sEEG brainstorm

import os
import numpy as np
import pandas as pd
from mayavi import mlab

import mne
from mne.viz import plot_alignment
import neo

subjects_dir = os.path.join(os.getcwd(), 'subjects')

electrodes_fname = os.path.join(os.getcwd(), 'anat/implantation/elec_pos_patient.txt')
electrodes = pd.read_csv(electrodes_fname, sep='\t', header=None)
electrodes.columns = ['ch_name', 'x', 'y', 'z']
electrodes = electrodes.set_index('ch_name')
ch_names = electrodes.index.values.tolist()
dig_ch_pos = dict(zip(ch_names, 1e-3 * electrodes.values))
montage = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)

info = mne.create_info(ch_names, 1000., 'seeg', montage=montage)
fig = plot_alignment(info, subject='epimap', subjects_dir=subjects_dir,
                     surfaces=['pial'], meg=False)
mlab.view(200, 70)


raw_fname = os.path.join(os.getcwd(), 'seeg/SZ1.TRC')

reader = neo.rawio.MicromedRawIO(filename=raw_fname)
reader.parse_header()
ch_names = list(reader.header['signal_channels']['name'])

def match_ch_type(name):
    out = 'seeg'
    if 'ecg' in name:
        out = 'ecg'
    if name in ['fz', 'cz']:
        out = 'eeg'
    return out

dig_ch_pos = {k:v for k, v in dig_ch_pos.items() if k in ch_names}
montage = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
ch_types = [match_ch_type(ch) for ch in ch_names]
data = np.array(reader.get_analogsignal_chunk())
data = reader.rescale_signal_raw_to_float(data).T
data *= 1e-6  # putdata from microvolts to volts

sfreq = reader.get_signal_sampling_rate()
info = mne.create_info(ch_names, sfreq, ch_types=ch_types, montage=montage)
raw = mne.io.RawArray(data, info)
raw.info['bads'] = ["v'1", "f'1"]
raw.plot()