200 for this
channel
min_freq = 5
max_freq = 100
st_power, itc, freqs = mn.time_frequency.tfr_array_stockwell(signal,Fs,
fmin=min_freq, fmax=max_freq)

This is the error message:

Traceback (most recent call last):
  File "/Users/fishbacp/Desktop/Python_Sabbatical/plot_stockwell.py", line
145, in <module>
    st_power, itc, freqs = mn.time_frequency.tfr_array_stockwell(signal,Fs,
fmin=min_freq, fmax=max_freq)
  File
"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/mne/time_frequency/_stockwell.py",
line 172, in tfr_array_stockwell
    n_out = data.shape[2] // decim + bool(data.shape[2] % decim)
IndexError: tuple index out of range

Lines-171-173 in the script, located at
https://github.com/mne-tools/mne-python/blob/maint/0.20/mne/time_frequency/_stockwell.py#L101-L199
are
as follows:

[image: image.png]

I don't see how data.shape[2] could even be defined if data lacks a third
dimension.

I'm not wed to using this implementation of the stockwell transform. I
tried it first because I enjoy using MNE, but if you know of other good
implementations, I would enjoy hearing about them.

Many, many thanks.

Best,

Paul

On Thu, Jul 9, 2020 at 10:38 AM Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

>         External Email - Use Caution
>
> hi Paul,
>
> try:
>
> import numpy as np
> import mne
>
> raw = mne.io.read_raw_edf('/Users/fishbacp/Desktop/EEG_file.edf',
> preload=True)
> Fs = raw.info['sfreq']
> data, times=raw[:, :]
> signal = data[[0]]
> min_freq = 5
> max_freq = 100
> st_power, itc, freqs = mne.time_frequency.tfr_array_stockwell(signal,
> Fs, fmin=min_freq, fmax=max_freq)
>
> --
>
> signal must be a 2d array with dimensions: channel x time
>
> Alex
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>


-- 
Professor of Mathematics, Grand Valley State University

Department of Mathematics (MAK C-2-408)
Grand Valley State University
1 Campus Dr.
Allendale, MI 49401
fishbacp at mail.gvsu.edu
616.331.2040
616.331.3120 (fax)
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