本文整理匯總了Python中mne.io.read_raw_fif方法的典型用法代碼示例。如果您正苦於以下問題：Python io.read_raw_fif方法的具體用法？Python io.read_raw_fif怎麽用？Python io.read_raw_fif使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mne.io的用法示例。
在下文中一共展示了io.read_raw_fif方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_coil_type
# 需要導入模塊: from mne import io [as 別名]
# 或者: from mne.io import read_raw_fif [as 別名]
def test_coil_type():
"""Test the correct coil type is retrieved."""
data_path = testing.data_path()
raw_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
raw = read_raw_fif(raw_fname)
assert coil_type(raw.info, 0) == 'meggradplanar'
assert coil_type(raw.info, 2) == 'megmag'
assert coil_type(raw.info, 306) == 'misc'
assert coil_type(raw.info, 315) == 'eeg'
raw.info['chs'][0]['coil_type'] = 1234
assert coil_type(raw.info, 0) == 'n/a' 示例2: load_fif
# 需要導入模塊: from mne import io [as 別名]
# 或者: from mne.io import read_raw_fif [as 別名]
def load_fif(file_path, **kwargs):
"""
File loader function for .fif and .raw.fif extension files.
Redirects to the mne.io.read_raw_fif function.
Loads all channels, non-needed channels are dropped in the extract function.
See utime.io.extractors.psg_extractors
Returns:
mne.io.Raw fif data array
"""
from mne.io import read_raw_fif
with mne_no_log_context():
raw = read_raw_fif(file_path)
return raw 示例3: init_data
# 需要導入模塊: from mne import io [as 別名]
# 或者: from mne.io import read_raw_fif [as 別名]
def init_data():
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
tmin, tmax, event_id = -0.2, 0.5, 1
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.find_events(raw, stim_channel='STI 014')
inverse_operator = read_inverse_operator(fname_inv)
# Setting the label
label = mne.read_label(data_path + '/MEG/sample/labels/Aud-lh.label')
include = []
raw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
stim=False, include=include, exclude='bads')
# Load condition 1
event_id = 1
# Use linear detrend to reduce any edge artifacts
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
preload=True, detrend=1)
return epochs, inverse_operator, label