Python setup_read_raw Beispiele

Beispiel #1

Datei: test_epochs.py Projekt: arokem/mne-python

def test_read_epochs():
    event_id = 1
    tmin = -0.2
    tmax = 0.5

    #   Setup for reading the raw data
    raw = fiff.setup_read_raw(raw_fname)
    events = mne.read_events(event_name)

    # Set up pick list: MEG + STI 014 - bad channels (modify to your needs)
    include = ["STI 014"]
    want_meg = True
    want_eeg = False
    want_stim = False
    picks = fiff.pick_types(raw["info"], want_meg, want_eeg, want_stim, include, raw["info"]["bads"])

    data, times, channel_names = mne.read_epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0))

Beispiel #2

Datei: plot_estimate_covariance_matrix.py Projekt: arokem/mne-python

"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)

print __doc__

import os
import mne
from mne import fiff

fname = os.environ['MNE_SAMPLE_DATASET_PATH']
fname += '/MEG/sample/sample_audvis_raw.fif'

raw = fiff.setup_read_raw(fname)

# Set up pick list: MEG + STI 014 - bad channels
want_meg = True
want_eeg = False
want_stim = False

picks = fiff.pick_types(raw['info'], meg=want_meg, eeg=want_eeg,
                        stim=want_stim, exclude=raw['info']['bads'])

print "Number of picked channels : %d" % len(picks)

full_cov = mne.Covariance(kind='full')
full_cov.estimate_from_raw(raw, picks=picks)
print full_cov

Beispiel #3

Datei: plot_read_epochs.py Projekt: arokem/mne-python

from mne import fiff

###############################################################################
# Set parameters
raw_fname = os.environ['MNE_SAMPLE_DATASET_PATH']
# raw_fname += '/MEG/sample/sample_audvis_raw.fif'
raw_fname += '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = os.environ['MNE_SAMPLE_DATASET_PATH']
# event_fname += '/MEG/sample/sample_audvis_raw-eve.fif'
event_fname += '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
event_id = 1
tmin = -0.2
tmax = 0.5

#   Setup for reading the raw data
raw = fiff.setup_read_raw(raw_fname)
events = mne.read_events(event_fname)

#   Set up pick list: MEG + STI 014 - bad channels (modify to your needs)
include = [] # or stim channel ['STI 014']
exclude = raw['info']['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more

# MEG Magnetometers
meg_mag_picks = fiff.pick_types(raw['info'], meg='mag', eeg=False, stim=False,
                                            include=include, exclude=exclude)
meg_mag_data, times, channel_names = mne.read_epochs(raw, events, event_id,
                            tmin, tmax, picks=meg_mag_picks, baseline=(None, 0))
meg_mag_epochs = np.array([d['epoch'] for d in meg_mag_data]) # as 3D matrix
meg_mag_evoked_data = np.mean(meg_mag_epochs, axis=0) # compute evoked fields

# MEG

Beispiel #4

Datei: read_write_raw.py Projekt: arokem/mne-python

"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)

print __doc__

import os
from math import ceil
from mne import fiff

infile = os.environ['MNE_SAMPLE_DATASET_PATH']
infile += '/MEG/sample/sample_audvis_raw.fif'
outfile = 'sample_audvis_small_raw.fif'

raw = fiff.setup_read_raw(infile)

# Set up pick list: MEG + STI 014 - bad channels
want_meg = True
want_eeg = False
want_stim = False
include = ['STI 014']
# include = []
# include = ['STI101', 'STI201', 'STI301']

picks = fiff.pick_types(raw['info'], meg=want_meg, eeg=want_eeg,
                        stim=want_stim, include=include,
                        exclude=raw['info']['bads'])

print "Number of picked channels : %d" % len(picks)