larsoner

import mne
import numpy as np
import vtk
from vtk.util import numpy_support
import pyvistaqt

raw = mne.io.read_raw_fif(
    mne.datasets.sample.data_path() + '/MEG/sample/sample_audvis_raw.fif')
data, times = raw[:, :]
n_points = 1000

larsoner

import sys
import mne
import numpy as np
from PyQt5.QtWidgets import QApplication
from PyQt5.QtCore import Qt
from qwt import QwtPlot, QwtLegend, QwtPlotCurve, QwtAbstractScaleDraw

raw = mne.io.read_raw_fif(
    mne.datasets.sample.data_path() + '/MEG/sample/sample_audvis_raw.fif')
data, times = raw[:, :]  # preload

larsoner

# convert MGZ->NII
#
# $ bet T1.nii stripped.nii.gz -n -A -v -e
#
import numpy as np
import nibabel as nib
import pyvista as pv
import mne

img = nib.load('T1.mgz')

larsoner

function HomerOfflineConverter(pathname)
%This function converts recorded NIRx datasets into Homer2 format (*.nirs)
%Please provide as input the root path, within all datasets are located
%If no probeInfo file can be found, it is necessary to choose it manually
%
%Note: to account for the original inter-optode distances, a few functions
%are added to properly import the 2D coordinates and convert them to 3D
%
%By: NIRx Medical Technologies
%Contact: [email protected]

larsoner

#!/usr/bin/env python3
"""
mailmanToMBox.py:  Inserts line feeds to create mbox format from Mailman Gzip'd
Text archives decompressed
Usage:   ./to-mbox.py  dir
Where dir is a directory containing .txt files pulled from mailman Gzip'd Text and decompressed

Adapted from https://gist.github.com/corydolphin/1728592#gistcomment-2583599
"""
import sys

larsoner

import os.path as op
import numpy as np
import mne
from mne.time_frequency import csd_morlet
from mne.beamformer import make_dics, apply_dics_csd

# Simulate bilateral auditory data
subject = 'sample'
data_path = mne.datasets.testing.data_path()
fname_raw = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif')

larsoner

import numpy as np
from scipy import stats
import mne
import matplotlib.pyplot as plt

# 1. Load data
raw = mne.io.read_raw_fif('CC_CP004_PN_L_RUN01_tsss.fif')
other = mne.io.read_raw_egi('CC_CP004_PN_L_Run01_20201016_110457.mff')
# 2. Get times for both instances that should match
t_raw = (mne.find_events(raw)[:, 0] - raw.first_samp) / raw.info['sfreq']

larsoner

import os.path as op
import mne
from mne.io.constants import FIFF

subjects = ('ANTS3-0Months3T', 'ANTS6-0Months3T', 'ANTS12-0Months3T')
subjects_dir = '.'

for subject in subjects:
    subject_dir = op.join(subjects_dir, subject)
    assert op.isdir(subject_dir), subject_dir

larsoner

import numpy as np
import mne

raws = list()

# kiloword
epo = mne.read_epochs(
    mne.datasets.kiloword.data_path() + '/kword_metadata-epo.fif')
epo.pick_types(meg=False, eeg=True)
# XXX this / 1000. is a bug with kiloword, should be in meters!

larsoner

import numpy as np


def output_lens(len_h, in_len, up, down):
    in_len_copy = in_len + (len_h + (-len_h % up)) // up - 1
    nt = in_len_copy * up
    need = nt // down
    if nt % down > 0:
        need += 1
    # need2 = int(np.ceil((in_len * up + len_h - 1) / down))