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