def visualize(bands=None, folder="", reduceSize = True):
#emptyDir("figs/temp")
print 1
data_path = somato.data_path()
raw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'
# read the raw data
raw = mne.io.read_raw_fif(raw_fname)
print 2
global images
#reduce the size of the data file for quick testing if specified
if reduceSize:
splitData = splitRaw(raw, 100)
sourceData=splitData[0]
else:
sourceData=raw
#creates an mne interval object with no measurements inside
streamedData = sourceData.crop(0,0)
print folder
print 3
global t1
streamInterval = 1
# thread streams data from [sourceData] to [streamedData]
t1 = threading.Thread(target=beginStreaming, args=([sourceData,streamedData,streamInterval]))
t1.start()
print 4
global t2
visInterval = 1
initial = 1
bufferSize = 5000
# thread visualizes the contents of the streamed data interval object
t2 = threading.Thread(target=beginVisualizing, args=([streamedData, images, bands, visInterval, initial, bufferSize, folder]))
t2.start()
print 5
# do not continue until both threads have terminated
t1.join()
t2.join()
# reset the number of figures generated in the current stream
global count
count = 0
print ("Done")
return images
def _get_somato_data():
from mne.datasets import somato
data_path = somato.data_path()
raw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'
event_id, tmin, tmax = 1, -1., 3.
# Setup for reading the raw data
raw = io.Raw(raw_fname, preload=True)
raw.filter(1, 40, n_jobs=-1)
baseline = (None, 0)
events = mne.find_events(raw, stim_channel='STI 014')
# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,
stim=False)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=baseline,
reject=dict(grad=4000e-13, eog=350e-6))
return epochs, epochs.info['sfreq']
###############################################################################
# First, we will import the packages needed for computing the inverse solution
# from the MNE somatosensory dataset. `MNE`_ can be installed with
# ``pip install mne``, and the somatosensory dataset can be downloaded by
# importing ``somato`` from ``mne.datasets``.
import os.path as op
import matplotlib.pyplot as plt
import mne
from mne.datasets import somato
from mne.minimum_norm import apply_inverse, make_inverse_operator
###############################################################################
# Now we set the the path of the ``somato`` dataset for subject ``'01'``.
data_path = somato.data_path()
subject = '01'
task = 'somato'
raw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg',
'sub-{}_task-{}_meg.fif'.format(subject, task))
fwd_fname = op.join(data_path, 'derivatives', 'sub-{}'.format(subject),
'sub-{}_task-{}-fwd.fif'.format(subject, task))
subjects_dir = op.join(data_path, 'derivatives', 'freesurfer', 'subjects')
###############################################################################
# Then, we load the raw data and estimate the inverse operator.
# Read and band-pass filter the raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True)
l_freq, h_freq = 1, 40
raw.filter(l_freq, h_freq)
We will use the somatosensory dataset that contains so-called
event related synchronizations (ERS) / desynchronizations (ERD) in
the beta band.
"""
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet, psd_multitaper
from mne.datasets import somato
###############################################################################
# Set parameters
data_path = somato.data_path()
raw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'
# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname)
events = mne.find_events(raw, stim_channel='STI 014')
# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True, stim=False)
# Construct Epochs
event_id, tmin, tmax = 1, -1., 3.
baseline = (None, 0)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=baseline, reject=dict(grad=4000e-13, eog=350e-6),
preload=True)
from mne.datasets import somato
from mne_bids import (BIDSPath, read_raw_bids, print_dir_tree)
###############################################################################
# We will be using the `MNE somato data <mne_somato_data_>`_, which
# is already stored in BIDS format.
# For more information, you can checkout the
# respective :ref:`example <ex-convert-mne-sample>`.
###############################################################################
# Step 1: Download/Get a BIDS dataset
# -----------------------------------
#
# Get the MNE somato data
bids_root = somato.data_path()
subject_id = '01'
task = 'somato'
datatype = 'meg'
bids_path = BIDSPath(subject=subject_id,
task=task,
datatype=datatype,
suffix=datatype,
root=bids_root)
# bids basename is nicely formatted
print(bids_path)
###############################################################################
# Print the directory tree
